Efficient Demand Side Management using Hybridization of Elephant ...

Efficient Demand Side Management using Hybridization of Elephant Herding Optimization and Firefly Optimization Iqra Fatima1 , Sikandar Asif1 , Sundas Shafiq1 , Itrat Fatima1 , Muhammad Hassan Rahim1 , Nadeem Javaid1 , * * COMSATS *

Institute of Information Technology, Islamabad 44000, Pakistan Correspondence: www.njavaid.com, [email protected]

Abstract—This paper presents a new algorithm to solve the problem of electricity cost reduction by hybridization of two meta-heuristic techniques, i.e., Elephant Herding Optimization (EHO) and Firefly Optimization (FF). A home energy management controller (HEMC), based on scheduling of different household electrical tasks is proposed to maintain balance between load and demand profile. The objective of this study is to determine lowest cost while considering user comfort maximization factor and peak to average ratio (PAR). The proposed algorithm, i.e., Hybrid Elephant and Firefly (HEF) optimization is analyzed comparatively to its’ separate implemented versions to evaluate the performance and behavior towards scheduling process. Moreover, three different pricing models are used to calculate the total power consumption rate. Simulation results show that our proposed hybrid optimization technique performs more efficiently to achieve lowest cost and maximum consumer satisfaction. Index Terms—Residential area domain, Elephant herding, Firefly, Home energy management

I. I NTRODUCTION Since decades, smart grid is considered as a reliable, flexible, secure, sustain and market enable network that strongly supports demand response programs and advance services [1]. In order to improve and to increase the reliability of the electric grid, smart grid has become a very famous approach in technological era. Moreover, to intelligently control high electricity demand, smart grid consists of highly automated computers, smart meters (SM), energy management controllers (EMC) and electronic control devices (ECD). The smart grid application is a great opportunity to provide an efficient and consistent transmission of electricity for the consumers as well as the sellers (power traders). The benefits of smart grid includes: quick restoration, lower power consumption rates, integration with renewable energy sources (RESs), effective and beneficial power trading and etc [2]. However, including all of mutual advantages, smart grid also faces some keen issues to be solved. These issues consists of: large transmission failures, power losses, instabilities, vulnerable to cyber attacks due to online applications, intermittent generation, irregular supply and etc. So, in order to tackle these sudden uncertainties, a number of solutions which have been suggested in literature are found to be very effective. The methods which are followed by most of the studies in literature to deal with smart grid real life problems comprises of numerous optimization algorithms, heuristic and

meta-heuristic techniques, convex optimization and linear programming based methods. The above mentioned issues are sometimes difficult to solve through analytical methodologies [10] which embraces other approaches to be followed. Meanwhile, in state of art analysis, nature-inspired techniques are playing an influence role to solve various reallife problems as well. According to [3], authors comparatively analyzed the performance of bio-inspired and nature-inspired techniques, i.e., genetic algorithm (GA), binary particle swarm optimization (BPSO), and ant colony optimization (ACO). In this paper, the integration of renewable energy sources (RESs) are considered along with combined price signal, i.e., time of use (ToU) and inclined block rate (IBR). The aim of this study is to minimize the total electricity bill and peak to average ratio (PAR) at the great extent, to balance the load and demand profile. Further, the authors considered the feature of user satisfaction to check that how much waiting time is required for a user to fulfill his demand associated to next appliance usage. Similarly, in [4], authors proposed a heuristic based method, i.e., glowworm swarm optimization algorithm (GSO) in order to perform optimal management in smart micro-grid. In this paper, authors mentioned a challenge of optimally dispatch energy sources in order to manage the load intelligently. A multi-objective approach is followed to achieve multiple objectives regarding load and resource dispatch management at the same time. Moreover, the performance of the implemented heuristic technique is compared with the NSGA-II method, to evaluate the optimality of the solution. In [5], authors implemented linear programming based heuristic algorithm in order to perform scheduling of electric vehicles’ (EV) charging and discharging aspects. Likewise, in [6], authors presented scheduling of appliances in order to achieve objective regarding electricity cost minimization. In studies [16-20], demand side management (DSM) approach is followed while using integer linear programming (ILP), mixed integer linear programming (MILP) and heuristic optimization algorithms. However, in above mentioned references, except [16] authors completely ignored the user comfort factor. By using five different pricing tariffs, in [7] authors formulated optimization problem as an mixed integer linear program in order to optimally manage the load demand of an industry. However, customer satisfaction regarding waiting time is again completely ignored.

Generation

Transmission and Distribution

Residential

Nuclear Power Plant

nces

Electronic Control Display

Wide Area Monitoring Cross-Border Interconnection

Grid Automation

Substation

Smart Switch and Distribution

Meter

Local Generation

Renewable Generation

Power Network Communication Path

ng

Fig. 1: Smart Grid Architecture

system. A systematic controller for a residential area domain is presented in Fig. 2. A single home with a SM and ECD is considered. The ECD is directly connected to the SM through which the stored data relating daily consumption is send to the main utility. This approach further enables a two way communication between the consumer and the energy provider. Moreover, it creates a flexible environment and ease of management for the whole community. For the purpose of computational experiment, a taxonomy of appliances is considered from literature [9]. The total number of appliances used in our simulations are 12. For further assumptions, we have divided the appliances in three categories depending on their operation and functions. These categories of appliances comprises of the following types of tasks. 1) Interruptible Appliances: This category of household electrical tasks consists of appliances which can be temporarily stop if once their running time have been started. Interrupting an appliance refers to stopping the running time or directly shift the time slot to another hour of the day. It happens when a user discontinue using an appliance after switching it ON. Such appliances are: Dishwasher, microwave, oven, cooker, laptop, desktop and vacuum cleaner. 2) Uninterruptible Appliances: This category of appliances consists of the electrical tasks which cannot be interrupted while in a running state. When the user start an appliance, the utility directly is not allowed to schedule or stop the time of uninterruptible appliances during usage. Such appliances consists of: Washing machine and cloth spinner. 3) Hard-time Appliances: Hard-time appliances are those appliances which are not able to compromise changing their running state. The length of operation time is fixed and these appliances get start and stop at their predefined time slots. Furthermore, for the purpose of achieving user comfort the simulations are not performed in this category due to fix time of usage. Such appliances includes: Refrigerator, electric vehicles ad interior lightings. As the appliances classification is taken from one of the papers shown in the bibliography, so, only the number of

While taking into account the DSM and supply-side management, Tan, Wooi-nee et al introduced linear programming models in [11] to minimize the electricity bill rates. In this paper, a single home is considered with total 6 appliances. Authors aimed to achieve minimum cost at demand side management whereas, maximum load at supply side management. However, user satisfaction is not considered. For the purpose of customer participation in energy management, authors in [12-14] implemented heuristic optimization techniques while considering user comfort aspect. This feature encourages and motivates electricity consumers to set priorities to the usage of appliances. Refer to the industrial area domain, and smart grid applications in industries, authors in [7], [15] and [16], successfully implemented heuristic algorithms to solve the problems related to the management of load profiles of industrial power consumption. As, there is no consideration of user satisfaction factor in most of the literature, which shows a notorious flaw of these researches, so, we have presented an idea of combining two optimization algorithms to find a new optimal solution for above mentioned problem. Considering above discussed problems, we have proposed an optimal solution regarding hybridization of two metaheuristic techniques, i.e., elephant herding optimization (EHO) and firefly optimization (FF). Our algorithm calculates waiting time of the user along with minimization of electricity cost and peak to average ratio (PAR). Moreover, we analyze whether our proposed technique gives global optimum results more quickly in comparison to other techniques to achieve maximum user satisfaction, reduced electricity cost and maximumum PAR reduction. This section includes the introduction and some state of the art analysis. Section 2 comprises of system model, section 3 discusses simulation results and section 4 briefly mentions conclusion. II. P ROPOSED S YSTEM A. System Description In this section, we have discussed our proposed system that shows a smart frame of proactive energy management

2

appliances are involved in our simulations process. We have performed our simulations by considering three different pricing tariffs in order to let users decide that which one of them would be appropriate to lower their electricity bills. Following are the equations shows how the problem is formulated. The total time intervals are represented as T= 48 hours and t 𝜖 T such that: 𝑇 = {1ℎ, 2ℎ, 3ℎ........48ℎ}

2) Time of Use: The time of use price signal depends on the time at which the peak formations occur. Whenever, there is a peak creation due to high demand, the pricing rates become high and when the peaks of demand or load is less, the price will also be less.

(1)

Let I denotes the total number of interruptible appliances such that i 𝜖 I where I belongs to seven appliances. Power rating of these appliances is denoted by P and PT belongs to the electricity pricing tariff. X shows the status of an appliance which is either 1 or 0 incase if an appliance is on or off respectively. Total energy consumption of interruptible appliances is denoted by Ec

Utility grid

Transmission lines Smart meter Two way communication Power flow

𝐸𝑐𝑖 =

𝑇 ∑︁ 𝐼 ∑︁

𝐸𝑐𝑡𝑖

Fig. 2: Energy Management Controller (EMC)

(2)

𝑡=1 𝑖=1

3) Critical Peak Pricing: The critical peak pricing rates are high during the few critical hours of the year when the electricity demand is very high. Over the year, the utility uses this price signal for approximately 12-15 days a year. Sometimes the CPP signal is used with the combination of ToU signal in order to balance the effect of high electricity cost.

Let Ui denotes the total number of uninterruptible appliances such that u 𝜖 Ui where Ui belongs to two appliances. Similarly the power rating of these appliances is denoted by P and PT is the electricity pricing tariff. X, again here belongs to the on/off status of an appliance. Total energy consumption for uninterruptible appliances is denoted by Uia 𝑈 𝑖𝑎 =

𝑇 ∑︁ 𝑈𝑖 ∑︁

𝑈 𝑖𝑡𝑢

(3)

III. C OMPUTATIONAL R ESULTS AND D ISCUSSION

𝑡=1 𝑢=1

In this section, simulations results are discussed in order to demonstrate the effectiveness and performance of our proposed solution. We performed our simulations in MATLAB environment and compared the results to evaluate the performance of each technique. The results are simulated on Intel (R) Core (TM) I5-7200U CPU@ 2.50GHz processor with 4GB RAM. The objectives regarding electricity bill minimization, user comfort maximization and PAR reduction are fulfilled using three different meta-heuristic techniques. The electricity bill calculation depends on the tariff which is provided by the utility. In this regard, three different pricing tariffs are considered in this study which includes: RTP, ToU and CPP. According to these pricing models, we divide the 24 hours price in 48 hours time interval, with each rate divided into two equal time periods. In case of RTP, the peak hours in which the electricity price is high are from 14𝑡ℎ hour to 22𝑛𝑑 hour. While, in case of ToU, the peak hours are from 22𝑛𝑑 hour to 34𝑡ℎ hour of the day. The other peak created in ToU is during the hours of (14-21) and (34-38). Whereas, in case of CPP, the price is high between 22𝑛𝑑 and 34𝑡ℎ hour of the day. Price rates for the peak hours in all three cases are 27.3 cents/kWh, 18.0 cents/kWh and 123.4 cents/kWh respectively. The reason behind performing simulations for all three pricing models is to enable the users to consume electricity intelligently. The simulations results show that how the overall electricity bill can be reduced by using each pricing scheme. The pricing tariff is always mentioned on the electricity bill by the utility

Let H denotes the total number of hard-time appliances such that h 𝜖 H where h belongs to three appliances. The power rating of these appliances is denoted by P and PT shows the electricity price rate. X is for on/off status . Total energy consumption of the hard-time appliances is denoted by HT. 𝐻𝑡 =

𝐻 𝑇 ∑︁ ∑︁

𝐻𝑡

(4)

𝑡=1 ℎ=1

The overall energy consumption for all the appliances is now evaluated by: 𝑇 𝐸𝐶 = 𝐸𝑐𝑖 + 𝑈 𝑖𝑎 + 𝐻𝑡

(5)

By means of this total energy consumption, PAR for residential customers is calculated and is stated in equation (6) PAR =

𝑀 𝑎𝑥𝑖𝑚𝑢𝑚(𝑈 ) 𝑀 𝑒𝑎𝑛(𝑈 )

(6)

To calculate the electricity bill, three different pricing tariffs are considered. These pricing tariffs includes: real time pricing (RTP), time of use (ToU) and critical peak pricing (CPP). B. Electricity Pricing Tariff 1) Real Time Pricing: According to real time pricing, the electricity consumers can adjust the usage pattern of electricity they want to use. This pricing signal varies from hour to hour and provides real time data usage.

3

3000

500

500

2500

400

300

200

100

0

Total Cost (cents)

600

Total Cost (cents)

Total Cost (cents)

600

400

300

200

100

Un Scheduled EHO Scheduled FF Scheduled

HEF Scheduled

0

2000

1500

1000

500

Un Scheduled EHO Scheduled FF Scheduled

(a) RTP

HEF Scheduled

0

Un Scheduled EHO Scheduled FF Scheduled

(b) TOU

HEF Scheduled

(c) CPP

Fig. 3: Total Electricity Cost.

20

40

15

20

0

18 UnScheduled EHO Scheduled FF Scheduled HEF Scheduled RTP

80

Electricity cost (cents)

25

60

16

60

14

40

12

10

20

10

5

0

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

8 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

Time (hours)

Time (hours)

(a) RTP

(b) TOU 140 UnScheduled EHO Scheduled FF Scheduled HEF Scheduled RTP

600

500

120

100

400

80

300

60

200

40

100

20

0

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

Time (hours)

(c) CPP

Fig. 4: Total Electricity Cost per Hour.

4

cents/kWh

700

Electricity cost (cents)

Electricity cost (cents)

80

100

cents/kWh

30 UnScheduled EHO Scheduled FF Scheduled HEF Scheduled RTP

cents/kWh

100

TABLE I: Appliances Parameters used in Simulation Process Appliances Washing Machine Dish Washer Spin Dryer Cooker Hob Cooker Oven Electric Car Interior Lighting Microwave Desktop Vaccum Cleaner Fridge Laptop

Power rating (kWh) 2.5 2 2.5 3 5 3.5 0.84 1.7 0.3 1.2 0.3 0.1

Earliest starting time (h) 9 9 13 8 18 18 18 8 18 9 0 18

Latest finishing time (h) 12 17 18 9 19 8 24 9 24 17 24 24

Operational time interval (h) 3 8 5 1 1 14 6 1 6 8 6

5

7

Average EHO Average FF Average HEF

6

Duration (h) 1.5 2 1 0.5 0.5 3 6 0.5 3 0.5 24 2

8

Average EHO Average FF Average HEF

4.5

Average EHO Average FF Average HEF

7

4 6

4 3 2

3.5

Waiting Time (hours)

Waiting Time (hours)

Waiting Time (hours)

5

3 2.5 2 1.5

5 4 3 2

1

1

1

0.5

0

Interruptible Load

0

NonInterruptible Load

(a) RTP

Interruptible Load

0

NonInterruptible Load

(b) TOU

Interruptible Load

NonInterruptible Load

(c) CPP

7

7

6

6

6

5

5

5

4

4

4

PAR

7

PAR

PAR

Fig. 5: User Comfort Analysis.

3

3

3

2

2

2

1

1

1

0

Un Scheduled

EHO Scheduled

0

FF Scheduled

(a) RTP

HEF Scheduled

Un Scheduled EHO Scheduled FF Scheduled

HEF Scheduled

(b) TOU

0

Un Scheduled EHO Scheduled FF Scheduled

HEF Scheduled

(c) CPP

Fig. 6: Peak to Average Ratio.

each month in order to inform its users. Moreover, users can easily check the tariff in the smart meter fixed in their houses.

the best electricity cost achieved after performing simulations is proved by our proposed hybrid technique HEF. In Fig. 3 (b), when ToU scenario is applied the cost in unscheduled case is 546.8 cents/kWh whereas in case of EHO scheduled, the cost is reduced to 398.2 cents/kWh. Relatively, the total cost in case of FF scheduled and HEF scheduled is 0.73% and 0.65% less than the unscheduled cost respectively. The third pricing model cost according to Fig. 3 (c), i.e., CPP’s unscheduled cost is 2796.0 cents/kWh which reduces the total cost to 990.6 cents/kWh, 1055.6 cents/kWh and 1071.5 cents/kWh in case of EHO, FF and HEF scheduled respectively.

According to Fig. 3 (a), the total unscheduled electricity cost is 514.6 cents/kWh. The unscheduled cost presented in this figure shows that the user runs the electrical appliances with insufficient attention. As, without waiting for a single second and by ignoring the value of pricing tariff unknowingly in a particular hour, user effects the grid stability by creating huge electricity load. In case of EHO scheduled, the unscheduled cost is reduced to 445.9 cents/kWh. While in case of FF scheduled and HEF scheduled, the cost is further reduced to 403.2 cents/kWh and 341.5 cents/kWh respectively. Therefore,

In Fig. 4 (a), the electricity cost per hour in case of RTP is

5

6

6 Un Scheduled EHO Scheduled FF Scheduled HEF Scheduled

5

5

4

Load (kWh)

4

Load (kWh)

Un Scheduled EHO Scheduled FF Scheduled HEF Scheduled

3

3

2

2

1

1

0

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

Time (hours)

Time (hours)

(a) RTP

(b) TOU 6 Un Scheduled EHO Scheduled FF Scheduled HEF Scheduled

5

Load (kWh)

4

3

2

1

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

Time (hours)

(c) CPP

Fig. 7: Total Load per Hour.

shown. The total electricity consumption pattern in this figure represents how peak hour load is shifted to the off-peak hours of the day. It can be easily observed that all of the three algorithms performed efficiently in shifting the peak load to the hours where load is very less. According to Fig. 4 (a), the peak is created during the day time approximately after 10:00 am. In ToU scenario, the hourly power consumption pattern is shown in Fig. 4 (b). In this figure, the peak shaving is achieved by all three algorithms related to user demand. However, the maximum energy consumption cost is equal to approximately 52.5 cents/kWh while scheduling through FF algorithm.

the scheduler for peak load shaving. For practical consideration, simulations regarding user comfort are also performed which has been shown in Fig. 5 (a), (b) and (c) separately. We have evaluated the waiting time according to the classification of the appliances adopted to perform simulations, whereas, here we will discuss the collective waiting time at first. Under the RTP scenario, if the user wants to run an appliance while using the EHO based schedular, he has to wait for 55 minutes and 2 seconds collectively. Moreover, for the case of FF based schedular and HEF based schedular, user has to wait for approximately 2 hours and 6 minutes and 41 minutes respectively. So, by following RTP scenario, the best waiting time is achieved by the HEF algorithm which further makes a user to pay a small amount of electricity bill, i.e., 341.5 cents/kWh. As, the total electricity cost in this case is lesser as compared to the

According to CPP scenario, as peak hours are between (22𝑛𝑑 -34𝑡ℎ ) hour of day in Fig. 4 (c), the implemented algorithms reduced and divided the peak efficiently among different hours to lower electricity bill. With respect to simulation results, EHO is proved as the best technique to enable

6

cost of other algorithms, so, we can say that our proposed algorithm is performing best among others while following RTP state . In Fig. 5 (b), the minimum waiting time to run an appliance is 44 minutes and 4 seconds resulting in case of our proposed hybrid algorithm HEF. However, in CPP scenario, the minimum waiting time is produced by the EHO algorithm, i.e., 37 minutes and 8 seconds. As there is a tradeoff between achieving user comfort and total electricity bill reduction, even then our hybrid technique performs better among two other techniques. Relating to waiting time results, the user comfort is best achieved by HEF as shown in Fig. 5 (a), (b) and (c). The collective waiting time is calculated by division of sum of appliances matrices to the total number of appliances and then taking mean of appliances’ waiting time. Results show that if the consumers perform their electricity tasks intelligently, it would be beneficial for both the utility and the customers. The satisfaction of user deals in both aspects, i.e., electricity cost minimization and waiting time reduction. In order to handle the uncertainties in supply projected by huge demand from consumer-side, the strategy to optimally consume electricity creates a great positive difference. This aspect parallel reflects the utility and energy providers to deliver stable and secure output. As, due to uneven power consumption, peak formations and inadequate supply happen which correspondingly effects both the user and the electricity provider. Moreover, it causes a severe type of load shedding. In Fig. 6, PAR reduction is calculated for each scenario. According to RTP scheme, the highly PAR reduction is achieved by FF based scheduler. Next, in case of ToU, again FF based scheduler reduces the PAR more optimally than that of other schedulers. Similarly, in case of CPP, the overall best PAR reduction is achieved by the proposed hybrid technique, i.e., HEF which is approximately 1.71.

[2] ℎ𝑡𝑡𝑝𝑠 : //𝑤𝑤𝑤.𝑠𝑚𝑎𝑟𝑡𝑔𝑟𝑖𝑑.𝑔𝑜𝑣/𝑡ℎ𝑒𝑠 𝑚𝑎𝑟𝑡𝑔 𝑟𝑖𝑑/𝑠𝑚𝑎𝑟𝑡𝑔 𝑟𝑖𝑑.ℎ𝑡𝑚𝑙 [3] Rahim, S., Javaid, N., Ahmad, A., Khan, S.A., Khan, Z.A., Alrajeh, N. and Qasim, U., 2016. Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy and Buildings, 129, pp.452-470. [4] Graditi, G., Di Silvestre, M.L., Gallea, R. and Sanseverino, E.R., 2015. Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Transactions on Industrial Informatics, 11(1), pp.271-280. [5] Umetani, S., Fukushima, Y. and Morita, H., 2017. A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system. Omega, 67, pp.115122. [6] Qayyum, F.A., Naeem, M., Khwaja, A.S., Anpalagan, A., Guan, L. and Venkatesh, B., 2015. Appliance scheduling optimization in smart home networks. IEEE Access, 3, pp.2176-2190. [7] Gholian, A., Mohsenian-Rad, H. and Hua, Y., 2016. Optimal industrial load control in smart grid. IEEE Transactions on Smart Grid, 7(5), pp.2305-2316. [8] Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A. and Alrajeh, N., ˘ Rside ˇ 2015. A generic demandâA management model for smart grid. International Journal of Energy Research, 39(7), pp.954-964. [9] Zhang, D., Evangelisti, S., Lettieri, P. and Papageorgiou, L.G., 2016. Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks. Energy Conversion and Management, 110, pp.113-124. [10] Meena, N.K., Parashar, S., Swarnkar, A., Gupta, N. and Niazi, K.R., 2017. Improved Elephant Herding Optimization for Multiobjective DER Accommodation in Distribution Systems. IEEE Transactions on Industrial Informatics. [11] Tan, W.N., Gan, M.T. and Tan, Z.L., 2016, June. Optimization models for demand-side and supply-side scheduling in smart grids. In Environment and Electrical Engineering (EEEIC), 2016 IEEE 16th International Conference on (pp. 1-5). IEEE. [12] Rasheed, M.B., Javaid, N., Awais, M., Khan, Z.A., Qasim, U., Alrajeh, N., Iqbal, Z. and Javaid, Q., 2016. Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies, 9(7), p.542. [13] Javaid, N., Ahmed, F., Ullah, I., Abid, S., Abdul, W., Alamri, A. and Almogren, A.S., 2017. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid. Energies, 10(10), p.1546. [14] Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A. and Niaz, I.A., 2017. A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies, 10(3), p.319. [15] Ahmad, A., Javaid, N., Guizani, M., Alrajeh, N. and Khan, Z.A., 2017. An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Transactions on Industrial Informatics, 13(5), pp.2587-2596. [16] Finn, P. and Fitzpatrick, C., 2014. Demand side management of industrial electricity consumption: promoting the use of renewable energy through real-time pricing. Applied Energy, 113, pp.11-21. [17] Ogunjuyigbe, A.S.O., Ayodele, T.R. and Akinola, O.A., 2017. User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, pp.352-366. [18] Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H. and Fan, Z., 2012, January. An integer linear programming based optimization for home demand-side management in smart grid. In Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES (pp. 1-5). IEEE. [19] Shakouri, H. and Kazemi, A., 2017. Multi-objective cost-load optimization for demand side management of a residential area in smart grids. Sustainable Cities and Society, 32, pp.171-180. [20] Melhem, F.Y., Grunder, O., Hammoudan, Z. and Moubayed, N., 2017, June. Optimal residential load scheduling model in smart grid environment. In Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe), 2017 IEEE International Conference on (pp. 1-6). IEEE.

IV. C ONCLUSION In this paper, we have performed comparative analysis between two meta-heuristic techniques. Moreover, we proposed an hybrid technique of EHO and FF algorithm, i.e., HEF in order to achieve our objective optimally. Our aim in this study is to minimize electricity cost along with user comfort maximization. Further, this study includes the parameters of PAR reduction which is beneficial for the supply side as if it increases, can cause severe load shedding due to increased user demand. We used three pricing schemes separately, in order to demonstrate the effect of each on electricity cost, PAR and waiting time. It is now justified and verified through results that our proposed hybrid technique is best among other techniques. Although there is a trade-off between some parameters. If user focus on the maximum comfort level, he must compromise on paying an excessive amount of electricity cost, whereas, if user can compromise on comfort factor, he can surely enjoy low prices. In future, we will focus on the privacy and security issue of the smart grid.

....

R EFERENCES [1] ℎ𝑡𝑡𝑝𝑠 : //𝑒𝑛.𝑤𝑖𝑘𝑖𝑝𝑒𝑑𝑖𝑎.𝑜𝑟𝑔/𝑤𝑖𝑘𝑖/𝑆𝑚𝑎𝑟𝑡𝑔 𝑟𝑖𝑑

7