Abstract：The strong uncertainty of renewable energy poses significant challenges to the reliability and safety of multi-microgrid (MMG). As the integration of renewable sources continues to grow globally, their inherent variability could lead to fluctuations in energy supply that complicate grid management. To address this, a distributed robust cooperative optimization scheduling model for MMG distribution systems based on an improved conditional generative adversarial network (CGAN) is proposed. Firstly, to accurately describe the uncertainty of renewable energy, the model uses scenario sets generated by the improved CGAN and reduced by the K-means++ clustering algorithm as the initial renewable energy scenarios for the distributionally robust optimization set. The improved CGAN model uses the Wasserstein distance as the discriminator loss function, effectively addressing issues of gradient vanishing and mode collapse during the optimization training process. By using day-ahead forecasts of renewable energy output as conditional variables, the improved CGAN learns the mapping between the conditional noise distribution and actual data, enhancing its ability to capture the stochastic characteristics of renewable energy production. Secondly, a DSO-MMG model considering dynamic power flow constraints and cloud storage leasing is constructed. The upper layer uses a Stackelberg game to describe the relationship between the DSO and MMG, while the lower layer establishes a benefit distribution method based on the contribution rate of multi-energy peer-to-peer (P2P) transactions. Dynamic power flow constraints reasonably limit the electric power flow during energy transactions, preventing overloads or voltage anomalies, thereby enhancing system stability and economic efficiency. The application of cloud energy storage reduces investment and management costs for microgrid storage, enabling them to store excess energy during surplus periods and release it during shortages, which decreases reliance on traditional grids and reduces operational costs. To address the benefit allocation issue, an asymmetric Nash bargaining model based on multi-energy P2P transaction contribution rates is proposed, ensuring fair distribution among participants and encouraging collaboration among microgrids. Then, a bisection method that couples parallel computing C&CG is proposed to solve the energy trading problem, allowing parallel operation of multiple computing units to accelerate the solving process. the improved ADMM is used to solve the benefit distribution problem. safeguarding participant privacy and improving algorithmic efficiency. The results show that the improved CGAN method proposed in this paper can describe the uncertainty of renewable energy more accurately and effectively, and the two-stage DRO model can avoid the overly conservative nature of traditional two-stage robust optimization while ensuring robustness, effectively balancing the economic efficiency and conservativeness of the microgrid system. In addition, the constructed hybrid game model effectively reduces the operation cost of multi-microgrids and stimulates the active participation of various stakeholders in energy trading. The solution algorithm of the model significantly improves the solution efficiency and protects the privacy of each subject.
李虹, 韩雨萌. 计及新能源不确定性的配电网-多微电网协同优化调度[J]. 电工技术学报, 2025, 40(17): 5571-5588.
Li Hong, Han Yumeng. Coordinated Optimization Scheduling of Distribution Networks and Multiple Microgrids Considering the Uncertainty of New Energy. Transactions of China Electrotechnical Society, 2025, 40(17): 5571-5588.
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2025, Vol. 40 
