Integrated energy system planning considering renewable energy uncertainties based on multi-scenario confidence gap decision

The problem of renewable energy uncertainties in the capacity planning of integrated energy system (IES) is prominent. To handle the multiple uncertainties, multi-scenario clustering analysis and classified confidence intervals of Gaussian mixture model (GMM) are combined, along with the robustness idea of information gap decision theory (IGDT), so a novel multi-scenario confidence gap decision theory (MCGDT) is proposed. Considering the comprehensive optimization objectives of maximizing exergy efficiency and minimizing annualized total cost, a robust capacity planning model for IES based on MCGDT is constructed to promote the cascade utilization of multiple energy and the economic performance of IES. Moreover, a new cross entropy-radar scanning differential evolution (CE-RSDE) algorithm is designed to improve solution efficiency and avoid prematurity in the optimization process. The simulation results of a typical case suggest that the proposed method leads to better capacity planning of IES, with up to 7.55% reduction of the annualized total cost and 14.03% increase in the exergy efficiency, while keeping strong robustness under the environment of multiple uncertainties.