Title :
Power market long-term stability: a hybrid MADM/GA comprehensive framework
Author :
Moghaddam, Mohsen Parsa ; Sheik-El-Eslami, Mohammad Kazem ; Jadid, Shahram
Author_Institution :
Dept. of Electr. Eng., Tarbiat Modares Univ., Tehran, Iran
Abstract :
Stabilizing the long-term electricity markets by providing new generation resources is one of the most important challenges that are surfaced to the industry regulators. To deal with this complex problem, this paper proposes a comprehensive multiple attribute decision making (MADM) framework, in which the genetic algorithm (GA) is used to model the investment decisions of the market generation firms. The fitness function of the GA is itself a decentralized optimization problem that simulates the short-term behavior of these profit-oriented firms. A simple fuzzy inference system and an elasticity relation between price and demand represent the power market, as the link among all firms. The framework is augmented by tradeoff/risk analysis to incorporate the effects of the uncertainties. Finally, a realistic case study is presented to show the advantages of the proposed framework.
Keywords :
decision support systems; fuzzy neural nets; game theory; genetic algorithms; investment; power generation planning; power markets; power system analysis computing; power system stability; risk analysis; decentralized optimization; decision support system; fuzzy inference system; game theory; generation expansion planning; genetic algorithm; industry regulator; investment decision; market long-term stability; multiple attribute decision making; neural network; power market; tradeoff-risk analysis; Decision making; Electricity supply industry; Fuzzy systems; Genetic algorithms; Investments; Power generation; Power markets; Power system modeling; Regulators; Stability; Competitive electricity markets; decision support systems; fuzzy inference systems (FIS); game theory; generation expansion planning (GEP); genetic algorithm (GA); market long-term stability; neural networks; risk analysis;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2005.857934