Resource Endowment-Oriented Capacity Configuration of Hybrid Wind-Solar-Storage Systems: A Dynamic Coupling Mechanism and Multi-Scale Spatiotemporal Optimization

Against the backdrop of the second phase of the Paris Agreement's emission reduction target (2025–203), solar power generation in China surpasses 28%, yet the wind and solar curtailment rate remains at 5.2%, revealing inherent deficiencies in traditional energy planning concerning spatiotemporal variability, climate resilience, and multi-level coordination. This research introduces a novel “spatiotemporal multi-scale optimization framework.” By integrating the WRF-Chem regional climate model (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1km}\times \mathbf{1km}$</tex> resolution) with the Copula-Vine nonlinear statistical approach, the study quantifies the spatiotemporal correlation of wind and solar outputs. Subsequently, a two-stage stochastic programming model is devised to facilitate comprehensive collaborative optimization spanning from seconds to yearly intervals. The study introduced a novel “climate-energy” coupling analysis framework<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup>, enhancing the flexibility of energy storage configuration by 23.6%. In practical applications, this framework was implemented in large-scale projects in Xinjiang and Qinghai, recommending diverse strategies such as “wind power + liquid flow battery” <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup>. The utilization of decision support tools derived from this framework led to a 17.8% reduction in energy storage investment inefficiencies within the national grid pilot program. Empirical findings revealed a daily power generation of 67.2kWh, achieving a self-sufficiency rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\geq 90\%}$</tex>. The estimated payback period, based on a comprehensive life cycle cost analysis, ranged from 7 to 9 years (assuming an electricity price of. 8 yuan/kWh), with an annual carbon footprint reduction of 20 tons. These results offer a comprehensive solution, spanning from precise modeling (digital twin platform latency <50ms) to practical implementation (modular scalability and carbon impact assessment), facilitating global energy transition efforts.