A framework for the day-ahead optimal dispatch of active power regulation with shared energy storage participating in the new power system

To address the challenges of multi-time-scale coupling, multi-agent decision-making, and heterogeneous resource coordination when shared energy storage participates in active power regulation in high-wind power systems across all time scales, this paper proposes a collaborative regulation mechanism between shared energy storage and conventional units to achieve coordination and responsibility allocation between the two across multiple time scales. A day-ahead reserve capacity reservation optimization scheduling model is constructed, with the objective of minimizing the total system cost. Under the premise of ensuring frequency security, resisting intra-day disturbances, and considering wind power curtailment risks, the advantages of shared energy storage are fully exploited. Meanwhile, a data-driven artificial intelligence prediction algorithm is introduced to build a wind power output prediction model to solve the uncertainty of wind power. Simulation analysis shows that this framework can optimally coordinate the three heterogeneous resources of shared energy storage, conventional units, and wind power, improving the economic operation of the system and the accommodation capacity of wind power while ensuring frequency security.