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Open AccessArticle

Towards the Handling Demand Response Optimization Model for Home Appliances

Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza—CE 60811-905, Brazil
Department of Computing, Federal University of Piauí (UFPI), Teresina—PI 64049-550, Brazil
Authors to whom correspondence should be addressed.
Sustainability 2018, 10(3), 616;
Received: 25 January 2018 / Revised: 16 February 2018 / Accepted: 22 February 2018 / Published: 27 February 2018
(This article belongs to the Special Issue Sustainable Smart Cities and Smart Villages Research)
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. View Full-Text
Keywords: demand response; genetic algorithm; load scheduling demand response; genetic algorithm; load scheduling
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Veras, J.M.; Silva, I.R.S.; Pinheiro, P.R.; Rabêlo, R.A.L. Towards the Handling Demand Response Optimization Model for Home Appliances. Sustainability 2018, 10, 616.

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