2018
DOI: 10.3390/s18103207
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A Multi-Objective Demand Response Optimization Model for Scheduling Loads in a Home Energy Management System

Abstract: Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisf… Show more

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Cited by 73 publications
(35 citation statements)
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References 52 publications
(68 reference statements)
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“…Restrição 7 (31) estabelece que a operação dos eletrodomésticos da categoria A III deve ser entre seu começo (T I i ) e fim (T F i ), como definindo pelo consumidor.É ininterruptível e não adiável para o intervalo de tempo Req i no horizonte de tempo T : (Veras et al, 2018) foi usado como uma arquitetura, na qual o modelo de DR proposto nesse artigoé responsável por determinar o agendamento das cargas e o ciclo de carga/descarga do SAE.…”
Section: Modelagem Do Problemaunclassified
“…Restrição 7 (31) estabelece que a operação dos eletrodomésticos da categoria A III deve ser entre seu começo (T I i ) e fim (T F i ), como definindo pelo consumidor.É ininterruptível e não adiável para o intervalo de tempo Req i no horizonte de tempo T : (Veras et al, 2018) foi usado como uma arquitetura, na qual o modelo de DR proposto nesse artigoé responsável por determinar o agendamento das cargas e o ciclo de carga/descarga do SAE.…”
Section: Modelagem Do Problemaunclassified
“…In addition, peak load and electricity bill are minimized. A multi-objective DR optimization model is proposed in [24] to minimize customers' convenience level as well as the electricity cost using the non-dominated sorted genetic algorithm (NSGA-II). Oprea et al [25] propose informatics solution that optimizes daily operational appliances, minimizes consumption peak and reduces stress on the main grid using the artificial neural network (ANN).…”
Section: Related Workmentioning
confidence: 99%
“…Algorithm 1 presents the optimal scheduling technique. The execution steps of the proposed optimal scheduling techniques depend on the complexity at lines (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) in the for-loop. The complexities of other lines (i.e., 10-22) are more than these lines.…”
Section: Algorithm 1: Proposed Optimal Scheduling Technique [26]mentioning
confidence: 99%
“…The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied in [14,15] to generate more economic usage profiles for controllable appliances. A drawback of the former is that it cannot be applied to household appliances with fixed operating times, limiting its applicability, whereas the latter does not consider renewable energy generation and uses simplistic models of the household appliances.…”
Section: Introductionmentioning
confidence: 99%