Complementary Optimization of Multiple Renewable Energy Generation Based on Confidence Level Checking

Aiming at the uncertainty of renewable energy generation, taking the photovoltaic (PV) station and wind turbine (WT) station as the research object, a complementary optimization method based on confidence check was proposed. With the purpose of reducing reserve requirement and improving the economic benefit, the complementary probability optimization model of multiple renewable energy generation was established. Particle swarm optimization algorithm combined with interior point method was used for the calculation of optimal power of PV and WT station. In order to meet the uncertainty constraints in the complementary optimization model, the optimal power passed the confidence level checking. The proposed complementary optimization method was programmed in MATLAB to optimize the new energy output ofAnqing Power Grid in 2020. The results slow that the renewable energy output can be optimized by using the complementary optimization method proposed in this paper. The new energy output can be increased by 159%, the standby capacity can be reduced by 100%, and the economic benefit can be increased by 69%, which verifies the effectiveness of the proposed complementary optimization method.