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      Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power

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          Abstract

          Cybersecurity and privacy are of the utmost importance for safe, reliable operation of the electric grid. It is well known that the increased connectivity/interoperability between all stakeholders (e.g., utilities, suppliers, and consumers) will enable personal information collection. Significant advanced metering infrastructure (AMI) deployment and demand response (DR) programs across the country, while enable enhanced automation, also generate energy data on individual consumers that can potentially be used for exploiting privacy. Inspired by existing works which consider DR, battery-based perturbation, and differential privacy noise adding, we novelly consider the aggregator (cluster) level privacy issue in the DR framework of solar photovoltaic (PV) generation following. Different from most of the existing works which mainly rely on the charging/discharging scheduling of rechargeable batteries, we utilize controllable building loads to serve as virtual storage devices to absorb a large portion of the PV generation while delicately keeping desired noisy terms to satisfy the differential privacy for the raw load profiles at the aggregator level. This not only ensures differential privacy, but also improves the DR efficiency in load following since part of the noisy signal in solar PV generation has been filtered out. In particular, a mixed integer quadratic optimization problem is formulated to optimally dispatch a population of on/off controllable loads to achieve this privacy preserving DR service.

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          Mechanism Design via Differential Privacy

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            State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance

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              EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications

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                Author and article information

                Journal
                28 November 2018
                Article
                1811.11933
                06673e4f-2841-4d73-b05d-351faf47742e

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                5 pages, 6 figures, Presented in IEEE e-Grid 2018
                cs.SY

                Performance, Systems & Control
                Performance, Systems & Control

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