A Distributed Algorithm for Managing Residential Demand Response in Smart Grids
A Distributed Algorithm for Managing Residential Demand Response in Smart Grids
复制标题
DOI：
10.1109/tii.2014.2316639
复制
发表时间：
2014-11-01
影响因子：
12.3
通讯作者：
Lehtonen, Matti
中科院分区：
文献类型：
--
作者：
Safdarian, Amir;Fotuhi-Firuzabad, Mahmud;Lehtonen, Matti
由时变价格实现的需求响应可以推动电力行业提高效率。然而，客户的不协调反应可能会导致价格较低时期的严重峰值反弹。在这方面，协调的需求响应计划可以减轻对峰值反弹的担忧。本文提出了一种全系统的需求响应管理模型，以协调住宅客户提供的需求响应。该模型的目标是使总负载曲线变平，使客户的个人成本最小化。该模型首先被表述为双层优化问题。然后将其转化为等效的单级问题，并通过迭代分布式算法来解决。嵌入客户智能电表中的家庭负载管理 (HLM) 模块是与算法相关的自主代理。在该算法中，首先，HLM模块根据公用事业公司公布的价格，优化家用电器的日常运行并发送回预定的负载曲线。然后，总负荷曲线由公用事业公司计算并发布。此后，HLM 模块异步更新其调度，以便在能量消耗最少的情况下实现最均匀分布的总负载曲线。实用程序和 HLM 模块之间的相互交互一直持续到没有获得进一步改进的程度。证明了算法的收敛性和最优性。
Demand response enabled by time-varying prices can propel the power industry toward a greater efficiency. However, a noncoordinated response of customers may lead to severe peak rebounds at periods with lower prices. In this regard, a coordinated demand response scheme can mitigate concerns about the peak rebounds. This paper presents a system-wide demand response management model to coordinate demand response provided by residential customers. The objective of the model is to flatten the total load profile that is subject to minimum individual cost of customers. The model is first formulated as a bi-level optimization problem. It is then casted into equivalent single-level problems, which are solved via an iterative distributed algorithm. Home load management (HLM) modules embedded in customers' smart meters are autonomous agents associated with the algorithm. In the algorithm, at first, HLM modules, in response to prices announced by the utility, optimize the daily operation of household appliances and send back the scheduled load profiles. Then, the total load profile is calculated and released by the utility. Thereafter, the HLM modules asynchronously update their schedule such that, given their least energy expenses, the most evenly distributed total load profile is achieved. The mutual interaction between the utility and HLM modules is continued to the point in which no further improvement is obtained. Convergence and optimality of the algorithm are proved.