Expansion Planning of Photovoltaic-Storage for Distribution Networks Based on Distributionally Robust Optimization

Abstract

A large proportion of distributed photovoltaic (DPV) and energy storage equipment is gradually being integrated into distribution networks, which increases the complexity of distribution network planning. To achieve flexible and efficient utilization of energy storage and DPV, a distributionally robust optimization expansion planning method for distribution networks based on Kullback-Leibler (KL) divergence is proposed. First, considering the power-flow, radial network, and energy storage constraints, a stochastic optimization planning model is established to minimize the cost of distribution network planning. Subsequently, the fuzzy sets of the DPV output based on KL divergence are embedded into the stochastic optimization planning model. This transforms it into a min-max-min three-level two-stage distributionally robust optimization model that can better balance economy and stability. Finally, the model is solved using the column-and-constraint generation (C&CG) method. The effectiveness and feasibility of the proposed model and algorithm are validated using an improved IEEE 33-node system.