Resilience-Oriented Bi-Level Planning of Photovoltaic and Energy Storage Systems for Power Distribution Networks under Extreme Heat Conditions

In recent years, extreme high-temperature weather events have posed significant challenges to power systems. Research on the rational planning of distributed photovoltaic and energy storage resources is of great importance for enhancing distribution network resilience under extreme heat conditions. This paper proposes a Photovoltaic (PV) and Energy Storage System (ESS) planning method based on a bi-level model. First, typical fault scenarios are generated according to an improved high-temperature heat wave line failure model, and clustering is performed using multi-dimensional vectors characterizing scenario characteristics. Second, the resilience enhancement problem is decomposed into upper-level planning and lower-level scheduling, corresponding to pre-disaster prevention and mid-disaster resistance phases. The upper level uses economic indicators as the objective function, while the lower level aims to maximize load recovery weight and voltage stability. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to optimize across both time and space dimensions, resulting in the optimal planning scheme for the PV and ESS. Finally, simulation verifies the effectiveness of the method. Results show that this planning approach can significantly improve system resilience under extreme high temperatures while maintaining economic efficiency and voltage quality.