A robust hybrid algorithm AI and GA for optimizing wind power in electricity market

Abstract

This paper proposes a robust hybrid method to optimize benefits under adverse conditions due to the uncertainty of wind power when integrated into competitive electricity markets. The hybrid algorithm synergizes an artificial intelligence technique to enhance the optimization efficiency of evolutionary algorithms. Results from the novel hybrid algorithm significantly enhance optimization speed and surpass local optima to achieve more favorable global optimum results. Experimental validation on the IEEE 30-bus power system, compared with previous studies and the original evolutionary algorithm, demonstrates notably higher profitability with the proposed algorithm. Based on experimental findings, the hybrid wind power-thermal power plant model also proves to mitigate compensation risks stemming from wind speed uncertainty, thereby stabilizing the electricity market and enhancing energy security. Encouraging optimal wind power capacity bidding on the electricity market in this context should entail a reduction of 15% to 18% compared to predictive expectations to attain optimal benefits.