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Open AccessArticle

Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition

Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78201, USA
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Energies 2019, 12(13), 2470; https://doi.org/10.3390/en12132470
Received: 8 April 2019 / Revised: 19 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition. View Full-Text
Keywords: load morphing; NSGA-II; smart grid; grid partition; multi-objective optimization; Pareto theory load morphing; NSGA-II; smart grid; grid partition; multi-objective optimization; Pareto theory
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Alamaniotis, M.; Gatsis, N. Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition. Energies 2019, 12, 2470.

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