Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/34780
Title: CO-EVOLUTIONARY BIDDING AND COOPERATION STRATEGIES FOR BUYERS IN POWER MARKETS
Authors: LY TRONG TRUNG
Keywords: Co-evolutionary Algorithms, Electric Power Markets, Cooperation Strategies, Optimal Coalition Structure.
Issue Date: 3-Feb-2012
Source: LY TRONG TRUNG (2012-02-03). CO-EVOLUTIONARY BIDDING AND COOPERATION STRATEGIES FOR BUYERS IN POWER MARKETS. ScholarBank@NUS Repository.
Abstract: Deregulation of electric power industries in recent years has opened many opportunities for electricity buyers. However, the strong influence of network physical constraints may result in economic decisions that adversely affect the interests of the consumers. Compared to the monopolistic economy of yesteryears, electricity buyers may now actually be able to influence the market by cooperating with other buyers in the electrical power network. This research presents different models using agent-based co-evolutionary framework for evolving individual and cooperative strategies of electricity buyers in a power market. To realize the above objectives, simulations involving evolutionary algorithms and multi-agent systems are used to study a single-node system, where economic agents are modeled by their supply / demand functions, and then a multi-node system, where the technical constraints of the power distribution network are fully taken into account. The results of the single-node model show that it is of great benefit to cooperate but the free rider problem may arise when an individual buyer gains more profit due to the cooperative effort of the others. The multi-node model is investigated through two situations. First, we focus on deterministic cases where buyers choose their bidding strategies to maximize the profits in different scenarios of playing individually or cooperatively. It is also found that by evolutionary learning, buyers can benefit from cooperation. Next, the uncertain nature of the market is modeled where buyers find optimal cooperation strategies to hedge against the risk of low payoffs. Our approach is universal since it can be applied to study the behaviors of buyers with any objective for cooperation. We proved a theorem to link the payoff distribution problem in cooperative game theory with the optimal coalition structure generation problem in combinatorial optimization theory. The statistically consistent simulation results show that our approach is able to discover interesting cooperation strategies, and can be easily extended for practical networks with large number of buyers.
URI: http://scholarbank.nus.edu.sg/handle/10635/34780
Appears in Collections:Master's Theses (Open)

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