1. Introduction
The increase of the global population has caused a greater complexity of the electricity supply. Due to this, there is a need for studies and research concerning the quality and reliability of electric power systems in order to avoid interruptions in the supply of electricity and in price increases, among other problems [
1,
2,
3,
4]. At the same time, the pressure on natural resources worldwide and concern for the environment is also increasing rapidly. One of the solutions to help overcome such problems is to use a smart grid (SG). An SG is a system that applies information and communication technologies (ICT) to improve the interaction between all the devices of an electrical power system (EPS) and consumers connected to it [
5]. This interaction can be used by end consumers to improve their electricity consumption pattern in order to reduce the cost of electricity.
The authors in [
6] state that the demand response control methodologies and smart appliances can optimize the use of electrical resources more efficiently. In this sense, the authors in [
7,
8,
9] defined a demand response (DR), from the point of view of a smart grid, as a program that provides various incentives and benefits to end consumers to change their electricity consumption patterns in response to changes in the price of electricity over time or when electrical power network reliability is compromised by any EPS overhead.
The most commonly used DR programs (DRPs) are based on price, following one of three tariff models: (1) Time-of-Use (TOU), which offers consumers different electric energy tariffs during different periods of the day [
10,
11] and is generally based on the average cost of generation and delivery of energy over a 24-h period [
12]; (2) Real-Time Pricing (RTP), when the price of electricity is modified hourly throughout the day, and this may reflect the cost for generation or the wholesale price level; and finally, (3) Critical-Peak Pricing (CPP), which is a dynamic pricing mechanism that uses elements of TOU and RTP to adjust tariffs as a temporary response to events or conditions such as high market prices, or decreasing reserves [
10]. The authors in [
5,
13] affirm that RTP has a much greater flexibility than TOU and CPP. Therefore, the increase in the price of the tariff is linked to the increase in demand for electricity or the low energy productivity of the EPS.
Thus, the DRPs can be regarded as one of the most important tools for Home Energy Management Systems (HEMS). DRPs are able to interrupt, control, regulate, or curtail the energy of the devices and end consumers have financial support to modify their electricity consumption patterns in order to improve the reliability and efficiency of EPS [
14]. Moreover, DRPs help the utility companies to shift the load from peak hours to off-peak hours in order to reduce electricity prices as well as to balance the supply and demand [
15].
Due to the costs and restrictions related to energy, HEMS is of great importance nowadays because it is becoming essential for modern societies, cities, and smart homes [
16,
17]. HEMS manages home energy consumption in order to increase the stability and efficiency of the EPS using Internet of Things (IoT) and optimization algorithms. The authors in [
14] describe IoT as a technological revolution in terms of information and communication. IoT allows Radio Frequency Identification (RFID) tags, sensors, actuators, smartphones, etc. into a network where they are able to inter-communicate without human intervention for a common purpose. The IoT has introduced fresh applications, i.e., smart homes, smart cities and so on. Therefore, different techniques are being studied to improve residential energy usage. The main technique to improve energy usage is by adjusting the planning of residential appliances to maximize the consumption. Such adjustments allow a reduction in the final amount of energy required and, by operating the appliances in periods when the cost of electricity is lower, reduce the final costs even further; moreover, the use of appliances in off-peak hours with cheaper rates reduces the demand during peak-hours [
14,
15].
End consumers have home appliances [
18,
19,
20] that need to be programmed in an orderly manner to guarantee a balance between supply and demand of electric energy [
18,
20,
21]. However, the programming of these home appliances within the same time interval requires specific knowledge and availability of time on the part of the consumer [
22]. In addition, residential management scheduling must take into account consumer preferences regarding the usage of these appliances and the price variation of electricity. Consequently, an infrastructure able to program the operational periods of these home appliances over the planning horizon is required. This program must be able to adjust itself in relation to the peak periods, and thus improve the reliability and efficiency of the EPS without modifying the satisfaction/comfort of the consumers. Although problems of DR in smart grid environments have been investigated in recent studies [
23,
24,
25,
26,
27], the scheduling of residential loads considering the different peculiarities that involve the communication system, the operating characteristics of the different categories of home appliances and the level of satisfaction/comfort of the end consumers have not been well analyzed.
This paper proposes the general architecture of an HEMS and presents a mathematically formulated multi-objective DR optimization model as a nonlinear programming (NLP) problem to determine the optimal scheduling of home appliances considering real-time pricing (RTP) as well as different categories of appliance. The multi-objective DR optimization model aims to minimize the cost of energy consumption and minimally affect convenience (satisfaction/comfort) of end consumers. The main constraints are: minimum and maximum load limits for each time period; ramp limits; minimum consumption within the planning horizon; and some restrictions for the different home appliance categories. Although it is difficult to overcome the NLP problem, it was solved by applying the Non-Dominated Sorted Genetic Algorithm (NSGA-II) [
28] and an optimal solution was obtained.
The main contributions of this paper are as follows:
- (1)
The HEMS and multi-objective DR optimization model present in this work can optimize the scheduling of different categories of home appliances considering different planning horizons and real-time pricing. Thus, with these smart tools, families can reduce the level of dissatisfaction/discomfort as well as energy costs;
- (2)
The DR model presented here can be set up in any country, worldwide for any energy layout;
- (3)
The impact of different energy consumption profiles can be analyzed considering the management of home appliances;
- (4)
The system takes into account various different effects on residential energy consumption, such as geographic location, different climates and temperatures, consumer preferences and the hourly price of electricity.
- (5)
The ability to assess any inconvenience to end consumers so they can decide whether or not to join the DR program;
- (6)
A statistical evaluation of the multi-objective model with NSGA-II was performed to verify its overall performance compared to a random search algorithm;
- (7)
The DR model can also offer greater flexibility so that end consumers can choose their preferences considering satisfaction and costs.
The rest of this paper is organized as follows.
Section 2 reviews the related work on the topic;
Section 3 shows the layout of the home energy management system;
Section 4 presents the multi-objective DR optimization model for electricity load scheduling and the NSGA-II optimization technique;
Section 5 details a case study that shows the experimental scenarios and the numerical results obtained through simulations of the HEMS using the multi-objective model to minimize the cost of electricity associated with consumption as well as the level of inconvenience of end consumers; and, finally,
Section 6 explains the main contributions of this work and outlines possible future research work.
2. Related Work
Significant research, in recent years, has been carried out to manage home appliances in SG environments. The authors in [
5] proposed an home energy management system architecture in order to minimize the cost of electricity and the peak-to-average ratio. The proposal contemplates the management of loads through DR that was formulated mathematically as a nonlinear programming problem. The optimization problem is solved using a genetic algorithm. The approach was limited to evaluation of nine home appliances; however, all of the parameters (start and stop times; time intervals between operations) of these appliances must be programmed by the consumers.
In Ref. [
13], the authors proposed a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method. The proposal was developed a linear programming (LP) problem. The main objective of the work is to shift load profiles by home appliances as well as cut down on peak energy demands through a new constrained swarm intelligence-based residential consumer-centric demand-side management (DSM) method. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering the user’s comfort and satisfaction for the implementation fo the DR. However, the proposal only evaluated the programming of nine appliances in a household. Thus, the proposal does not consider the different categories of home appliances.
The authors in [
29] proposed a DR optimization model that takes into account a set of energy-related constraints to determine the optimal operation schedule for home appliances. The objective is to minimize the cost associated with energy consumption, taking into account the satisfaction and comfort of final consumers and the various constraints associated with the consumption of electric energy. The problem was formulated as nonlinear programming. The results of the computational simulations show that the optimization process by means of a Genetic Algorithm (GA) using the model proposed in this work effectively manage the different categories of appliances in the ten Brazilian households. Thus, the proposed DR model is able to reduce the cost associated with the consumption of electric energy and the level of inconvenience of the families when considering the preferences of the consumers in relation to the use of the home appliances. However, the paper does not have a multi-objective perspective and it does not use statistical techniques to analyze and validate the DR model.
In order to cut unnecessary consumption and minimize energy costs, the authors in [
17] applied a management system that integrated automatic switching off with load balancing and a planning algorithm. Cost minimization was dealt with as a mixed-integer programming problem. All appliances were scheduled to a least slack time (LST) algorithm while also taking user comfort into consideration. The computational simulations showed that the LST plan reduced the costs of energy consumption. However, the different classes of appliances were not considered in this study by the planning algorithm.
A home load control (HLC) system was put forward by the authors [
22] to manage the operational planning of home appliances. The novel day-ahead HLC program was established to plan the home appliances and a plug-in hybrid electric vehicle (PHEV) in such a way as to minimize overall costs for the following day. In this work, only seven appliances, which included a heating system and a PHEV, were evaluated. Moreover, no details of the bidirectional communication between consumer and the utility were given nor information concerning the control of the residential devices by the HLC. In addition, according to the computational simulations, the use of different classes of appliances simultaneously were not considered in the new planning system proposed in this work.
A Home Energy Management as a Service (HEMaaS) method was investigated by the authors in [
30]. The aim of this method was to reduce the demand at peak times and total energy consumption by moving and reducing the usage of residential appliances. HEM was expressed as a set of discrete states, which represent the binary formulation of the power levels of domestic devices. The Main Command and Control Unit (MCCU) control the power states, which were expressed as a Markov Decision Process (MDP). Reinforcement learning (RL) based on a Neural Fitted Q-Iteration (NFQI) algorithm was applied to obtain the solutions. However, the computational simulations showed that the simultaneous use of the different classes of home devices were not considered in this work when faced with the new planning criteria.
In Ref. [
31], the authors proposed an algorithm that determines the thermostat settings that minimize the electricity bill for a consumer. The main goal was to use energy storage to minimize the electricity costs. The problem was formulated as dynamic programming and the results showed that the algorithm was able to reduce the cost associated with the consumption. However, it was restricted to only programming thermal devices without taking into account the other categories of home appliances. The goal of the authors in [
32] was to introduce voltage hopper technology for autonomous and automated grid ancillary services and load control without a centralized controller using an electronic interface and a hybrid direct current (DC)/alternating current (AC) grid concept. Validation of the system was carried out in an interfaced dSPACE/OPAL-RT real-time simulator (Isfahan, Iran). However, the simultaneous use of the different classes of devices and the customer satisfaction were not considered.
A hybrid scheme for planning residential loads, named GAPSO, was introduced by the authors in [
33]. The goal was to cut electricity cost and user discomfort but also consider the peak energy consumption. A multiple knapsack problem (MKP) was used to express the binary optimization problem. The simulation showed that GAPSO performed well to reduce costs and consumer discomfort, but the different classes of home appliances were not considered. Here, the authors in [
34] developed an algorithm for the planning of residential loads to control the operational times and consumption of all the household devices. A mixed integer nonlinear programming (MINLP) problem was developed. A Benders decomposition approach was used to overcome the problem with low computational complexity. However, this work only evaluated one residence and the impact of changing the programing of the appliances was not assessed in terms of consumer satisfaction.
The authors in [
35] proposed a real-time closed-loop residential electricity price-based DR system to modify consumer behavior on a smart grid. The proposal was expressed mathematically as a linear programming problem. However, neither the different classes of devices nor consumer satisfaction were evaluated. A home energy management planner algorithm to reduce residential consumption and costs using stochastic dynamic programming was presented by the authors in [
36]. However, only seven home appliances were appraised and the impact of changing the times of these appliances on the consumer satisfaction was not considered.
The coordination of residential loads using a DR management distribution algorithm was presented by the authors in [
37] and was expressed mathematically as a bi-level programming problem. The distributed algorithm enhanced the general load profile, the magnitude of the network voltage and the system reliability. Simulations were performed in the MATLAB environment and problems associated with home load management (HLMs’) rescheduling were solved by the General Algebraic Modeling System (GAMS). However, the simultaneous use of different classes of home devices was not assessed nor was client satisfaction evaluated.
In order to reduce the peak-to-average ratio (PAR) in aggregate load demand, two interactive algorithms based on the stochastic approximation technique were introduced by the authors in [
38]. However, the algorithms did not consider the simultaneous use of different classes of residential appliances nor the client satisfaction level with this new improved planning. A DR algorithm was set up to manage energy consumption, which was expressed mathematically as a mixed integer programming problem, in order to modify residential electricity consumption profiles by the authors in [
39]. The daily price of electricity and the client preferences for the use of their home appliances were taken into account, then modeled with MATLAB and solved using a GUROBI-MATLAB interface. However, only five consumers with similar consumption profiles and seven appliances were evaluated.
In [
40], the authors suggested an operational planning algorithm for home appliances to reduce electricity costs based on real-time pricing. A stochastic scheduling technique based on deterministic linear programming was used to manage the times the appliances were in use. However, the different classes of the home appliances were not taken into account. A novel Traversal-and-Pruning (TP) algorithm to schedule thermostatically controlled household loads was introduced by the authors in [
41]. To meet the objective of the project, both payment and comfort settings were considered. The planning of the loads was considered a mixed integer nonlinear programming (MINLP) problem. However, only thermal devices were evaluated and the other classes of domestic appliances were ignored.
Most of the recent studies presented here show that the main goal is to minimize the cost associated with the consumption of electric energy without considering the preferences/needs of end consumers. Therefore, we can say that these works do not consider the real difficulty of the problem which involves scheduling the use of home appliances and they do not evaluate aspects such as: (a) different residential scenarios; (b) various categories of home appliances; (c) the level of satisfaction/comfort of consumers with the new scheduling of their home appliances. Moreover, the studies that dealt with the inconvenience aspect performed simulations without taking into account the different categories of home appliances, thus reducing the complexity of the method.
This paper and other works in the literature have the following differences:
- (1)
HEMS using the EMC with the DR multi-objective optimization model allows the different categories of home appliances and the levels of satisfaction/comfort of end consumers for the new scheduling of the home appliances to be considered;
- (2)
The impact of different energy consumption profiles can be evaluated in relation to the management of home appliances;
- (3)
The HEMS using the multi-objective DR optimization model in the EMC reduced the cost of electricity for all the used scenarios, minimally affecting the satisfaction/comfort of end consumers as well as taking into account all the restrictions;
- (4)
HEMS can be used in any country worldwide and with any energy scenario.
3. Architecture of Home Energy Management System (HEMS)
HEMS is defined as the system that provides power management services in order to efficiently monitor the generation, storage and consumption of electricity in smart homes. Therefore, HEMS consists of demand response programs, automation services, power management, data visualization/analysis, auditing and security services [
42].
Thus, HEMS provides a bidirectional communication between homes and the electric utility to monitor, control and analyze the data that involves the consumption of electricity in smart homes [
42]. In this sense, the communication technologies, Wide Area Network (WAN), Neighborhood Area Network (NAN) and Home Area Network (HAN) [
43,
44,
45] used in the smart grid serve as the basis for the HEMS as proposed in this work.
Thus, the HEMS proposed in this work is basically composed of advanced metering infrastructure (AMI), smart meter (SM), an energy management controller (EMC) and home appliances. The architecture from HEMS is presented in
Figure 1.
The smart meter is equivalent to a communication interface and is usually mounted between the AMI and EMC in order to collect the electrical energy consumption data from each device using ZigBee (IEEE 802.15.4) technology [
46] and it also receives the price of electricity from the utility company in real time.
The AMI provides intelligent bidirectional communication between the SMs and the utility company. This enables automated measurement functions and also enables the utility company to send real-time data on energy consumption and price. The information is transmitted or received from the utility company through commonly available fixed networks such as PLC (Power Line Communication), GSM (Global System for Mobile Communications) or WiMax [
47,
48]. Thus, this data can be used for further analysis such as: each consumer’s demand for energy in a specific area or the schedules with the lowest electricity prices that can be used for moving loads.
The EMC is considered the operating nucleus of the home network and is responsible for the management of the consumption and production of energy. Based on this, the proposed HEMS can manage various devices such as electric vehicles, electrical energy storage systems, renewable energy generation, and home appliances. The HEMS uses an algorithm to allow consumers to monitor and/or reschedule the configurations of the existing devices in the residence according to their needs and the DRP data provided by the AMI, received via the smart meter.
The integration of multiple technologies combined with the optimized control of the EMC enables intelligent decision making, reliability and security. An application of this architecture envisages that the generated and stored electricity can be used over a time horizon to charge not only electric vehicles but also to provide loads to the other residential devices when, for example, the cost of electricity is high. In addition, HEMS’s communications infrastructure allows the consumers to participate actively. This is because consumers can access the whole process of monitoring, controlling and managing household energy through an Internet Mobile App. Consumers, with an HEMS Mobile App, can obtain information about energy consumption, demand and price of electricity for a certain interval of time via the SMs. Thus, consumers can make the decision to intervene or not in the optimized programming as suggested by the EMC.
This work proposes an EMC that aims to minimize the cost associated with the consumption of electricity, the peak-to-average ratio and the level of inconvenience (dissatisfaction/discomfort) of consumers as well as to guarantee the stability and safety of the EPS.
Figure 2 shows the communication between the EMC and the different devices used in the residential load management process.
In an HEMS, EMC has an important role because it manages all home appliances through the multi-objective DR model of this work and the ZigBee communication technology involved in switching gadgets on/off. The EMC schedules all operations based on the energy consumption records, the real-time electricity price and the client’s preferences. In this work, the residential devices are divided into three classes [
49] as follows: interruptible and deferrable; uninterruptible and deferrable; and, uninterruptible and non-deferrable. Uninterruptible indicates that an operation cannot be interrupted until it has finished. Non-deferrable and Deferrable refer to whether an operation may start at the first time slot of the operational window, or not.
HEMS makes it easier to control and manage home appliances, to reduce the electricity consumption costs, the level of inconvenience associated with the use of appliances and it results in a lower peak-to-average ratio, which contributes to improving the reliability of the EPS operation.
6. Conclusions
Scheduling management of home appliances in smart grids enables the EPS to be more efficient and effective because issues such as power interruptions during peak demands can be minimized. Thus, DR plays a key role in managing energy consumption in order to avoid overloading as well as reducing the cost of electricity for end consumers. However, this optimized operation of home appliances requires an infrastructure capable of scheduling the operating periods of the devices over the planning horizon, and thus reducing the periods of peak demand, and improving the reliability and efficiency of the EPS minimally affecting the satisfaction/comfort of end consumers. This paper proposes an architecture of a home energy management system (HEMS) and presents a multi-objective DR optimization model to manage the scheduling of electrical appliances in residencies, aiming at minimizing the cost associated to the energy consumption, as well as minimizing the inconvenience (dissatisfaction/discomfort) of end consumers.
The performance of the HEMS using the DR optimization model was evaluated through simulations. First, the efficiency of the HEMS was analyzed for cost minimization associated with the consumption of electric energy as well as inconvenience (dissatisfaction/discomfort) minimization of end consumers of the different residential Scenarios. In addition, the HEMS performance was evaluated for the load scheduling of some appliances (
Table 5,
Table 9 and
Table 13) in order to verify the influence of such appliances to reduce the cost of electricity. Next, through the diversity, coverage and hypervolume metrics, the characteristics of the solutions for the problem of scheduling the home appliances were evaluated.
The results of the study showed that there is a significant reduction in the total cost associated with the consumption of electric power for the three scenarios analyzed. The families that obtained the greatest reductions were the residents in Teresina—PI where the total cost of electricity was reduced from US
$ 99.31 to US
$ 90.72, from US
$ 250.66 to US
$ 229.08, and from US
$ 57.45 to US
$ 52.52 for Scenarios 1, 2 and 3, respectively. Moreover, when the level of inconvenience and the trade-off were analyzed, after optimizing the use of home appliances, the highest values for
trade-off were 0.11, 0.17 and 0.12 for the Scenarios 1, 2 and 3, respectively, for the families living in Teresina—PI. Thus, these families achieved a reduction in the price of electricity equivalent to US
$ 0.11, US
$ 0.17 and US
$ 0.12. In addition, the statistical results in
Table 15 show that, when the HEMS applied the NSGA-II technique, with the multi-objective DR model, in the EMC, it obtained the best results of the simulations when compared to the random search algorithm for all the metrics (Diversity, Coverage and Hypervolume) used in this work.
Future research could further improve in several directions. One possibility would be to improve the model so that some microgrid characteristics, such as the use of electric vehicles and renewable sources for the generation of electric energy, can be included. Another direction could be to evaluate the performance of our model using the NSGA-II technique for other residential scenarios. In addition, a third option could be to solve the multi-objective problems presented in this work with other optimization techniques.