Protecting Customer Privacy Through Distributed Energy Resource Anonymization

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2023 IEEE Power & Energy Society General Meeting (PESGM)

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Due to their stochastic nature, the increase of Renewable Energy Resources as primary sources of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in grid services is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and Grid Service Providers. Obtaining detailed information about customers’ power consumption can lead to privacy risks that may prevent users from willingly participating in services. Anonymization of individual data is one method of privacy protection that should be explored. This paper discusses the use of the IEEE 2030.5 [1] flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of DERs by making it more difficult to determine the individual characteristics of each device. We discuss the results of applying this form of anonymization to a set of simulated DER load profiles and examine the effectiveness of the anonymization through the use of a linear Support Vector Machine classifier.

Index Terms—Distributed Energy Resource, Anonymization, Renewable Energy Resource, Flow Reservation, Support Vector Machine


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