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Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

机译：强化学习的动态定价和能耗调度

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摘要

In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.
机译：在本文中，我们研究了微电网中的动态定价和能耗调度问题，在该微电网中，服务提供商通过从公用事业公司购买电能并将其出售给顾客来充当公用事业公司与客户之间的经纪人。对于服务提供商而言，尽管动态定价是管理微电网的有效工具，但由于缺乏客户方信息以及微电网中各种不确定性，动态定价的实施仍具有很高的挑战性。同样，由于零售电价的不确定性，客户在安排能源消耗方面也面临挑战。为了克服实施动态定价和能耗调度的挑战，我们开发了强化学习算法，该算法使服务提供商和客户中的每个人都可以了解其策略而无需有关微电网的先验信息。通过数值结果，我们表明，提出的基于强化学习的动态定价算法可以在没有系统动力学先验信息的情况下有效地工作，并且由于每个客户的学习能力，提出的能耗调度算法可以进一步降低系统成本。

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