对于以可再生能源为主导的配电网络,在深度学习辅助的层次优化中进行多代理协调和不确定性适应
Yongle Zheng1, Huixuan Li1, Shiqian Wang1
1State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou, China.
Scientific reports
|January 13, 2026
概括
本研究介绍了一种深度学习增强的强大优化框架,用于电力系统,通过整合可再生能源来提高经济效率和可靠性. 新的深度DRO模型在不确定性下平衡了成本,可靠性和可再生能源的使用.
科学领域:
- 电力系统工程 电力系统工程
- 优化理论 优化理论
- 机器学习 机器学习
背景情况:
- 越来越多的可再生能源整合引入了电力系统运行中的复杂不确定性.
- 传统的优化方法很难管理来自太阳能和风能等来源的多层次不确定性.
- 现有的框架在平衡经济效率,运营可靠性和可再生能源利用方面面临挑战.
研究的目的:
- 提出一个新的深度学习辅助的分布式稳健优化 (Deep-DRO) 框架,以提高电力系统运行.
- 提高可再生能源透率高的电网的经济效率和运行可靠性.
- 通过集成深度学习来进行概率推断和强大的优化来实现全系统的弹性来动态管理不确定性.
主要方法:
- 开发了一个带有深度学习模块的层次协调架构,用于推断不确定的变量分布 (太阳能,风能,负载).
- 采用多代理调度结构 (县,料层,DER层) 与强化学习用于适应性协调.
- 综合深度网络用于场景分布估计和强大的优化核心,以最大限度地降低成本和可靠性处罚在模两可的情况下.
主要成果:
- 深度DRO模型将运营成本降低了11.0-13.5%,可靠性指数从0.864提高到0.911.
- 可再生能源利用率从85.6%增加到89.7%,在30%更高的不确定性变量下,弹性保持不变.
- 实现了28.6%的碳排放减少,证明了经济,环境和可靠性目标之间的平衡.
结论:
- 深度DRO框架为电力系统中的智能,风险意识的能源管理提供了一个可概括的范式.
- 层次学习和强大的优化有效地提高了在不确定性下适应性协调和绩效一致性.
- 该研究为未来的智能电网自主,可持续调度和电力系统恢复提供了理论和实际意义.
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