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author:

Shi, Pengjia (Shi, Pengjia.) [1] | Zhang, Linyao (Zhang, Linyao.) [2] | Zhu, Zhenshan (Zhu, Zhenshan.) [3] (Scholars:朱振山) | Zheng, Hailin (Zheng, Hailin.) [4] | Weng, Zhimin (Weng, Zhimin.) [5] | Wen, Buying (Wen, Buying.) [6] (Scholars:温步瀛)

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EI

Abstract:

With the further open of the electricity market, the electricity retailers often need to configure appropriate amount of energy storage to reduce the operation cost. In order to solve this problem, an energy storage capacity optimization model is proposed to minimize the operation cost of the electricity retailers under the participation of multiple flexible resources. The adjustable resources and operation cost of the electricity retailer is analyzed. And the economic model of energy storage allocation for the electricity retailer is built based on the life cycle cost. In order to solve the sequential decision-making model, the Soft Actor Critic algorithm (SAC) is used to find the optimal storage capacity allocation for electricity retailer based on grid search. Finally, the proposed model is verified by various simulations. © 2021 IEEE

Keyword:

Decision making Deep learning Electric energy storage Life cycle Operating costs Reinforcement learning Sales

Community:

  • [ 1 ] [Shi, Pengjia]Economic Technology Research Institute, State Grid Fujian Electric Power Company, Fujian, China
  • [ 2 ] [Zhang, Linyao]Economic Technology Research Institute, State Grid Fujian Electric Power Company, Fujian, China
  • [ 3 ] [Zhu, Zhenshan]College of Electrical and Automation, Fuzhou University, Fujian, China
  • [ 4 ] [Zheng, Hailin]College of Electrical and Automation, Fuzhou University, Fujian, China
  • [ 5 ] [Weng, Zhimin]College of Electrical and Automation, Fuzhou University, Fujian, China
  • [ 6 ] [Wen, Buying]College of Electrical and Automation, Fuzhou University, Fujian, China

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Source :

Year: 2021

Page: 3810-3815

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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