Abstract--The US wholesale power market comprises a large commodity market. The growth in power trading is due to the ongoing deregulation policy of the electric power industry. Most of deregulation scenarios indicate a further separation of power production from transmission and retailing. The power production is opened to more competition. Unfortunately, the power trading mechanism is not clearly investigated in the level that we can predict a price change in the US wholesale power market. Such a price change in the US wholesale power market is explored from a simulation system with learning capabilities. Using the new intelligence system, we investigate the bidding strategies of traders in the wholesale power market and examine how the price change occurs under different economic and engineering environments.

Abstract--This study documents the practicality of an agentbased approach by examining how two groups of agents handle business complexity related to power trading. Three important findings are identified in this research. First, the proposed approach can estimate fluctuations of electricity prices as well as other well-known methods such as neural networks and genetic algorithms. Second, multiple learning capabilities incorporated in adaptive agents do not have an advantage over limited learning capabilities in predicting the market price of electricity. Finally, a theoretical extension of multiple learning capabilities may have potential for developing the agent-based approach for power trading.

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This paper describes a new agent framework that fuses an HTN planner, through its underlying conceptual model, with the mental attitudes of the BDI agent architecture, thus exploiting the strengths of each. On the one hand, the practical and proven ability to reason about actions that is the strength of HTN planning fleshes out the option generation function in the inference loop of the BDI model; on the other hand, the mental attitudes make explicit the knowledge that plays an essential role in plan selection, an important aspect that is not considered in the traditional formulation of the planning problem. The result is a coherent framework that allows for the design and implementation of activitycentric rational agents.