World increasing population, global warming, the rise in carbon emissions and increasing electricity demand create an alarming situation for electricity producing and distributing companies as well as for governments to take any strong action against these alarming situations. The electricity producing companies are detained from integrating renewable energy sources (RESs) to overcome global warming and carbon emissions [1
]. The present fossil fuel based electric grid is working on the centralized approach: only a few large electricity producing plants are operating at 50 Hz or more. High-power electricity plants are operating at very high voltage (i.e., 400 kV or more). Then, the produced electricity from large plants is distributed to the electricity consumers. A large number of supply lines supply the high voltage load to heavy industries and low voltage load to residential consumers and small-scale industries.
The power flow in the present power system is unidirectional due to the centralized approach. Electricity consumers are considered just passive users; they cannot play any role in the stability and reliability of the electric grid. According to [2
], more than 65% of total electricity is wasted during generation, transmission and distribution of electricity. The basic reasons behind electricity wastage are that the present electricity system has unidirectional communication and a lack of monitoring technologies. The novel approach is distributed based on bidirectional communication. It provides the widely and higher distributed intelligence in electric power generation, distribution and flow of information. Furthermore, the novel approach provides the multiple opportunities for electricity consumers to manage their electricity consumption for bill reduction and reliable grid operation.
The novel approach is a smart grid and integration of cost-efficient RESs hold promise to tackle the above-discussed problems in the traditional electric grid. In the smart grid, electricity is generated via cheaper and efficient resources and then distributed to electricity consumers through smart transmission lines. Electricity prosumers are the consumers they can utilize as well as produce the electricity from their own local microgrid, which consists of multiple RESs, i.e., solar panel, wind turbine, hydro power plant, etc. They utilize electricity from their own generation and are also interconnected with the commercial grid. In case of less electricity generation, as compared to load demand, they purchase electricity from utilities or neighbors. If the electricity production from their own microgrid is more than the load demand, then the excess electricity is sold back to the commercial grid or stored in batteries for future use when electricity generation is low. The batteries may discharge only when electricity production from the microgrid is low or per unit electricity price is high.
At present, RESs generate few kilowatts (kW) or megawatts (mW) of electricity in residential areas and integration of RESs on a large-scale is widely diffused around the globe. Furthermore, electricity generation via RESs and integration of storage systems are enabling smart homes and small-scale industries to gain profit by selling excess electricity to the grid or neighbors. Moreover, end users may purchase energy when electricity tariffs are low and sell back electricity when prices are high.
In addition, approximately 10–30% electricity consumption can be saved through demand side management (DSM) [3
]. There are many dynamic pricing schemes (to calculate electricity consumption cost) for consumers’ motivation to alleviate their electricity consumption in ON-peak hours. These pricing schemes include real-time pricing (RTP), time of use (ToU), critical peak pricing (CPP) and critical peak rebate (CPR) [4
]. Electricity consumers have the option to select the best electricity tariff according to their satisfaction.
In smart grids, two-way communication provides an opportunity to optimize consumption costs along with peak to average ratio (PAR) minimization. Due to the advent of a smart grid, a lot of studies have focused in regard to cost and PAR reduction via DSM [5
]. However, none of this work has included the capability to generate and store electricity for future use. The authors in [9
] have proposed a DSM scheme by considering different types of electricity consumers. Optimum electricity consumption with maximum user comfort is determined in [10
] within a smart home containing different types of smart appliances. They also investigate their proposed scheme on smart buildings, comprised of multiple smart homes with different living patterns (power rating and load demand). A cost efficient home energy management scheme has been proposed in [11
]. The authors of [11
] also integrate the RESs to minimize the electricity cost and carbon emissions. Furthermore, the consumers are able to store excess electricity in batteries for future use; when electricity rates are high, stored electricity is consumed. The authors of [12
] have proposed a new smart grid architecture for electricity consumers. They also integrate the RESs for electricity generation. According to their work, the consumers are able to consume, generate, store and sell excess electricity. The excess electricity is sold back to the electric grid for earnings maximization. Most of the recently proposed schemes used look at the problem from the grid or electricity consumer’s perspective.
In this work, a DSM scheme has been proposed for electricity cost and PAR reduction via integrating the RESs and energy storage system (ESS) in a residential area. We consider a smart home that not only utilizes electricity from the commercial grid but is also capable of generating electricity from its own microgrid and storing electricity for future use. Furthermore, the smart home is independent from making autonomous decisions for electricity cost and PAR reduction with revenue maximization in each hour. Therefore, maximizing the revenue generated from power trading is also the objective of this work. A smart home makes decisions according to electricity tariffs. When prices are high, the home tries to reduce load demand and excess electricity is sold back to the commercial grid. The home purchases electricity in low price time slots while minimizing the PAR.
Furthermore, two different RTP schemes are considered for electricity cost calculation in terms of purchasing and selling electricity. At the end, a comparative analysis is performed to show the performance of the cuckoo search algorithm (CSA) and strawberry algorithm (SA). Our main contributions in this work are as follows:
Two heuristic approaches CSA and SA are implemented to schedule appliances for efficient power trading.
An analysis is performed to investigate the fact that CSA and SA are adaptable and capable of making autonomous decisions for effective scheduling of an appliance and optimal power trading with a commercial grid.
We enable a smart home to interact with the grid and make autonomous decisions for selling power to the grid for getting financial benefits.
In order to validate the effectiveness of CSA and SA, extensive simulations are performed in MATLAB (2017a) and the performance parameters are total cost and PAR along with earnings.
Simulation results show that our proposed scheme significantly reduces the electricity cost and PAR with earning maximization.
The remainder of the document is organized as follows: a literature review is presented in Section 2
. Section 3
explains the problem statement and proposed system model. In Section 4
, we explain our proposed scheme. Case studies are explained in Section 5
and simulation results are presented in Section 6
. Finally, paper findings and future work are explained in Section 7
2. Related Work
Electricity cost reduction and load equilibrium between demand and supply are the interesting and challenging research problems that have been tackled by researchers in the last few decades. Many DSM strategies have been presented in the last few years for electricity costs and PAR minimization while maximizing user comfort. Some of the existing work is presented below.
], a home appliances scheduling scheme was proposed to reduce the total electricity cost and balance the load demand by mixed integer linear programming (MILP) in residential areas. The experimental results present that their proposed scheme rapidly obtains the desired targets, i.e., reduction in peak load and electricity cost. An integer linear programming (ILP) based strategy is proposed in [14
]. The basic objective of this study is to find equilibrium between electricity supply and demand in the residential area. Their proposed strategy efficiently shifts optimal operation time and optimal power for time-shiftable and power-shiftable appliances, respectively. Experimental results present that their proposed technique sharply archived the claimed objectives.
A MILP based model is presented in [15
] for PAR and electricity cost reduction along with RESs integration. The simulation results validate this proposed model for electricity cost and PAR alleviation with efficient integration of RESs. Another optimization technique is presented by Mohamed et al., in [16
] using a genetic algorithm (GA). The objectives of this technique are energy cost reduction and load balancing between electricity supply and load demand. The authors of [17
] further proposed a heuristic based technique using particle swarm optimization (PSO) and GA. The optimization problem is formulated through a multiple knapsack problem (MKP) along with three different pricing signals: ToU, RTP and CPP. Electricity bill and peak load reduction are the main objectives of this work. Experimental analysis shows the efficacy of proposed scheme using PSO and GA. Moreover, they also provide the comparison between GA and PSO, which shows that GA outperforms PSO. In [18
], a dynamic programming (DP) based electricity cost reduction scheme was proposed. Electricity costs are reduced through scheduling of home appliances from ON-peak to OFF-peak time intervals. Furthermore, the game theory based approach is adopted to interact with the electricity consumers with extra electricity generation. In [19
], the electricity cost optimization problem under the ToU environment is presented. Furthermore, they categorize the total load into three categories known as: interruptible load, shiftable load and weather based load. The authors of [20
] considered three main objectives for the optimization problem, which are scheduling preference optimization, cost minimization and climatic comfort maximization. The demand response (DR) policy is presented in [21
] and intends to obtain PAR reduction and electricity cost savings via scheduling of smart appliances according to hourly electricity prices. The home energy management system is presented in [22
] using MILP to adjust the load demand among the PV panel, electric grid, ESS and electric vehicles. An electricity cost minimization scheme using GA was presented in [23
], with RES and ESS integration. The ESS is used to balance the electricity supply and load demand. Experimental analysis shows the efficacy of their proposed scheme.
An intelligent home energy management scheme was presented in [24
] for PAR and electricity cost minimization with the integration of RESs. In [11
], an MILP based cost reduction and load balancing scheme is presented in a residential area. An MILP based home energy management system (HEMS) [12
] considers electricity users as prosumers. They proposed HEMS for a single smart home and for a community of 39 prosumers. Each smart home has its own PV panel for electricity generation and is also connected with a commercial grid to meet the load demand. The prosumers store electricity in batteries when they have more generation than the requirements. However, they are not able to export electricity in high price hours for maximizing profit. In [13
], a scheme is presented for cost minimization via integrating distributed energy sources. Moreover, each consumer has its own solar panel for electricity generation along with battery storage. Electricity consumers are able to sell or purchase electricity according to price signals. A RTP scheme is used in their work for electricity cost calculation. However, this work only considers non-interruptible appliances, which is not realistic for a smart home because there are appliances that can be interrupted, for instance, TV, etc. The authors of [25
] studied the trading problem in smart grids. Electricity consumers are able to generate, purchase and sell electricity. However, they put their excessive amount of electricity on an auction market for bidding instead of selling to the main grid or single utility. Nevertheless, the ability of electricity consumers to generate electricity, store electricity and adjust their demand according to electricity tariffs, and selling excess electricity back to the electric grid simultaneously has not been considered in [11
]. Here, we propose a novel energy management scheme using CSA and SA to tackle the above-mentioned problems.
5. Case Studies
In this work, three case studies are presented to demonstrate the performance of our proposed scheme from both the perspectives of the smart home/consumer and the electric grid. In the first case study, the home has no intelligence capabilities; put simply, we consider a conventional home and the electric grid. The home purchases and utilizes the electricity conventionally without knowing the electricity tariff. In the other two cases, households with different properties are considered (i.e., integration of wind turbine, solar panel and ESS) and compared with each other from both customer and electric grid perspectives. Each case study has a transient phase in which every smart home makes autonomous decisions in interaction with the electric grid; the smart home can purchase, sell, store the electricity or shift the load. Eventually, the overall performance of the proposed system will reach equilibrium. Multiple parameters defined for our case studies are shown in Table 4
are average hourly electricity generation from a wind turbine, solar panel and average storage capacity of ESS, respectively.
show the cut-in and cut-out speed of wind where the wind turbine generates minimum and maximum electricity, respectively.
Furthermore, we also consider assumptions for the operation of ESS. The minimum remaining storage, maximum charging level and rate of charging/discharging are the constraints that are considered in this work.
6. Simulation Results
In this section, to evaluate and demonstrate the versatility of our proposed scheme, simulations are carried out. Then, simulation results are shown to uncover an optimal scheduling and power trading for the household. We perform simulations multiple times and then we present the results’ average of 20 runs. Two algorithms (CSA and SA) are implemented to critically analyze and validate our proposed scheme. We consider a smart home consisting of 12 different smart appliances and each have different living habits (i.e., power rating and LOT) explained in Table 1
Furthermore, these appliances are categorized into three different categories (see Section 3.4
). All of the appliances’ parameters that are used in our simulations are presented in Table 1
. Base-load appliances may not contribute to minimizing electricity costs or PAR because these appliances cannot be shifted and must be ON according to user preferences. In our simulation setting, the operation time is considered 24 slots, 1-h each, beginning from 8 a.m. to 8 a.m. on the next day. We consider RTP signals for electricity cost calculation and RTP signals are presented in Figure 4
. Experiments/simulations of our proposed model were executed on a computer system with an Intel Core i7 CPU 2.8 GHz processor, 8 GB RAM, and Windows 10 Operating system. Furthermore, MATLAB is used as a simulator in this work.
6.1. Case 1: Home without Energy Management and Microgrid
In this case, we have studied the operation of a conventional home without a microgrid and ESS integration. This conventional home is not able to manage its electricity consumption and does not have any excess electricity to sell back to the electric grid. In addition, the home cannot make any decisions about electricity usage. It blindly purchases and utilizes electricity while ignoring the electricity tariff or any other parameters.
depicts the electricity bought by conventional homes from electric grid and pricing signals. The results clearly demonstrate that the conventional home ignores the pricing signals and blindly utilizes the electricity. The conventional home purchases electricity in time slots 9 and 10 when the price is at a maximum and also creates peaks due to maximum electricity consumption. Therefore, the conventional home would pay maximum electricity costs against blind utilization of electricity. The hourly and total electricity cost against unscheduled consumption are presented in Figure 6
and Figure 7
, respectively. However, the results presented in this section consider a baseline for our later comparisons.
6.2. Case 2: Home with Energy Management but without Microgrid
Energy management of a home is considered in this case and the smart home is able to manage its electricity consumption. The shiftable appliances may have shifted from ON-peak to OFF-peak hours. EMC is installed in this case and EMC shifts the load according to price and load information. Figure 8
represents the hourly electricity consumption without energy management and with energy management using SA and CSA. The result demonstrates that electricity consumption is high without energy management in ON-peak hours; however, electricity consumption is low in ON-peak hours using SA and CSA. Our proposed algorithms efficiently shift the load while minimizing the PAR, which is presented in Figure 9
. CSA shows high performance for PAR reduction as compared to SA and an unscheduled case. The PAR reduction is 15% and 45% using SA and CSA, respectively.
We minimize the hourly electricity cost by energy management, and, eventually, per day total electricity cost is minimized. Figure 6
depicts the hourly electricity cost, which clearly shows that electricity cost is at a minimum in ON-peak hours using our proposed scheme, as compared to unscheduled energy consumption patterns. The total electricity consumption cost against one day is presented in Figure 7
. It may be observed from the results that the electricity cost is at a minimum using CSA when we compare with SA and unscheduled consumption of electricity. However, the electricity cost paid using SA is lower than the unscheduled electricity consumption. In case 2, the total electricity cost reduction is 26% and 36% using SA and CSA, respectively.
6.3. Case 3: Home with Energy Management and Microgrid
In this case, the smart home is similar to case 2; however, a microgrid with ESS is considered here. The smart home is able to make decisions at every hour to shift the load, purchase, sell or store electricity. In this case, a smart home imports electricity when rates are low and in ON-peak hours the load requirement is met by the microgrid, and the excess electricity is sold back to the commercial grid against high prices. The smart home earns maximum profit with this activity. Figure 10
shows the electricity generation from a wind turbine and solar panel. The electricity generation from the microgrid is at a maximum in ON-peak hours because, in the daytime, the wind speed and solar irradiation are at a maximum. However, the electricity generation is low at nighttime or in the morning when solar irradiation and wind speed are minimum or 0. Figure 11
and Figure 12
represent the relationship of electricity generation from a wind turbine and wind speed, and electricity generation from solar panels and temperature, respectively. When wind speed is at a maximum, electricity generation is at a maximum and vice versa.
The total imported and sold electricity by both algorithms SA and CSA are presented in Figure 13
. The results represent both algorithms showing high performance. Firstly, load is shifted in OFF-peak hours and then the generated and stored electricity in ON-peak hours is sold. If a home performs this act efficiently, it earns a maximum profit through electricity trading. Furthermore, our proposed CSA shows high performance as compared to SA in terms of load shifting and power selling in high price hours. Figure 14
depicts the total electricity cost against imported electricity and total earnings against sold electricity. Our proposed algorithm CSA shows supremacy in terms of electricity cost reduction and earnings maximization as compared to SA. The basic reason behind efficacy of CSA is that CSA has lesser parameters to be fine-tuned.
6.4. Performance Comparison
In this section, we provide the comparison of the three case studies previously presented and our proposed scheme using SA with CSA. According to Figure 7
, the proposed scheme using SA and CSA minimized the total electricity cost as compared to case 1. Furthermore, when we compare case 2 with case 3, the electricity cost is reduced more in case 3 and presented in Figure 14
. The total imported electricity from the external grid is also minimized in case 3 when we compare with case 1 and case 2, which is shown in Figure 13
. Figure 9
shows the PAR against case 1 and case 2, and results clearly demonstrate that case 2 has minimum PAR as compared to case 1. In case 3, ESS is also taken into account for reliable and stable grid operation. Nevertheless, power trading is only possible in case 3 and the total revenue generated from power trading is depicted in Figure 14
. The overall comparison is presented in Table 5
. It is clearly seen from Table 5
that our proposed scheme using CSA outperforms as compared to SA in all case studies.
In this paper, an electricity load management scheme is developed using SA and CSA along with an RTP scheme for DSM. We considered the load scheduling and power trading problem simultaneously in a smart home that has a grid-connected microgrid. An ESS is also installed to enhance the microgrid performance and reliable operation. The smart home makes autonomous decisions against selling, purchasing or storing electricity according to electricity generation and pricing signals. Furthermore, a comparison of different case studies has been provided to investigate the effectiveness of our proposed scheme. Simulation results demonstrate that our proposed scheme outperforms in terms of electricity cost and PAR reduction with maximizing the earnings. It has been observed from simulation results that the proposed scheme using SA and CSA minimized the electricity cost by 26.63% and 36.42%, respectively, in case 2, and by 56.26% and 62.83%, respectively, in case 3. In addition, our proposed scheme using SA and CSA earns 173.39 and 249.39 (cents), respectively. The overall performance of CSA is superior to SA because the CSA spends a maximum amount of time on global search instead of local search.
In the future, we will focus our research on implementing the mathematical techniques for cost and PAR alleviation in addition to electrical vehicle integration as mobile storage. We will also implement this model in different electricity consuming sectors i.e., industrial and commercial.