Digital Twin‐Driven Two‐Stage Bi‐Level Collaborative Operation Strategy for Multi‐Microgrids Considering Energy Sharing and Price Incentives

ABSTRACT To enhance the techno‐economic performance and robustness of multi‐microgrids (MMG) systems, this paper proposes a two‐stage bi‐level collaborative optimisation strategy integrating energy sharing and price incentives. In the day‐ahead stage, the shared energy storage operator (SESO) at the upper level employs conditional Wasserstein generative adversarial network (CWGAN) and conditional value‐at‐risk (CVaR) to quantify renewable uncertainty risks, formulating day‐ahead transaction prices to maximise profit while deriving internal clearing prices based on supply‐demand ratios. The digital twin operator (DTO) is further utilised to execute the internal energy‐sharing clearing process, incorporating grid‐constrained pre‐dispatch to ensure the feasibility of the derived schedule. Simultaneously, the lower‐level MMG system minimises operational costs by optimising internal energy‐sharing and resource scheduling schemes. During the real‐time stage, the SESO adjusts real‐time prices based on a coupling mechanism with day‐ahead shared energy volumes, whereas MGs execute rolling optimisation via model predictive control (MPC). The bi‐level problem is solved using an improved particle swarm optimisation algorithm. Case studies demonstrate that compared to traditional P2P bidding models, the proposed method significantly reduces system operational costs and boosts SESO revenue. Specifically, compared to the traditional P2P bidding model, the proposed strategy reduces the aggregate dispatch cost of the MG cluster by 8.29% while simultaneously ensuring the operational profitability of the SESO. Furthermore, the standard deviation of grid exchange power decreases by 40.5%, indicating effective smoothing of power fluctuations and mitigating impact on the main grid.