Distributed distributionally robust optimization of distribution network incorporating novel battery charging and swapping station

This paper addresses the joint optimization problem of the distribution network (DN) and novel battery charging and swapping (NBCSS) under multi-entities and uncertainties. A distributed distributionally robust optimization (DRO) framework is proposed to facilitate the coordination of DN and NBCSS. Firstly, the DRO models for the DN and NBCSS are developed respectively for the DN and NBCSS, taking into account the uncertainties associated with wind and photovoltaic power. Both models minimize the worst-case expected total cost over a family of distributions characterized by an ambiguity set. Then, the constraints of exchanging power are used to couple the DN and NBCSS. Specifically, in the model of NBCSS, an integrated modeling method of batteries based on the state of charge (SOC) interval is constructed, and the charging and discharging priorities are embedded. Further, the primitive DRO model is transformed into a tractable mixed-integer linear program (MILP) via the affine decision rule and duality theory. Moreover, to protect the information privacy of different entities, the analytical target cascading (ATC) method is developed to decouple the centralized DRO model into two independent subproblems, which can be solved separately. Finally, case studies are carried out to demonstrate the effec-tiveness of the proposed method.