A data-driven chance-constrained BESS planning in distribution networks by a decoupling solution method

In this study, a novel approach for the battery energy storage system (BESS) planning considering the integration of photovoltaic (PV) is proposed. A data-driven chance-constrained BESS programming model including the dynamic network reconfiguration (DNR) is developed. Then, a decoupling solution approach for sitting selection and sizing optimization is proposed to obtain optimal solution. According to the approach, comprehensive cost sensitivity factor (CCSF) is formulated for the sitting selection, and the particle swarm optimization (PSO) combined with the Minty algorithm and the switch operation time reduction algorithm is developed to obtain more accurate sizing. In addition, based on the scenarios generated by the spectral clustering approach, a continuous scenario increment approach is proposed to handle chance-constrained constraints. The numerical simulations on a practical 31-bus distribution network show the superiority of the proposed algorithm over existing methods.