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Efficient utilization of energy employing heuristic techniques with the incorporation of green energy resources in smart cities

  • September 2019
Authors:
Asif Khan at COMSATS University Islamabad
Asif Khan
Nadeem Javaid
Nadeem Javaid
Nadeem Javaid
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Mariam Akbar at COMSATS University Islamabad
Mariam Akbar
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Efficient utilization of energy employing
heuristic techniques with the incorporation
of green energy resources in smart cities
Asif Khan
CIIT/FA15-PCS-004/ISB
Supervisor: Dr. Nadeem Javaid
Co-Supervisor: Dr. Mariam Akbar
Presentation theme
•Introduction
•Literature review
•Problem statement
•Problem formulation
•System model
•Proposed solution
•Simulations and discussion
•Conclusion
2
Introduction
Smart grid
•Traditional grid + information and communication technology
•Bi-directional communication
•Consumer-interactive
Challenges
•Stochastic environment i.e. Renewable energy sources
•Load balancing
•User comfort maximization
•Uninterrupted power supply
Solution
•Supply side management (SSM)
•Demand side management (DSM)
•Demand response (DR) programs
•Price-based DR
•Incentive-based DR
3
Literature review (1/2)
4
[1]. Vardakas, John S., Nizar Zorba, and Christos V. Verikoukis. “Power demand control scenarios for smart grid applications with finite number of
appliances.”Applied Energy 162 (2016): 83-98.
[2]. Shirazi, Elham, and Shahram Jadid. "Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS." Energy and Buildings 93
(2015): 40-49.
[3]. Derakhshan, Ghasem, Heidar Ali Shayanfar, and Ahad Kazemi.“The optimization of demand response programs in smart grids.”Energy Policy 94 (2016):
295-306.
Technique(s)
Objective(s)
Feature (s)
Limitation(s)
Analytical model
[1]
Compress peak power
demand
Recursive formulas to determine power
units in use in 4
different scenarios
Does
not consider appliance
energy cycles
MILP [2]
Minimize cost and
peak
energy demand
Reduced cost and integrated RESs,
storage, combined heat and power unit,
User comfort
Computational complexity,
priority
ignored
TLBO and SFL
[3]
Consumers’ bill
reduction
TOU, CPP, RTP prices signals taken,
TLBO provides more optimized results
than SFL
User comfort and PAR are
ignored
Table 1 : Literature review
Literature review (2/2)
5
[4]. Muralitharan, K., Rathinasamy Sakthivel, and Yan Shi. "Multiobjective optimization technique for demand side management with load balancing
approach in smart grid." Neurocomputing 177 (2016): 110-119.
[5]. Logenthiran, Thillainathan, Dipti Srinivasan, and Tan Zong Shun. “Demand side management in smart grid using heuristic optimization.”IEEE
transactions on smart grid 3.3 (2012): 1244-1252.
[6]. Ogunjuyigbe, A. S. O., T. R. Ayodele, and O. A. Akinola. “User satisfaction-induced demand side load management in residential buildings with user
budget constraint.”Applied Energy 187 (2017): 352-366.
Technique(s)
Objective(s)
Feature (s)
Limitation(s)
Multi
-objective evolutionary
algorithm [4]
Minimize cost and waiting
time
Threshold policy along with
penalty has been considered
PAR is ignored
EA [5]
Minimize electricity bill and
peak demand
Considered large number of
devices
in residential,
commercial and industrial
areas
User comfort and priority are
ignored
Load satisfaction algorithm
[6]
Cost reduction and user
comfort maximization
Multi
-objective function,
Three different budget
scenarios are
implemented
Ignores PAR and
energy
cycles of an appliance
Table 1 : Literature review cont.
Problem statement (1/2)
•Optimal stopping rule [7]
•Minimization in electricity bill and average delay
•Peak energy appliances cycles consideration
•However, ignores PAR
•GA, ACO and BPSO used for home appliance scheduling [8]
•Cost reduction
•Problem formulation using multiple knapsack
•User priority is not considered
In [7], there is no PAR evaluation, which may cause high load during off-peak time slots. In [8],
actual load profiles are replaced by maximum or the average load of devices which may not give
accurate results when compared it with the real load profile taking different energy cycles of an
appliance and also neglected the priority. We consider the limitations of above papers and propose
a solution where real load profile of three appliances are taken with priority and PAR
consideration.
6
[7]. Yi, Peizhong, Xihua Dong, Abiodun Iwayemi, Chi Zhou, and Shufang Li.“Real-time opportunistic scheduling for residential demand response.”IEEE
Transactions on smart grid 4, no. 1 (2013): 227-234.
[8]. Rahim, Sahar, Nadeem Javaid, Ashfaq Ahmad, Shahid Ahmed Khan, Zahoor Ali Khan, Nabil Alrajeh, and Umar Qasim. “Exploiting heuristic algorithms
to efficiently utilize energy management controllers with renewable energy sources.”Energy and Buildings 129 (2016): 452-470.
Problem statement (Future) (2/2)
•Linear programming [9]
•Minimization in electricity bill and user
•RESs and ESS are ignored
•Fog-to-cloud (F2C) layered approach [10]
•Bring different heterogeneous fog/cloud layers into a hierarchical architecture
•Establish a real need for coordinated management
•IoT-based optimization framework [11]
•Energy efficient: scheduling and use of lightweight protocols (UPnP, 6lowPAN)
•Energy harvesting: from ambient sources i.e. solar, wind.
•Fog computing environment [12]
•Big data is preprocessed and computed before transmitting it to the cloud servers
•The three-tier model use (SG, fog, and cloud layers)
However, [10-12] lack the integration of social grid data
7
[9]. Ma, Kai, Ting Yao, Jie Yang, and Xinping Guan. "Residential power scheduling for demand response in smart grid." International Journal of Electrical Power &
Energy Systems 78 (2016): 320-325.
[10]. Masip-Bruin, Xavi, Eva Marn-Tordera, Ghazal Tashakor, Admela Jukan, and Guang-Jie Ren. “Foggy clouds and cloudy fogs: a real need for coordinated management
of fog-to-cloud computing systems.”IEEE Wireless Communications23, no. 5 (2016): 120-128.
[11]. Ejaz, Waleed, Muhammad Naeem, Adnan Shahid, Alagan Anpalagan, and Minho Jo.“Efficient energy management for the internet of things in smart cities.”IEEE
Communications Magazine55, no. 1 (2017): 84-91.
[12]. Okay, Feyza Yildirim, and Suat Ozdemir. “A fog computing based smart grid model.”In Networks, Computers and Communications (ISNCC), 2016 International
Symposium on, pp. 1-6. IEEE, 2016.
Problem formulation (1/2)
8


Subject to:
=

 ……… (1)

 ……………… (2)

 

 ….. (3)


……………………..……….. (4)

>
……………………….….…..…(5)
=  
  ….…. (6)
Problem formulation (2/2)
9
Threshold [13]:
 
….…..…(7)
Where:
are maximum and minimum EP values at time slot
represents appliance priority value
energy consumption
PAR:   

…………… (8)
[13]. Yi, Peizhong, Xihua Dong, Abiodun Iwayemi, Chi Zhou, and Shufang Li.“Real-time opportunistic scheduling for residential demand response.”IEEE
Transactions on smart grid 4, no. 1 (2013): 227-234.T
System model (1/2)
10
▪Smart home
•Home area network (HAN)
•Energy management
controller (EMC)
•Appliances energy pattern
▪EMC
•Communicates with SM
•SM receives pricing signal
•EMC collects appliances power
consumption pattern
•Schedule home appliances
Smart
Clothes
dryer
Smart TV
Smart
Refrigerator
PC
Mobile
Juicer/ grinder
Fan
Smart Home
Smart
Dishwasher
Energy Management
Controller (EMC)
Wi-Fi/
ZigBee
routers
Utility network
NAN
Smart meter
Smart meter
Smart meter
RTP signals
Appliances
usage data
HAN
AMI
Smart plug
Fig. 1: Proposed system model
System model (2/2)
11
▪Advance metering infrastructure
•Bidirectional communication
•NAN used to send RTP
▪Appliances energy consumption
pattern [14]
•Clothes dryer (CD)
•Dishwasher (DW)
•Refrigerator (REF)
[14]. Iwayemi, Abiodun, PeizhongYi, Xihua Dong, and Chi Zhou. “Knowing when to act: An optimal stopping method for smart grid demand
response.” Ieee Network 25, no. 5 (2011).
246810 12 14 16 18 20 22 24
Hour of the day
0
0.02
0.04
0.06
0.08
0.1
0.12
Normalized hourly energy usage
Clothes dryer
Dishwasher
Refrigerator
Fig. 2: Appliance energy profile data
Proposed solution
12
▪Enhanced Differential Evaluation (EDE) [15]
•Evolutionary algorithm, enhanced DE
•Control parameters reduced (NP, F, and CR) to (NP and F)
•Improve strategy by create five different trial vectors
•Good convergence rate (4th trial vector)
•Increase diversity of search space (5th trial vector)
▪Genetic algorithm (GA)
•Evolutionary algorithm
•Good convergence rate (0.9 cross over)
•Parallel search
•May stuck on local optima
▪Binary particle swarm optimization (BPSO)
•Combines self-experience with social experience
•Find approximate solutions of problems
•Easy to implement
•Few parameters to adjust
[15] Arafa, M., Sallam, E. A., & Fahmy, M. M. (2014, May). An enhanced differential evolution optimization algorithm. In Digital Information and
Communication Technology and it’s Applications (DICTAP), 2014 Fourth International Conference on (pp. 216-225). IEEE.
Simulations and discussion (1/14)
▪Pricing scheme
•Day ahead real time pricing (DA-RTP)
▪Performance parameters
•Cost
•Delay
•Energy consumption
•PAR
▪Two scenarios
•Scenario-1 without knapsack capacity limit
•Scenario-2 with knapsack capacity limit
13
Fig. 3: DA-RTP signal
14
12345678910 11 12
Time (Month)
5
10
15
20
25
30
Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
Fig. 4: Average monthly cost of clothes dryer
12345678910 11 12
Time (Month)
6
8
10
12
14
16
18
20
22
24
Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 6: Average monthly cost of refrigerator
12345678910 11 12
Time (Month)
20
25
30
35
40
45
Cost (dollar)
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Fig. 5: Average monthly cost of dishwasher
Scenario –1
Monthly Cost Plots
Simulations and discussion (2/14)
15
Fig. 7: Average yearly cost of clothes dryer
Fig. 8: Average yearly cost of dishwasher
0Cases
0
50
100
150
200
Average Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 9: Average yearly cost of refrigerator
0Cases
0
50
100
150
200
250
300
350
400
Cost (dollar)
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
App.
N
-Sch.
($)
EDE ($) GA ($) BPSO ($)
Low
High
Low
High
Low
High
CD
234.90
84.42
157.14
94.21
159.75
84.49
158.07
DW
199.54
102.10
131.96
105.35
135.06
102.10
132.74
REF
367.48
332.89
352.17
337.63
352.17
333.55
352.35
Simulations and discussion (3/14)
Table 3 : Yearly cost
0Cases
0
50
100
150
200
250
Average Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
16
Fig. 10: Average delay of clothes dryer
0Cases
0
1
2
3
4
5
6
7
8
Time (Hours)
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
EDE (hrs) GA (hrs) BPSO (hrs)
Low
High
Low
High
Low
High
CD
7.62
2.38
6.83
3.19
7.57
3.00
DW
5.00
0.75
4.50
1.22
5.00
0.93
REF
11.88
5.50
11.88
5.50
11.88
5.50
Fig. 11: Average delay of dishwasher
0Cases
0
1
2
3
4
5
Time (Hours)
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 12: Average delay of refrigerator
0Time (Day)
0
2
4
6
8
10
12
Time (Hours)
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (4/14)
Table 4 : Average delay
17
Fig. 13: Energy consumption of clothes dryer
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
0.1
0.2
0.3
0.4
0.5
0.6
Energy consumption (kWh)
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
N
-Sch.
(kWh)
EDE (kWh) GA (kWh) BPSO (kWh)
Low
High
Low
High
Low
High
CD
0.50
0.56
0.33
0.52
0.30
0.56
0.50
DW
0.17
0.18
0.13
0.18
0.13
0.18
0.13
REF
0.16
0.22
0.16
0.22
0.16
0.22
0.16
Fig. 14: Energy consumption of dishwasher
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Energy consumption (kWh)
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 15: Energy consumption of clothes refrigerator
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
Energy consumption (kWh)
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (5/14)
Table 5 : Energy consumption
18
Fig. 16: PAR of clothes dryer
0Cases
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
PAR
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
N
-Sch.
(PAR)
EDE GA BPSO
Low
High
Low
High
Low
High
CD
4
4.44
2.67
4.15
2.37
4.44
2.67
DW
2.93
3.06
2.26
3.06
2.26
3.06
2.26
REF
1.85
2.46
1.85
2.46
1.85
2.46
1.85
Fig. 17: PAR of dishwasher
0Cases
0
0.5
1
1.5
2
2.5
3
3.5
PAR
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 18: PAR of refrigerator
0Cases
0
0.5
1
1.5
2
2.5
PAR
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (6/14)
Table 6 : PAR
19
Fig. 19: Average monthly cost of clothes dryer (knapsack)
12345678910 11 12
Time (Month)
5
10
15
20
25
30
Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
Fig. 20: Average monthly cost of dishwasher (knapsack)
12345678910 11 12
Time (Month)
6
8
10
12
14
16
18
20
22
24
Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 21: Average monthly cost of refrigerator (knapsack)
12345678910 11 12
Time (Month)
20
25
30
35
40
45
Cost (dollar)
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (7/14)
Scenario –2 (knapsack)
Monthly Cost Plots
20
Fig. 22: Average yearly cost of clothes dryer (knapsack)
0Cases
0
50
100
150
200
250
Average Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
N
-Sch.
($)
EDE ($) GA ($) BPSO ($)
Low
High
Low
High
Low
High
CD
234.90
118.23
163.83
120.92
163.99
126.05
165.40
DW
199.54
110.87
134.62
112.10
136.27
111.14
138.93
REF
367.48
343.37
352.17
343.37
352.17
343.51
352.22
Fig. 23: Average yearly cost of dishwasher (knapsack)
0Cases
0
50
100
150
200
Average Cost (dollar)
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 24: Average yearly cost of refrigerator (knapsack)
0Cases
0
50
100
150
200
250
300
350
400
Cost (dollar)
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (8/14)
Table 7 : Yearly cost (knapsack)
21
Fig. 25: Average delay of clothes dryer (knapsack)
0Cases
0
1
2
3
4
5
6
Time (Hours)
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
EDE (hrs) GA (hrs) BPSO (hrs)
Low
High
Low
High
Low
High
CD
5.86
3.01
5.86
3.38
5.86
3.6
DW
4.79
1.22
4.65
1.22
4.79
0.94
REF
6.38
5.50
6.38
5.50
5.75
5.50
Fig. 26: Average delay of dishwasher (knapsack)
0Cases
0
1
2
3
4
5
Time (Hours)
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 27: Average delay of refrigerator (knapsack)
0Time (Day)
0
1
2
3
4
5
6
7
Time (Hours)
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (9/14)
Table 8 : Average delay (knapsack)
22
Fig. 28: Energy consumption of clothes dryer (knapsack)
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
0.1
0.2
0.3
0.4
0.5
Energy consumption (kWh)
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
N
-Sch.
(kWh)
EDE (kWh) GA (kWh) BPSO (kWh)
Low
High
Low
High
Low
High
CD
0.50
0.50
0.33
0.50
0.30
0.50
0.50
DW
0.17
0.17
0.13
0.17
0.13
0.17
0.13
REF
0.16
0.16
0.16
0.16
0.16
0.16
0.16
Fig. 30: Energy consumption of dishwasher (knapsack)
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Energy consumption (kWh)
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 29: Energy consumption of refrigerator (knapsack)
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Energy consumption (kWh)
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (10/14)
Table 9 : Energy consumption (knapsack)
23
Fig. 31: PAR of clothes dryer (knapsack)
0Cases
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
PAR
Non-schedule
EDE mu=0.001
EDE mu=0.13
GA mu=0.001
GA mu=0.13
BPSO mu=0.001
BPSO mu=0.13
App.
N
-Sch.
(PAR)
EDE GA BPSO
Low
High
Low
High
Low
High
CD
4
4.00
2.67
4.00
2.37
4.00
4.00
DW
2.93
2.93
2.26
2.93
2.26
2.93
2.26
REF
1.85
1.85
1.85
1.85
1.85
1.85
1.85
Fig. 32: PAR of dishwasher (knapsack)
0Cases
0
0.5
1
1.5
2
2.5
3
PAR
Non-schedule
EDE mu=0.001
EDE mu=0.017
GA mu=0.001
GA mu=0.017
BPSO mu=0.001
BPSO mu=0.017
Fig. 33: PAR of refrigerator (knapsack)
0Cases
0
0.5
1
1.5
2
PAR
Non-schedule
EDE mu=0.0033
EDE mu=0.0089
GA mu=0.0033
GA mu=0.0089
BPSO mu=0.0033
BPSO mu=0.0089
Simulations and discussion (11/14)
Table 10 : PAR (knapsack)
24
Feasible region (FR) of
cost-power consumption
relationship
Fig. 34: FR cost-energy of clothes dryer
00.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Power consumption (kWh)
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Cost per hour ($)
P1(0.0185, 0.00152)
P2(0.0185, 0.0025)
P3(0.5, 0.0679)
P4(0.5, 0.0041)
P5(0.073, 0.0102)
P6(0.5, 0.0102)
00.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18
Power consumption (kWh)
-0.005
0
0.005
0.01
0.015
0.02
0.025
Cost per hour ($)
P1(0.0031, 0.00025)
P2(0.0031, 0.0042)
P3(0.1707, 0.0232)
P4(0.1707, 0.0014)
P5(0.09, 0.0120) P6(0.1707, 0.0120)
Fig. 35: FR cost-energy of dishwasher Fig. 36: FR cost-energy of refrigerator
0.06 0.08 0.1 0.12 0.14 0.16 0.18
Power consumption (kWh)
-0.005
0
0.005
0.01
0.015
0.02
0.025
Cost per hour ($)
P1(0.0718, 0.0013)
P2(0.0718, 0.0098)
P3(0.1615, 0.0219)
P4(0.1615, 0.00588)
P5(0.13, 0.0176)
P6(0.1615, 0.0176)
Simulations and discussion (12/14)
25
Feasible region (FR) cost-
delay relationship
Fig. 38: FR cost-delay of dishwasher
00.5 11.5 22.5 33.5 44.5 5
Delay (hours)
0
50
100
150
200
Cost (dollar)
P1(0, 199.54)
P2(0.75, 131.96)
P3(5, 102.10)
P4(4.79, 103)
P5(4.79, 105)
012345678
Delay (hours)
0
50
100
150
200
Cost (dollar)
P1(0, 234.90)
P2(2.38, 157.14)
P3(7.62, 84.42)
P4(5.86, 109)
P5(5.86, 122)
Fig. 37: FR cost-delay of clothes dryer
Fig. 39: FR cost-delay of refrigerator
0246810 12
Delay (hours)
320
330
340
350
360
370
Cost (dollar)
P1(0, 367.48)
P2(5.50, 352.17)
P3(11.88, 332.89)
P4(6.38, 332.89)
P5(6.38, 350)
Simulations and discussion (13/14)
26
Performance trade-off
1.
  

2.


3.


Simulations and discussion (14/14)
27
Conclusion and future work
•Appliances priority implemented
•Two scenarios used
1. Scenario-1
•EDE performed well in reducing cost
•GA performed well in reducing delay
2. Scenario-2 (KS)
•Knapsack implemented to control peaks
•GA reduced PAR value
•Future Work
•Consider large number of appliances in a home
•Integration of RESs and ESS in grid-connected and stand-alone modes
•Fog- induced SG architecture
Thank you!
28
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