PeHEMS: Privacy enabled HEMS and load balancing prototype

G. Kalogridis, Saraansh Dave
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引用次数: 11

Abstract

Smart grid efficient load balancing and the need for privacy are, in principle, contradictory. While richer information obtained from frequent energy readings help improve both the prediction and the control of the demand, and, effectively, improve the efficiency of the energy equilibrium production problem, it also gives rise to consumer privacy concerns. This is possible by analysing energy signatures to detect appliance usage and home living patterns of behaviour, which in effect cascades to a range of privacy invasion risks. This paper argues that the objective of energy efficiency might not necessarily be contradictory to protecting user privacy. In particular, we introduce a new notion of smart meter privacy which we call reconciled privacy and we connect it with a simple energy management algorithm that caps the energy a home may consume in 30 minute intervals by using a rechargeable battery system. System benchmarking is underpinned by formulating a methodology to assess a) utility cost savings, b) wholesale energy savings, and c) privacy protection. Our results suggest that the proposed algorithm will protect customer privacy and will improve energy production efficiency as compared with other energy management schemes. This is due to the algorithm's principle of promoting a universal consumption pattern that is close to its average, which in retrospect allows individual usage differences to be absorbed. To support this work, we use data from trials in Bristol city, which forms part of the 3eHouses EU FP7 project, and we present a prototype implementation showcasing the visualisation of privacy and energy control.
PeHEMS:支持隐私的HEMS和负载平衡原型
从原则上讲,智能电网的高效负载平衡和对隐私的需求是矛盾的。虽然从频繁的能源读数中获得的更丰富的信息有助于改善需求的预测和控制,并有效地提高能源平衡生产问题的效率,但它也引起了消费者隐私问题。这可以通过分析能源特征来检测家电使用和家庭生活行为模式,这实际上会导致一系列隐私侵犯风险。本文认为,能源效率的目标可能不一定与保护用户隐私相矛盾。特别是,我们引入了智能电表隐私的新概念,我们称之为协调隐私,我们将其与一个简单的能源管理算法联系起来,该算法通过使用可充电电池系统来限制家庭在30分钟内可能消耗的能源。系统基准是通过制定一种方法来评估a)公用事业成本节约,b)批发能源节约,以及c)隐私保护。我们的研究结果表明,与其他能源管理方案相比,所提出的算法将保护客户隐私并提高能源生产效率。这是由于该算法的原则是促进接近其平均值的普遍消费模式,回顾起来,这允许吸收个人使用差异。为了支持这项工作,我们使用了布里斯托尔市的试验数据,这是3eHouses欧盟FP7项目的一部分,我们提出了一个原型实现,展示了隐私和能源控制的可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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