increase – increasing the penetration of renewable energy

INCREASE – Increasing the penetration of renewable energy sources in the distribution grid by

developing control strategies and using ancillary services

D3.3 – Report on simulation results and evaluation of the integrated simulation platform

31.08.2015 1

INCREASE

INCREASING THE PENETRATION OF RENEWABLE

ENERGY SOURCES IN THE DISTRIBUTION GRID BY

DEVELOPING CONTROL STRATEGIES AND USING

ANCILLARY SERVICES

D3.3 – Report on simulation results and evaluation

of the integrated simulation platform




31.08.2015 2

Document info

Project Number 608998 – INCREASE

Funding Scheme Collaborative Project

Work Programme

Topic ENERGY.2013.7.1.1: Development and validation of methods

and tools for network integration of distributed renewable

resources

Number D3.3

Title Report on simulation results and evaluation of the integrated

simulation platform

Dissemination Level

Date 21.07.2015

Nature

Authors

Andreas I. Chrysochos (AUTH), Georgios C. Kryonidis (AUTH),

Eleftherios O. Kontis (AUTH), Matthias Strobbe (UGent), Charis S.

Demoulias (AUTH), Grigoris K. Papagiannis (AUTH)

Contributors

Reviewers Bart Meersman (UGent), Andrej Gubina (UL)

Document History

Date Authors Action Status

21/07/2015

Andreas I. Chrysochos

Georgios C. Kryonidis

Eleftherios O. Kontis

Matthias Strobbe

Charis S. Demoulias

Grigoris K. Papagiannis

Preparation of the 1st draft

05/08/2015 Bart Meersman Reviewed first draft

24/08/2015 Grigoris K. Papagiannis Preparation 2nd

draft

28/08/2015 Andrej Gubina Reviewed 2nd

draft

31/08/2015 Grigoris K. Papagiannis Final version

Comments

This deliverable report corresponds to Task 3.3 – ‘Development of a toolset capable to

access jointly the power and the communication in the integrated platform’.




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Contents

Document info ..................................................................................................................... 2

Document History ................................................................................................................ 2

Comments ............................................................................................................................ 2

List of tables ......................................................................................................................... 5

List of figures ........................................................................................................................ 6

1. Introduction ............................................................................................................... 10

1.1. Context ............................................................................................................... 10

1.2. Goals ................................................................................................................... 10

1.3. Report outline .................................................................................................... 11

2. Overview of the INCREASE simulation platform ....................................................... 13

2.1. General architecture .......................................................................................... 14

2.2. Tool components ................................................................................................ 15

2.2.1. Core platform .............................................................................................. 15

2.2.2. Draw tool ..................................................................................................... 16

2.2.3. OpenDSS simulator ..................................................................................... 17

2.2.4. JADE environment ....................................................................................... 18

2.2.5. OMNeT++ simulator .................................................................................... 18

2.3. Local Control scheme ......................................................................................... 18

3. Implementation of the Overlaying Control ............................................................... 20

3.1. OLTC control algorithm ...................................................................................... 20

3.1.1. Problem formulation ................................................................................... 20

3.1.2. Proposed methodology ............................................................................... 21

3.2. Congestion management algorithm .................................................................. 22

3.2.1. Introduction ................................................................................................ 22

3.2.2. Proposed methodology ............................................................................... 22

3.3. FPS control algorithm ......................................................................................... 24

3.4. Integration with Local Control ........................................................................... 25

3.5. Actual implementation ....................................................................................... 26




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3.5.1. JADE and Core components ........................................................................ 26

3.6. Tasks performed by JADE and Core components .............................................. 28

4. Incorporation of LAN simulator component ............................................................. 30

4.1. Introduction ........................................................................................................ 30

4.2. Communication network simulation .................................................................. 30

4.2.1. Network Simulator (ns-2 and ns-3) ............................................................. 30

4.2.2. OMNeT++ .................................................................................................... 31

4.2.3. NeSSi ........................................................................................................... 31

4.2.4. OPNET Modeller .......................................................................................... 32

4.2.5. Discussion .................................................................................................... 32

4.3. Overview of OMNeT++ and INET ....................................................................... 32

4.3.1. Introduction ................................................................................................ 32

4.3.2. OMNeT++/INET simulator configuration .................................................... 33

4.4. INCREASE modules ............................................................................................. 35

4.5. Interface with the INCREASE simulation platform ............................................. 36

5. Simulation results ...................................................................................................... 40

5.1. Normal Case ....................................................................................................... 43

5.2. Case of high DRES penetration ........................................................................... 51

5.3. Communication network performance evaluation............................................ 59

5.3.1. Wired communication network .................................................................. 61

5.3.2. Wireless communication network .............................................................. 65

6. Conclusions ................................................................................................................ 70

References ......................................................................................................................... 71




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List of tables

Table 5-1: PV units rated power ........................................................................................ 41

Table 5-2: Distribution transformer characteristics .......................................................... 42

Table 5-3: PV units rated power for the case of high DRES penetration .......................... 51




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List of figures

Fig. 2.1: Overview of the INCREASE simulation platform. ................................................. 15

Fig. 2.2: Main window of draw library ............................................................................... 16

Fig. 2.3: Indicative GUI of agent ......................................................................................... 17

Fig. 2.4: Typical droop curve of controllable DG units ...................................................... 18

Fig. 3.1: LV network with conflict on voltage regulation ................................................... 21

Fig. 3.2: The 15-min timeslot concept ............................................................................... 26

Fig. 3.3: Conceptual implementation of Overlaying Control ............................................. 27

Fig. 3.4: Simulation flowchart ............................................................................................ 29

Fig. 4.1: Simple and compound modules .......................................................................... 34

Fig. 4.2: TCP/IP 5-layer protocol stack ............................................................................... 35

Fig. 4.3: High-level view on the implemented INCREASE modules on top of OMNeT++ and

INET .......................................................................................................................................... 36

Fig. 4.4: Interface between power and communication network simulators ................... 37

Fig. 4.5: Visual overview of the exchanged messages on application level between a

regular and an aggregator agent .............................................................................................. 38

Fig. 4.6: Visual overview of the exhanged messages on application, TCP and IP level

between a regular and an aggregator agent............................................................................ 38

Fig. 4.7: Example graph for the measured throughput for a setup with 10 regular agents,

one router and an aggregator agent ........................................................................................ 39

Fig. 5.1: Elektro Gorenjska modified network topology .................................................... 41

Fig. 5.2: Active power profile of the aggregated load vs. time ......................................... 42

Fig. 5.3 Reactive power profile of the aggregated load vs. time ....................................... 43

Fig. 5.4: Injected active power of PV units vs. time ........................................................... 44

Fig. 5.5: Injected active power of PV units from 10:00 to 16:00 ....................................... 44

Fig. 5.6: Curtailed active power of PV units vs. time ......................................................... 45

Fig. 5.7: Curtailed active power of PV units from 10:00 to 16:00 ..................................... 45

Fig. 5.8: Network losses vs. time ....................................................................................... 46

Fig. 5.9: Network losses vs. time from 10:00 to 16:00 ...................................................... 46

Fig. 5.10: Voltage of the network vs. time ......................................................................... 47

Fig. 5.11: Tap setting of the transformer vs. time ............................................................. 48

Fig. 5.12: Daily energy losses ............................................................................................. 48

Fig. 5.13: Daily energy production ..................................................................................... 49

Fig. 5.14: Total injected active power of PV units along a single feeder ........................... 50

Fig. 5.15: Positive-sequence voltage profile along a single feeder ................................... 51

Fig. 5.16: Transformer total apparent power vs. time ...................................................... 53

Fig. 5.17: Transformer total apparent power from 10:00 to 16:00 .................................. 53




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Fig. 5.18: Injected active power of PV units vs. time......................................................... 54

Fig. 5.19: Injected active power of PV units from 10:00 to 16:00 ..................................... 55

Fig. 5.20: Curtailed active power of PV units vs. time ....................................................... 55

Fig. 5.21: Curtailed active power of PV units from 10:00 to 16:00 ................................... 56

Fig. 5.22: Daily energy production ..................................................................................... 56

Fig. 5.23: Daily energy losses ............................................................................................. 57

Fig. 5.24: Network losses vs. time ..................................................................................... 57

Fig. 5.25: Voltage of the network vs. time ......................................................................... 58

Fig. 5.26: Tap setting of the transformer vs. time ............................................................. 59

Fig. 5.27: Overview of the examined grid using the GUI interface of the INCREASE

simulation platform .................................................................................................................. 60

Fig. 5.28: Overview of the simulated wired communication network .............................. 62

Fig. 5.29: Throughput per link and direction ..................................................................... 63

Fig. 5.30: Channel utilization for link between router and aggregator agent ................... 63

Fig. 5.31: End-to-end delays .............................................................................................. 65

Fig. 5.32: Overview of the locations of the simulated agents ........................................... 66

Fig. 5.33: End-to-end delays via TCP measured at 2 regular agents located at the closest

and furthest from the aggregator agent .................................................................................. 67

Fig. 5.34: End-to-end delays via TCP measured at the aggregator agent ......................... 67

Fig. 5.35: End-to-end delays via UDP measured at 2 regular agents located at the closes

and furthest from the aggregator agent .................................................................................. 68

Fig. 5.36: End-to-end delays via UDP measured at the aggregator agent ........................ 68




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List of symbols and abbreviations

Symbol Description Symbol Description i

a Coefficient for fair distribution of

curtailed active power to all DRESs

along the feeder

i

GP Generated active power of the

DRES at the i-th connection point

m

curtc Curtailment coefficient tot

LP Total active power consumption of

Load

∆Ρ Curtailed active power of the DRES

with the minimum i

MPPP of the m-

th feeder

transf

curtP Total curtailed active power

according to the congestion

algorithm

∆i

gV Change in voltage magnitude of

the i-th connection point

tot

MPPP Total amount of DRES MPP

generation

|ΔVtap| Dead-band of the fixed step width

of tap changer ,

m

FPS curtP FPS actual curtailment of the m-th

feeder

g1 Fundamental input conductance ,

m

Cong curtP Actual active power curtailment of

the m-th feeder

gd Damping conductance totQ Transformer reactive power

tot

lossP Total active power losses rated

S Apparent rated power of the

transformer i

LP Absorbed active power of the Load

at the i-th connection point

transfS Cumulative apparent flow of the

transformer

PMPP Maximum power point active

power

ʋmin, EN

lowV

Minimum allowable voltage in the

grid as defined by the

corresponding Standards

Pinj Injected active power ʋ0, ʋ1,

ʋ2

zero-, positive- and the negative-

sequence components of the grid

voltage tot

GP Total DRES generation of the

network

ʋmax Maximum allowable voltage in the

grid as defined by the

corresponding Standards m

curtP Active power curtailment of the m-

th feeder

υcpb Constant power band voltage

m

MPPP Total DRES MPP power at the m-th

feeder

ʋg Voltage at the connection point

,

m

LC curtP Active power curtailment of the m-

th feeder caused by the Local

Control

Vg Voltage at the PCC

i

curtP Final curtailed active power of the

DRES at the i-th connection point

Vmin Minimum voltage along a feeder

Pref Reference power of the control w Coefficient vector




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inverter i

MPPP Maximum power point active

power of the DRES at the i-th

connection point

xij Elements of the sensitivity matrix

D Deliverable MAS Multi-Agent System

DG Distributed Generation MV Medium-Voltage

DRES Distributed Renewable Energy

Source

MPP Maximum Power Point

DSO Distribution System Operator OL OLTC and Local

DVB Desired Voltage-Bandwidth OLF OLTC, Local and FPS

EPRI Electric Power Research Institute OLFC OLTC, Local, FPS and Congestion

FIPA Foundation for Intelligent Physical

Agents

OLTC On-Load Tap Changer

FPS Fair Power Sharing algorithm PCC Point of Common Coupling

GUI Graphical User Interface PLC Power Line Communication

JADE JAVA Agent Development

Framework

pu per unit

LF Local and FPS TSO Transmission System Operator

LV Low-Voltage WP Work Package




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1. Introduction

1.1. Context

The INCREASE project aims to manage distributed renewable energy sources (DRES) in

low- (LV) and medium-voltage (MV) networks. This is succeeded by providing ancillary

services, namely voltage control and provision of reserve, towards distribution (DSOs) and

transmission system operators (TSOs). The cornerstone of the project activities is the

introduction of three-phase four-wire inverter-interfaced DRES that provides high flexibility

and advanced features, further supported by an intelligent multi-agent-based control system

with enhanced structure and algorithms. INCREASE focuses on providing solutions for

operational problems in power systems, allowing increased penetration of DRES as well as

providing advanced technological solutions and intelligent control strategies for the

prosumers.

Under the INCREASE framework (WP 3), an integrated simulation platform is developed,

enabling the design, analysis, and optimization of the developed solutions. This simulation

platform is a valuable tool for DSOs in order to investigate the performance of DRES in their

distribution grids, implementing either the proposed INCREASE solutions or any other

control scheme. The simulation platform is developed using existing open-source software

and includes the following major features:

• Simulation of the distribution system (both LV and MV networks) with the presence

of unbalanced loads and generation.

• Integration of the locally controlled, inverter-interfaced, distributed generation (DG)

units, i.e. of the Local Control scheme for overvoltage and voltage unbalance

mitigation.

• Incorporation of a Multi-Agent System (MAS) taking into account the multi-objective

Overlaying and Scheduling Control algorithms.

• Implementation of a communication network simulator for the evaluation of the

existing infrastructure and the communication requirements for the MAS control

system.

The software platform architecture also allows the integration of other external and

independent software modules, such as forecasting algorithms, demand side management

and demand response simulation modules, as well as constraint optimization tools.

1.2. Goals

In this report, the incorporation of the MAS-based Overlaying Control algorithm and of

the communication network simulator in the INCREASE simulation platform is thoroughly




31.08.2015 11

described. The MAS-based multi-objective algorithm is developed to cope with overvoltages

and congestion problems, which are significant technical issues arising when integrating

multiple DRES units in the distribution grid. The LAN simulator component aims to enhance

the features of the INCREASE simulation platform, by focusing on the evaluation of the

communication infrastructure and of the requirements posed by the MAS control system

and the implemented control algorithms.

First, all features of the Overlaying Control are presented, including the transformer on-

load tap-changer (OLTC) algorithm, the congestion control technique, and the fair power

sharing (FPS) algorithm presented in Deliverable D3.2. The combination of Local and

Overlaying Control is also discussed, presenting the consecutive implementation of both

control schemes and the update of the various control parameters in each simulation time

interval. Furthermore, the actual incorporation of MAS in JADE environment is analyzed,

highlighting the bi-directional communication between the Core and JADE components of

the INCREASE simulation platform.

Next, the LAN simulator component included in the INCREASE simulation platform is

presented. The general framework and the different simulation programs are described,

while the simulation functionalities and features of the open-source OMNeT++ software are

further analyzed. The necessary operational functions and their implementation in the

INCREASE simulation platform are thoroughly discussed, while the actual integration is

presented, focusing on the interface between GUI, Core and LAN components.

The performance of the full Overlaying Control is demonstrated on a selected pilot

installation and is compared to the cases of no DRES power curtailment as well as to the

Local Control scheme. Results show the efficiency of the proposed algorithm in mitigating

overvoltages by changing the OLTC state and enforcing a fair contribution of the curtailed

power among the installed PV inverters, while maintaining low levels of power losses in the

distribution network. Moreover, the efficiency of the congestion control technique is also

examined, assuming a high DRES penetration level. Finally, a performance evaluation of the

communication layer is conducted in the examined pilot installation using the LAN simulator

component, while assuming different wired and wireless communication technologies.

1.3. Report outline

This introductory chapter is followed by:

Chapter 2: Overview of the INCREASE simulation platform. The overall structure and

the individual components of the INCREASE simulation platform are presented. The

overview mainly focuses on the interface between the Core and JADE components as well as




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between GUI, Core and LAN components. This section also briefly describes the Local Control

scheme, originally presented in D3.1.

Chapter 3: Implementation of the Overlaying Control. The developed full multi-

objective Overlaying Control scheme is incorporated in the INCREASE simulation platform,

featuring the OLTC control algorithm, the congestion management technique and the FPS

among the connected DRES. The integrated model is generalized, introducing proper

weighting factors in order to be also applicable to feeders and PV inverters with arbitrary

characteristics and nameplate ratings.

Chapter 4: Incorporation of LAN simulator component. The integration of the LAN

simulator in the INCREASE simulation platform is described with special emphasis on the

necessary functions to investigate the performance of the communication system,

supporting the MAS-based control. Moreover, the interface between GUI, Core and LAN

components is described.

Chapter 5: Simulation results. Simulation results from a pilot installation are presented,

revealing the effectiveness of the full multi-objective Overlaying Control under normal or

high PV penetration conditions. Results are compared with other control schemes, whereas

a detailed investigation is also performed on the total active power injection and losses of

the distribution network. The communication infrastructure of the same pilot grid is also

simulated to evaluate its performance in supporting the INCREASE distributed control

scheme, while different wired and wireless technologies are investigated.

Chapter 6: Conclusion. General conclusions are summarized and a plan for the next

research steps is proposed.




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2. Overview of the INCREASE simulation platform

The main objective of the INCREASE simulation platform is to simulate and analyze

MV/LV electrical power grids, including inverter-interfaced DRES, different types of loads as

well as the implementation of various control schemes for the DRESs. The INCREASE

simulation platform is able to handle unbalanced AC power flow calculations, using the

phase-domain approach [1], as well as profile-based power flow calculations. Due to the

required analysis over extended observation times, a quasi-dynamic solution has been

selected and implemented instead of a dynamic simulation model. The quasi-dynamic

solution is based on sequential steady-state power flow calculations over a user-defined

time span with a variable time step. This approach is preferred over detailed dynamic

simulations, since it can provide a better insight into system steady-state conditions while it

is also very efficient numerically, requiring significantly smaller execution times [2]. A

detailed description and evaluation of the INCREASE simulation platform can be found in

Deliverable D3.1.

In the INCREASE project framework, three distinct control schemes are proposed and

developed, namely Local, Overlaying and Scheduling Control. Each one of these control

schemes is fully integrated in the INCREASE simulation platform. The incorporated INCREASE

control schemes can be summarized in the following:

• The first level control, denoted as Local Control, is a low-level control applied to

controllable DG units via grid-interfaced inverters. Its main objective is to perform

voltage control in low voltage networks by mitigating overvoltages and voltage

unbalances [3]-[7]. The Local Control works continuously, adjusting the total

amount of the inverter active power output and its distribution among the three

phases according to the voltage at the Point of Common Coupling (PCC).

• The second level control, characterized as Overlaying Control, is related to the MAS

coordination algorithms for the application of specific control strategies and is

focused on voltage control and transformer congestion management [8], [9]. It is

an event-driven control, interacting with the Local Control system by varying its

operational parameters, such as the slopes of the inverter droop curves and the

corresponding set-points, as well as the MV/LV transformer OLTC settings.

Generally, the Overlaying Control is addressed to all controlled DRES in the

examined network.

• The third level control, named as Scheduling Control, addresses problems on a

longer time scale, which varies from minutes to days. This control focuses on the

optimization of the grid performance according to predefined criteria and




31.08.2015 14

constraints, which include load and generation forecasts, demand response, as well

as possible energy market inputs [10]. Scheduling Control is also implemented

through the MAS.

Finally, in the INCREASE simulation platform a discrete event simulator of the

communication infrastructure is employed. This simulator is used to evaluate the

communication performance of the MAS control system and to analyze possible

contingencies of the communications in the operation of the MAS control system.

Furthermore, it can be used to investigate alternative options on the design of the necessary

infrastructure, and to examine the communication system vulnerability and its risks on the

control system performance.

2.1. General architecture

An overview of the INCREASE simulation platform is presented in Fig. 2.1. The developed

software comprises different open-source tool components and their mutual

interconnections. More, specifically, the INCREASE simulation platform includes:

• The Core, which is the base of the simulation platform. The Core controls the

interaction of the different INCREASE platform components. It is also the base for

the implementation of the algorithms of the proposed Local Control.

• The Draw tool, which is a graphical pre-processor with design capabilities to allow

the user-friendly input and configuration of the distribution or transmission

network under investigation.

• The OpenDSS software [11], which is a phasor-domain grid simulator, capable of

handling unbalanced power flow problems, and also allowing the development and

implementation of the necessary power system component models.

• The JADE software [12], which is the tool integrating the MAS and the

corresponding communication in the INCREASE platform for the implementation of

both the Overlaying and Scheduling Control algorithms.

• The OMNeT++ simulator [13], which is used for the analysis and the evaluation of

the communication infrastructure of the examined network.




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Fig. 2.1: Overview of the INCREASE simulation platform.

2.2. Tool components

In this section a brief description of the components incorporated in the INCREASE

simulation platform is presented. A more detailed analysis of all required tools can be found

in D3.1.

2.2.1. Core platform

The core of the platform is developed in MATLAB, which is a high-level language,

suitable for numerical computations, visualization and programming [14]. In the developed

platform the initial GUI windows defining the main simulation parameters as well as the

post-processing tools for the results are implemented within the Core component.

Furthermore, the Core is responsible for the interconnections between the different

components of the developed platform. More specifically, as shown in Fig. 2.1, four distinct

interconnections among the key elements of the INCREASE simulation platform are

implemented. These are:

• A one-way interconnection between the Core and the Draw tool. This interaction is

used for transferring all necessary data from the Draw input pre-processor to the

appropriate variables and structures in the Core.

• A two-way interconnection between Core and OpenDSS. This interconnection uses

the well-established COM interface, transmitting all necessary input files (.dss files)

to the OpenDSS simulator. Furthermore, the corresponding results from the power

Core

Draw

Network Designing Tool

JADEAgents’ Environment

OMNeT++LAN Simulation

Framework

OpenDSSDistribution System

Simulator

2-way

2-way1-way

1-way




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flow simulations performed in OpenDSS are transferred back to the Core via the

same connection.

• A two-way interconnection between Core and JADE. This link allows the

communication of the MAS components with the core of the INCREASE simulation

platform.

• A one-way interconnection between the Core and the OMNeT++. This

interconnection is responsible for the transmission of the necessary data to define

the grid and MAS structure, as well as to create the corresponding input files of the

LAN simulator (.ned and .ini files).

2.2.2. Draw tool

The graphic library for the input of all individual network components is based on

SIMULINK models [14] and contains all components defined in the toolbox of Fig 2.2. This

collection includes all basic power system components, such as loads and generation units,

as well as the more advanced models of controllable inverters and agents, which have been

presented thoroughly in D3.1. All elements work in a drag and drop environment by simply

putting them in the design area, configuring their required parameters, and making the

appropriate connections. Furthermore, all elements are accompanied by brief help

descriptions, when pressing the corresponding button. Finally, the final version of the Draw

component will also include a data conversion tool to provide basic import capabilities from

file formats used in other simulation software packages and design environments to

variables and structures compatible with the INCREASE simulation platform.

Fig. 2.2: Main window of draw library

The GUI for the agent component is shown in Fig. 2.3. By selecting the corresponding

checkboxes, result reports regarding voltages, currents, active and reactive power can be

easily obtained from the power flow simulations performed in OpenDSS. Furthermore,

compared to previous versions of the Draw tool, two new data fields are incorporated in the




31.08.2015 17

GUI of the agent. In the first field, the user is able to define if the connection between the

agents will be wired (power line communication - PLC) or wireless. In the second field, the

relative to the aggregator agent geographical coordinates of each agent are inserted,

allowing the simulation of wireless connections.

Fig. 2.3: Indicative GUI of agent

2.2.3. OpenDSS simulator

The OpenDSS simulation tool [11], [15] has been chosen as the power flow solver within

the INCREASE simulation platform. OpenDSS is a comprehensive, open-source simulation

tool for power distribution systems, developed and distributed by the Electric Power

Research Institute (EPRI). OpenDSS provides highly accurate results, remarkable numerical

performance and vast communication abilities with external programs. Furthermore, the

most common power system analysis algorithms for both steady-state and dynamic analysis

are incorporated in the OpenDSS. It also includes various quasi-dynamic solution modes,

such as snapshot-daily-yearly power flows and harmonic analysis, making it ideal for

sequential time simulations. The time period can be arbitrary selected, whereas the user

may also implement external macros to drive the load models in any arbitrary manner. The

results of a power flow generally include bus voltages, branch currents, grid losses and other




31.08.2015 18

information available for the total system, for each component, and for certain defined

areas.

2.2.4. JADE environment

A detailed presentation of the JADE environment is given in Section 3.

2.2.5. OMNeT++ simulator

A thorough description of OMNeT++ simulator is given in Section 4.

2.3. Local Control scheme

The Local Control scheme is a low-level control applied to controllable DG units via grid-

interfaced inverters. Its main objective is to perform voltage control in low voltage networks

by using exclusively local parameters measured at each inverter PCC, such as the grid voltage

and the available power, offering the ability to immediately react on grid disturbances and

ensuring the safe operation of the distribution grid.

The proposed Local Control incorporates two distinct basic control features, namely the

droop control of the injected active power and the voltage unbalance mitigation strategy.

Since, in low voltage distribution grids the R/X ratio is relatively high as distribution lines

have mainly resistive characteristics, voltage control can be accomplished more efficiently by

controlling the active power output of the DRESs connected to the LV network. Thus, the

droop control curtails the active power of DG units, avoiding unacceptable overvoltages

along the distribution feeders. The droop control is based on the voltage at the point of

common coupling (PCC) gυ , while a typical droop curve is depicted in Fig. 2.4.

Fig. 2.4: Typical droop curve of controllable DG units

Voltages cpbυ and

maxυ are the thresholds for the activation of active power curtailment

and for the complete cut-off of power injection, respectively. Additionally, min

υ is the

inverter minimum operating voltage, whereas refP is the maximum active power the inverter




31.08.2015 19

can deliver at a specific time instant, defined from the available power of the primary

source.

In the second integrated control scheme of the Local Control, inverters mitigate voltage

unbalance by injecting zero- and negative-sequence currents proportionally to the zero- and

negative-sequence voltages at the PCC, respectively. A proportionality term is introduced,

called damping conductance d

g , which results in a resistive behaviour of the inverter

towards the zero- and negative-sequence components of the grid voltage. The injected

currents in symmetrical components are calculated according to the following equation [3]-

[7],

0 0

1 1 1

2 2

0 0

0 0

0 0

d

d

i g

i g

i g

υυυ

= ⋅

(2.1)

where 0

υ , 1

υ , 2

υ are the zero-, positive- and negative-sequence components of the voltage

at the PCC, and 1

g refers to the injected active power and is the fundamental conductance

of the inverter having an opposite sign of d

g in case of generation.

A detailed presentation of the Local Control is available in D2.4. Further results

considering the performance and the effectiveness of the proposed Local Control scheme

are presented in D3.1.




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3. Implementation of the Overlaying Control

A significant drawback of the Local Control is that the active power curtailment is mainly

observed in the DRES units connected close to the end of the radial LV feeders. The

Overlaying Control and especially the OLTC control algorithm acts complementary to the

Local Control, adjusting the voltage level of the network and thus reducing the total curtailed

power of the DRES. Furthermore, the FPS algorithm of the Overlaying Control is responsible

to redistribute the curtailed active power in a fair way among the DRES, while the

Congestion management acts supplementary to the other control schemes in order to

mitigate any transformer congestion.

3.1. OLTC control algorithm

In principle, the MV/LV transformers are equipped with off-load tap changers, which can

only be adjusted off-line during the installation or after a topology change in the network.

However, it is expected that future distribution networks will be equipped with OLTC-based

MV/LV distribution transformers to independently adjust the voltage level of LV networks

during their operation without affecting the corresponding level of the MV network. The

OLTC is capable of adjusting the secondary voltage on-line by controlling a tap changer on

the MV side, thus mitigating voltage fluctuations due to DRES [16], [17]. The incorporated

automatic voltage regulator compares the network voltages with a reference voltage,

adjusting the taps of the transformer when necessary. This is usually done in a predefined

number of steps up and down the rated voltage, and with a predefined voltage change rate

for each step. In the following text the term ‘tap down’ implies a voltage reduction at the LV

side by the pre-defined tap changers step rate, while ‘tap up’ implies a voltage increase at

the LV side, irrelevant of the actual OLTC manufacturer definitions.

3.1.1. Problem formulation

The MV/LV distribution network of Fig. 3.1 is considered, where several feeders are

connected. For simplicity reasons and without limiting the generalized application of the

methodology it is also assumed that feeder k explicitly hosts a number of DRES, while feeder

l contains only loads. In this case, the former feeder is mainly characterized by a voltage rise

when the DRES operate at their peak generation capacities, while in the latter one a voltage

drop is generally observed due to the presence of passive loads. If the OLTC is tapped down

to mitigate the voltage rise problem, e.g. controlling the transformer voltage ratio to lower

the LV side voltage for steady feeding voltage from the MV, a severe undervoltage situation

may be observed in feeder l, violating the low voltage limit EN

lowV according to the EN 50160

standard. As a result, a coordinated voltage regulation among DRES and OLTC must be




31.08.2015 21

developed, maintaining the voltage level of all feeders in the desired voltage-bandwidth

(DVB).

Fig. 3.1: LV network with conflict on voltage regulation

3.1.2. Proposed methodology

Since feeder k includes n DRES, it is expected that this feeder experiences the maximum

voltage among the feeders. The DRES voltages at the PCC and the corresponding droop-

control thresholds can be grouped in vectors gV and

cpbV , respectively. Meanwhile, feeder l

experiences the minimum voltage min

V , which is also the lowest among the feeders due to

the sole presence of passive loads. In case of mitigating the voltage rise or drop in these two

feeders, the voltage levels of the remaining feeders definitively remain in the desired DVB,

which is determined by the difference between the maximum element of gV and

minV .

The operational procedure of the proposed OLTC control can be summarized in the

following situations, assuming a certain dead-band tapV∆ due to the fixed step width of the

tap changer:

• Situation #1a - No tap action: g cpb≤V V and EN

min lowV V≥

• Situation #1b - No tap action: g cpb>V V and EN

min tap lowV V V∆− <

• Situation #2 - Tap up: EN

min lowV V<

• Situation #3 - Tap down: g cpb>V V and EN

min tap lowV V V∆− ≥




31.08.2015 22

In more detail, if all voltages remain within DVB, then Situation #1a is met and neither

OLTC nor DRES curtailment is activated. In Situation #1b, although there is active power

curtailment due to the activation of the Local Control, no tap action is performed since it

would lead to the violation of EN

lowV . In case of undervoltage, Situation #2 is met and a tap up

action is performed, until the problem is mitigated. Finally, the tap down action of

Situation #3 is activated when both the Local Control is activated and no violation of EN

lowV is

observed.

In general, the DRES power generation is maximised using the tap down action of the

OLTC, while the DRES power generation is curtailed only when the OLTC cannot tap down

because of a lower voltage in another feeder. This control is activated in an iterative way to

determine the exact required number of tap actions, while the execution is performed after

a user-defined time delay and only after the confirmation that the voltage issue persists.

3.2. Congestion management algorithm

3.2.1. Introduction

In distribution networks with high penetration of DRES, extremely high reverse power

flows may lead to operational points for network elements beyond their physical capacity

limits, and thus congestions may occur. The most congestion vulnerable network elements

in a distribution grid with high DRES penetration at the LV level are the MV/LV transformers

[18], [19]. As a result, DSOs with a high share of DRES in their networks may face challenges

in maintaining the reliability of the network. These challenges are expected to become more

frequent, depending on the different types of connected resources, their geographic

location and the voltage level of the connection.

One aspect of the Overlaying Control aims to deal with the impact of high DRES

generation into the loading conditions of the distribution network and to treat effectively

the resulting congestion issues. For this purpose, the implemented MAS-based active power

curtailment mechanism is modified to also tackle the potential congestion of the MV/LV

transformers in LV networks. This curtailment strategy is thoroughly investigated and

successfully combined with the FPS algorithm of D3.2, aiming to fairly curtail the active

power of the connected DRES in terms of their feed-in capacity in order to solve the

congestion issue.

3.2.2. Proposed methodology

Given that i

GP is the generated DRES active power at the i-th connection point, the total

DRES generation tot

GP of the network is given by:




31.08.2015 23

1

Ntot i

G G

i

P P=

=∑ (3.1)

where N is the total number of connection points in the network. Similarly, the total load tot

LP is calculated as:

1

Ntot i

L L

i

P P=

=∑ (3.2)

whereas the corresponding line and cable total active power losses are denoted by tot

lossP .

In each bus of the grid, reverse power flow occurs when the local generation i

GP exceeds

the local load i

LP . This reverse power flow can cause transformer overload when the

cumulative flow exceeds the transformer rating ratedS :

( ) ( )2 2transf tot tot tot tot

G L l rates dosS P P P Q S= − − + > (3.3)

where tot

Q corresponds to the transformer reactive power. In this case, congestion occurs

and the necessary amount of total active power curtailment is approximately calculated by:

transf transf

c ratet durP S S≅ − (3.4)

The proposed methodology aims to distribute the required curtailment among all the

feeders on the basis of a fair curtailment mechanism. This is performed by assigning a

suitable curtailment coefficient m

curtc for each feeder m, based on the individual total feeder

DRES generation on MPP conditions. The MPP value is prefered over the installed capacity,

since this is the maximum power that a PV unit can generate in any moment given the

radiation level. The coefficient is defined as the ratio of the total DRES MPP power at m-th

feeder m

MPPP to the total amount of DRES MPP generation in the network tot

MPPP :

m

MPm

cP

ur tot

P

t

MP

P

Pc = (3.5)

Thus, the total required active power curtailment can be distributed evenly among the

feeders by multiplying it with the corresponding weighing coefficients. Therefore, the

curtailment at each feeder m

curtP is given by:




31.08.2015 24

m

cur

m transf

curt curttP Pc ⋅= (3.6)

Finally, the actual active power curtailment of the m-th feeder, with reference on its MPP

conditions ,

m

Cong curtP , is calculated in (3.7) by taking into account the corresponding feeder

curtailment caused by the Local Control on the DRES connected to the m-th feeder ,

m

LC curtP :

, ,

m m m

Cong curt curt LC curtP P P= + (3.7)

3.3. FPS control algorithm

The last goal of the Overlaying Control is to obtain a fair power curtailment among the

DRES connected to a radial feeder of the LV network, while mitigating the overvoltage in the

feeder. This is thoroughly presented in D3.2, where the sensitivity matrix methodology is

used in order to uniformly curtail the active power of DRES.

According to the FPS formulation, the curtailed active power Ρ∆ of the DRES with the

minimum i

MPPP on the m-th feeder is given by (3.8), while the active power curtailments of

all the other DRES in this feeder are given by (3.9):

1

m

i ji

i

i

g

w

V

x=

Ρ =⋅∑

∆∆

(3.8)

,FPS curt = Ρ ⋅∆P w (3.9)

Here, i

gV∆ is the change in the voltage magnitude of the i-th connection point,

jix are the

corresponding elements of the sensitivity matrix, and w the coefficient vector related to the

MPP conditions. By summing the elements of (3.9), the FPS actual curtailment of the m-th

feeder is acquired with reference on its MPP conditions:

, ,

m

FPS curt FPS curtP =∑P ; for the m-th feeder (3.10)

The combination of the Congestion and FPS control algorithms is achieved by comparing

the actual curtail values of (3.7) and (3.10), which correspond to the m-th feeder. The largest

value is selected as the final power curtailment for the m-th feeder, yielding voltages on the

safe side, while the total amount of curtailed active power is fairly distributed to all DRES

connected to this feeder. This is calculated in (3.11) for the DRES at the i-th connection

point:




31.08.2015 25

( ), ,,

i i m m

curt Cong curt FPS curtP a max P P= ⋅ (3.11)

In (3.11) the weighting factor ia is given by:

ii MPP

m

MPP

Pa

P= (3.12)

The curtailed active power components of (3.11) lead to new power injections for all

inverters, thus a new power flow solution is performed to calculate the corresponding

voltages at the inverter PCCs. From the acquired set-points, the droop characteristics of all

inverters are then reconfigured as proposed in D3.2.

3.4. Integration with Local Control

The integration concept of the Local and Overlaying Control is presented in Fig. 3.2,

where a detail of an arbitrary selected 15-min timeslot is also shown. Since the Local Control

is designed to be embedded at the hardware level of the inverter, it is continuously active

during the 15-min timeslot. After the first 5 min, the MAS component of the INCREASE

platform detects any possible voltage issue and calculates the new tap setting of the

transformer OLTC according to Section 3.1. Then all the data considering the voltages at PCC,

the inverter power injections and the net power flows are monitored and communicated to

the MAS layer. In case of detecting any congestion issue or unfairness in curtailing the DRES

injections, the combined Congestion and FPS algorithm is activated according to Sections 3.2

and 3.3, and the new reference control signals are sent to each inverter. Then, the droop

curves are reconfigured and this state retains until the end of the 15-min timeslot, where the

droop curves reset and the loads as well as the MPP inverter values are updated to new

values. At this point, the OLTC control algorithm executes the tap change after checking

whether the voltage issue persists. The length of the specific time slot is fully flexible and can

be adjusted according to the sampling rates and all other response times of the actual grid

elements and of the monitoring system implemented in each specific distribution grid.




31.08.2015 26

Fig. 3.2: The 15-min timeslot concept

3.5. Actual implementation

3.5.1. JADE and Core components

JADE (Java Agent Development Framework) is a software development framework

aimed at developing MAS and applications conforming to FIPA standards for intelligent

agents. JADE is written in an object-oriented programming language, namely JAVA, because

of the many attractive features it provides.

In the framework of the INCREASE simulation platform, JADE is employed as a MAS

developing environment where the Overlaying Control is implemented. More specifically,

the code referring to the Overlaying Control is actually written in JADE, making it an essential

component of the simulation platform. The link of the Core module, which is implemented in

MATLAB, with JADE is done through TCP/IP communication. Considering a specific time

t t+15 min t+30 min t+45 min

Local Control

FPS & Congestion FPS & Congestion FPS & Congestion

t+30 min

Local Control

FPS & Congestion

t+15 min

Reset

Load, MPP

OLTCOLTC OLTC

OLTC

Detection of Voltage Issue

Calculate New Tap Setting

Check for OLTC

activation




31.08.2015 27

instant, the Overlaying Control procedure is depicted in Fig. 3.3 comprising 6 steps which are

analyzed as follows:

Fig. 3.3: Conceptual implementation of Overlaying Control

Step 1: The results of the Local Control regarding voltages, injected active power of DRES

etc., are forwarded to the Core module of the INCREASE simulation platform. Then,

the Core module checks whether the Overlaying Control is activated. In the case of

DRES with curtailed active power, the FPS algorithm is launched, whereas the

Congestion and OLTC algorithms are used to address transformer overloading and

voltage violation issues, respectively. Otherwise, the procedure moves to Step 6 and

the Overlaying Control remains deactivated.

Step 2: This step is implemented via a TCP/IP communication channel, established between

Core module and JADE. The data transferred to JADE refer to the network

configuration, the outputs from the Local Control, i.e. voltages, loads, injected active

power of the DG units, etc., and the activated algorithms of the Overlaying Control.

Step 3: Considering the aforementioned Congestion and FPS algorithms, two auxiliary power

flow calculations are necessary. In the first power flow solution, each DG unit is

assumed to inject its maximum available power, i.e. MPP operation, whereas in the

second it injects the active power defined by the Congestion or FPS algorithm. The

power flow calculations are implemented in MATLAB and OpenDSS but are initiated

by the MAS module established in JADE.

Step 4: The results obtained from the power flow calculations are transferred to JADE. Then,

the combined Congestion and FPS algorithm calculates the sensitivity matrix and the

new droop curves based on the first and the second power flow, respectively,

whereas the OLTC algorithm determines the new setting of the transformer on-load

tap changer.




31.08.2015 28

Step 5: The new droop curves of the DG units and the new transformer on-load tap setting

are forwarded back to the Core module via another TCP/IP communication channel.

Step 6: Finally, the droop curves of the DG units and the tap settings are passed to the Local

Control, moving one time step forward and initiating a new iteration.

3.6. Tasks performed by JADE and Core components

The complete simulation flowchart is depicted in Fig. 3.4, including the coordination of

the Local and Overlaying Control, based on the 15-minute timeslot concept of Section 3.4.

Initially, the network data referring to the consumption, generation and network

configuration are loaded. Then, a part of the OLTC algorithm of the Overlaying Control is

executed. An internal Local Control is initiated and the Core module checks whether the tap

setting should change in the new value. Next, the Local Control for the first five minutes is

executed.

The results obtained from the Local Control are forwarded to the Core module, which

first checks whether a new setting of the on-load tap changer is needed, according to the

OLTC algorithm of Section 3.1, which will be forwarded at the next 15-min timeslot. Then,

the Core module compares the calculated apparent power of each transformer with the

corresponding rated one. In case a violation is observed, the Congestion algorithm is

activated and executed in JADE by calculating the required amount of active power to be

curtailed, as described in Section 3.2. Then, depending on the condition that there are DG

units operating in the droop region, the Core module activates the FPS algorithm of Section

3.3, which calculates the new injected active power for each DG unit.

Finally, the new droop curves of the DG units, derived from the combined operation of

Congestion and FPS algorithms are used as inputs to the Local Control, corresponding to the

last ten minutes of the timeslot. The procedure is repeated until the end of the pre-defined

total simulation time.




31.08.2015 29

Fig. 3.4: Simulation flowchart

Start of Simulation

(t = 0)

End of Simulation

Last timeslot

Load network data of the

timeslot (consumption,

generation, etc.)

Stransf

> Srated

Congestion management

scheme

Vg > Vcpb FPS control scheme

Ov

erl

ay

ing

Co

ntr

ol

Ov

erl

ay

ing

Co

ntr

ol

YES

NO

NO

NO

YES

YES

Internal Local Control

Local Control

(first 5 minutes)

Local Control

(last 10 minutes)

Core (MATLAB)

MATLAB/OpenDSS

JADE

Check for OLTC

activation

OLTC control scheme

(new tap setting for the

next timeslot)

NO

YES

Check for the tap setting

change

t = t + 15




31.08.2015 30

4. Incorporation of LAN simulator component

4.1. Introduction

The goal of the communication network simulator is to simulate the communication

traffic for the control mechanisms developed within INCREASE. The Overlaying Control

scheme is implemented as a MAS with different kinds of agents that communicate with each

other to apply the specific control strategies focused on voltage control and transformer

congestion management.

The communication between these agents can be realized using different

communication technologies. Power-line communication can be used using the existing

feeders, wireless technologies can be also deployed or other deployed telecom solutions like

DSL, coax and fiber. In all cases, it is important to analyse to what extent these technologies

can support the communication traffic associated with the Overlaying Control features in

terms of acceptable delays, needed bandwidth, reliability, etc.

In this section a short overview of the available communication network simulation

platforms is presented and the OMNET++/INET platform used in the INCREASE simulator is

further described. Then, the developed components within the simulation platform for

modeling the different kinds of agents and associated communication patterns are

described, while the interface between the communication network simulator and the

power grid simulator is also investigated.

4.2. Communication network simulation

A short overview of a number of communication network simulators is presented that

are widely used for the development and evaluation of communication architectures and

protocols, and have been used successfully in a smart grid context [20].

4.2.1. Network Simulator (ns-2 and ns-3)

The Network Simulator version 2 (ns-2) is a widely used open-source discrete event

network simulator, created for research and educational purposes. It is targeted at

networking research, with a strong focus on Internet systems. Therefore, it includes a rich

library of network models to support the simulation of e.g. IP-based applications (including

TCP, UDP, etc.), routing, multicast protocols, over wired and/or wireless networks. The ns-2

core is written in the C++ programming language. Users can create new network models or

protocols using the C++ language. Simulation scripts to control the simulation and configure

aspects such as the network topology are created using the OTcl language interface. As a

result, users can create and modify simulations without having to resort to C++

programming and recompiling ns-2.




31.08.2015 31

Development of ns-3, the successor to ns-2, is ongoing. New features include support for

the Python programming language as a scripting interface instead of OTcl, improved

scalability, more attention to realism, better software integration, etc. [21]. However, when

selecting a specific version of ns, it is important to consider that ns-3 is not backwards

compatible with ns-2, i.e. existing ns-2 simulation models must implemented again for ns-3.

Both are widely used for networking research, while both ns-2 and ns-3 are also adopted

in a smart grid context, e.g. a co-simulation approach [22], [23].

4.2.2. OMNeT++

The open-source OMNeT++ discrete event simulation environment has been designed

for the simulation of communication networks (wired and wireless) and distributed systems

in general [13], [24]. The simulation environment has a general design, i.e. it is not limited to

simulating communication networks, and therefore has been used in various domains, such

as wireless network simulations, business process simulation and peer-to-peer networking.

However, OMNeT++ is mostly applied in the domain of communication network simulation.

A comprehensive set of Internet-based protocols is provided by means of the INET

framework extension [25], which includes support for IPv4, IPv6, TCP, UDP, Ethernet, and

many other protocols. Other extensions provide simulation support for mobility scenarios

(e.g. VNS), ad-hoc wireless networks (e.g. INET-MANET), wireless sensor networks (e.g.

MiXiM, Castalia), etc. Distributed parallel simulation is also supported to enable simulation

of large-scale networks. Additionally, federation support based on the High-Level

Architecture (HLA) standard is provided in OMNEST, the commercial version of OMNeT++.

An OMNeT++ simulation model consists of simple modules implemented in C++.

Compound modules consist of other simple or compound modules, and are defined using

the OMNeT++ Network Description Language (NED). Modules communicate by passing

messages via gates, which are the input and output interfaces of the modules that are linked

to each other by so-called connections, forming communication links between modules.

Apart from the networking community, OMNeT++ has also received substantial attention

from the smart grid community for developing smart grid simulators [26], [27], [28].

Examples that focus on the communication aspect of the smart grid include the design and

evaluation of different smart grid communication architectures, the performance of smart

grid protocols, etc.

4.2.3. NeSSi

NeSSi (Network Security Simulator) is an open-source discrete event network simulator

developed at DAI-Labor (Distributed Artificial Intelligence Laboratory) and sponsored by

Deutsche Telekom Laboratories. The primary focus of the tool is on network security related

scenarios in IP networks [29]. Features described to support security related scenarios are




31.08.2015 32

attack modeling, attack detection, security metrics, etc. Distributed simulation is supported

to enable simulation of large-scale networks.

4.2.4. OPNET Modeller

OPNET Modeller is a powerful commercial discrete event network simulator with built-

in, validated models including LTE, WIMAX, UMTS, ZigBee, Wi-Fi, etc. It enables modeling of

various kinds of communication networks, incorporating terrain, mobility, and path-loss

characteristics in the simulation models. OPNET Modeller has a visual high-level user

interface, offering access to a large library of C and C++ source code blocks, representing the

different models and functions. It comes with an open interface for integrating external

object files, libraries, other simulators (co-simulation) and even hardware-in-the-loop.

4.2.5. Discussion

The discussed communication network simulators have been used successfully in the

context of smart grid research. OMNeT++ and ns-2/ns-3 are used extensively in academia

due to their open-source nature. In terms of supported simulation models, a wide range of

models is available for each simulator, and the choice mainly depends on prior knowledge

and preferences of the user regarding modeling language and tools, extensibility and

supported programming languages, presence of extensive GUI tools, etc. For example,

OMNeT++ and NeSSi provide an integrated development environment (IDE) that includes

GUIs for building and configuring simulation models, visualization of topologies, result

processing, etc. However, ns-2/ns-3 lacks an extensive set of GUI tools as found in

OMNeT++, making it more complex in its usage. OPNET Modeller on the other hand is a

commercial simulator that has a visual high level interface. Another aspect that may

influence the choice of simulator is the commercial support, which is available for OMNeT++

(i.e., OMNEST) and OPNET. NeSSi, also an open source simulator, distinguishes itself from

the other tools due to its primary focus being network security. Consequently, the OMNeT++

in combination with the INET framework is chosen for the INCREASE simulation platform,

mainly due to prior positive experiences with this simulator.

4.3. Overview of OMNeT++ and INET

4.3.1. Introduction

OMNeT++ is an extensible, modular, component-based C++ simulation library and

framework, primarily for building network simulators [13]. It offers an Eclipse-based IDE, a

graphical runtime environment, and a host of other tools. It runs on Windows, Linux, Mac OS

X, and other Unix-like systems. OMNeT++ provides a component architecture for models.

Components (modules) are programmed in C++, then assembled into larger components and




31.08.2015 33

models using a high-level language (NED) in order to be easily reused. OMNeT++ has

extensive GUI support, and due to its modular architecture, the simulation kernel (and

models) can be embedded into applications.

OMNeT++ provides the basic machinery and tools to write simulations, but it does not

provide itself any components specifically for computer network simulations, queuing

network simulations, system architecture simulations or any other area. Instead, these

application areas are supported by various simulation models and frameworks, such as INET

[25]. INET is an open-source model library for OMNeT++ providing protocols, agents and

other models for researchers and students working with communication networks.

Furthermore, it contains models for the Internet stack (TCP, UDP, IPv4, IPv6, OSPF, BGP,

etc.), wired and wireless link layer protocols (Ethernet, PPP, IEEE 802.11, etc), support for

mobility, MANET protocols, DiffServ, MPLS with LDP and RSVP-TE signaling, several

application models, and many other protocols and components.

INET is built around the concept of modules that communicate by message passing.

Agents and network protocols are represented by components, which can be freely

combined to form hosts, routers, switches, and other networking devices. New components

can be programmed by the user, and existing components have been written so that they

are easy to understand and modify. INET also benefits from the infrastructure provided by

OMNeT++. Beyond making use of the services provided by the OMNeT++ simulation kernel

and library (component model, parameterization, result recording, etc.), this also means that

models may be developed, assembled, parameterized, run, and their results evaluated from

the comfort of the OMNeT++ Simulation IDE, or from the command line.

4.3.2. OMNeT++/INET simulator configuration

An OMNeT++ model consists of the following parts:




31.08.2015 34

• Modules that communicate with message passing. The active modules are

termed simple modules and they are written in C++, using the simulation class library.

Simple modules can be grouped into compound modules and so forth, thus the

number of hierarchy levels is unlimited. The whole model, called network in

OMNeT++, is itself a compound module. Messages can be sent either via connections

that span modules or directly to other modules.

Fig. 4.1: Simple and compound modules

• NED language topology description(s) (.ned files) that describe the module structure

with parameters, gates, etc. NED files can be written using any text editor, but the

OMNeT++ IDE provides support for two-way graphical and text editing.

• Message definitions (.msg files). Various message types can be defined and data

fields added to them. OMNeT++ will translate message definitions into full-fledged

C++ classes.

• A configuration file (typically called omnetpp.ini). This file contains settings that

control how the simulation is executed, values for model parameters, etc. The

configuration file can also prescribe several simulation runs.

The output of the simulation is written into result files: Output vector files , output scalar

files, and possibly the user's own output files. OMNeT++ contains an Integrated

Development Environment (IDE) that provides an environment for analyzing these files.

Output files are line-oriented text files, which make it possible to process them with a variety

of tools and programming languages as well, including Matlab, GNU R, Perl, Python, and

spreadsheet programs.




31.08.2015 35

4.4. INCREASE modules

In Fig. 4.2 an overview of the 5-layer Internet protocol stack and some typical protocols

used in every layer is given. INET provides modules for most of these protocols. In order to

simulate the typical communication patterns between the INCREASE agents, extra modules

are added in the application layer.

Fig. 4.2: TCP/IP 5-layer protocol stack

The IncreaseAgent and IncreaseAggregator are implemented with small differences in

case TCP or UDP is used as transport protocol on top of OMNeT++ and INET, as shown in Fig.

4.3. The IncreaseAgent contains a number of parameters such as the time period by which

measurement messages are sent to the Aggregator agent (measurementInterval, e.g. 1

minute) and the size of such a measurement message (sendBytesMeasurement). When the

Aggregator agent sends a control message, the regular agent will send a reply message of a

certain size (sendBytesControlReply), possibly after a certain delay

(replyControlMessageDelay), e.g. to update the settings of the associated PV inverter. A

specific start time of the agent can be specified (startTime) and furthermore a number of

variables are defined to collect statistics on the communication traffic.




31.08.2015 36

Fig. 4.3: High-level view on the implemented INCREASE modules on top of OMNeT++ and

INET

An OMNeT++/INET module consists of a C++ header (.h), a class file (.cc) and a NED file

which contains the different parameters that can be defined in the simulation configuration

file (.ini file) with often a default value, and a number of additional statistics that are

calculated, e.g. the end-to-end delay of the received messages.

4.5. Interface with the INCREASE simulation platform

To perform a simulation with the communication network simulator two files are

essentially needed: A NED file connecting the different modules, i.e. INCREASE agents with

the relevant internet protocols on the different layers, and a configuration file (.ini file) to

provide specific values for the different module and simulation parameters.

In Fig. 4.4 the interface between the INCREASE power simulator and the communication

network simulator is shown. The power simulator contains a GUI representation of the

power grid under study which is converted to Matlab matrices containing information for

the number and names of present Agents in the grid, the type of communication (wired or

wireless), the coordinates of the regular agents on a map and the distances between the

regular agents and the aggregator agent.




31.08.2015 37

Fig. 4.4: Interface between power and communication network simulators

A Matlab parsing script is developed to transform these matrices into NED and INI files,

which can be used by the communication network simulator. Using these files an actual

simulation of the communication aspects can be executed. The results of such a simulation

run can be visually inspected in the OMNeT++ IDE, e.g. the exchanged messages between all

the involved actors and different layers of the network stack.

In Fig. 4.5 an example is provided, showing the exchange of messages on the highest

(application) level between one regular agent and one aggregator agent. In Fig. 4.6 a more

detailed view is given, also showing the TCP and IP layers of the agents and the intermediate

router for the exchange of a measurement message and associated reply. One can now also

see the exchanged TCP ACK messages.




31.08.2015 38

Fig. 4.5: Visual overview of the exchanged messages on application level between a regular

and an aggregator agent

Fig. 4.6: Visual overview of the exhanged messages on application, TCP and IP level

between a regular and an aggregator agent

The OMNeT++ IDE also provides some tools to easily make graphs of the different

measured statistics. Of course, for detailed evaluations any tools can be used to process the

results, but OMNeT++ tools are handy to check if everything works as expected when

defining and testing your simulations. As an example, in Fig. 4.7 the measured throughput

for a setup with 10 regular agents, one aggregator agent and an intermediate router is




31.08.2015 39

shown. As expected, the throughput on the link between router and aggregator is the largest

and could potentially become a bottleneck if for example PLC communication with low

datarates is used.

Fig. 4.7: Example graph for the measured throughput for a setup with 10 regular agents,

one router and an aggregator agent




31.08.2015 40

5. Simulation results

The combined operation of Local Control with the different versions of Overlaying

Control is demonstrated in the pilot installation of the Slovenian DSO, Elektro Gorenjska. The

examined LV network is depicted in Fig. 5.1 and consists of 79 nodes, where 70 inductive

unbalanced loads and a typical MV/LV distribution transformer are connected. The green

nodes denote the location of the existing controllable PV units.

Since there is no feeder with at least two PV units, this network configuration does not

allow the examination of the FPS feature of the Overlaying Control. Thus, 24 additional PV

units are considered in the installation as shown by the red nodes in Fig. 5.1. The rated

power of the PV units, as well as the transformer data are shown in Table 5-1 and Table 5-2,

respectively. The MV/LV transformer is equipped with an OLTC, while the MV side voltage is

assumed equal to 1.05 pu. The transformer tap range is ±2 % with a voltage dead-band of

2.5 %. Finally, the 4-wire distribution lines have cross sections ranging from 4x16 mm2 to

4x150 mm2, while their lengths vary from 10 m up to 176 m.

In the following simulations 6 different control schemes are assumed and investigated:

1. No Control, where no control is considered and the PV units inject their nominal

active power.

2. Local, where the droop control of the injected active power of PV units is

implemented, according to D3.1.

3. Local & FPS (LF), which is the combined operation of Local Control with the FPS

algorithm of Overlaying Control, according to the 15-minute timeslot concept of

D3.2.

4. OLTC & Local (OL), which is the cooperation of OLTC algorithm with Local Control,

according to Section 3.1.

5. OLTC & Local & FPS (OLF), which is an enhanced version of OL with the incorporation

of the FPS algorithm.

6. OLTC & Local & FPS & Congestion (OLFC), which is the full version of Overlaying

Control that includes the FPS, OLTC, and Congestion algorithms, and it is combined

with Local Control, according to the 15-minute timeslot concept of Section 3.4.




31.08.2015 41

Fig. 5.1: Elektro Gorenjska modified network topology

Table 5-1: PV units rated power

Name Node

Rated

Power

(kWp)

Name Node

Rated

Power

(kWp)

PV 1 13 10 PV 16 50 10

PV 2 14 12 PV 17 47 10

PV 3 18 12 PV 18 52 10

PV 4 20 6 PV 19 54 6

PV 5 22 10 PV 20 9 6

PV 6 24 10 PV 21 57 10

PV 7 25 10 PV 22 58 10

PV 8 4 6 PV 23 59 10

PV 9 5 10 PV 24 60 10

PV 10 6 4 PV 25 63 10

PV 11 7 10 PV 26 64 12

PV 12 8 10 PV 27 70 4

PV 13 31 10 PV 28 74 10




31.08.2015 42

PV 14 41 10 PV 29 76 10

PV 15 45 10 PV 30 78 6

Table 5-2: Distribution transformer characteristics

Sn (kVA) Un (kV) Vector group uk (%) No-load losses (W) Load losses (W)

250 20 / 0.4 Dyn5 4 425 3250

This chapter is divided into three sections. In the first one, namely the normal case, a

time-series simulation is presented, where all control schemes (except the OLFC) are

evaluated. Each timeslot is considered equal to 15 min and the simulation covers a period of

24 hours. The active and reactive power profiles of the aggregated load are presented in Fig.

5.2 and Fig. 5.3, respectively, as derived from real-time measurements provided by the DSO.

The generation profile is also acquired by real-time measurements of the current PV

installations and is further applied to all PV units after appropriate scaling. In the second

section, an extreme case with high PV penetration is demonstrated in order to validate the

proposed OLFC and compare it with the OL and OLF control schemes. Finally, in the third

section the different performance metrics of the communication network for both wired and

wireless communication technologies are calculated for the examined LV grid.

Fig. 5.2: Active power profile of the aggregated load vs. time

0 4 8 12 16 20 240

10

20

30

40

50

60

Time (h)

Act

ive

Po

we

r (k

W)

Phase a Phase b Phase c




31.08.2015 43

Fig. 5.3 Reactive power profile of the aggregated load vs. time

5.1. Normal Case

The total injected active power of all PV units with respect to time is presented in Fig.

5.4. In Fig. 5.5 a zoom over a specific time period is shown to clearly highlight the differences

among the proposed control schemes. The corresponding total curtailed active power is

depicted in Fig. 5.6 and Fig. 5.7. The LF control scheme results in a more uniform active

power curtailment among PV units. The main drawback of this method is the further

reduction of the total injected active power compared to the Local control scheme. By

employing the OL control scheme, the total injected power is considerably improved

compared to the Local control scheme. Finally, the integration of the FPS algorithm to the OL

control scheme, i.e. the OLF scheme, reduces the injected power due to the uniform power

curtailment which, however, remains higher compared to the other control schemes.

Therefore, the incorporation of the OLTC algorithm into the Overlaying Control increases the

overall injected active power of PV units.

Considering the network losses, the high PV penetration results in a reverse power flow

during the high generation periods. Thus, the active power losses are approximately

proportional to the square of the generation and present similar trend as shown in Fig. 5.8

and Fig. 5.9.

0 4 8 12 16 20 240

5

10

15

20

25

30

Time (h)

Re

act

ive

Po

we

r (k

VA

r)

Phase a Phase b Phase c




31.08.2015 44

Fig. 5.4: Injected active power of PV units vs. time

Fig. 5.5: Injected active power of PV units from 10:00 to 16:00

0 4 8 12 16 20 240

50

100

150

200

250

300

Time (h)

Act

ive

Po

we

r (k

W)

Local LF OL OLF

10 12 14 1650

100

150

200

250

300

Time (h)

Act

ive

Po

we

r (k

W)

Local LF OL OLF




31.08.2015 45

Fig. 5.6: Curtailed active power of PV units vs. time

Fig. 5.7: Curtailed active power of PV units from 10:00 to 16:00

0 4 8 12 16 20 240

10

20

30

40

50

60

70

80

90

Time (h)

Act

ive

Po

we

r (k

W)

Local LF OL OLF

10 12 14 160

10

20

30

40

50

60

70

80

90

Time (h)

Act

ive

Po

we

r (k

W)

Local LF OL OLF




31.08.2015 46

Fig. 5.8: Network losses vs. time

Fig. 5.9: Network losses vs. time from 10:00 to 16:00

In Fig. 5.10, the voltage profile of each network node is depicted for the different control

schemes. It is evident that, since the No Control scheme results in zero active power

curtailment, there are overvoltages, especially during the high generation periods. However,

the use of Local, LF, OL, and OLF control schemes mitigate the overvoltages, while the

0 4 8 12 16 20 240

2

4

6

8

10

12

Time (h)

Act

ive

Po

we

r (k

W)

Local LF OL OLF

10 12 14 161

2

3

4

5

6

7

8

9

10

11

Time (h)

Act

ive

Po

we

r (k

W)

Local LF OL OLF




31.08.2015 47

voltage is also efficiently controlled. In the cases of OL and OLF, the voltage at several nodes

is less than 1.06 pu due to the OLTC operation, and thus the droop control for a number of

PV units is also avoided.

The tap setting of the transformer is depicted in Fig. 5.11, where only two tap changes

are performed during the examined day. Therefore, the equipment is not stressed, whereas

the injected active power of PV units is further increased. Finally, the daily energy losses and

the produced energy are presented in Fig. 5.12 and Fig. 5.13, respectively, where similar

conclusions can be drawn.

Fig. 5.10: Voltage of the network vs. time

0 4 8 12 16 20 240.95

1

1.05

1.1

1.15

a) No Control

Time (h)

Vo

lta

ge

(p

u)

0 4 8 12 16 20 240.95

1

1.05

1.1

1.15

Time (h)

Vo

lta

ge

(p

u)

b) Local

0 4 8 12 16 20 240.95

1

1.05

1.1

1.15

Time (h)

Vo

lta

ge

(p

u)

c) LF

0 4 8 12 16 20 240.9

0.95

1

1.05

1.1

1.15

Time (h)

Vo

lta

ge

(p

u)

d) OL

0 4 8 12 16 20 240.9

0.95

1

1.05

1.1

1.15

e) OLF

Time (h)

Vo

lta

ge

(p

u)




31.08.2015 48

Fig. 5.11: Tap setting of the transformer vs. time

Fig. 5.12: Daily energy losses

0 4 8 12 16 20 24

-2

-1

0

1

2

Time (h)

Ta

p P

osi

tio

n

OL OLF

Local LF OL OLF0

10

20

30

40

50

60

En

erg

y L

oss

es

(kW

h)

Control Scheme




31.08.2015 49

Fig. 5.13: Daily energy production

The injected active power of all PV units on the feeder indicated by the blue line in Fig.

5.1 is presented in Fig. 5.14 after the implementation of the different control schemes. The

corresponding positive-sequence voltage profiles are also presented in Fig. 5.15. All data are

shown in a pu scale based on the rated voltage and power of each PV unit and correspond to

the timeslot between 12:00 and 12:15 where the most severe overvoltages occur as shown

in Fig. 5.10.

Considering the No Control scheme, each PV unit injects its nominal active power. Since

no active power curtailment is employed, the voltage at the last nodes of the feeder exceeds

1.1 pu which is the maximum permissible voltage, as defined by the EN 50160 standard. This

overvoltage is avoided by applying the Local control scheme. However, in this case the PV

units located at the end of the feeder suffer from a severe active power curtailment

compared to the ones located at the beginning of the feeder. To overcome this problem, the

LF control scheme is applied, resulting in a uniform active power curtailment among the PV

units of the same feeder, as shown in Fig. 5.14. According to the results of the LF control, the

injected active power of PV units located at the end of the feeder is increased, whereas the

PV units at the beginning of the feeder have now an increased active power curtailment,

compared to the previous Local control case.

The introduction of the OLTC algorithm improves considerably the performance of the

Overlaying Control. More specifically, considering the OL control scheme, only the last three

Local LF OL OLF500

750

1000

1250

1500

Control Scheme

En

erg

y P

rod

uct

ion

(k

Wh

)




31.08.2015 50

PV units operate at the droop region, compared to the Local control scheme, due to the

reduction of the positive-sequence voltage at the LV side of the transformer as shown in Fig.

5.15. Furthermore, the OLF control scheme results in a considerable increase of the injected

active power, compared to the LF control scheme, due to the incorporation of the OLTC into

the Overlaying Control.

Fig. 5.14: Total injected active power of PV units along a single feeder

PV 1 PV 2 PV 3 PV 4 PV 5 PV 6 PV 740

50

60

70

80

90

100

110

PV unit

Act

ive

Po

we

r (%

)

No Control Local LF OL OLF




31.08.2015 51

Fig. 5.15: Positive-sequence voltage profile along a single feeder

5.2. Case of high DRES penetration

The performance of the various control schemes are validated once again in the pilot

installation of Elektro Gorenjska. As mentioned previously, the network configuration of this

specific installation is not suitable for the implementation of the full Overlaying control. In

fact, despite the assumptions and modifications made in the previous section, the

congestion management control cannot be activated, since for the given load the total

injected power from all installed inverters in the network does not cause high enough

reverse power flow to lead to congestion.

Thus, in order to evaluate the congestion management control, a modified network

topology of Elektro Gorenjska, as shown in Fig. 5.1, is used. However, in this simulation case

the rated power of all DRES is assumed to be considerably higher, compared to the rated

power of all inverters in the previous section. The corresponding data are presented in Table

5-3, while the data of the MV/LV transformer, as well as the unbalanced, time-varying load is

shown in Table 5-2, and in Fig. 5.2 and Fig. 5.3, respectively. Finally, in this case the MV side

voltage of the transformer is considered equal to 1 pu.

Table 5-3: PV units rated power for the case of high DRES penetration

Name Node

Rated

Power

(kWp)

Name Node

Rated

Power

(kWp)

MV LV 11 13 14 18 19 20 21 22 23 24 250.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

Node

Vo

lta

ge

Ma

gn

itu

de

(p

u)

No Control Local LF OL OLF Voltage threshold




31.08.2015 52

PV 1 13 15.4 PV 16 50 15.4

PV 2 14 17.6 PV 17 47 15.4

PV 3 18 17.6 PV 18 52 15.4

PV 4 20 8.8 PV 19 54 8.8

PV 5 22 15.4 PV 20 9 8.8

PV 6 24 15.4 PV 21 57 15.4

PV 7 25 15.4 PV 22 58 15.4

PV 8 4 8.8 PV 23 59 15.4

PV 9 5 15.4 PV 24 60 15.4

PV 10 6 6.6 PV 25 63 15.4

PV 11 7 15.4 PV 26 64 17.6

PV 12 8 15.4 PV 27 70 6.6

PV 13 31 15.4 PV 28 74 15.4

PV 14 41 15.4 PV 29 76 15.4

PV 15 45 15.4 PV 30 78 8.8

The total apparent power which flows during the day through the MV/LV transformer is

depicted in Fig. 5.16. In Fig. 5.17 a zoom over a specific time period from 10:00 to 16:00 is

illustrated, to clearly highlight the congestion events and the necessity of the congestion

management control scheme. By observing Fig. 5.17 it is evident that after 10:00 three

congestion events occur. In the first event the total apparent power is equal to 251.2 kVA,

while both OLF and OLFC can handle efficiently the problem. In fact, the result of both

control strategies is exactly the same, since the activation of the FPS control curtails

adequate amount of active power from the PV units and thus mitigates the problem without

the activation of the congestion management control. However, regarding the second and

the third congestion events, it is clear that the OLF control strategy cannot mitigate

efficiently the congestion issues. Thus, the congestion management control must be applied.




31.08.2015 53

Fig. 5.16: Transformer total apparent power vs. time

Fig. 5.17: Transformer total apparent power from 10:00 to 16:00

The total injected active power of all PV units is presented in Fig. 5.18 and Fig. 5.19.

Furthermore, the corresponding total curtailed active power for all the examined control

strategies is depicted in Fig. 5.20 and Fig. 5.21. Using the OL control scheme, the total

injected active power is maximized compared to the OLF and the OLFC control strategies.

0 4 8 12 16 20 240

50

100

150

200

250

300

Time (h)

Ap

pa

ren

t P

ow

er

(kV

A)

OL OLF OLFC Transformer Limit

10 12 14 160

50

100

150

200

250

300

Time (h)

Ap

pa

ren

t P

ow

er

(kV

A)

OL OLF OLFC Transformer Limit




31.08.2015 54

However, the implementation of the OL control cannot ensure a uniform active power

curtailment among the installed PV units. On the other hand, the OLF control results in a

uniform power curtailment, nevertheless reducing the total injected active power. Finally,

the proposed OLFC control scheme curtails higher amount of active power compared to OL

and OLF controls, however, its main advantage is the efficient mitigation of the congestion

issues. Finally, the total daily injected power and energy losses are depicted in Fig. 5.22 and

Fig. 5.23, where similar conclusions can be drawn.

Fig. 5.18: Injected active power of PV units vs. time

0 4 8 12 16 20 240

50

100

150

200

250

300

350

400

Time (h)

Act

ive

Po

we

r (k

W)

OL OLF OLFC




31.08.2015 55

Fig. 5.19: Injected active power of PV units from 10:00 to 16:00

Fig. 5.20: Curtailed active power of PV units vs. time

10 12 14 1650

100

150

200

250

300

350

400

Time (h)

Act

ive

Po

we

r (k

W)

OL OLF OLFC

0 4 8 12 16 20 240

20

40

60

80

100

120

Time (h)

Act

ive

Po

we

r (k

W)

OL OLF OLFC




31.08.2015 56

Fig. 5.21: Curtailed active power of PV units from 10:00 to 16:00

Fig. 5.22: Daily energy production

10 12 14 160

20

40

60

80

100

120

Time (h)

Act

ive

Po

we

r (k

W)

OL OLF OLFC

OL OLF OLFC2000

2050

2100

Control Scheme

En

erg

y P

rod

uct

ion

(k

Wh

)




31.08.2015 57

Fig. 5.23: Daily energy losses

Regarding the network losses, once again the high PV penetration results in a reverse

power flow during high generation periods. Thus, the active power losses are approximately

proportional to the square of the generation and present the same trend as shown in Fig.

5.24.

Fig. 5.24: Network losses vs. time

OL OLF OLFC70

75

80

85

Control Scheme

En

erg

y L

oss

es

(kW

h)

0 4 8 12 16 20 240

2

4

6

8

10

12

14

16

18

20

Time (h)

Act

ive

Po

we

r (k

W)

OL OLF OLFC




31.08.2015 58

The voltage profile of each network node is presented in Fig. 5.25. It is clear that all

control strategies can mitigate efficiently any possible overvoltages, ensuring the safe

operation of the network. Furthermore, the incorporation of the OLTC control ensures that

the voltages at the majority of the network nodes are less than 1.06 pu during the day. Thus,

the activation of the droop control, with the Vcpb indicated by the red dotted line, is avoided

and the total injected active power is maximized. The tap setting of the transformer is

illustrated in Fig. 5.26. It is clear that during the day only two tap changes are required for all

the examined control strategies.

Fig. 5.25: Voltage of the network vs. time

0 4 8 12 16 20 240.9

0.95

1

1.05

1.1

Time (h)

Vo

lta

ge

(p

u)

a) OL

0 4 8 12 16 20 240.9

0.95

1

1.05

1.1

Time (h)

Vo

lta

ge

(p

u)

b) OLF

0 4 8 12 16 20 240.9

0.95

1

1.05

1.1

Time (h)

Vo

lta

ge

(p

u)

c) OLFC




31.08.2015 59

Fig. 5.26: Tap setting of the transformer vs. time

5.3. Communication network performance evaluation

In Fig. 5.27 the different performance metrics of the communication network for both

wired and wireless communication technologies are calculated for a part of the examined LV

distribution grid.

0 4 8 12 16 20 24-3

-2

-1

0

1

2

3

Time (h)

Ta

p P

osi

tio

n

OL OLF OLFC




31.08.2015 60

Fig. 5.27: Overview of the examined grid using the GUI interface of the INCREASE

simulation platform

The examined grid contains 30 regular agents that control the PV inverters and 1

aggregator agent located in the transformer LV bus. The following parameters are used in

the simulations:

• Simulated time period: 1 hour

• Message size: The agents use ACL messages for their communication with an

envelope size of 200 bytes. On top of that the actual data values need to be added.

o A measurement sample from a regular agent sent towards the aggregator

contains values for the PV injection, the active power exchanged and the

voltage for each of the three phases, thus 12 values in total. Assuming 4 bytes

per measurement value, a total of 48 bytes of data is added to the packet

resulting in a total message of 248 bytes of application data.

o For a control message from the aggregator towards a regular agent one value

per phase is assumed, thus 12 bytes extra and a total message size of 212

bytes.

o Further it is assumed that the reply messages to measurement and control

messages do not contain extra data, thus their size is 200 bytes.




31.08.2015 61

• Frequency: Every regular agent sends a measurement message every minute, which

is acknowledged by the aggregator with a reply message and is immediately followed

by a control message from the aggregator which is on its turn acknowledged by the

regular agent. As a result, 4 application messages are exchanged every 60 seconds

between the aggregator and each of the regular agents.

For the evaluations presented in this section much details of the physical layer are not

taken into account, thus generic communication channels are assumed, but with realistic

data rates for different technologies. Results are compared for the 2 main transport

protocols TCP and UDP:

• TCP stands for Transmission Control Protocol. It is a connection oriented protocol

where messages are acknowledged when they arrive and are resent in case a

message gets lost. Messages are also rearranged at the receiver side in the order that

they were sent. It is suited for applications that require high reliability (e.g. web

browsing, file transfer, email).

• UDP stands for User Datagram Protocol. It is a more lightweight protocol without

guarantee that messages reach their destination and without reordering of packets.

It has a smaller header than TCP (8 bytes vs 20 bytes), so it is especially useful for

applications that require fast and efficient transmission (e.g. games, VoIP).

For the INCREASE control strategies TCP seems the most appropriate protocol, although

UDP could work as well since within the application layer reply messages are sent when

measurement or control messages are received. So even with UDP the communication could

be made reliable on application level, therefore the overhead from TCP to UDP is compared

in the simulations.

5.3.1. Wired communication network

In this case a wired communication network is assumed, where all regular agents and

the aggregator agent are connected to each other via a router, as shown in Fig. 5.28.




31.08.2015 62

Fig. 5.28: Overview of the simulated wired communication network

5.3.1.1. Throughput and channel utilization

The amount of data that is sent per time unit, i.e. the throughput, is first examined using

the settings described above. In Fig. 5.29 the results for TCP and UDP are shown. Logically,

the throughput on the link between the router and the aggregator is much higher than on

the links between the router and the individual regular agents as the former link transports

data from and towards 30 agents. The throughput towards the aggregator is the highest as

the measurement messages are a little larger than the control messages.

Furthermore, it is observed that the throughput for TCP is about 25 % higher than for

UDP. A first reason is because the TCP header is larger than the UDP header and a second

reason is that messages in TCP are acknowledged by additional ACK messages, as shown in

Fig. 4.6.

To calculate the utilization of the communication channel the throughputs in both

directions on a link are aggregated and divided by the maximum data rate of the channel. In

Fig. 5.30 the results for the most used link between router and aggregator are shown for a

few wired technologies: Standard Ethernet with 100 Mbps and 2 types of PLC with their

maximum data rates. As can be seen from the figure there is no problem to transmit all the

data. The maximum channel utilization in case of G3-PLC with TCP is about 15 %.




31.08.2015 63

Fig. 5.29: Throughput per link and direction

Fig. 5.30: Channel utilization for link between router and aggregator agent

In case lower data rates are used for PLC communication to make the communication

more robust, e.g. the most robust transmission rate of PRIME is 5.4 kbps, channel utilizations

close to 100 % are obtained. Thus, in case of larger networks with more agents or more




31.08.2015 64

frequent message exchanges, a pure PLC based network will not support the traffic anymore

and a different, e.g. hybrid communication infrastructure, would be needed. Part of the

communication network could still be based on PLC, e.g. to connect regular agents with the

router, whereas the link between router and aggregator should be replaced by technologies

with faster data rates like Ethernet or fiber. The optimal combination of PLC with other

technologies of course depends on the actual topology of the grid.

5.3.1.2. End-to-end delay

Next, the latencies for the messages exchanged between the agents are compared for

different technologies and the two transport protocols in order to assess if the information

reaches its destination always on time.

In Fig. 5.31 the results for the different communication technologies and transport

protocols are shown. The bars present the average delays for all messages that are received

at a certain agent for a unit of time (in this case within one minute as all messages

(measurement, control and reply messages are sent every minute). For regular agents this is

the average of 2 messages (a control message and a measurement reply message). For the

aggregator agent this is the average of 60 messages (30 measurement messages and 30

control reply messages). The whiskers show the minimum and maximum values.

Note that the y axis is logarithmic to show the values for Ethernet as these are very

small. Furthermore, note that as the channel utilization in all cases is well below 100 %, the

delays are always the same over the whole simulation period of 1 hour.




31.08.2015 65

Fig. 5.31: End-to-end delays

As it can be seen from the graph, the delay is always lower than 1 second. Thus, there is

enough time left for processing and sending back messages within the algorithm frequency

of 1 minute.

During the simulation, messages are simulated in order of number of agent, so that the

first message that arrives at the first agent as well as at the side of the aggregator has a very

low delay, whereas later messages have higher delays due the amount of traffic that is

generated at the same moment and corresponding waiting in queues. Therefore, for the last

regular agent all message experience some delay due to the other traffic which explains the

small difference between the minimum and maximum value.

5.3.2. Wireless communication network

In this section a wireless communication network is examined. In this case, the location

and the distance between the regular agents and aggregator agent is relevant. Since the

exact locations of the agents for the EG network are not known, some typical distances are

assumed in Fig. 5.32, varying from 10 m up to 750 m between the regular agents and the

aggregator agent.




31.08.2015 66

A simple abstracted wireless network is simulated with a transmission range that is high

enough for all regular agents to directly communicate with the aggregator agent and a

relatively limited data rate of 2 Mbps.

Fig. 5.32: Overview of the locations of the simulated agents

In Fig. 5.33 the recorded delays at the closest (Agent 8) and furthest located agent

(Agent 18) from the aggregator are shown assuming TCP communication. Compared to the

frequency of transmission of measurement messages and control messages, i.e. 1 minute, all

delays are very small, but the delays for the furthest agent are clearly higher than for the

closest agent. Similarly, in Fig. 5.34 the delays for all messages arriving at the aggregator

agent can be observed, where there is also a clear difference in delays mainly because all

messages are sent at the same moment, and thus some of them are kept up in queues

before they can be processed. In Fig. 5.35 and Fig. 5.36 the same results for UDP

communication are presented. In this case the delays are smaller than the corresponding of

the TCP communication.




31.08.2015 67

Fig. 5.33: End-to-end delays via TCP measured at 2 regular agents located at the closest

and furthest from the aggregator agent

Fig. 5.34: End-to-end delays via TCP measured at the aggregator agent




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Fig. 5.35: End-to-end delays via UDP measured at 2 regular agents located at the closes and

furthest from the aggregator agent

Fig. 5.36: End-to-end delays via UDP measured at the aggregator agent




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6. Conclusions

This report presents a detailed description of the Overlaying Control and of the

communication network simulator that is incorporated in the INCREASE simulation platform.

The full version of the Overlaying Control consists of the OLTC scheme, the congestion

control technique, and the FPS algorithm, which are developed to cope with overvoltages

and congestion problems, when integrating multiple DRES units in the distribution grid. The

combination of Local and Overlaying Control is also discussed, presenting the consecutive

implementation of both control schemes and the update of the various control parameters

in each simulation time interval. Furthermore, the actual incorporation of MAS in JADE

environment is analyzed, highlighting the bi-directional communication between the Core

and JADE components of the INCREASE simulation platform.

The performance of the full Overlaying Control is demonstrated on a selected pilot

installation and is compared to the cases of no DRES power curtailment as well as to the

Local Control scheme. Results show the efficiency of the proposed algorithm in mitigating

overvoltages by changing the OLTC state and enforcing a fair contribution of the curtailed

power among the installed PV inverters, while maintaining low levels of power losses in the

distribution network. Moreover, the efficiency of the congestion control technique is also

shown, assuming a high DRES penetration level.

Next, the LAN simulator component is presented aiming to enhance the features of the

INCREASE simulation platform, by focusing on the evaluation of the communication

infrastructure and of the requirements posed by the MAS control system and the

implemented control algorithms. The general framework and the different simulation

programs are described, while the simulation functionalities and features of the open-source

OMNeT++ software are further analyzed. The necessary operational functions and their

implementation in the INCREASE simulation platform are thoroughly discussed, while the

actual integration is presented, focusing on the interface between GUI, Core and LAN

components. The performance evaluation of the communication layer is conducted in the

examined pilot installation using the LAN simulator component, while assuming different

wired and wireless communication technologies.




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References

[1] X.-P. Zhang, “Fast three phase load flow methods,” IEEE Trans. Power Syst., vol. 11, no.

3, pp. 1547-1553, Aug. 1996.

[2] G. C. Kryonidis, E. O. Kontis, A. I. Chrysochos, C. S. Demoulias, D. Bozalakov, B.

Meersman, T. L. Vandoorn, and L. Vandevelde, “A Simulation tool for extended

distribution grids with controlled distributed generation,” in IEEE Eindhoven PowerTech

2015, Eindhoven, Netherlands, June 29 - July 2, 2015, accepted for presentation.

[3] B. Meersman, B. Renders, L. Degroote, T. Vandoorn, and L. Vandevelde, “Three-phase

inverter-connected DG-units and voltage unbalance,” Elec. Power Syst. Res., vol. 81,

pp. 899-906, 2011.

[4] B. Renders, W. R. Ryckaert, K. De Gussemé, K. Stockman, and L. Vandevelde,

“Improving the voltage dip immunity of converter-connected distributed generation

units,” Renewable Energy, vol. 33, pp. 1011-1018, 2008.

[5] B. Renders, K. De Gussemé, W. R. Ryckaert, and L. Vandevelde, “Converter-connected

distributed generation units with integrated harmonic voltage damping and harmonic

current compensation function,” Elec. Power Syst. Res., vol. 79, pp. 65-70, 2009.

[6] T. L. Vandoorn, B. Meersman, L. Degroote, B. Renders, and L. Vandevelde, “A control

strategy for islanded microgrids with DC-link voltage control,” IEEE Trans. Power Del.,

vol. 26, no. 2, pp. 703-713, 2011.

[7] D. Bozalokov, T.L. Vandoorn, B. Meersman, C. Demoulias, L. Vandevelde, “Voltage dip

mitigation capabilities of three-phase damping control strategy”, Elec. Power Syst. Res.,

vol. 121, pp. 192-199, 2015

[8] V. M. A. M. Mansoor, P. H. Nguyen, and W. L. Kling, “An integrated control for

overvoltage mitigation in the distribution network,” in IEEE PES Innov. Smart Grid

Tech., 2014.

[9] M. M. M. A. Viyathukattuva, P. H. Nguyen, W. L. Kling, A. I. Chrysochos, T. A.

Papadopoulos, and G. K. Papagiannis, “Fair power curtailment of distributed

renewable energy sources to mitigate overvoltages in low-voltage networks,” in IEEE

Eindhoven PowerTech 2015, Eindhoven, Netherlands, June 29 - July 2, 2015, accepted

for presentation.

[10] A. F. Gubina, A. Tuerk, and G. Taljan, “Policy framework conditions for provision of

ancillary services in a distribution network by distributed RES generation,” in IEEE

Eindhoven PowerTech 2015, Eindhoven, Netherlands, June 29 - July 2, 2015, accepted

for presentation.

[11] http://sourceforge.net/projects/electricdss/

[12] http://jade.tilab.com/

[13] http://inet.omnetpp.org/




31.08.2015 72

[14] http://www.mathworks.com/

[15] http://smartgrid.epri.com/SimulationTool.aspx

[16] P. Kadurek, J. F. G. Cobben, and W. L. Kling, “Smart MV/LV transformer for future

grids,” in SPEEDAM 2010, Pisa, Italy, June 14-16, 2010.

[17] P. Kadurek, J. F. G. Cobben, and W. L. Kling, “Future LV distribution network design and

current practices in the Netherlands,” in 2nd IEEE PES Innovative Smart Grid

Technologies, Manchester, UK, December 5-7, 2011.

[18] P. Bach Andersen, J. Hu, and K. Heussen, “Coordination strategies for distribution grid

congestion management in a multi-actor, multi-objective setting,” in 3rd IEEE PES

Innovative Smart Grid Technologies, Berlin, Germany, October 14-17, 2012.

[19] B. Asare-Bediako, W. L. Kling, and P. F. Ribeiro, “Future residential load profiles:

Scenario-based analysis of high penetration of heavy loads and distributed

generation,” Energy Build., vol. 75, pp. 228–238, 2014.

[20] K. Mets, J. Aparicio and C. Develder, “Combining power and communication network

simulation for cost-effective smart grid analysis,” IEEE Commun. Surveys Tutorials, vol.

16, no. 3, pp. 1771-1796, 2014.

[21] T. R. Henderson, M. Lacage, and G. F. Riley, “Network simulations with the ns-3

simulator,” in Proc. ACM Conf. of the Special Interest Group on Data Communication

2008 (SIGCOMM ’08), August 17-22, 2008.

[22] J. Bergmann, C. Glomb, J. Gö, J. Heuer, R. Kuntschke, and M. Winter, “Scalability of

smart grid protocols: Protocols and their simulative evaluation for massively

distributed DERs,” in Proc. 1st

IEEE Int. Conf. on Smart Grid Communications 2010

(SmartGridComm ’10), Oct. 2010.

[23] H. Lin, S. Sambamoorthy, S. Shukla, J. Thorp, and L. Mili, “Power system and

communication network co-simulation for smart grid applications,” in Proc. IEEE/PES

Innovative Smart Grid Technologies 2011 (ISGT ’11), 2011.

[24] A. Varga, “An overview of the OMNeT++ simulation environment,” in Proc. 1st

Int. Conf.

Simulation Tools and Techniques for Communication Networks and Systems

(SIMUTools ’08), Marseille, France, March 3-7, 2008.

[25] “INET Framework: An open-source OMNeT++ model suite for wired, wireless and

mobile networks”, https://inet.omnetpp.org/.

[26] J. Juarez, C. Rodrıguez-Morcillo, and J. A. Rodrıguez-Mondejar, “Simulation of IEC

61850-based substations under OMNeT++,” in Proc. of the 5th Int. ICST Conference on

Simulation Tools and Techniques. ICST (Institute for Computer Sciences, Social-

Informatics and Telecommunications Engineering), pp. 319-326, 2012.

[27] K. Mets, T. Verschueren, C. Develder, T. L. Vandoor, and L. Vandevelde, “Integrated

simulation of power and communication networks for smart grid applications,” in Proc.




31.08.2015 73

IEEE 16th Int. Workshop on Computer Aided Modeling and Design of Communication

Links and Networks 2011 (CAMAD ’11), Kyoto, Japan, June 10-11, 2011.

[28] C. Müller, H. Georg, and C. Wietfeld, “A modularized and distributed simulation

environment for scalability analysis of smart grid ICT infrastructures,” in Proc. 5th Int.

ICST Conf. on Simulation Tools and Techniques (SIMUTools ’12), Brussels, Belgium, pp.

327–330, 2012.

[29] T. Konnerth, J. Chinnow, D. Grunewald, and S. Kaiser, “Integration of simulations and

MAS for smart grid management systems,” in Proc. 3rd Int. Workshop on Agent

Technologies for Energy Systems (ATES 2012) at 11th Int. Conf. on Autonomos Agents

and Multi-agent Systems (AAMAS ’12), Valencia, Spain, June 5 2012.

increase – increasing the penetration of renewable energy

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