Scenario-Based Optimal Bidding Strategies of GENCOs in the Incomplete Information Electricity Market Using a New Improved Prey—Predator Optimization Algorithm

Abstract

In order to find out the optimal bidding strategies (BSs) of generating companies (GENCOs) in a competitive electricity market, it is necessary to solve a bilevel optimization problem. The first level of the problem relates to the GENCOs to strategically bid, and the second level solves the independent system operator's market clearing problem based on the maximization of social welfare. In order to model the incomplete information of participants in the market about cost coefficients of opponents and their forecast errors, a scenario-based programming framework is presented. In addition, a roulette wheel mechanism is used for scenario-generation process so that the forecast errors of coefficients are considered as random variables with known probability distribution functions. Then, each GENCO solves the bilevel optimization problem and maximizes its expected profit function. These bilevel problems are nonconvex, and the mathematical-based optimization technique is unable to handle the problem and obtain the nearly global optima. In order to resolve this issue, a novel prey–predator optimization algorithm is suggested to solve the first level of the bilevel problem and using the iterative method to find out the supply function equilibrium that is the optimal BSs of GENCOs. Applying to the IEEE 57- and 118-bus test systems with incomplete information studies, the performance of the proposed approach is successfully approved.