1. Introduction
The dream of smart electric power system is become true in the past few decades with the emergence of smart grid (SG). SG provides the vision of integrating sensing and communication technologies for controlled power flow between the utility and a consumer [
1]. Moreover, it revolutionized the traditional grid by enabling bi-directional communication in the electricity system. Additionally, the use of sensing and communication technologies and control mechanisms enabled real time monitoring and distribution of electricity at consumer level in more friendly way [
2]. Furthermore, it has the ability of self healing, attack resistance and capable to store and generate all types of electricity in real time.
With the emergence of SG, it is very convenient for the utility to provide consumers electricity based on the time dependent prices which includes real time and day ahead pricing. By receiving information from the utility, consumer can adjust the operations of an appliance according to the pricing scheme [
3]. Moreover, the advanced metering infrastructure (AMI) allows the consumers to store the information and make timely decisions. The stored data helps to optimally schedule the appliances for minimum electricity cost using the energy management controller (EMC). This is a programmable logic controller which allows the consumer to implement the demand response algorithms for controlling the operations of smart devices as per the requirements.
In the era of SG, demand side management (DSM) plays an important role to achieve a balanced load on the utility. Due to DSM strategies, it is possible for end consumers to minimize the electricity cost and PAR. However, customer participation is an important aspect to successfully implement the DSM strategies. Moreover, it is necessary to examine the behavior of consumers for predicting the habitual energy consumption pattern [
1].
Furthermore, demand response (DR) programs are offered helping end users to reduce peak power demand in peak time slots. The DR consists of incentive and price-based programs. In the former, utility provides the monetary incentives to a consumer for minimizing the load over specific time intervals [
2,
3]. However, to achieve this objective, an agreement between the consumer and utility is necessary [
4]. In the latter, electricity consumers are offered with time-varying prices according to the power consumption pattern. As, electricity price varies dynamically over the time period of day, week or month and every consumer wants to operate appliances at the lowest pricing hour. Thus price-based scheme motivates the consumer to schedule appliances from on-peak to off-peak hours [
5].
Different pricing schemes are interchangeably used by the electricity market including critical peak pricing (CPP), time of use (ToU), real time pricing (RTP) and inclined block rate (IBR). In the implementation of CPP, it is very difficult to select a suitable critical day because CPP is a dynamic pricing signal which is based on ToU and RTP. Moreover, it augments a time invariant structure with high price during the period of system stress [
6]. Moreover, the CPP is not economical as compared to RTP; however, it reduces the price risk associated with RTP by reflecting the short-term cost of critical periods. The price of CPP moments is double as compared to day ahead RTP (DA-RTP). In the proposed model, both pricing signals: CPP and DA-RTP are implemented for calculating the electricity cost.
Research community is striving hard to optimize the scheduling of appliances for efficient and cost effective electricity consumption. In this regard, various optimization strategies are proposed for instance in [
7,
8], an optimization-based model is used to shift the load from on-peak to off-peak hours. Authors used the concept of agents to manage the electricity load and generation. Moreover, the electric vehicles are used as mobile storage to provide electricity in ON-peak hours to achieve a flattened electric load curve. Similarly, Mitra et al. used the load shifting technique to decrease the PAR and electricity cost [
9]. The authors used a modified form of the particle swarm optimization (PSO) for DSM to reduce the utility production cost and minimize the peak demand.
However, in the existing state of the art schemes [
7,
8,
9], when consumer minimizes the electricity cost, the average waiting time increases resulting high discomfort. Moreover, the sudden shift of load from off-peak to on-peak hours results in the generation of peaks. To bring the balance between electricity consumption, peak demand and user satisfaction, a dire need of an efficient algorithm emerges which can satisfy the demands of the consumers.
To overcome the aforesaid issues, we have implemented two heuristic algorithms including TLBO and EDE, to solve the electricity cost and PAR minimization problem. We propose a hybrid technique (HT) by combining the best properties of TLBO and EDE. These algorithms are preferred over PSO and other heuristic techniques due to their self-organization, self-optimization, self-protection and self-healing abilities [
10]. These algorithms are tested via simulations for a home energy management controller (HEMC) under DA-RTP and CPP. The results demonstrate that significant reduction in electricity cost and PAR is observed along with the considerable improvement in the user satisfaction. The key contributions of this work are:
The reduction of electricity cost and minimization of PAR based on the consumer defined priorities for each appliance.
A hybrid meta-heuristic technique is developed to optimize the schedule of shift-able appliances for achieving less electricity cost along with minimum waiting time for maximum user comfort.
HT, TLBO and EDE are implemented for multiple homes to analyze the performance of each algorithm against for different number of appliances.
Extensive simulations in MATLAB are performed to check the performance of HT, TLBO and EDE against electricity consumption cost, PAR, and waiting time.
The rest of the work is organized as follows:
Section 2 describes the related work, the problem description is in
Section 3 and
Section 4 deals with problem formulation, system model in
Section 5,
Section 6 describes proposed solution. Simulation results and discussion in
Section 7,
Section 8 presents the feasible region and finally,
Section 9 concludes the work.
2. Related Work
In the literature, many DSM-based load scheduling techniques are presented to reduce the electricity bill and PAR. A Home energy management system (HEMS) plays a crucial role in residential scheduling. In this regard, some of the related work is presented below.
Ahmed et al. in [
11] focused on the DSM for plug-in hybrid electric vehicle (PHEV) charging at low voltage transformer. The primary objectives are to flatten the electricity load curve and fulfill the consumers’ requirement for PHEV charging in OFF-peak hours. A convex programming is used to solve this load management problem. Simulation results show that the proposed algorithm efficiently achieves the desired power load curve.
A distributed algorithm used to manage sparse load shifting for DSM [
12]. Authors make the contribution in three forms: (i) remodel the DSM to improve sparsity, (ii) a bidirectional framework to seek the Nash equilibrium and (iii) Newton method used to accelerate the convergence of coordination updates on the supply side. The objective of this work includes the scheduling of home appliances for the purpose of cost reduction. This way the consumer helps to achieve maximum user comfort by implementing the sparse load shifting techniques.
Authors in [
13] use stackelberg game theory approach for appliance scheduling; using this concept of game theory, a relationship is created between the energy supplier and the consumer. Authors solved the problem of multiple appliances by considering the demand side along with market and supply side. The authors also covered a new dimension for improving the utility companies’ profit. The authors propose a decision process for both demand and supply side as a multi-leader multi-follower stakelberg game.
In [
14], the authors used a genetic algorithm (GA) in DSM and benefited with an overall reduction of 21.91%. The load is divided into three service areas: industrial, residential and commercial. The use of electricity in peak hours vary in every service area with the objective to minimize the power utilization during the peak hours.
Authors in [
15] proposed a scheduling model for appliances. Dynamic prices are considered for shifting the load from on-peak to off-peak hours. In this scheme, an optimization model is proposed which minimizes the electricity cost for the consumer. Moreover, different pricing schemes are applied in this model to make it more realistic. Additionally, user comfort and social welfare are also incorporated in this model. However, PAR is not considered in this model.
Electricity consumption cost is minimized by appliance scheduling in [
16]. They considered twenty-four appliances for scheduling with the aim of reducing energy consumption. Simulation result shows that BPSO performs well as compared to GA and WDO. However, user comfort is not considered in this work.
The authors in [
17] proposed a model that calculates robust price for consumers using day ahead pricing (DAP). Moreover, convergent distribution algorithm is used to find a robust price for the consumer. This algorithm significantly reduced the electricity consumption cost and provided a reliable estimated production cost to the energy supplier. The DSM mainly focuses on calculating an optimal policy for monetary saving on the consumer side. The distributed robust algorithm has very little computational complexity and a significant monetary saving.
An electricity consumer can reduce its energy consumption cost by scheduling the appliances according to electricity tariff and controlling the charging and discharging of energy storage devices. Proximal decomposition algorithm (PDA) allowed a consumer to update its energy consumption pattern. Simulation results show that PDA minimizes the energy consumption cost and peak load of the system [
18].
The authors of [
19] proposed an optimization model to utilize the energy optimally in smart homes. This optimization model is based on multi-objective mixed integer nonlinear programming (MO-MINLP) technique. The proposed model minimizes the tradeoff between energy consumption cost and user comfort. The MO-MINLP based smart energy management system (EMS) tests different residential loads and checks its performance through different operating conditions. Simulations are conducted to verify the efficiency and robustness of the proposed model.
In [
20], the authors reduced the peak demand by shifting the appliances. They considered the default scenario with finite delay requests and compressed demand scenarios. Recursive formulas are used to calculate the peak demand which show an efficient reduction in the peak demand for a finite number of devices. However, in the proposed scheme system complexity and computational time are increased. Authors presented an energy management model, which is based on co-evolutionary PSO. This optimization technique is used to schedule the “must-run” resistive load and the load associated with PHEV. The consumer defines monetary benefits of the energy usage [
21]. Yang et al. proposed an improved form of PSO to schedule the appliances [
22]. This work considers the CPP, ToU and demand response signals to minimize the cost and PAR. Authors also analyze the effect of energy management on distribution transformer. The proposed algorithm is also favorable for reducing the PAR and cost. An enhanced GA-based EMS is proposed in [
23] to control home appliance power consumption. In this work, RTP signals are used to calculate the electricity cost.
It is observed from the literature, research community is working to find an optimal solution for energy management problems. In this regard, heuristics optimization algorithms are used in recent studies [
8,
9,
14,
16,
17] due to their abilities of self-healing, self-adaptation and low computational complexity. Moreover, mathematical techniques are also implemented for energy management [
11,
19]; however, when size of the problem increases, high computational complexity is the limitation of mathematical techniques.
3. Problem Description
In SG, energy optimization is a challenging task due to randomness in consumers’ energy consumption pattern. Mathematical optimization techniques are not considered feasible due to their high computational complexity. To tackle aforementioned issues, heuristic techniques are widely adopted due to their self-adaptive behavior during exploration and exploitation of the search space. The DSM deals with the activities that modify the electricity consumption pattern to minimize the cost. Ahmed et al. in [
11] devoted their efforts to maximize the user comfort via implementing the energy consumption threshold. In [
12], the authors proposed a two-stage Stackelberg game theory approach, where power supplier is considered as the leader to maximize the monetary benefits. However, the cost factor and user comfort are not considered in this work.
The mathematical techniques i.e., MINLP and mixed integer linear programming (MILP) are beneficial for appliance scheduling, however, the computational complexity is high [
24]. The heuristic algorithms: TLBO and EDE are flexible for specified constraints which are easy to implement and have less computational complexity. In this work, we have proposed a HT along with the implementation of TLBO and EDE to solve the optimization problem. These algorithms are chosen due to their decentralized control system, self-optimization, self-protection and self-organization. CPP and DA-RTP are investigated for the calculation of electricity cost. Simulation results illustrate that each heuristic algorithm reduces the electricity cost and PAR as compared to unscheduled consumption. Furthermore, the proposed scheme HT shows better performance as compared to its counter parts in terms of aforesaid objectives.
4. Problem Formulation
In the DSM plan, some of the major objectives are to reduce the electricity cost, maximize the user comfort and minimize the PAR to benefit the utility and consumer. These objectives are achieved by shifting the load from on-peak to off-peak hours and through the proper management of electricity consumption. In the proposed model, primary objectives are to minimize the cost and waiting time without compromising on PAR. Furthermore, we formulate our problem as multiple knapsack problem (MKP) as follows:
Furthermore, Equation (
2) is normalized in Equation (
3) as [
25]:
We first consider the cost factor
${C}_{t}^{i}$ of the objective function. Whereas, the electricity consumption cost is calculated by considering three shiftable appliances (SA). In Equation (
2), power rating is represented by
${p}_{i}^{r}$ where
i shows an appliance,
${x}_{i}^{t}$ shows the status of
ith appliance and
$\psi (t)$ denotes the price of an electricity at time interval
t. The cumulative power consumed by appliances is shown in Equation (
4). In Equations (
5) and (
6) show that power capacity must be greater than or equal to the consumed energy. Different constraints for HEMC are shown in Equations (
7)–(
9).
Capacity = total daily energy consumption.
Furthermore, Equation (
10) is normalized as [
25]:
Here, ${t}_{a}$ is the request time and ${t}_{a}^{\prime}$ is switch ON time for a given appliance.
In Equation (
10) waiting cost is represented by
${W}_{i}^{t}$ and p is a parameter that varies with different time slot and the value of
k is 2 [
26].
All these appliances must be scheduled from starting time (
${t}_{s}$) to finishing time (
${t}_{f}$) and maximum delay of 8 h is allowed. The total delay of an appliance can be calculated using Equation (
12). Our major objectives are to minimize the cost and maximize the user comfort by minimizing the average waiting time without compromising on PAR. In Equation (
18), mathematical expression for PAR is given as:
5. System Model
In our proposed model, a smart home consisting of various electrical appliances including washing machine, refrigerator, and cloth dryer etc. A smart meter is installed providing a two-way communication from smart home to the power distribution company. The similar communication takes place between power distribution and transmission. Finally, transmission is connected with power generation with a two way communication link. All appliances are capable to communicate with each other using home area network (HAN) [
27,
28,
29]. The flow of power between the utility and SM is shown by the bold line in
Figure 1 and this work is the extension of [
5,
30]. The communication between EMC, SM and utility is carried out using the wifi or wimax technology. The flow of information between the utility and EMC is bidirectional. We use the DA-RTP and CPP schemes to calculate the electricity cost of multiple homes (single, twenty and fifty homes).
5.1. Day-Ahead Real Time Pricing
Using RTP signals, the massive data flows between the utility and smart home, using DAP an efficient energy consumption scheme is proposed [
31]. A wide scope of this strategy is used to tackle the load management problem. In [
32], the basic objectives are to improve the system performance and user’s participation in DA-RTP and CPP. However, flat pricing refers to that method, where utility companies charge fix price for all periods. Authors consider flat pricing because price fluctuation is not suited for each consumer, so we optimized the price profile using DA-RTP option. Authors’ basic objective is to make the DA-RTP profile in such a way that the consumer will pay the minimum energy consumption cost by observing the user’s satisfaction.
A DA-RTP model is proposed to resolve the energy management issue between the provider and distribution company by offering optimal DA hourly prices using smart meter. The recent development in the smart metering technologies increases price responsive demands. Several utilities executes a variety of time-based pricing programs; however, most of the utilities used DA-RTP scheme.
In the case of DA-RTP, utilities need to provide planning tools to aid and evaluate the benefits of DA-RTP. The latest example of a system to assist building operator in responding to DA-RTP rates, is the ashere RTP software package which belongs to DA-RTP in the system memory. The basic concept of designing such controller is to help building operator to utilize DA-RTP in an efficient way.
To take advantages from DA-RTP scheme, we require more support system and services. The EMS is able to an automatic scheduling of electricity use under DA-RTP. In this process, EMS requires ongoing monitoring of condition and daily prediction of energy consumption pattern, real time calculation and decision control system. Electricity peak demand makes the power management system unpredictable and peak occurs very infrequently due to which only 20% of the utility side is constructed to handle these peak points.
5.2. Critical Peak Pricing
CPP provides timely information to the consumers about energy cost, specially, during high energy usage periods. This information helps to make more accurate decisions for the efficient utilization energy consumption [
33,
34]. The CPP provides more accurate information regarding the cost of energy to the consumer, especially, during high energy usage periods, for more accurate decision regarding the electricity consumption. While the price of electricity is higher in CPP events, the CPP rate offers low prices during all other times. This distinction provides a consumer with the opportunity to better assess and potentially reduce your overall annual energy cost.
CPP events mostly occur during an extreme heat wave in summer, extreme cool wave in winter, on Christmas or any other religious event. The CPP events mostly occurred up to four times per year. There is no range of minimum and maximum number of CPP events in a year.San Diego gas and electric (SDGE) has the capability to start CPP events only when it determines the need to call customers for temporary reductions in electricity demand. SDGE notifies to consumers 24 h before the occurrence of event.
9. Conclusions
DSM has the potential to provide lot of advantages to the entire smart grid, particularly at distribution network level. In this work, we have proposed a DSM scheme for electricity cost and PAR minimization with maximum consumers’ satisfaction. For comparative analysis, existing algorithms TLBO and EDE are implemented in our optimization problem with the same parameters. We have tested the proposed scheme on multiple smart homes with the objective to schedule the smart appliances where consumer pays less electricity cost along with maximum satisfaction level and minimum PAR. To validate the proposed scheme, extensive simulations are conducted against the parameters of PAR, electricity expenses and average waiting time. It is evident from the results that proposed HT scheme outperforms existing techniques with minimum cost, waiting time and PAR. Moreover, the monthly electricity cost of HT, TLBO and EDE is reduced by 45%, 40% and 39%, respectively. Additionally, significant reduction is observed on average PAR which is 58%, 52% and 48% of HT, TLBO and EDE, respectively.