« EMR and inversion-based control of a multi-source … · Gas Turbine PMSM Rectifier DC ... 10 «...

EMR’14CoïmbraJune 2014

Summer School EMR’14“Energetic Macroscopic Representation”

« EMR and inversion -based control of a multi-source power plant »

Dr. Xavier KESTELYN, Mr. Oleg GOMOZOV, Dr. Frédéric COLASL2EP, Arts et Métiers ParisTech, France

EMR’14, Coimbra, June 20142

« EMR and inversion-based control of a multi-source power plant »

- Outline -

1. Introduction

2. Deduction of a Hierarchical and Predictive contro l structure of a multi-source power plant

3. Implementation and tuning of a reduced-order mode l predictive controller

4. Conclusion and perspectives



« INTRODUCTION »



- Introduction -

Distributed generation, which generates electrical energy from many smallfacilities, could be considered as a good solution for reducing environmentalimpacts.

The increasing level of renewable energy and energy storagesystems indistributed energy architectures imposes advanced and efficient controlschemes that can cope with non controllable power systems and low maximumpower generation systems (maximum power limits are often reached ).

Model predictive control, able to manage with non controllable sources andpower limits, is then well adapted.



- Introduction -

We propose to deal with theactive power control of amulti-source power plantcomposed of:- A micro gaz turbine (30kW

peak)- A bank of supercaps

(30kWpeak-10kWmin)- Photovoltaic panels (17kW

peak)

PMSMAir

Fuel

Exhaust

gases

Grid

F

i

l

t

e

r

PGridref

F

i

l

t

e

r

Super

caps

F

i

l

t

e

r

Photovoltaic panels

Loads

PGrid

PGT

PSC

PPP

F

i

l

t

e

r

F

i

l

t

e

r

Micro

turbine

Overall control



- Introduction -

The number of variables to control is too important to use a centralized controlsufficiently scalable which could be able to compute all references in real-time.

We propose to control the system using a three layer hierarchical structure:- The first layer of the control structure is deduced by the inversion of the EMR ofthe system.- The second layer is composed of local strategies often based on power balances.- The third layer is a model predictive control of a reduced order model of thesystem.



« Deduction of a Hierarchical and Predictive control structure of a multi-

source power plant »



- First step: EMR of the system -

GT

gm.

gtT

gtΩ

gtΩ

emT

mser

msir

msir

recvr

Gas Turbine PMSM RectifierDCBus Inverter Filter

PVpvi

pvv fili chopv gridvr

Photovoltaic panels

PVBus Chopper

DC Bus Inverter FilterFilter

pvv fili

busV

busV

busV

busV

gridvr

gtiv

recmr

invmr

invmr

chopm

Grid

SCsci

scv

ChopperDC Bus Inverter FilterFilter

fili

busV

busV

Supercaps

chopi

vsii

vsivr

reci

vsii

vsivr

gtiv

pviv

pviv

chopi

vsii

vsivr

sciv

sciv

gridvr

gridvr

gridir

chopvchopm invm

r



- Second step: Local Control Structures and Strategi es -

PVpvi

pvv fili chopv gridvr

Photovoltaic panels

PVBus Chopper

DC Bus Inverter FilterFilter

pvv fili

busV

busV

invmr

chopm

Grid

chopi

vsii

vsivr

pviv

pviv

gridvr

ifil* vchop

*vpv*

vpv*

ipv

vpv

MPPTstrategy

*busV

PFCstrategy

*RMSpvI −

*pviv

*vsivr

Exemple: Practical Control Structure deduction of the PV system

Busstrategy

gridir



- Third step: Reduced models and Predictive Control -

It is not possible to use theexact model of each controlledsubsystem to apply a modelpredictive control in real-time.A reduced order model foreach controlled subsystem isthen deduced.

PMSMAir

Fuel

Exhaust

gases

Grid

F

i

l

t

e

r

PGTref

F

i

l

t

e

r

Super

caps

PSCref

F

i

l

t

e

r

Photovoltaic panels

Loads

PGrid

PGT

PSC

PPP

F

i

l

t

e

r

F

i

l

t

e

r

Micro

turbine

Gaz turbine control

Reduced-

Order Model

Predictive

ControlSupercaps control

PV control

Gaz turbine strategy

Supercaps strategyl

PV strategy

PGridref

PGTest

PSCest

SOCSCest

PPVest




For the sake of simplicity, each controlled system is considered as a first ordersystem or a random source.

sP

P

GTref

GT

estGT

τ+=

1

1

F

i

l

t

e

r

Super

caps

PSCref

PSC

F

i

l

t

e

r

Supercaps control

Supercaps strategy

PSCest

SOCSCest

sP

P

SCref

SC

estSC

τ+=

1

1

estSC

SOCinitSC

estSC P

s

kSOCSOC −=

refGTP est

GTP

refSCP est

SCP




For the sake of simplicity, each controlled system is considered as a zero or firstorder system.

)(trandomP estPV =

The reduced-order modelpredictive controller can thenbe implemented and tuned.

PV

estPVP



« Implementation and tuning of the reduced -order model predictive

controller »



- Implementation of the Model Predictive Controller-

A model predictive controller give an optimal solution to a problem underconstraints over a prediction horizonp.




In our case the problem consists in minimizing at each instant i over a predictionhorizonN the cost functionJ:

Output vector: Input vector:

Q, R – positive semi-defined weights matrices

Reference vector:With:

=

iSC

igridi

SOC

Py

=

iSC

igridi

SOC

Pref

*

*

=

iSC

iGTi

P

Pu

( ) ( ) ( )∑+=

=

+−−=

Nni

ni

iiTiiiiTii uRuyrefQyrefJ




The chosen parameters are:

9.03.0

10

300*

*

≤≤

≤∆≤

≤≤

estSC

GT

GT

SOC

kWP

kWP

For the constraints:

=

=

00

00

1.00

01

R

Q

For the weights:

Initial states:

3.0=initSCSOC

Tracking ofpower referenceis favoured

Cost of systeminputs areignored

Supercaps are considered atthe minimum level of energy

Controller parameters:

10

10

02.0

==

=

predictionControl

predictionHorizon

stimeSampling



- Simulation results -

Grid power reference tracked

Max SOC not exceeded

Min SOC not exceeded

Average SOC around 0.6

Gas turbine power capacities not exceeded

Supercaps power capacities not exceeded

PV power is random and acts as a disturbance

+



« CONCLUSION AND PERSPECTIVES »



- Conclusion and Perspectives -

The EMR is a good tool to structure the model and then to help the controldesigner to find a suitable control structure.

The different variables than can be manipulated via local strategies are exhibited.

The model reductions, often necessary to implement a globalstrategy (as ModelPredictive Control based strategies), are simplified.

As a perspective, the control of reactive power is planned and a custom MPCcode is under construction in order to optimally control systems with specialfeatures.



« BIOGRAPHIES AND REFERENCES »



- Authors -

Dr. Xavier KESTELYN Arts et Métiers ParisTech, L2EP, FranceAssociate Professor HdR in Electrical EngineeringPhD in Electrical Engineering at University of Lille1 (2003)Research topics: Control of multi-input electromechanical systems with coupled dynamics, EMR

Mr. Oleg GOMOZOV Arts et Métiers ParisTech, L2EP, FranceMaster student in Electrical EngineeringEngineering degree in Industrial Heat Power Engineering (2011)Research topics: energy management and control systems, model predictive control and modeling of hybrid and multi-domain systems

Dr. Frédéric COLAS Arts et Métiers ParisTech, L2EP, FranceResearch Ingenior in Electrical EngineeringPhD in Automatic Control at Ecole Centrale de Lille (2007)Research topics: Power Systems and Grids



- References -

[1] L. Xie, Y. Gu, A. Eskandari, and M. Ehsani, «Fast MPC-Based Coordination of Wind Power and BatteryEnergy Storage Systems», Journal of Energy Engineering, p.138, 2, с. 43–53, 2012.

[2] M. Trifkovic, M. Sheikhzadeh, K. Nigim, and P. Daoutidis, «Modeling and Control of a RenewableHybrid Energy System With Hydrogen Storage», IEEE Transactions on Control Systems Technology, p.22, 1, с. 169–179, Jan. 2014.

[3] W. Qi, J. Liu, X. Chen, and P. D. Christofides, «Supervisory Predictive Control of Standalone Wind/SolarEnergy Generation Systems», IEEE Transactions on Control Systems Technology, p. 19, 1, с. 199–207, Jan. 2011.

[4] P. Li " Design formalism for the supervision of dispersedmulti source and hybrid power systems:Application for the management of microgrids " 19-06-2009,PhD of Ecole Centrale de Lille-France.

[5] L.Chalal « Coordination de systèmes multisources pour favoriser la production d’énergie électriquerenouvelable ». 14-03-2013, PhD of University Lille1-France.

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