Appliances Scheduling using Hybrid Scheme of ...

Appliances Scheduling using Hybrid Scheme of Genetic Algorithm and Elephant Herd Optimization for Residential Demand Response Rasool Bukhsh1,2 , Nadeem Javaid1,∗ , Zafar Iqbal3 , Usman Ahmed2 , Zeeshan Ahmad2 , Muhammad Nadeem Iqbal4 1 COMSATS

2

Institute of Information Technology, Islamabad 44000, Pakistan NFC Institute of Engineering and Fertilizer Research, Jaranwal Road Faisalabad, Pakistan 3 PMAS Agriculture University, Rawalpindi 46000, Pakistan 4 COMSATS Institute of Information and Technology, Wah Cantt, Pakistan ∗ Corresponding author: www.njavaid.com, [email protected]

Abstract—The invention of Smart Grid (SG) have revolutionized the traditional electricity consumption pattern as well as distribution. The technology of communication and information have been involved in almost every domain so for Smart Grids. Production of electricity is not cheap, hence with the help of information and technology, Smart Meters (SM) play vital role to control, manage and perform optimization to utilize the electric power efficiently on consumer side and called Demand Side Management (DSM). In the proposed research paper, a Home Energy Management System (HEMS) have been proposed to optimize the home appliances to reduce the maximum cost. Elephant Herd Optimization (EHO) algorithm have been implemented along with our own hybrid of EHO algorithm. This EHO is hybrid of Genetic Algorithm (GA) and called Genetic Elephant Herd Optimization (GEHO). Results of simulation shows that GEHO scheduled the appliance more efficiently to reduce maximum cost when comparing with regular EHO and unscheduled schemes. Peak to Average Ration (PAR) also have been observed. GEHO and unscheduled have equal PAR due to scheduling of maximum appliances on either sides but EHO have small PAR. For further understanding of cost optimization different Operation Time Interval (OTI) have been applied. Trends of load and cost with all of three schemes have been discussed in detail. Index Terms—Smart Meter (SM), Genetic Elephant Herd Optimization (GEHO), Elephant Herd Optimization (EHO), Smart Home (SM), Operation Time Interval (OTI)

I. I NTRODUCTION Electricity is being provided to almost 80% of the world’s population. This is still increasing due to adaptation of modern life style and easy access on the globe [1]. This has increased the demand drastically. Electricity generation, transmission distribution and their controls are performed at grid stations. With the involvement of Information and Technology (IT) the grids are termed as Smart Grid. A SG communicates with SM at consumer’s side to enhance, control, self-heal as well as suggesting solutions for cost efficiency. SHs are the key units of SG. The concept of SHs have been existing since nineties century [2]. With the inventions of more electrical and electronic appliances the use of electricity is increasing rapidly, specially in smart homes.

The increase in demand of electricity enforce the production. Grid stations run the more generators to meet the demands. Generators consume more fuel which add the carbon in environment which is the main cause of global warming. In this case, solution lies in the optimization of energy usage. Consumer should optimize the appliances at least for two reasons. One, consume less energy while meeting his needs. Second, reduce the utility bills. For both the cases, consumer should shift the running of appliances from on-peak time-slots to off-peak time slots wisely. The optimization of energy is challenging for grids and consumers [3]. Researchers and scientists have been proposing different solutions for Energy Management System (EMS). EMS have two major applications Demand Side Management (DSM) and Supply Side Management (SSM). SSM manages energy generation resources and ensures the reliability of energy supply. DSM educates the consumer how to use electricity with optimization to have reduce cost. DSM encourages to use electricity during off-peak times to meet their requirements rather in on-peak hours. DSM is the key part of HEMS. DSM manages the appliances of consumer efficiently. It helps him to reduce maximum utility bill. The optimization of appliances is achieved by load technologies like shifting of loads, managing the loads and direct controlling of loads [4], [5]. Different energy models also have been proposed to reduce the consumer’s cost. Centralized energy management model may work better for small scale but it has lesser competencies with large scale management. Semi centralized and fully decentralized management system models may work better with large scale [6]. To support all such management model systems Artificial Intelligence (AI) base is a promising approach for SHs, Smart Buildings (SB) and SGs [7], [8]. In this paper AI based heuristic techniques have been proposed to optimize appliances of home to reduce the cost. Hybrid of EHO with GA have been proposed. The proposed hybrid technique is compared with regular EHO, GA and unscheduled techniques. Multiple OTIs have been used to

study the behavior of load, cost and PAR. In section II related work have been discussed. The problem statement is discussed in section III. Section IV is dedicated to simulatioin results and discussion. Section V has the conclusion. II. R ELATED W ORK It has been encouraged to create SG and SH using new technology called Internet of Things (IoT) [9]. IoT gives more freedom to control appliances with mobile gadgets and others using wireless technologies. The systems developed for such controls are HEMS. The softwares suggest the consumers how to save maximum cost and reduce maximum load by full filling their satisfaction [10], [11]. These softwares are AI based which suggest intelligently by monitoring the beahvior of use, load and expected costs. DSM is a functional unit of HEMS [12], [13]. It reduces the consumption of electricity and makes the use more efficiently for consumers. Implementation to load shift enabled the use of energy in off-peak hours. Market of electricity is higher in on-peak hours rather off-peak hours. With the aid of DSM load shifted to off-peak hours resulting in reduced cost. DSM enables the consumer to spend less and use the electricity efficiently. An energy management controller (EMC) has been proposed in [14] which was a design of three different algorithms. The algorithms ant colony optimization (ACO), Binary Partical Swarm Optimization (BPSO) and GA were the parts of EMC. Residential energy management targeted the avoidance of peaks where Time of Use (TOU) and Inclined Block Rate (IBR) were the pricing singnals. Appliances were classified into three classes. The results showed reduction of PAR, electricity bills, execution time with maximum comfort level of user using GA. Privacy and security issues were not discussed between utility and consumer. An other intelligent technique have been proposed for energy optimization, Multi-objective brainstorm algorithm (MOBSA) in [15]. Brainstorm is the technique to find a solution for problem among the people with different backgrounds. An other technique also proposed called multi-objective firefly algorithm (MO-FA). These techniques were compared with GA II (a non-dominated sorting algorithm). MOBSA showed better performance for energy efficiency and time execution. In [18] authors proposed a HEMS for SH. The proposed system handled the surplus power when supply exceeded the demand. For the proposed system no scheduling techniques was used. Formation of peak and user comfort were neglected. While, in [19] the impllimentation of HEMS was proposed with multi-agent system for SH. The demand of consumer is measured by two priorities, user comfort and lower consumption. Electricity consumption and cost were reduced along with power demand by consumer. PAR reduction was ignored. A control system with multi-agent was proposed in [16]. Energy consumption of grid is controlled with four intelligent agents. These were load, switch, central control and local coordinator agent. These agents were controlled by a heuristic

technique, PSO. A case study for energy optimization have been proposed in [17]. The aim was to reduce cost and improve efficiency of plant. The novel techniques of dispatch optimizer and predictive controlled algorithms reduced cost bill by 12% and improved efficiency 1.56% annually. In this proposed research two techniques were implemented to reduce the cost. The behavior of cost, load and PAR have been studied using different OTIs. A hybrid EHO also have been proposed. In next section Problem statement is explained later simulation results and discussion is presented. In last section conclusion is presented. In [21] GA and cuckoo search algorithms have been used to propose energy efficiency for SH and a smart building of 30 homes.Real Time Pricing (RTP) signal was used for the said techniques. Results demonstrated the effectiveness of proposed technique for single and multiple homes. The cost and PAR was reduced. However, trade-off between electricity cost and waiting time for user was observed. Load scheduling under utility and photolytic (PV) renewable energy resources were used in [23]. GA, Binary Particle Swarm Optimization (BPSO), Wind Driven Optimization (WDO) and they proposed Genetic Wind Driven Optimization (GWDO) algorithms were used to optimize the interruptible and non-interruptible appliances. Two pricing signals RTP and Inclined Block Rate (IBR) were used. The proposed scheme shifted loads from on-peak hours to off-peak hours and hours when PV generate high loads. The proposed techniques reduced the cost and PAR effectively. Authors, did not discussed the user comfort. Three case studies have been discussed in [23] to study the saving of energy for Singapore household with smart technologies. Authors suggested to adopt smart technologies for smart homes to save energy. Studies showed that individual may not change energy consumption usage even though full awareness of environmental pollution unless security and comfort is offered. Immaturity of technology and design do not encourage the consumer to adopt smart technology. So, technologies with AI should be encouraged and the integrate with public services and utility sectors. SM should detect behavior of consumer and take action rather making the consumer active operator for appliances control in Singapore. Consumer should be notified with mobile gadgets and other automated house consul should be provided. Research finding showed that SM are at immature phases but projects like Singapore Smart Nation might be one of the leading projects to improve the technology and smart homes in the near future. III. P ROBLEM S TATEMENT GA is biological evolutionary algorithm in which coding of chromosomes provide solution for the problem. chromosomes update them by crossover or mutation. In crossover parents are selected (usually random) which swap the information according to fitness function. In mutation random chromosomes are selected and exchange the information. GA algorithm is simple to implement and provide optimize solution. However, scaling the complexity is the flaw of the algorithm. In the

research hybrid of GA and EHO have been proposed which overcome the flaw by reducing the randomization [24]. EHO algorithm has been derived from the social life style of elephants. Elephants maintain herding rules in multiple clans, each ruled by female leader ’matriarch’. Females and calves live under matriarch and males live nearby the group but separated. Male calves at their adulthood are separated from main herd but bond is maintained with respective clans using low frequencies [?]. For the proposed problem DA-RTP is taken for a day or 24 hours from [20]. Appliances have their own behavior of statuses to run or off for each hour. Power rating of each appliance is different. In proposed research appliances have been scheduled to reduce the cost. DA-RTP has 24 time-slots for a day and statuses of appliances also have been observed for 24 time-slots. In proposed research paper, different OTIs have been taken to have variant time-slots. The purpose is to study change in total cost with variant number of time-slots in a day.

IV. S IMULATION R ESULTS AND D ISCUSSION In proposed research, different OTIs have been used to understand the behavior of PAR, Cost and Load for GEHO. Five OTIs of 60, 30, 20, 15 and 12 minutes have been incorporated in the research. These OTIs also, have been implemented with a new proposed technique. This is a hybrid of regular EHO and genetic algorithm (GA). Both of the techniques have reduced the cost when compared with unscheduled technique, though hybrid EHO (GEHO) showed improved cost efficiency. 20 19

Toit is the length of time for one OTI. In the research paper OTIs of 12, 15, 20, 30 and 60 minutes were taken. Tslt are number of time-slots for an hour. Time slots for a day is calculated as, day Tslt = Tslt × 24 (2)

12 1

The total load of all appliances for given time-slots,  App=1,n X  App App Loadtotal = P rslt × Sslt

21

Fig. 1. Loads of appliances (OTI = 60 min.)

2

1.5

1

0.5

(5) 0 Unscheduled

(6)

(7)

slt=1,n

And the total cost of all appliances for given time-slots,  App=1,n X  App App hour Cost(total) = P rslt ×Sslt ×DART Pslt (8) slt=1,n

11

OTI 60-Minutes

PAR

(4)

And the cost of an appliance for a specific time-slots is, App hour CostApp slt = Loadslt × DART Pslt

15

(3)

App P rslt is the power rating of an appliance which decomposed to 1/Tslt part of an hour. So, the load of an appliance for specific time-slot is, App App LoadApp slt = P rslt × Sslt

16

13

hour DART Pslt is the 1/Tslt portion of a price for DART P hour hour of a given hour. DART Pslt and statuses of appliances are iterated for Tslt times for the specific hour. Power rating also converted from KWh to KW tslt . Where tslt is the 1/Tslt portion of an hour. App P rslt = P rApp /Tslt

17

14

day Tslt are the total number of slots for a day. DA-RTP for a day day also converted into Tslt time-slots. For the case, number of DA-RTP time-slots of an hour is calculated as, hour DART Pslt = DART P hour /Tslt

Unscheduled EHO GA GEHO

18

(1) Load (kW60-Min.)

Tslt = 60/Toit

Total load of all appliances for a day remains same. Total cost may change as the time-slots increase with smaller OTI and vice versa. The purpose of proposed research is to reduce maximum cost and study the behavior of load and cost of appliances for a day with the change of OTIs.

EHO

GA

GEHO

Fig. 2. PAR (OTI=60 min.)

The simulation of OTI with 60 minutes shows the shift of appliances from high tariff time-slots to low tariff timeslots, using GA, EHO but GEHO shifted more loads. In Fig.1 load of EHO and GA shift from high tariff time-slots to low tariff but peak of this load remained below the peak load of unscheduled. This has reduced the peak-to-average ration (PAR) compared with unscheduled as shown in Fig.2. The PAR of GEHO is equal to PAR of unscheduled. To understand

10 9.5 Unscheduled EHO GA GEHO

9

Load (kW30-Min.)

this, Fig.1 have to be re-examine. GEHO has rescheduled the optimized appliances so efficiently that it has further reduced the cost by adjusting the load from high tariff time-slots to low tariff time-slots, which increased the peak load to maximum. Peak load of GEHO and unscheduled turned to be equal but cost of GEHO remained lowest among unscheduled, GA and EHO, shown in figure in Fig.3. The cost of EHO is higher compared to unscheduled, in beginning time-slots as shown in Fig.4. This is because of shifting of appliances from higher tariff to low tariff time-slots. During high tariff time-slots, the cost is low, which all-together, makes less total cost compared to unscheduled. GEHO has similar behavior in Fig.4 but it has bit more higher cost in early time-slots compared to EHO and more higher cost compared to unscheduled. But in last few time-slots cost is lesser because EGHO has shifted more appliances to early time-slots making higher cost in early timeslots.

8.5 8 7.5 7 6.5 6 1

11

21

31

41

OTI 30-Minutes

Fig. 5. Loads of appliances (OTI = 30 min.)

2

1200

1.5

PAR

Total Cost (cent)

1000 1

800

600

0.5

400 0 Unscheduled

200

0 Unscheduled

EHO

GA

GEHO

Fig. 3. Total costs (OTI=60 min.)

120 Unscheduled EHO GA GEHO

Electricity Cost (Cent)

100

80

60

40

20

0 1

11

21

OTI 60-Minutes

Fig. 4. Electricity cost (OTI = 60 min.)

OTI is decreased to 30 minutes which increased the timeslots. In other words loads of appliances have more space to sparse. The appliances approach to closer their actual energy cycles too which may reduce more cost. Hence, more space to sparse and actual energy cycles of appliances decrease the over all cost.

EHO

GA

GEHO

Fig. 6. PAR (OTI=30)

More over EHO schedule the appliance to have minimum over all cost. Appliances shifted to low tariff time-slots from high tariff time-slots. In Fig.5 loads of appliances shifted to low tariff time-slots. The peak load with EHO is less than that of unscheduled. So, PAR of EHO is less than unscheduled. GEHO scheduled the appliances in so efficient manner that maximum loads of appliances shifted to low tariff time-slots that peak load is equal to that of unscheduled. This has forced the PAR of GEHO equal to PAR of unscheduled, shown in Fig.6. Because of adjustment more load in low tariff time-slots, it reduced the over all cost Fig.8 compared to unscheduled and regular EHO. Cost in every time-slot changes Fig.7 as load has changed after the shift. In proposed research OTI have further reduced to 20 minutes which turned more time slots. There were 48 time-slots when OTI was of 30 minutes and it has 72 time-slots now and OTI is 20 minutes. There is further space for loads of appliances to be sparse in and scheduling algorithm shift these loads in given time-slots with less costs. Regular EHO has scheduled the appliance to reduce the total cost by shifting appliances from high tariff time-slots to low tariff time-slots. Fig.9 shows, EHO has shifted appliances to low tariff time-slots but peak load is less than that of unscheduled making PAR less than unscheduled. PAR of GEHO is equal to that of unscheduled because GEHO has

2

30 Unscheduled EHO GA GEHO

25

PAR

Electricity Cost (Cent)

1.5 20

15

1

10

0.5 5

0

0 1

11

21

31

Unscheduled

41

EHO

GA

GEHO

OTI 30-Minutes

Fig. 10. PAR (OTI=20 min.)

Fig. 7. Electricity cost (OTI = 30 min.) 600

TABLE I C OST D IFFERENCES WITH OTI S IN PERCENTAGE

Total Cost (cent)

500

Unscheduled OTIs Minutes 12 15 20 30 60

400

300

200

222.352 12 0 20 40 60 79.98

277.94 15 20 0 25 50 74.98

370.5867 20 40 25 0 33.33 66.64

555.88 30 60 50 33.33 0 49.97

1111 60 79.98 74.98 66.64 49.97 0

100

0 Unscheduled

EHO

GA

GEHO

Fig. 8. Total cost (OTI = 30 min.)

7

Load (kW20-Min.)

6.5 Unscheduled EHO GA GEHO

6

slots more higher than regular EHO and lower in last few time-slots where tariff is high, as explained in Fig.11. OTI further reduced to 15 minutes which increased the timeslots to 96. Each slot with length of 15 minutes. Tariff is also divided accordingly for each time-slots. Appliances with further space for load sparse may reduce more over all cost. Energy cycles of appliances may have closer time length with these small time-slots which help in reducing more cost when adjusted in low tariff time-slots. Regular EHO has shifted the appliances in low tariff time-

5.5

TABLE II C OST D IFFERENCES WITH OTI S IN PERCENTAGE

5

4.5

4 1

11

21

31

41

51

61

71

OTI 20-Minutes

Fig. 9. Loads of appliances (OTI=20 min.)

shifted more appliances in lesser tariff time-slots to reduce more cost which increased the loads in low tariff time-slots. Ultimately, higher load peak than regular EHO and equal to unscheduled Fig.10. When more load shifted to low tariff time-slots there shall be higher cost during these time-slots. There should be lesser cost in high tariff time-slots. Regular EHO have more costs then unscheduled in early time-slots where tariff is low actually. GEHO has similar characteristics but costs of in early time-

EHO Cost OTIs Minutes 12 15 20 30 60

193.9022 12 0 20.15 40.10 60.05 79.99

242.8451 15 20.15 0 24.98 49.96 74.94

323.7138 20 40.10 24.98 0 33.30 66.60

485.3315 30 60.05 49.96 33.30 0 49.93

969.228 60 79.99 74.94 66.60 49.93 0

TABLE III C OST D IFFERENCES WITH OTI S IN PERCENTAGE GEHO Cost OTIs Minutes 12 15 20 30 60

192.08 12 0 19.87 39.88 59.90 79.92

239.70 15 19.87 0 25.17 50.09 75.00

319.51 20 39.88 25.17 0 33.30 66.60

479.03 30 59.90 50.09 33.30 0 49.93

956.628 60 79.92 75.00 66.60 49.93 0

400

5 4.8

350

Unscheduled EHO GA GEHO

4.6

Load (kW15-Min.)

Total Cost (cent)

300 250 200 150

4.4 4.2 4 3.8 3.6

100 3.4

50

3.2

0

3

Unscheduled

EHO

GA

1

GEHO

11

21

31

41

51

61

71

81

91

OTI 15-Minutes

Fig. 11. Electricity cost (OTI = 20 min.)

Fig. 13. Loads of appliances (OTI = 15 min.)

2

12 Unscheduled EHO GA GEHO

10

PAR

Electricity Cost (Cent)

1.5 8

6

1

4

0.5 2

0

0 1

11

21

31

41

51

61

Unscheduled

71

EHO

GA

GEHO

OTI 20-Minutes

Fig. 12. Total cost (OTI = 20 min.)

Fig. 14. PAR (OTI=15 min.)

slots which makes less over all cost compared to unscheduled. GEHO has made more appliances to shift in low tariff timeslots which eventually, helped to reduce over all cost. This made the GEHO more efficient in cost reduction compared to EHO and unscheduled as shown in Fig.12. In Fig.13 loads of appliances are sparse in 96 time-slots. Unscheduled loads have higher peak in time-slots where tariff is high. Regular EHO has higher peak where tariff is low. When compare both, unscheduled have higher peak than EHO this is why unscheduled have more PAR value Fig.14. GEHO’s load is also scheduled similar to regular EHO but more loads of appliances are shifted in less tariff time-slots. So, load reduced from high tariff time-slots Fig.13. This made the peak load of GEHO equal to that of unscheduled and higher than regular GEHO. This shift of loads from high tariff time-slots to low tariff time-slots have increased the cost in low tariff time-slots. As, there left less in loads in high tariff time-slots so, cost is also reduced when scheduled the appliances with EHO and GEHO. In Fig.15, regular EHO has higher costs in during less tariff time-slots and lower during the high tariff time-slots comparing with unscheduled. GEHO has alsmot similar cost behavior but more higher than regular EHO at low tariff timeslots and more less during high-tariff time-slots. This makes

reduced overall cost for EHO comparing with unscheduled and higher than GEHO over all cost. This is observed in Fig.16. So, PAR of unscheduled and GEHO are equal and PAR of EHO is less than both of these techniques. Overall cost of GEHO is lesser than regular EHO and unscheduled techniques. Time-slots reach to 120 when OTI made to 12 minutes. In proposed research, further division of time-slots is made to have more space for sparse of loads. Again there should be

7 Unscheduled EHO GA GEHO

Electricity Cost (Cent)

6

5

4

3

2

1

0 1

11

21

31

41

51

61

71

81

91

OTI 15-Minutes

Fig. 15. Electricity cost (OTI = 15 min.)

300

2

250

PAR

Total Cost (cent)

1.5 200

150

1

100 0.5 50

0

0 Unscheduled

EHO

GA

GEHO

Unscheduled

Fig. 16. Total cost (OTI = 15 min.)

4.5

3.8

4 Unscheduled EHO GA GEHO

Unscheduled

Unscheduled EHO GA GEHO

3.5

Electricity Cost (Cent)

Load (kW12-Min.)

GEHO

Fig. 18. PAR (OTI=12 min.)

4

3.6

EHO

3.4 3.2 3 2.8

3 2.5 2 1.5 1

2.6

0.5

2.4

0 1

11

21

31

41

51

61

71

81

91

101

111

OTI 12-Minutes

1

11

21

31

41

51

61

71

81

91

101

111

OTI 12-Minutes

Fig. 17. Loads of appliances (OTI = 12 min.)

Fig. 19. Electricity cost (OTI = 12 min.)

more reduced cost comparing with 96 time-slots and other lesser time-slots due to having more space for load shifting and close to even small energy cycles of appliances. These energy cycles may finish within given time-slot (12 minutes) or even if surpass still there less load compared to 96 timeslots with 15 minutes time length of each as well as compared to other longer time length with lesser time-slots. In proposed research, OTI of 12 minutes have 120 timeslots and unlike unscheduled EHO has shifted the loads of appliances in low tariff time-slots. More the spaces for load to shift more efficient cost reduction is. This can be observed in Fig.17. EHO has higher loads than unscheduled where tariff is low and lower loads where tariff is high in time-slots. Unscheduled have highest peak compared to regular EHO. This has made unscheduled with more PAR than regular EHO Fig.18. GEHO showed more efficient in shifting of loads of appliances in low tariff time-slots. Ultimately, peak of load risen equal to unscheduled load peak. This is why PAR of GEHO and unscheduled are equal Fig.18. As loads have been shifted from high tariff time-slots to low tariff time-slots the cost should increase in low tariff timeslots and reduce in high tariff time-slots. This trend can be observed in Fig.19. Regular EHO have higher costs during low tariff time-slots due to shifting of appliances in these time-

slots. Even costs of EHO surpass the unscheduled cost during these time-slots. The costs of EHO decreased during high tariff time-slots as it already shifted loads of appliances from these time-slots to low tariff time-slots. GEHO have bit higher costs during low tariff time-time slots because GEHO shifted more loads of appliances in these time-slots and accordingly, has reduced costs during high tariff time-slots. So, such optimizations of EHO and GEHO have lesser overall cost compared to unscheduled. As GEHO shifted more loads in less tariff time-slots so, it has lesser overall cost from regular EHO as well as from unscheduled overall costs Fig.20. PAR of unscheduled and GEHO are equal and EHO has lesser PAR. While costs of EHO and GEHO are lesser than unscheduled. GEHO have less cost from EHO and unscheduled. After implementing all five different OTIs with unscheduled and scheduled techniques, common behavior have been ob served in all of them. In all graphs of OTIs show shift of appliances from high tariff time-slots to low tariff time-slots. Similar, behavior of costs loads have been observed in graphs of all OTIs. In TABLE 1 difference of unscheduled costs have been summarized. For example OTI of 12 minutes has 20 % less cost from OTI of 15 minutes. Similarly,OTI have 40 % , 60 % and 79.98 % more efficient (less cost) from

250

Total Cost (cent)

200

150

100

50

0 Unscheduled

EHO

GA

GEHO

Fig. 20. Total cost (OTI = 12 min.)

OTIs 20,30 and 60 minutes respectively. Similarly, OTI of 15 minutes have 20% more cost from OTI of 12 minutes and 25%, 50% and 74.98% lesser cost from OTIs of 20,30 and 60 minutes OTIs. Rest of the cost differences can be read and observed accordingly from TABLE 1. There are almost similar differences of percentage of costs with one another, using EHO and GEHO with given OTIs in TABLE 2 and TABLE 3.For example, cost with OTI of 12 minutes is 20.15% lesser from OTI of 15 minutes. Similarly, 40.10% , 60.05% and 79.99% lesser in cost from OTI of 20, 30 and 60 minutes respectively by using EHO technique TABLE 2. Differences with rest of OTIs for EHO are given in TABLE 2.Differences of costs for our proposed technique are given in TABLE 3. The proposed GEHO reduced 6.86% cost compared to GA with OTI of 60 minutes and 7.22% with 30 minutes of OTI. The cost of GEHO reduced by 8.5%, 8.47% and 8.3% with OTIs of 20, 15 and 12 minutes compared to GA. V. C ONCLUSIOIN Regular EHO scheduled the appliances efficiently such that it reduced the cost. Our proposed hybrid of EHO (GEHO) reduced more cost compared to regular EHO and unscheduled. Both EHO and GEHO shifted the load from on-peak hours to off-peak hours. A behavior of cost reduction with smaller OTIs and more time-slots have been observed. A draw back of smaller OTI is that it increased the execution time. Because of more time-slots produced with smaller OTIs. Bigger the OTI lesser the execution time due to lesser time-slots. R EFERENCES [1] Tayab, Usman Bashir, Mohd Azrik Bin Roslan, Leong Jenn Hwai, and Muhammad Kashif. ”A review of droop control techniques for microgrid.” Renewable and Sustainable Energy Reviews 76 (2017): 717727. [2] Lobaccaro, Gabriele, Salvatore Carlucci, and Erica Lfstrm. A Review of Systems and Technologies for Smart Homes and Smart Grids. Energies 9, no. 5 (2016): 348. [3] Zhou, Bin, Wentao Li, Ka Wing Chan, Yijia Cao, Yonghong Kuang, Xi Liu, and Xiong Wang. Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews 61 (2016): 30-40.

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