Approach to Multi-Timescale Optimization for Distributed Energy Resources Clusters Considering Flexibility Margin

Abstract

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China’s power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid.