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A Privacy-Preserving Online Learning Approach for Incentive-Based Demand Response in Smart Grid

机译：智能电网中基于激励的需求响应的隐私保护在线学习方法

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摘要

Incentive-based demand response (IDR) programs enable smart grid customers to participate in the demand reduction, triggered by system contingencies or peak load, to improve reliability, sustainability, security, and efficiency of the power grid. However, the deployment of smart meters and the increasing number of customers in IDR programs make the data generated in the smart grid at a large scale. Meanwhile, fine-grained data from smart meters can deluge customer's lifestyle and usage pattern, posing threat to customer privacy. Therefore, privacy-preserving demand source management techniques that support increasingly large-scale datasets are in urgent need. In this paper, we propose an online privacy-preserving IDR management system, in which social welfare is maximized through recommending the optimal consumer to the utility company. Since the contexts of electricity curtailment offers from the utility company are different, an adaptive context partition method is proposed to enable the system context awareness. In addition, we cluster the customers in a tree structure to make the analyses of the customers in the cluster level and thus enable the algorithm to support the large-scale system. Furthermore, a tree-based noise aggregation method is applied to guarantee both the differential privacy of customer's sensitive information and the utility of the data. Theoretical analysis shows that our proposal guarantees differential privacy of customers, while converging to the optimal policy in a long run. Numerical results validate that our proposed algorithm supports the large-scale dataset while striking a balance between the privacy-preserving level and social welfare.
机译：基于激励的需求响应（IDR）程序使智能电网客户能够参与因系统突发事件或峰值负载而触发的需求减少，从而提高电网的可靠性，可持续性，安全性和效率。但是，智能电表的部署以及IDR计划中越来越多的客户，使得智能电网中生成的数据量很大。同时，来自智能电表的细粒度数据可能会淹没客户的生活方式和使用方式，从而对客户隐私构成威胁。因此，迫切需要支持越来越多的大规模数据集的保护隐私的需求源管理技术。在本文中，我们提出了一种在线隐私保护IDR管理系统，该系统通过向公用事业公司推荐最佳消费者来最大化社会福利。由于公用事业公司削减电力的情境不同，因此提出了一种自适应的情境划分方法来实现系统情境感知。另外，我们将客户聚类为树状结构，以在聚类级别对客户进行分析，从而使该算法能够支持大型系统。此外，基于树的噪声聚合方法被应用来保证客户敏感信息的差异隐私和数据的实用性。理论分析表明，我们的建议保证了客户的差异隐私，同时从长远来看会收敛到最佳策略。数值结果验证了我们提出的算法在保持隐私级别和社会福利之间取得平衡的同时，支持大规模数据集。

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  • 来源
    《IEEE systems journal》 |2019年第4期|4208-4218|共11页
  • 作者

    Chen Wenbo; Zhou Anni; Zhou Pan; Gao Liang; Ji Shouling; Wu Dapeng;

  • 作者单位

    Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Dept Ind & Mfg Syst Engn Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China;

    Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China|Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA;

    Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Differential privacy; incentive-based demand response (IDR); large-scale datasets; smart grid;

    机译：差异性隐私;基于激励的需求响应（IDR）;大规模数据集智能电网;

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