Optimal operational strategies for a day-ahead electricity market in the presence of market power using multi-objective evolutionary algorithms
Dynamic bidding analysis in power market based on the supply function
Computers & Mathematics with Applications
Bidding strategies in dynamic electricity markets
Decision Support Systems - Challenges of restructuring the power industry
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
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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.