deliverable 5.2/3: congestion management in distribution ... · ide4l deliverable 5.2/3 2 ide4l is...

IDE4L is a project co-funded by the European Commission

Project no: 608860

Project acronym: IDE4L

Project title: IDEAL GRID FOR ALL

Deliverable 5.2/3:

Congestion Management in Distribution Networks

Due date of deliverable: 01.09.2015

Actual submission date: 01.09.2015

Start date of project: 01.09.2013 Duration: 36 months

Lead beneficiary name: Dansk Energi, Denmark

Authors:

Dansk Energi (DE), Universidad Carlos III de Madrid (UC3M), Tampere University of Technology (TUT)

Project co-funded by the European Commission within the Seventh Framework Programme (2013-2016)

Dissemination level PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

IDE4L Deliverable 5.2/3

2 IDE4L is a project co-funded by the European Commission

Track Changes

Version Date Description Revised Approved

0.1 26.05.2015 Template Jasmin Mehmedalic

0.2 03.07.2015 First Draft Anna Kulmala, Mónica Alonso, Hortensia Amaris, Mª Ángeles Moreno

0.3 16.07.2015 Second Draft Anna Kulmala, Farzad Azimzadeh Moghaddam, Sami Repo, Mónica Alonso, Hortensia Amaris, Mª Ángeles Moreno, Jasmin Mehmedalic, Philip Douglass

0.4 28.07.2015 Third Draft Anna Kulmala, Farzad Azimzadeh Moghaddam, Mónica Alonso, Hortensia Amaris, Mª Ángeles Moreno, Jasmin Mehmedalic, Zaid Al-Jassim

0.5 07.08.2015 Fourth Draft Anna Kulmala, Farzad Azimzadeh Moghaddam, Philip Douglass

0.6 13.08.2015 Final Draft Sami Repo, Jasmin Mehmedalic

1.0 14.08.2015 Final version Zaid Al-Jassim



TableofContents

1 Introduction ......................................................................................................................................... 9

2 State of the Art ...................................................................................................................................11

2.1 Congestion Management in Distribution Networks Utilizing Voltage Control ...............................11

2.1.1 Coordinated Voltage Control................................................................................................11

2.1.2 Optimal Power Flow Formulation.........................................................................................12

2.1.3 Classification of OPF Formulations .......................................................................................13

2.1.4 Deterministic Optimization Methods ...................................................................................13

2.1.5 Summary of the Deterministic Methods Used in OPF ...........................................................14

2.1.6 Nondeterministic Optimization Methods .............................................................................14

2.1.7 Summary of Nondeterministic Methods Used in OPF ...........................................................15

2.1.8 Hybrid Optimization Methods ..............................................................................................15

2.2 Congestion Management in Distribution Networks Utilizing Power Flow Control .........................15

2.2.1 DER (1): FACTS .....................................................................................................................16

2.2.2 DER (2): Distributed Generation ...........................................................................................17

2.2.3 Generation Curtailment .......................................................................................................17

2.2.4 Load Shedding or Load Curtailment .....................................................................................18

2.2.5 Flexible Loads (EVs) ..............................................................................................................19

2.3 Congestion Management in Distribution Networks Utilizing Network Reconfiguration ................19

2.3.1 Classical Optimisation Methods ...........................................................................................20

2.3.2 Heuristic Algorithms ............................................................................................................20

2.4 Selection of Objective Function ...................................................................................................22

2.4.1 Overload of Lines/Cables and Transformers .........................................................................22

2.4.2 Overload as a Single Objective Function ...............................................................................22

2.4.3 Overload as a Part of a Multi-objective Formulation ............................................................22

2.4.4 Voltage Deviation ................................................................................................................23

2.4.5 Voltage Deviation as a Part of a Multi-objective Formulation ...............................................23

2.4.6 Multi-objective Formulations ...............................................................................................23

2.5 Congestion Management Concept ...............................................................................................24

2.6 Recommendation in Regard to the IDE4L Concept .......................................................................25

3 Design Specifications ...........................................................................................................................27

3.1 Control Concept ..........................................................................................................................27



3.1.1 Control System Hierarchy ....................................................................................................27

3.1.2 Direct and Indirect Control of DERs ......................................................................................28

3.1.3 Detailed Interactions of the Control System .........................................................................31

3.1.4 Algorithm Execution and Use of Flags in the Real-time Time Frame .....................................34

3.2 Secondary Control .......................................................................................................................35

3.2.1 Real-time Power Control ......................................................................................................35

3.2.2 Offline Cost Parameter Update ............................................................................................36

3.2.3 Block OLTCs of Transformers (BOT) ......................................................................................36

3.3 Tertiary Control ...........................................................................................................................37

3.3.1 Real-time Validation ............................................................................................................38

3.3.2 Offline Validation: ................................................................................................................39

3.3.3 Network Reconfiguration .....................................................................................................41

3.3.4 Market Agent.......................................................................................................................41

4 Algorithms ..........................................................................................................................................43

4.1 Secondary Control .......................................................................................................................43

4.1.1 Real-time Power Control ......................................................................................................43

4.1.2 Offline Cost Parameter Update ............................................................................................68

4.1.3 Block OLTCs of Transformers (BOT) ......................................................................................78

4.2 Tertiary Control ...........................................................................................................................88

4.2.1 Network Reconfiguration (NR) .............................................................................................88

4.2.2 Market Agent (MA) ............................................................................................................100

References ................................................................................................................................................113

Appendix 1 ...............................................................................................................................................121



AbbreviationsACO Ant Colony Optimization

AVC Automatic Voltage Control

AVCO Automatic Voltage Control Relay Operating

AVR Automatic Voltage Regulator

BAU Business as Usual

BOT Block OLTCs of Transformers

CA Commercial Aggregator

CC Control Centre

CHP Combined Heat and Power

CIM Common Information Model

CIS Customer Information System

CRP Conditional Re-profiling

CVC Coordinated Voltage Control

DC Direct Current

DER Distributed Energy Resource

DG Distributed Generation

DMS Distribution Management System

DR Demand Response

DSO Distribution System Operator

DXP Data Exchange Platform (this is equivalent to SAU database)

EA Evolutionary Algorithm

EV Electric Vehicle

FACTS Flexible AC Transmission System

FLISR Fault Location, Isolation and Service Restoration

GA Genetic Algorithm

GF Generation Forecaster

GOOSE Generic Object Oriented Substation Events

GT Graded Time



HEMS Home Energy Management System

HV High Voltage

ID Intraday Market Session

IED Intelligent Electronic Device

IED.CTRL.AVC Automatic Voltage Control Relay for OLTC

IPM Interior Point Method

KPI Key Performance Indicator

LP Linear Programming

LV Low Voltage

LVPC Low Voltage Power Control

MA Market Agent

MILP Mixed Integer Linear Programming

MINLP Mixed Integer Nonlinear Programming

MV Medium Voltage

MVPC Medium Voltage Power Control

NIS Network Information System

NLP Nonlinear Programming

NR Network Reconfiguration

OLTC On Load Tap Changer

OLV Offline Validation

OPF Optimal Power Flow

PC Power Control

PCPU Power Control Offline Cost Parameter Update

PSAU Primary Substation Automation Unit

PSO Particle Swarm Optimization

PV Photovoltaic

QP Quadratic Programming

RDBMS Relational Database Management System

RTDS Real Time Digital Simulator



RTV Real-time Validation

SAU Substation Automation Unit

SCADA Supervisory Control And Data Acquisition

SE State Estimation

SF State Forecast

SLP Sequential Linear Programming

SQP Sequential Quadratic Programming

SRP Scheduled Re-profiling

SSAU Secondary Substation Automation Unit

STATCOM Static Synchronous Compensator

SVC Static VAR Compensator

TC Tertiary Control

TCO Tap Changer Operating

TSC Thyristor-switched Capacitor

TSO Transmission System Operator

WP Work Package



ExecutiveSummaryCongestion management in distribution networks is one of the main subjects addressed in the IDE4L project. In this report, a congestion management system for distribution networks is described.

The presented congestion management system is a modular hierarchical control system. It consists of three levels – primary, secondary and tertiary control. Primary control consists of controllers for each unit (i.e. embedded unit controllers, such as that of a PV inverter). Secondary control consists of distributed network controllers, working at either LV or MV level, controlling the resources in their network area, by supplying reference signals for primary controllers. Tertiary control is a central controller located in the DSO control centre, coordinating the overall state of the network and communication with other market actors.

This approach was chosen to ensure good response times, distribute computational burden, provide flexibility for future upgrades of individual parts/algorithms of the control system and facilitate the different objectives of each control level.

This report presents the developed controllers for secondary and tertiary control. All controllers are based on optimization, with robust fallback routines for dealing with technical difficulties that may occur in the field (e.g. missing signals, communication errors or insufficient control resources).

Secondary control consists of 5 controllers – a voltage/power controller for each voltage level, LV and MV respectively, their associated supporting parameter update controllers and a supplementary controller ensuring coordinated use of cascaded tap changers. Each of these controllers can run individually or in cooperation with the other controllers. These controllers are located at either primary or secondary substations.

Tertiary control consists of three algorithms – each dealing with its own objectives and working in close cooperation with each other. Two of these algorithms are covered by this deliverable. One algorithm deals with direct network control in the form of network reconfiguration. The other deals with indirect network control, in the form of flexibility services purchased from commercial aggregators through a market. The third algorithm deals with dynamic grid tariffs and is covered by the future deliverable D5.4.

Simulation tests have been performed for each algorithm to evaluate its performance. The results show that the individual controllers and algorithms have good performance. Further testing and demonstration of the algorithms will be done in the context of work package 7. Integration labs will be used to test the communication interfaces of the controllers and algorithms and the entire congestion management system will be tested in RTDS simulations and field demonstrations.



1 IntroductionThe future distribution system is challenged with the increasing penetration of distributed energy resources (DERs) such as wind, solar, heat pumps and electric vehicles. Such technologies depend on external and uncontrollable factors such as wind speed, solar radiation, time of day, human behaviour, etc. Congestions in the form of violations of network constraints are therefore expected to occur during short periods of time. The classical way of handling such challenges has been to reinforce the distribution network to handle these short extremes, even though the network capacity utilization at other times of day is moderate or low.

The IDE4L project is dealing with, developing and demonstrating new technologies and methods to eliminate the eventual congestions in the distribution network and improve utilization of the capacity of the distribution network.

Several studies and projects have been dealing with the subject of congestion management. Therefore, in order to build on former experiences, a state of the art study has been carried out as a first step of the development process. In chapter 2 of this report, the outcomes of these studies [IDE4L2014c] have been presented briefly in order to introduce the background of each of the developed functions. The state of the art analysis phase has been followed by documenting the design specifications for each individual algorithm, in order to create a complete system and ensure harmony and cohesion of the functionality of all the developed functions in the IDE4L project. These design specifications are presented in chapter 3 together with the overall congestion management concept. Chapter 4 gives a thorough technical description of each developed algorithm and presents simulation results.

During the very first stages of the project (specifically the writing process of the description of work) it has been specified that in order to create a fully functioning congestion management system, a power control algorithm mitigating overloads and a voltage control algorithm mitigating over- and undervoltage shall be created. However, after carrying out the first two phases of the analysis, the below mentioned points have been identified:

1. The power control algorithm and voltage control algorithm have almost the same inputs and outputs and creating two separate algorithms will cause duplication of work.

2. As power and voltage are closely interconnected, making separate controllers for each can lead to conflicts between the controllers, reducing their performance.

3. Different time frames and thus speed of action are required depending on which control resources are used and in what capacity those resources are used (i.e. for voltage control or power control). To balance computational complexity and speed of action, it is beneficial to use a hierarchical control structure with multiple controllers.

4. In order to create harmony amongst the work of all controllers, which are located at different network locations, voltage levels and hierarchy levels, it is necessary to create a coordination scheme that considers all the controllers.

Based on the above mentioned points and internal discussions amongst all the partners involved in work package 5 (WP5), the following has been decided:



1. Instead of developing two separate algorithms (voltage controller and power controller), and increasing the complexity of the system, one generic algorithm called power control (PC) algorithm will be developed that will perform both voltage control and power control.

2. A new DSO function is identified and developed called the tertiary controller (TC). The TC is located at the control centre. It monitors and optimizes the state of the network by finding the optimal configuration of the network and optimizing the use of market based services offered by commercial aggregators. By operating on demand and considering forecasts of load and production, it can participate in markets. Its complete knowledge of the network also allows it to aid in service restoration after faults and use market products to do so.

3. A hierarchical control scheme with supporting algorithms and controllers has been developed and introduced to overcome the challenges that could be faced if no coordination between controllers was present.

Based on all the above mentioned points, and in order to create a document that best describes the IDE4L proposed method for congestion management, the deliverables D5.2 and D5.3 have been merged in one single deliverable called D5.2/3.

In Appendix 1 an offline simulation analysis of the low voltage power control (LVPC) at UFD and Østkraft demonstration sites is included. The analysis has been performed to evaluate the LVPC under scenarios that are infeasible to demonstrate in the field and to prepare for field demonstrations by identifying scenarios and algorithm parameters that can be used to evaluate the LVPC’s performance. The offline simulations have also helped in testing, debugging and improving the performance of the developed algorithms.



2 StateoftheArt

2.1 CongestionManagementinDistributionNetworksUtilizingVoltageControlThe lack of capacity in the power system for the power flow leads to congestion. Congestion can be caused either by voltage exceeding the allowed limits or overload of components. Congestion due to violation of voltage limits can be mitigated by utilizing voltage control. [IDE4L2014c]

Connecting generation to the present distribution networks often leads to voltage rise problems. An acceptable voltage profile can be achieved by passive voltage control methods such as increasing the conductor size and connecting generation on a dedicated feeder. However, by using active voltage control methods, voltage control can be done in a more cost-effective way. Active voltage control methods utilize the controllable network resources in the voltage regulation. These controllable resources include on-load tap changers (OLTCs), active and reactive capability of distributed generation (DG) units and other controllable devices connected to the network. [Kulmala2014a]

Active voltage control methods are divided into two categories; control based on local measurements and control based on information of the entire power system. Control based on local measurements means that controllable resources like DG units are operating only considering local measurements, for instance, local voltage. If information of the entire system is used, a combination of active voltage controls can be used for voltage regulation meaning that voltage control is achieved by taking all controllable resources into consideration at the same time. The latter case is called coordinated voltage control (CVC). The needed network state data can be obtained either from state estimation (SE) or can be directly measured. Most of the CVC algorithms use a centralized unit for control purposes. [Kulmala2014a]

2.1.1 CoordinatedVoltageControlCVC methods can be divided into two categories; one is based on rule based algorithms and the other is based on optimization algorithms. [Kulmala2014a]

2.1.1.1 RuleBasedMethodsHaving a simple network structure and few controllable resources rule based methods can be suitable options. In the simplest rule based CVC method, substation voltage is controlled based on network maximum and minimum voltages to keep all network voltages in the allowed range. Substation voltage is lowered if network maximum voltage exceeds its limit and increased if network minimum voltage falls below its limit. When both network maximum and minimum voltage limits are violated this method stops execution. [Hird2004, Kulmala2007]

It is also possible to combine the coordinated operation of substation voltage with local active and reactive power control of, for instance, DG units. In this case, the local control will operate faster than substation control, because the transformer automatic voltage control relay and tap changer delays are much larger than the delay of the local active and reactive power controllers. This means that substation voltage is used as the last control option. [Kulmala2014a]

Several methods that include active and/or reactive power control in the CVC algorithm have been proposed in publications (e.g. [Pfajfar2007, Kulmala2014b, Bignugolo2008, Conti2007, Brenna2013, Zhou2007]). Control sequences have been selected differently in the papers. [Kulmala2014a]



2.1.1.2 MethodsUtilizingOptimizationSince an optimization-based approach has been considered for voltage control in the IDE4L project, the state of the art is focused on optimization-based methods. In the power system, any optimization problem which contains a set of power flow equations in the constraints can be treated as an optimal power flow (OPF) problem [Frank2012a]. In general, OPF problems are nonlinear and nonconvex and may contain both continuous and discrete variables [Biskas2005].

2.1.2 OptimalPowerFlowFormulationOPF problems can be presented on the following standard form [Kundur1994] [Zhang2005]:

min ( , ). . ( , ) = 0ℎ( , ) ≤ 0

where

is the controllable system variable is the dependent or state variable ( , ) is the objective function which determines the optimization goal

Vector function ( , ) = 0 determines the equality constraints Vector function ℎ( , ) ≤ 0 determines the inequality constraints

Having nonconvex objective function and constraints, the OPF problem will be more complex from a computational and theoretical point of view [Almeida1996]. Since the structurally complex constraints are difficult to handle in nondeterministic search techniques, these constraints are usually applied as penalties to the objective function. [Frank2012a]

Based on the requirements, different objectives can be considered in an OPF problem. Some possible objectives are as follows [IDE4L2014c]:

· Reduction of the network losses · Reduction of the production curtailment · Minimizing the load control actions · Reducing the cost of changing the normally open disconnectors · Reducing the cost of the reactive power flow supplied by the transmission network · Reducing the cost of the active power flow supplied by the transmission network · Reducing the voltage variation at each node (difference between current and reference value of

voltage)

Variables involved in OPF problems are divided into two categories, state variables and controllable variables. Usually, bus voltage magnitude, bus voltage angle and real and reactive power injections at each node of the network are taken as state variables. All state variables are continuous. On the other hand, controllable variables are a subset of state variables (e.g. real and reactive power injections at generation buses). In addition, switching device settings (e.g. OLTC ratios) are considered as controllable variables. Controllable variables can be continuous or discrete. [Frank2012a]

OPF constraints can be divided into two categories, equality constraints and inequality constraints. All balance equations are taken as equality constraint [Frank2012a]. The technical limits and limits for controllable resource capabilities define inequality constraints [Kulmala2014a].



2.1.3 ClassificationofOPFFormulationsThe OPF formulation has an effect on both solution method design and solution accuracy [Frank2012a]. Classification of OPF formulations has been presented in much more detail in the internal state of the art document [IDE4L2014c]. In this subsection, OPF formulation methods are briefly presented. OPF formulation methods are as follows [Frank2012a]:

Nonlinear programming (NLP) formulation method considers only continuous variables. Therefore, discrete control variables are taken as continuous. [Frank2012a]

Linear programming (LP) is quite efficient when dealing with separable and convex objective functions. It is, nevertheless, inaccurate when dealing with nonseparable objectives like minimization of losses. Speed and excellent convergence properties are the main advantages of LP solution methods. The use of DC power flow formulation with linear objective function is a good example of applied LP to OPF problems [Rau2003]. [Frank2012a]

Quadratic programming (QP) is a special form of NLP. The objective function is in quadratic form and all the constraints are linear. In [Contaxis1986], a decoupled QP formulation has been implemented. [Frank2012a]

Mixed integer linear programming (MILP) can take discrete parameters into consideration. MILP can be applied after linearization of the system under study. In addition, MILP includes most advantages of LP. Since the power system has nonlinear characteristics, applying MILP will result in some inaccuracies. [Frank2012a]

Mixed integer nonlinear programming (MINLP) is the most precise and complicated formulation method of the power system while taking the discrete variables into consideration. [Frank2012a]

2.1.4 DeterministicOptimizationMethodsDeterministic (classical) optimization methods applied to OPF problems are as follows [Frank2012a]:

Gradient methods are divided into three categories, the reduced gradient (RG), conjugate gradient (CG) and generalized reduced gradient (GRG) methods. The objective function and used constraints in this method should be differentiable. Using the first order derivative of the NLP objective function and during an iterative process, a search direction toward the solution is found. These methods can find the global optimum only in the case of convex problems. [Frank2012a]

Newton’s method requires the Lagrangian function to be applicable to OPF problems [Frank2012a]. The basic idea of the Newton method, used in optimization problems, is to make a second order approximation of the objective function and then minimize it. It should be noticed that due to second order approximation, the Hessian matrix needs to be calculated.

Simplex method is most likely the best solution method for LP. This method can be directly utilized with DC OPF formulations. The simplex method searches in the feasible region and tries to find the best possible solution. [Frank2012a]

Sequential linear programming (SLP) is an upgraded form of LP. SLP is able to optimize nonlinear problems using linear approximations. Comparing with LP, the SLP method has the same speed, but better accuracy on nonlinear problems. [Frank2012a]



In Sequential quadratic programming (SQP), a quadratic program which is an approximation of the original NLP (around a particular operation point) is obtained at each iteration. Afterwards, this QP is solved to get the optimum. The obtained optimal point (at each iteration) is used as the initial point for the next iteration of SQP and this process continues until the final optimal point is found. Deterministic methods can be utilized in order to solve the generated QP at each iteration. [Frank2012a]

Interior point methods (IPMs) have the capability of finding a path to the optimal solution through the feasible region. Due to successful application of IPMs to LP problems, it has also been applied to NLP problems. One of the main advantages of IPMs is the rapid convergence. IPMs may be divided into four categories, primal-dual interior point methods (PDIPMs), predictor-corrector PDIPM, multiple centrality corrections PDIPM and trust region interior point methods (TRIPMs). [Frank2012a]

2.1.5 SummaryoftheDeterministicMethodsUsedinOPFActive-set SLP, SQP methods and different types of PDIPMs are the best deterministic algorithms which are fast, time saving and most accurate in handling nonlinearity [Frank2012a]. Deterministic methods have two main disadvantages [Frank2012a]:

· All provide local optimum · Most are continuous solvers

The disadvantages of deterministic methods have led to significant work on nondeterministic (heuristic) methods and also hybrid methods [Frank2012a].

2.1.6 NondeterministicOptimizationMethodsNondeterministic (heuristic) methods have been developed mainly due to weaknesses of the deterministic methods in finding the global optimum [Frank2012b]. Heuristic optimization methods applied to OPF problems are as follows [Frank2012b]:

Ant Colony Optimization “(ACO) was inspired by the observation of ant colonies establishing shortest route paths between the colony and food sources” [Frank2012b]. In [Teng2003], the authors have successfully applied an ACO approach to an optimum switch relocation problem. [Frank2012b]

Artificial Neural Networks (ANNs) are based on parallel processing similar to the human brain. Parallel processing has led to high speed even when working with large amounts of data with unknown mathematical relations. In [Das2001], optimal capacitor switching in a distribution network has been done by an ANN-based technique. [Frank2012b]

Bacterial Foraging Algorithm (BFA) is based on bacteria’s behaviour when looking for food (foraging) [Passino2002]. In [Tripathy2007], the authors have used a bacteria foraging-based solution to optimize both the real power loss and voltage stability limit. [Frank2012b]

Chaos Optimization Algorithms (COAs) use chaotic variables to find the optimum. In [Jiang1999], authors have used a chaotic optimization method for economical operation of hydro power plants. [Frank2012b]

Evolutionary Algorithms (EAs) are suitable for problems which have evolving behaviour. EAs are good options when dealing with multi-objective functions. The outcome of this algorithm will be a set of solutions. In [Yu2010], “Introduction to Evolutionary Algorithms”, authors have discussed and compared different EAs. The most commonly applied EAs to OPF are artificial immune systems (AIS), differential evolution (DE), evolutionary programming (EP) and genetic algorithms (GAs). [Frank2012b]



Particle Swarm Optimization “(PSO) is based on processes arising naturally in socially organized colonies such as flocks of birds and schools of fish” [Frank2012b]. In [Mo2007], the authors have used PSO to solve OPF problems considering transient stability constraints.

“Simulated Annealing (SA) is a generic, probabilistic meta-heuristic for global optimization that was proposed by Kirpatrick et al. [Kirpatrick1983]” [Frank2012b]. Due to slow convergence and high computational time, this method is proposed to be used with other methods to form a hybrid method. [Frank2012b]

In Tabu Search (TS) method there is a “tabu list that controls search directions so that the solution escapes from local minima and prevents cycling by using flexible memory structures [Frank2012b]”.

2.1.7 SummaryofNondeterministicMethodsUsedinOPFNondeterministic methods are meta-heuristic. Meta-heuristic approaches can deal with non-convexities and complex constraints present in the OPF problems. In addition, meta-heuristics usually do not get trapped in local optimums and, based on the theory, converge to the global optimum. However, meta-heuristics need more time (relative to deterministic methods) to get a result. [Frank2012b]

2.1.8 HybridOptimizationMethodsHybrid methods consist of different optimization techniques which have been packed into one single algorithm (as an entity). In this way, the advantages of each method can cover the disadvantages of the other method. Having a good combination, great results can be achieved. [Frank2012b]

Hybrid methods can be divided into four categories as follows [Frank2012b]:

· Deterministic methods combined · Deterministic and nondeterministic methods combined · Nondeterministic methods combined · Fuzzy logic combined with OPF (“Fuzzy logic is not an optimization algorithm but rather a

mathematical approach for dealing with incomplete or imprecise information” [Frank2012b])

2.2 Congestion Management in Distribution Networks Utilizing Power FlowControl

To improve the control of power flow in electric power systems, the first step is to define the equations that govern the flow of power in the system. Equations (2.2.1) and (2.2.2) represent the active and reactive components of power, respectively, injected at a node. The options for controlling power flow are changes in the network configuration (line conductance Gik, line susceptance Bik) or the active or reactive power at the nodes.

1

( cos sin )N

i i k ik ik ik iki

P V V G Bq q=

= +å (2.2.1)

1

( sin cos )N

i i k ik ik ik iki

Q V V G Bq q=

= -å (2.2.2)



Once the behaviour of a power system is known, it is possible to look for ways to improve the flow of power through the network. In the literature, the main methods for controlling the flow of power are the following:

· Flexible AC transmission systems (FACTS) · Distributed Generation · Generation curtailment · Load shedding · Distributed energy resources

2.2.1 DER(1):FACTSFACTS incorporate electronic power converters that facilitate quick adjustments and control of the electrical system. These devices can be connected in series, in parallel, or in a combination of both and can be used to improve voltage stability, minimise power losses and control power flows to increase grid efficiency [Shi2006]. Reactive support in power systems can alleviate congestion problems by improving the voltage in one bus or area of the network or by supplying sufficient reactive power to reduce the power flow in congested lines. FACTS are considered key components in the operation of smart grids and can be used to control the power flow in a transmission system to relieve congestion and to improve the utilisation of the transmission and distribution system capacity [Divan2007].

There are many types of systems that can be classified as FACTS, which are defined by IEEE as systems using power electronics and other static devices that enhance controllability, and the transfer of power. The following are some of the most common FACTS devices and recent studies of their use in controlling power flow:

· The Static VAR Compensator (SVC) is a shunt-connected static VAR generator or absorber whose output is adjusted to exchange capacitive or inductive current to maintain or control specific parameters of the electrical power system (typically, the bus voltage) [Mithulananthan2003]. Typical SVCs are the thyristor-controlled reactor (TCR), the thyristor-switched reactor (TSR) and the thyristor-switched capacitor (TSC).

· The Static Synchronous Compensator (STATCOM) can be defined as a static synchronous generator operated as a shunt-connected static VAR compensator whose capacitive or inductive output current can be controlled independently of the AC system voltage [Mithulananthan2003], [Moore1998]. The basic element is the voltage source converter, which converts an input DC voltage to an AC voltage at the fundamental frequency with a given magnitude and a controllable phase. The AC output voltage is dynamically controlled to provide the required reactive power to the network.

· In [Wibowo2009] the optimal allocation of FACTS devices was studied to address the problem of congestion management. The locations, types, and sizes of the FACTS devices were the decision variables for the problem. The objective of the congestion management problem was to minimise the generation cost, as shown in (2.2.3), and the installation cost of the FACTS devices (i.e., TSCs), as shown in (2.2.4). The power flow equations and the line voltage and thermal limits are incorporated in the formulation as constraints. Once the optimal allocation was found, the OPF was obtained using sequential quadratic programming.



= + + (2.2.3)

= 0,0003 − 0.03051 + 127.38 (2.2.4)

· In [Yousefi2012] FACTS devices (TSCs and SVCs) were coordinated with the demand response (DR) and conventional generators to relieve congestion. The objective of the optimisation problem was to minimise the generation and load re-dispatch costs given in (2.2.5). The constraints were the power flow equations, the nodal voltage constraints, the generator capacities and the FACTS operating limits.

( + ∆ ) − ( ) + ∆∈

(2.2.5)

In [Reddy2010] TSCs and SVCs were used for congestion management. A multi-objective genetic algorithm was developed to determine the locations and sizes of the TSCs and SVCs to relieve congestion. Three objectives related to congestion relief – minimising branch loading, maximising voltage stability and reducing line losses – were included in the optimisation problem. The GA provided a set of solutions (i.e., a Pareto optimal solution) for the various objectives.

2.2.2 DER(2):DistributedGenerationThe main advantages of DG can be found in the literature [Arief2011], where it has been demonstrated that DG can be a good alternative for congestion management. In [Alonso2010a] DG units are optimally controlled to inject reactive power to the grid, enhancing voltage stability at the connection point.

2.2.3 GenerationCurtailmentIn [Arram2012], generation curtailment of a wind farm was used to improve the behaviour of the power system in terms of voltage and thermal capacity in an active distribution network. Curtailment is introduced in the formulation as a negative generation injected at the DG point of connection.

In [Zhou2007], generation curtailment of DG was used for congestion management in a power distribution network because voltage control methods were not effective in relieving congestion. Generation curtailment can be formulated as an optimisation problem where the objective of the optimisation problem is to maximise the net exported generation, as given by (2.2.6):

max ( − ∆ ) − − ∆ (2.2.6)

where ΔLossi is the loss reduction from generation curtailment at node i, and which can be expressed as (2.2.7):

∆ = ∆ − ∆ (2.2.7)

and γi and ωi are the loss sensitivity factors with respect to active and reactive power injection at the i’th node, respectively, and ΔPi and ΔQi are the active and reactive load curtailments at node i, respectively.



Pi and Loss represent the active power injected at node i and the losses in the power system, respectively, and are independent of the curtailment, so the objective function can be simplified and expressed as in (2.2.8):

min [∆ + ∆ − ∆ ] (2.2.8)

The constraints are the active and reactive generator power capacities, the voltage and the branch thermal limits. The control variables in the optimisation problem are the active and reactive generation curtailments of both dispatchable thermal units and nondispatchable renewable generation units. The optimisation problem was solved using an OPF.

2.2.4 LoadSheddingorLoadCurtailmentIn literature, load curtailment is traditionally approached in two ways:

· Curtailing some loads while supplying energy to critical customers. · Curtailing load demands with the aim of both maximising voltage stability and minimising power

losses.

Regarding these objectives, the problem of load curtailment can be summarised as follows:

· Determine the optimal value of load demand that must be curtailed. · Choose the optimal location of the load to be curtailed.

In [Arief2011], it is shown that load curtailment can successfully alleviate congestion problems in grids with high renewable penetration levels. In this paper, the problem of optimal load curtailment was formulated in terms of the curtailment cost which was defined as follows:

The objective of the optimisation problem was to minimise the total curtailed load cost:

min ∆ (2.2.9)

The optimal solution must satisfy the power flow equations, the voltage requirements and load limits. The problem formulation considered that certain loads cannot be shed, so there was a constraint related to the minimum load level for selected customers. Moreover, an inequality constraint was included to avoid reverse power flow in the radial network.

In [Malekpour2008], the optimal load curtailment problem with DG was formulated in terms of minimising the sum of the curtailed loads and the real power losses during deficiency generation conditions. The optimal load curtailment problem was defined as follows:

· The objective of the load shedding problem was:

min + , (2.2.10)

where Wk is a weighting factor on customer k, Pk is the curtailed load of the k’th customer, Rh is the resistance of branch h and Ih is the absolute current in the h’th branch.



· The constraints in the problem included the power flow equations, the voltage limits, the generating capacity limits and the line thermal capacities.

· A GA was used to obtain the solution of the problem.

The formulation was applied successfully in a simulated radial distribution network.

2.2.5 FlexibleLoads(EVs)The controllability and flexibility of the EV load offers the DSO new possibilities for congestion management. Examples of the capabilities offered by EVs can be found in the literature; see, for example, [Hu2013] and [Leemput2012].

Management of EVs was analysed in [Han2012]. The load management problem was defined as follows:

· The EV charging strategy was selected to minimise power losses. The objective was to minimise the active power losses in terms of the line characteristics (resistance) and the line current.

· Constraints included the state of charge of the batteries and the maximum power of the chargers. · ZigBee technology was used to relay the charging parameters of the EVs (initial state of charge,

battery capacity and connection time). · Quadratic programming was used to find the optimal solution.

2.3 Congestion Management in Distribution Networks Utilizing NetworkReconfiguration

Automatic network reconfiguration has been applied to distribution networks to find a radial operating configuration that optimises certain objectives while satisfying all the operational constraints without islanding any nodes.

Distribution systems may be designed as weakly meshed networked systems in urban areas, but the majority of distribution systems operate with a radial topology for technical reasons. Thus, the topology constraint is present in nearly all distribution expansion and operational planning problems.

Given a network containing n switches, there will be 2n possible configurations corresponding to the states of the switches (i.e., open or closed). Some of these configurations are not permissible because they yield either a disconnected system with several islands or a nonradial configuration.

The goal of network reconfiguration is to change the topological structure of the distribution feeders by closing some normally open switches and opening some normally closed switches in their place. The network configuration should remain radial after the switching operations.

The problem of distribution network reconfiguration is a highly complex, combinatorial, nondifferentiable optimisation problem because of the large number of discrete switching elements. Furthermore, the radial constraint typically introduces additional complexity in the reconfiguration problem for large distribution networks [Shariatkhah2012]. The problem of distribution network reconfiguration belongs to the category of nondeterministic combinatorial optimisation problems [Enacheanu2008] and has conventionally been considered as a mixed integer nonlinear programming problem. Classical methods using a mixed integer linear programming formulation have been used for solving reconfiguration problems in large-scale distribution systems, but these methods are prone to converge to a local minimum and not to the global minimum.



In many studies, reconfiguration has been used in normal conditions to improve system parameters such as power loss, the voltage profile and power balancing. In several studies, reconfiguration was used to restore the interrupted loads in emergency conditions.

In [Ramaswamy2011], feeder reconfiguration was investigated for distribution network congestion management. The main objective was to minimise branch overloads. However, if there was no congestion in the network, it was assumed that the configuration with the minimum loss was the optimum configuration.

2.3.1 ClassicalOptimisationMethodsIn [Abur1996], reconfiguration was achieved using a modified linear programming algorithm for a minimum-cost network flow problem, where the simplex algorithm was modified to solve for a radial network configuration that did not violate any line capacity limits and minimised losses. The algorithm starts from the power balance equations at each node and neglects network losses and voltage constraints. With these assumptions, a feasible solution to the network reconfiguration problem for minimum loss can be obtained using a simple and fast linear programming approach. Although this approach is useful for loss reduction, it is not able to address other objectives such as minimising the number of switching operations. Moreover, this method gives only a sub-optimal solution. In [Celli2005], the same approach was extended to distribution networks with distributed generation, defining the objective function as the weighted sum of the absolute power flows through all of the network branches, the power generation from each controllable DG unit and, if necessary, the load constrained by the DR actions. Consequently, only the real power injections and the branch resistances were considered in the optimisation procedure.

A MILP-based approach for minimising losses and the number of switching operations was presented in [Moradzadeh2011]. The objective function, which included branch overloads and the number of control actions (consisting of suitable line-opening operations), was minimised. The deterministic “branch-and-bound” decomposition algorithm used can provide optimal solutions for problems with convex constraints.

In [Franco2013], the reconfiguration problem for distribution systems with distributed generation was posed as a MILP problem. The demands of the electric distribution system were modelled using linear approximations in terms of the real and imaginary parts of the voltage, taking into account the typical operating conditions of the electrical distribution system. This MILP approach was applied to one test system and two actual systems, showing good performance. It should be noted that the loads were modelled as constant power, constant current and constant impedance loads.

2.3.2 HeuristicAlgorithmsHeuristic algorithms have been applied to the problem of network reconfiguration for loss reduction in several studies. The objective of reconfiguration in [Baran1989] was to reduce losses and balance the loads, and various search algorithms were used to obtain solutions. A branch-exchange strategy was used to guarantee the radial structure of the system. The differences between the search algorithms depend on the sensitivity analysis used to decide which branch should be removed/opened at each step. Thus, the radial topology constraint of the system is imposed implicitly by the heuristic algorithms and not explicitly in the model. Heuristic techniques attempt to find solutions to optimisation problems using information from a performance index based on intuition. Evolutionary algorithms, genetic algorithms, simulated annealing and ant colony optimisation are examples of heuristic algorithms that have been used for network reconfiguration. These techniques can be summarised as follows:



· Evolutionary Algorithms: It is known that the computation times for evolutionary algorithms are large when used for reconfiguration of large-scale networks because of the encoding process. In [Delbem2005], the authors proposed a new tree encoding based on graph chains that reduced the computation time.

· Genetic Algorithms: References [Mendoza2006] and [Carreo2008] used two specialised genetic algorithms for network reconfiguration. In [Mendoza2006], a new GA was proposed that was based on the construction of an initial population of feasible individuals using the system loops and applying the specialised genetic operations of accentuated crossover and directed mutation. This approach reduces the search space so that only feasible radial topologies are evaluated, minimising the required memory and CPU time. All approaches implicitly considered the radial operation constraint. Reference [Carreo2008] proposed a new codification that permits the genetic operations of recombination and mutation so that radial topologies are always generated. In [Delbem2005], the radial operation constraint was assured using graph theory, whereas in [Mendoza2006] and [Gandomkar2005], the constraint was imposed inside the genetic operations. The problem of choosing the states of the switching devices (open or closed) in primary distribution networks to minimise total loss was addressed in [Schmidt2005]. In [Ramos2005], a genetic algorithm approach was compared with a classical MILP. All bus voltage magnitudes were assumed to be 1 p.u. in the evaluation of the losses, and the radial topology constraint was enforced using a heuristic based on a comparison between the node-to-substation path resistance and the resistance of the shortest path to the substation for that node. Results showed that the performance of the GA was significantly affected by certain parameters such as the population size, the number of generations and the mutation probability, and it was necessary to execute the GA several times to properly tune these parameters. Moreover, the computational load of the GA was higher than that of the classical MILP approach. The performance of the methodology proposed when embedded generators were included was not detailed.

· Simulated annealing: In general, only offline approaches have been studied, but smart distribution grids are characterised by extensive networks with continuous demand and generation fluctuations, requiring real-time optimal network reconfiguration. A real-time approach was presented in [Vargas2012]. In collaboration with an Italian distribution company, the feasibility of this approach for loss reduction and congestion management in real time was tested on a smart grid project. The results were obtained using a detailed simulation of a realistically sized urban distribution network (approximately 1000 nodes). The starting configuration was identified through a topology processor and a state estimator. The topology module collected the status of each switch and returned an actual snapshot of the grid. The state estimator module calculated voltages, currents and angles based on pseudo-measurements from the field. An automatic network reconfiguration module evaluated the necessary switching to achieve the grid configuration with the smallest losses or requiring the least control effort during active power rescheduling. The reconfiguration problem was solved through a simulated annealing algorithm that allowed straightforward integration with other system analysis and optimisation tools [Bruno2012] [Granelli2006].



2.4 SelectionofObjectiveFunction

2.4.1 OverloadofLines/CablesandTransformersCongestion management must consider the problem of exceeding the thermal capacity of lines, cables and transformers in power systems. In the literature, overloads in lines are frequently included as inequality constraints in the problem formulation, as shown in (2.4.1) [Bhattacharjee2012]:

| | ≤ (2.4.1)

where Pb is the power flow in line b and Pbmax is the thermal capacity of line b.

In other studies line overloads are included in the objective function.

2.4.2 OverloadasaSingleObjectiveFunctionThere are several studies in the literature that incorporated the capacity limit as a single objective. In [Hazra2007], the line overload was used as the objective function, as shown in (2.4.2), to satisfy the congestion management requirements.

min − (2.4.2)

where LFb is the power flow in line b and Lcapb is the thermal capacity of line b.

2.4.3 OverloadasaPartofaMulti-objectiveFormulationIn [Vijayakumar2012], a multi-objective formulation was used for congestion management in transmission networks. Overload congestion was alleviated by rescheduling generators and/or load shedding. The multi-objective formulation included three objectives.

· The first objective was to minimise the total congestion cost:

= ∆ + ∆ (2.4.3)

· The second objective was to minimise the thermal congestion :

=0, ≤

( − ) , ≥ (2.4.4)

Thermal congestion was represented as in (2.4.4) where if the power flow in line ij exceeds the thermal capacity limit, then the thermal congestion cost is proportional to a penalty factor (α=1000) and the square of the excess power.

· The third objective was to minimise the voltage deviation index:

=0, ≤ ≤

( − ) , ≤( − ) , ≥

(2.4.5)

Heuristic techniques were used because the proposed problem is a complex, combinatorial optimisation problem. Conventional optimisation techniques such as gradient-based methods cannot be applied because they require the existence of the first and second derivatives [Vijayakumar2012].



2.4.4 VoltageDeviationVoltage deviation is another important issue in congestion management. Traditionally, techniques for congestion management have included voltage deviations as constraints in the optimisation problem [Arram2012], [Alonso2012], [Conejo2006], [Reddy2010], [Balaraman2010], as shown in (2.4.6), where , and are the voltage and the upper and lower voltage limits, respectively, at bus i.

≤ ≤ (2.4.6)

2.4.5 VoltageDeviationasaPartofaMulti-objectiveFormulationThere are several studies in the literature in which voltage deviations were included in an optimisation problem using a multi-objective formulation.

Reference [Vijayakumar2012] is one such example in which a multi-objective formulation was used. One of the objectives was to minimise the nodal voltage deviations. In this case, a penalty factor β, as shown in (2.4.5), was used to penalise those solutions that violated the voltage constraints.

In [Balaraman2010], differential evolution was used to solve the congestion management problem. The goal was to minimise deviations from previous generation schedules and the congestion cost. The penalty on voltage deviations was defined using the sum of the squares of the voltage deviations at every bus in the system, as shown in (2.4.7), and incorporated in the multi-objective function shown in (2.4.8) with a penalty factor (pf).

(∆ )2

=1

(2.4.7)

min = + − + (∆ ) (2.4.8)

2.4.6 Multi-objectiveFormulationsSeveral multi-objective formulations have been developed for the congestion management problem and solved it successfully. In the literature, there are a large number of examples; several are described in this subsection.

· In [Hazra2007] PSO was used to solve a multi-objective optimisation problem to alleviate congestion in the lines and minimise the cost of production. The objectives were the line overloads and the costs of shifting generation and loads, as given by:

min ( + + ) + ( + ′ + ′ ) (2.4.9)

The equality constraints were related to the power flow equations, and the inequality constraints included the active and reactive capacities of the generation units and the nodal voltage limits for every bus.

PSO was used because of its ability to find a global minimum for complex nonlinear optimisation problems such as congestion management.



· In [Vijayakumar2012], a multi-objective approach that combined the minimisation of rescheduled generation congestion costs, as given by (2.4.3), transmission congestion in terms of line flows, as given by (2.4.4), and voltage deviations, as given by (2.4.5) was developed. The equality constraints were related to the power flow equations, and the inequality constraints included the active and reactive capacities of the generation units, the voltages at the buses and the thermal capacities of the lines. The control variables were the active and reactive reference signals and the loads shed.

Two approaches were used to obtain a solution to the problem: fuzzy evolutionary programming and a no-dominated-sorting genetic algorithm. Fuzzy evolutionary programming performed well in finding a unique solution to both objectives of the optimisation problem. The no-dominated-sorting genetic algorithm found a set of Pareto optimal solutions. The feasibility of the proposed methods was demonstrated on IEEE 30 node test feeder for three cases: Case A was a line outage with the total load increased by 40%, Case B was a line outage and a loss of generator 2, and Case C was a line outage and a 50% increase in the loads at all the buses.

· In [Reddy2010], the congestion management problem was formulated as a multi-objective optimisation problem that maximised both branch capacity, voltage stability, and minimised active power losses. In this optimal allocation problem, the types and sizes of the FACTS devices were the control variables. A genetic algorithm was used to solve the optimisation problem.

· In [Balaraman2010], the costs incurred in generator rescheduling, voltage deviations and power flows in the lines were combined to define a multi-objective problem for congestion relief. The objective function was defined as the total cost of rescheduling power generation from the selected generators, as defined in (2.4.8), and pf is penalty factor used to weight line overloads and voltage deviations in the multi-objective formulation.

The equality constraints were related to the power flow equations, and the inequality constraints included the active and reactive capacities of the generation units and reliability constraints (the voltages at the buses and the thermal capacities of the lines).

A differential evolutionary algorithm provided the solution to the optimisation problem.

2.5 CongestionManagementConceptSeveral aspects of the congestion management concept in IDE4L affect the recommendation that follows from the state of the art study. In the IDE4L congestion management concept a hierarchical control structure is used together with state estimation and forecasting.

The forecasting allows for more accurate state estimation, as it gives more accurate estimates of unmeasured load and production than the estimates normally used. State estimation in turn allows the controllers full access to any network state, removing the concern of input availability when evaluating congestion management controllers.

The hierarchical control structure divides the congestion management into secondary and tertiary control – each of these having a different role in the control structure, and thus also different requirements. The recommendation is therefore divided into a recommendation for each control level.



2.6 RecommendationinRegardtotheIDE4LConceptThere are several schemes to fulfil congestion management: voltage control, load management, network reconfiguration and micro production regulation.

· Control methods based on only local measurements (local reactive and active power control) can improve the network voltage status. However, due to access to a state estimator, it is beneficial to use the information from the SE for control purposes (coordinated method). Performance of the coordinated methods is better than methods using local measurements. Using SE information, data transfer platforms should also be considered. [Kulmala2014a]

· Voltage control can be done by OLTC regulation and regulation of reactive power from capacitor banks and/or STATCOM devices.

· DG units offer the possibility to inject reactive power to the network by reducing their active power injection (power curtailment). This option should be considered in the IDE4L project as an effective way to regulate voltage in the whole distribution network.

· The optimisation algorithm for congestion management should be defined in terms of multi-objective formulation, because this formulation offers the advantage of dealing with different objectives simultaneously.

· Congestion management algorithms can be implemented in secondary controllers (MV and LV) and can have a 1 minute time frame and also in the tertiary controller which can be executed in scheduled time frame (i.e., after the day-ahead market clearing or after each of the intraday markets clearing) and also “on demand” (post-fault situations or/and MV secondary controller requests). Several optimization solvers can be applied for congestion management purposes such as interior point methods or genetic algorithms. However, considering the IDE4L network dimension it is expected that the application of simpler algorithms such as IPMs could provide successful results. If IPM algorithms fail to converge to the optimal solution, a hybrid-based algorithm can be used, where the problem is divided into two sub-problems. IPM methods can be utilized for the deterministic part of the algorithm and one of the nondeterministic algorithms can be used to deal with discrete control variables [IDE4L2014c]. A summary of the main aspects are shown in the following tables.

CONGESTION MANAGEMENT ALGORITHM: Secondary Controllers

· Located in both secondary controllers (MV and LV) · Execution interval: 1 minute

Objective function Main constraints Output signal Action on

· Minimise power losses

· Minimise production curtailment

· Minimise load shedding

· Minimise Number of tap changer operations

· DER capability limit · DG capability limit · Load curtailment limit

(for those customers which offer this service)

· Congestion management constraints: voltage limits, branch current thermal constraints.

OLTC Transformers

Qref STATCOMs

Controllable VAR devices

PDG,ref

QDGref

Controllable DG sources

Pload,ref Specific customers



· Minimise voltage deviation

which offer load curtailment service

CONGESTION MANAGEMENT ALGORITHM: Tertiary Controller

Located in DSO control centre

Execution interval: in scheduled time frame (i.e., after the day-ahead market clearing or after each of the intraday markets clearing) and also “on demand” (post-fault situations or/and MV secondary controller requests)


· Minimise the number of switching operations after restoration

· Minimise power losses

· Minimise the cost of flexibility products used to solve the congestion

· The distribution system should be radial without meshes after network reconfiguration

· Fulfilment of connectivity of all customers demand through the state of switching devices.

· DER capability limit. · Congestion

management constraints: voltage limits, branch current thermal constraints.

· Flexibility limit (from commercial aggregators).

Switching status (on/off)

Switching devices of MV network

OLTC Transformers

Qref Controllable VAR devices

Accepted flexibility bids

(purchase/activation)

Commercial aggregators



3 DesignSpecificationsThis chapter describes the overall control concept and the design specifications from which the control algorithms were designed.

3.1 ControlConcept

3.1.1 ControlSystemHierarchyThe control concept within the IDE4L project is based on a hierarchy of controllers. These controllers are tertiary, secondary and primary controllers.

Primary controllers are always located next to the control resource (DERs or DSO’s units like OLTC or STATCOM) and they operate independently based on local measurements and therefore they respond immediately to disturbances. The setpoints of primary controllers may be adjusted remotely. In the semantic of the automation system they are called intelligent electronic devices (IEDs) [IDE4L2014e, IDE4L2015a]. Primary controllers are for example: automatic voltage control (AVC) relay of OLTC, automatic voltage regulator (AVR) of DG units in droop or power factor control mode, and active power curtailment of DG units.

Secondary controllers are located at substation level (primary or secondary substation) depending on which network, MV or LV, they are managing. The aim of a secondary controller is to coordinate the operation of primary controllers of the selected control area in order to avoid congestions and to optimize network operation. Therefore, it requires information on the state of the control area (measurement data, topology of the network, connection of DG units) in order to optimize the operation of the network in the control area. The secondary controller adjusts the setpoints of primary controllers within its control area, which is the method to integrate primary and secondary controllers. The secondary controller consists of a control function (e.g. OPF software), necessary interfaces to receive and send information, and a database (also called DXP) within the primary substation automation unit (PSAU) or secondary substation automation unit (SSAU) in the semantic of the automation system [IDE4L2014e, IDE4L2015a].

The tertiary controller has the responsibility to optimize the whole network. Due to scalability issues the tertiary controller focuses on the TSO-DSO interface and the MV network. While primary and secondary controllers are running in real time, the tertiary controller is running both day-ahead and in real time in specific situations (i.e. post-fault situations or/and when the medium voltage power control (MVPC) requests help). The tertiary controller is planning the optimal network topology (network reconfiguration) for the next day while considering boundary conditions between control areas (TSO-DSO and between secondary control areas) and forecasted states of the network to prevent congestion. In addition to network reconfiguration the tertiary controller takes responsibility to validate network acceptance for demand response actions of commercial aggregators within the DSO’s network, and to purchase flexibility services (scheduled and conditional re-profiling [IDE4L2015d]) from commercial aggregators if needed to solve congestion. In real-time mode the tertiary controller is supporting fault location, isolation and supply restoration (FLISR) functionality. The fast isolation and restoration phase is realized by IED-IED GOOSE communication within open ring MV feeders [IDE4L2015b]. The role of the tertiary controller is to act as slow restoration functionality after the fast isolation and restoration phase in order to enlarge the area where supply is restored by utilizing remotely and manually controlled switches. The real-time tertiary controller can also be called by the MVPC algorithm if the MVPC is unable to solve some congestion.



Figure 3.1.1 illustrates the hierarchy of the controllers and their interactions. Primary controllers are located at the bottom of the figure because they are the most distributed part of the system. Several primary controllers and IEDs are presented in order to indicate different location (MV or LV) and ownership (DSO or customer). Secondary controllers are in the middle and both MV and LV grid level controllers are presented. They coordinate primary controllers belonging to their control area. Coordination and interaction between secondary controllers is explained in the next subsections where more details are presented. The tertiary controller is at the top and adjusts network topology remotely via the entity of DMS/SCADA, and manually via the workforce management system to send work orders to field personnel.

HEMS / Microgrid

Tertiary controllerCommercial aggregator

Secondary controller (MV grid)

Secondary controller (LV grid)

Primary controller (DERs with direct control, AVR of

DG)

Primary controller (DERs with direct control, AVR of

DG or load control)

Tertiary controller (Day-ahead scheduling)

Primary controller (DERs with

indirect control)

Secondary controller

(Coordination of resources within HEMS/microgrid)

Primary controller (DSO’s units, AVC

of OLTC)

SCADA / DMS

Network topology

IED of Circuit breaker or remotely

controlled disconnector

Workforce management

system

IED of manually controlled

disconnector

Flexibility services

Reference points (e.g. voltage, reactive

power)

Reference points (e.g. reactive power flow)

Open / close switch remotely

Primary controller (DSO’s units, AVC

of OLTC)

Reference points (e.g. voltage, reactive

power)

Network topology

Open / close switch locally

Figure 3.1.1. Hierarchy of controllers (colours are for visual clarity).

3.1.2 DirectandIndirectControlofDERsControl commands to primary controllers may be sent directly (like described in previous paragraphs) or indirectly. Direct control of customer owned DERs requires a bilateral contract between customer and DSO. The grid code, which determines connection rules for DERs, might be used to mandate control and communication capabilities for DERs. Retroactive modifications of DERs hardware and software might be very costly or almost impossible in practice. A good example of such requirements is a voltage or reactive power control of PV inverters and a standard based communication interface (e.g. IEC 61850 MMS server) to communicate with the inverter. Without a specific requirement in the grid code, end-customers probably have no interest to invest in capabilities which will provide benefits for the DSO and therefore all customers of the DSO. Typically grid codes are changed only if really needed, but then the changes come too late from the power system viewpoint. A good example is the so called 50.2 Hz problem in Germany [FedMin2012, VDE2011]. Therefore necessary changes in the grid code should be introduced proactively based on long-term scenarios of power system development and needs.

The DSO should also have the right to directly control DERs in emergency conditions. The emergency control of DERs should be restricted to situations when the DSO’s own resources and market based flexibility services are not able to solve the congestion problem. These are unexpected conditions like unavailability of flexibility services for congestion management when a long-term contract of flexibility



service between commercial aggregator and DSO exists. The aim of emergency control is to ensure stable operation of the power system during a failure of the flexibility service market. Emergency control of DERs might include load shedding, increment of noncritical loads and DG curtailment. The cost of the emergency control to the DSO should be significantly higher than the cost of using the DSO’s own resources or market based flexibility services to solve the congestion.

Emergency control actions should also be coordinated with the protection system i.e. emergency control should always act before protection in congestion conditions, but should not act before all normal control resources are utilized. Of course during fault conditions emergency control should not prevent or delay the functioning of the protection system.

Figure 3.1.2 presents an example of coordination of the primary voltage control (AVR of DG unit), emergency control (production curtailment of DG unit and load shedding) and loss of mains protection of a DG unit (disconnection of a DG unit in case of unintended islanding). The example utilizes both the time and the voltage grading to coordinate controls and protection. The AVR of the DG unit has a dead-band presented by the green area in the figure. Voltage droop is active within the orange area in order to keep the voltage between acceptable limits defined by the DSO. Voltage droop is adjusted to fully utilize reactive power resources of a DG unit when the voltage measured by the AVR reaches the minimum (90 %) or the maximum (110 %) limit. The OLTC actions will also impact voltages at the terminal of the DG units and therefore the operating time of control is extended to 60 s which is assumed as a control delay of the AVC of the OLTC. Lines in the figure represent protection and emergency control settings. The settings of over- and under-voltage protection (of loss of mains protection) are presented with the outermost lines. The red line is the fast protection stage and the blue line is the slow protection stage of definite time voltage relays. The chosen settings of Figure 3.1.2 are modified from existing grid codes to fulfil the coordination requirements. Correspondingly the orange line represents the fast emergency control stage and the green line represents the slow emergency control stage. Next the basic ideas of coordination of the example case are explained starting from the left side of the figure. Time grading is utilized to coordinate the fast stages. The fast stage of emergency control (horizontal orange line) has to be the fastest. When the voltage drops below 85 %, load shedding is needed to improve the voltage level and to prevent more serious consequences like disconnection of a DG unit, which would happen if voltage drops below 80 %. The fast stage of under-voltage protection should, however, be faster than the fast auto-reclosing of the MV feeder protection (horizontal red line at 400 ms) in order to enable successful auto-reclosing. The slow stages are coordinated in a similar way, but settings should not be set too close to the lower limit of normal voltage (90 %). The coordination of over-voltage protection and emergency control (production curtailment of a DG unit) is realized by principles similar to the coordination of under-voltage stages. The time delays of the slow stages are adjusted to relatively high values in order to ensure the operation of the OLTC before emergency control or over-voltage protection actions.



Ope

ratin

g tim

e [s

]

60 s

10 s

1 s

400 ms

50 ms

0 % ... 80 % 85 % 90 % 95 % 100 % 105 % 110 % 115 % 120 %

Voltage [%]

Figure 3.1.2. Example of coordination of controls and protection of a DG unit.

Indirect control of DERs is based on flexibility services of commercial aggregators like scheduled or conditional re-profiling. The tertiary controller purchases flexibility services from commercial aggregators in order to influence the power demand, storage or production within the congested network. Therefore the location, capacity, and moment and duration of availability of flexibility services are necessary information for the tertiary controller. At the moment a local market for DSO level flexibility services does not exist. Therefore, bilateral contracts between the DSO and commercial aggregators will be utilized first.

Another important aspect would be the transparency and liquidity of such a market in order to ensure the proper functioning of the market. Congestion management would be very dependent on flexibility services and therefore the availability, trust and predictability of the price of flexibility services should be at a very high level. The commercial aggregator has contracts with end-customers and microgrids to control their DERs (volume based control of conditional re-profiling service) or to indirectly influence the behaviour of customers (price based control of scheduled re-profiling service). The commercial aggregator will utilize DERs in different electricity markets like day-ahead, intraday, intra-hour, balancing power and reserve markets in order to maximize profit from the flexibility of DERs. Most probably the tertiary controller will purchase conditional re-profiling service from commercial aggregators for occasional congestion conditions (e.g. extreme and unlikely combination of load and production) and scheduled re-profiling services for predictable congestion conditions (e.g. peak load condition of cold/heat wave).



3.1.3 DetailedInteractionsoftheControlSystemFigure 3.1.3 presents the detailed description of interactions between the hierarchical levels of the control system and the functioning of the congestion management in different time frames. The time frames consist of three slots called day-ahead, intra-hour, and real time.

The hierarchy of the control system is visualized with colours in Figure 3.1.3. Different functionalities of the controllers are presented in separate blocks in order to illustrate the interactions of the control system more clearly. For example the tertiary controller consists of dynamic grid tariff calculation (tariff calculation), offline validation which consists of two functions (network reconfiguration and market agent (MA)), slow restoration (real-time implementation of network reconfiguration), and real-time validation. Details of tariff calculation will be published later in D5.4 [IDE4L2016].

In addition to the controllers Figure 3.1.3 also includes supporting functionalities. These are forecasting, monitoring/estimation and market functionalities. A detailed description of forecasting and estimation functionalities is presented in [IDE4L2015c]. The monitoring system and functionalities are described as part of the automation system specification [IDE4L2014e, IDE4L2015a]. Market functionalities are published in [IDE4L2015d].

Figure 3.1.3. Detailed interactions of congestion management.

Intr

a-ho

urRe

al-t

ime

Day-

ahea

d



3.1.3.1 Day-aheadTimeFrameThe sequence of congestion management starts day-before actual operating hour (well before day-ahead market closing). Long-term forecasts of load demand and production (day-ahead forecasts) feed hourly forecasts in tariff calculation and offline validation. Dynamic grid tariffs are published for all commercial aggregators/end-customers in order to encourage customers to move their load (load shifting) from network peak hours to off-peak hours. The dynamic grid tariff is based on a locational pricing scheme in order to allocate load shifting correctly from a congestion management viewpoint. Because the commercial aggregator has the role of retailer, the dynamic grid tariff has an impact on the purchase of energy from the day-ahead market. After the day-ahead market bidding process, the DSO will validate if the proposed schedules for load demand and production will fit within the local distribution network constraints i.e. validate if there exist congestion during the next day. If congestion does not exist then the market may be closed, otherwise the tertiary controller will try to mitigate congestion by reconfiguring the network and considering the coordination of voltage controllers. If these do not solve the problem, then help is requested from the market agent to purchase scheduled and/or conditional re-profiling services from commercial aggregators in the day-ahead flexibility market. The purpose of this flexibility market is to handle flexibility transactions for solving technical restrictions through the presentation of flexibility sale bids from commercial aggregators that can be used by the network operator. Scheduled re-profiling services may be purchased from e.g. day-ahead energy markets. Conditional re-profiling is purchased from a local flexibility market. Clearing of such a market might be organized by the DSO in a similar way as the balancing power market at TSO level or these markets might be combined. Independent flexibility market clearing ensures transparent participation of several commercial aggregators in this market.

The market agent will inform the short-term forecast about purchased services in order to adapt intraday and intra-hour forecasts. The day-ahead energy market and flexibility market clearing will in turn inform commercial aggregators about accepted bids, which together will create a final schedule for commercial aggregators. Commercial aggregators will forward the price incentives for the operation day to its consumers/prosumers in order to activate scheduled re-profiling products. The market agent will also send activation signals of conditional re-profiling to commercial aggregators. The third interaction between controllers is between the tertiary and secondary controllers. Because the secondary controller requires an accurate and up-to-date model of the network of its control area, it has to be informed of the changes to the network topology. The tertiary controller working in real time does not have any conflict with the secondary controller (OPF.MVPC), because the real-time tertiary controller acts only after post-fault situations and secondary controller requests.

3.1.3.2 Intra-hourTimeFrameThe intra-hour time frame has two purposes from a congestion management viewpoint. First the commercial aggregator should send control signals to DER control, if scheduled re-profiling has been activated by the market agent. Internal processes of the commercial aggregator are not presented in the figure, only final outcome of portfolio optimization as a form of control setpoints (price or volume) is presented.

Secondly the cost parameters of the OPF cost function within the secondary controllers may be updated in order to adapt them to forecasted power flow conditions provided by the state forecast, and to changes in network topology. The formulation of the OPF in the secondary controller is a multi-objective problem which may have multiple optimal operation points dependent on the preferences of the DSO. These preferences are indicated in the form of OPF cost parameters. The power control parameter update



functionality updates the OPF cost parameters of the real-time secondary controllers (for more details see subsection 4.1.2).

3.1.3.3 Real-timeTimeFrameThe real-time time frame follows a structure similar to the previous time frames except real-time monitoring is utilized instead of forecasting. Monitoring functionality collects and stores all available measurement and status data from the control area into the database of the SAU. The same clients and servers as those used for monitoring will also be used for sending control commands and requests to other controllers. Therefore, other real-time functionalities like state estimation and secondary controller receive necessary data from the SAU database.

Because measurements may be erroneous or incomplete state estimation is needed to provide necessary inputs for the real-time secondary controller. The state estimation utilizes pseudo measurements to enhance the observability of the network. Pseudo measurements are typically customer group based load profiles for end-customers. Smart metering data from a few recent years is needed to create such profiles. In IDE4L project, the load and production forecast algorithms provide the pseudo measurement input data for the state estimation algorithm. For example temperature dependency of load and generation profiles is included in this way.

The secondary controller focuses on coordination of voltage controllers which are under direct control of the DSO and minimizes the amount of production curtailment or load control needed in the control area. Therefore the secondary controller is adjusting settings of two different kinds of primary controllers: IED and DER control. IEDs include for example AVC of OLTC and AVR of STATCOM, both owned by the DSO. DER control includes both the contracted control and the emergency control of DERs. Contracted control includes for example AVR of DER or production curtailment in case of nonfirm connection contract. See reference [IDE4L2014d] to find more details about firm and nonfirm connection of DERs. Emergency control should be mandated by the grid code and may include all controllable DERs. The secondary controller will also inform the commercial aggregator in case of emergency control of DERs.

The real-time secondary controllers may also request help from the tertiary controller (slow restoration and real-time validation in Figure 3.1.3) to solve the congestion problem. Slow restoration may reconfigure the network to solve the problem. The SCADA/DMS needed to communicate with IEDs in that case is not visualized in Figure 3.1.3 but was included in Figure 3.1.1. If network reconfiguration is not able to solve the congestion problem, then the MA real-time validation is requested. In that case the real-time validation will request activation of conditional re-profiling services from commercial aggregators.

The emergency control of the secondary controller is activated if the congestion problem is not solved within the required response time (e.g. 15 min). The response time might depend on the severity of the congestion, thermo-dynamic time constant of overloaded components, applied safety margins (margin between operational limits and protection/emergency limits) and knowledge of actual emergency limits (e.g. if dynamic line rating or real-time monitoring/estimation of the transformer hot-spot temperature is used or not).

The BOT (Block OLTCs of Transformers) coordinates the functioning of real-time secondary controllers and AVC relays of OLTCs in case where two or more OLTCs are operated in cascade. Depending on the location of voltage disturbance, the BOT will indicate which OLTC and corresponding part of the secondary controller should be blocked and when the blocking may be released.



If a fault has occurred, then the slow restoration is activated to isolate the fault and restore supply. Other functionalities (secondary and tertiary controller) are waiting meanwhile.

3.1.4 AlgorithmExecutionandUseofFlagsintheReal-timeTimeFrameThe algorithms making up the control concept of the IDE4L project are dependent on each other. This dependency means that the algorithms are running sequentially. Forecasting provides inputs for state estimation, so state estimation cannot begin execution before forecasting is finished. In the same way, state estimation provides inputs to the power control algorithms, so they cannot begin execution before the state estimation is finished. It is therefore necessary to coordinate the execution of algorithms in time.

This temporal coordination of algorithms can be performed in several ways. One must first consider that every part of the control system is essentially a discrete time component (i.e. software and digital hardware running with a fixed sample time). As this is a control system it is necessary to consider the time delay of the system. Each algorithm in the control concept has different execution times, and measurements come in at fixed intervals. This must be considered when choosing the temporal coordination of algorithms.

Two approaches are considered; execution triggered by the sample clock of the system and execution triggered by flags. For the purpose of this discussion the first approach will referred to as parallel execution, while the second approach will be referred to as sequential execution.

The main differences between parallel and sequential execution are the system delay and the performance requirements of the algorithms. As all the algorithms in WP5 are dependent on each other in a sequential manner, the system delay with parallel execution is:

= ⋅

Where is the system delay, is the system sample time and is the number of algorithms. In the real-time time frame = 2, as only state estimation and power control (for the sake of simplicity, all power controllers are considered as one entity here) are relevant.

With sequential execution, all the algorithms run within the time frame of a single sample and thus the system delay is:

<

When it comes to the performance requirements for the algorithms, the difference is just as pronounced. With parallel execution the performance requirements for the algorithms are:

< <

Where tse is the execution time of state estimation and tpc is the execution time of power control. With sequential execution the performance requirements for the algorithms are:

+ <

Looking at performance, there are two extremes, with each of the extremes favoring one of the execution approaches. In a situation where = the system delay would be identical for the two execution approaches, but the sample time would be twice as long for sequential execution. The longer sample time



of the sequential execution would likely result in the parallel execution giving better system performance due to the higher temporal resolution.

In the situation where ≫ , the system delay with parallel execution would be nearly twice that of observed with sequential execution, while the sample time would be practically identical. In this case the lower delay of sequential execution would result in it displaying better system performance.

Since the execution times of the power controllers are expected to be significantly longer than those of forecasters and state estimators, the time delay of the control system is reduced to a minimum with sequential execution (i.e. the use of flags). Therefore, the use of flags is the approach chosen in the IDE4L project. Each algorithm in the control structure will set a flag once it is done executing to inform the other algorithms that the data it provides is now ready.

Apart from temporal coordination, additional flags are implemented to improve algorithm execution time and reduce load on the database. These flags are mainly used to avoid reading and importing of static data, such as topological information, at each execution of the algorithms. These are large data sets that rarely change and require processing in the algorithms.

3.2 SecondaryControlThe secondary control consists of three parts: Real-time power control (PC) aims to keep the network in an acceptable state and to optimize its operation based on the current network state. Power control offline cost parameter update (PCPU) utilizes forecaster data and aims to prevent back and forth operations (i.e. hunting) of the tap changers by changing the cost parameters of the real-time optimization algorithm. The BOT unit (Block OLTCs of Transformers) aims at preventing simultaneous operation of the HV/MV and MV/LV tap changers and the MV and LV power control algorithms in cases where operation of, for instance, the HV/MV tap changer will restore the network to an acceptable state.

Both the real-time power control and the offline cost parameter update algorithms are designed so that the same algorithm can be used for both medium and low voltage control and separate design specifications for MV and LV networks are not presented here. Small differences in the medium voltage power control (MVPC) and low voltage power control (LVPC) algorithms are discussed in subsections 4.1.1.3 and 4.1.1.5.

The medium voltage PC and PCPU functions are run on the PSAU and optimize the MV network operation. The low voltage PC and PCPU functions are run on the SSAU and optimize the LV network operation. The BOT function is coordinating the MV and LV voltage control functions and is located in the PSAU. All input data of the algorithms is obtained from the SAU database and the outputs are written to the database. The SAUs also include interfaces to the IEDs and other SAUs that send for instance the new setpoints calculated by the PC algorithm to the IEDs.

3.2.1 Real-timePowerControlThe most significant objective of the real-time power control is to mitigate congestions in the distribution network. The algorithm aims at keeping network voltages between acceptable limits and feeder and transformer currents below the thermal limits. The second objective of the real-time power control is to optimize the network state taking the following objectives into account:



· Minimizing network losses · Minimizing production curtailment · Minimizing load control actions · Minimizing the number of tap changer operations · Minimizing the voltage variation at each node (difference between estimated and reference value

of voltage)

In the MVPC algorithm it is possible to include also the costs of real and reactive power transfer between the distribution and transmission systems in the objective function.

To be able to perform the optimization, the PC algorithm needs detailed information from the network it is optimizing. Static network data, state estimation results and data on which controllable resources are available and at what cost is required. The BOT unit blocking signal and cost parameters for the objective function are also needed. The inputs are discussed in detail in subsection 4.1.1.3.

Based on the input data, the PC algorithm calculates optimal setpoints for the available controllable resources that can include transformer tap changers, distributed generators, reactive power compensators and controllable loads. If the PC algorithm is not able to solve a congestion, an alarm signal is sent to the operator.

The PC algorithm uses sequential quadratic programming to solve the optimization problem. In the development phase MATLAB has been used. The final implementation is an Octave program that is run on the SAU Linux machine.

3.2.2 OfflineCostParameterUpdateThe PCPU algorithm determines the cost parameter values of the PC algorithm. Its purpose is to define the cost parameters so that unnecessary control actions are prevented. In the IDE4L project, the algorithm will concentrate on preventing continuous tap changer actions.

The PCPU algorithm utilizes state forecaster data to determine the control actions that the PC algorithm will take in future time steps. If it observes frequent tap changer operations back and forth, it changes the PC algorithm cost parameters such that other resources than the tap changer are used during the short-term changes in the network state.

The inputs of the PCPU algorithm are mostly the same as the inputs of the PC algorithm but instead of state estimation data the algorithm uses state forecaster data. In its calculations the PCPU algorithm utilizes the forecasted data of the whole forecaster horizon and not only data from a single moment as is the case with the PC algorithm. As an output the PCPU algorithm gives cost parameter values for the PC algorithm. The cost parameters are written to the database from where the PC algorithm reads them before each execution round.

3.2.3 BlockOLTCsofTransformers(BOT)The BOT unit is a centralized unit that is located at the primary substation automation unit. The main aim of the BOT unit (PSAU.BOT) is to coordinate the operation of the MVPC and LVPC. This is achieved by coordination of distribution transformers. Since the MVPC does not take the MV/LV transformer operation into account and similarly the LVPC does not consider the HV/MV transformer operation in its algorithm, lack of coordination between these cascaded OLTCs in both power controllers can lead to unnecessary tap changer actions and voltage fluctuations at consumption points [Moghaddam2015]. In addition, inaccurate operation of OLTCs will result in additional actions by other controllable resources that are controlled by



the power controllers. The BOT unit is assigned to manage the cascaded HV/MV and MV/LV OLTCs so that coordination is achieved.

Coordination is realized by sending OLTC block signals and block validity time/unblock signals to the AVC relays of HV/MV and MV/LV transformers. The main purpose of the BOT unit is to [Moghaddam2015]:

· solve the voltage problem as locally as possible · reduce the voltage fluctuations at consumption points · minimize the total number of tap actions · prevent hunting phenomenon of OLTCs

The BOT unit is dependent on medium voltage state estimation (MVSE). The connection of the state estimation unit and the BOT unit is achieved through the MV database.

The BOT outputs are sent to the MV database. Then the MVPC, the HV/MV transformer AVC relay, the LVPC and the MV/LV transformer AVC relay will read these data. The MVPC and HV/MV transformer AVC relay read data locally from the MV database, but the LVPC and MV/LV transformer AVC relay will read data from the LV database. The complete list of BOT inputs and outputs is presented in subsection 4.1.3.5.

The BOT algorithm has been implemented in MATLAB, however, for the final implementation Octave will be used.

3.3 TertiaryControlThe primary objective of the tertiary controller is to define and develop functions for the congestion management of the MV distribution network. These functions are implemented on control centre (CC) level to manage MV networks by means of:

· Network related measures, in particular through network reconfiguration and changes in the settings of voltage controllers in the MV network.

Market related measures to propose changes of scheduled generation/consumption values of DER units, through flexibility offers/bids to provide a feasible combination of schedules. Figure 3.3.1 clarifies the connections between the different steps. The design specification documents [IDE4L2014a] and [IDE4L2014b] include detailed descriptions for all the information that is either read from the database or written to the database.



Start

Day-ahead scheduling from WP6

Are there any congestions?

Check grid constraints

Fast restorationmessage from WP4

No

Yes

Congestion solved?

Yes

No

On demand from WP5

Finish

Validate WP4 or WP6 inputs

Validate NETWORK RECONFIGURATION

outputs

Validate MARKET AGENT outputs

Run MA

Send request to NR

Receive response from NR

Figure 3.3.1. General tertiary controller manager main function flow chart (NR: Network Reconfiguration and MA: Market Agent).

Tertiary control offers two different ways of operation: offline validation and real-time validation.

3.3.1 Real-timeValidationThe tertiary control works in real time and tries to solve MV network congestions that appear during real-time operation which corresponds to post-fault situations and MVPC requests. Congestions appearing in the distribution grid (MV level) during real-time operation (e.g. post-fault situations and MVPC requests) should be solved first through the DSO distribution asset resources or by network reconfiguration. If these



actions are insufficient to solve the congestions, or are not practical, then the purchase/activation of flexibility products from commercial aggregators (CAs) is evaluated through the market agent tool.

Table 3.3.1 contains the step-by-step description for the tertiary control algorithm during real-time operation.

Table 3.3.1. Tertiary controller performance during real-time validation.

1 Initialization:

· Read updated topology from CC database · Read active and reactive power (state estimator) for all MV nodes, MV/LV aggregated

substation load demand (active/reactive) · Read MV load demand (active/reactive) forecast, MV production forecast. · The tertiary controller reads the information related to the available flexibility in the MV grid

and its price. 2 Check MV grid constraints: Run power flow to check the feasibility of the switches status once the

Fast Restoration Process is completed or MVPC signal has been received. The tertiary controller sends its outputs to the CC database if there are no MV congestion problems.

3 Validate input data: Validate that the initial switches status received from WP4 (after the FLISR process) do not produce any congestion in the MV network. Outputs are sent to the CC database.

4 Send request message to network reconfiguration algorithm (NR): In case of congestion problems the tertiary controller manager will send a request message to the NR to execute the network reconfiguration algorithm. NR will calculate the optimal network configuration and/or voltage control setpoints to alleviate MV congestion problems.

5 Receive message from NR: Tertiary controller manager will receive a message from NR as soon as the network reconfiguration algorithm has finished. The message from NR can be: “Modified switches status” and “Modified voltage control setpoints” if the congestion problem has been solved by the network reconfiguration algorithm or “Finished” if NR is not able to find a solution.

6 Validate network reconfiguration outputs: Outputs are sent to the CC database.

7 Run market agent: Execute market agent algorithm (real-time validation algorithm) if NR message is “Finished”.

8 Validate market agent outputs: Outputs are sent to the CC database and CAs.

9 Finish.

3.3.2 OfflineValidation:Offline operation of the tertiary controller tries to solve MV network congestions for the day-ahead right after the market clearing. The primary objective of the tertiary controller during offline operation is to define and develop functions for the congestion management of the MV distribution network for the next hours (day-ahead market or intraday market time frame) by means of distribution asset resources or by network reconfiguration and market based methods.



The main program running the tertiary controller algorithm during offline operation is executed at scheduled fixed time periods (e.g., each 24 hours) considering timeslots of one hour independently.

Table 3.3.2 contains the step-by-step description for the tertiary control algorithm during offline operation.

Table 3.3.2. Tertiary controller performance during offline validation.

1 Initialization:

· Read updated topology from CC database · Read active and reactive power (state estimator) for all MV nodes, MV/LV aggregated

substation load demand (active/reactive) · Read MV load demand (active/reactive) forecast, MV production · The tertiary controller reads the information related to the available flexibility in the MV grid

and its price 2 Check MV grid constraints: Run power flow to check the technical validation of the schedule coming

from the market clearing program.

3 Validate input data: Validate that the initial schedule coming from the market clearing program does not produce any congestion in the MV network. Outputs are sent to the CC database and CA.

4 Send request message to NR: In case of congestion problems the tertiary controller manager will send a request message to the NR to execute the network reconfiguration algorithm. NR will calculate the optimal network configuration and/or voltage control setpoints to alleviate MV congestion problems.

5 Receive message from NR: Tertiary controller manager will receive a message from NR as soon as the Network reconfiguration algorithm has finished. The message from NR can be: “Modified switches status” and “Modified voltage control setpoints” if the congestion problem has been solved by the network reconfiguration algorithm or “Finished” if NR is not able to find a solution.

6 Validate network reconfiguration outputs: Outputs are sent to the CC database.

7 Run market agent: Execute market agent algorithm (offline validation algorithm) if NR message is “Finished”.

8 Validate market agent outputs: Outputs are sent to the CC database and CAs.

9 Finish.

Although one of the new functionalities of the DSO within the smart grid concept is the validation of flexibility products procured by other agents different from the DSO, the algorithms developed within the scope of WP5 in the tertiary controller do not considered this functionality, and new algorithms should be developed for that purpose. A high level description of those algorithms can be found in [IDE4L2015d]. For example, the TSO can procure flexibility products to deal with various constraints of the transmission system. However the activation of such products must not cause new violations of constraints, in terms of voltage, congestions or imbalances in the distribution grid. This is the reason why these products must be checked and validated before their activation.



3.3.3 NetworkReconfigurationAutomatic network reconfiguration is a function of the tertiary controller which will be applied for medium voltage grid congestion management during real-time validation and offline validation. The goal of network reconfiguration is to change the topological structure of the distribution feeders by closing some normally open switches and opening some normally closed switches in their place and to select the optimal setpoint of the voltage controllers (on-load tap changers, MV capacitors, MV reactive power sources). The network configuration should remain radial after the switching operations.

In normal operation, the network reconfiguration algorithm will be executed to reduce system losses, balance loads (exchange between feeders) and to avoid overload of network elements. During emergency operations such as post-fault situations network reconfiguration will be run on demand after the fault has occurred and services have been restored by the FLISR system. The tertiary controller finds the optimal network configuration to restore the remaining unrestored costumers and at the same time solve congestion.

Inputs to the NR will be:

· Network topology (including information of switches states), MV state estimation/forecaster outputs.

· During real-time validation: Input from WP4 about fault details (phases affected, fault currents, fault location, breakers, switches status etc.).

· During Offline validation: Input from WP6 about day-ahead schedule coming from the market clearing program.

The algorithm will find the optimal configuration for the MV network according to the objective functions and equality and inequality constraints.

Outputs from network reconfiguration will be saved in the CC level DXP:

· Switches status, signals to the automation system, setpoints for MV voltage controllers etc.

3.3.4 MarketAgentThe market agent is a function of the tertiary controller for MV grid congestion management through flexibility services or products. Its main objective is to propose changes of scheduled generation/consumption of DER units through flexibility offers/bids to provide a feasible combination of schedules. The market agent concept developed in this document covers the purchase or activation of flexibility products to solve a congestion (overload and voltage limits). Flexibility products include:

• Modification of consumption (consumers, storage systems) • Modification of production (DG, storage systems)

Another functionality of the market agent not developed in the scope of WP5 is the technical validation for the activation of flexibility products requested by other agents different from the DSO.

For the sake of simplicity, and in order to develop the algorithm on time for the scope of WP5, several assumptions have been considered:

• Only one CA operating within the regional area of the DSO, which makes flexibility offers per node.



• The CA adopts the role of retailer, flexibility provider and balance responsible party (BRP). There are no other actors involved in the congestion management.

• The DSO has made a previous request for flexibility, so flexibility offers from the CA are known in advance.

• TSOs are not considered in the process, because 1) DSO is the main beneficial party, and 2) there is no TSO participating in the IDE4L project.

Two main types of flexibility products are considered, taken from [ADDRESS2009]: scheduled re-profiling (SRP) or conditional re-profiling (CRP). SRP is the offer of one or more DERs to produce/consume an assigned power during an assigned period of time, while CRP is the offer of one or more DERs to be ready and available to change the production/consumption in a certain range for an assigned period of time. It is also assumed that DSO contracts CRPs by signing a bilateral contract with a service provider (a CA or a local producer/consumer) and SRPs are selected with a market-based mechanism. Regarding the flexibility products (SRPs and CRPs), each product is characterized by the time of service, volume and price (no pay-back effect is considered).

Inputs to the MA will be:

• Network topology. • During offline validation, input from WP6 about day-ahead schedule coming from the market

clearing program, marginal system price and available flexibility bids. • During real-time validation: energy schedule for the next hour and flexibility bids with

acquisition/activation prices.

The algorithm will find the optimal schedule according to the objective function and equality and inequality constraints.

Outputs from MA will be saved in the CC level DXP and sent to CA:

• Accepted flexibility bids or selected flexibility products activation • Final energy schedule



4 AlgorithmsThis chapter gives a detailed technical description of the algorithms developed within WP5.

4.1 SecondaryControlThis section describes the secondary control designed and implemented in the IDE4L project. The purpose of the secondary control is to guarantee that the network state is always acceptable and to optimize its operation in real time. As already stated in section 3.2, the secondary control consists of real-time power control, offline cost parameter update and BOT.

The reliable, correct and relatively fast operation of the real-time power control algorithm is vital to the distribution network as it is used to mitigate network congestions. Hence, it is the most important part of the secondary control. The other parts – PCPU and BOT – are used to prevent unnecessary control actions such as multiple tap changer operations during a short period of time and they enhance the operation of the control system. They are not, however, as critical to the distribution system operation as the PC algorithm.

In the IDE4L project, a modular control architecture is implemented and each of the algorithms is implemented as its own independent instance. All data between the algorithms and IEDs is sent through the database and the coordination between different algorithms is realized by using flags. Modularity has several advantages: If one of the algorithms is wanted to be replaced by another one performing the same task, it can be easily done without a need to modify the other algorithms. Also, possible failures in the noncritical algorithms (PCPU and BOT) do not lead to failures in the PC algorithm.

4.1.1 Real-timePowerControlThe real-time power control algorithm is an optimizing algorithm. The algorithm is developed based on [Kulmala2014b]. In this project, the formulation of the objective function has been extended to include also demand response actions, on-load tap changer operations and voltage variation from a reference value. Controllable loads have been added as controllable variables. The algorithm is also implemented in Octave (previously only a MATLAB version was available) and some changes to the problem formulation (relative to the MATLAB version) have been made to have acceptable operation when using Octave’s SQP solver. Moreover, the implementation has been modified to be suitable for the modular control architecture of IDE4L.

4.1.1.1 AlgorithmStepsThis subsection will present the operational principle of the power control algorithm. The following subsections will give more detailed information on the main functionalities presented in this subsection. The flow chart of the PC algorithm operation is presented in Figure 4.1.1.



1. Initialization of the algorithm

2. Waiting for OPF starting conditions to be fulfilled

START

Static network data updated ?

4. Static network data reading and processing

Yes

Input data available?

Yes

No

3. Exceptional situation handling 1

No

5. Dynamic input data reading and processing

6. Optimization

Acceptable solution found?

7. Exceptional situation handling 2

8. Check if setpoints should be changed and determine the

data that is written to the database

9. Write output data to database

Yes

No

Figure 4.1.1. The flow chart of the PC algorithm operation.

The first step is the initialization of the algorithm. At this step the connection to the database is opened and the static network data is read and processed to a form that the optimization algorithm can utilize. After the initialization phase, the algorithm enters a loop that executes the optimization once every minute (other execution intervals can also be used).

At the beginning of step 2, the algorithm waits until the execution interval delay has elapsed. Then it starts monitoring flags in the database. When it observes that all algorithms that need to be executed before the PC algorithm are ready, it moves forward from the waiting state (for details see subsection 4.1.1.4). If adequate input data is not available, the algorithm moves to exceptional situation handling state #1 which is further discussed in subsection 4.1.1.5. If input data is available, it is read from the database and



processed to a form that the optimization algorithm can utilize. Static network data is read only if it has been updated.

After input data reading, the optimization algorithm is called. The optimization algorithm initializes the optimization problem, runs the SQP algorithm and uses a heuristic method to assign the discrete variables. If an acceptable solution is not found, the algorithm enters exceptional situation handling state #2 (for details see subsection 4.1.1.5). If an acceptable solution is found, the algorithm checks if setpoints should be changed. It is not reasonable to change the setpoints of control devices, if the improvement in the objective function value is small. At step 8, the algorithm calculates the value of the objective function using the current setpoints and the setpoints calculated by the optimization algorithm. The new setpoints are only written to the database, if the objective function value decreases more than a set limit or if the network state prior to the optimization is not acceptable. At the last step, output data is written to the database and the algorithm returns to the waiting state (step 2). SAU interfaces will take care of transferring the new setpoints and other relevant data to the IEDs and other SAUs.

4.1.1.2 FormulationoftheOptimizationProblemThe optimization of distribution network voltage control is a mixed integer nonlinear programming problem:

minimize f(x,ud,uc)

subject to g(x,ud,uc) = 0 (4.1.1)

h(x,ud,uc) ≤ 0

, where x is the vector of dependent variables, ud is the vector of discrete control variables and uc is the vector of continuous control variables. The optimization aims to minimize the objective function f(x,ud,uc) subject to equality constraints g(x,ud,uc) = 0 and inequality constraints h(x,ud,uc) ≤ 0.

In the IDE4L project, nonlinear programming is used to solve the MINLP problem of equation (4.1.1) and a heuristic method is used to assign the discrete variables. The controllable variables are transformer tap changer positions, real and reactive powers of distributed generators, reactive powers of reactive power compensators and real powers of controllable loads.

4.1.1.2.1 StateVariablesThe vector of dependent variables contains the voltage magnitudes V and voltage angles d of all n distribution network nodes.

x = [V1,…,Vn,d1,…, dn] (4.1.2)

The vector of continuous control variables contains DG real powers, reactive powers of controllable resources and real power changes of controllable loads. The controllable reactive power resources can be either DG units or reactive power compensators.

uc = [PDG1,…,PDGj,Qcont1,…,Qcontk,DPDR1up,...,DPDRmup,DPDR1down,...,DPDRmdown] (4.1.3)

, where PDGj is the real power setpoint of the j’th DG unit, Qcontk is the reactive power setpoint of the k’th controllable reactive power resource, DPDRmup is the real power increase of the m’th controllable load and



DPDRmdown is the real power decrease of the m’th controllable load. The real power change of the m’th controllable load can be calculated by summing the two variables.

DPDR = DPDRup + DPDRdown (4.1.4)

, where DPDRup ³ 0 and DPDRdown ≤ 0. DPDR is divided into two variables DPDRup and DPDRdown to avoid using absolute values in the objective function. MATLAB SQP manages absolute values in the objective function although the absolute value function is not differentiable. When Octave SQP is used, absolute values in the objective function can lead to poor optimization results and, hence, reformulation of the optimization problem was needed.

The vector of discrete control variables contains only the change in transformer tap changer position m.

ud = [Dmup,Dmdown] (4.1.5)

Also the tap changer position variable is divided into two parts to avoid absolute values in the objective function calculation. The change in the tap changer position can be calculated similarly as for the demand response state variable.

Dm = Dmup + Dmdown (4.1.6)

, where Dmup ³ 0 and Dmdown≤ 0.

4.1.1.2.2 ObjectiveFunctionThe objective function considers network losses, generation curtailment, demand response actions, the number of tap changer operations and the voltage variation from a reference value.

f(x,ud,uc) = Closses×Plosses + S(Ccur × Pcur) + S(CDR×(DPDRup-DPDRdown) )+ Ctap ×(ntapup-ntapdown) + S(CVdiff ×(Vi,r-Vi)2)

(4.1.7)

, where Closses is the cost parameter for losses, Plosses is the amount of losses, Ccur is the cost parameter for curtailed generation, Pcur is the amount of curtailed generation, CDR is the cost parameter for load control, DPDR is the amount of controlled load, Ctap is the cost parameter for one tap step, ntap is the number of tap changer operations, CVdiff is the cost parameter for voltage variation, Vi,r is the reference voltage and Vi is the estimated voltage of node i. It should be noted that the cost parameters Ccur, CDR and CVdiff are not necessarily the same for each resource and each network node. The algorithm is formulated such that giving different cost parameters for each resource and network node is possible.

The losses can be calculated as the sum of real power injections of all network nodes.

Plosses = SPi (4.1.8)

The bus power injections can be computed from the following equation:

P+jQ = diag(V)(YbusV)* (4.1.9)

, where V is the node voltage vector [V1ejd1,…,Vnejdn] and Ybus the bus admittance matrix [Nagrath1994].

It should be noted that in the current formulation of the OPF problem, it is assumed that the generation aims to operate at its maximum possible power all the time and, hence, its real power output cannot be



increased by the OPF algorithm but only curtailment is possible. This is a reasonable assumption for resources such as PV and wind but the formulation should be changed for dispatchable generators where both up and down regulation is possible.

4.1.1.2.3 EqualityConstraintsThe equality constraints model the power flow equations at each network node. In the IDE4L project optimization formulation, the secondary side of the tap changing transformer i.e. the substation bus is defined to be the slack node. If there is no tap changer, the slack node can also be set to be at the primary side of the transformer. All other network nodes are defined to be PQ nodes because all active resources operate in reactive power control mode instead of voltage control mode.

In the slack node, the voltage magnitude and angle are known. The substation node voltage depends on the tap changer position. Also the current through the impedances upward from the substation secondary bus affects the voltage. The value for transformer secondary side voltage after control actions can be calculated from:

= ( + ∆ ) ∙ (4.1.10)

, where Vssmeas is the measured substation voltage, DVss is the change in voltage due to changes in power flow through the transformer and feeding network impedance, morig is the original tap changer position and mnew the new tap changer position. The tap changer is located on the primary winding of the transformer.

Some simplifying assumptions are made when DVss is calculated. It is assumed that the voltages are at the nominal value and that the voltage angle difference is small. Also, changes in network losses due to changes in real and reactive powers of active resources are not taken into account. With these assumptions, the change in secondary side voltage due to changes in real and reactive powers of active resources can be calculated from:

∆ = ∆ + ∆ (4.1.11)

, where R and X are the combined resistance and reactance of the transformer and the feeding network, DP consists of changes in controllable generation and loads and DQ is the change in controllable reactive powers. All values are in per unit. DP and DQ can be obtained directly from the state variables of the optimization problem. If the upward impedances do not need to be taken into account, the values of R and X can be set to zero.

The slack node is defined to be at the secondary of the tap changing transformer for computational reasons. It would also be possible to take the tap changer into account by modifying the bus admittance matrix needed to calculate the bus power injections in equation (4.1.9). This approach was not selected because it is slower and more susceptible to non-convergence.

So to conclude, the following equality constraints have to be fulfilled in the slack node:

− ( + ∆ ) ∙ = 0 (4.1.12)

dslack = 0 (4.1.13)



In the PQ nodes the real and reactive powers are known and the following equality constraints have to be fulfilled:

Pi - Pgen,i + Pload,i = 0 (4.1.14)

Qi - Qgen,i + Qload,i = 0 (4.1.15)

, where injected powers Pi and Qi are calculated from equation (4.1.9), Pgen,i is the generated real power at the i'th node, Pload,i is the consumed real power at the i'th node, Qgen,i is the generated reactive power at the i'th node and Qload,i is the consumed reactive power at the i’th node. The generated and consumed real and reactive powers are calculated by combining the uncontrollable node powers and controllable powers from the state variable vector uc.

The total number of equality constraints is 2⋅n where n is the total number of distribution network nodes.

4.1.1.2.4 InequalityConstraintsThe inequality constraints are used to model network technical constraints and the capability limits of the controllable resources. The following constraints are used:

Pactive,i,min ≤ Pactive,i ≤ Pactive,i,max (4.1.16)

Qactive,i,min ≤ Qactive,i ≤ Qactive,i,max (4.1.17)

mmin ≤ m ≤ mmax (4.1.18)

Vlower ≤ Vi ≤ Vupper (4.1.19)

Iij ≤ Iij,max (4.1.20)

The first three constraints set the capability limits of the controllable resources. The first constraint (4.1.16) sets the limits for real powers of controllable DERs and the second constraint (4.1.17) sets the limits for reactive power of controllable DERs. Constraint (4.1.18) limits the transformer tap ratio. The two following constraints model the network technical constraints. Constraint (4.1.19) states that all network voltages have to remain between feeder voltage limits and constraint (4.1.20) limits the currents in all network branches below the maximum allowed value. Line currents are calculated from the voltage magnitudes and angles of (4.1.2) and the feeder impedances using a p-model for the feeders.

4.1.1.2.5 AssigningtheDiscreteVariablesIn the SQP algorithm all variables are treated as continuous and some calculations are needed also after the first SQP algorithm operation to assign the discrete variables. In the IDE4L project implementation, transformer tap changer position is the only discrete variable and a three-stage procedure is used to assign a value for it. In the first round, SQP is executed assuming that also the tap changer position is a continuous variable. After the first round, the two tap changer positions on both sides of the calculated value of the tap changer position are selected. The second and the third round execute SQP using the two previously selected tap changer positions. The alternative with the smallest value of the objective function is selected.

4.1.1.3 InputsandOutputsThe inputs and outputs of the real-time power control algorithm are described in this subsection. All input data is read from the SAU database and all output data is written to the database. SAU interfaces are then used to transfer the output data to IEDs or other SAUs if needed.



The inputs of the PC algorithm consist of static network data, dynamic measurement and estimation data, algorithm parameters and data used to coordinate the operation of different algorithms (flags and blocking signals). The outputs consist of setpoints to controllable resources, alarm and log messages and data needed for post-demonstration evaluation of the algorithm operation.

Detailed listing of the PC algorithm inputs and outputs is presented in Table 4.1.1 and Table 4.1.2 respectively. The inputs and outputs are almost the same for the MV and LV power control algorithms and, hence, are presented in common tables. The small differences between the inputs and outputs are shown in the tables using different colours.

The tables present the data that is read and written by the main function of the power control algorithm. Inside the main function the data is further processed to a form understood by the function that realizes the optimization.

Table 4.1.1. The inputs of the LVPC and MVPC algorithms. The data common to both MVPC and LVPC algorithms is on the white background, the data that is needed only by the LVPC algorithm is on the green background and the data needed only by the MVPC algorithm is on the blue background.

Input Data exchanged Source Local / Remote

Update schedule

Data Unit Data Format

Network model, CIM (static input)

Grid static data

1. Line from node # to node # 2. Line impedances and capacitances 3. Conductors current limits 4. Phase info (which phases the line is connected to) 5. Switch (breaker and disconnector) and fuse locations

SAU.DXP Local On request (quite rarely)

1. No unit 2. Ohm [Ω], Siemens [S] 3. Ampere [A] 4. No unit 5. No unit

1. Integer 2. Floating point 3. Floating point 4. 3 * binary (one for each phase) or integer 5. Integer

Substation static Info

1. From node # to node # 2. Transformer power rating 3. Tap limits 4. Step size 5. Location of the tap changer (primary or


1. No unit 2. [MVA] 3. No unit 4. No unit 5. No unit 6. Ohm [Ω]

1. Integer 2. Floating point 3. Floating point 4. Floating point 5. Integer (primary or



secondary of the transformer) 6. Transformer resistance, reactance and etc.

secondary) 6. Floating point

Load static data

1. Connection point 2. Phase info 3. DR node or not


1. No unit 2. No unit 3. No unit

1. Integer 2. 3 * binary (one for each phase) or integer 3. Binary

Static DG information

1. Connection point 2. Phase info 3. Nominal power


1. No unit 2. No unit 3. [kVA]

1. Integer 2. 3 * binary (one for each phase) or integer 3. Floating point

Reactive power compensator information (static)

1. Connection point 2. Phase info 3. Reactive power limits


1. No unit 2. No unit 3. [kVAr]

1. Integer 2. 3* binary (one for each phase) or integer 3. Floating point

Switch and fuse status



No unit Table with binary numbers

Parameters of algorithms (static input)

Network static voltage info

1.Voltage limits for each network node (usually the same in all nodes) 2. Reference voltage values for objective function calculation for each network

SAU.DXP Local 1. Usually fixed limits 2. Usually fixed

1. [V] 2. [V]

1. Floating point 2. Floating point



node

Block message and flags (dynamic input) Block signal from the BOT unit

1. Block signal 2. Validity time

PSAU.BOT Stored in PSAU.DXP and transferred also to SSAU.DXP

Local for MVPC, remote for LVPC

When BOT is ready

1. No unit 2. Timestamp

1. Binary 2. Timestamp

TCO or AVCO

1. Tap changer operating 2. AVC relay operating

IED.CTRL.AVC Stored in SAU.DXP

Remote When the TCO or AVCO signal changes

1. No unit 2. No unit

1. Binary 2. Binary

BOT ready flag

BOT ready flag PSAU.BOT Stored in PSAU.DXP and transferred also to SSAU.DXP

Remote When BOT is ready

Timestamp of the last execution of BOT

SE ready flag

SE ready flag SAU.SE Stored in SAU.DXP

Local When SE is ready

Timestamp of the last execution of SE

Static network data changed flag


NIS, CIS, DMS Stored in SAU.DXP

Remote When static network data changes

Timestamp of the last change in static network data

Network switching state changed flag


DMS FLISR Stored in SAU.DXP

Remote When network switching state changes

Timestamp of the last change in network switching state

Network state flag

Network state (normal, faulted or in unusual switching state)

DMS FLISR Stored in SAU.DXP

Remote When the DMS or FLISR updates the flag

Integer



Measurements and estimates (dynamic input)

Dynamic load info (nodes without DR)

1. Real power 2. Reactive power

SAU.SE Stored in SAU.DXP

Local On a fixed schedule (e.g. once a minute)

1. [kW] 2. [kVAr]


Dynamic load info (nodes with DR)

1. Real power (from SE, includes previous PC control actions) 2. Reactive power (from SE) 3. Minimum limit for real power change DP (from e.g. HEMS) 3. Maximum limit for real power change DP (from e.g. HEMS)

SAU.SE, HEMS Stored in SAU.DXP

1 and 2 local 3 and 4 remote

On a fixed schedule (e.g. once a minute)

1. [kW] 2. [kVAr] 3. [kW] 4. [kW]

1. Floating point 2. Floating point 3. Floating point 4. Floating point

Dynamic DG info

1. Real power (from SE, includes previous PC control actions) 2. Reactive power (from SE, includes previous PC control actions) 3. Real power limits (from generation forecasting, maximum limit is the power without PC control actions) 4. Reactive power limits (can depend on

SAU.SE, SAU.GF Stored in SAU.DXP


1. [kW] 2. [kVAr] 3. [kW] 4. [kVAr] 5. No unit

1. Floating point 2. Floating point 3. Floating point 4. Floating point 5. Binary



the real power or be constant) 5. Status (connected / disconnected)

Dynamic reactive power compensator info

Reactive power (from SE)



[kVAr] Floating point

Dynamic voltage info

Nodal voltage info



[V] Floating point

Dynamic current info

Branch current info



[A] Floating point

Substation dynamic info

Tap position IED.CTRL.AVC Stored in SAU.DXP

Remote On a fixed schedule (e.g. once a minute)

No unit Floating point

Algorithm parameters (dynamic input) Weighting factors for objective function calculation

1. Cost/weighting factor for losses 2. Cost/weighting factors for generation curtailment for each generator 3. Cost/weighting factor for demand response actions for each DR resource 4. Cost/weighting factor for tap changer

SAU.DXP SAU.PCPU can update these


No unit or euro

Floating point



operations 5. Cost/weighting factor for voltage difference to the reference value for each network node

Inputs needed only by the LVPC algorithm Reference real power calculated by PSAU.PC

Value of reference real power which is taken as reference in the SSAU.PC (in case that the PSAU.PC algorithm needs to use resources located in LV networks)

PSAU.PC Stored in PSAU.DXP and transferred also to SSAU.DXP


[kW] Floating point

Reference reactive power calculated by PSAU.PC

Value of reference reactive power which is taken as reference in the SSAU.PC (in case that the PSAU.PC algorithm needs to use resources located in LV networks)

PSAU.PC Stored in PSAU.DXP and transferred also to SSAU.DXP



Inputs needed only by the MVPC algorithm Static data that is read only rarely Power flow limits from the HV network

1. Real power limit 2. Reactive power limits

TSO & DSO Stored in PSAU.DXP

Local On request (quite rarely)

1. [kW] 2. [kVAr]


Dynamic data that is read every time the algorithm is executed Help request from SSAU.PC

1. PSAU help request flag 2. LV network maximum and

SSAU.PC Through SSAU.DXP

Remote Whenever the value is updated

1. No unit 2. [V]

1. Binary 2. Floating point



minimum voltages

and PSAU.DXP

Table 4.1.2. The outputs of the LVPC and MVPC algorithms. The data common to both MVPC and LVPC algorithms is on the white background, the data outputted only by the LVPC algorithm is on the green background and the data outputted only by the MVPC algorithm is on the blue background.

Output Data exchanged

Destination Local / Remote

Update schedule


Setpoints Transformer OLTC AVC relay voltage setpoint

Voltage setpoint

IED.CTRL.AVC Through SAU.DXP

Remote Whenever the OPF algorithm updates the output

[V] Floating point

Setpoints for reactive power compensators

Reactive power setpoint for each compensator

Controllers of reactive power compensators Through SAU.DXP



DG unit reactive power output

Reactive power setpoint for each DG unit

Voltage controllers of DG units Through SAU.DXP



DG unit real power output

Real power setpoint for each DG unit

Power controllers of DG units Through SAU.DXP


[kW] Floating point

Real power change of controllable loads

Real power change DP setpoint for each controllable load

DR controller, e.g. HEMS Through SAU.DXP


[kW] Floating point

Reference real power for the SSAU.PC calculated by PSAU.PC

Value of reference real power which is taken as reference in the

SSAU.PC Through PSAU.DXP and SSAU.DXP


[kW] Floating point



SSAU.PC (in case that the PSAU.PC algorithm needs to use resources located in LV networks)

Reference reactive power for the SSAU.PC calculated by PSAU.PC

Value of reference reactive power which is taken as reference in the SSAU.PC (in case that the PSAU.PC algorithm needs to use resources located in LV networks)

SSAU.PC Through PSAU.DXP and SSAU.DXP



Alarms and log signals Alarm signal If PC is unable

to restore the network to an acceptable state, an alarm signal is sent to the operator

Operator Through SSAU.DXP and PSAU.DXP


No unit Integer (or string)

Log message If PC has some problems but the network state can be restored to an acceptable state, a log message is written

At least SAU.DXP, possibly also to the operator

Local (remote)

Whenever the OPF algorithm updates the output


Help request to PSAU.PC if SSAU.PC is unable to restore the network voltages to an

1. PSAU help request flag 2. LV network maximum and minimum voltages

PSAU.PC Through SSAU.DXP and PSAU.DXP


1. No unit 2. [V]

1. Binary 2. Floating point



acceptable range Help request to the tertiary control if PSAU.PC is unable to restore the network to an acceptable state

Tertiary control help request flag

Tertiary control Through PSAU.DXP


No unit Binary

Data needed for evaluating the operation of the algorithm Execution time

OPF algorithm execution time

SAU.DXP Local Whenever the OPF algorithm updates the output

[s] Floating point

Objective function value

OPF algorithm objective function value



Voltages calculated by the OPF algorithm

1. Voltages of each network node 2. Voltage reference values used in the objective function calculation


1. [V] 2. [V]


Branch currents calculated by the OPF algorithm

Currents of each network branch


[A] Floating point

Losses calculated by the OPF algorithm

Network losses SAU.DXP Local Whenever the OPF algorithm updates the output

[kW] Floating point

Generation curtailment calculated by the OPF

Generation curtailment of each DG unit

SAU.DXP Local Whenever the OPF algorithm updates the

[kW] Floating point



algorithm output Demand response actions calculated by the OPF algorithm

DR actions of each controllable load


[kW] Floating point

Tap position calculated by the OPF algorithm

Tap position SAU.DXP Local Whenever the OPF algorithm updates the output


4.1.1.4 AlgorithmStartingConditionsThis subsection will present the starting conditions for the PC algorithm in detail. A flow chart of the PC algorithm operation before the actual optimization function is called is depicted in Figure 4.1.2. The state estimation algorithm and the BOT algorithm produce input data for the PC algorithm and, hence, need to be executed before the PC algorithm. The operation of the PC algorithm also depends on the network state.

In the IDE4L project, the PC algorithm is executed once every minute and the algorithm starts by waiting for the next minute to start. The optimization algorithm is not run when the network is in fault location, fault isolation or supply restoration states.

The algorithm utilizes flags to identify whether the network static data has been updated. The static network data changes only rarely and reading it from the database every algorithm execution round is not reasonable.

State estimation data is a mandatory input for the power control algorithm. The PC algorithm proceeds to the next step only after the state estimation ready flag is raised. If the state estimation results are not available before a predefined maximum delay time has elapsed, the PC algorithm concludes that adequate input data is not available and moves to the exception handling state that will be further discussed in subsection 4.1.1.5.

The PC algorithm also utilizes the BOT algorithm output if it is available. After the state estimation data is available, the PC algorithm waits for the BOT results until the BOT ready flag is raised or a predefined time delay has elapsed. The algorithm can continue operation also without the BOT results if the BOT for some reason fails.

When the SE and BOT algorithms are done, the PC algorithm checks the network state and static network updated flags once more. If their values have changed during the SE and BOT operation, it is not reasonable to continue as the SE results are based on the network state at the beginning of the minute and the PC algorithm returns to the waiting state.



Wait for start of minute

Network state

Normal

Read network state flag


Static network data reading and processing

Faulted

Yes

Exceptional situation handling 1

No

Dynamic input data reading and processing

Either one changed during SE and BOT execution ?

AVC relay or tap changer operating ?

Yes

Yes

No

No

Read static network data updated flag

Wait for SE ready flag

SE ready before the delay exceeds a limit?

Wait for BOT ready flag or for a predefined delay if BOT

unsuccesful

Read network state and static network data updated flags

Read AVCO and TCO signals

Yes

No

To optimization (step 6 in Fig. 4.1.1)

From initialization (step 1 in Fig. 4.1.1)

Figure 4.1.2. A detailed flow chart of the usage of flags in the PC algorithm. The flow chart is a more detailed presentation of the parts between steps 2-5 of Figure 4.1.1.



Finally the PC algorithm checks if the AVC relay or the tap changer are operating. A change in the tap changer position changes the network state substantially and it is not reasonable to make changes to setpoints before the local tap changer control has finished its control operations.

Finally, the dynamic input data such as state estimation results is read from the database and processed to a form that can be utilized by the optimization algorithm.

4.1.1.5 OperationinExceptionalSituationsThe PC algorithm operation in exceptional situations is discussed in this subsection. Three exceptional situations are considered:

1. Lack of updated state information of control area. The PC algorithm requires state estimation data as its input which can, however, be unavailable due to e.g. communication or state estimation algorithm failures.

2. Acceptable solution not found by the optimization algorithm. The convergence of the optimizing algorithm cannot be guaranteed. It is also possible that there are not enough controllable resources to restore the network to an acceptable state.

3. The network is in fault location, isolation or supply restoration state.

4.1.1.5.1 AdequateInputDataNotAvailableWhen adequate input data is not available, the PC algorithm does not run the optimization algorithm. It sends an alarm message to the operator and calls a function that tries to solve the problem. The function first determines where the problem is (interfaces, database, communication, measurement devices, state estimation algorithm) and after that tries to solve the problem. It can for instance reboot interface clients, measurement devices or even the whole SAU.

If the input data has been missing for longer than a predefined time, the PC algorithm sends predefined control setpoints to the controllable resources. The setpoints are determined by offline simulations of the network. The simulations need to be conducted in the worst case conditions that are maximum loading – minimum generation and minimum loading – maximum generation. The parameters need to be such that the network always remains in an acceptable state but also operation of for instance DG overvoltage protection can be taken into account. The flow chart of this exception handling is depicted in Figure 4.1.3 (Exceptional situations handling 1 – block in Figure 4.1.1).



Send an alarm signal to the operator

Data missing for a predefined time ?

Yes

No

No

Set predefined setpoints to the controllable resources

Adequate input data not available

Call the function that tries to solve the problem

Write the new setpoints to the database

To the waiting state

Figure 4.1.3. Flow chart of the exception handling when adequate input data is not available.

4.1.1.5.2 AcceptableSolutionNotFoundbytheOptimizationAlgorithmThis subsection gives a detailed description of the exception handling when the optimization algorithm is unable to find an acceptable solution. There are two possible reasons for this: either the optimization algorithm does not converge or the available controllable resources are not adequate to restore the network to an acceptable state. Figure 4.1.4 depicts a flow chart of the exception handling in these cases (Exceptional situations handling 2 – block in Figure 4.1.1).



OPF with recovery methods

Acceptable solution?Yes

No

No

Set predefined setpoints to the controllable resources

OPF did not reach an acceptable solution

Send an alarm signal to the operator

To step 8 in Figure 4.1.1

Original network state acceptable?

Yes

OPF did not converge – log message to the database

Run rule based algorithm

Able to restore network to an acceptable state?

YesTo step 9 in Figure 4.1.1

No

To step 9 in Figure 4.1.1

Send a help request to an upper level controller

Unacceptable network state remained longer than

a predefined time ?

Yes

To the waiting stateNo


Figure 4.1.4. Flow chart of the exception handling when the optimization algorithm does not find an acceptable solution.



The first action that is taken is to use recovery methods in the OPF algorithm. The parameters of the optimization algorithm can be changed (initial values, tolerances, maximum number of iterations). Also soft limits can be used instead of hard limits. If the OPF converges after the recovery methods and the network state after the optimization is acceptable, the algorithm moves to step 8 in Figure 4.1.1.

If the OPF does not converge even after the recovery methods, the algorithm writes a log message to the database and checks if the current network state is acceptable or not i.e. are all node voltages between feeder voltage limits and all branch currents below the thermal limits of the branches. If the network state is acceptable, the algorithm returns to the waiting state. If the original network state is unacceptable, a rule based algorithm based on [Kulmala2014b] is called. The rule based algorithm cannot have convergence problems but also it can be unsuccessful in restoring the network to an acceptable state if the amount of controllable resources is not adequate. If the rule based algorithm is able to restore the network to an acceptable state, the algorithm moves to step 9 in Figure 4.1.1 i.e. writes the new setpoints to the database.

If the rule based algorithm is not able to restore the network to an acceptable state, an alarm message is sent to the operator and a help request is sent to an upper level controller. The upper level controller is different for the LVPC and the MVPC algorithms. The LVPC sends the help request to the MVPC and the MVPC to the tertiary control. The help request is sent only in cases where the upper level controller can possibly improve the situation. If there is a voltage problem in the LV network, the MVPC can possibly help but if there is a problem with branch currents, the MVPC cannot do anything. The tertiary controller executes the network reconfiguration algorithm and can affect both voltages and currents and, hence, MVPC can request for help in both types of congestions.

If the unacceptable network state remains longer than a predefined time, predefined setpoints are sent to the controllable resources. The setpoints are the same ones that are utilized in Figure 4.1.3 when input data has been missing for a predefined time.

4.1.1.5.3 NetworkinFaultLocation,IsolationorSupplyRestorationStateDuring network fault location, isolation and supply restoration the PC algorithm is not executed as the network switching state is constantly changing. When the network is in normal state or the fault has been isolated and the repair is ongoing, the PC algorithm is executed normally. If the network switching state is unusual (e.g. in fault repair state), the voltage limits used in the optimization can be relaxed.

4.1.1.6 PerformanceTestsThe algorithm has been implemented both as a MATLAB and an Octave program. At the development phase, MATLAB was used and the final implementation that will be used in the demonstrations is an Octave program. The original assumption was that the operation in MATLAB and Octave would be basically the same but further studies have shown that the MATLAB SQP and the Octave SQP are not the same algorithms. In MATLAB, convergence problems were not encountered and the optimization algorithm was able to cope with absolute values in the objective function. In Octave, reformulation of the optimization problem was required to avoid absolute values in the objective function and some cases where the algorithm did not converge were found. Moreover, Octave SQP seems to be more sensitive to selection of the initial values for the optimization problem and proper scaling of state variables i.e. selection of the base power for per unit calculations is more important in Octave. Also, the execution times of the Octave SQP are longer than MATLAB optimization. However, despite these shortcomings in the Octave SQP it will be used in the IDE4L project. If the algorithm is not able to find an acceptable solution fast enough, the



exception handling procedure presented in subsection 4.1.1.5 is used to guarantee that the network state will be acceptable in all cases. In commercial applications, a more robust optimization algorithm should be used but for the IDE4L project purposes Octave SQP combined with the other functionalities represented in the previous subsections is adequate. The differences between the MATLAB and the Octave implementations will be further studied using RTDS simulations.

Extensive simulation results will not be presented in this subsection. One example PSCAD simulation will be presented to show the time domain operation of the algorithm and more offline simulation results are presented in appendix 1. The example PSCAD simulation uses the MATLAB implementation of the PC algorithm because a MATLAB interface is available in PSCAD. The PowerFactory simulations in the appendix use the Octave implementation of the PC algorithm. The appendix considers the algorithm operation in two of the demonstration networks of the IDE4L project. The final testing of the algorithm is done in the RTDS simulation environment and finally the algorithm will be used in the real distribution network demonstrations.

4.1.1.6.1 ExamplePSCADSimulationThe simulation network model is constructed based on a real Finnish distribution network and consists of two 20 kV feeders which are fed from the same substation. Three DG units are connected to feeder 1 and feeder 2 is a pure load feeder. The structure of the simulation network is represented in Figure 4.1.5 and more detailed network data can be found in [Kulmala2014b]. The network model includes a representation of the substation AVC relay and the tap changer mechanism [Calovic1984]. The AVC relay deadband is 1.5 % and the delay 3 s. Line drop compensation is not used. The main transformer tap step can be changed ±9⋅1.67 % and the delay of the tap changer is 1 s. The selected loading condition represents an average loading condition. Synchronous generators operating in reactive power control mode are used to model the DG units. From the point of view of PC algorithm testing, the simulation results, however, apply to all kinds of DERs whose real or reactive power can be controlled. The simulation sequence used in the simulations is presented in Table 4.1.3.

Figure 4.1.5. The structure of the simulation network.

Feeder 2Feeder 1

G

G

G

Substation (Ss)

Gen 1Gen 2

Gen 3

12

3

45

6

7 8 9

10 1112

1314

1516

17

1819

20

2122

23

24 2526

27

28

29

30 31

32

3334

35

3637

38 39

40 41

42

434445



Table 4.1.3. The simulation sequence in the example simulation case. The rated real power of all generators is 1.36 MW.

Time [s] Pset1 [p.u.] Pset2 [p.u.] Pset3 [p.u.] 0 0.1 0.1 0.1

20 1.0 0.1 0.1 50 1.0 1.0 0.1 80 1.0 1.0 1.0

130 0.1 1.0 1.0 160 0.1 0.1 1.0 190 0.1 0.1 0.1

The feeder voltage limits in the simulation are set to 0.95-1.05 p.u.. If the PC algorithm is not used, the voltages in the network do not remain at an acceptable level as can be seen from Figure 4.1.6. In this simulation, the substation AVC relay setpoint is 1.03 p.u. and the DG units are operated at unity power factor. Significant network reinforcement would be required if the DG units are connected to the network without changes in the control principles.

Figure 4.1.6. Voltages of all network nodes without the PC algorithm.

Simulation results with the PC algorithm are represented in Figure 4.1.7 and Figure 4.1.8. In Figure 4.1.7 the optimization cost parameters are selected such that only losses and generation curtailment are taken into account in the optimization. In this case the cost parameters can be selected based on real cost data. The cost parameter for losses Closses is set to 44.6 €/MWh which is an average value of Nordpool Finland spot price in years 2006-2010. The price of curtailed energy is assumed to be 83.5 €/MWh which is the feed-in

0 20 40 60 80 100 120 140 160 180 200 220 2401

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

[pu]

t [s]

Feeder 1

Substation and feeder 2



tariff for wind generators in Finland and the distribution charge is assumed to be 0.7 €/MWh which is the maximum allowed distribution charge for production units in Finland. Hence, the cost parameter for generation curtailment Ccur is set to 82.8 (83.5-0.7) €/MWh. [Kulmala2011] The cost parameters for tap changer operation Ctap and voltage variation CVdiff are both set to 0.0.

In Figure 4.1.8 also the tap changer operation and voltage variation are taken into account. Determining cost parameters for these is not as straightforward as for the losses and generation curtailment. Tap changer operations cause wear of the tap changer and can increase its maintenance need. The tap changers need an overhaul after some number of tap changer operations (e.g. 100000) or after some number of years of service (e.g. five years) [ABB]. Hence, the additional tap changer operations start to increase the tap changer maintenance costs only after the interval between overhauls diminishes due to the operation of the secondary control. Costs will increase also if the secondary control increases the tap changer operations so much that replacement of the contacts is necessary during the life of the transformer. Therefore, determining the cost of one tap changer operation is not easy. Determining a real cost for voltage quality related parameters such as the voltage variation is even more difficult. In Figure 4.1.8 the cost parameters Ctap and CVdiff are set to 1 and 10, respectively, to obtain the desired operation of the algorithm. The cost parameters Closses and Ccur are the same as in Figure 4.1.7. Controllable load is not available in the example case and, therefore, the cost parameter for load control CDR is not needed.

Figure 4.1.7. Time domain operation of the PC algorithm when the cost function includes only losses and generation curtailment. The algorithm is executed every fourth second. The uppermost figure represents the maximum voltage Vmax and minimum voltage Vmin in the network. The second figure depicts the substation voltage Vss and the setpoint of the AVC relay Vref. The third figure represents the reactive power setpoints of each generator and the lowest figure the amount of generation curtailment of each generator.

0 20 40 60 80 100 120 140 160 180 200 220 2400.95

1.0

1.05

1.1

[pu]

Vmax Vmin

0 20 40 60 80 100 120 140 160 180 200 220 2400.95

1

1.05

[pu]

Vss Vref

0 20 40 60 80 100 120 140 160 180 200 220 240

-0.4

-0.2

0

0.2

[MV

Ar]

Qref1 Qref2 Qref3

0 20 40 60 80 100 120 140 160 180 200 220 240

-0.4

-0.2

0

0.2

t [s]

[MW

]

Pcvc1 Pcvc2 Pcvc3

Feeder voltage limits

AVC relaydeadband



Figure 4.1.8. Time domain operation of the PC algorithm when the whole objective function is used. The algorithm is executed every fourth second. The uppermost figure represents the maximum voltage Vmax and minimum voltage Vmin in the network. The second figure depicts the substation voltage Vss and the setpoint of the AVC relay Vref. The third figure represents the reactive power setpoints of each generator and the lowest figure the amount of generation curtailment of each generator.

The simulations show that the PC algorithm operates as expected: It is able to restore network voltages between acceptable limits and its output depends on the cost parameters of the objective function. Figure 4.1.9 represents some comparison between the cases where the cost parameters are different. The figure shows that when the objective function takes only losses and generation curtailment into account, the voltage level of the network remains at a high level throughout the simulation to minimize the losses. When also the voltage variation from the reference voltage 1.0 p.u. is taken into account, losses increase in some generation situations but the voltage level is kept at a lower level if it is possible.

0 20 40 60 80 100 120 140 160 180 200 220 2400.95

1

1.05

1.1[p

u]

VmaxVmin

0 20 40 60 80 100 120 140 160 180 200 220 2400.95

1

1.05

[pu]

Vss Vref

0 20 40 60 80 100 120 140 160 180 200 220 240

-0.4

-0.2

0

[MV

Ar]

Qref1Qref2Qref3

0 20 40 60 80 100 120 140 160 180 200 220 240

-0.4

-0.2

0

0.2

t [s]

[MW

]

Pcvc1 Pcvc2 Pcvc3

Feeder voltage limits

AVC relaydeadband



Figure 4.1.9. Losses and network maximum voltage with the two different cost parameters. The blue lines represent the result in the simulation case of Figure 4.1.7 and the green lines are from the simulation case of Figure 4.1.8.

4.1.2 OfflineCostParameterUpdateThe offline cost parameter update algorithm determines the cost parameters for the real-time power control algorithm. It utilizes state forecasting data as its input and its aim is to prevent constant operation of tap changers. It has several similarities with the real-time power control algorithm but is implemented as an independent function to decouple the time-critical real-time control operation from the possibly time-consuming PCPU algorithm operation.

4.1.2.1 AlgorithmStepsThis subsection will present the operational principle of the offline cost parameter update algorithm. The flow chart of the PCPU algorithm operation is presented in Figure 4.1.10.

0 50 100 150 200 250-0.1

0

0.1

0.2

0.3

0.4

[MW

]

Losses + curtailmentComplete objective function

0 50 100 150 2000.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

[pu]

Losses + curtailmentComplete objective function

Losses

Maximum voltage



Initialization of the algorithm

Waiting for algorithm starting conditions to be fulfilled

START



Yes

Input data available?

Yes

No


No

Dynamic input data reading and processing at time t

Run PC algorithm optimization

Acceptable solution found?


Check if setpoints will be changed and store results

Write output data to database

Yes

No

t = tpresent

t = t+1

t > tstop?

Determine new cost parameters for the PC

algorithm using a rule based inference algorithm

No

Yes

Figure 4.1.10. The flow chart of the PCPU algorithm operation.



The first parts of the PCPU algorithm are similar to the first parts of the PC algorithm. At the beginning, the algorithm is initialized and the algorithm enters a loop that executes the algorithm using a predefined execution interval. The algorithm is called when the starting conditions (execution interval elapsed and state forecasting algorithm ready) are fulfilled and static network data is read from the database only if it has been updated since the last execution round.

After the static network data is read, the algorithm operation starts to differ from the PC algorithm operation. In the PC algorithm, calculations are made based on the present network state and the future states are not taken into account. The PCPU algorithm utilizes the state forecaster information and bases its calculations on several time steps. The algorithm runs the PC algorithm optimization for each of the available forecasted time steps and stores the relevant network operation data that is needed when determining whether to change the PC algorithm cost parameters. When all time steps are gone through, the algorithm uses a rule based inference algorithm to determine possible new cost parameter values.

The rule based inference algorithm concentrates on preventing multiple tap changer operations during a short time. At first, it checks if the monitoring period contains back and forth tap changer operations that happen inside a predefined time window. If such tap changer operations are detected, the algorithm increases the cost parameter for tap changer operation to a large value that should prevent tap changer operations if the network state can be kept acceptable without them. Then, the algorithm runs the PC algorithm optimizations for the whole monitoring period again with the new cost parameters and checks whether the operation has improved. If the multiple tap changer operations are visible also with the new cost parameters, the algorithm assumes that tap changer operations are necessary for acceptable network operation and does not change the cost parameters. If the multiple tap changer operations disappear, the algorithm still checks the change in the objective function values and new cost parameters are set if the increase in the objective function value sum is not larger than a predefined limit value. In the objective function value calculations, the original cost parameters are used for both cases.

Finally, the new cost parameter values are written to the database from where the PC algorithm retrieves them when it is executed the next time.

It should be noted that the PCPU algorithm defined in the IDE4L project is quite simple and takes into account only the tap changer operations. It would also be possible to use more advanced algorithms that would optimize the PC algorithm cost parameters based on the forecasted data. In the modular control architecture, replacing the PCPU algorithm with another one is easy.

4.1.2.2 InputsandOutputsThe inputs of the offline cost parameter update algorithm are partly the same as the inputs of the real-time power control algorithm. The inputs consist of static network data, dynamic measurement and forecasted data, original parameters of the PC algorithm and data used to coordinate the operation of different algorithms (flags). The outputs consist of cost parameters for the PC algorithm, log messages and data needed for evaluation of the algorithm operation.

Detailed listing of the PCPU algorithm inputs and outputs is presented in Table 4.1.4 and Table 4.1.5 respectively. The inputs and outputs are the same for the MV and LV algorithms.



Table 4.1.4. The inputs of the PCPU algorithm.


Update schedule


Network model, CIM (static data) Grid static data

1. Line from node # to node # 2. Line impedances and capacitances 3. Conductors current limits 4. Phase info (which phases the line is connected to) 5. Switch (breaker and disconnector) and fuse locations


1. No unit 2. Ohm [ Ω], Siemens [S] 3. Ampere [A] 4. No unit 5. No unit

1. Integer 2. Floating point 3. Floating point 4. 3 * binary (one for each phase) or integer 5. Integer

Substation static Info

1. From node # to node # 2. Transformer power rating 3. Tap limits 4. Step size 5. Location of the tap changer 6. Transformer resistance, reactance and etc.


1. No unit 2. [MVA] 3. No unit 4. No unit 5. No unit 6. Ohm [ Ω]

1. Integer 2. Floating point 3. Floating point 4. Floating point 5. Integer (primary or secondary) 6. Floating point




No unit Table with binary numbers

Load static data

1. Connection point 2. Phase info 3. DR node or not


1. No unit 2. No unit 3. No unit

1. Integer 2. 3 * binary (one for each phase) or integer 3. Binary

Static DG information

1. Connection point 2. Phase info 3. Nominal power


1. No unit 2. No unit 3. [kVA]

1. Integer 2. 3 * binary (one for each phase) or integer 3. Floating



point

Reactive power compensator information (static)

1. Connection point 2. Phase info 3. Reactive power limits


1. No unit 2. No unit 3. [kVAr]

1. Integer 2. 3* binary (one for each phase) or integer 3. Floating point

Parameters of algorithms (static input)

Static voltage info

1.Voltage limits for each network node (usually the same in all nodes) 2. Reference voltage values for objective function calculation for each network node

SAU.DXP Local 1. Usually fixed limits 2. Can be fixed or can be requested when changed

1. [V] 2. [V]


Flags (dynamic input)

TCO or AVCO

1. Tap changer operating 2. AVC relay operating


Remote When the TCO or AVCO signal changes

1. No unit 2. No unit

1. Binary 2. Binary

SF ready flag

SF ready flag SAU.SF Stored in SAU.DXP

Local When SF is ready

Timestamp of the last execution of SF



NIS, CIS, DMS Stored in SAU.DXP

Remote When static network data changes

Timestamp of the last change in static network data



DMS FLISR Stored in SSAU.DXP

Remote When network switching state changes

Timestamp of the last change in network switching state

Network state flag

Network state (normal, faulted or

DMS FLISR

Remote When the DMS or FLISR

Integer



in unusual switching state)

Stored in SAU.DXP

updates the flag

Measurements and forecasts (dynamic input) Forecasted dynamic load info (nodes without DR)

1. Real power 2. Reactive power

SAU.SF Stored in SAU.DXP

Local On a fixed schedule (e.g. every 10 minutes)

1. [kW] 2. [kVAr]


Forecasted dynamic load info (nodes with DR)

1. Real power 2. Reactive power 3. Limits for real power change DP 4. Maximum DR activation time



1. [kW] 2. [kVAr] 3. [kW] 4. [s]

1. Floating point 2. Floating point 3. Floating point 4. Floating point

Forecasted dynamic DG info

1. Real power 2. Reactive power 3. Real power limits 4. Reactive power limits 5. Status (connected / disconnected)



1. [kW] 2. [kVAr] 3. [kW] 4. [kVAr] 5. No unit

1. Floating point 2. Floating point 3. Floating point 4. Floating point 5. Binary

Forecasted dynamic voltage info

Nodal voltage info SAU.SF Stored in SAU.DXP


[V] Floating point

Forecasted dynamic current info

Branch current info SAU.SF Stored in SAU.DXP


[A] Floating point

Substation dynamic info

Tap position at the moment




PC algorithm parameters (dynamic input) Original weighting

1. Cost/weighting factor for losses

SAU.DXP

Local On a fixed schedule (e.g.

No unit or euro

Floating point



factors for objective function calculation

2. Cost/weighting factors for generation curtailment for each generator 3. Cost/weighting factors for demand response actions for each DR resource 4. Cost/weighting factor for tap changer operations 5. Cost/weighting factors for voltage difference to the reference value for each network node

every 10 minutes)

Table 4.1.5. The outputs of the PCPU algorithm.

Output Data exchanged Destination Local / Remote

Update schedule


Weighting factors for objective function calculation

1. Cost/weighting factor for losses 2. Cost/weighting factors for generation curtailment for each generator 3. Cost/weighting factors for demand response actions for each DR resource 4. Cost/weighting factor for tap changer operations 5. Cost/weighting factors for voltage difference to the reference value for each network node

SAU.DXP Local Whenever the SAU.PCPU algorithm updates the output

No unit or euro

Floating point

Log message

Message used to track the operation of the SAU.PCPU

SAU.DXP Local Whenever the SAU.PCPU algorithm




algorithm updates the output

Data needed for evaluating the operation of the algorithm Execution time

SAU.PCPU algorithm execution time

SAU.DXP Local Whenever the SAU.PCPU algorithm updates the output

[s] Floating point

4.1.2.3 AlgorithmStartingConditionsThe starting conditions for the PCPU algorithm are quite similar to the starting conditions for the PC algorithm. The flow chart of the PCPU algorithm starting conditions is presented in Figure 4.1.11. The only difference compared to PC algorithm starting conditions is that the state forecasting ready flag is read instead of the state estimation ready flag and that the BOT unit does not affect the operation of the PCPU algorithm.



Wait until the execution interval has elapsed

Network state

Normal

Read network state flag



Faulted

Yes


No

Either one changed during SF execution ?

AVC relay or tap changer operating ?

Yes

Yes

No

No

Read static network data updated flag

Wait for SF ready flag

SF ready before the delay exceeds a limit?

Read network state and static network data updated flags

Read AVCO and TCO signals

Yes

No

To optimization calculations

From initialization

Figure 4.1.11. Flow chart of the PCPU algorithm starting conditions.



4.1.2.4 OperationinExceptionalSituationsThe same exceptional situations as in PC algorithm are considered also in the PCPU algorithm: lack of proper input data, convergence problem in optimization and the network in a fault location, isolation or supply restoration state. The exception handling in the PCPU algorithm is simpler than in the PC algorithm because failure of the PCPU algorithm is not as critical as failure of the PC algorithm.

If proper input data is not available, the algorithm writes a log message to the database and calls a function that tries to solve the problem. If the problem persists for longer than a predefined time, predefined cost parameters are written to the database. The flow chart of this operation is presented in Figure 4.1.12.

Write a log message to the database

Data missing for a predefined time ?

Yes

No

No

Set predefined cost parameters

Adequate input data not available

Call the function that tries to solve the problem

Write the new cost parameters to the database


Figure 4.1.12. Flow chart of the PCPU exception handling when adequate input data is not available.

If the PC algorithm optimization does not converge at some time step of the PCPU calculations, the algorithm writes a log message to the database. After that it checks whether there was a convergence problem already at the previous execution round of the algorithm. If the OPF did not converge at the previous execution round either, predefined cost parameters are written to the database. The flow chart of this operation is presented in Figure 4.1.13.



Write a log message to the database

OPF converged at the last execution round?

Yes

No

Set predefined cost parameters

OPF does not converge

Write the new cost parameters to the database


Figure 4.1.13. Flow chart of the PCPU exception handling when there is a convergence problem with the optimization algorithm.

In the fault location, isolation and supply restoration states, the PCPU algorithm operates in a similar way as the PC algorithm.

4.1.3 BlockOLTCsofTransformers(BOT)This subsection is dedicated to explanation of the BOT unit developed in the IDE4L project. The BOT unit is responsible for managing the transformers operating in series in a way that coordination of the secondary controllers (i.e. the MVPC and the LVPC) is realized. The BOT unit is also capable of voltage regulation in absence of power controllers.

In standalone operation of the BOT unit, the AVC relays of transformers control the secondary side (LV side) voltage based on local measurements and coordination of the cascaded OLTCs is achieved by OLTC block signal and block validity time/unblock signal generated by the BOT unit.

In integrated operation of the BOT unit (i.e. in presence of the MVPC and the LVPC), the BOT provides block signal and block validity time/unblock signal for the power controllers and the AVC relays of transformers as inputs.

4.1.3.1 NeedfortheBOTThe most commonly used control method for transformers equipped with on-load tap changer and operating in series is the graded time (GT) method. The main idea of this method is to assign different time delays for cascaded OLTCs. The upper level OLTCs have shorter time delays in comparison to lower level OLTCs. This is to ensure that the upper level OLTC operates first and then the lower level OLTC acts if required. The GT method considers the worst case scenario for voltage regulation time. [Smith2003][Gao2010] This is the main disadvantage of the GT method which leads to delay in customer voltage restoration time [Moghaddam2015].



Since data exchange platforms will be considered and developed in the IDE4L project, a communication based approach will be utilized for managing the OLTCs. As it has already been mentioned in subsection 3.2.3, the BOT unit is located at the primary substation. The BOT will utilize the data exchange platforms to send the OLTC block signals and block validity time/unblock signals. The main advantage of the BOT unit over the GT method is that the BOT makes it possible to set equal time delays for cascaded transformers [Moghaddam2015]. This leads to reduction in customer’s voltage restoration time [Moghaddam2015].

OLTCs are usually used to adjust the voltage on the secondary side (LV side) of transformers. In order to adjust the secondary side voltage of the transformer, the AVC relay should provide settings for the OLTC. The AVC relay is not able to determine whether the voltage change has originated from the primary or secondary side of the transformer. Therefore, finding the origin of voltage change is a momentous task for proper coordination of cascaded OLTCs. The BOT unit is capable of finding the origin of voltage variations in the network by tracking active and reactive power changes. The BOT unit is specifically designed for a bidirectional power flow environment. Hence, it is able to consider reverse power flow through the transformers due to integration of distributed generation. [Moghaddam2015]

4.1.3.2 BOTUnitOperationalLogicThe BOT unit has been divided into two steps. At first, the origin of the voltage change is located. Afterwards, an OLTC block signal is sent to the AVC relay of the transformer whose operation should be delayed or avoided [Moghaddam2015]. The developed control scheme for the BOT unit is depicted in Figure 4.1.14. The variables P and Q in the figure are active and reactive powers, respectively.

HV/MV transformerMV/LV transformer

AVC relay of HV/MV

transformer

BOT

P,Q measurements from secondary side of HV/MV

transformer

AVC relay of MV/LV

transformer

Tap changer Tap changer

P,Q measurements

from primary side of MV/LV

transformer

Figure 4.1.14. Developed BOT control scheme. [Moghaddam2015]



In order to determine the operational principle of the BOT unit, a simplified model of a distribution network in PSCAD, is used. The network is depicted in Figure 4.1.15. It has also been assumed that the MV/LV transformer is equipped with an OLTC.

Main power supply

HV/MV transformer

MV/LV transformer

M1

M2

Figure 4.1.15. Simplified distribution network model in PSCAD. [Moghaddam2015]

4.1.3.2.1 LocatingtheOriginofVoltageVariationMeters M1 and M2 in Figure 4.1.15 are providing active and reactive power flow information. Power flow recorded by M1 indicates the power flow to/from the whole MV network including LV networks. However, M2 presents mainly the power flow to/from the LV network. Since the system has a cascaded nature, changes in power flow recorded at M2 are also sensed by M1. This means that power changes in the LV network are also visible at M1 in the same direction. On the other hand, power variation in the MV network is not affecting the power flow at M2 that much. The power flow measured by M2 is dependent on the LV network loading and generation unless there are some outages or critical problems in the higher voltage levels. This logic can be utilized for detecting the origin of voltage variations in the network. [Moghaddam2015]

4.1.3.2.2 OrderingOLTCBlockSignalsThe principle, used inside the BOT algorithm, for generating the block signals is quite simple. Having voltage changes originating from the MV network, the BOT sends a block signal to the AVC relay of the MV/LV transformer that prevents the MV/LV OLTC operation. Similarly, voltage changes caused by the LV network lead to blocking of the HV/MV OLTC. The OLTC block signals are generated along with block validity time. This is to make sure that in the case of communication failure between the BOT and the AVC relay, the blocked OLTC will not stay blocked for an indefinite and possibly very long period of time. [Moghaddam2015]



4.1.3.3 ImportantSettingsRelatedtotheBOTThe BOT unit is set in a way that it does not react to small power variations. This means that in normal network conditions no block signals are sent and the AVC relays of transformers take care of the voltage regulation locally if any OLTC action is needed. The time delay of the AVC relays should be set equal for all cascaded transformers, which will result in fast restoration of voltage at consumption points [Moghaddam2015]. Block validity time should be carefully selected. This time should not be very small as that can weaken the coordination of cascaded transformers. Also, it should not be set to a high value as that can lead to blocking the OLTC for a long period of time.

4.1.3.4 BOTUnitOperationinthePresenceofPowerControllersFigure 4.1.16 presents the BOT unit operational flow chart in the presence of the power controllers. The BOT unit first checks that there is no fault in the network. It also checks if the MV database contains new data from the SE unit. If no new data is available from the SE unit, the BOT returns to a waiting state. If new data is available, it is read and the BOT algorithm is run. The generated outputs are saved in the MV database. The generated outputs of the BOT unit will be used as inputs by the MVPC, the LVPC and the AVC relays of transformers.

Yes

No

No

BOT reads the value of its input flags from MV database

Fault in the network?

New data update by SE?

Yes

BOT reads its inputs from MV database

BOT algorithm is run

BOT inserts its outputs into MV database

Figure 4.1.16. Operational flow chart of the BOT unit in the presence of power controllers.



4.1.3.5 InputsandOutputsThe inputs and outputs for the BOT unit are presented in Table 4.1.6. and Table 4.1.7., respectively.

Table 4.1.6. Inputs of the BOT unit.


Update schedule


Network state flag

Network state (normal, faulted or in unusual switching state)

DMS FLISR Stored in PSAU.DXP.RDBMS

Remote When the DMS or FLISR updates the flag

No unit Integer

SE ready flag SE ready flag PSAU.SE Stored in PSAU.DXP.RDBMS

Local When SE is ready

No unit Binary and timestamp of the last execution of SE

Measured active power at secondary side (MV side) of HV/MV transformers

1. Value of active power

2. Which transformer

PSAU.SE Stored in PSAU.DXP.RDBMS

Local On a fixed schedule

1. [kW] 2. No unit

1. Floating point

2. Integer

Measured reactive power at secondary side (MV side) of HV/MV transformers

1. Value of reactive power




1. [kVAr] 2. No unit

1. Floating point

2. Integer

Measured active power at primary side (MV side) of MV/LV transformers

1. Value of active power




1. [kW] 2. No unit

1. Floating point

2. Integer

Measured reactive power at primary side (MV side) of MV/LV

1. Value of reactive power




1. [kVAr] 2. No unit

1. Floating point

2. Integer



transformers

Network topology

1. Lines from node # to node #

2. Substations from node # to node #

3. Location of meters that provide active and reactive power values (i.e. which transformer)

PSAU.DXP.RDBMS

Local On request (quite rarely)

No unit Integer

Table 4.1.7. Outputs generated by the BOT unit.


Update schedule


Block signal (for PSAU.PC, AVC relay of HV/MV transformers as well as SSAU.PC and AVC relay of MV/LV transformers)

Block signal which prevents the OLTC operation

block signal= 1 means that tap position should not be altered by PSAU.PC & SSAU.PC As a default, the value of block signal is zero in secondary controllers i.e. tap is a control variable

PSAU.DXP.RDBMS SSAU.DXP.RDBMS (via PSAU.DXP.RDBMS)

Local for PSAU.DXP.RDBMS Remote for SSAU.DXP.RDBMS

After each run of the PSAU.BOT unit. The value of the block signal is 0 unless the PSAU.BOT unit changes it to 1

No unit Binary

Unblock signal (for PSAU.PC, AVC relay of HV/MV transformers as well as

Unblock signal (this is used when block validity time is not considered)



After each run of the PSAU.BOT

No unit Binary



SSAU.PC and AVC relay of MV/LV transformers)

Validity time

If the value of the block signal is 1, then this signal is sent along with it.

Validity time of block signal



After each run of the PSAU.BOT unit

Second [s] Datetime

End time: MM/DD/YY HH:MM:SS

BOT ready flag Flag signal that shows PSAU.BOT unit is done with its calculations and outputs are available in the database



After each run of the PSAU.BOT unit

No unit Binary and timestamp of the last execution of BOT

PSAU.BOT output error log message (for operator)

Flag signal indicating error inside PSAU.BOT i.e. PSAU.BOT cannot generate its outputs for any reason

Operator via PSAU.DXP.RDBMS

Remote After each run of the PSAU.BOT unit

No unit Binary (or string)

4.1.3.6 PerformanceTestsFor the BOT algorithm performance test, the network in Figure 4.1.15 is used. A test sequence is created and the performance of the BOT in standalone operation is tested. The BOT performance is also compared with the conventional method where the same time delay is assigned to the AVC relays of the cascaded transformers. The network model has been constructed in PSCAD and the BOT algorithm has been implemented in MATLAB. PSCAD/MATLAB interface is utilized in simulations.

Table 4.1.8. and Table 4.1.9. present the transformer parameters and loading condition in the network, respectively.



Table 4.1.8. Transformer parameters. [Moghaddam2015]

Transformer Voltage ratio Full capacity Deadband Tap steps Number of available taps

HV/MV 110/21 kV 16 MVA 1.5 % 1.67 % 9 up

9 down

MV/LV 20/0.4 kV 0.1 MVA 1.5 % 1.67 % 9 up

9 down

Table 4.1.9. Loading condition in the network. [Moghaddam2015]

Load at HV/MV transformer at minimum loading case

323.933 kW

-83.213 kVAr

Load at MV/LV transformer at minimum loading case

4.71 kW

1.7 kVAr

Table 4.1.10. presents the used time delays for tap operations for both BOT and conventional methods.

Table 4.1.10. Time delay used in simulations. [Moghaddam2015]

Control approach Time delay of first tap operation

Time delay of consecutive tap operation Mechanical time delay

Coordinated using BOT 3 s 2 s 1 s

Conventional method 3 s 2 s 1 s

A test sequence as shown in Table 4.1.11. is created and control methods are compared.

Table 4.1.11. Test sequence. [Moghaddam2015]

Time of change Change level Type of change Value of change

4 s LV Active and reactive load increase

8 kW, 2 kVAr

12 s

MV Active load increase 9 MW

21 s LV Power production by DG units 20 kW

28 s MV Inductive load increase 4 MVAr

38 s MV Capacitor bank connection 3 MVAr

46 s LV Reactive power generated by DG units 20 kVAr



The voltage reference has been set to 1.03 per unit for the HV/MV transformer and 1 per unit for the MV/LV transformer. Line drop compensation is set to zero meaning that the AVC relays are controlling the transformer secondary bus voltage. Figure 4.1.17 presents the operation of the HV/MV OLTC which is the same for both methods. Operation of the MV/LV OLTC for both methods is depicted in Figure 4.1.18.

Figure 4.1.17. HV/MV OLTC operation for both control methods. [Moghaddam2015]

Figure 4.1.18. MV/LV OLTC operations. [Moghaddam2015]

As can be seen from Figure 4.1.17 and Figure 4.1.18, using the BOT unit prevents the extra tap actions that can occur when using the conventional method. The extra tap actions made by the conventional method are due to simultaneous operation of the HV/MV and MV/LV OLTCs at time 16 s. However, using the conventional method a faster tap action is done at time 32 s. The same action is done with a small delay when the BOT unit is used. The reason for this is the long block validity time (3 s) assigned by the BOT to the AVC relay of the MV/LV transformer.



Figure 4.1.19 shows the voltage at the secondary side of the MV/LV transformer for both methods.

Figure 4.1.19. Voltage at secondary side of MV/LV transformer. [Moghaddam2015]

As can be observed from Figure 4.1.19, using the BOT unit decreases the voltage fluctuations. However, due to the long block validity time assigned by the BOT, voltage drop at time 28 s is restored with some delay in comparision to the conventional method. This can be solved by setting a smaller value for the block validity time. In order to see the effect of reduced block validity time, the simulation is conducted again. Figure 4.1.20 and Figure 4.1.21 present the MV/LV OLTC operation and secondary bus voltage of the MV/LV transformer with reduced block validity time (1.1 s), respectively.

Figure 4.1.20. MV/LV OLTC operation with reduced block validity time.



Figure 4.1.21. Voltage at secondary side of MV/LV transformer with reduced block validity time.

Figure 4.1.20 and Figure 4.1.21 show that when the value of block validity time is reduced, the BOT method shows a much faster reaction in respect to the voltage drop at time 28 s. To sum up, the BOT unit is able to prevent extra actions that may occur when using the conventional method. In contrast to GT methods where the time delays of cascaded transformers are set based on the worst case scenario, using the BOT makes it possible to set equal time delays for cascaded transformers which reduces the customer’s voltage regulation time. [Moghaddam2015]

4.1.3.7 OperationinAlternativeScenariosIn standalone operation, if for any reason the BOT unit fails to generate its outputs, the AVC relays of transformers will take care of the voltage regulation locally. If the BOT fails to provide outputs to the power controllers (the MVPC and the LVPC), they will operate without considering the coordination of transformers.

4.2 TertiaryControl

4.2.1 NetworkReconfiguration(NR)NR is a function of the tertiary controller which will be applied for medium voltage grid congestion management. The goal of network reconfiguration is to change the topological structure of the distribution feeders by closing some normally open switches and opening some normally closed switches in their place and to select the optimal setpoint of the voltage controllers (on-load tap changers, MV capacitors, MV reactive power sources). The network configuration should remain radial after the switching operations.

NR updates network switches status and/or MV voltage controllers’ setpoints in order to alleviate congestion problems that are related to a specific MV network. The NR algorithm runs on demand (real-time validation) and also runs on fixed intervals such as once a day or scheduled hours to find the optimal configuration of the network (offline validation).



4.2.1.1 NormalOperationIn normal operation the network reconfiguration algorithm will be executed to reduce system losses, balance loads (exchange between feeders) and avoid overload of network elements.

The main program running the network reconfiguration algorithm can be divided into the following steps:

1) Received request from the tertiary controller manager. The network reconfiguration algorithm runs on demand (real-time validation) and also runs on fixed intervals such as once a day or scheduled hours to find the optimal configuration of the network (offline validation). The activation process is initiated by the tertiary controller manager in case a network reconfiguration is needed to solve a congestion problem.

2) Read network topology and switch status from the control centre level DXP. Two types of switches will be considered: normally closed switches which connect line sections (sectionalizing switches) and normally open switches on the tie-lines which connect two feeders.

3) Read real-time measurements and forecaster outputs from the control centre level DXP: a. DER information and availability b. FLISR data (i.e.: fault location) c. Forecaster outputs d. OLTC setpoints e. MV VAR controllable resources setpoints

4) Topology information processing. In case of faults, it is expected that FLISR clears the fault and restores service to as many customers as possible.

5) Optimal network reconfiguration execution a. Network reconfiguration is a MINLP optimization problem containing both binary variables

(operative status of switching devices, on/off) and continuous variables (branch currents, power injections, nodal voltages and the optimal setpoints of voltage controllers for on-load tap changers, MV capacitors, MV reactive power sources).

b. Optimization solver: A genetic algorithm is used to solve the network reconfiguration problem considering the radiality constraints and the network graph theory. The flow chart of the genetic algorithm is shown in Figure 4.2.1.

c. On demand execution the network reconfiguration algorithm determines both the optimal topology of the power system and the optimal setpoints of voltage control units (STATCOMs, capacitors banks and OLTCs).

NETWORK RECONFIGURATION ALGORITHM

Located in control centre

Execution interval: “on demand”* and “scheduled hours”


• Minimize the number of switching operations after restoration

• Minimize active power losses

• Minimize number or size of load

• Radiality: The distribution system should be radial without meshes after network reconfiguration

• Fulfilment of connectivity of all customers demand through the state of switching devices (avoiding load islands).

• Status of remotely controlled sectionalizing switches, tie switches, and breakers. (on/off)

• MV VAR controllable resources

• Switching device of MV network

• MV VAR controllable resources *

• OLTCs*



shedding • Balancing

(exchange between feeders)

• Congestion management constraints: voltage limits, branch current thermal constraints

• DER power capabilities constraints

setpoints* • OLTC setpoints*

6) Exporting network reconfiguration results: status of switches, signals to the automation systems,

messages to the tertiary controller manager.

Load power system

Create initial population

Evaluate all the individuals of the

population

Crossover

Mutation

Elitism

New offspring

All constraints satisfied?

Assign operational cost to each individual

Yes

No

Assign penalty operational cost to

the individual

i=1

i=i+1

Reach stop criteria?

Yes

Record best individual topology

Stop

No

i=population’s individuals?

Yes

No

Figure 4.2.1. Genetic algorithm flow chart.



4.2.1.2 InputsandOutputsThe inputs and outputs of the NR algorithm are presented in Table 4.2.1 and Table 4.2.2, respectively.

Table 4.2.1. Network reconfiguration inputs.


Update schedule

Unit Format

Network reconfiguration request

Request message from tertiary controller manager

Tertiary controller manager

Local On demand No unit Message

(start)

Network topology

1. Line parameters:

connection (from/to) resistance reactance capacitance current limits phase

2. OLTC location, position, transformer power rating, tap limits, step size, transformer resistance, reactance and etc.

3. MV STATCOM location, setpoint and VAR limits

4. MV Capacitors location, setpoint and VAR limits

CC database Local Whenever the NR request is received

1. No unit, Ohm [Ω], Ohm [Ω], Siemens [S], Ampere [A], no unit

2. No unit, [MVA], no unit, no unit, Ohm [Ω]

3. No unit, no unit, [kVAr]

4. No unit, no unit, [kVAr]

Table with integer and floating point numbers

MV state estimation outputs

1. MV customer connection point: Load demand (active/reactive) for the current instant of time

2. MV/LV transformer load demand

SE

Stored in CC database

Local Whenever the NR request is received

1. [kW/kVAr]

2. [kW/kVAr]

3. [kW], no unit

4. No unit, [kVAr]




(active/reactive) for the current instant of time

3. MV DG generation and status for the current instant of time

4. MV VAR compensators status and injection for the current instant of time

5. OLTC tap values for the current instant of time

5. No unit

MV forecaster outputs

1. MV forecast Load demand (active/reactive) k steps ahead

2. MV/LV aggregated substation load demand (active/reactive)

3. MV production forecast

SF

Stored in CC database

Local Whenever the NR request is received

1. [kW/kVAr]

2. [kW/kVAr]

3. [kW]

Table with floating point numbers

DER information

1. connection point

2. phase

3. nominal power and capability curves

4. type

5. status (connected or disconnected)


1. No unit

2. No unit

3. [kW] and [kVAr]

4. No unit

5. No unit

Table with integer, binary and floating point numbers



Switches information

1. From and to Location

2. Status (normally open or closed)

3. Remote controlled or not?

4. Type (C.B. or disconnector)


1. No unit

2. No unit

3. No unit

4. No unit

Table with binary and integer

Operational Costs

1. Network losses

2. Costs of changing the status of remotely controlled switches

3. VAR costs

CC database Local To be defined

1. [€/MW] power losses

2. [€/switch]

3. [€/MVAr]

Table with floating point numbers

FLISR data Input from WP4 about fault details (Phases, Fault Currents, Fault location, breakers, switches status etc.)

CC database Local Whenever FLISR_START flag is activated

No unit To be defined

MV network static voltage info

Voltage limits CC database Local Usually fixed limits

[V] Floating point

Table 4.2.2. Network reconfiguration outputs.


Update schedule

Units Format

Optimal network topology

New status of network switches

CC database Local Whenever needed

No unit Table with binary numbers (logic)



Feedback to the automation system

Commands to the SAU

SAU To be defined

Whenever needed

No unit To be defined

Optimal MV voltage controllers setpoints

1. OLTC setpoints

2. MV STATCOM setpoints

3. MV capacitors setpoints

CC database Local Whenever needed

1. No unit

2. [MW]

3. [MW]

Floating point

Feedback to tertiary controller manager

message Tertiary controller manager

To be defined

Whenever needed

No unit Messages:

· “Modified switches status”

· “Modified voltage control setpoints”

· “Finished”



4.2.1.3 Step-by-stepDescriptions

Table 4.2.3. Step-by-step description of the NR algorithm.

1 Received request message from tertiary controller manager

2 Connect to the control centre level database and read network topology and switch data position at the current time.

3 Read real-time measurements and forecast outputs from the CC database

· Information about DER units (active and reactive power production at the current time). · MV customer load demand · MV/LV transformer load demand

4 Run optimization algorithm until convergence

· Select switching pattern from all the 2n switching combinations · Create a radial network configuration based on graph theory · Evaluate objective function for each network topology · Calculate equality and inequality constraints · Calculate MV controllers setpoints

5 Save network reconfiguration outputs and MV voltage controllers setpoints to the CC level DXP

6 Send messages to the tertiary controller manager



Read data from DXP

Select initial switching configuration

Find new switching configuration

Yes

NoOptimum reached?

Evaluate objective function

Calculate technical constraints

Request from TC?

Create network graph

No

YesExport optimal on/off patterns of switches, OLTC, STATCOM

and capacitors setpoints to DXP

Max iter reached?

Send “finished” message to TC

manager

Send “modified switches status and

MV controllers setpoint” message to

TC manager

End

Yes

No

Start

Figure 4.2.2. General network reconfiguration main function flow chart.



4.2.1.4 PerformanceTestsofNetworkReconfiguration

4.2.1.4.1 StudySystemThe performance of the network reconfiguration algorithm is evaluated by doing a simulation study. The power system used in the simulations is the MV network from A2A’s demonstration site. This network has three feeders (blue, pink and green) and 39 MV buses. On each MV bus an LV power system is connected through a transformer and represented by its aggregated load demand. Moreover, PV units are available in the LV power systems and represented in the MV power system by their aggregated production.

The blue boxes of Figure 4.2.3 represent the switches available in the power system under study. The total number of switches is 29, and only three of them can be used to connect the different feeders to each other. These three switches allow the power system to be operated as a mesh.

4.2.1.4.2 OnlineValidationThe operation of the network reconfiguration algorithm during real-time validation has been described in subsection 3.3.1 of this document. When a fault occurs, and without an optimal solution from the FLISR actions, the network reconfiguration algorithm is run and tries to look for the optimal configuration of the power system minimizing real power losses, taking into account voltage and load limits, with the minimum number of switch actions. Fault scenarios are listed in Table 4.2.4 and shown in Figure 4.2.4.

Table 4.2.4. Fault scenarios for real-time NR validation.

Fault No. 1 2 3 4 5 6

Branch in fault 1340-603 1354-827 827-1006 1006-1464 117-378 378-145

Figure 4.2.3. A2A power system.



Figure 4.2.4. A2A power system fault situation.

Table 4.2.5 shows the KPIs of the NR algorithm optimal solution for the fault scenarios shown in Figure 4.2.4.

Fault scenario number 4 is analysed. Under a fault condition between buses 1006 and 1464, and after the FLISR actions, the NR algorithm looks for the optimal configuration. In the optimal configuration the switches located at the right side of bus number 1006 and the left side of bus number 1464 have been opened. This leaves the nodes between bus 1006 and 603 unsupplied (i.e. everything downstream of the fault). In order to supply these loads the switches located between buses 603 and 117 has been closed, as can be seen in Figure 4.2.5. These open/close switch actions supply blue loads from the pink feeder. There are some loads which cannot be supplied. These loads are located in the section under fault.

Branch loading under this condition is shown in Figure 4.2.6. It can be observed that none of the lines are overloaded with the NR optimal configuration. Moreover, Figure 4.2.7 demonstrates that the NR algorithm is able to optimize the configuration of the power system while keeping the voltage at any node within range limits.



Table 4.2.5. Real-time validation NR KPIs.

KPI Scenario / Fault 1 2 3 4 5 6 Pload [kW] 20190 19690 19760 18890 18390 18760 Pgeneration PV [kWh] 0 0 0 0 0 0 Curtailed production [kWh] Lost production

0 0 0 0 0 0

Curtailed/moved load [kWh] Not supplied load

0 1800 2370 4470 1800 2730

Network losses [kWh] 194,24 196,02 212,22 257,86 177,56 184,28 P setpoint changes [pcs] 0 0 0 0 0 0 Q setpoint changes [pcs] 0 0 0 0 0 0 V setpoint changes [pcs] 0 0 0 0 0 0 Switch activations [pcs] 1 3 3 3 1 3 Capacity utilization See Figure 4.2.6 Duration the voltage/current is out of bounds [s]

0 0 0 0 0 0

Number of overvoltage events [pcs] 0 0 0 0 0 0 Number of undervoltage events [pcs] 0 0 0 0 0 0 Number of overcurrent events [pcs] 0 0 0 0 0 0

Figure 4.2.5. NR optimal solution for fault scenario no. 4.



Figure 4.2.6. Line loading for fault scenario 4.

Figure 4.2.7. Voltage profile for the A2A power systems under fault scenario 4.

4.2.2 MarketAgent(MA)The market agent is a function of the tertiary controller for MV network congestion management through flexibility services or products. Its main objective is to propose changes of scheduled generation/consumption of DER units through flexibility offers/bids, to provide a feasible combination of schedules [Sebastian2008].

The market agent performs two different algorithms, depending on the time frame, to validate the energy schedule coming from the market and procure and/or activate flexibility products in order to alleviate congestions in the distribution grid:



1) Offline validation (OLV): in case of problems detected in the day-ahead or in the intraday markets, and if the NR algorithm is not able to find a solution1, flexibility offers/bids presented by the CAs are evaluated whereby the network states (voltages and currents) are kept within limits, trying to avoid generation curtailment or load shedding.

2) Real-time validation (RTV): in the real-time time frame, secondary controllers are continuously checking the state of the network. If problems arise and they are not able to solve them, they request help from the tertiary controller. This algorithm evaluates the activation of CRP flexibility products previously contracted by the DSO and the CA or the purchase of new SRP products.

4.2.2.1 OfflineValidation(OLV)The offline validation algorithm is used for the validation of the provisional energy schedule given by the energy market clearing in the daily and intraday markets. The process is applied right after the day-ahead market clearing, or after each of the intraday markets clearing, if network reconfiguration is not able to solve the possible congestions. The validation algorithm is run for timeslots of one hour independently.

The OLV needs the forecasted state of the network (loads, generation, topology), the provisional schedule from the market and the market clearing price. The OLV needs to know the aggregated generation/demand of each MV node from the market clearing and to have forecasts of DG of independent producers not included in any CA portfolio . Input data for the OLV algorithm is provided by the data exchange platform (DXP) located at the control centre. It is assumed that LV network data and MV network data are sent to the CC DXP from the LV DXP and MV DXP respectively. In this way, the source of all the data will be considered as local from the OLV point of view.

A power flow calculation is run first in order to check for any limit violation in the network (voltage, branch loading and power capability constraints of the network). If violations are detected, the algorithm will evaluate flexibility products offered by the aggregators, through an OPF. The flexibility products can be SRPs or CRPs.

The OPF is run to find the changes of scheduled generation/consumption values of DER units that should be applied in order to satisfy all the network constraints with the least cost. It is formulated as a single period AC OPF whose objective function to be minimized is the cost of the flexibility products. The OPF should consider a high cost associated to load or generation curtailment in order to reduce them to the unavoidable cases. The equality constraints are the power flow equations in each node, and inequality constaints are voltage constraints, branch capacity constraints and power generation/consumption capabilities of DERs.

Once a solution is obtained, the accepted bids are sent to the CAs and the final schedule is stored in the DXP. A solution will always be obtained because generation or load curtailment is always a feasible solution. The flow chart of the OLV algorithm is depicted in Figure 4.2.8. The time slots considered in the flow chart are consistent with those of the daily market. For intraday (ID) markets the time slots would vary depending on the session (in the Iberian Electricity Market the time slots for each session are 28 (ID1), 24 (ID2), 20 (ID3), 17 (ID4), 13 (ID5) and 9 (ID6)).

1 The NR algorithm changes the setting of voltage controllers in the network or applies topological changes in the network. Only when these actions are insufficient to avoid problems or are not practicle, other types of action will be considered.



Figure 4.2.8. Offline validation algorithm flow chart.

4.2.2.2 Real-TimeValidation(RTV)RTV is similar to OLV but based on a different time frame. This function runs “on demand” under request of the tertiary controller manager in real-time operation. Its main objective is the activation of CRP flexibility products, if previous actions from real-time secondary controller and real-time network reconfiguration have not been able to alleviate the congestions in the distribution grid.

The core procedure is almost the same as in the OLV tool. Due to the close to real time use of this tool, the real description of the system is used (topology, load, generation etc.). Input data for the RTV algorithm is provided by the DXP located at control centre. It is assumed that LV network data and MV network data are sent to the CC DXP from the LV DXP and MV DXP respectively. In this way, the source of all the data will be considered as local from the RTV point of view.

A power flow calculation is run first in order to check for any limit violation in the network (voltage, branch loading and power capability constraints of the network). Then, an OPF will be run in order to decide among the activation of CRPs or the purchase of new SRPs, again with the objective of satisfying all the network constraints with the least cost. The OPF is formulated as a single period AC OPF with a high cost associated to load and generation curtailment. The equality constraints are the power flow equations in each node, and the inequality constraints are voltage constraints, branch capacity constraints and power generation/consumption capabilities of DERs.

Once a solution is obtained, the accepted bids are sent to the CAs and the final schedule is stored in the DXP. A solution will always be obtained because generation or load curtailment is always a feasible solution. The flow chart of the algorithm is shown in Figure 4.2.9.



Figure 4.2.9. Real-time validation algorithm flow chart.

4.2.2.3 OptimalPowerFlowThe main core of both algorithms is the optimal power flow which is a standard AC OPF found in the MATPOWER 5.0b1 package [Zimmerman2011]. In this subsection, the main formulation is summarized.

The optimization vector x for the standard AC OPF problem consists of the bn x 1 vectors of voltage angles

Q and magnitudes mV and the gn x 1 vectors of generator real and reactive power injections gP and gQ .

m

g

g

Vx

PQ

Qé ùê úê ú=ê úê úê úë û

(0.0.1)

The objective function is simply a summation of individual polynomial cost functions iPf and i

Qf of real and

reactive power injections, respectively, for each generator:

( ) ( ); ; ;

1

ming

m g g

ni i i i

P g Q gV P Qi

f p f qQ

=

+å (0.0.2)

At the moment, reactive power costs are not considered, but they could be added.

The equality constraints are the full set of 2 bn× nonlinear real and reactive power balance equations:



( ) ( ) ,, , , 0P m g bus m d g busg V P P V P PQ = Q + - = (0.0.3)

( ) ( ) ,, , , 0,Q m g bus m d g busg V Q Q V Q QQ = Q + - = (0.0.4)

where busP and busQ are the real and reactive power injected to the system at each bus; dP and dQ are

the real and reactive power demanded at each bus; and ,g busP and ,g busQ are the real and reactive power

generated at each bus.

The inequality constraints consist of two sets of ln branch flow limits as nonlinear functions of the bus

voltage angles and magnitudes, one for the from end and one for the to end of each branch:

( ) ( ) max, , 0f m f mh V F V FQ = Q - £ (0.0.5)

( ) ( ) max, , 0.t m t mh V F V FQ = Q - £ (0.0.6)

The flows are typically apparent power flows ( )fS expressed in MVA, but can be real power ( )fP or

current flows ( )fI . The vector of flow limits maxF has the appropriate units for the type of constraint.

The variable limits include an equality constraint on any reference bus angle ( )iq and upper and lower

limits on all bus voltage magnitudes ( )imv and real and reactive generator injections ( ),i i

g gp q :

,ref refi i i refi Iq q q£ £ Î (0.0.7)

, , , 1i min i i maxm m m bv v v i n£ £ = ¼ (0.0.8)

, , , 1i min i i maxg g g gp p p i n£ £ = ¼ (0.0.9)

, , , 1 .i min i i maxg g g gq q q i n£ £ = ¼ (0.0.10)

In order to handle the nonsmooth convex piecewise linear cost functions that typically arise from discrete bids and offers in electricity markets, a constrained cost variable method is used. The convex piecewise linear cost function

(0.0.11)

is defined by a sequence of points ( ), 0,j jx c j n= ¼ , where jm denotes the slope of the j-th segment



1

1

, 1...j jj

j j

c cm j n

x x-

-

-= =

- (0.0.12)

and and .

This piecewise linear cost function ( )c x is replaced by a helper variable y and a set of linear constraints that form a convex “basin” requiring the cost variable y, in the form:

( ) , 1 .j j jy m x x c j n³ - + = K (0.0.13)

The cost term added to the objective function in place of ( )c x is simply the variable .y

Dispatchable loads or price-sensitive loads are modelled using a simple approach consisting in modelling them as negative real power injections with associated negative costs. This is done by specifying a generator with a negative output, ranging from a minimum injection equal to the negative of the largest possible load to a maximum injection of zero [Zimmerman2009]. Taking negative costs, the lowest cost would push the generator to the minimum injection (that is, to the maximum load), which is equal to consider no demand response. It also leads to a more general formulation regarding a future prosumer modelling with consumption/production behaviour.

The solver is MIPS (MATLAB Interior Point Solver), a primal/dual interior point method [Wang2007].

The optimal power flow main function requires the following inputs:

1) Bus data matrix, containing the bus numbers, aggregated consumption/production for each bus and voltage limits. Data for each period of interest are needed (e.g., 24 hours in the day-ahead market time frame).

2) Branch data matrix, containing resistance, impedance, capacitive susceptance, MVA rating (long term rating, short-term and emergency rating) for each branch and status. If the branch is a transformer also the nominal turns ratio (taps at “from” bus, impedance at “to” bus), and the phase shift angle in degrees (positive implies a delay).

3) Generator data matrix, containing the bus numbers, the scheduled production and limits, the voltage magnitude setpoint (p.u.), the total MVA base of the machine and the status. Additional parameters defining the generator P-Q capability curve are optional.

4) Generator costs matrix, containing the cost model (piecewise linear or polynomial), the number of cost coefficients for a polynomial cost function or the number of data points for a piecewise linear

cost function, and the parameters defining the total cost functions ( )P gf p and ( )Q gf q .



The outputs of this function are:

1) Final objective function value 2) Final value of optimization variables 3) Lagrange multiplier on real and reactive power mismatch 4) Karush-Kuhn-Tucker (KKT) multipliers on voltage limits 5) KKT multipliers on active power generation limits 6) KKT multipliers on reactive power generation limits 7) KKT multipliers on flow limit at from and to buses

4.2.2.4 InputsandOutputsThe inputs and outputs of the market agent are presented in Table 4.2.6 and Table 4.2.7, respectively.

Table 4.2.6. Market agent inputs.

Input Data exchanged

Source Local / Remote

Update schedule

Format Unit

Grid model and network topology

Branch parameters:

- connection (from/to)

- resistance

- reactance

- susceptance

- loading limit

- status

CC DXP Local Once a day Table with integer and floating point numbers

Resistance/ reactance: [W]

Susceptance: [S]

loading limit: [kVA]

Status: no unit

Actual

system price (hourly based)

Day-ahead system prices

CC DXP Local Once a day (after the market clearing)

Table of float numbers

[€/MWh]

Provisional schedule (hourly based)

Aggregated generation/consumption per node

CC DXP Local Once a day (after the market clearing)

Table of float numbers

[kW]

Last available forecast of DER

Location and quantity

CC DXP

(From CA)

Local According to contract with prediction provider


Location: no unit

DER quantity: kW per node



per hour

Flexibility offers and bids

Activation price

Quantity up/down and price

CC DXP

(From CA)

Local - Table of float numbers

Quantity up/down: [kW]

Price: [€/MWh]

Table 4.2.7. Market agent outputs.

Output Data exchanged

Destination Local / Remote

Update schedule

Format Units

Accepted flexibility or load/generation shedding

Quantity

Location

CC DXP

(To CA)

Local To be defined

Quantity: [kW]

Location: no unit.

Final schedule (hourly based)

Aggregated generation/consumption per node and hour

CC DXP

(To CA and TSO)

Local - Table of float numbers

[kW]

4.2.2.5 PerformanceTestsofMarketAgentIn order to test the performance of the MA, a simulation with the OLV in the day-ahead time frame has been selected. In the example, the purchase of SRP flexibility products is considered to solve congestions in the grid, and the flexibility price is viewed as the SRP price, but the same example could be used to evaluate the activation of CRPs regarding the flexibility price as CRP activation price.

The study case is based on the European MV distribution benchmark [CIGRE2013]. The topology of the MV network is reproduced in Figure 4.2.10.



Figure 4.2.10. Topology of the MV distribution network.

Different generation units are added at various nodes, as listed in Table 4.2.8. All of them are considered to be at their maximum capacity except the CHP diesel generation unit connected to node 9, which provides 200 kW but can change its production in the range of 0-1000 kW. This unit is used to provide DER flexibility.

Table 4.2.8. Parameters of DER units.

Node DER type Pcommitted [kW] Pmax [kW] 3 Photovoltaic 20 20 4 Photovoltaic 20 20 5 Photovoltaic 30 30 5 Residential fuel cell 33 33 6 Photovoltaic 30 30 7 Wind turbine 1500 1500 8 Photovoltaic 30 30 9 CHP diesela 200 1000 9 Photovoltaic 30 30 9 CHP fuel cell 212 212

10 Photovoltaic 40 40 10 Residential fuel cell 14 14 11 Photovoltaic 10 10

a. This generation unit offers flexibility.



Line and transformer parameters have been taken from [CIGRE2013], as well as the load parameters listed in Table 4.2.9. Load values given for nodes 1 and 12 are much larger than those given for the other nodes, because these loads represent additional feeders served by the transformers. It is assumed that all the loads are balanced. The load connected at node 14 is considered as a flexible load. It is supposed that flexible loads maintain a constant power factor.

Table 4.2.9. Load parameters of the European MV distribution network benchmark.

Node Apparent Power, S [kVA] Power Factor, pf Residential Commercial /

Industrial Residential Commercial /

Industrial 1 15,300 5,100 0.98 0.95 2 - - - - 3 285 265 0.97 0.85 4 445 - 0.97 - 5 750 - 0.97 - 6 565 - 0.97 - 7 - 90 - 0.85 8 605 - 0.97 - 9 - 675 - 0.85

10 490 80 0.97 0.85 11 340 - 0.97 - 12 15,300 5,280 0.98 0.95 13 - 40 - 0.85 14 215 390 0.97 0.85

Maximum and minimum voltage limits are set to 1.05 and 0.95 p.u. at all the nodes. The transmission capacity of all the lines is 5 MVA. An hourly marginal price of 35 €/MWh is considered at the power supply point, node 0, for the operating time t, according to the level of Spanish electricity prices in the daily market. For example, the Spanish average price in 2014 was 41.97 €/MWh, and the average price in January of that year was 33.62 €/MWh [OMIE2015].

A power flow simulation is run with the committed generation and load (base case), and no limit violations appear in this case (case A in Table 4.2.10). The cost function of this base case is 1,498.38 €/h and it is taken as a reference to check the cost increase of using flexibility products.

In order to test the OLV, different artificial constraints have been applied. For the sake of simplicity, flexibility offers and bids came from energy offers and bids presented in the day-ahead market. In each case, the optimal power flow inside the OLV algorithm finds the least-cost dispatch solution.

The constraints applied, the actions taken to solve them and the cost function are depicted in Table 4.2.10. Case A is the base case, without any congestion. In cases B and C, a single constraint is applied consisting in limiting the transmission capacity of a line (line 13-14 of Feeder 2 in case B and line 2-3 of Feeder 1 in case C); the first one is solved with demand flexibility (decreasing the consumption at node 14 with 210 kW) and the second one is solved with generation flexibility from the CHP diesel unit at node 9 (the generation of this unit increases with 250 kW). In case D, both constraints are applied simultaneously and thus the previous actions used to solve each one separately are combined. Cases E and F derive from case D, adding additional constraints which cause an increase in the production of the CHP diesel unit. In case E, the power



from the wind turbine connected at node 7 is limited to 1200 kW, and in case F, the voltage deviation margins at node 0 are limited to ±1%.

Table 4.2.10. Constraints, flexibility solution and cost function.

Constraint applied Solution Cost function, f [€/h] A None (base case) - 1,498.38 B Smax, 13-14 = 0.4 MVA DPl, 14 = - 0.210 MW 1,509.96 (+11.30) C Smax, 2-3 = 2 MVA DPg, 9 = + 0.250 MW 1,506.10 (+7.72) D Smax, 13-14 = 0.4 MVA

Smax, 2-3 = 2 MVA DPl, 14 = - 0.210 MW DPg, 9 = + 0.250 MW

1,517.39 (+19.01)

E Smax, 13-14 = 0.4 MVA Smax, 2-3 = 2 MVA Pmax, WT = 1200 kW

DPl, 14 = - 0.210 MW DPg, 9 = + 0.540 MW

1,541.22 (+42.84)

F Smax, 13-14 = 0.4 MVA Smax, 2-3 = 2 MVA 0.99 £ u0 £ 1.01

DPl, 14 = - 0.210 MW DPg, 9 = + 0.370 MW

1,523.29 (+24.91)

In the previous tests, as flexible devices are placed at different feeders, they cannot compete to solve congestions in the grid, i.e. there is only one flexible device capable of solving each type of constraint. Thus a new study has been performed modifying the fixed load at node 5 and making it flexible. In a new test, only the congestion at line 2-3 in Feeder 1 is applied, which can be solved either by increasing generation at node 9 or by decreasing load at node 5, depending on their flexibility price. Three different possibilities have been considered, as shown in Table 4.2.11.

For the sake of simplicity, flexibility bids are taken from generator offers and load bids previously sent to the daily market. In the daily market clearing, the market operator has already accepted all the load bids with price above the system marginal price (35 €/MWh) and all the generation offers with price below the system marginal price. In the OLV algorithm, flexible generator offers and flexible load bids are put together in a merit order procedure. The option with less cost for the system, solving the congestion is the one selected (“flexibility clearing”). If both flexible devices sign contracts with the same aggregator, it could manage their flexibility in a profitable way; otherwise the aggregators would compete in the flexibility market.

Table 4.2.11. Flexibility bids for solving congestions (the accepted bids in bold).

Generator offers Load bids Action to solve the congestion G +0.1 MW, 50 €/MWh

+0.7 MW, 80 €/MWh -0.728 MW, 100 €/MWh

-0.4 MW, 150 €/MWh -0.1 MW, 180 €/MWh

DPg = + 0.250 MW

H +0.1 MW, 50 €/MWh +0.7 MW, 80 €/MWh

-0.728 MW, 40 €/MWh -0.4 MW, 50 €/MWh -0.1 MW, 80 €/MWh

DPl = - 0.23 MW

I +0.1 MW, 50 €/MWh +0.7 MW, 80 €/MWh

-0.728 MW, 79 €/MWh -0.4 MW, 90 €/MWh

-0.1 MW, 180 €/MWh

DPg = + 0.10 MW DPl = - 0.13 MW



The flexibility bids of the load and generator in case G are depicted in Figure 4.2.11. Increasing generation is cheaper than decreasing load, so the first block available from generation is accepted (at a price of 50 €/MWh); as this is not sufficient to solve the congestion, the following block is partially accepted (at a price of 80 €/MWh). The accepted blocks are highlighted in green. The result of the optimal power flow is an increase in generation at node 9 of 0.25 MW. In this case, congestion at line 2-3 is solved with flexible generation.

Figure 4.2.11. Flexibility curves for case G in Table 4.2.11.

In case H, the flexibility curves are the shown in Figure 4.2.12. In this case, it is cheaper to reduce the demand at node 5 than to increase generation. The congestion in line 2-3 is now solved with the flexible demand at a cost of 40 €/MWh.

Figure 4.2.12. Flexibility curves for case H in Table 4.2.11.

Finally, in case I, the curves are those of Figure 4.2.13. Now, the first block from generation is accepted, but as this is insufficient to solve the congestion, the cheapest following block, in this case load, is also accepted. The optimal power flow gives an increase in generation of 100 kW and a decrease in demand of 130 kW. Congestion in line 2-3 is now solved by the combined action of reducing demand and increasing generation.



Figure 4.2.13. Flexibility curves for case I in Table 4.2.11.

With these examples the performance of the MA has been checked and the use of flexibility products can be viewed by the DSO as a way of managing congestions in its network.



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IDE4L is a project co-funded by the European Commission

Project no: 608860

Project acronym: IDE4L

Project title: IDEAL GRID FOR ALL

Deliverable 5.2/3 – Appendix 1:

Offline Simulations of Low Voltage Power Controller

Due date of deliverable: 01.09.2015

Actual submission date: 01.09.2015

Start date of project: 01.09.2013 Duration: 36 months

Lead beneficiary name: Dansk Energi, Denmark

Author:

Dansk Energi (DE)

Project co-funded by the European Commission within the Seventh Framework Programme (2013-2016)

Dissemination level PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)



Track Changes

Version Date Description Revised Approved

0.1 01.07.2015 First Draft Philip Douglass

0.2 05.08.2015 Second Draft Philip Douglass 0.3 13.08.2015 Final Draft Jasmin Mehmedalic 1.0 14.08.2015 Final Version Zaid Al-Jassim



TableofContents

1 Executive Summary ...........................................................................................................................126

2 Introduction ......................................................................................................................................127

3 Optimization by the Low Voltage Power Controller ...........................................................................128

3.1 Voltage Constraints ...................................................................................................................128

3.2 Current Constraints ...................................................................................................................128

3.3 Flexible Resources .....................................................................................................................128

3.4 Cost Function ............................................................................................................................128

4 UFD, Madrid......................................................................................................................................130

4.1 Network Model .........................................................................................................................130

4.2 Load and Production Profiles .....................................................................................................131

4.3 Controllable Resources ..............................................................................................................131

4.4 Scenarios List – UFD...................................................................................................................131

5 Østkraft, Bornholm ...........................................................................................................................133

5.1 Network Model .........................................................................................................................133

5.2 Load Scenarios ..........................................................................................................................134

5.3 Production.................................................................................................................................135

5.4 Controllable Resources Scenarios ..............................................................................................136

5.5 Scenario List - Østkraft ...............................................................................................................138

6 Results ..............................................................................................................................................140

6.1 UFD ...........................................................................................................................................140

6.1.1 Congested Networks ..........................................................................................................140

6.1.2 Cost Optimization ..............................................................................................................141

6.2 Bornholm ..................................................................................................................................142

6.2.1 Business as Usual (BAU) .....................................................................................................142

6.2.2 Congested Networks – Few Controllable Resources ...........................................................144

6.2.3 Many Controllable Resources.............................................................................................146

6.2.4 Cost Optimization ..............................................................................................................149

7 Discussion .........................................................................................................................................153

8 Conclusion ........................................................................................................................................154

9 Bibliography ......................................................................................................................................155



ListofFigures

Figure 4-1 Schematic diagram of UFD test network. ..................................................................................130 Figure 4-2 Load profiles of Load active power (P-Load), load reactive power (Q-Load), uncontrolled DG active power (PV-no_ctr-P), uncontrolled DG reactive power (PV-no_ctrl-Q), and finally controllable PV active power (PV-Ctrl-P). ...........................................................................................................................131 Figure 5-1 Overview of demonstration site on Bornholm. Arial photos courtesy of Google Maps. ............133 Figure 5-2 Timeseries of 3 load profile scenarios. Note that these profiles are for the load only, DG production has not been added to the load profiles. .................................................................................135 Figure 5-3 Ideal aggregate PV production on a cloud-free summer day. ....................................................136 Figure 5-4 Heating system block diagram. .................................................................................................137 Figure 6-1 Net power in BAU (1a.) and congested network (2b.) scenarios, with PV curtailment shown for the congested network scenario. ..............................................................................................................140 Figure 6-2 Net reactive power flow for BAU (1a.), and cost optimization scenarios (1b, 1c). ......................141 Figure 6-3 Reactive power output of controllable PV inverter in cost optimization scenarios (1b, 1c). .......141 Figure 6-4 Net load in 3 BAU scenarios (1, 2a and 3a). ...............................................................................142 Figure 6-5 Most loaded line in BAU scenarions (1, 2a and 3a). ...................................................................143 Figure 6-6 Voltage at bus with lowest voltage in BAU scenarios (1, 2a and 3a). .........................................143 Figure 6-7 Voltage at bus with highest voltage in BAU scenarios (1,2a, 3a). ...............................................144 Figure 6-8 Voltage at bus with lowest voltage in congested networks with few controllable resources (2b and 2c), compared to BAU scenario 2a. .....................................................................................................145 Figure 6-9 Line loading on the line with heaviest load in congested network scenarios (2b and 2c) compared to BAU. Note that the line capacity is reduced in scenario 2c, but can’t stay within limits with the available resources. .................................................................................................................................................145 Figure 6-10 Maximum voltage in congested network with high DG production (3b) is identical to BAU (3a) because overvoltage alarms in scenario 3b timesteps 9-16 resulted in no curtailment. .............................146 Figure 6-11 Maximum line loading in scenarios with reduced line capacity (2c and 2e), compared to BAU (2a). ..........................................................................................................................................................147 Figure 6-12 Minimum voltage in scenarios with tight voltage constraints and high load (2b and 2d), compared to BAU (2a). .............................................................................................................................147 Figure 6-13 Maximum voltage in scenarios with low load and high DG production (3b and 3c), compared to BAU (3a). ..................................................................................................................................................148 Figure 6-14 Curtailed DG production in congested network scenarios 3b and 3c. ......................................149 Figure 6-15 Value of cost function for all scenarios with congested networks. ..........................................149 Figure 6-16 Minimum voltage in 5 cost optimizing scenarios (4a-4e). ........................................................150 Figure 6-17 Line losses in 5 cost optimizing scenarios (4a-4e). ...................................................................150 Figure 6-18 Cost function value for each timestep in 5 cost optimization scenarios (4a-4e). ......................151 Figure 6-19 Reactive power output of DG in 5 cost optimizing scenarios (4a-4e). ......................................151 Figure 6-20 Cumulative value of cost function in 5 cost optimization scenarios (4a-4e). *Scenario 4.d uses a corrected value (timestep 21) for the negative cost at timestep 22. ..........................................................152 Figure 6-21 Average and maximum execution times for LVPC in cost optimizing scenarios (4a-4e). ...........152



1 ExecutiveSummaryThis report describes the creation of a set of simulation scenarios for performance evaluation of the low voltage power controller (LVPC), an optimizing controller in the IDE4L substation automation unit.

The simulation environment is used to model the demonstration sites located in UFD (Madrid, Spain) and Østkraft (Bornholm, Denmark). The Spanish demonstration site has a very simple topology, and controllable resources are limited to a single PV inverter. The demonstration site in Denmark contains typical residential LV feeders, with 50 kW of PV generators and about 10 % of households containing flexible loads.

In the simulations, the LVPC generally performed well, successfully reducing costs within operational constraints. The simulations showed that the amount of flexible loads available in the Danish demonstration site was inadequate for the LVPC to significantly improve system performance. The LVPC was able to significantly mitigate the negative effects of PV overproduction by controlling PV inverters’ active and reactive power.

The LVPC occasionally failed to find feasible solutions to the optimization problems, and execution times could exceed 2 minutes. Both of these issues need to be address with suitable error handling in the final real-time implementation.



2 IntroductionThe IDE4L project aims to develop and demonstrate an active distribution network with an IP-based, standards-compliant communication infrastructure. This active distribution system includes a low voltage power controller module which optimizes flexible loads and controllable distributed generation (DG) to avoid network congestion and to optimize network operation.

The optimization algorithm used in the LVPC has been tested in an offline testbench before its deployment in the field demonstration. The primary goal of the offline simulations is to evaluate the LVPC under scenarios that are infeasible to demonstrate in the field trial. A secondary goal is to prepare for field demonstrations by identifying scenarios and algorithm parameters that can be used to evaluate the LVPC’s performance. Finally, the offline simulations will help debug and validate the LVPC algorithm implementation.

Offline simulations allow a wide range of scenarios to be tested. A subset of these scenarios will be tested in online laboratory tests (with RTDS) and a smaller subset will be tested in the field demonstrations. The scenarios will also be applied to evaluate the medium voltage power controller, but this is outside the scope of the present report.

Network congestion occurs when large loads (or large power injections from DG) cause voltage and/or thermal constraint violations. Congestion management activates controllable resources to alleviate congestion. The LVPC acts to manage congestion and optimize costs within the constraints of the 400 V distribution system. These constraints can be thermal constraints of the conductors (related to current magnitude), voltage constraints, and/or constraints imposed by the controllable resources.

This document describes the setup and results of the offline simulations. It is organized as follows: First, the demonstration networks are described, including simulation scenarios. Next, the results from simulating the scenarios are presented, and finally, conclusions are drawn.

The scenarios include situations where there is no congestion (reference scenarios). In these reference scenarios, the LVPC optimizes the system and minimizes the cost. Other scenarios will have situations where the congestion is too extreme for the LVPC to successfully alleviate it. In these scenarios the LVPC will raise an alarm signal. Finally, there will be scenarios where the congestion is mild enough or controllable resources large enough, for the LVPC to successfully avert constraint violations.

The offline simulations will test the LVPC algorithm in isolation. The other parts of the system – i.e. state estimation, state forecaster – are not included in the offline simulation. Perfect knowledge of all system states is assumed.

The inputs to the LVPC algorithm (node voltages, line currents) are generated by load flow simulations in DIgSILENT PowerFactory. The output of the algorithm will be used to configure the load flow simulations. The LVPC algorithm itself is executed in Octave, the same scripts and execution environment as in the field demonstration. Executing the actual Octave implementation gave the LVPC developers valuable feedback because bugs in the code were fixed before the field demonstration.

This document builds on the earlier work of the LVPC use cases, and the KPIs for the LVPC .



3 OptimizationbytheLowVoltagePowerControllerThe LVPC is based on a nonlinear optimization function to minimize costs while complying with technical constraints. This chapter describes the inputs for the optimization function: the constraints and the cost function.

3.1 VoltageConstraintsThe standard EN50160 defines acceptable voltage levels at customer connection points (the street cabinet). The 10-minute average voltage must be within +/-10 % of nominal voltage at all times.

In the field demonstrations, there are not expected to be voltage constraint violations, therefore the demonstration will adjust the voltage limits in the LVPC to be narrower, and more restrictive. This will be done because the distribution system operator (DSO) wants to have a design margin to account for uncertainties, or to give an allowance for voltage drop within the customers’ premises, or to maintain reserve capacity.

In addition to hard constraints on allowable voltage levels, the LVPC acts to improve voltage quality by assigning costs to voltage deviations. Voltage deviations are given a cost that increases quadratically when the voltage deviates from the target voltage. The voltage target for all nodes is 1 p.u.

Note: Voltage constraints are only relevant for the Danish test case (Bornholm). The cables in the Spanish test network at too short to show significant voltage drop.

3.2 CurrentConstraintsCable and transformer manufacturers publish a rated current for their equipment which is defined as the level at which they can operate indefinitely without overheating. This current can be exceeded for short periods. Normally a DSO will choose to set limits for the utilization of equipment under the rated capacity to reserve network capacity, or to extend the operating lifespan of the equipment.

The field demonstration sites are not expected to contain overloaded equipment. Therefore to test the ability of the LVPC to act on overcurrent constraints, the maximum current of some equipment will be configured below the manufacturers’ rated currents in the LVPC.

3.3 FlexibleResourcesThe LVPC achieves its goals by controlling flexible resources. In IDE4L, these flexible resources belong to one of two categories: DG assets where the active power can be curtailed and the power factor adjusted between preset limits, and demand response (DR) which are loads that can shift their power consumption in time within preset limits.

3.4 CostFunctionThe operation costs of the system is the sum of costs for active power losses, the cost of voltage deviations, the cost of activating DR, and the cost of curtailing DG. These costs are unitless. No attempt was made to associate cost function parameters with actual financial costs of network operation.

Costs were chosen such that the highest cost was for DR (150/p.u.), second most costly was the DG curtailment (75/p.u.), then active power losses (1/p.u.), and finally, voltage deviations (0.1/p.u.).

These widely different costs were intended to give easily predictable results: the influence of voltage deviations are very low because voltage deviations are generally without consequence today. The cost of active power losses are direct costs to the DSO. These costs are low compared to the costs of demand or



generation curtailment. It is assumed that demand curtailment has higher costs than generation curtailment, because of a risk of degrading energy services to the customer.

Note that the cost parameters will be unchanged in all scenarios, except that voltage variation costs nothing in the Spanish test cases (explained in the next chapter).



4 UFD,MadridThe IDE4L project will demonstrate the substation automation unit in a test network located at UFD’s Madrid headquarters. The system was simulated over 10 days which were assumed to include the full range of operating conditions.

The load and production profiles were supplied by UFD. Load data only covered a small portion (1/20th) of the total load. Therefore the load data used in the model is not representative of the whole system load, but the limited load was useful nevertheless to show the range of response of the LVPC.

Two scenarios tested the effect of different constraints on the controllable DERs: large flexibility with full curtailment and a power factor between 0.9 leading/lagging, and limited flexibility with curtailment of 50 % available power and the power factor restricted to be greater than 0.97 leading/lagging. Other scenarios considered the LVPC response when the transformer capacity was reduced.

The cost of voltage deviations was set to 0, and the allowable voltage limits were set to +/- 20 %, thereby disabling voltage regulation. Only losses and DG curtailment contribute to the cost function.

4.1 NetworkModelThe demonstration network at UFD in Madrid contains commercial loads, and a number of DG assets. The low voltage cables connecting these loads and assets are so short that the voltage drop is negligible; therefore voltage regulation will not be tested nor simulated. The primary constraint which the LVPC will act upon is the transformer capacity. When the transformer is not overloaded, costs are optimized with respect to active power losses.

The network model for UFD is reduced to a single impedance that connects an ideal voltage source (MV grid) to the DG and load. The impedance Z of the transformer is set to be (0.321 + j 0.078) Ω, with a nominal maximum current of 217 A2. The real capacity of the installed transformer, 150 kVA, is much greater than the loads described in UFD’s load profiles. Therefore the network congestion will be simulated by constraining the transformer capacity to be below the load observed in the baseline (BAU) scenario.

Figure 4-1 Schematic diagram of UFD test network.

2 The impedance is not derived from technical data of the transformer because this data was unavailable. The impedance comes from typical cable impedances found in low voltage power systems. The uncertainty introduced by this assumption affects the magnitude of active power losses, but does not decisively alter the results of the test.



4.2 LoadandProductionProfilesUFD provided load profiles over a 6 month period, with hourly resolution. Ten days in June were selected for the simulations. In the daytime, the DG production exceeds the load, and on days when the office is closed (the 1st, 8th and 9th days), the power flow through the transformer is at its highest.

Figure 4-2 Load profiles of Load active power (P-Load), load reactive power (Q-Load), uncontrolled DG active power (PV-no_ctr-P), uncontrolled DG reactive power (PV-no_ctrl-Q), and finally controllable PV active power (PV-Ctrl-P).

4.3 ControllableResourcesThe only controllable resources in the UFD network is a 20 kW PV system. The active power of the PV can be curtailed, and reactive power can be chosen such that the power factor is between 0.9 leading and 0.9 lagging.

A reduced controllability scenario will be simulated, where the controllable resource is half capacity: 10 kW active power and power factor above 0.97.

4.4 ScenariosList–UFDThe following table lists the simulation scenarios used to evaluate the LVPC in the UFD test network. The scenarios are characterized by the amount of controllable resources available, and the capacity of the transformer.

Tabel 4-1 UFD scenario list.

-28000-23000-18000-13000-8000-300020007000

120001700022000

UFD Load & Production Profiles

over 10 day period

P-Load PV-Ctrl-P Q-Load PV-no_ctrl-P PV-no_ctrl-Q

Time [hour]

Activ

e/R

eact

ive P

ower

[W/V

AR]



Nr. Scenario Name

Controllable Resources

Transformer Capacity

Expected Result

1a BAU None Rated ampacity BAU

Congested Network (tight operating constraints)

2a Thermal Constraints – no DG

None 20% ampacity Overcurrent violation alarms

2b Thermal Constraints – Full DG

Curtailable PV: 20 kW , PF > 0.9

20% ampacity PV Curtailment

LVPC Optimizes costs 1b Few DG

resources Partly Controllable PV: 10 kW curtailable, PF > 0.97

Rated ampacity Cost reduction (relative to BAU) through PF adjustment

1c Full DG resources

Curtailable PV: 20 kW , PF > 0.9

Rated ampacity Cost reduction (relative to BAU & 1b.) through PF adjustment



5 Østkraft,BornholmThe low voltage network on Bornholm contains the full complexity that the substation automation unit, and the LVPC should be able to handle.

5.1 NetworkModelThere are approximately 120 customers under this substation. Most of these customers are single family households. The area also contains a church, a kindergarten, and a small business.

Figure 5-1 Overview of demonstration site on Bornholm. Arial photos courtesy of Google Maps.



The conventional, passive, uncontrollable loads were aggregated so that there was one load object for each street cabinet, 51 in total. Generic load profiles from representative days from [DE2015] were scaled according to the annual energy consumption of each customer, and assumed to have a power factor of 0.95 inductive.

Houses with electric heating (both heatpumps and direct resistive heating) were considered flexible loads. PV systems were modelled as negative loads with unity power factor.

The network topology and impedances are given by the network operator. This includes data on DG production capacity.

5.2 LoadScenariosThe extremes of distribution system operation will be tested: from maximum load and no DG production to minimum load and full DG production.

Load data from Bornholm is taken from billing data, with the total annual power consumption available for each customer. The annual energy use was used to scale the generic load profiles. Note that these generic load profiles represent the 95 % quantile of load, therefore, the “typical” and “minimum” load are actually significantly higher than what is expected in the field.

The load profiles are described below:

Typical – from a weekday that is judged as normal (April 14th).

Maximum – the day when the maximum load power is observed (December 24th).

Minimum – the day with minimum aggregate3 (including load and DG production) power consumption (June 22nd).

3 The minimum load power would be at night, but there is no risk of overvoltage in this situation, because there is no PV production. Therefore, it is the aggregate load that is interesting.



Figure 5-2 Timeseries of 3 load profile scenarios. Note that these profiles are for the load only, DG production has not been added to the load profiles.

5.3 ProductionThere are over 50 kW of PV panels installed on the residences in the demonstration area. The DG may or may not be controllable by the LVPC (see next section).

To compensate for the relatively high load found in the generic profiles, the reported installed PV capacity was doubled. Doubling the PV capacity achieved reverse power flows comparable to those observed in the field.

Different amounts of PV production will feature in different scenarios:

None – self explanatory.

Maximum – The theoretical energy production on a cloud-free day.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

0,05

0,1

0,15

0,2

0,25

Load Profiles

Typical-load-no-DG Max-load-no-DG Min-load-no-DG

Time [hours]

Tota

l Act

ive

Pow

er [M

W]



Figure 5-3 Ideal aggregate PV production on a cloud-free summer day.

5.4 ControllableResourcesScenariosThere was significant uncertainty about the amount of controllable resources available at the demonstration site, so the offline tests will probe the whole feasible range of controllable resources.

Electric Space Heating – These flexible loads are heat pumps or resistive heaters installed in single family homes. They are modelled as a simple energy buffer with a constant energy consumption. The available flexibility is estimated based on the energy buffer level, and the power consumed by the heating element. The heat source has flexibility to increase power if it is not already producing heat at full power, and the buffer is not already full. The heat source can decrease power if it is on, and if the buffer is not empty. When not being controlled by the LVPC, a hysteresis thermostat turns the energy source on when the buffer level declines below a given threshold, and turns the heater off when the buffer is full. The model parameters are the maximum power of the energy source, the size of the energy buffer, and the rate of energy loss.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

-0,12

-0,1

-0,08

-0,06

-0,04

-0,02

0

0,02

PV Production Profile

Time [hours]

Tota

l Act

ive

Pow

er [M

W]



Figure 5-4 Heating system block diagram.

The initial buffer level will be a uniformly distributed random value. For simplicity, all the houses will have the same parameter values:

Power: 4,5 kW

Energy Buffer Size: 5,3 kWh

Energy Withdraw: 2,65 kW kWh/h

At minimum load, the power consumption of flexible loads represents 70% of the total load. At peak load, the power consumption constitutes 12 % of total load.

The LVPC controls the heating loads as if their output power were continuously variable. Many electric heating systems installed today operate discretely (using ON/OFF relays), which means that in these simulations, the LVPC overestimates the amount of flexibility available from relay-controlled heating systems.

Generation – By default, renewable generators produce active power based on resource availability, but the LVPC has the option of curtailing their output. The LVPC can also control the power factor of the generators. The PV model produces energy based on the theoretical cloud-free radiation. The LVPC can curtail the active power production, and when the PV is producing power, the LVPC can control the power factor. The ratings of the various generators are as follows:

PV Rated Power: between 6 and 12 kW

Power Factor: 0.9 lagging to 0.9 leading



5.5 ScenarioList-ØstkraftThe following table lists the simulation scenarios. The scenarios are characterized by the configuration of loads, DG production, the amount of controllable resources available, and the constraints on node voltage and line amperage.

Nr. Scenario Name

Load DG Production

Controllable Resources

U -limits I – limits Expected Result

Business as Usual

1 Normal Day Typical None None +/-10 % Rated ampacity

BAU

2a High Load Maximum None None +/-10 % Rated ampacity

BAU

3a High DG Minimum Maximum None +/-10 % Rated ampacity

BAU

Congested Network (tight operating constraints)

2b Low U – Few resources

Maximum None Few,

DR: 20 %

DG: N/A

Narrow,

+0 %,

-2.5 %

Rated ampacity

Undervoltage violations

2c High I – Few resources

Maximum None Few,

DR: 20 %

DG: N/A

+/-10 % 50 % Rated ampacity

Overcurrent violations

3b High U – Few resources

Minimum Maximum Few,

DR: 0 %

DG: 20 %

Narrow, +1 %,

- 2 %

Rated ampacity

Overvoltage violations

Many Controllable Resources (& tight operating constraints)

2d Low U – Many resources

Maximum None Many

DR:100 %

DG: N/A

Narrow,

+0 %,

-2.5 %

Rated ampacity

LVPC avoids undervoltage

2e High I – Many resources

Maximum None Many

DR:100 %

DG: N/A

+/-10 % 50 % Rated ampacity

LVPC avoids overcurrent



3c Overproduction – Many resources

Minimum Maximum Many

DR:100 %

DG:100 %

Narrow, +1 %,

- 2 %

Rated ampacity

LVPC avoids overvoltage

LVPC Optimizes costs

4a Optimize – 0 resources

Typical Maximum DR: 0 %

DG: 0 %

+/-10 % Rated ampacity

Baseline cost

4b Optimize – 30 % resources


DG: 30 %


LVPC reduces costs relative to 4a.

4c Optimize – 50 % resources


DG: 50 %


LVPC reduces costs relative to 4b.

4d Optimize – 70 % resources


DG: 70 %


LVPC reduces costs relative to 4c.

4e Optimize–100 % resources

Typical Maximum DR:100 %

DG:100 %


LVPC reduces costs relative to 4d.

Tabel 5-1 Bornholm scenario list.



6 ResultsThis chapter presents the results of the offline simulations of the LVPC in the UFD and Østkraft demonstration sites.

6.1 UFDIn the UFD test case, the power controller had very limited resources under control: a single PV inverter. Active power curtailment was possible, but curtailment was only utilized when the transformer risked being overloaded (in scenario 2b.). When the LVPC optimizes for cost (without capacity constraints, scenarios 1b. and 1c.), the only variable the LVPC changed was the reactive power of the generator.

When executing the simulations, there were no situations when the LVPC failed to find a solution, when a solution obviously existed. Execution times for the LVPC on a 64-bit Intel i7 2.9 GHz mobile processor with 8 GB RAM was always less than 700 ms and greater than 45 ms.

6.1.1 CongestedNetworksWhen network capacity is restricted to 26 kW, and controllable DG resources are available, the LVPC successfully limits the load on the transformer because the peak load is a reverse power flow which can be mitigated by DG curtailment. During the 10 test days, there are 3 days with light load and strong PV generation that require PV curtailment, as shown in Figure 6-1. The cost of curtailment eclipses the cost of losses, therefore the cost function mirrors the DG curtailment timeseries.

In scenario 2a, capacity is restricted without any controllable resources available. In this scenario, all the timesteps when the uncontrolled power flows exceeded the transformer capacity resulted in an alarm from the LVPC, as expected.

Figure 6-1 Net power in BAU (1a.) and congested network (2b.) scenarios, with PV curtailment shown for the congested network scenario.

-40000

-30000

-20000

-10000

0

10000

20000

Active Power in Congested Network

1a. BAU – Net Load 2b. Congestion – Net Load 2b. Congestion – Curtailed DG

Time [hours]

Activ

e Po

wer

[W]



6.1.2 CostOptimizationIn scenarios 1b. and 1c. the LVPC acts to reduce the cost of losses. Losses were reduced by reducing reactive power flows, as shown in Figure 6-2. This was achieved by controlling the reactive power production of the controllable DG to balance the reactive power of the load and reactive power of the uncontrolled PV production. Modulating the power factor of the DG was considered to be without cost. There was little difference between having 100 % fully controllable DG (scenario 1c: 20 kW, 0.9 minimum power factor) or having only 50 % of the controllable capacity (scenario 1b: 10 kW, 0.97 minimum power factor), because the optimal reactive power output was only slightly above what the 10 kW controllable DG could deliver, see Figure 6-3. The reduction in the value of the cost function (relative to scenario 1a.) was 3.1 % and 3.7 % for scenarios 1b. and 1c. respectively.

Figure 6-2 Net reactive power flow for BAU (1a.), and cost optimization scenarios (1b, 1c).

Figure 6-3 Reactive power output of controllable PV inverter in cost optimization scenarios (1b, 1c).

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

Reactive Power Flow at Grid Connection

1a.BAU 1b.50%-DER 1c.100%-DER

Time [hours]

Rea

ctiv

e Po

wer

[VAR

]

-3000-2000-1000

0100020003000400050006000

Reactive Power produced by controllable DG

1b.50%-DER 1c.100%-DER

Time [hours]

Rea

ctive

Pow

er [V

AR]



6.2 BornholmIn contrast to the simplified model utilized in the UFD simulations previously described, the system model for Bornholm was large enough to stress the optimization algorithms. Occasionally, the LVPC optimizer failed to find feasible solutions, even though inspection of the scenarios appeared to confirm the presence of solutions. These exceptional cases are noted in the discussion of the results.

6.2.1 BusinessasUsual(BAU)The BAU, or reference scenario, showed only 3 % voltage drop at the bus with the lowest voltage in the high load scenario (2a), as shown in Figure 6-6. In the high DG production scenario (3a), voltage rises 3 % above nominal, as shown in Figure 6-7. Line loadings are likewise far from their rated limits, with the highest loading observed to be 63 %, see Figure 6-5.

The LVPC failed to return a result in scenario 3a, timesteps 8, 12 and 13.

Figure 6-4 Net load in 3 BAU scenarios (1, 2a and 3a).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

-0,1

-0,05

0

0,05

0,1

0,15

0,2

0,25

0,3

Net Load

1.typical-load-no-DG 2a.high-load-no-DG 3a.low-load-max-DG

Time [hours]

Activ

e Po

wer

[MW

]



Figure 6-5 Most loaded line in BAU scenarions (1, 2a and 3a).

Figure 6-6 Voltage at bus with lowest voltage in BAU scenarios (1, 2a and 3a).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

10

20

30

40

50

60

70

Maximum Line Loading


Time [hours]

Line

Loa

ding

[%]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240,955

0,96

0,965

0,97

0,975

0,98

0,985

0,99

0,995

1

Minimum Voltage


Time [hours]

Volta

ge [p

u]



Figure 6-7 Voltage at bus with highest voltage in BAU scenarios (1,2a, 3a).

6.2.2 CongestedNetworks–FewControllableResourcesAs described in section 5.4, the potential flexible resources are 10 heat pumps, and 10 PV systems. In these scenarios, only 20 % of the potential flexible resources are available. Network constraints were chosen after observing the BAU scenarios to find thresholds that the BAU scenarios exceeded. These thresholds were: under high load a voltage range of 1.0 to 0.975 p.u. (2.5 % variation in total), and 50 % reduction in line limits, and under high DG production a voltage range of 1.01 – 0.98 p.u. (3 % variation in total).

The LVPC failed to return a result in scenario 2b timestep 16, 2c timestep 16, and 3b timesteps 9-16. In all cases, the LVPC correctly raised an alarm that no control action could avoid constraint violations.

The DR was not decisive in avoiding constraint violations; there was no significant difference with or without it as shown in Figure 6-8 and Figure 6-9. Similarly, DG curtailment was unable to prevent overvoltage constraint violations. All timesteps in the BAU scenario that exceed the overvoltage threshold (scenario 3a timestep 9-16), resulted in alarms in scenario 3b, see Figure 6-10. Recall that alarms result in no control actions being taken, though in this case, a better response would have been to curtail the DG as much as possible.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240,98

0,99

1

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Figure 6-8 Voltage at bus with lowest voltage in congested networks with few controllable resources (2b and 2c), compared to BAU scenario 2a.

Figure 6-9 Line loading on the line with heaviest load in congested network scenarios (2b and 2c) compared to BAU. Note that the line capacity is reduced in scenario 2c, but can’t stay within limits with the available resources.

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Figure 6-10 Maximum voltage in congested network with high DG production (3b) is identical to BAU (3a) because overvoltage alarms in scenario 3b timesteps 9-16 resulted in no curtailment.

6.2.3 ManyControllableResourcesThese scenarios represent the upper limit to the availability of flexible resources in the chosen demonstration site. All 10 heat pumps are controllable, representing around 12 % of the peak load. All 10 PV systems are controllable, with a production capacity approximatively equal to the peak uncontrolled load. These scenarios are directly compared to the BAU scenarios (2a and 3a) and to the scenarios with few controllable resources (2b, 2c and 3b), see Figure 6-11, Figure 6-12 and Figure 6-13.

From Figure 6-11 it can be seen that even with full availability of DR, there is little difference with the BAU scenario. The LVPC failed to find a feasible solution during peak load, timestep 16, in scenario 2c and 2d (as well as scenario 2b as noted in the previous subsection).

DG curtailment was effective in preventing overvoltage violations, see Figure 6-13, however the value of the cost function reflected the high cost of production curtailment, shown in Figure 6-15.

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Figure 6-11 Maximum line loading in scenarios with reduced line capacity (2c and 2e), compared to BAU (2a).

Figure 6-12 Minimum voltage in scenarios with tight voltage constraints and high load (2b and 2d), compared to BAU (2a).

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Figure 6-13 Maximum voltage in scenarios with low load and high DG production (3b and 3c), compared to BAU (3a).

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Figure 6-14 Curtailed DG production in congested network scenarios 3b and 3c.

Figure 6-15 Value of cost function for all scenarios with congested networks.

6.2.4 CostOptimizationThe cost optimization scenarios presented a typical load, and varying degrees of controllable resources. Both DR and DG resources were increased from total absence to their full potential in 5 increments.

The results show that even the full potential of controllable resources can only marginally reduce system costs, as shown in Figure 6-18 and Figure 6-20. Figure 6-18 shows an outlier in scenario 4.d, timestep 22, when costs become strongly negative. The cumulative cost of this scenario excluded this outlier, and instead substituted the cost from the previous timestep (scenario 4.d timestep 21).

This incorrect result happens because of a combination of two things: First, the Octave optimization function allows the variables to go outside the constraints during the optimization process. And second, the optimization is stopped when the step size becomes smaller than a set tolerance. If the optimization is stopped because the step size becomes small when the variables are outside the constraints, the cost function can become negative.

From Figure 6-20 it can be seen that the lowest cost is achieved when 100 % of the available flexible resources are controllable. From Figure 6-16, Figure 6-17 and Figure 6-19 it can be seen that LVPC produced very little response.

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Average execution time of the LVPC, shown in Figure 6-21, gradually decreased as more flexible resources were made available. The maximum execution times, which are interesting in the context of the IDE4L demonstration where the LVPC will be used in a real-time system, were in excess of 100 seconds and were greatest when 50 % of the potentially controllable resources were available.

Figure 6-16 Minimum voltage in 5 cost optimizing scenarios (4a-4e).

Figure 6-17 Line losses in 5 cost optimizing scenarios (4a-4e).

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Figure 6-18 Cost function value for each timestep in 5 cost optimization scenarios (4a-4e).

Figure 6-19 Reactive power output of DG in 5 cost optimizing scenarios (4a-4e).

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Figure 6-20 Cumulative value of cost function in 5 cost optimization scenarios (4a-4e). *Scenario 4.d uses a corrected value (timestep 21) for the negative cost at timestep 22.

Figure 6-21 Average and maximum execution times for LVPC in cost optimizing scenarios (4a-4e).

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7 DiscussionThe potential DR resources available today on Bornholm in 2015 do not provide enough flexibility to significantly reduce the risk of cable overload nor undervoltage. With respect to flexible loads with an energy buffer, the fundamental constraint to provide sufficient energy greatly limited the available flexibility. Because the LVPC was unable to optimize utilization of the DR across timesteps, the LVPC had a tendency to exhaust the DR flexibility early in the simulation period, instead of waiting until the DR could provide more value.

PV curtailment is a reliable means to prevent overvoltage and overload caused by reverse power flow.

In the UFD demonstration network, the controller was able to reduce losses caused by reactive power flows, but aggregate reactive power flows were not reduced to 0.

In this type of system there is no guarantee that the optimization algorithm gives the global optimum result. This was observed when optimization results were sensitive to the formulation of equations, the initial conditions, and the solver implementation. Small changes to any input parameter, including the random number seed, can result in widely different outcomes. Alternative implementation of the optimization in MATLAB and Octave gave different results, and different execution times. In simulation runs during the testing phase (not presented in these results), Octave execution times could exceed 2 minutes.

The Octave solver allows constraint violations within a “small” tolerance. This means that mathematically equivalent formulations of the system configuration give very different results. In particular, the order of magnitude of the input variables (i.e. MW or kW) has a decisive effect on the outcome. Rescaling the variables, by changing the base of the p.u. system, was required to get the LVPC to produce feasible solutions.

The method of choosing vastly different cost values to create a more predictable outcome (cost = 150 for DR, 75 for DG, 1 for losses and 0.1 for voltage deviations) backfired because small constraint violations in DR and DG had a large effect on the final cost because the costs of losses and voltage deviations were so small.



8 ConclusionThis offline simulation task had two main goals. The first goal was to evaluate the LVPC in scenarios that were infeasible to demonstrate in the field. These scenarios showed that the LVPC was effective at using active power control of DG to avoid overvoltage and current constraints caused by reverse power flow. The LVPC also effectively used reactive power control of the DG to reduce active power losses. Demand response, modelled by a simple energy buffer, was not effective in avoiding constraint violations, but was marginally useful in improving system operation costs.

The second objective of testing the LVPC implementation was not directly discussed in this report, but was nevertheless successful. While performing these simulations the LVPC algorithm implementation was tested, and several bugs were found that were subsequently corrected. This document naturally focused on the successful simulation runs; the far more numerous unsuccessful runs contributed to making the LVPC more robust.



9 Bibliography

[DE2015] Dansk Energi, “RA594- Load Profiles for Dimensioning Low Voltage Networks”

deliverable 5.2/3: congestion management in distribution ... · ide4l deliverable 5.2/3 2 ide4l is...

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