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 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.

Um Modelo de Otimização Multiobjetivo para Gerenciamento de Microgrids

“…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.…”

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

“…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).…”

“…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.…”

A Heuristic Home Electric Energy Management System Considering Renewable Energy Availability

“…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.…”