Multi-objective robust optimization allocation for energy storage using a novel confidence gap decision method. (June 2022) - Peng, Chunhua; Xiong, Zhisheng; Zhang, Yi; Zheng, Cong
Highlights: A novel confidence gap decision (CGD) method based on confidence level driving robust optimization is proposed. A new multi-objective robust optimization allocation model for energy storage in distribution network based on CGD is established. A new adaptive harmonic aliasing multi-objective compound differential evolution (AHA-MOCDE) algorithm is designed. Abstract: With the increasing penetration rate of uncertain wind/photovoltaic power, robust optimization allocation for energy storage becomes more and more important in the distribution network. By introducing chance constraints and the classified probability confidence intervals, and integrating the robust idea of information gap decision theory (IGDT), a novel confidence gap decision (CGD) method based on confidence level driving robust optimization is proposed. Considering the comprehensive optimization objectives of maximizing voltage profile improvement index and minimizing annual investment cost, a multi-objective robust optimization allocation model of energy storage based on CGD is established. The proposed CGD model can not only mitigate the conservativeness of conventional robust optimization, but also overcome the roughness of uncertain set and the subjectivity of objective deviation factor in IGDT model, so that a more reasonable and accurate uncertainty planning can be achieved. Moreover, the chance constraints in CGD model are transformed into the equivalent deterministic constraints according to uncertainty theory, and a new adaptive harmonic aliasing multi-objective compound differential evolution algorithm is proposed to solve above model. Finally, sample applications are applied to demonstrate the advantages of the proposed theory and method.