CJEE

Chinese Journal of Electrical Engineering ›› 2023, Vol. 9 ›› Issue (3): 15-25.doi: 10.23919/CJEE.2023.000031

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Resilience-oriented Valuation for Energy Storage Amidst Extreme Events*

Youzhen Wu1, Jianxiao Wang2,*, Yiyang Song3, Yunyun Xie4   

  1. 1. College of Engineering, Peking University, Beijing 100871, China;
    2. National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China;
    3. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China;
    4. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
  • Received:2023-07-16 Revised:2023-07-28 Accepted:2023-08-04 Online:2023-09-25 Published:2023-08-11
  • Contact: *E-mail: wang-jx@pku.edu.cn
  • About author:Youzhen Wu received the B.S. degree in Communication Engineering from Yuan Ze University, Taiwan, China, in 2018. Currently, she is a Ph.D. candidate in the Department of Industrial Engineering and Management at Peking University in Beijing, China. Her research interests focus on smart grid resilience and data center operation and planning.
    Jianxiao Wang (Member, IEEE) received the B.S. and Ph.D. degrees in Electrical Engineering from Tsinghua University, Beijing, China, in 2014 and 2019. He was a Visiting Student Researcher at Stanford University, Stanford, CA, USA. He is currently an Assistant Professor at Peking University, Beijing, China. His research interests include smart grid operation and planning, hydrogen and storage technology, transportation and energy systems integration, electricity markets, and data analytics.
    Yiyang Song received the B.S. degree in Electrical Engineering from the Shanghai University of Electric Power, Shanghai, China, in June 2021. He is currently pursuing a master’s degree at the College of Electrical Engineering, North China Electric Power University, Beijing, China. His research interests include distribution network planning and operation, and the electricity market.
    Yunyun Xie received the B.Eng. degree in Electrical Power Engineering and the Ph.D. degree in Control Science and Engineering from Nanjing University of Science and Technology, Nanjing, China, in 2007 and 2013, respectively. He is currently an Assistant Professor at the School of Automation, Nanjing University of Science and Technology. His main research interests include power system restoration and power system transient stability.
  • Supported by:
    *National Key Research and Development Program (No. 2022YFB2405600) and the National Natural Science Foundation of China (No. 52277092).

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Abstract: In power grids, the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate attacks. This has sparked discussions on the resilience of power grids. Energy-storage systems (ESSs) are critical for enhancing the resilience of power grids. ESSs, with their mechanism of flexible charging and discharging, adjust energy usage as needed during disasters, thereby mitigating the impact on the grid and enhancing security and resilience. This, in turn, ensures the power system’s stable operation. Currently, there is limited systematic research quantifying the economic value of energy storage in resilience scenarios. Therefore, a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events. A two-stage stochastic optimization mathematical model was developed. The first stage involves pre-deployment based on day-ahead expectations, and the second stage involves simulating potential failure scenarios through real-time scheduling. Considering the temporal dimension, the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of load-shedding. Here, a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity, as reflected in the emergency dispatch model. This index helps determine the balance between the energy storage investment cost and resilience value. Finally, an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed method. The findings revealed a significant improvement in the resilience value, with a 23.49% increase observed when energy storage systems were implemented compared to the scenario without energy storage systems. The optimal capacity configurations for the flywheel, lithium-ion batteries, and pumped hydro storage were 10 MW, 11 MW, and 12 MW, respectively, highlight their potential to maximize value in experimental system.

Key words: Energy storage dispatch model, power system resilience, resilience-oriented valuation, two-stage optimization model