Low-carbon optimal scheduling of integrated energy system with concentrating solar power based on entropy weight IGDT

Concentrating solar power (CSP) can effectively mitigate the randomness and intermittency of solar resources. To accurately characterize the operation characteristics of the CSP and address the multiple uncertainties, this study establishes a low-carbon optimal scheduling model of an integrated energy system with CSP based on entropy weight information gap decision theory (IGDT). Firstly, based on the start-up characteristics of the CSP, a refined model of a multi-period cumulative heat start-up for the CSP is established. Then, considering the multiple uncertainties, the IGDT is applied to address the new uncertainties in energy and load power. The entropy weight method is employed to eliminate the subjectivity associated with the weights of multiple uncertain variables and their corresponding deviation factors. Finally, the IGDT optimization model is compared with the established stochastic model, robust model, and robust stochastic model. The sensitivity of the uncertainties of the opportunity and robust strategies is also analyzed. The case studies verify the validity and feasibility of the established model of IES with CSP based on entropy weight IGDT, which provides a reference for operational research in CSP and uncertainty research in energy systems.