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

Tang Huiling,Wu Jiekang,Chen Lingmin,Liu Zhijun,Wu Fan,Li Kangxing,Wu Zhijiang, Yu Fangming,Bi Qilin

International Journal of Electrical Power & Energy Systems（2021）

Cited 15|Views8
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Abstract
•A CVaR-based method is used to determine power control value of PVs, EVs and DSs.•Energy risk due to power loss and voltage offset is controlled by optimizing method.•A method based on second-order cone programming is proposed to control CVaR risk.•Q-learning driven adaptive differential evolution method is used for faster solution.
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Key words
Risk control,CVaR risk,Q-learning powered adaptive differential evolution algorithm,Distributed generation,Electric vehicle,Energy storage device
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