论文标题
智能电网中分布式能源市场的多代理深入增强学习方法
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids
论文作者
论文摘要
本文介绍了基于增强的学习（RL）的能源市场，以占主导地位的微电网。拟议的市场模型促进了实时和需求的动态定价环境，从而降低了电网成本并改善了生产商的经济利益。此外，该市场模型使电网操作员能够利用造型的存储容量作为电网支持应用程序的可调度资产。基于深QNETWORK（DQN）框架的仿真结果表明，造型器和电网运营商的24小时累积利润以及网格储备功率利用率的重大减少。
This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers. Furthermore, this market model enables the grid operator to leverage prosumers storage capacity as a dispatchable asset for grid support applications. Simulation results based on the Deep QNetwork (DQN) framework demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the grid operator, as well as major reductions in grid reserve power utilization.
