Towards the Handling Demand Response Optimization Model for Home Appliances

Jaclason M. Veras, Igor Rafael S. Silva, Plácido R. Pinheiro and Ricardo A. L. Rabêlo
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Jaclason M. Veras: Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza—CE 60811-905, Brazil
Igor Rafael S. Silva: Department of Computing, Federal University of Piauí (UFPI), Teresina—PI 64049-550, Brazil
Plácido R. Pinheiro: Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza—CE 60811-905, Brazil
Ricardo A. L. Rabêlo: Department of Computing, Federal University of Piauí (UFPI), Teresina—PI 64049-550, Brazil

Sustainability, 2018, vol. 10, issue 3, 1-18

Abstract: The Demand Response (DR) is used by public electric utilities to encourage consumers to change their consumption profiles to improve the reliability and efficiency of the electric power system (EPS) and at the same time to minimize the electricity costs for the final consumers. Normally, DR optimization models only aim to reduce the energy consumption and reduce the final cost. However, this disregards the needs of the consumer. Therefore, proposals which appear excellent in theory are usually impracticable and non-commercial. This paper proposes a real-time Demand Response (DR) optimization model to minimize the electricity costs associated with consumption without compromising the satisfaction or comfort of residential consumers. The proposed DR here considered the different home appliance categories and level of consumer satisfaction for the new load scheduling of the appliances and is much more comprehensive than the other models analyzed. Moreover, it can be applied in any country, under any energy scenario. This model was developed as a nonlinear programming problem subject to a set of constraints. An energy consumption analysis of 10 families for 2015 from five geographic and climatic regions of Brazil was carried out. A computational validation of the model was performed using a genetic algorithm (GA) to determine the programming of residential devices for the time horizon. The computational simulations showed a decrease in the cost of the electricity. Moreover, the results showed that there was no impairment to consumer satisfaction and comfort due to the scheduling of appliances.

Keywords: demand response; genetic algorithm; load scheduling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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