A decision support system for the electrical power districting problem

Many national electricity industries around the globe are being restructured from regulated monopolies to deregulated marketplaces with competitive business units. The business units responsible for transmission and distribution must be given physical property rights to certain parts of the power grid in order to provide reliable service and make effective business decisions. However, partitioning a physical power grid into economically viable districts (distribution companies) involves many considerations. We refer to this complex problem as the electrical power districting problem (EPDP). This research identifies the fundamental characteristics required to appropriately model and solve an EPDP. The proposed solution methodology is implemented as a decision support system (DSS) featuring a visualization tool that allows decision makers (DMs) to explore what we refer to as a "soft efficient frontier." This DSS was found to effectively support DMs at The World Bank in solving an EPDP in the context of a case study for the Republic of Ghana.

[1]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[2]  W. Vickrey On the Prevention of Gerrymandering , 1961 .

[3]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[4]  Jeffrey L. Ringuest,et al.  A sampling-based method for generating nondominated solutions in stochastic MOMP problems , 2000, Eur. J. Oper. Res..

[5]  F. Schweppe Spot Pricing of Electricity , 1988 .

[6]  G. Nemhauser,et al.  Optimal Political Districting by Implicit Enumeration Techniques , 1970 .

[7]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[8]  Michael S. Heschel Effective Sales Territory Development , 1977 .

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Prabhakant Sinha,et al.  Integer Programming Models for Sales Resource Allocation , 1980 .

[11]  Micah Altman,et al.  Is automation the answer: the computational complexity of automated redistricting , 1997 .

[12]  Stephen J. Rassenti,et al.  Deregulating Electric Power: Market Design Issues and Experiments , 1998 .

[13]  R. Slark Electricity : trading in the new market , 1998 .

[14]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[15]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[16]  William W. Hogan,et al.  A Market Power Model with Strategic Interaction in Electricity Networks , 1997 .

[17]  J. Weaver,et al.  Nonpartisan Political Redistricting by Computer , 1965 .

[18]  Charles Gide,et al.  Cours d'économie politique , 1911 .

[19]  Mark A Jamison Industry structure and pricing , 1999 .

[20]  Hung-po Chao,et al.  Introduction: Economic and Technological Principles in Designing Power Markets , 1998 .

[21]  Chris Easingwood A Heuristic Approach to Selecting Sales Regions and Territories , 1973 .

[22]  M. Granger Morgan,et al.  The Role of Research and New Technology in a Restructured Networked Energy System , 1998 .

[23]  Cliff T. Ragsdale,et al.  A Simulated Annealing Genetic Algorithm for the Electrical Power Districting Problem , 2003, Ann. Oper. Res..

[24]  Sidney W. Hess,et al.  Experiences with a Sales Districting Model: Criteria and Implementation , 1971 .

[25]  Gautam Mitra,et al.  Solution of Set-Covering and Set-Partitioning Problems Using Assignment Relaxations , 1992 .

[26]  G. Nemhauser,et al.  An Optimization Based Heuristic for Political Districting , 1998 .

[27]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[28]  William W. Hogan,et al.  Nodes and Zones in Electricity Markets: Seeking Simplified Congestion Pricing , 1998 .

[29]  G. Marakas Decision Support Systems in the 21st Century , 1998 .

[30]  Leonard M. Lodish,et al.  Sales Territory Alignment to Maximize Profit , 1975 .

[31]  Stephen C. Peck,et al.  A market mechanism for electric power transmission , 1996 .

[32]  Jacques A. Ferland,et al.  Decision Support System for the School Districting Problem , 1990, Oper. Res..

[33]  R. E. Turner,et al.  Sales Territory Design: An Integrated Approach , 1975 .

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[35]  Michelle H Browdy Simulated Annealing: An Improved Computer Model for Political Redistricting , 1990 .

[36]  S. Nagel Simplified Bipartisan Computer Redistricting , 1965 .

[37]  Micah Altman,et al.  The computational complexity of automated redistricting : Is automation the answer ? , 1997 .

[38]  John R. Koza,et al.  Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems , 1990 .