Distributed Demand Response for Multienergy Residential Communities With Incomplete Information

Distributed Demand Response for Multienergy Residential Communities With Incomplete Information
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不完全信息多能源住宅社区的分布式需求响应

DOI:
10.1109/tii.2020.2973008
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发表时间:
2021-01
影响因子:
12.3
通讯作者:
Shengli Xie
Shengli Xie
中科院分区:
计算机科学1区
文献类型:
--
作者:
Weifeng Zhong;Yi Liu;Chao Yang;Shengli Xie

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本文提出了多能源住宅社区的分布式需求响应(DR)方法,该社区配备了各种能量转换和存储设备,以服务于多个住宅负载(例如,电力、天然气和加热负荷)。在所提出的DR方法中,每个能量设备和负载都是单独的决策者,也是随机连接的通信网络中的节点。DR方法对网络中节点和链路的随机不作为所导致的不完全信息具有容忍性。首先,为了协调分布式DR中节点的行为,节点之间采用不同的信息传输机制。特别地,提出了Steiner树广播,其中节点根据其能量类型进行组网,以降低节点的计算复杂度和网络的通信开销。基于信息传输机制,初始DR问题转化为可在随机网络中求解的网络问题。然后,基于乘子随机交替方向法,设计了求解不完全信息网络问题的分布式算法。在仿真中,使用真实世界的多个能源负荷和价格的数据集,并提出了DR方法的收敛性能和通信开销进行了比较。
This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes’ behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes’ computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.
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