论文标题
需求响应式动态定价框架，用于使用多重强化学习，以占主导地位的微电网占主导地位
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
论文作者
论文摘要
需求响应（DR）具有改善电网稳定性和可靠性的同时，在减少客户能源账单的同时，具有广泛认可的潜力。但是，传统的DR技术带有几个缺点，例如无法处理运营不确定性和产生客户的分离，这阻碍了他们在现实世界中广泛采用的广泛利用。本文提出了一种新的多重增强学习（RL）的决策环境，用于在占主导地位的微电网中实现实时定价（RTP）DR技术。该提出的技术解决了传统DR方法常见的几个缺点，并为电网操作员和生产者提供了重大的经济利益。为了显示其更好的功效，将提出的DR方法与小型微电网系统中的基线传统操作方案进行了比较。最后，在此微电网中使用制造商储能容量的调查突出了拟议方法建立平衡市场设置的优势。
Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle operational uncertainties and incurring customer disutility, impeding their wide spread adoption in real-world applications. This paper proposes a new multiagent Reinforcement Learning (RL) based decision-making environment for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated microgrid. The proposed technique addresses several shortcomings common to traditional DR methods and provides significant economic benefits to the grid operator and prosumers. To show its better efficacy, the proposed DR method is compared to a baseline traditional operation scenario in a small-scale microgrid system. Finally, investigations on the use of prosumers energy storage capacity in this microgrid highlight the advantages of the proposed method in establishing a balanced market setup.
