Visible to the public Data-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile

TitleData-Driven Optimization for Utility Providers with Differential Privacy of Users' Energy Profile
Publication TypeConference Paper
Year of Publication2018
AuthorsWang, J., Zhang, X., Zhang, H., Lin, H., Tode, H., Pan, M., Han, Z.
Conference Name2018 IEEE Global Communications Conference (GLOBECOM)
ISBN Number978-1-5386-4727-1
KeywordsData analysis, data privacy, data- driven cost minimization, data-driven optimization, Differential privacy, differential private noises, digitally enabled smart grid, distributed differential privacy techniques, electricity grid, energy consumption, espionage meters, fine-grained energy consumption data, historical users differentially private data, Human Behavior, Metrics, minimisation, Noise measurement, noisy data collection, policy-based governance, power generation, power generation cost reduction, power generation economics, power system simulation, privacy, privacy risks, pubcrawl, Resiliency, simulated energy consumption data, Smart Grid Consumeer Privacy, smart grid consumer privacy, Smart grids, smart meters, smart power grids, users demand distribution, users energy profile differential privacy, utility companys real data analysis, utility provider

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those "espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

Citation Keywang_data-driven_2018