multi-dc mid term event, 19.09.2019 kriegersflak master ... · multi-dc mid term event, 19.09.2019...

MULTI-DC MID TERM EVENT, 19.09.2019

KriegersFlak Master Controller (MIO)

Overview and Demo

Dr. Rüdiger Franke, Carsten Dietrich, Marcin Bartosz, ABB

Kriegers Flak Combined Grid Solution – KF CGS

- Overview

- Grid/Assets

Operation

- Concept

- Challenges

MIO implementation

- ABB OPTIMAX® implementing Dynamic Optimization

- Function

October 21, 2019 Slide 2

Agenda

Detail


Kriegers Flak Combined Grid Solution

DEDK2

New 400 MW interconnector utilizing existingor to be build offshore wind power collectorgrids of the two TSO’s 50Hertz Transmissionand Energinet.dk

KFCGS interconnector

Power Flow



Total of +900 MW wind infeed

220/150 kV 450 MVA transformer

380/150 kV Back to Back converter:

– 400 ±MW

– 100 ±MVAr

KFCGS interconnector

TRBtB

TR

Baltic 2

Baltic 1

Bentwisch

BentwischWind

Bjaeverskov

IshojTolstrupGaarde

KriegersFlak A

KriegersFlak B

KF Extension

600MW

220 kV

400MW

150 kV

Operation concept



– Wind power has prioritized access to the grid

– Interconnector is used to provide remainingcapacity of the wind farm connections to theelectricity market

Priorities

MW

1 Year

Wind

Ci

Cw

Market

Topology to be considered



Asset No. Considered

Cables 12

Transformers 12

Busbars 18

Reactors 15

Circuit breakers 50

Back-to-Back converter 1

MSCDN 1

Wind farms 4

TRBtB

TR

Baltic 2

Baltic 1

Bentwisch

BentwischWind

Bjaeverskov

IshojTolstrupGaarde

KriegersFlak A

KriegersFlak B

KFE

Operation concept



1. Forecast available transfer(market)capacity at KFE (POR)

Regulation steps

Pcap?

P

P

P

P

Operation concept



1. Forecast available transfer(market)capacityat KFE (POR)

2. Set BtB power order according to agreedmarket flow and wind power of Baltic 1 and2

3. Adjust BtB power order if POR deviates dueto forecast errors of Baltic 1 and/or 2

4. Adjust BtB and/or limit wind farms if anycomponents are overloaded due to:

- Forecast errors or;

- Contingencies

Regulation steps

TRBtB

TR

Baltic 2

Baltic 1

Bentwisch

BentwischWind

Bjaeverskov

IshojTolstrupGaarde

KriegersFlak A

KriegersFlak B

KFE Ptrade

Plim

Plim

Plim

Plim

P?

Operation concept



1. Forecast available transfer(market)capacity at KFE (POR)

2. Set BtB power order according to agreedmarket flow and wind power of Baltic 1 and2

3. Adjust BtB power order if POR deviates dueto forecast errors of Baltic 1 and/or 2

4. Adjust BtB and/or limit wind farms if anycomponents are overloaded due to:

- Forecast errors or;

- Contingencies

5. Adjust V reference of BtB and provide Vreference remommendations for windfarms, in order to keep Q exchange at KFE

Regulation steps

V?

+-40 Mvar

V?

V?

V?

V?

TRBtB

TR

Baltic 2

Baltic 1

Bentwisch

BentwischWind

Bjaeverskov

IshojTolstrupGaarde

KriegersFlak A

KriegersFlak B

KFE

Overview

MIO interfaces


Interfaces

Overview

MIO implementation


– Real-time data processing and evaluating of P/Q references to control active and reactive power flow through the interconnector

– Optimal power flow calculation (OPF) based on CGS grid model

– Predictive and forecast functions

– Complete delivery of hardware and software for MIO

The MIO power flow optimization is based on ABB‘s OPTIMAX® PowerFit

Challenge and solution



Situation

Germany and Denmark interconnect theironshore transmission systems through HVACcables with 4 offshore wind farms and a Back-to-Back converter station.

To manage the optimal power flow (OPF) in theCombined Grid Solution (CGS) a controller(MIO) is required, which incorporates thecomplete offshore grid, the wind farms and theconverter station with its controlling functions.The following shows a MIO OPF cycle.

CalculateSystem/grid

model

Calculate real-time & predictive

OPF

Distributesetpoints

Physicalresponse of CGS

Measure andUpload all Inputs


ABB OPTIMAX® – standardized model-based applications

FMI (Functional Model Interface) for deployment (www.fmi-standard.org)

Modelica for application engineering (www.modelica.org)

Power Plants Power Pools

IPC Server Cloud

Basis for several OPTIMAX applications

Renewables

OpenModelica features

• Use of pre-built Modelica libraries

• Graphical composition of application model

• Model translation to fast executable C++ code

Model characteristics

• Equations/Variables: 4722

• Non-triviial equations: 788

• Size of core equation system: 81

• Model inputs u, z: 144

• Model outputs y: 335

Model-based predictive optimization

• Optimize power flows over K time steps,e.g. 96 steps for 15min intervals of one day

• Real-time need: solve time steps in parallel

Basing on PowerSystems library using dq phase system (RMS)


Modelica system model

Dynamic Optimization

For dynamic system model and sample time points tk , t0 < t1 < … < tK

find control u (and/or initial states x(0)) that minimize criterion Jsubject to mixed discrete/continuous model, initial conditionsand further constraints g

FMU MEJ = , ( )( ) , ( )

( ) → min(0)( ), (0)

( )

+ 1 = , , , , 0 = , = 0,1, … ,

= , , , , = , ∈ ,

, , ≥ 0

Treat optimal control programs basing on simulation models

FMI

= ℎ , , , , = 0,1, … ,

Numerical treatment


Dynamic Optimization problem

Optimize over time horizon

Degrees of freedom: optimal control u(t) and possiblyinitial states x(t0)

Numerical treatment:

• parameterize control trajectories:u(t) = u(uk), k=0,1,…,K-1

• Treat constraints and state equations in discrete timeCollocation: implicit state equations

Multistage: explicit state equations

• Collect all discrete-time control and state variables inone large vector of optimization variables

Alt. simple treatment:

1,,0,

),(

),,(0

0)(1,,0,0),(

..

min),()(

1

1

,

1K

0k00 0

-=

ïïî

ïïí

ì

-

=

³

-=³

¾®¾+=

+

+

-

=å

Kk

xuxf

xuxf

xcKkuxc

ts

uxfxfJ

kkkk

kkkk

KK

kkk

ux

kkKk

K

K

Collocation:

Multistage/shooting:= ( , , , , … , , , )

= , , … ,

∈ [ , ]

21.10.2019

Describe control trajectory withcontrol parameters

Introduce initial states of each interval asoptimization variables

Parallel solution of initial value andsensitivity problems for each interval

Treat junction conditions betweenintervals as optimization constraints

Parallel Optimization with control vector parameterization (parallel multiple shooting)

MIO model-based control solving Optimal Power Flow in real-time


ABB OPTIMAX® applied to real-time control

CGS MIO OPF RT

Iterations

OPF Constraintsand Targets

Tuned ModelInputs (u)

Measurements (z)CGS Model

Optimized Variables (y)

Model variables

Measurements z

– Status of switches and tap changers

– Actual voltages and power flows

– Actual line loadings

Tuned Model Inputs u

– Controlled voltages and power flows

Optimized Variables y

– Active power set point of HVDC

– Active power limitations of wind farms

– Voltage / reactive power set points

OPF Constraints and Targets

– Equipment limits, e.g. line loadings

– Target power flow, power losses

Summary

MIO implementation


Functions

Control

– P and V references to BtB and wind farms

Monitor

– Topology of CGS

– Asset loading

– Power exchange between both TSOs

Predict

– Power flow forecasts and grid restrictions

Advice

– Warnings/alarms

Import

Process DB

OPF PredictOPF Predict

OPF Predict

OPF PredictOPF PredictOPF RT

Interface

Export

Process DB

OPF PredictOPF Predict

OPF Predict

OPF PredictOPF PredictOPF RT

Interface

OperatorOperatorWind prod. Forecasts,

scheduled powertransfer

Reports, predictions

Summary

MIO implementation


– Designed and implemented by ABB

– Based on ABB OPTIMAX PowerFit

– Optimal power flow calculation in real time using grid model developed by ABB (Modelica)

– Verification using customers system simulation model (PowerFactory)

– Geo-redundant servers

– IEC 60870-5-104 communication

Highlights

multi-dc mid term event, 19.09.2019 kriegersflak master ... · multi-dc mid term event, 19.09.2019...

Documents