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Barcelona 12-15 May 2003

Session 5 – Block 2

Geographical Information System and Geographical Information System and Genetic Algorithm based planning tool for Genetic Algorithm based planning tool for

MV distribution networksMV distribution networks

Minea Skok, Davor Skrlec, Slavko Krajcar

Faculty of Electrical Engineering and Computing

Department of Power Systems

University of Zagreb

Croatiaemail:caddin@zvne.fer.hr

2Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Presentation outlinePresentation outline

introductory remarks on use of evolutionary algorithms (EA) in distribution systems

CADDiN - GA application in long-term large-scale urban distribution network planning

3Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Evolutionary algorithmEvolutionary algorithm

computer-based problem solving systems which model evolution mechanisms …

genetic algorithms

evolutionary algorithms

evolution strategies

genetic programming

classifier systems

4Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Why interest in EA?Why interest in EA? well suited to deal with problems with …

integer variables

non-convex functions

non-differentiable functions

domains not connected

multiple local optima

multiple objectives

fuzzy data, etc.

5Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Surveys on application of EA in power systemsSurveys on application of EA in power systems

V. Miranda, D. Srinivasan, L.M. Proenca, Evolutionary computation in power systems, Electrical Power & Energy Systems, Vol.20, No.2, 1998, pp. 89-98.

D. Srinivasan, F.S. Wen, C.S. Chang, A.C. Liew, A survey of applications of evolutionary computation to power systems, Proceedings of ISAP’96, Orlando, USA, 1996, pp. 35-43.

J.T. Alexander, An indexed bibliography of genetic algorithms in power engineering, Report 94-1-Power, Department of Information Technology and Production Economics, University of Vassa, Finland, February 1996.

M.A. Laughton, Genetic algorithms in power system planning and operation, IEE Coloquium on Artificial Intelligence in Power Systems, IEE Digest No. 075, London, UK, 1995, pp. 5/1 -5/3.

6Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Application of EA in distribution systems Area Field GA ES EP GP Hybrid

Expansion planning

distribution X X GA+Fuzzy

VAr planning, capacitor placement

X X

Distribution operation

Loss minimization, switching

X

Fault diagnosis X GA+NN

Service restorationX GA+Exp.Sys.

PGA

Load management X

Load forecasting X X GA+NN

Analysis Harmonics X

7Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

CADDiNCADDiN – – CComputer omputer AAided ided DDesign of esign of DiDistribution stribution NNetworksetworks

Geographical Information System Extensions

Preparing necessary data – collecting, converting, calculating

Interpreting and analyzing the expansion planning results.

Evaluating different expansion alternatives

Analizing the existing DS –distribution of load, transfer capability of existing cable system capacity limitations and supply areas of substations,etc.

Load forecasting

Optimization modules

1. urban areas (EA):

• open-loop• link (connective, clasp)

2. rural areas

8Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Link distribution networksLink distribution networks

planned link distribution network

3 HV/MV substations 1 switching station

tick lines – feeders

thin lines – possible routing corridors (GIS)

9Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Evolutionary algorithm – functions

optimal feeders routing

switching station & HV/MV substations sitting and sizing

service areas of HV/MV substations

contingency switching (tie-lines)

10Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Evolutionary algorithm – objectives

minimal capital investments

new substations and transformers costs costs of new feeder sections costs of adding new feeders to supply & switching

stations

minimal power and energy losses costsminimal maintenance costs

11Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Evolutionary algorithm – constraints

voltage drop

loading limits

contingency margin rules

network layout

number of feeders emanating from HV/MV and

switching station

the total load with each link

the total number of MV/LV substations per link

12Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Evolutionary algorithm – codingEvolutionary algorithm – coding

load pointsupply substation

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Preprocessing of link’s routes

chromosome 14 12 15 8 9 13

decoding

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The result of the first step of the decoding procedure

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The result of the second step of the decoding procedure

13Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

Evolutionary algorithm – operatorsEvolutionary algorithm – operators

Crossover

fragment reordering crossover (FRX)cycle crossover (CX)

Mutation

order based mutation (OBM)

14Minea Skok, Davor Skrlec, Slavko Krajcar Croatia Session 5 – Block 2

Barcelona 12-15 May 2003

caddin@zvne.fer.hrTel: +385 1 6129 907Fax: +385 1 6129 890

Department of Power systemsFaculty of Electrical Engineering and ComputingUniversity of ZagrebPP 148Zagreb HR-10000Croatia

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