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Demand response management using cloud enabled load optimization

机译:使用基于云的负载优化进行需求响应管理

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Energy demand for users of residential, commercial, industrial and agriculture has been largely uncontrollable and inelastic in relation to power production. There is urgency in power sector to provide flexibility in energy use by various sectors so as to maintain the stability and efficiency of the electric system. At the same time it is important to maintain the minimum peak average ratio (PVR). DSM can smoothen the peak to-average ratio (PAR) of power usage in electricity supply. In this paper, the main aim is to encourage the consumers to use less energy during peak hours by sending message alerts to the particular consumer with the cost allocated for the unit usage and to maintain the maximum profit profile for generation companies. For this we simulated power generation and the distribution models with LabVIEW technology. From this simulated model we collect the data of the generation and the distribution systems for every interval. This is stored in the data base of the cloud platform. When the demand profile is more than the generation profile from the collected cloud data, peak load time is observed on that time and the collected data is compared by the optimized data which is taken by the maximum profit optimized solution. In this case sending the message alerts to the consumer and consumer also be a part of DSM of the electricity system.
机译:相对于电力生产,住宅,商业,工业和农业用户的能源需求在很大程度上是不可控制的且缺乏弹性。电力部门迫切需要为各个部门的能源使用提供灵活性,以保持电力系统的稳定性和效率。同时,保持最小峰均比(PVR)很重要。 DSM可以平滑电力供应中的用电高峰平均比(PAR)。在本文中,主要目的是通过向特定消费者发送消息警报(通过为单位使用分配费用)来鼓励消费者在高峰时段使用更少的能量,并保持发电公司的最大利润。为此,我们使用LabVIEW技术模拟了发电和配电模型。从这个模拟模型中,我们收集每个时间间隔的发电和配电系统数据。这存储在云平台的数据库中。当需求概况大于收集的云数据的生成概况时,会观察到该时间的峰值负载时间,并将收集的数据与最大利润优化解决方案所获取的优化数据进行比较。在这种情况下,将消息警报发送给用户,用户也将成为电力系统DSM的一部分。

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