Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

Abstract

As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster’s task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably.