Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

Abstract

The increasing integration of renewable energy sources (RESs), particularly wind power plants (WPP), into deregulated power markets introduces complexities in optimizing social welfare (SW). This article proposes a recent metaheuristic algorithm to address this challenge and maximize SW while accounting for the presence of WPP and the inherent uncertainty associated with wind power forecasting. The proposed algorithm optimizes generation scheduling and demand-side bidding strategies in the deregulated power market to maximize SW while ensuring economic efficiency. To validate the effectiveness and robustness of the proposed algorithm, MATLAB simulations are conducted on IEEE 30 and IEEE 118-bus systems. The results demonstrate that the proposed algorithm provides promising solutions for maximizing SW, especially in the context of incorporating WPP. This research contributes to the advancement of power market optimization methods and promotes the seamless integration of RESs, fostering a more sustainable energy future.