Comprehensive energy system planning with a focus on electric-thermal load correlations

Abstract

Deepening research on electrothermal integrated energy systems has heightened the coupling between electric power and thermal systems. Accurate electrothermal load scenario modeling and thorough consideration of their interdependencies are crucial for effective planning and scheduling. The traditional method of generating scenarios cannot fully reflect the full complexity of the original power load. To address this, our paper introduces an enhanced clustering approach. Employing the Frank-Copula function to express the correlation between electric and thermal loads, we optimize the clustering and scene reduction sequence, yielding correlated typical electric and thermal load datasets. These refined load profiles serve as the foundation for comprehensive planning and analysis of the integrated energy system.