A control optimization model for CVaR risk of distribution systems with PVs/DSs/EVs using Q-learning powered adaptive differential evolution algorithm

摘要

Distributed generation and energy storage brings opportunities and risks of distribution systems, such as greater power loss and abnormal voltage fluctuation. A Q-learning powered optimization model for these risk control of is presented in this paper. Considering the uncertainties of output power of distributed generation systems, charging and discharging power of electric vehicles and energy storage devices, a CVaR-based energy risk control model for distribution system with renewable energy is presented to determine the control value of output power of distributed generation systems. Q-learning powered adaptive differential evolution algorithm is used to solve the proposed optimization problem. Second order cone programming is used to simplify the objective function and constraints of the optimization model, and Q-learning driven adaptive differential evolution algorithm is used to enhance the ability of solving, simplify the calculation, and make the solution faster and more stable. The feasibility and applicability of the proposed model and algorithm are verified by simulating IEEE-118 distribution system.