Individual Welfare Maximization in Electricity Markets Including Consumer and Full Transmission System Modeling

ii RED-BORDERED FORM iii ABSTRACT This dissertation presents a new algorithm that allows a market participant to maximize its individual welfare in the electricity spot market. The use of such an algorithm in determining market equilibrium points, called Nash equilibria, is also demonstrated. The start of the algorithm is a spot market model that uses the optimal power flow (OPF), with a full representation of the transmission system. The OPF is also extended to model consumer behavior, and a thorough mathematical justification for the inclusion of the consumer model in the OPF is presented. The algorithm utilizes price and dispatch sensitivities, available from the Hessian matrix of the OPF, to help determine an optimal change in an individual's bid. The algorithm is shown to be successful in determining local welfare maxima, and the prospects for scaling the algorithm up to realistically sized systems are very good. Assuming a market in which all participants maximize their individual welfare, economic equilibrium points, called Nash equilibria, are investigated. This is done by iteratively solving the individual welfare maximization algorithm for each participant until a point is reached where all individuals stop modifying their bids. It is shown that these Nash equilibria can be located in this manner. However, it is also demonstrated that equilibria do not always exist, and are not always unique when they do exist. It is also shown that individual welfare is a highly nonconcave function resulting in many local maxima. As a result, a more global optimization technique, using a genetic algorithm (GA), is investigated. The genetic algorithm is successfully demonstrated on several systems. It is also shown that a GA can be developed using special niche methods, which allow a GA to converge to several local optima at once. iv Finally, the last chapter of this dissertation covers the development of a new computer visualization routine for power system analysis: contouring. The contouring algorithm is demonstrated to be useful in visualizing bus-based and transmission line-based quantities. v ACKNOWLEDGMENTS I would like to thank Professor Thomas J. Overbye for his knowledge, guidance, and support throughout my doctoral studies. He and his family have been a great help to me. I would also like to thank all the other professors who have helped and continue to help me in my academic career. I also say thanks the University of Illinois Power Affiliates Program and Grainger Foundation for their generous financial …

[1]  Thomas J. Overbye,et al.  Voltage contours for power system visualization , 2000 .

[2]  A. J. Svoboda,et al.  Revenue adequate bidding strategies in competitive electricity markets , 1999 .

[3]  A. David,et al.  Optimal dispatch under transmission contracts , 1999 .

[4]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods , 1999 .

[5]  Thomas J. Overbye,et al.  Modeling the consumer benefit in the optimal power flow , 1999, Decis. Support Syst..

[6]  P. Skantze,et al.  Price dynamics in the deregulated California energy market , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[7]  Eric W. Weisstein,et al.  The CRC concise encyclopedia of mathematics , 1999 .

[8]  F. Li,et al.  A comparison of genetic algorithms with conventional techniques on a spectrum of power economic dispatch problems , 1998 .

[9]  A. Papalexopoulos,et al.  Transmission congestion management in competitive electricity markets , 1998 .

[10]  T. Alvey,et al.  A security-constrained bid-clearing system for the New Zealand wholesale electricity market , 1998 .

[11]  S. M. Shahidehpour,et al.  Application of games with incomplete information for pricing electricity in deregulated power pools , 1998 .

[12]  G. Sheblé,et al.  Genetic algorithm evolution of utility bidding strategies for the competitive marketplace , 1998 .

[13]  Günter Neumann,et al.  Interleaving Natural Language Parsing and Generation Through Uniform Processing , 1998, Artif. Intell..

[14]  M.A. Abido,et al.  A genetic-based fuzzy logic power system stabilizer for multimachine power systems , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[15]  J. R. McDonald,et al.  Unit commitment for power systems using a heuristically augmented genetic algorithm , 1997 .

[16]  S. M. Shahidehpour,et al.  Transaction analysis in deregulated power systems using game theory , 1997 .

[17]  S. M. Shahidehpour,et al.  Transmission analysis by Nash game method , 1997 .

[18]  R. D. Christie,et al.  Visualizing voltage profiles for large scale power systems , 1997 .

[19]  H. Glavitsch,et al.  Management of multiple congested conditions in unbundled operation of a power system , 1997, Proceedings of the 20th International Conference on Power Industry Computer Applications.

[20]  J. Cardell Market power and strategic interaction in electricity networks , 1997 .

[21]  Shangyou Hao,et al.  Reactive power pricing and management , 1997 .

[22]  James A. Momoh,et al.  Challenges to optimal power flow , 1997 .

[23]  Thomas J. Overbye,et al.  A simulation tool for analysis of alternative paradigms for the new electricity business , 1997, Proceedings of the Thirtieth Hawaii International Conference on System Sciences.

[24]  James Daniel Weber,et al.  Implementation of a Newton-based optimal power flow into a power system simulation environment , 1997 .

[25]  Thomas J. Overbye,et al.  Visualizing power system operations in an open market , 1997 .

[26]  Hugh Rudnick,et al.  Pioneering electricity reform in South America , 1996 .

[27]  R. D. Christie,et al.  Minimizing user interaction in energy management systems: task adaptive visualization , 1996 .

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

[29]  S. Hunt,et al.  Unlocking the grid [electricity industry restructuring] , 1996 .

[30]  B. Gorenstin,et al.  Some fundamental, technical concepts about cost based transmission pricing , 1996 .

[31]  Ping Ju,et al.  Genetic algorithm aided controller design with application to SVC , 1996 .

[32]  Jun Hasegawa,et al.  An application of genetic algorithms to the network reconfiguration in distribution for loss minimization and load balancing problem. II , 1995, Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95.

[33]  Jyoti K. Parikh,et al.  Optimal reactive power planning and its spot-pricing: an integrated approach , 1995 .

[34]  Márk Jelasity,et al.  GAs, a Concept of Modeling Species in Genetic Algorithms , 1995, Artificial Evolution.

[35]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[36]  G. Pires de Azevedo,et al.  Enhancing the human-computer interface of power system applications , 1995, Proceedings of Power Industry Computer Applications Conference.

[37]  David E. Goldberg,et al.  Parallel Recombinative Simulated Annealing: A Genetic Algorithm , 1995, Parallel Comput..

[38]  David John Finlay Optimal bidding strategies in competitive electric power pools , 1995 .

[39]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[40]  R. Seydel Practical Bifurcation and Stability Analysis , 1994 .

[41]  Richard D. Christie,et al.  Case study: visualization of an electric power transmission system , 1994, VIS '94.

[42]  R. D. Christie Towards a higher level of user interaction in the energy management task , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[43]  K. Ghoshal,et al.  GUI display guidelines drive winning SCADA projects , 1994, IEEE Computer Applications in Power.

[44]  Q. H. Wu,et al.  Genetic search for optimal reactive power dispatch of power systems , 1994 .

[45]  W.R. Block,et al.  Modern user interface revolutionizes supervisory systems , 1994, IEEE Computer Applications in Power.

[46]  R. D. Christie,et al.  Envisioning power system data: concepts and a prototype system state representation , 1993 .

[47]  S. Mokhtari,et al.  Seeing results in a full graphics environment , 1993, IEEE Computer Applications in Power.

[48]  R. E. Marsten,et al.  A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows , 1993, Conference Proceedings Power Industry Computer Application Conference.

[49]  W. Hogan Markets in Real Electric Networks Require Reactive Prices , 1993 .

[50]  Ian Dobson,et al.  Voltage collapse precipitated by the immediate change in stability when generator reactive power limits are encountered , 1992 .

[51]  Samir W. Mahfoud Crowding and Preselection Revisited , 1992, PPSN.

[52]  Francisco D. Galiana,et al.  An integrated personal computer graphics environment for power system education, analysis and design , 1991 .

[53]  M. L. Baughman,et al.  Real-time pricing of reactive power: theory and case study results , 1991, IEEE Power Engineering Review.

[54]  David M. Kreps,et al.  A Course in Microeconomic Theory , 2020 .

[55]  B. Stott,et al.  Further developments in LP-based optimal power flow , 1990 .

[56]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[57]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[58]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

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

[60]  W. Tinney,et al.  Optimal Power Flow by Newton Approach , 1984, IEEE Power Engineering Review.

[61]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[62]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[63]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .