Local privacy‐friendly verification of customer participation in frequency regulation services using smart meter data

• Published in: IET smart grid 2024-10, Vol.7 (5), p.593-609
• Main Authors: Palacios‐Garcia, Emilio J., Vrabel, Vladimir, Deconinck, Geert
• Format: Article
• Language: English
• Subjects: artificial intelligence and data analytics; data privacy; energy demand management; frequency control
• ISSN: 2515-2947; 2515-2947
• DOI: 10.1049/stg2.12165

The delivery of flexibility from distributed assets guarantees the stable operation of the power system as increasing volumes of renewable energy are deployed. Nevertheless, verifying the adequate provision is challenging when considering behind‐the‐meter resources. A cost‐effective alternative to dedicated metring is using measurements from smart meters. However, flexibility activations must be discerned from the rest of the loads in the household. Furthermore, privacy issues arise since electricity consumption contains personal data. The authors tackle both issues by developing a data‐driven privacy‐friendly verification algorithm for participation in frequency containment reserves (FCRs). Our methodology evaluated three machine learning (ML) classification models, deployed locally, and fed with total consumption measurements and activation set points to verify users' participation. The amount of information that leaves the premises was reduced from low‐granularity power measurements to simple compliance indicators. The models were trained and evaluated using a real dataset of households, where FCR was delivered by behind‐the‐meter batteries, resulting in an accuracy close to 0.90. A proof‐of‐concept setup was employed to test the algorithms under real circumstances. Even with several background loads, an accuracy of up to 0.83 was observed, promising results considering the privacy‐friendly features, use of simple ML models, and embedded deployment. The authors developed a privacy‐friendly verification algorithm for participation in frequency containment response services. The methodology used data‐driven models, local consumption measurements obtained from the smart meter, and the activation set points to estimate whether a user participated in the flexibility provision as requested by the aggregator. The proposed models were trained and evaluated using datasets from real households, and, subsequently, tested in a proof‐of‐concept setup with commercially available devices.