在VPP中大规模协调分布式能源资源:一个基于RL的双层优化方法
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了一种新的双层优化方法,使用平均场强化学习 (MFRL) 来协调虚拟发电厂 (VPP) 中的大规模分布式能源 (DER),显著降低成本并改善融合.
科学领域:
- 电力系统工程 电力系统工程
- 人工智能的人工智能
- 优化理论 优化理论
背景情况:
- 在虚拟发电厂 (VPP) 中协调分布式能源 (DER) 提供了经济和稳定性的好处.
- 多样化和不确定的DER的大规模协调构成了重大研究挑战.
- 目前的方法与VPP中的DER的规模和异质性作斗争.
研究的目的:
- 提出一个新的双层优化框架,用于在VPP中大规模协调DER.
- 解决DERs中异质性和不确定性的挑战.
- 提高VPP运营的效率和经济可行性.
主要方法:
- 一种双层优化方法,结合了平均场强化学习 (MFRL) 和混合整数线性编程 (MILP).
- 上层优化利用MFRL与快速Shapley信贷分配进行大规模协调.
- 下级优化使用MILP来管理异质集成能源系统 (IES).
主要成果:
- 拟议的方法显著提高了VPP运行的趋同速度.
- 显示了全球运营成本的大幅降低,特别是在大规模场景中.
- 与基线方法相比,在10-500种药物中实现了4.8%-26.6%的客观改善.
结论:
- 双层优化方法有效地使VPP中的DERs的大规模协调成为可能.
- 与MILP相结合的MFRL为复杂的VPP管理提供了可扩展和高效的解决方案.
- 该方法为现代电力系统提供了显著的经济效益和运营改进.
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