A novel dynamic reconfiguration approach for active distribution networks with soft open points and energy storage systems

The active distribution network (ADN) integrates a substantial amount of renewable energy sources (RESs), which exhibits considerable variability and uncertainty. To solve the challenges of renewable energy volatility and the lack of intelligence and flexibility in traditional distribution networks, this paper firstly constructs a bi-level optimization model for the dynamic reconfiguration of ADNs, incorporating soft open points (SOPs) and energy storage systems (ESSs). The mathematical model formulated in this paper is inherently characterized by its high-dimensionality, complexity, non-linearity, and stochastic optimization nature. Secondly, a double deep Q network algorithm embedded with physical knowledge (PK-DDQN) is developed to resolve the constructed model accurately and rapidly. The upper-level model optimizes the topology of the distribution network using the double deep Q network algorithm. In contrast, the lower-level model employs second-order cone programming to optimize the operation of ADNs incorporating SOPs and ESSs. This divide-and-conquer approach enhances the solution’s efficiency. Finally, the superiority and scalability of the proposed algorithm are verified on the modified IEEE 33 and 69-bus distribution systems. The simulation results demonstrate that compared with the genetic algorithm (GA), the power losses are reduced by 3.77% and 23.47%, and the voltage deviations are reduced by 28.63% and 23.41%, respectively. Additionally, compared with the mixed-integer second-order cone programming (MISOCP), the computational efficiency is increased by 21.84 times and 36.15 times.