Two-stage distributionally robust optimization model of integrated energy system considering wind–solar joint uncertainties and flexible resources management

As the proportion of renewable energy in new power system continues to grow, wind and solar energy play a significant role in energy conservation and emission reduction. However, the uncertainty of wind and solar leads to insufficient flexibility in integrated energy system (IES), which greatly affects the safe and stable operation of IES. This paper proposes a Copula-based two-stage distributionally robust optimization (CTSDRO) model to improve the flexibility of IES. First, the optimal time-varying Copula function is selected to describe the wind–solar joint uncertainties and to generate sample data, which are applied to construct ambiguity set based on the Wasserstein metric. Subsequently, the proposed model is developed by incorporating affinely adjustable policy and flexible resources management and is reformulated into a tractable model using duality theory and convex optimization techniques. The simulation results show: Compared to the traditional algorithm, the CTSDRO algorithm achieves a balance among economic performance, robustness, and computational efficiency. By incorporating flexible resources into the proposed model, the flexibility shortage is effectively mitigated and the upward flexibility increases by 22 % and the downward flexibility improves by 2.4 times under extreme operating conditions, without sacrificing economic performance.