可解释的分布鲁棒优化在电池储能系统规划中的应用

A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lacks interpret-ability in its results,obscuring the causal relationships between data distribution characteristics and the outcomes.Further-more,the current approach to battery type selection is not in-cluded in traditional BESS planning,hindering comprehensive optimization.To tackle these BESS planning problems,this pa-per presents a universal method for BESS planning,which is designed to enhance the interpretability of DRO.First,mathe-matical definitions of interpretable DRO(IDRO)are intro-duced.Next,the uncertainties in wind power,photovoltaic pow-er,and loads are modeled by using second-order cone ambigui-ty sets(SOCASs).In addition,the proposed method integrates selection,sizing,and siting.Moreover,a second-order cone bidi-rectional-orthogonal strategy is proposed to solve the BESS planning problems.Finally,the effectiveness of the proposed method is demonstrated through case studies,offering planners richer decision-making insights.