Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid
Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid
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DOI：
10.1109/tsg.2010.2089069
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发表时间：
2010-12-01
影响因子：
9.6
通讯作者：
Leon-Garcia, Alberto
中科院分区：
文献类型：
--
作者：
Mohsenian-Rad, Amir-Hamed;Wong, Vincent W. S.;Leon-Garcia, Alberto
大多数现有需求端管理计划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，我们在用户之间提出了一个自主和分布式需求端的能源管理系统，它利用了双向数字通信基础架构，该基础架构在未来的智能电网中被设想。我们使用游戏理论并制定一种能源消耗计划游戏，其中玩家是用户，他们的策略是他们家用电器和负载的每日时间表。假定公用事业公司可以采用足够的定价关税，从而在时间和级别上区分能源的使用情况。我们表明，对于一个常见的情况，在一家单一公用事业公司为多个客户提供服务时，在最小化能源成本方面的全球最佳性能是在配制的能源消耗计划游戏的NASH均衡中实现的。拟议的分布式需求侧能源管理策略要求每个用户将其最佳响应策略简单地应用于电源分配系统中当前的总负载和关税。用户可以维护隐私，不需要向其他用户揭示其能耗时间表的详细信息。我们还表明，用户将有激励措施参与能源消耗计划游戏并订阅此类服务。仿真结果证实，所提出的方法可以降低总能源需求，总能源成本以及每个用户的个人每日电费的峰值与平均值。
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.