New Electrical Engineering Data Have Been Reported by Researchers at Huazhong University of Science and Technology (A Privacy-Preserving Online Learning Approach for Incentive-Based Demand Response in Smart Grid)
- Jan 17, 2020 3:24 pm GMTJan 17, 2020 4:07 pm GMT
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2020 JAN 16 (NewsRx) -- By a News Reporter-Staff News Editor at Electronics Daily -- Investigators discuss new findings in Engineering - Electrical Engineering. According to news reporting from Hubei, People’s Republic of China, by NewsRx journalists, research stated, “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.”
The news correspondents obtained a quote from the research from the Huazhong University of Science and Technology, “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.”
According to the news reporters, the research concluded: “Numerical results validate that our proposed algorithm supports the large-scale dataset while striking a balance between the privacy-preserving level and social welfare.”
For more information on this research see: A Privacy-Preserving Online Learning Approach for Incentive-Based Demand Response in Smart Grid. IEEE Systems Journal, 2019;13(4):4208-4218. IEEE Systems Journal can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Systems Journal - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4267003)
Our news journalists report that additional information may be obtained by contacting P. Zhou, Huazhong University of Science and Technology, Wuhan 430074, Hubei, People’s Republic of China. Additional authors for this research include W.B. Chen, A.N. Zhou, L. Gao, S.L. Ji and D.P. Wu.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/jsyst.2018.2883448. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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