Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game

Yaohong Xie; Xiuli Wang; Zhicheng Wang; Meng Yang; Hujun Li; Fangzhao Deng

doi:10.1109/EI259745.2023.10512635

Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game

Yaohong Xie
, Xiuli Wang
, Zhicheng Wang
, Meng Yang
, Hujun Li
, Fangzhao Deng

School of Electrical Engineering

Xi'an Jiaotong University
State Grid Henan Economic Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

With the rapid growth of wind power installed capacity, battery energy storage system (BESS) on the wind power side has become an important method to alleviate the randomness and volatility of wind power. In order to study how BESS helps wind power manufacturers to participate in the real-time electricity market according to wind power fluctuation and load forecasting, a real-time charging and discharging strategy model of energy storage based on Bayesian game is proposed. The model uses the current wind power ultra-short-term forecast data and load ultra-short-term forecast data, and the impact of expected profits at several moments in the future to determine the current energy storage charge and discharge volume and provide guidance for the charge and discharge behavior of BESS. Finally, the validity of the model is verified by an example. The results show that wind power producer (WPP) equipped with energy storage can obtain a satisfactory income of participating in the real-time market.

Original language	English
Title of host publication	2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3305-3310
Number of pages	6
ISBN (Electronic)	9798350345094
DOIs	https://doi.org/10.1109/EI259745.2023.10512635
State	Published - 2023
Event	7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023 - Hangzhou, China Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023

Conference

Conference	7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023
Country/Territory	China
City	Hangzhou
Period	15/12/23 → 18/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Bayesian game
battery energy storage system
real-time market uncertainty
strategy optimization

Access to Document

10.1109/EI259745.2023.10512635

Cite this

Xie, Y., Wang, X., Wang, Z., Yang, M., Li, H., & Deng, F. (2023). Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game. In 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023 (pp. 3305-3310). (2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EI259745.2023.10512635

Xie, Yaohong ; Wang, Xiuli ; Wang, Zhicheng et al. / Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game. 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 3305-3310 (2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023).

@inproceedings{e75f8f62f436448eb6b910224887ee59,

title = "Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game",

abstract = "With the rapid growth of wind power installed capacity, battery energy storage system (BESS) on the wind power side has become an important method to alleviate the randomness and volatility of wind power. In order to study how BESS helps wind power manufacturers to participate in the real-time electricity market according to wind power fluctuation and load forecasting, a real-time charging and discharging strategy model of energy storage based on Bayesian game is proposed. The model uses the current wind power ultra-short-term forecast data and load ultra-short-term forecast data, and the impact of expected profits at several moments in the future to determine the current energy storage charge and discharge volume and provide guidance for the charge and discharge behavior of BESS. Finally, the validity of the model is verified by an example. The results show that wind power producer (WPP) equipped with energy storage can obtain a satisfactory income of participating in the real-time market.",

keywords = "Bayesian game, battery energy storage system, real-time market uncertainty, strategy optimization",

author = "Yaohong Xie and Xiuli Wang and Zhicheng Wang and Meng Yang and Hujun Li and Fangzhao Deng",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

doi = "10.1109/EI259745.2023.10512635",

language = "英语",

series = "2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3305--3310",

booktitle = "2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023",

}

Xie, Y, Wang, X, Wang, Z, Yang, M, Li, H & Deng, F 2023, Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game. in 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023. 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3305-3310, 7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023, Hangzhou, China, 15/12/23. https://doi.org/10.1109/EI259745.2023.10512635

Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game. / Xie, Yaohong; Wang, Xiuli; Wang, Zhicheng et al.
2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3305-3310 (2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game

AU - Xie, Yaohong

AU - Wang, Xiuli

AU - Wang, Zhicheng

AU - Yang, Meng

AU - Li, Hujun

AU - Deng, Fangzhao

PY - 2023

Y1 - 2023

N2 - With the rapid growth of wind power installed capacity, battery energy storage system (BESS) on the wind power side has become an important method to alleviate the randomness and volatility of wind power. In order to study how BESS helps wind power manufacturers to participate in the real-time electricity market according to wind power fluctuation and load forecasting, a real-time charging and discharging strategy model of energy storage based on Bayesian game is proposed. The model uses the current wind power ultra-short-term forecast data and load ultra-short-term forecast data, and the impact of expected profits at several moments in the future to determine the current energy storage charge and discharge volume and provide guidance for the charge and discharge behavior of BESS. Finally, the validity of the model is verified by an example. The results show that wind power producer (WPP) equipped with energy storage can obtain a satisfactory income of participating in the real-time market.

AB - With the rapid growth of wind power installed capacity, battery energy storage system (BESS) on the wind power side has become an important method to alleviate the randomness and volatility of wind power. In order to study how BESS helps wind power manufacturers to participate in the real-time electricity market according to wind power fluctuation and load forecasting, a real-time charging and discharging strategy model of energy storage based on Bayesian game is proposed. The model uses the current wind power ultra-short-term forecast data and load ultra-short-term forecast data, and the impact of expected profits at several moments in the future to determine the current energy storage charge and discharge volume and provide guidance for the charge and discharge behavior of BESS. Finally, the validity of the model is verified by an example. The results show that wind power producer (WPP) equipped with energy storage can obtain a satisfactory income of participating in the real-time market.

KW - Bayesian game

KW - battery energy storage system

KW - real-time market uncertainty

KW - strategy optimization

UR - https://www.scopus.com/pages/publications/85194193944

U2 - 10.1109/EI259745.2023.10512635

DO - 10.1109/EI259745.2023.10512635

M3 - 会议稿件

AN - SCOPUS:85194193944

T3 - 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023

SP - 3305

EP - 3310

BT - 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

Y2 - 15 December 2023 through 18 December 2023

ER -

Xie Y, Wang X, Wang Z, Yang M, Li H, Deng F. Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game. In 2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3305-3310. (2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023). doi: 10.1109/EI259745.2023.10512635

Real-Time Charging and Discharging Strategy of Energy Storage Considering Uncertainty of Wind Power and Load via Bayesian Game

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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