Power System Resource Adequacy Assessment and Capacity Remuneration Mechanism Considering Spatiotemporal Correlation of Generation and Load

To address heightened source–load uncertainty and strengthened spatiotemporal dependence under high-penetration wind and photovoltaic integration, and to support a low-carbon and sustainable transition of power systems without compromising reliability, this study aims to develop a practical framework that converts spatiotemporally correlated uncertainty into actionable inputs for adequacy evaluation and reliability-constrained capacity-compensation decisions. First, a spatiotemporally correlated joint source–load forecasting model is established to generate statistically consistent joint uncertainty scenarios for operational risk analysis. Second, system adequacy is quantified using Loss of Load Probability and Expected Energy Not Served, and the computational burden is reduced through typical-day/representative-scenario construction with probability weighting, enabling efficient yet risk-preserving adequacy assessment. Finally, a risk-driven unified capacity-compensation clearing model is formulated that incorporates resource marginal costs and an unserved-energy penalty, while enforcing explicit reliability constraints to obtain economically optimal compensation decisions. Case studies demonstrate that the proposed framework effectively mitigates loss-of-load risk and improves both the economic performance and computational efficiency of compensation clearing. These results can support system operators and market operators in scenario-based adequacy studies and reliability-constrained clearing, and provide regulators and planners with quantitative evidence for designing capacity-remuneration mechanisms that facilitate secure renewable integration and sustainable power system operation.