Distributed Demand Side Management with Energy Storage in Smart Grid
Distributed Demand Side Management with Energy Storage in Smart Grid
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DOI：
10.1109/tpds.2014.2372781
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发表时间：
2015-12-01
影响因子：
5.3
通讯作者：
Han, Zhu
中科院分区：
文献类型：
--
作者：
Hung Khanh Nguyen;Song, Ju Bin;Han, Zhu
在提高电网效率和可靠性的过程中，需求侧管理和分布式储能的整合起着至关重要的作用。在本文中，我们考虑了一个智能电力系统，其中用户配备了储能设备。用户将向能源供应商请求他们的能源需求，能源供应商根据用户的负荷情况确定他们的能源支付。通过调度由中央控制器调节的用户的能源消耗和存储，能源供应商试图最小化电力系统的瞬时能源需求和平均需求之间的平方欧几里德距离。用户打算通过联合调度他们的电器和控制他们的储能设备的充放电过程来减少他们的能源支付。应用博弈论建立了分布式设计中的能量消耗与存储博弈模型，参与者是用户，他们的策略是家电和存储设备的能耗计划。基于博弈论的建立和近似分解，我们还提出了两种由用户执行的分布式需求侧管理算法，其中每个用户试图最小化其能量支付，同时仍然保护用户的隐私以及最小化与中央控制器之间所需的信令量。仿真结果表明，所提出的算法对能源供应者和用户都是最优的。
Demand-side management, together with the integration of distributed energy storage have an essential role in the process of improving the efficiency and reliability of the power grid. In this paper, we consider a smart power system in which users are equipped with energy storage devices. Users will request their energy demands from an energy provider who determines their energy payments based on the load profiles of users. By scheduling the energy consumption and storage of users regulated by a central controller, the energy provider tries to minimize the square euclidean distance between the instantaneous energy demand and the average demand of the power system. The users intend to reduce their energy payment by jointly scheduling their appliances and controlling the charging and discharging process for their energy storage devices. We apply game theory to formulate the energy consumption and storage game for the distributed design, in which the players are the users and their strategies are the energy consumption schedules for appliances and storage devices. Based on the game theory setup and proximal decomposition, we also propose two distributed demand side management algorithms executed by users in which each user tries to minimize its energy payment, while still preserving the privacy of users as well as minimizing the amount of required signaling with the central controller. In simulation results, we show that the proposed algorithms provide optimality for both energy provider and users.