Applications of Equilibrium Decision Theory in Low-Carbon Economic Operation of Wind-Solar-Hydrogen Integrated Energy System Considering Prediction Uncertainties

With the increasing integration of renewable energy into power grids, integrated energy systems (IES) have gained global attention. However, the stochastic and uncertain characteristics of wind and solar challenge the stable grid operation significantly. To address these issues, this article proposes an optimal scheduling model for wind-solar-hydrogen IES, incorporating prediction uncertainty and demand response (DR). The equilibrium decision theory framework, combined with frequency enhanced decomposed transformer, interval prediction and decision, is proposed to quantify the uncertainties and determine the action plans. Then, an optimal scheduling model is designed to maximize economic revenue and minimize pollution emissions (PE), which is solved using multiobjective elephant clan optimization algorithm. Furthermore, DR strategy is integrated to enhance system flexibility and stability. Empirical results demonstrate that increased uncertainty leads to more conservative scheduling, improving system robustness. Additionally, DR boosts ER by over 3.50% and reduces PE by more than 3.20% across various scenarios.