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 satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém—PA, Teresina—PI, Cuiabá—MT, Florianópolis—SC and São Paulo—SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina—PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers.

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In this paper, a preference-based multi-objective optimization model is presented, considering the real-time energy price to solve the problem of optimal scheduling of residential loads. It aims to minimize the cost associated with consumption and the inconvenience caused to consumers. The model was formalized using a nonlinear programming problem, subject to constraints related to energy consumption and operational aspects related to appliance categories. The computational resolution of the model used the NSGA-III genetical algorithm to determine the scheduling of the devices, renewable energy resources and the use of the storage system for the entire time horizon, considering consumer preferences. The results show that the proposed model and the technique used can minimize the total cost associated with energy consumption and the inconvenience of consumers without the need for manual intervention. Resumo: Para minimizar o custo associado ao consumo e a inconveniência causada ao consumidor, este artigo apresenta um modelo de otimização multiobjetivo baseado em preferências, que considera o preço da energia em tempo real para resolver o problema do agendamentó otimo das cargas residenciais. O modelo foi formalizado como um problema de programação não linear, sujeito a restrições associadas ao consumo de energia e aspectos operacionais relacionadosàs categorias de eletrodomésticos. A resolução computacional do modelo utilizou o algoritmo genético NSGA-III para determinar o agendamento dos aparelhos, dos recursos de energia renovável e a utilização do sistema de armazenamento para todo o horizonte de tempo, considerando as preferências do consumidor. Os resultados mostram que a abordagem proposta minimiza o custo total associado ao consumo de energia e a inconveniência dos consumidores.