基于利亚普诺夫的微电网能源管理安全增强学习
概括
本研究介绍了微电网 (MG) 能源管理的安全强化学习 (SRL) 框架. 通过完善能源管理方案,SRL确保了安全和高效的运行,改善了经济结果.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 控制理论 控制理论
背景情况:
- 可再生能源 (RES) 融入微电网 (MGs) 需要先进的能源管理.
- 基于模型的方法由于重新设计的需要而缺乏效率,而无模型的方法在培训期间面临安全挑战.
研究的目的:
- 为高效和安全的MG能源管理提出安全强化学习 (SRL) 框架.
- 解决在机械机械运营中无模型方法的安全问题.
主要方法:
- 开发安全评估优化模型 (SAOM) 以评估和完善能源管理方案.
- 制定MG能源管理作为一个基于评估的受约束的马尔科夫决策过程 (A-CMDP).
- 应用基于Lyapunov的安全政策优化来学习代理政策,以确保运营安全.
主要成果:
- 拟议的SRL框架有效地学习了MG的能源管理政策.
- 该框架确保了整个学习过程中MG操作的安全性.
- 在MG的经济运营中表现出卓越的表现.
结论:
- 该SRL框架为MG能源管理提供了安全有效的解决方案.
- 该方法通过优化能源运营来提高MG的经济可行性.
- 这种方法克服了传统基于模型和无模型方法的局限性.
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