Energy Loss Reduction for Distribution Networks with Energy Storage Systems via Loss Sensitive Factor Method

Xiangming Wu, Chenguang Yang, Guang Han, Zisong Ye and Yinlong Hu
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Xiangming Wu: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Chenguang Yang: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Guang Han: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Zisong Ye: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Yinlong Hu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

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

Abstract: The loss of distribution networks caused by various electrical motors including transformers and generators can significantly affect the efficiency and economical operation of the power grid, especially for new power systems with high penetration of renewable energies. In this paper, the potential of using an energy storage system (ESS) for loss reduction is investigated, where a novel two-stage method for key-bus selection and ESS scheduling is proposed. At the first stage, the most sensitive key buses to the variation of load are selected by using the loss sensitive factors (LSF) method. At the second stage, ESS scheduling is conducted by solving an optimization problem with uncertainties caused by high penetration of renewable energies, where the uncertainties are characterized by confidence levels. The optimal scheduling of ESS including locations, capacities, and working modes are obtained at the second stage. The effectiveness of the proposed method is demonstrated via numerical simulations. The influences of capacities of ESS and confidence levels with respect to uncertainties are also analyzed. It is demonstrated that the loss-reduction performances can be improved if the ESSs are deployed on the buses selected by the LSF method and operated under the developed optimal scheduling method.

Keywords: loss reduction; energy storage system; loss sensitivity factors; renewable energy; chance constraint (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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