Optimization Configuration of Hybrid Energy Storage System Capacity with Grid-connecting Wind Power
编号:47 访问权限:仅限参会人 更新:2022-09-26 21:36:28 浏览:276次 张贴报告

报告开始:2022年11月04日 15:06(Asia/Shanghai)

报告时间:12min

所在会场:[S] Power System and Automation [PS5] Poster Session 5

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摘要
In order to improve the scheduling flexibility of grid connected wind power generation system, it is necessary to apply energy storage technology, and the main key technology of energy storage system is how to determine the capacity configuration of energy storage system. Using the individual advantages of superconducting magnetic energy storage (SMES), battery energy storage and hydrogen storage, the capacity is configured, which is an energy management strategy based on the principle of meeting the load power shortage rate and improving the overall economy of the energy storage system. According to the whole life cycle cost theory, the annual average cost function expression of energy storage device is established, and the optimal allocation model of energy storage capacity is proposed with the minimum value of the function as the goal and the operation indices such as load shortage rate as constraints. An example is calculated by using the improved particle swarm optimization algorithm to verify the correctness, effectiveness and stability of the optimization model and algorithm.
关键词
Grid-connected wind power generation system, hybrid energy storage, improved particle swarm optimization algorithm, load power shortage rate, superconducting magnetic energy storage (SMES).
报告人
Jianhong Wang
North China Electric Power University

稿件作者
Jianhong Wang North China Electric Power University
Yinshun Wang North China Electric Power University
Lecheng Wang North China Electric Power University
Zikun Zhao North China Electric Power University
Yubo Gao North China Electric Power University
Xindan Zhang North China Electric Power University
Wei Liu North China Electric Power University
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重要日期
  • 会议日期

    11月03日

    2022

    11月05日

    2022

  • 08月01日 2022

    初稿截稿日期

  • 11月04日 2022

    注册截止日期

  • 11月05日 2022

    报告提交截止日期

主办单位
Huazhong University of Science and Technology
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