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Hierarchical Demand Response for Peak Minimisation using Dantzig-Wolfe Decomposition

机译:使用Dantzig-Wolfe分解的峰值最小化的分层需求响应

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Summary form only given: Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.
机译:仅给出摘要形式:需求响应(DR)算法操纵可控负载的能耗计划,以满足电网目标。出于可伸缩性的原因,使用集中式代理实现DR算法可能会出现问题,并且存在与数据隐私和通信故障的鲁棒性有关的问题。因此,期望使用可扩展的分散算法来实现DR。在本文中,提出了一种基于Dantzig-Wolfe分解(DWD)的峰值最小化(PM)的分层DR方案。此外,成本函数中还包含一个时间加权最大化选项,该选项可提高寻求早日接收其期望能量的设备的服务质量。本文还演示了如何通过在每次DWD迭代之后计算上下限来更有效地实现DWD算法。

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