A Sustainable Planning and Evaluation Framework for Renewable Energy Bases Under Extreme Weather

Large-scale renewable energy bases are important for the low-carbon transition and sustainable development of power systems. However, extreme weather may cause prolonged low output or rapid fluctuations in wind and photovoltaic generation, resulting in capacity shortages, limited flexibility, and renewable energy curtailment. To address the inability of conventional planning methods to fully capture extreme-weather risks and multi-timescale flexibility requirements, this paper proposes a sustainable planning and evaluation method for renewable energy bases under extreme weather. First, typical extreme weather events are identified from wind speed and solar irradiance characteristics, and a Copula model combined with CEEMDAN-GRU is used to generate scenarios and forecast available wind and photovoltaic power. Second, a bi-level model integrating upper-level flexibility resource planning and lower-level 8760 h production simulation is developed to jointly optimize long-duration energy storage, coal-fired power, hydrogen conversion, transmission corridors, and demand response under carbon cost and emission constraints. A sustainability-oriented evaluation is then conducted from four dimensions: energy utilization, economic performance, operational flexibility, and environmental performance. Results show that under prolonged low-wind and low-solar conditions, the 48 h average combined wind and photovoltaic output decreases from 1549.11 MW to 811.70 MW, a reduction of 47.60%; during high-wind cut-out events, wind power drops from 3000 MW to 47.45 MW. The CEEMDAN-GRU model achieves average RMSEs of 243.318 MW for wind power and 113.752 MW for photovoltaic power, with R2 values of 0.8626 and 0.9356, respectively. The optimal plan includes 928.60 MW of long-duration energy storage, 536.40 MW of incremental coal-fired capacity, 183.70 MW of electrolyzer capacity, 146.80 MW of hydrogen-based generation, 138.50 MW of transmission expansion, and 48.60 MW of demand response. The proposed method can identify differentiated capacity and flexibility needs under extreme weather and support sustainable and resilient planning through coordinated multi-resource allocation.