Abstract
In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings.
Highlights
Today, Internet of Things (IoT) scenarios where widespread technology is constantly monitoring user activities is crucial to develop algorithms for data collection, monitoring, and control that are guaranteeing the user privacy
We assume that the Distribution System Operator (DSO) updates its power constraint signal every minutes and that each node can adapt the consumption of flexible loads in a purely proportional fashion
demand response (DR) schemes working in a community of smart buildings, acting as a virtual microgrid regardless of their physical location
Summary
Internet of Things (IoT) scenarios where widespread technology is constantly monitoring user activities is crucial to develop algorithms for data collection, monitoring, and control that are guaranteeing the user privacy. Exploiting the IoT paradigm, a smart grid is an innovative energy distribution network able to improve the conventional electrical grid so to be more reliable, cooperative, responsive, and economical. DR refers to the possibility of end users to change their consumption patterns in response to a dynamic price signal, or (called load control) to the possibility of directly switching or tuning specific user appliances off during peak demand. Control (DLC), for example, is a specific mechanism of demand side management that allows electric utilities to turn specific users’ appliances off during peak demand periods and critical events. Most of the current DLC programs work on thermostatic loads [3,4], such as air conditioners and heating systems because they allow a fine-tuning regulation of power demand. Other solutions are considering the use of enhanced smart plugs which consider user habits and consumption profiles to optimize controllable resources [7]
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