Multi-Objective Coordinated Scheduling of Virtual Power Plants for Economic, Low-Carbon, and Stability Objectives: A DOA-NSGAII Hybrid Optimization Strategy

To achieve low-carbon, economical, and secure operation of power systems under the “dual carbon” goals, virtual power plants (VPPs) serve as a key technology for aggregating flexible resources such as distributed energy, storage, and demand response, making multi-objective coordinated scheduling critically important. However, the high dimensionality, multiple constraints, and conflicting objectives of this problem pose challenges for traditional optimization methods. Therefore, this paper proposes a hybrid intelligent optimization framework based on an improved Dream Optimization Algorithm (DOA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II), termed DOA-NSGAII, to solve the multi-energy scheduling problem of VPPs with objectives of minimizing economic cost, carbon emissions, and peak-valley load difference. Through customized encoding, decoding, and constraint-handling mechanisms, the framework integrates DOA’s global search capability with NSGA-II’s multi-objective decision-making advantages. Simulation experiments on the IEEE 14-bus system with three comparative schemes show that, compared with the single-objective economic optimization scheme (S1), the proposed multi-objective coordinated optimization scheme (S3) achieves significant comprehensive benefits—reducing carbon emissions by 30.5% and improving the peak-valley load difference by 12.0%—at the cost of only a marginal 4.9% increase in economic cost. This study validates the effectiveness of the DOA-NSGAII framework in solving complex engineering optimization problems and provides a new approach for multi-objective intelligent scheduling of VPPs.