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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1444791
This article is part of the Research Topic Optimal Scheduling of Demand Response Resources In Energy Markets For High Trading Revenue and Low Carbon Emissions View all 29 articles

Optimal scheduling of distributed shared energy storage based on optimal SOC interval

Provisionally accepted
Liudong Zhang Liudong Zhang 1Tong Zhang Tong Zhang 2*Yan Chen Yan Chen 3Zhiqiang Peng Zhiqiang Peng 2
  • 1 State Grid Jiangsu Electric Power Company, Nanjing, Liaoning Province, China
  • 2 State Grid Jiangsu Electric Power Co., LTD, Nanjing, China
  • 3 Other, Yangzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    Proposed within the framework of the sharing economy, Shared Energy Storage (SES) aims to enhance the efficiency of Energy Storage Systems (ESS) and drive down costs. This study focuses on an innovative approach to emphasize the multifaceted utilization of individual ESS units and the centralized use of dispersed ESS resources. Renewable Energy Power Plants (REPPs) collaborate to create SES pools, leveraging their ESS assets. Beyond meeting the needs of REPPs, these resources are shared for ancillary services like Secondary Frequency Regulation (SFR) to yield additional benefits. The paper delves into the scheduling techniques for SES. While conventional day-ahead robust optimization algorithms specify ESS power output for each period, they struggle to adjust schedules due to time-dependent constraints like renewable energy output and ESS state limitations. To address this, a distributed SES scheduling method based on optimal operating intervals is proposed. This method introduces an optimal interval variable for Energy Storage State of Charge (SOC) into the traditional three-layer optimization problem, effectively decoupling time-related constraints. Furthermore, a novel Nested Column and Constraint Generation (Nested C&CG) algorithm is presented to solve the mathematical model. Lastly, a revenue sharing model grounded in cooperative game theory is introduced, along with an illustrative example showcasing the efficacy of the proposed approach in managing uncertainties.

    Keywords: distributed shared energy storage1, optimization scheduling method2, the optimal SOC interval3, multiple auxiliary services4, time coupling5. (Min.5-Max. 8

    Received: 06 Jun 2024; Accepted: 25 Sep 2024.

    Copyright: © 2024 Zhang, Zhang, Chen and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Tong Zhang, State Grid Jiangsu Electric Power Co., LTD, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.