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

Hongwei Li, Qing Xu, Shitao Wang and Huihui Song
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Hongwei Li: Educational Administration Center, State Grid of China Technology College, Jinan 250002, China
Qing Xu: College of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China
Shitao Wang: Educational Administration Center, State Grid of China Technology College, Jinan 250002, China
Huihui Song: College of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China

Energies, 2022, vol. 15, issue 19, 1-17

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With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable.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.

Keywords: dynamic evaluation; peak shaving; distributed generation cluster; self-renewal mechanism; optimal dispatching (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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