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Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

机译:基于博弈论能耗调度的未来智能电网自主需求侧管理

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Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
机译:现有的大多数需求方管理程序都主要关注公用事业公司与其客户/用户之间的交互。在本文中,我们介绍了一种用户之间的自治和分布式需求侧能源管理系统,该系统利用了未来智能电网中设想的双向数字通信基础架构。我们运用博弈论,制定了能源消耗调度游戏,其中玩家为用户,策略为家用电器和负荷的日常调度。假定公用事业公司可以采用适当的定价费率,以区分时间和级别上的能源使用。我们表明,在通常情况下,只有一家公用事业公司为多个客户提供服务,就可以在制定的能耗计划博弈的纳什均衡下实现就最小化能源成本而言的全球最佳性能。提议的分布式需求侧能源管理策略要求每个用户简单地将其最佳响应策略应用于配电系统中当前的总负荷和电价。用户可以维护隐私,而无需向其他用户透露其能源消耗时间表的详细信息。我们还表明,用户将有动力参加能耗调度游戏并订阅此类服务。仿真结果表明,该方法可以降低总能量需求,总能量成本以及每个用户的每日电费的峰均比。

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