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Object-Oriented Simulation Software for a Competitive Environment Application to Transmission Expansion Planning Rodrigo Palma B., Luis S. Vargas, Oscar Moya A. Departamento de Ingeniería Eléctrica Universidad de Chile Av. 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 adjusted in order to include the new industry requirements. This paper links the object-oriented approach with 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 developed inside Deep-Edit with the purpose of determining an economically adapted electric transmission system in a competitive open access environment. Keywords: object-oriented programming, transmission expansion planning, market simulation, Deep-Edit, genetic programming, JAVA.

I. INTRODUCTION An important consequence arising from the development of competitive electricity markets is its impact on classical analysis tools. Whereas in supply structures with vertically integrated utilities the system operation function is well defined, in competitive structures there is a need to incorporate market behaviour into system operation. Existing simulation software tools have to be adjusted in order to include the actors and their impact on power system operation and planning [1-4]. In this research field, the objectoriented programming [5] (OOP) approach has gained wide spread importance and acceptance in software development due to its advantages concerning flexibility, expandability, maintainability, and data integrity [6-8].

components in the hydro database (HDB). The market database (MDB) contains market related objects like market actors and contracts. Due to their high hydro generation dependency, an accurate HDB representation is an important issue in countries like Chile and Brazil. The individual characteristics of network, hydro, and market elements are described by object attributes and the information exchange between objects is performed by messages following the objectoriented programming paradigm (fig. 1). The object modelling technique in [5] has been used for developing the object models presented in this paper. System Component NDB Component

HDB Component

MDB Component

...

...

...

Figure 1: Object Model of the System In the OOP terminology, a generalisation of a data object along with its data variables and methods is a class of data objects. The data variables are referred to as class attributes and an instance of a class is called an object. The concept of inheritance makes it possible to define subclasses of a class that share characteristics of the parent class. Network Database (NDB) NDB Component 1-Pole

Plan. Object

Power transmission, with open and third party access, is a central aspect in competitive electricity supply sectors with competition in the generation and trading sectors. A necessary condition for competition is that generators are able to reach consumers through the transmission network. This can be achieved through open access schemes and a transmission expansion planning that integrates market behaviour in an explicit way [9,13]. The paper assumes a basic knowledge on object oriented programming and genetic algorithms [5,12].

II. SYSTEM MODELLING The modelling approach presented in this paper can be summarised as follows [7,8,12,13]. The system representation is based on physical power system objects in the network database (NDB) and on hydrosystem

: inheritance

Node

Injection

Network Feeder

Ground

Generator

2-Pole

Load

Compensation

Asynch. Machine

Branch

Transformer

Switch

Line

Figure 2: Hierarchy Chart of NDB Figure 2 shows the hierarchy chart of the NDB. “NDB component” is the most general class and its attributes and methods are available for all subclasses [12]. Since simulation models are typically based on a node/branch-representation, these classes are explicitly included in the object-oriented data model. The class “Branch” is a child-class of the abstract class “2-Pole” and it contains all branch facilities having an impe-

dance like transmission lines and transformer subclasses. A main advantage of the object-oriented network model is that the model can be easily expanded by introducing new classes for network facilities like FACTS-devices. A planning object allows the representation of an expansion planning alternative through a group of NDB-Objects with specific planning attributes [12]. NDB-Objects store electrical location information in attributes. The potential network topology can be described using these references.

actor. It is responsible of a reliable system operation and co-ordination. One of the main advantages of the object oriented approach is that additional market actors and contract types can be implemented in an easy way. Furthermore, the methods related to the classes can be easily adjusted to the given access policies and the existing regulatory framework. Relationship Between Objects

Hydro Database (HDB) Several classes of each database, NDB, HDB, and MDB, are closely related. A direct relationship between objects from different databases occurs through references to objects, which are given as attributes of the individual classes in following forms:

Affluent

Hydro Unit

Link

Series Unit

Isolated Run of River

• • • •

Reservoir Unit

Figure 3: Hierarchy Chart of HDB

Market Database (MDB) Market Component Market Actor

Supplier.

Customer

ISO / RTO

Broker Trader

Contract

Wheeling Service Use of Infrastructure

Bilateral Contract

Ancillary Services

Figure 4: Hierarchy Chart of MDB The market database is an object-oriented representation of the electricity market place. In figure 4 the hierarchy chart of the MDB is shown. The classes are divided into three main categories: market actor, system operator (ISO/RTO) and contracts. At present, the three classes, customer, supplier, and energy broker/trader represent actors behaviour of the electricity market. Typical methods related to market actors are “select supplier” for the class “customer” and “make offer” for the class “supplier”. The independent system operator (ISO) is also included in the MDB. In contrast to the other actors, the ISO is not viewed as a market

•

These references define information that is directly available to the different objects. Therefore, the supplier has, for example, direct access to the technical parameters 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 be classified in the following three basic categories [8, 12]: Pool market, market based on physical bilateral contracts, and market based on financial bilateral contracts. Actual markets throughout the countries correspond to a mix of these categories. System Operator

Technical parameters

Hydraulic power generation systems can be represented in a simplified way by five main classes (Affluent, Link, Run of River, Series, and Reservoir Units) and an abstract “Hydro Unit” generation class, as shown in figure 3. Connections between hydro units and affluent are determined by “Link” objects. For Hydrothermal co-ordination models a splitting of run of river plants in series and isolated units is recommended.

suppliers own or manage generators and/or network-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 information about all network objects in its control area and about the actual network topology, hydro units processed water is electrically generated by generators and/or network-feeders.

Market Operator Offers, costs Supplier

manage Network Feeder

Technical parameters

Hydraulic Component

Customer manage Generator

manage : aggregation : multiple

Load

Figure 5: Pool Market Object Model Figure 5 shows the object model of a Pool based power market. Suppliers and Customers, with their respective NDB related objects, exchange technical and eco-

Object-Oriented Simulation Software for a Competitive Environment

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nomic information with the System and Market Operator. A direct relationship between Customers and Suppliers is not allowed under the Pool approach. A co-ordinated work between Market and System operator defines the final operation schedule of the power system, typically for the next day in a day ahead market. III. DEEP-EDIT SIMULATION SOFTWARE A general purpose simulation software, called DeepEdit was developed and implemented using JAVA technology. Its general structure and the client/server architecture are shown in figure 6. Deep-Edit is built based on the object-oriented representation presented in section II. External Event Event Process and Update Client & Server

Man-Machine Interface

PSAA Library

Client

Client & Server

Market Editor

Object Oriented Database

NDB

MDB

HDB

Source/Data Files

Server Client Hydro Editor

Client

Source/Data File Editor

Geographic Information System GIS

Network Editor

Client

Client

Figure 6: Deep-Edit Client-Server Architecture The Object Oriented Database constitutes the core of the application. Source-file and specific Network, Hydro and Market editors allow users to interact with the system information and options. Server-socket connections do the information exchange between Internet users and the system. Analysis Tools

utilize

utilize

utilize • initialize (abstract) • execution (abstract) • save results

HDB Object MDB Object

Control Area own

Market Analysis Tool utilize

Network Analysis Tool

Pool Market

OPF

. ..

• execute

Pricing Model

Allowing and promoting the development of competitive markets in generation and commercialisation of electrical energy is increasingly accepted as the main objective of transmission expansion planning in the new market structure. In this context, the cost minimisation paradigm changed to the ability of transmission planners to create non discriminatory open access conditions at minimum cost with predefined minimum security and quality levels [10, 12]. For the midterm time horizon (2-10 years) transmission expansion can be formulated as a decision/optimisation problem with the following characteristics: multiobjective function, continuos & integer variables, dynamic decisions, high dimensionality, non linearity, non convexity, and multiple uncertainties. The optimisation variables of this problem are defined in this work as the entry period of a planning project in the system (see figure 2), that internally represents a group of “Branch” objects. The model assumed the existence of a set of possible and well defined planning projects. Nevertheless, the high complexity of solving this optimisation problem using the current computational capabilities is recognised by several authors [12]. In order to deal with these limitations, this work identifies the need of using a combination of an interactive and a computer aided approach. Interactive Transmission Expansion Planning

NDB Object

. ..

The interactive transmission planning system takes full advantage of the Deep-Edit platform presented in sections II and III. It allows the following capabilities: •

• execute

Power Flow

. ..

• execute

Market of Bil. Cont. . .. • execute

IV. TRANSMISSION EXPANSION PROBLEM

own

• initialize

• initialize

.. .

Subnetwork

Flow calculation, Optimal-Power-Flow, pricing models, market simulation applications, etc.. All these applications use the information stored in the objectoriented database. Figure 7 shows the object oriented general model of PSAA. These applications specialise in Market and Network Analysis Tool classes. Usually, Market Analysis Tools like a Pool market simulation make use of several Power System Tools in each simulation step.

. ..

• execute

.. .

Sensitivity . .. analysis • execute

Figure 7: Object Oriented Model of PSAA The software package contains a library of Power System Analysis Applications (PSAA) including Load-

• • • •

easy edition of technical and market data of the system for the study time horizon, detailed market and network operation analysis of the existing system using the PSAA library, definition of alternative transmission expansion plans using expert knowledge, detailed comparison of alternative expansion alternatives in order to give the final recommendation, flexible incorporation of new analysis tools to the PSAA library resulting from the OOP approach.


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Computer Aided Transmission Expansion Planning

System Description NDB

The computer aided transmission expansion planning follows the model presented by the authors in [8, 12]. Due the complexity level of the general problem, simplifications in the system representation and simulation tools were applied. A dynamic transmission planning methodology (DTPM), using genetic algorithms, is developed for the purpose of determining an economically adapted electric transmission system in a competitive open access environment. DTPM makes use of PSAA in tasks like classic load flow calculation, economic dispatch, and sensitivity analysis. Figure 8 shows the proposed DTPM.

HDB

Parameters • Horizon • Genetic Param.

MDB

Sensitivity Analysis

Initialize Population

Simulation Tool

Individuals Evaluation Selection Recombination Mutation Evaluation of New Individuals

Decision Modul

Population Creation Convergency

Master Problem

Expansion Plan of Lines and Transformers

Slave Problem

Planning Problem

Impact of the Decision on Selected Objective Functions

Branch Object • Electrical Parameters • Planning Type: FIX, OPT, UPD • Suggested? • Investment Costs • Lifetime • Topological Information • Min. Entry Period • Max. Exit Period

Genetic Coding • Investmet Costs • Operation Costs • Power Losses • Unserved Energy • Wheeling Income • Network Constrains • Voltage Quality

Optimization Variables Entry Period

• Simulation Time Horizon N Bits • Number of OPT- and UPD-Type M Variables • Code Length NxM Bits

Random Num. Generator

No

Yes

Results • Best Plans • Multiobj. Evaluation

Figure 9: DTPM Flow Diagram [12] Well defined recombination masks and mutation rates are used within a standard genetic algorithm procedure. A combination of Network and Market analysis tools, conforming a simulation framework, are used for the evaluation steps. After convergence is reached, the best expansion solution can be saved for the purpose of further detailed evaluations with the interactive planning tool. DTPM is incorporated as part of the PSAA library of Deep-Edit. Simulation Results

Figure 8: Dynamic Planning Methodology The investment decisions represents the Master Problem in the DTPM while its impact is evaluated through a Slave Problem according to the selected objective function (see figure 8). All the characteristics of a planning project are stored in the attributes of the included “Branch” objects. A set of “Planning Projects” defines a genetic code in a unique way. Each “Planning Project” uses a segment of the whole genetic code and is decoded as its entry period in the system [12]. Figure 9 shows the flow diagram of the DTPM. For the problem formulation, the information stored in the object oriented database (see figure 6) and the general runtime options are used. The initial population is built based on a sensitivity analysis of the proposed solution given by the expert and it considers additional randomly created expansion plans.

Figure 10: 3-Bus Test Case Figure 10 shows the diagram of a 3-Bus test case with four independent planning projects for a simulation period between 1999 and 2005 [12]. The network topology and all network data are stored on Deep-Edit system. The system operation is simulated using a mandatory Pool market model with audited costs.


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The following charts summarise some results were different combination of objective functions were selected. Each dot represents the result for a complete expansion alternative. The arrow indicate the best result in each case.

Figure 14: Convergence behaviour V. CONCLUSIONS

Figure 11: Operation Costs vs. Investment Costs

It is shown that the OOP approach is adequate to define the information flow between the individual market actors and their relation with technical components. The resulting simulation platform, Deep-Edit is flexibly adapted to the transmission expansion problem. Deep-Edit is able to work in both, an interactive and a computer aided way. The 3-Bus test case shows the potential application of Deep-Edit to real scenarios. The software is currently used at the University of Chile for several studies on expansion planning, wheeling transactions, and evaluation of power exchange structures for the Chilean market. VI. ACKNOWLEDGMENT

Figure 12: Power Losses Costs vs. Investment Costs

This paper has been partially supported by grants Fondecyt #1000866 y #1000940, and the Facultad de Ciencias Físicas y Matemáticas of University of Chile. We also appreciate the helpful collaboration of Mrs. Anita Araneda. VII. REFERENCES

Figure 13: Unserved Energy Costs vs. Investment Costs Finally, figure 14 shows convergence behaviour along the generations.

[1] Casazza, J., A., Eunson, E., M., Manzoni, G., Schwarz, J., Stam, E.: „Challenges for Power System Planners and Operators due to Changing Institutional Arrangements – Special Report“, CIGRE, Session Paris, 1996. [2] Otero-Novas, C. Moseguer, C. Battle.:"A Simulation Model for a Competitive Generation Market", IEEE Transactions on Power Systems, Vol. 15, Nº 1, Feb. 2000, pp 250-256. [3] Wolak F., Patrick R., “The Impact of Market Rules And Market Structure on the Price Determination Process in the England and Wales Electricity Market” Technical Report PWP-047, Power-series, The University of California Energy Institute, Feb. 1996. [4] Ancona, J., "A Bid and Selection Method for Developing a Competitive Spot Priced Electric Market", Vol. 12, N° 2, 1997, pp. 743-748. [5] Rumbaugh, J., et al., "Object-Oriented Modeling and Design", London: Prentice-Hall International, 1991. [6] Daly, J., Miller, J., Brooks, A., Roper, M., Wood, M.: „A survey of experiences amongs object-oriented practitioners“, IEEE Conference Proceedings, Software Engineering Conference, 1995 Asia Pacific, ISBN: 0-8186-7171-8, Dec. 1995.


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[7] Handschin, E., Heine, M., König, D., Nikodem, T., Seibt, T., Palma, R., "Object-oriented software engineering for transmission planning in open access schemes", 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 Simulation and Management of Re-regulated Energy Markets”, IEEE-ANDESCON99, 8-10, Sept, 1999, Isla Margarita, Venezuela, Vol. 1 pp. 94-97. [9] Rudnick, H., Soto, M., Palma, R.: „Use of system approaches 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., "Economically adapted transmission systems in open access schemes- Application of genetic algorithms", IEEE Trans. on Power Systems, Vol. 11, No. 3, Aug. 1996. [11] Wood, A. J., Wollenberg, B. F.: „Power Generation Operation and Control“, Wiley-Interscience, Second Edition, ISBN 0-471-58699-4, 1996. [12] R. Palma: “Ausbauplanung von elektrischen Übertragungsnetzen unter wettbewerbsorientierten Rahmenbedingungen”, doctoral thesis, Lehrstuhl für Elektrische Energieversorgung, University of Dortmund, VDE Verlag, ISBN 3-8007-2547-9, 2000. [13] Handschin E., Nikodem T., Palma R.: „ObjectOriented Simulation Software for Transmission System Management in Open Access Schemes“, EPSOM’98, International Con-ference on Electrical Power Systems Operation and Management, Zürich, September 1998.

VIII. BIOGRAPHIES Rodrigo Palma B. was born 1968 in Antofagasta, Chile. He received his B.Sc. and M.Sc. in electrical engineering from 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 the University 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 of Chile, Santiago de Chile, and his Ph.D. from University

of Waterloo, Canada. He is now working as a professor 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 now working as a professor at the University of Chile, Santiago de Chile.


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