Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
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DOI：
10.1109/naps50074.2021.9449714
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Amin Shojaeighadikolaei;Arman Ghasemi;Kailani R. Jones;Alexandru G. Bardas;M. Hashemi;R. Ahmadi
中科院分区：
文献类型：
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作者：
Amin Shojaeighadikolaei;Arman Ghasemi;Kailani R. Jones;Alexandru G. Bardas;M. Hashemi;R. Ahmadi
需求响应（DR）在提高电网稳定性和可靠性的同时减少客户的能源账单方面具有广泛认可的潜力。然而，传统的DR技术存在一些缺点，例如无法处理操作的不确定性和引起客户的不利影响，从而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，用于在产消主导的微电网中实现实时定价（RTP） DR技术。该技术解决了传统DR方法常见的几个缺点，为电网运营商和消费者提供了显著的经济效益。为了证明该方法的有效性，将该方法与小型微电网系统的基线传统运行场景进行了比较。最后，对产消者储能容量在该微电网中的使用情况进行了调查，突出了该方法在建立平衡市场设置方面的优势。
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.