Designing a Bidding-Agent for Electricity Markets: A Multi Agent Cooperative Learning Approach

Abstract

In the restructuring process of power systems, bidding strategies are the main routes for making more profit and therefore, there has been a wide research on them. In this paper, we consider a bidding model that is based on the residual demand (RD). Our approach concerns the identification of RD and learning how to bid according to it. When the agents bid in a market, some knowledge about the environment is obtained, which may be more influential than the obtained sheer profit. So, the agent should learn when and how to pay attention to the environment. The agent's expertise is measured in each part of the residual demand curve and, then its shortcomings are identified. Our agent makes a balance between the profit making and knowledge increasing processes. The designed agent's mind consists of many subagents, each learning its own task and also cooperating with others simultaneously. In this regard, a credit assignment system is implemented among the agents and the cooperative learning trends are applied. Finally, through a few case studies our agent design method is verified.