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Object-Oriented Simulation Software for a Competitive EnvironmentApplication to Transmission Expansion Planning

Rodrigo Palma B., Luis S. Vargas, Oscar Moya A.Departamento de Ingeniera Elctrica

Universidad de ChileAv. Tupper 2007, Santiago, Chile

[email protected] , http://146.83.6.6/bdmc

Abstract: During the past two decades, liberalisation of

electricity sectors has taken place in many countries,

changing significantly the general industry framework.

Existing simulation software tools have had to be ad-

justed in order to include the new industry require-

ments. This paper links the object-oriented approachwith the new energy market structure focusing on the

transmission expansion problem. A simulation software

using JAVA-technology, called Deep-Edit, is developed

to solve this problem in both, an interactive and an

computer aided way. A dynamic transmission planning

methodology (DTPM), using genetic algorithms, is de-

veloped inside Deep-Edit with the purpose of determin-

ing an economically adapted electric transmission system

in a competitive open access environment.

Keywords: object-oriented programming, transmissionexpansion planning, market simulation, Deep-Edit, geneticprogramming, JAVA.

I. INTRODUCTION

An important consequence arising from the development ofcompetitive electricity markets is its impact on classicalanalysis tools. Whereas in supply structures with verticallyintegrated utilities the system operation function is welldefined, in competitive structures there is a need to incorpo-rate market behaviour into system operation. Existing

simulation software tools have to be adjusted in order toinclude the actors and their impact on power system opera-tion and planning [1-4]. In this research field, the object-oriented programming [5] (OOP) approach has gained widespread importance and acceptance in software developmentdue to its advantages concerning flexibility, expandability,maintainability, and data integrity [6-8].

Power transmission, with open and third party access, is acentral aspect in competitive electricity supply sectors withcompetition in the generation and trading sectors. A neces-sary condition for competition is that generators are able toreach consumers through the transmission network. Thiscan be achieved through open access schemes and a trans-mission expansion planning that integrates market behav-iour in an explicit way [9,13]. The paper assumes a basicknowledge on object oriented programming and geneticalgorithms [5,12].

II. SYSTEM MODELLING

The modelling approach presented in this paper can besummarised as follows [7,8,12,13]. The system repre-

sentation is based on physical power system objects inthe network database (NDB) and on hydrosystem

components in the hydro database (HDB). The marketdatabase (MDB) contains market related objects likemarket actors and contracts. Due to their high hydrogeneration dependency, an accurate HDB representa-tion is an important issue in countries like Chile andBrazil. The individual characteristics of network,hydro, and market elements are described by objectattributes and the information exchange between ob-jects is performed by messages following the object-oriented programming paradigm (fig. 1). The objectmodelling technique in [5] has been used for develop-ing the object models presented in this paper.

Figure 1: Object Model of the System

In the OOP terminology, a generalisation of a dataobject along with its data variables and methods is aclass of data objects. The data variables are referred toas class attributes and an instance of a class is called anobject. The concept of inheritance makes it possible todefine subclasses of a class that share characteristics ofthe parent class.

Network Database (NDB)

Figure 2: Hierarchy Chart of NDB

Figure 2 shows the hierarchy chart of the NDB. NDBcomponent is the most general class and its attributesand methods are available for all subclasses [12]. Sincesimulation models are typically based on a no-de/branch-representation, these classes are explicitlyincluded in the object-oriented data model. The class

Branch is a child-class of the abstract class 2-Poleand it contains all branch facilities having an impe-

: inheritance

SystemComponent

NDBComponent

MDBComponent

HDBComponent

... ... ...

1-Pole 2-Pole

Node Injection Compen-sationLoadGround

NetworkFeeder

Asynch.Machine

Branch Switch

LineTrans-formerGene-rator

Plan. Object

NDBComponent

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Object-Oriented Simulation Software for a Competitive Environment 3

dance like transmission lines and transformer sub-classes. A main advantage of the object-oriented net-work model is that the model can be easily expandedby introducing new classes for network facilities likeFACTS-devices. A planning object allows the repre-sentation of an expansion planning alternative througha group of NDB-Objects with specific planning attrib-utes [12]. NDB-Objects store electrical location infor-mation in attributes. The potential network topologycan be described using these references.

Hydro Database (HDB)

Figure 3: Hierarchy Chart of HDB

Hydraulic power generation systems can be repre-sented in a simplified way by five main classes (Afflu-ent, Link, Run of River, Series, and Reservoir Units)and an abstract Hydro Unit generation class, asshown in figure 3. Connections between hydro unitsand affluent are determined by Link objects. ForHydrothermal co-ordination models a splitting of runof river plants in series and isolated units is recom-mended.

Market Database (MDB)

Figure 4: Hierarchy Chart of MDB

The market database is an object-oriented representa-tion of the electricity market place. In figure 4 thehierarchy chart of the MDB is shown. The classes aredivided into three main categories: market actor, sys-tem operator (ISO/RTO) and contracts. At present, thethree classes, customer, supplier, and energy bro-ker/trader represent actors behaviour of the electricitymarket. Typical methods related to market actors areselect supplier for the class customer and makeoffer for the class supplier. The independent system

operator (ISO) is also included in the MDB. In contrastto the other actors, the ISO is not viewed as a market

actor. It is responsible of a reliable system operationand co-ordination.

One of the main advantages of the object orientedapproach is that additional market actors and contracttypes can be implemented in an easy way. Further-more, the methods related to the classes can be easilyadjusted to the given access policies and the existingregulatory framework.

Relationship Between Objects

Several classes of each database, NDB, HDB, andMDB, are closely related. A direct relationship be-tween objects from different databases occurs through

references to objects, which are given as attributes ofthe individual classes in following forms:

suppliers own or manage generators and/or net-work-feeders,

suppliers own or manage hydraulic units, customers own or manage loads, the ISO object has a reference to sub-networks

(container of objects), which means that it has in-formation about all network objects in its controlarea and about the actual network topology,

hydro units processed water is electrically gener-ated by generators and/or network-feeders.

These references define information that is directlyavailable to the different objects. Therefore, the sup-plier has, for example, direct access to the technicalparameters of the generators that it is representing.

Market Behaviour Object Oriented Model

World-wide market organisation of the power industry

presents a wide range of arrangements, that may beclassified in the following three basic categories [8,12]: Pool market, market based on physical bilateralcontracts, and market based on financial bilateral con-tracts. Actual markets throughout the countries corre-spond to a mix of these categories.

Figure 5: Pool Market Object Model

Figure 5 shows the object model of a Pool based power

market. Suppliers and Customers, with their respectiveNDB related objects, exchange technical and eco-

HydraulicComponent

Affluent

SeriesUnit

ReservoirUnit

HydroUnit

Link

IsolatedRun of River

MarketComponent

MarketActor

Supplier. WheelingService

AncillaryServices

Use ofInfrastructure

ContractISO / RTO

Customer BrokerTraderBilateralContract

Market Operator

Supplier

System Operator

: aggregation

Customer

NetworkFeeder

Gene-rator Load

: multiple

Offers,costsT

echnicalparameters

Technicalparameters

managemanagemanage

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nomic information with the System and Market Op-erator. A direct relationship between Customers andSuppliers is not allowed under the Pool approach. Aco-ordinated work between Market and System op-erator defines the final operation schedule of the powersystem, typically for the next day in a day ahead mar-ket.

III. DEEP-EDIT SIMULATION SOFTWARE

A general purpose simulation software, called Deep-Edit was developed and implemented using JAVAtechnology. Its general structure and the client/serverarchitecture are shown in figure 6. Deep-Edit is builtbased on the object-oriented representation presented

in section II.

Figure 6: Deep-Edit Client-Server Architecture

The Object Oriented Database constitutes the core ofthe application. Source-file and specific Network,

Hydro and Market editors allow users to interact withthe system information and options. Server-socketconnections do the information exchange betweenInternet users and the system.

Figure 7: Object Oriented Model of PSAA

The software package contains a library of PowerSystem Analysis Applications (PSAA) including Load-

Flow calculation, Optimal-Power-Flow, pricing mod-els, market simulation applications, etc.. All theseapplications use the information stored in the object-oriented database. Figure 7 shows the object orientedgeneral model of PSAA. These applications specialisein Market and Network Analysis Tool classes. Usu-ally, Market Analysis Tools like a Pool market simula-tion make use of several Power System Tools in eachsimulation step.

IV. TRANSMISSION EXPANSION PROBLEM

Allowing and promoting the development of competi-tive markets in generation and commercialisation ofelectrical energy is increasingly accepted as the main

objective of transmission expansion planning in thenew market structure. In this context, the cost minimi-sation paradigm changed to the ability of transmissionplanners to create non discriminatory open accessconditions at minimum cost with predefined minimumsecurity and quality levels [10, 12].

For the midterm time horizon (2-10 years) transmis-sion expansion can be formulated as a deci-sion/optimisation problem with the following charac-teristics: multiobjective function, continuos & integervariables, dynamic decisions, high dimensionality, nonlinearity, non convexity, and multiple uncertainties.

The optimisation variables of this problem are definedin this work as the entry period of a planning projectin the system (see figure 2), that internally represents agroup of Branch objects. The model assumed theexistence of a set of possible and well defined planningprojects. Nevertheless, the high complexity of solvingthis optimisation problem using the current computa-tional capabilities is recognised by several authors

[12].

In order to deal with these limitations, this work identi-fies the need of using a combination of an interactiveand a computer aided approach.

Interactive Transmission Expansion Planning

The interactive transmission planning system takes fulladvantage of the Deep-Edit platform presented insections II and III. It allows the following capabilities:

easy edition of technical and market data of thesystem for the study time horizon,

detailed market and network operation analysis ofthe existing system using the PSAA library,

definition of alternative transmission expansionplans using expert knowledge,

detailed comparison of alternative expansionalternatives in order to give the final recommen-dation,

flexible incorporation of new analysis tools to the

PSAA library resulting from the OOP approach.

NDB

Client & Server

Client

Server

Client & Server

Client

ClientClient

Hydro Editor

Client

MDB HDB

Object Oriented Database

PSAA Library

Market Editor

Network Editor

Man-MachineInterface

Source/DataFiles

Source/DataFile Editor

External Event

GeographicInformationSystem GIS

Event Processand Update

Analysis Tools

Sub-network

ControlArea

initialize (abstract) execution (abstract) save results

Market AnalysisTool

MDBObject

utilize utilizeutilize

PoolMarket

Marketof Bil. Cont.

. ..

execute

execute

..

.

utilize

initialize

. ..

PricingModel

execute

. ..

OPF

Sensitivityanalysis

. ..

execute

execute

. ..

PowerFlow

execute

. ..

..

.

Network AnalysisTool

initialize

NDBObject

own own

HDBObject

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Computer Aided Transmission Expansion Planning

The computer aided transmission expansion planningfollows the model presented by the authors in [8, 12].Due the complexity level of the general problem, sim-plifications in the system representation and simulationtools were applied. A dynamic transmission planningmethodology (DTPM), using genetic algorithms, isdeveloped for the purpose of determining an economi-cally adapted electric transmission system in a com-petitive open access environment. DTPM makes use ofPSAA in tasks like classic load flow calculation, eco-nomic dispatch, and sensitivity analysis. Figure 8shows the proposed DTPM.

Planning Problem

Expansion Plan

of Lines and

Transformers

Impact of the Decision

on Selected Objective

Functions

Master

Problem

Slave

Problem

Branch Object

Genetic Coding

Optimization

Variables

Entry Period

Simulation Time HorizonN Bits

Number of OPT- andUPD-Type

M Variables Code Length

NxM Bits

Investmet Costs

Operation Costs

Power Losses

Unserved Energy

Wheeling Income

Network Constrains

Voltage Quality

Electrical Parameters

Planning Type:

FIX, OPT, UPD

Suggested?

Investment Costs

Lifetime

Topological Information

Min. Entry Period

Max. Exit Period

Figure 8: Dynamic Planning Methodology

The investment decisions represents the Master Prob-lem in the DTPM while its impact is evaluated througha Slave Problem according to the selected objectivefunction (see figure 8). All the characteristics of aplanning project are stored in the attributes of theincluded Branch objects. A set of Planning Proj-ects defines a genetic code in a unique way. EachPlanning Project uses a segment of the whole ge-netic code and is decoded as its entry period in thesystem [12].

Figure 9 shows the flow diagram of the DTPM. For theproblem formulation, the information stored in theobject oriented database (see figure 6) and the generalruntime options are used. The initial population is builtbased on a sensitivity analysis of the proposed solutiongiven by the expert and it considers additional ran-domly created expansion plans.

System Description Parameters

NDB Horizon Genetic Param.

Initialize

PopulationSensitivity

Analysis

Random Num.

Generator

Individuals EvaluationSimulation

Tool

Selection

Recombination

Mutation

Evaluation of New Individuals

Population Creation

Convergency No

Yes

Results

Decision

Modul

Best Plans Multiobj. Evaluation

HDB MDB

Figure 9: DTPM Flow Diagram [12]

Well defined recombination masks and mutation ratesare used within a standard genetic algorithm proce-dure. A combination of Network and Market analysistools, conforming a simulation framework, are used forthe evaluation steps. After convergence is reached, thebest expansion solution can be saved for the purpose offurther detailed evaluations with the interactive plan-ning tool. DTPM is incorporated as part of the PSAAlibrary of Deep-Edit.

Simulation Results

Figure 10: 3-Bus Test CaseFigure 10 shows the diagram of a 3-Bus test case withfour independent planning projects for a simulationperiod between 1999 and 2005 [12]. The networktopology and all network data are stored on Deep-Editsystem. The system operation is simulated using amandatory Pool market model with audited costs.

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The following charts summarise some results weredifferent combination of objective functions wereselected. Each dot represents the result for a completeexpansion alternative. The arrow indicate the bestresult in each case.

Figure 11: Operation Costs vs. Investment Costs

Figure 12: Power Losses Costs vs. Investment Costs

Figure 13: Unserved Energy Costs vs. InvestmentCosts

Finally, figure 14 shows convergence behaviour alongthe generations.

Figure 14: Convergence behaviour

V. CONCLUSIONS

It is shown that the OOP approach is adequate to de-

fine the information flow between the individual mar-ket actors and their relation with technical compo-nents. The resulting simulation platform, Deep-Edit isflexibly adapted to the transmission expansion prob-lem. Deep-Edit is able to work in both, an interactiveand a computer aided way. The 3-Bus test case showsthe potential application of Deep-Edit to real scenarios.

The software is currently used at the University ofChile for several studies on expansion planning,wheeling transactions, and evaluation of power ex-change structures for the Chilean market.

VI. ACKNOWLEDGMENT

This paper has been partially supported by grants Fon-decyt #1000866 y #1000940, and the Facultad deCiencias Fsicas y Matemticas of University of Chile.We also appreciate the helpful collaboration of Mrs.Anita Araneda.

VII. REFERENCES

[1] Casazza, J., A., Eunson, E., M., Manzoni, G.,Schwarz, J., Stam, E.: Challenges for Power SystemPlanners and Operators due to Changing InstitutionalArrangements Special Report, CIGRE, SessionParis, 1996.

[2] Otero-Novas, C. Moseguer, C. Battle.:"A SimulationModel for a Competitive Generation Market", IEEETransactions on Power Systems, Vol. 15, N 1, Feb.2000, pp 250-256.

[3] Wolak F., Patrick R., The Impact of Market RulesAnd Market Structure on the Price DeterminationProcess in the England and Wales Electricity Mar-ket Technical Report PWP-047, Power-series, TheUniversity of California Energy Institute, Feb. 1996.

[4] Ancona, J., "A Bid and Selection Method for Devel-oping a Competitive Spot Priced Electric Market",Vol. 12, N 2, 1997, pp. 743-748.

[5] Rumbaugh, J., et al., "Object-Oriented Modeling andDesign", London: Prentice-Hall International, 1991.

[6] Daly, J., Miller, J., Brooks, A., Roper, M., Wood, M.:A survey of experiences amongs object-orientedpractitioners, IEEE Conference Proceedings, Soft-

ware Engineering Conference, 1995 Asia Pacific,ISBN: 0-8186-7171-8, Dec. 1995.

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[7] Handschin, E., Heine, M., Knig, D., Nikodem, T.,Seibt, T., Palma, R., "Object-oriented software engi-neering for transmission planning in open accessschemes", IEEE Transactions on Power Systems,Vol. 13, N 1, 1998, pp. 94-100.

[8] Handschin, E., Mueller, L., Nikodem, T., Palma, R.:Object-Oriented Software Package for Simulationand Management of Re-regulated Energy Markets,IEEE-ANDESCON99, 8-10, Sept, 1999, Isla Marga-rita, Venezuela, Vol. 1 pp. 94-97.

[9] Rudnick, H., Soto, M., Palma, R.: Use of system ap-proaches for transmission open access pricing,ELSEVIER, Electrical Power and Energy Systems,Vol 21, N 2, S. 125 ff., 1999.

[10] Rudnick, H., Palma, R., Cura, E., Silva, C., "Eco-nomically adapted transmission systems in open ac-cess schemes- Application of genetic algorithms",

IEEE Trans. on Power Systems, Vol. 11, No. 3, Aug.1996.

[11] Wood, A. J., Wollenberg, B. F.: Power GenerationOperation and Control, Wiley-Interscience, SecondEdition, ISBN 0-471-58699-4, 1996.

[12] R. Palma: Ausbauplanung von elektrischen ber-tragungsnetzen unter wettbewerbsorientierten Rah-menbedingungen, doctoral thesis, Lehrstuhl frElektrische Energieversorgung, University of Dort-mund, VDE Verlag, ISBN 3-8007-2547-9, 2000.

[13] Handschin E., Nikodem T., Palma R.: Object-Oriented Simulation Software for Transmission Sys-tem Management in Open Access Schemes,EPSOM98, International Con-ference on ElectricalPower Systems Operation and Management, Zrich,September 1998.

VIII. BIOGRAPHIES

Rodrigo Palma B. was born 1968 in Antofagasta, Chile.He received his B.Sc. and M.Sc. in electrical engineeringfrom the Catholic University of Chile, Santiago de Chile,and his Ph.D. in 1999 from University of Dortmund,Germany. He is now working as a professor at theUniversity of Chile, Santiago de Chile.

Luis Vargas D. was born in Chile. He received his B.Sc.and M.Sc. in electrical engineering from the University ofChile, Santiago de Chile, and his Ph.D. from Universityof Waterloo, Canada. He is now working as a pro-

fessor at the University of Chile, Santiago de Chile.

Oscar Moya A. was born in Chile. He received his B.Sc.in electrical engineering from the University of Chile,Santiago de Chile, and his Ph.D. from Imperial College,UK. He is an IEEE Senior Member. He is nowworking as a professor at the University of Chile,Santiago de Chile.

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