Demand-Side Management Optimization Using Genetic Algorithms: A Case Study

Lauro Correa dos Santos Junior (), Jonathan Muñoz Tabora (), Josivan Reis (), Vinicius Andrade, Carminda Carvalho, Allan Manito, Maria Tostes, Edson Matos () and Ubiratan Bezerra
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Lauro Correa dos Santos Junior: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Jonathan Muñoz Tabora: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Josivan Reis: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Vinicius Andrade: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Carminda Carvalho: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Allan Manito: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Maria Tostes: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Edson Matos: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil
Ubiratan Bezerra: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém 66075-110, PA, Brazil

Energies, 2024, vol. 17, issue 6, 1-14

Abstract: This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption.

Keywords: genetic algorithms; demand-side management; energy efficiency; optimization; active power demand (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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