Two-Stage Multi-Objective Optimization and Decision-Making Method for Integrated Energy System Under Wind Generation Disturbances

Although integrated energy systems (IES) are currently modest in size, their scheduling faces strong challenges, stemming from both wind generation disturbances and the system's complexity, including intrinsic heterogeneity and pronounced non-linearity. For this reason, a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration (MOGSOPE) is proposed to efficiently achieve the optimal solution under wind generation disturbances. The optimizer has an embedded trainable surrogate model, Deep Neural Networks (DNNs), to explore the common features of the multi-scenario search space in advance, guiding the population toward a more efficient search in each scenario. Furthermore, a multi-scenario Multi-Attribute Decision Making (MADM) approach is proposed to make the final decision from all alternatives in different wind scenarios. It reflects not only the decision-maker's (DM) interests in other indicators of IES but also their risk preference for wind generation disturbances. A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms. With respect to numerical performance metrics HV, IGD, and SI, the proposed optimizer exhibits improvements of 3.1036%, 4.8740%, and 4.2443% over MOGSO, and 4.2435%, 6.2479%, and 52.9230% over NSGAII, respectively. What's more, the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated, particularly in optimal scheduling of IES under wind generation disturbances.