储能科学与技术 ›› 2025, Vol. 14 ›› Issue (6): 2555-2557.doi: 10.19799/j.cnki.2095-4239.2025.0484

• 储能测试与评价 • 上一篇    下一篇

基于深度学习的电网-储能联合调度策略设计

刘震宇(), 陈建, 尹兆磊, 杨慢慢   

  1. 国网冀北电力有限公司承德供电公司，河北 承德 067000
  • 收稿日期:2025-05-26 修回日期:2025-05-30 出版日期:2025-06-28 发布日期:2025-06-27
  • 通讯作者: 刘震宇 E-mail:liuzhenyu1021@163.com
  • 作者简介:刘震宇（1976—），男，硕士，正高级工程师，研究方向为电力系统及其自动化、继电保护、电网调控、电力通信及网络安全、新能源，E-mail：liuzhenyu1021@163.com。

Design of power grid energy storage joint dispatch strategy based on deep learning

Zhenyu LIU(), Jian CHEN, Zhaolei YIN, Manman YANG   

  1. State Grid jibei electric power co. ltd chengde power supply company, Chengde 067000, Hebei, China
  • Received:2025-05-26 Revised:2025-05-30 Online:2025-06-28 Published:2025-06-27
  • Contact: Zhenyu LIU E-mail:liuzhenyu1021@163.com

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摘要：

随着可再生能源的快速发展和智能电网技术的不断进步，电网-储能联合调度成为提高电力系统运行效率和可靠性的重要手段。本文提出了一种基于深度学习的电网-储能联合调度策略，通过数据预处理与特征提取、深度学习模型构建与优化、调度策略制定与实施等步骤，实现了对电网和储能系统的智能调度。实验结果表明，该策略能够显著提高电力系统的能源利用效率，降低运行成本，为电力系统的可持续发展提供了有力支持。

关键词: 深度学习, 调度, 储能

Abstract:

With the rapid development of renewable energy and the continuous advancement of smart grid technology, grid energy storage joint scheduling has become an important means to improve the operational efficiency and reliability of the power system. This article proposes a deep learning based power grid energy storage joint scheduling strategy, which achieves intelligent scheduling of the power grid and energy storage system through data preprocessing and feature extraction, deep learning model construction and optimization, scheduling strategy formulation and implementation, and other steps. The experimental results show that this strategy can significantly improve the energy utilization efficiency of the power system, reduce operating costs, and provide strong support for the sustainable development of the power system.

Key words: deep learning, dispatch, energy storage

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