Privacy Cost Optimization of Smart Meters Using URLLC and Demand Side Energy Trading

In this article, we consider ultra-reliable low-latency communication (URLLC) for efficient energy trading over a smart grid (SG) network using home-based smart meters (SM). We develop a cost-friendly privacy preservation framework based on existing demand-side energy management by employing random...

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Bibliographic Details
Published in:IEEE transactions on services computing 2023-11, Vol.16 (6), p.4140-4153
Main Authors: Hossain, Mohammad Belayet, Pokhrel, Shiva Raj, Choi, Jinho
Format: Article
Language:English
Subjects:
Algorithms
Consumers
Costs
Customers
Data privacy
Demand
Energy
Energy costs
Energy management
Follow the regularized leader (FTRL)
Network latency
Optimization
Privacy
privacy-cost optimization
Reliability
Smart grid
smart grid (SG)
Smart grids
smart meter (SM)
ultra -reliable low-latency communication (URLLC)
Ultra reliable low latency communication
Wide area networks
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Summary:In this article, we consider ultra-reliable low-latency communication (URLLC) for efficient energy trading over a smart grid (SG) network using home-based smart meters (SM). We develop a cost-friendly privacy preservation framework based on existing demand-side energy management by employing random bidirectional energy trading among customers. Customers in our design can be either producers or consumers and mostly both (' prosumers '). Our aim is to develop a decentralized optimization framework that not only reduces energy costs, but also improves privacy preservation and energy trading ability directly from the customer's end. One of the vital costs for energy consumers is the supply charge. Our method can minimize it by orchestrating energy trading among customers in a decentralized adaptive fashion. To predict the energy demand by optimizing between privacy and cost, we employ an extension of the follow the regularized leader (FTRL) algorithm. We perform a theoretical analysis to demonstrate the convergence of the FTRL, the benefits of URLLC for the SG network, and the cost-effective privacy preservation ability of the proposed model. In addition to enabling energy trading efficiently, our extensive simulation results demonstrate that our proposed framework outperforms the state-of-the-art methods in terms of the cost-friendly privacy of SMs.
ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2023.3310939