Preserving privacy in distributed energy management

Viac o knihe

The smart power grid evolves into a distributed system with various stakeholders by integrating communication technology into the traditional grid. Distributed Energy Management (DEM) is essential for stabilizing energy consumption and generation, allowing for an increased share of renewable sources without blackout risks. However, privacy remains a critical yet often overlooked aspect, as detailed consumption data can lead to power signature analysis that compromises user privacy. This thesis presents innovative DEM algorithms that adhere to the privacy-by-design principle, addressing the intersection of DEM and privacy. A round-based procedure allows households to aggregate information about their devices in the first round, with deviations from DEM targets distributed fairly in subsequent rounds. The Privacy-Friendly Algorithm employs the Bucket Encryption Scheme (BES), adding encrypted noise during aggregation in a ring overlay network. Only the server can decrypt and subtract the noise, preserving individual privacy. Additionally, PrivADE utilizes the Homomorphic Encryption Scheme (HES) based on the Paillier cryptosystem, enabling operations on ciphertext and supporting various overlay networks. To facilitate the development and testing of DEM algorithms, the thesis introduces SiENA, a simulation platform capable of co-simulating power, heat, and communication with a realistic data basis for household appliances and energ