A coordinated strategy for resilient distribution network reconfiguration and demand response under high renewable penetration

In order to address the challenges introduced by high renewable energy penetration, including voltage instability, wind-solar curtailment, and significant load peak-valley differences, a bi-level planning method for resilient distribution networks that jointly incorporates demand response and network reconfiguration is proposed. First, hierarchical Latin hypercube sampling is employed to characterize uncertainties in wind power, photovoltaic (PV) generation, and load, followed by multi-scenario generation and reduction. Next, a bi-level optimization model is formulated: the upper layer aims to minimize overall system cost, while the lower layer minimizes distribution network operational cost across generated scenarios. To efficiently solve the model, an improved whale optimization algorithm is developed, integrating chaotic mapping and a nonlinear convergence factor to enhance convergence speed and global search ability. Case studies based on the IEEE 33-bus system are conducted to evaluate the impacts of demand response, network reconfiguration, and PV penetration on planning performance. Simulation results demonstrate that the proposed method significantly improves voltage stability, reduces planning and operating costs, and effectively suppresses wind-solar curtailment. Furthermore, the improved algorithm exhibits superior performance in solving the bi-level optimization problem.