Title :
Power market long-term stability: a hybrid MADM/GA comprehensive framework
Author :
Moghaddam, Mohsen Parsa ; Sheikh-El-Eslami, Mohammad ; Jadid, Shahram
Author_Institution :
Tarbiat Modares Univ.
Abstract :
Summary form only given. 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 making; genetic algorithms; inference mechanisms; investment; power engineering computing; power markets; power system economics; power system stability; pricing; risk analysis; decentralized optimization problem; fitness function; fuzzy inference system; generation resources; genetic algorithm; hybrid MADM/GA comprehensive framework; industry regulators; investment decisions; long-term electricity markets; long-term stability; market generation firms; multiple attribute decision making framework; power market; profit-oriented firms; tradeoff-risk analysis; Decision making; Electricity supply industry; Fuzzy systems; Genetic algorithms; Investments; Power generation; Power markets; Power system modeling; Regulators; Stability;
Conference_Titel :
Power Engineering Society General Meeting, 2006. IEEE
Conference_Location :
Montreal, Que.
Print_ISBN :
1-4244-0493-2
DOI :
10.1109/PES.2006.1708854