A new genetic algorithm approach for optimizing bidding strategy viewpoint of profit maximization of a generation company
This paper presents a new approach for bidding strategy in a day-ahead market from the viewpoint of a generation company (GENCO) in order to maximize its own profit as a participant in the market. It is assumed that each GENCO submits its own bid as pairs of price and quantity, and the sealed auction with a pay-as-bid market clearing price (MCP) is employed. The optimal bidding strategies are determined by solving an optimization problem with unit commitment constraints such as generating limitations. In this paper, the problem is solved from two different viewpoints including profit maximization of GENCO without considering rival's profit function, and profit maximization of GENCO by considering both rivals' bid and profit functions. Therefore, there is a multi-objective problem to be solved in this study. Since this problem is non-convex which is difficult to solve by traditional optimization techniques, hence, genetic algorithm (GA) has been employed to solve the problem. A simple test problem is designed to illustrate the efficiency of the proposed approach. (C) 2011 Elsevier Ltd. All rights reserved.
genetic algorithm bidding strategy profit maximization day-ahead market generation company (genco)