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Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management.

The optimal configuration of the rated capacity, rated power and daily output power is an important prerequisite for energy storage systems to participate in peak regulation on the grid side. Economic benefits are the main reason driving investment in energy storage systems. In this paper, the relationship between the economic indicators of an energy storage system and its configuration is first analyzed, and the optimization objective function is formulated. Then, according to the objective limitations of the energy storage system configuration and operation, the constraints are formulated. A set of typical parameters is selected, and the CPLEX (IBM ILOG CPLEX Optimization Studio) solver is used in MATLAB to solve the optimal configuration results. Several sets of optimization results are obtained by taking different subjective coefficient β values, and the economic and social benefits of the optimization results are analyzed. When the economic benefits of the energy storage system are more important, the value of β needs to be smaller, such as a value of 1000. Conversely, when the peak-regulation effect is more important, the value of β should be larger. Finally, configuration results of the control groups are given, and the effect of optimizing the calculation for improving economic benefits is verified.