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author:

Wu, Y. (Wu, Y..) [1] | Guo, Q. (Guo, Q..) [2] | Zheng, C. (Zheng, C..) [3] | Zheng, F. (Zheng, F..) [4] (Scholars:郑峰) | Pan, Z. (Pan, Z..) [5] | Zhang, J. (Zhang, J..) [6]

Indexed by:

Scopus PKU CSCD

Abstract:

Under the background of power market and low-carbon economy, in order to enhance the space-time complementarity between new energy power stations and improve the utilization rate of self-contained energy storage, the joint optimal operation and bidding strategy model of wind-solar reservoir group considering energy storage sharing is established. Firstly, based on the complementary characteristics of new energy power stations, the joint operation mechanism of wind-solar reservoirs considering energy storage sharing is designed, and the cooperative operation mechanism of wind-solar reservoirs is analyzed. Secondly, considering the market means to promote new energy consumption, the market revenue model of wind and solar power stations participating in the day-ahead energy market, self-contained energy storage and shared energy storage providing balanced fluctuation reserve capacity is established. On this basis, considering the uncertainty of electricity price and wind-solar output, a joint bidding strategy for wind-solar reservoir group is proposed to maximize the benefits of wind-solar reservoir group. Finally, the improved particle swarm optimization algorithm is used to solve the joint bidding and cooperative operation model. The simulation results show that the strategy proposed in this paper can effectively improve the utilization rate of self-provided energy storage, increase the income of wind-solar storage group, and play a positive role in the market consumption of new energy. © 2023 Authors. All rights reserved.

Keyword:

coordinated optimization scheduling improved particle swarm optimization algorithm market bidding strategy power market scenery storage group Shared energy storage

Community:

  • [ 1 ] [Wu Y.]Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde, 352100, China
  • [ 2 ] [Guo Q.]Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde, 352100, China
  • [ 3 ] [Zheng C.]Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde, 352100, China
  • [ 4 ] [Zheng F.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350000, China
  • [ 5 ] [Pan Z.]Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China
  • [ 6 ] [Zhang J.]Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China

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Source :

Journal of Electrical Engineering (China)

ISSN: 2095-9524

CN: 10-1289/TM

Year: 2023

Issue: 1

Volume: 18

Page: 219-227

Cited Count:

WoS CC Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 0

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