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.

中文翻译：

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使用多智能体强化学习的产消者主导微电网的需求响应动态定价框架

需求响应 (DR) 具有广泛认可的潜力，可以提高电网稳定性和可靠性，同时降低客户的能源费用。然而，传统的 DR 技术有几个缺点，例如无法处理操作不确定性和导致客户不实用，阻碍了它们在实际应用中的广泛采用。本文提出了一种新的基于多智能体强化学习 (RL) 的决策环境，用于在产消者主导的微电网中实施实时定价 (RTP) DR 技术。所提出的技术解决了传统 DR 方法常见的几个缺点，并为电网运营商和产消者提供了显着的经济效益。为了显示其更好的功效，将所提出的 DR 方法与小规模微电网系统中的基线传统运行场景进行比较。最后，对该微电网中产消者储能容量使用情况的调查突出了所提出的方法在建立平衡市场设置方面的优势。