Efficient Utilization of Energy Employing Meta-Heuristic Techniques with the Incorporation of Green Energy Resources in Smart Cities

Abstract

External Defense

Full text

Efficient Utilization of Energy Employing Meta-
Heuristic Techniques with the Incorporation of Green
Energy Resources in Smart Cities
Presented by: Asif Khan
CIIT/FA15-PCS-004/ISB
PhD (Scholar)
Supervisor: Dr. Nadeem Javaid
Co-Supervisor: Dr. Mariam Akbar
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019
1

Agenda
1. Introduction
2. Literature review
3. Problem statement
4. Proposed system models
5. Formulations of problems
6. Proposed solutions
7. Results and discussions
8. Conclusions and future work
2
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

1. Introduction
3
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

SMART CITY (1/4)
❑SC energy areas
❖A SC is defined by six characteristics [1]
❖The five energy-related SC intervention
areas [2]
➢Generation
➢Infrastructure
➢Transport
➢Facilities
➢Storage
Smart
city
Smart
environment
Smart
governance
Smart people
Smart
mobility
Smart
economy
Smart living
Fig. 1.1: Characteristics of smart city
[1] Kumar, T. V. (2015). E-governance for smart cities. In E-governance for smart cities (pp. 1-43). Springer, Singapore.
[2] Calvillo, C. F., Sanchez-Miralles, A., & Villar,J. (2016). Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews, 55, 273-287.
Introduction (1/4)
4
SC: Smart city

❑Focus => smart home
❖Types of buildings
➢Industrial
➢Commercial
➢Residential
❖Our focus is on residential building
➢Flexible- do not follow strict timelines and
milestones
❖The 37% of electricity consumed in US
is only due to residential buildings [3]
❖Energy management system for homes
is termed as HEMS
AMI, SMs
Smart appliances
HEMC
Sensors
Communication
Infrastructure
Smart home
Fig. 1.2: Key components enabling smart home
[3] Cetin, K. S., Tabares-Velasco, P. C., & Novoselac, A. (2014). Appliance daily energy use in new residential buildings: Use profiles and variation in time-of-use. Energy
and Buildings,84, 716-726.
Introduction (2/4)
5
HEMS: Home energy management system
AMI: Advanced metering infrastructure
SMs: Smart meters
HEMC: Home energy management controller
5

CHALLENGES (3/4)
❑Challenges
❖Increase in population => high power demand which results in
➢High consumption of fossil fuels for power generation
➢Increase of carbon emissions (serious ecological and environmental concerns)
❑Other challenges
❖UC maximization
❖UPS
❖Load balancing between power demand and supply
❖PAR reduction
❖Unit sizing
❖Integration of RESs
➢Intermittent nature of RESs
➢Reliability
Introduction (3/4)
6
UC: User comfort
UPS: Uninterrupted power supply
PAR: Peak to average ratio
RESs: Renewable energy sources

❑Smart grid
❖Traditional grid + ICT
❖Bi-directional communication
❖Consumer-interactive
❑Energy solutions
❖SSM
❖DSM
➢DR programs
✓Price-based DR (RTP, DA-RTP, ToU, CPP, etc.)
✓Incentive-based DR (DLC, Emergency programs, etc.)
Improves grid
stability Reduces costly
generation
Flexibility
to meet
demand at
low cost
Fig. 1.3: DSM benefits
Introduction (4/4)
7
ICT: Information communication technology
SSM: Supply side management
DSM: Demand side management
DR: Demand response
RTP: Real-time pricing
DA-RTP: Day-ahead RTP
ToU: Time-of-use
CPP: Critical peak pricing
DLC: Direct load control
7
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

2. Literature review
8
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

LITERATURE REVIEW (1/3)
Technique(s)
Objective(s)
User comfort
Limitation(s)
Convex optimization
[4]
Multi
-
objective function to
minimize cost and
discomfort
UC
is calculated using delay
Appliances’ delay
is only
associated with UC
Comfort demand
function based
on
DBR
[5]
Multi
-objective function
considering buying, selling
and comfort factors
Power and comfort function
calculated using
IBR and DBR
Only
thermal comfort
of AC is
considered
UC level framework for
HEMS [6]
Uni
-objective function to
minimize cost
UC calculated using the
appliances utility, electricity cost
savings and ROI
Appliances’ priority
may
account to
UC, which is not
considered
Load satisfaction
algorithm [7]
Uni
-objective function to
maximize absolute
satisfaction
User satisfaction calculated on
three
satisfaction postulation
Appliances’ priority
is not
considered
Table 2.1: Literature review (UC)
[4] Ma, K., Yao, T., Yang, J., & Guan, X. (2016). Residential power scheduling for demand response in smart grid. International Journal of Electrical Power & Energy Systems, 78, 320-325.
[5] Al Hasib, A., Nikitin, N., & Natvig, L. (2015, April). Cost-comfort balancing in a smart residential building with bidirectional energy trading. In Sustainable Internet and ICT for
Sustainability (SustainIT), 2015 (pp. 1-6). IEEE.
[6] Mahmood, D., Javaid, N., Ahmed, S., Ahmed, I., Niaz, I. A., Abdul, W., & Ghouzali, S. (2017). Orchestrating an effective formulation to investigate the impact of EMSs (energy
management systems) for residential units prior to installation. Energies, 10(3), 335.
[7] Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied
Energy, 187,352-366.
Literature review (1/3)
9
DBR: Declining block rate
IBR: Inclined block rate
ROI : Return on investment

Technique(s)
Objective(s)
Feature(s)
Limitation(s)
Analytical model [8]
Compress
peak power
demand
Recursive formulas to determine power units in use
in 4
different scenarios
Does
not consider
appliance energy cycles
MILP
[9]
Minimize
cost and peak
energy demand
Reduced cost and integrated RESs, storage,
combined heat and power unit,
UC
Computational complexity
,
priority
ignored
TLBO and SFL
[10]
Consumers’
bill reduction
TOU, CPP, RTP prices signals taken,
TLBO provides more optimized results than SFL
UC
and PAR are ignored
Multi
-objective EA
[11]
Minimize cost and
waiting
time
Threshold policy along with penalty has been
considered
PAR is ignored
EA
[12]
Minimize electricity bill and
peak demand
Considered large number of devices
in residential,
commercial and industrial areas
UC
and priority are ignored
PSO
[13]
Minimize cost
Complete
HEMS
UC
is not considered
Table 2.2: Literature review (Techniques)
[8]. 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.
[9] Shirazi, Elham, and Shahram Jadid. "Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS." Energy and Buildings 93 (2015): 40-49.
[10]. Derakhshan, Ghasem, Heidar Ali Shayanfar, and Ahad Kazemi.“The optimization of demand response programs in smart grids.”Energy Policy 94 (2016): 295-306.
[11] 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.
[12] 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.
[13] Huang, Y., Tian, H., & Wang, L. (2015). Demand response for home energy management system. International Journal of Electrical Power & Energy Systems,73, 448-455.
Literature review (2/3)
10
MILP: Mixed integer linear programming
TLBO: Teaching learning based optimization
SFL: Shuffled frog leaping
EA: Evolutionary algorithm
PSO: Particle swarm optimization

Technique(s)
Objective(s)
Feature(s)
Limitation(s)
Iterative method
and
FASA
[14]
Minimize
cost
of PV modules,
inverters, charge controllers,
and batteries
Considers MPPT and
standard charge controller
Iterative method used is
highly
computational
MILP
technique
applied in [15]
Find the optimum
combination of PVs and its
components
to minimize cost
Good in finding the optimal
solution in a deterministic
environment
Highly computational
HOMER
software tool
[16]
Cost
estimation
A techno
-
economic analysis
of wind, PV, and biomass
system is carried out for
Kallar Kahar,
Pakistan
The tool used does not support
multi
-objective problems, DoD
of the battery bank
Table 2.3: Literature review (Unit sizing)
[14] Aziz, N. I. A., Sulaiman, S. I., Shaari, S., Musirin, I., & Sopian, K. (2017). Optimal sizing of stand-alone photovoltaic system by minimizing the loss of power supply probability. Solar
Energy, 150, 220-228.
[15] Okoye, C. O., & Solyali, O. (2017). Optimal sizing of stand-alone photovoltaic systems in residential buildings. Energy, 126, 573-584.
[16] Ahmad, J., Imran, M., Khalid, A., Iqbal, W., Ashraf, S. R., Adnan, M., ... & Khokhar, K. S. (2018). Techno economic analysis of a wind- photovoltaic-biomass hybrid renewable energy
system for rural electrification: A case study of Kallar Kahar. Energy, 148, 208-234.
Literature review (3/3)
11
FASA: Firefly algorithm-based sizing algorithm
PV: Photovoltaic
MPPT: Maximum power point tracking
MILP: Mixed integer linear programming
HOMER: Hybrid optimization model for multiple energy resources
DoD: Depth of discharge

3. Problem statement
12
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

THESIS PROBLEM STATEMENT (1/2)
In the previous work, UC is associated with thermal comfort [5] and related to the appliances’
delay, cost savings and ROI parameters [4, 6]. Ogunjuyigbe et al. considered the UC from a
different perspective which is based on three satisfaction postulations [7]. However, none of the
referenced work has considered the UC which may be derived from time and device based
varying priorities. Further, the consumer’s budget limitation is also one of the prominent
constraints to the electricity usage mostly neglected in the literature as their primary focus is on
cost minimization.
In [17], there is no PAR evaluation, which results in peak formation during off-peak time slots. In
[18], 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 profiles taking different energy cycles of
an appliance and also neglected the priority.
[17] Yi, P., Dong, X., Iwayemi, A., Zhou, C., & Li, S. (2013). Real-time opportunistic scheduling for residential demand response. IEEE Transactions on smart grid,4(1), 227-234.
[18] Rahim, S., Javaid, N., Ahmad, A., Khan, S. A., Khan, Z. A., Alrajeh, N., & Qasim, U. (2016). Exploiting heuristic algorithms to efficiently utilize energy management controllers
with renewable energy sources. Energy and Buildings,129, 452-470.
Problem statement (1/2)
13

THESIS PROBLEM STATEMENT (2/2)
The use and combustion of fossil fuel cause toxic air emission that results in environmental
problems causing great risk to children [19]. Carbon dioxide accounts for an estimated 77%are
caused by the human-generated greenhouse gas emissions [20]. All these factors contribute to
toxic air emission in the environment, which also has an adverse impact on the climate change.
The RESs consist of solar and wind are focused in the literature, since these reduce carbon
dioxide and are ecological and universal [21,22]. However, these resources have unpredictable
and intermittent nature due to natural conditions. Thus, the reliability of RESs is a major concern
which needs to be tackled at minimum consumer cost.
[19] Perera, F. P. (2017). Multiple threats to child health from fossil fuel combustion: impacts of air pollution and climate change. Environmental health perspectives, 125(2), 141.
[20] Rahman, F. A., Aziz, M. M. A., Saidur, R., Bakar, W. A. W. A., Hainin, M. R., Putrajaya, R., & Hassan, N. A. (2017). Pollution to solution: Capture and sequestration of carbon
dioxide (CO2) and its utilization as a renewable energy source for a sustainable future. Renewable and Sustainable Energy Reviews, 71, 112-126.
[21] Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., & Kim, K. H. (2018). Solar energy: potential and future prospects. Renewable and Sustainable Energy Reviews, 82, 894-900.
[22] Wagh, S., & Walke, P. V. (2017). Review on wind-solar hybrid power system. International Journal of Research In Science & Engineering, 3, 71-76.
Problem statement (2/2)
14

4. Proposed system models
15
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

Utility AMI
WAN
S/
NSection Appliance with short
form
Power
rating
(kW)
Qty
1
TV lounge
LCD TV (TV) 0.1500 1
2 Lighting (Ligt.) 0.0200 5
3 AC 1.2000 1
4 Bathroom Water heater (WH) 2.0000 1
5 Lighting (Ligt.) 0.0200 2
6
Laundry
Washing machine (WM) 0.7000 1
7 Clothes dryer (CD) 1.8000 1
8 Lighting (Ligt.) 0.0300 2
9
Kitchen
Microwave oven (MO) 1.5000 1
10 Juicer 0.4000 1
11 Dish washer (DW)1.4000 1
12 Refrigerator (Ref.) 2.1000 1
13 Lighting (Ligt.) 0.0300 3
14 Surveillance Lighting (Ligt.) 0.0200 8
15 CCTV camera 0.0090 3
16
Master bedroom
Lighting (Ligt.) 0.0200 5
17 Laptop 0.0600 1
18 Mobile charger (MC) 0.0060 1
Fig. 4.1: Consumer UC enabled system
model for a home
Table 4.1: Energy consumption
Fixed price: 0.115 $/kWh [7]
System models (1/6)
❑UC model
16

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. 4.2: Proposed priority system model
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
Cost ($/kWh)
DA-RTP prices
DA-RTP signal
System models (2/6)
❑Priority model
❖DA-RTP signal used
❖Three appliances: CD, DW,
and Refrigerator are taken
for scheduling
17

Fig. 4.3: Proposed GC smart home
Shiftable loads
Non
-shiftable
loads
Washing machine
Personal
computers
Air conditioner
Security cameras
Clothes dryer
Microwave oven
Water heater
Refrigerator
Dish washer
Television
ESS
Lights
Table 4.2: Appliances classification
❑RES integration in GC-mode
System models (3/6)
18
GC: Grid connected
ESS : Energy storage system
AMI : Advanced metering infrastructure
SM: Smart meter
SS: Smart scheduler
MC: Master controller

❑RESs integration in SA-
mode model
❖Unit sizing of HRESs to
minimize cost in SA-mode
❖HRESs (intermittent nature)
❖ESS => H2 tanks
❖Performed simulations
(Pakistan)
ElectrolyzerCompressor
Fuel Cell
Solar
Irradiance
Converter
Wind Turbines
Load
AC
Bus
DC
Bus
H2
Tanks
Inverter
Fig. 4.4: Proposed SA model (Pakistan)
System models (4/6)
19
SA: Stand alone
HRESs: Hybrid renewable energy sources
H2: Hydrogen
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

Fig. 4.5: Proposed SA model (Iran)
❑RESs integration in SA-
mode model
❖Single DC bus
❖ESS=>battery bank
❖Performed simulations (Iran)
DC/DC
Solar
Irradiance
Wind Turbines
Load
DC Bus
PV panels
DC/DCAC/DC DC/DC
DC/AC
Batteries Bank
System models (5/6)
20
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

❑Integration of DG with
RESs and ESS
❖PVs
❖WTs
❖FCs
❖DG
Fig. 4.6: Proposed PV-WT-FC-DG model
System models (6/6)
21
DG: Diesel generator
PVs: Photovoltaics
WTs: Wind turbines
FCs: Fuel cells
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

5. Formulations of problems
22
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

❑5.1 UC formulation
❖UC objective function: Obj     (5.1.1)
❖Absolute comfort [7]: (5.1.2)
❖Desired comfort:  
 
(5.1.3)
❖Constraints:   (5.1.4)
   (5.1.5)

 (5.1.6)
 $
 
$ , and (5.1.7)
 . (5.1.8)

 


Formulations of problems (1/6)
23
: Absolute user comfort
: User defined budget

: Time-based priority of appliance J

: Device-based priority of appliance J
T_𝑒𝑥𝑝:"Total consumer’s expenditure
Total energy consumption
: Number of appliances
Total operational time
 otal power rating
𝑇𝐸𝐴: Total energy available
ET: Electricity tariff

❑5.2 Appliance’s priority
❖Priority objective function:



  (5.2.1)

=

    (5.2.2)
=  
  (5.2.3)
❖Threshold [17]:  
(5.2.4)
❖PAR [23]:   





. (5.2.5)
❖Subject to: PAR (5.2.6)

 
 (5.2.7)
Formulations of problems (2/6)
24
: Cost
: Number of appliances
: Time slot
: Power rating
: Boolean variable
: Electric price (EP)
𝑃𝐴𝑅: Peak-to-average ratio
 : Maximum knapsack capacity limit

: Energy consumption of non-
scheduled appliance

: Energy consumption of scheduled
appliance
Maximum and minimum EP
values, respectively
: Appliance priority value
[23] Javaid, N., Ahmed, A., Iqbal, S., & Ashraf, M. (2018). Day ahead real time pricing and critical peak pricing based power scheduling for smart homes with different
duty cycles. Energies,11(6), 1464.

❑5.3 RES integration in GC-mode formulation
❖Energy generation of PV system [24]:
 =      (5.3.1)
❖Energy consumption:
 
 



. (5.3.2)
❖Energy cost:
=         (5.3.3)
❖Objective function:
          (5.3.4)
Subject to:
  +     (5.3.5)
[24] Shirazi, Elham, and Shahram Jadid. "Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS." Energy and Buildings 93 (2015): 40-
49.
Formulations of problems (3/6)
25
 : Energy generation of PV system in
time-slot t
: Energy conversion efficiency of the PV
system in (%)
: PV area in (m2)
 time-slot
: Solar irradiance (kW/m2)
: Temperature correction factor
: Outdoor temperature (◦C)
: Standard room temperature (◦C)
: Total energy consumption
M: Shiftable appliances
N: Non−shiftable appliances
Electricity cost
: Energy consumed by shiftable appliances
: Energy consumed by non-shiftable
appliances
: Energy produced by PV panels system
: Energy provided by energy storage system
: Day-ahead pricing signal
𝐸_𝑔𝑟𝑖𝑑: Sanction load that an end−user can
import from the utility grid

❑5.4 RESs integration in SA-mode
❖Photovoltaic power [25]:        , (5.4.1)
    . (5.4.2)
❖Wind turbine power [25]:
         
   
   
   (5.4.3)
    (5.4.4)
❖Accumulative generation:
        (5.4.5)
❖Consumer’s load:  
. (5.4.6)
[25] Maleki, A., & Askarzadeh, A. (2014). Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering LPSP
concept. Solar Energy, 107, 227-235.
Formulations of problems (4/6)
26
: Total hourly PV panels power
(W) generated at time (t)
: Solar insolation (W/m2)
 : Area by a given set of PV panels
(m2)
: Instantaneous PV panels efficiency
 : Total produced PV energy
: Total number of PV panels
: Wind turbine (WT) power
: Speed of wind
: WT rated power
 represent rated, cut-in, and
cut-out wind speed, respectively
 : Total produced WT power
: Total number of WTs
: Total produced WT energy
: Accumulative energy generation
from both: PV and WT
: Efficiency of the inverter
: Consumer’s load
ower rating of appliance i
: Boolean integer showing an appliance
status

❑Storage capacity of hydrogen fuel tanks [25]:
           
, (5.4.7)
     
 
       (5.4.8)
❑Total cost:   + (5.4.9)
Subject to constraints
❖Energy storage: 
  
(5.4.10)
❖Loss of power supply probability [25]:  
    

 , (5.4.11)
. (5.4.12)
Surplus
Energy
Deficit
Energy
Formulations of problems (5/6)
27
: Total produced PV power
: Total produced WT power
: Efficiency of the inverter
: Efficiency of the electrolyzer
: Efficiency of the fuel cell
: Consumer’s load
: Annual capital cost
: Annual maintenance cost
Hourly time slot
: Loss of power supply probability
: Maximum allowable loss of power supply
probability
: Energy generation
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

❑5.5 Modeling of DG
❖Total cost:   +  . (5.5.1)
❖Modeling of DG fuel cost [26]:

 
 +
 (5.5.2)
 
,
 
 
  
(5.5.3)
❖Renewable factor [27]:
   
100. (5.5.4)
Formulations of problems (6/6)
28
Total annual cost
Annual capital cost
Annual maintenance cost
Cost of DG

Fuel consumed by the DG

DG rated power

DG output power
Coefficients of the
consumption curve
Fuel consumption hourly cost
Fuel price
: Renewable factor
: Power generated from DG
: Accumulative electricity
generation from PVs and WTs
[26] Maleki, A. (2018). Modeling and optimum design of an off-grid PV/WT/FC/diesel hybrid system considering different fuel prices. International Journal of
Low-Carbon Technologies,13(2), 140-147.
[27]Ramli, M. A., Bouchekara, H. R. E. H., & Alghamdi, A. S. (2018). Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-
adaptive differential evolution algorithm. Renewable energy,121, 400-411.

6. Proposed solutions
29
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

•EDE
•GA
•BPSO
•OSR
•EACA •GA
•BPSO
•WDO
•BFO
•Hybrid (HGPO)
•JAYA [28]
•GA
•BSA
•JAYA
•TLBO
•GA
•SSA
•EDE
•Hybrid JLBO,
EESA
1-UC 2-Priority 3-RES-GC 4-RES-SA 5-RES-SA
[28] Rao, R.(2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of
Industrial Engineering Computations, 7(1), 19-34.
Proposed solutions (1/6)
6-RES-
DG-SA
•JAYA
•TLBO
•Hybrid
TACMA
30
EACA: Enhanced accretive comfort algorithm
EDE: Enhanced differential evaluation
GA: Genetic algorithm
BPSO: Binary particle swarm optimization
OSR: Optimal stopping rule
WDO: Wind driven optimization
BFO: Binary foraging optimization
HGPO: Hybrid genetic particle optimization
BSA: Backtracking search algorithm
TLBO: Teaching learning based optimization
SSA: Salp swarm algorithm
GLBO: Jaya learning based optimization
EESA: Enhance evolutionary sizing algorithm
TACMA: Total annual cost minimizing
algorithm

❑EACA Solution
1: Procedure EACA
2: Input
3: Initialize parameters like population size, crossover rate, mutation rate, termination criteria
4: Read TB and DB priority tables, energy consumption cost table, user’s budget limitation, ET
5: Initialization generate an initial population;
6: Compute absolute comfort values by using Eq. (5.1.2)
7: Compute total energy availability by using Eq. (5.1.7)
8: Compute total desired comfort (μdesired) by using Eq. (5.1.3)
9: While stopping criteria is not met do
10: Selection Evaluate the population using fitness function by using Eq. (5.1.1) and sort solutions
11: Selects only those solutions that satisfy the budget and energy constraints by using Eq. (5.1.4) and Eq. (5.1.8)
12: Reproduction
13: if Crossoverrate > rand(1) then Parent vectors are selected as: Parent1 and Parent2
14: Reproduce the offspring (newchild) by applying a crossover operation
15: offspring1 = [parent1(1 : CP) Parent2(CP + 1 : end)]
16: end if
17: if Mutationrate > rand(1) then
18: Select an individual from the population as Parent3 and assigned it to variable Temp
19: Randomly invert two bits of selected individual
Proposed solutions (2/6)
31
TB: Time based
DB: Device based
ET: Electricity tariff
CP: Cut-off point

❑EACA Solution
20: if mutant row == 0 then
21: mutant row=randi(length(Parent3),1,1)
22: end if
23: Status bit = Parent3(mutant row)
24: if Status bit == 1 then
25: Temp(mutant row) = 0
26: else
27: if Status bit == 0 then
28: Temp(mutant row) = 1
29: end if
30: end if
31: offspring2=Temp
32: end if
33: end While
34: Return output
35: Return best solution Gbest
36: Compute total achieved comfort (μachieved) based on scheduling pattern
37: end procedure
Proposed solutions (3/6)
32

Fig. 6.1: Hybrid GA- BPSO (HGPO) algorithm
❑RESs integration via GC-mode
❖Why hybridization
➢BPSO performed better
=> Bill reduction
➢GA performed better
=> PAR reduction
❖Hybrid steps
➢All BPSO steps are followed
➢At end crossover and mutation
steps of GA are applied on best
results obtained by BPSO
Proposed solutions (4/6)
33

Start
1) Initialize population, i.e., X, 2) Set a termination criteria
Select the Best and Worst vectors (solutions) based on
the objective function and constraints in population X
Modify vectors based on the Best and Worst vectors using Jaya equation
Is modified vectors have better fitness values to
the corresponding X vectors?
Stop
No
No
Yes
Yes
Accept modified vectors and replace them
with corresponding X vectors
Is Xnew better fitness than Xold?
Select optimum solution
Keep the previous X vectors
Is Xm better than Xn?
Randomly select two solutions: Xm and
Xn from X
Xnew = Xold + r (Xm – Xn) Xnew = Xold + r (Xn – Xm)
Yes No
Accept the new solution and
replace the previous solution
Is termination criterion met?
Yes
No
Reject and keep the previous
solution
Learning phase of TLBO algorithm Jaya algorithm
Fig. 6.2: Hybrid JAYA- TLBO (JLBO) algorithm
❑RESs integration via SA-mode
❖Hybridization
➢High search abilities on more
promising areas of the search
space for finding best solutions
[29]
❖Hybrid composition JLBO
➢All Jaya steps
➢Followed by the learning phase
of TLBO
[29] Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in
combinatorial optimization: A survey. Applied Soft Computing, 11(6), 4135-4151.
Proposed solutions (5/6)
34

Fig. 6.3: Hybrid TLBO +EDE (EESA) algorithm
❑RESs integration via SA-mode
❖Hybrid composition EESA
➢All TLBO steps
➢The updated population of
TLBO is further explored by
using trial vectors of EDE
❖Results
➢Resulting in increased search
power around global solution
Proposed solutions (6/6)
35
Start
1) Initialize population of students (S) 2) Set a global termination criteria
Based on the fitness criteria, find the bes t solution (teacher)
Are modified solutions have better fitness values to
the corresponding S solutions?
Stop
No
No
Yes
YesIs S_new has better fitness than S_old?
Select optimum solution
Is Sm better than Sn?
Randomly select two solutions: Sm and Sn from S
S_new = S_old + r (Sm –Sn) S_new =S_old + r (Sn –Sm)
Yes No
Accept the new solution and replace
the previous solution
Is global termination criterion met?
YesNo
Reject and keep the previous solution
TLBO teacher phase
EDE algorithm steps
Based on the best solution (teacher), modify all other solutions
Reject
solution
Accept
solution
1. Updated population X_new achieved 2. Set a local termination criteria
TLBO student phase
Generate 5 trial vectors Tvec based on EDE e quations
NoYesIs Tvec has better f itness than X_new?
Accept the new solution and replace
the previous solution Reject and keep the previous solution
Is local termination criterion met?
No
Yes
Find mean (M) of each decision variable

7. Results and discussions
36
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

UC (1/2)
App.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
TV
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.2
0.4
0.7
0.8
1.0
1.0
0.7
0.5
Ligt
.
0.0
0.0
0.0
0.0
0.0
0.9
0.8
0.5
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.6
0.7
0.8
1.0
0.9
0.7
0.4
AC
0.0
0.0
0.0
0.1
0.2
0.4
0.3
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.2
0.3
0.5
1.0
0.9
0.5
0.2
MC
1.0
0.7
0.5
0.4
0.3
0.3
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.8
Table 7.2: TB priority
App
.
1
2
3
TV
0.0
0.0
0.0
Ligt
.
0.0
0.0
0.0
AC
0.0
0.0
0.0
WH
0.0
0.0
0.0
Ligt.
0.0
0.0
0.0
WM
0.0
0.0
0.0
CD
0.0
0.0
0.0
Ligt.
0.0
0.0
0.0
MO
0.0
0.0
0.0
Juicer
0.0
0.0
0.0
DW
0.0
0.0
0.0
Ref.
0.0
0.0
0.0
Ligt.
0.0
0.0
0.0
Ligt.
1.0
1.0
1.0
CCTV
1.0
1.0
1.0
Ligt.
0.2
0.1
0.1
Laptop
0.1
0.2
0.3
MC
1.0
0.9
0.7
Table 7.1: DB priority

 


Absolute UC [7]:
For instance: MC at 
    

Results and discussions (1/27) TB priority
DB priority Fuzzy values between 0 and 1
37

❑Simulation results
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
2
4
6
8
10
Hourly cumulative user comfort
Desired user comfort
Achieved user comfort
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
2
4
6
8
10
Hourly cumulative user comfort
Desired user comfort
Achieved user comfort
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
2
4
6
8
10
Hourly cumulative user comfort
Desired user comfort
Achieved user comfort
Fig. 7.1: Budget $1.5 per day Fig. 7.2: Budget $2.0 per day Fig. 7.3: Budget $2.5 per day
Actual expenditure ($/kWh): 1.50
Total absolute comfort (μ_desired): 95.10
Total achieved comfort, (μ_achieved): 57.00
Percentage comfort (%): 59.94
Cost per unit kWh- comfort, ($/kWh): 0.0263
Actual expenditure ($/kWh): 2.00
Total absolute comfort (μ_desired): 95.10
Total achieved comfort, (μ_achieved): 61.50
Percentage comfort (%): 64.67
Cost per unit kWh- comfort, ($/kWh): 0.0325
Actual expenditure ($/kWh): 2.50
Total absolute comfort (μ_desired): 95.10
Total achieved comfort, (μ_achieved): 65.00
Percentage comfort (%): 68.35
Cost per unit kWh- comfort, ($/kWh): 0.0385
Results and discussions (2/27)
❑EACA UC results
38
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

❖Two scenarios
➢Scenario-1 atomic appliances
Appliances
➢CD
➢DW
➢Refrigerator
➢Scenario-2 aggregate appliances
Without knapsack capacity limit (PAR created)
With knapsack capacity limit
Low and high
priorities
assignment
Results and discussions (3/27)
❑Priority results:
39
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

12345678910 11 12
Time (Month)
2
4
6
8
10
12
14
16
18
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
OSR mu=0.001
OSR mu=0.13
❑Priority results: scenario-1 atomic appliances
12345678910 11 12
Time (Month)
2
4
6
8
10
12
14
16
18
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
OSR mu=0.001
OSR mu=0.17
12345678910 11 12
Time (Month)
6
7
8
9
10
11
12
13
14
15
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
OSR mu=0.0033
OSR mu=0.0089
Fig. 7.4: Average monthly cost of CD Fig. 7.5: Average monthly cost of DW Fig. 7.6: Average monthly cost of refrigerator
Table 7.3: CD
Non-schedule cost ($) of CD: 153.32
Average values EDE GA BPSO OSR
LP Cost ($): 55.10 60.83 55.16 45.38
LP Delay (hrs): 7.62 7.42 7.62 6.57
HP Cost ($): 102.57 104.21 103.21 99.15
HP Delay (hrs): 2.38 3.09 3.40 2.69
Non-schedule cost ($) of DW: 143.10
Average values EDE GA BPSO OSR
LP Cost($): 73.22 75.20 73.23 63.18
LP Delay (hrs): 5.00 4.45 5.00 5.02
HP Cost ($): 94.64 96.79 95.13
102.14
HP Delay (hrs): 0.75 1.22 1.18 0.72
Non-schedule cost ($) of REF: 122.65
Average values EDE GA BPSO OSR
LP Cost ($): 111.10 112.62 111.26
106.03
LP Delay (hrs): 11.88 11.25 11.88 10.38
HP Cost ($): 117.54 117.54 117.66
112.61
HP Delay (hrs): 5.50 5.50 5.50 5.38
Table 7.4: DW Table 7.5: Refrigerator
Results and discussions (4/27)
40

0Cases
0
50
100
150
200
250
300
350
400
450
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
OSR mu=0.001
OSR mu=0.13
0Cases
0
50
100
150
200
250
300
350
400
450
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
OSR mu=0.001
OSR mu=0.13
0Cases
0
2
4
6
8
10
12
14
PAR
Non-schedule
EDE mu=0.001
EDE mu=0.105
GA mu=0.001
GA mu=0.105
BPSO mu=0.001
BPSO mu=0.105
OSR mu=0.001
OSR mu=0.105
0Cases
0
2
4
6
8
10
12
14
PAR
Non-schedule
EDE mu=0.001
EDE mu=0.105
GA mu=0.001
GA mu=0.105
BPSO mu=0.001
BPSO mu=0.105
OSR mu=0.001
OSR mu=0.105
Fig. 7.8: Average cost of
appliances (knapsack)
Fig. 7.7: Average cost of appliances
Scheme
Priority
value Without Knapsack Knapsack
Avg.
Cost
($)
Avg.
Delay
(hrs)
PAR Avg.
Cost
($)
Avg.
Delay
(hrs)
PAR
Non-Sch. -419.07 -13.37 419.07 -13.37
EDE 0.001 142.12 10.63 13.84 164.08 10.13 10.90
0.105 239.54 3.50 10.90 239.54 3.50 10.90
GA 0.001 142.18 10.63 13.84 164.14 10.13 10.90
0.105 240.89 4.13 10.90 240.98 4.13 10.90
BPSO 0.001 142.62 10.63 13.84 164.86 10.13 10.90
0.105 239.62 3.50 10.90 239.60 3.50 10.90
OSR 0.001 138.75 9.38 13.84 146.56 9.88 10.90
0.105 238.34 2.75 10.90 238.34 2.75 10.90
Fig. 7.9: PAR of appliances Fig. 7.10: PAR of appliances
(knapsack)
❑Priority results: scenario-2 aggregate
appliances
Table 7.6: Priority results
Results and discussions (5/27)
41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
20
40
60
80
100
120
Time(hours)
Electricity Cost (Cents)
Unsch hourly cost
Unsch+RES hourly cost
Unsch+RES+ESS hourly cost
Unscheduled Unscheduled+RES Unscheduled+RES+ESS
0
100
200
300
400
500
600
700
800
900
Total Cost (Cents)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
0.2
0.4
0.6
0.8
1
1.2
Time (hours)
RES Generation (kW/h)
Estimated RE
90% of Estimated RE
Remaing RE after ESS charging
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
1
2
3
4
5
6
7
Time (hours)
Energy consumption (kWh)
Unsch hourly load
Unsch+RES hourly load
Unsch+RES+ESS hourly load
Unscheduled Unscheduled+RES Unscheduled+RES+ESS
0
0.5
1
1.5
2
2.5
3
3.5
Case 1 PAR
Fig. 7.14: Total electricity cost in case 1
Fig. 7.12: Electricity cost in case 1
Fig. 7.11: Estimated renewable energy Fig. 7.13: Energy consumption in case 1
Fig. 7.15: Case 1 PAR
Scenario Cost (Cents) PAR
Unscheduled 862.66 3.04
Unscheduled +
RES 713.78 2.57
Unscheduled +
RES + ESS 690.63 2.39
Table 7.7: Comparison of cost and PAR
Same: 1-6 and
20-24
Results and discussions (6/27)
❑RESs integration via GC-Mode
❖Case 1: Integration of RES and ESS
42

❑Case 2: OHEMS with integrated RES and ESS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
10
20
30
40
50
60
70
80
Time (hours)
Electricity Cost (Cents)
Unsch+RES+ESS hourly cost
GA+RES+ESS hourly cost
BPSO+RES+ESS hourly cost
WDO+RES+ESS hourly cost
BFO+RES+ESS hourly cost
HGPO+RES+ESS hourly cost
Unsch+RES+ESS GA BPSO WDO BFO HGPO
0
100
200
300
400
500
600
700
Total Cost (Cents)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
1
2
3
4
5
6
7
Time (hours)
Energy consumption (kWh)
Unsch+RES+ESS hourly load
GA+RES+ESS hourly load
BPSO+RES+ESS hourly load
WDO+RES+ESS hourly load
BFO+RES+ESS hourly load
HGPO+RES+ESS hourly load
Unsch+RES+ESS GA BPSO WDO BFO HGPO
0
0.5
1
1.5
2
2.5
Case 2 PAR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
5
10
15
20
25
30
Time (hours)
Cost (Cents/kWh)
DAP
Fig. 7.19: Hourly energy consumption in case 2
Fig. 7.17: Hourly electricity cost in case 2 Fig. 7.18: Case 2 Total cost
Fig. 7.20: Case 2 PAR
Fig. 7.16: DAP signal
Scenario Cost (Cents) PAR
Unscheduled + RES + ESS 690.63 2.39
GA 622.97 2.05
BPSO 555.32 2.31
WDO 584.24 2.35
BFO 581.56 1.58
HGPO 517.15 1.79
Table 7.8: Comparison of cost and PAR
Results and discussions (7/27)
❑RESs integration via GC-mode
❖Case 2: OHEMS with integrated RES and ESS
43

11000 2000 3000 4000 5000 6000 7000 8000 9000
Time(h)
0
200
400
600
800
1000
Insolation(W/m2)
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time(h)
0
5
10
15
Wind speed(m/s)
Fig. 7.22: Hourly wind speed profile data
during a year
Fig. 7.21: Hourly insolation profile data
during a year
❖Data obtained from Hawksbay, Pakistan via AEDB [30]
❖The dataset contains data that are recorded each 10 min per day
❖Mean values of the irradiation and wind speed data (at a height of 10 m)
[30] Alternative Energy Development Board (AEDB), Ministry of Energy, Power Division, Government of Pakistan. http://www.aedb.org/ ae-technologies/wind-power/wind-data
(Accessed on 2nd April 2018).
Results and discussions (8/27)
❑RESs integration via SA-mode (Pakistan)
44
AEDB: Alternate energy development board

Fig. 7.23: Hourly produced PVs’ power for PV-WT-FC
system during a year
(b) LPSPmax = 2%
(a) LPSPmax = 0%
Systems LPSPmax (%) LPSP (%) Npv Nwt Nt TAC ($)
PV-WT-FC 0067 17083 1152600
21.84 62 15634 919800
PV-FC 0084 N/A 6448 1051200
21.78 79 N/A 4822 790000
WT-FC 00N/A 714169 2288200
20.60 N/A 610090 1633300
Table 7.9: Jaya results for the proposed hybrid systems
Fig. 7.24: Hourly produced WTs’ power for
PV-WT-FC system during a year
LPSPmax = 0% and 2%
❑RESs integration via SA-mode: PV-WT-FC scenario
Results and discussions (9/27)
45
: Maximum
allowable loss of power
supply probability
: Number of PVs
: Number of WTs
: Number of fuel tanks
TAC: Total annual cost
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
1
2
3
4
5
6
7
8
PVs (kW)
LPSPmax=0%
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
1
2
3
4
5
6
7
8
PVs (kW)
LPSPmax=2%
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
0.2
0.4
0.6
0.8
1
WTs (kW)
LPSPmax=0%

Fig. 7.25: Hourly produced PVs’ power for PV-FC system during a year
(b) LPSPmax = 2%
(a) LPSPmax = 0%
Systems LPSPmax (%) LPSP (%) Npv Nwt Nt TAC ($)
PV-WT-FC 0067 17083 1152600
21.84 62 15634 919800
PV-FC 0084 N/A 6448 1051200
21.78 79 N/A 4822 790000
WT-FC 00N/A 714169 2288200
20.60 N/A 610090 1633300
Table 7.10: Jaya results for the proposed hybrid systems
Results and discussions (10/27)
❑RESs integration via SA-mode: PV-FC scenario
46
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
2
4
6
8
10
PVs (kW)
LPSPmax=0%
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
2
4
6
8
10
PVs (kW)
LPSPmax=2%

(b) LPSPmax = 2%
(a) LPSPmax = 0%
Systems LPSPmax (%) LPSP (%) Npv Nwt Nt TAC ($)
PV-WT-FC 0067 17083 1152600
21.84 62 15634 919800
PV-FC 0084 N/A 6448 1051200
21.78 79 N/A 4822 790000
WT-FC 00N/A 714169 2288200
20.60 N/A 610090 1633300
Table 7.11: Jaya results for the proposed hybrid systems
Fig. 7.26: Hourly produced WTs’ power for WT-FC system during a year
Results and discussions (11/27)
❑RESs integration via SA-mode: WT-FC scenario
47
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
1
2
3
4
5
6
7
WTs (kW)
LPSPmax=0%
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time (h)
0
1
2
3
4
5
6
WTs (kW)
LPSPmax=2%

11000 2000 3000 4000 5000 6000 7000 8000 9000
Time(h)
0
500
1000
1500
2000
Energy storage tanks(kWh)
LPSPmax=0%
LPSPmax=2%
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time(h)
0
500
1000
1500
2000
2500
Energy storage tanks(kWh)
LPSPmax=0%
LPSPmax=2%
Fig. 7.27: Hourly expected mass of
stored energy in HFTs for PV-WT-FC
system during a year
11000 2000 3000 4000 5000 6000 7000 8000 9000
Time(h)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Energy storage tanks(kWh)
LPSPmax=0%
LPSPmax=2%
Fig. 7.28: Hourly expected mass of
stored energy in HFTs for PV-FC
system during a year
Fig. 7.29: Hourly expected mass of
stored energy in HFTs for WT-FC
system during a year
Systems LPSPmax (%) LPSP (%) Npv Nwt Nt TAC ($)
PV-WT-FC 0067 17083 1152600
21.84 62 15634 919800
PV-FC 0084 N/A 6448 1051200
21.78 79 N/A 4822 790000
WT-FC 00N/A 714169 2288200
20.60 N/A 610090 1633300
Table 7.12: Jaya results for the proposed hybrid systems
Results and discussions (12/27)
❑RESs integration via SA-mode: HFTs results
48
HFTs: Hybrid fuel tanks

(b) Hourly ambient temperature profile
data during a year and first eight days
(a) Hourly insolation profile data
during a year and first eight days
11000 2000 3000 4000 5000 6000 7000 8000 8760
Time (h)
0
200
400
600
800
1000
1200
Insolation (W/m2)
1 24 48 72 96 120 144 168 192
Time (h)
0
100
200
300
400
500
600
700
800
Insolation (W/m2)
11000 2000 3000 4000 5000 6000 7000 8000 8760
Time (h)
-10
0
10
20
30
40
Temperature (°C)
1 24 48 72 96 120 144 168 192
Time (h)
-10
-5
0
5
10
15
20
Temperature (°C)
[31] Iran, Ministry of energy, Statistics on renewable met mast stations (SATBA), http://www.satba.gov.ir/en/regions/kerman (Accessedon 2nd April 2018).
Results and discussions (13/27)
Fig. 7.30: Yearly and during first eight days insolation and ambient temperature input data
❑RESs integration via SA-mode: (Iran)
❖Data obtained for Rafsanjan, Iran [31]. Data are recorded each 10 min per day
❖Mean values of the insolation and ambient temperature data (at a height of 10 m)
49

Fig. 3.31: Yearly and during first eight days of the year wind speed
and consumer’s load input data
11000 2000 3000 4000 5000 6000 7000 8000 8760
Time (h)
0
5
10
15
20
25
Wind speed (m/s)
1 24 48 72 96 120 144 168 192
Time (h)
0
1
2
3
4
5
6
7
8
9
Wind speed (m/s)
Results and discussions (14/27)
❑RESs integration via SA-mode
50
(b) Hourly consumer’s load profile data during a year
(a) Hourly wind speed profile data during a year
11000 2000 3000 4000 5000 6000 7000 8000 8760
Time (h)
2
3
4
5
6
7
8
Power (kW)
1 24 48 72 96 120 144 168 192
Time (h)
3
3.5
4
4.5
5
Power (kW)

Hybrid systems
Index
Jaya TAC
($)
TLBO TAC
($)
JLBO TAC
($)
GA
TAC ($)
PV
-WT-Battery
Mean
50596
51458
50247
54626
Std.
173.1658
1790.2
0
4539.9
Best
50268
50268
50247
50247
Worst
50678
55621
50247
63565
PV
-Battery
Mean
67052
67052
67052
67052
Std.
0
0
0
0
Best
67052
67052
67052
67052
Worst
67052
67052
67052
67052
WT
-Battery
Mean
138250
138250
138250
138250
Std.
0
0
0
0
Best
138250
138250
138250
138250
Worst
138250
138250
138250
138250
Average rank
85299.33
85586.67
85183
86642
Final rank
2
3
1
4
Table 7.13: Summary of mean, standard deviation, best performance, worst performance, and ranks of
the schemes over 10 runs by the proposed hybrid systems at LPSP max = 1%
Results and discussions (15/27)
❑RESs integration via SA-mode
51
Similar
Results

Hybrid
systems
Jaya JLBO
LPSPmax (%) LPSP (%) Npv Nwt Nb TAC ($) LPSP (%) Npv Nwt Nb TAC ($)
PV
-WT-
Battery
0
0
145
15
1802
66863
0
150
14
1795
66542
0.3
0.2721
139
15
1620
61102
0.2962
144
14
1612
60752
1
0.9650
160
9
1296
50268
0.9817
165
8
1299
50247
2
1.8080
172
5
1084
43066
1.7976
168
6
1078
43046
5
4.6908
170
4
849
35555
4.8372
174
3
818
34464
PV
-
Battery
0
0
213
N/A
6448
88853
0
213
N/A
6448
88853
0.3
0.2097
210
N/A
5634
82790
0.2097
210
N/A
5634
82790
1
0.9715
202
N/A
5634
67052
0.9715
202
N/A
5634
67052
2
1.9158
193
N/A
5634
50424
1.9158
193
N/A
5634
50424
5
4.5991
187
N/A
5634
39409
4.5991
187
N/A
5634
39409
WT
-
Battery
0
0
N/A
56
4246
147730
0
N/A
56
4246
147730
0.3
0
N/A
55
4072
142150
0
N/A
55
4072
142150
1
0.8744
N/A
54
3954
138250
0.8744
N/A
54
3954
138250
2
0.8744
N/A
54
3954
138250
0.8744
N/A
54
3954
138250
5
0.8744
N/A
54
3954
138250
0.8744
N/A
54
3954
138250
Table 7.14: Jaya and JLBO results for the
proposed hybrid systems at different LPSPmax
values
Similar
Results
Results and discussions (16/27)
52

(b) Produced WTs’ power of PV-WT-Battery system (a) Produced PVs’ power of PV-WT-Battery system
Table 7.15: JLBO results for the proposed hybrid systems
(c) Batteries energy storage level of PV-WT-Battery system
Hybrid
system
JLBO
LPSPmax (%) LPSP (%) Npv Nwt Nb TAC ($)
PV
-WT-
Battery
0
0
150
14
1795
66542
0.3
0.2962
144
14
1612
60752
1
0.9817
165
8
1299
50247
2
1.7976
168
6
1078
43046
5
4.8372
174
3
818
34464
Results and discussions (17/27)❑PV-WT-Battery system results
Fig. 7.32: Hourly produced power and energy storage level of
PV-WT-Battery system by JLBO algorithm during a year at
different LPSPmax values
53

(b) ) Batteries energy storage level of PV-Battery system
(a) Produced PVs’ power of PV-Battery system
Table 7.16: JLBO results for the PV-Battery system
❑PV-Battery system results
Hybrid
system
JLBO
LPSPmax (%) LPSP (%) Npv Nwt Nb TAC ($)
PV
-
Battery
0
0
213
N/A
2601
88853
0.3
0.2097
210
N/A
2404
82790
1
0.9715
202
N/A
1893
76052
2
1.9158
193
N/A
1354
50424
5
4.5991
187
N/A
997
39409
Results and discussions (18/27)
Fig. 7.33: Hourly produced power and energy
storage level of PV-Battery system by JLBO
algorithm during a year at different LPSPmax
values
54

(b) ) Batteries energy storage level of WT-Battery system
(a)Produced WTs’ power of WT-Battery system
Table 7.17: JLBO results for the WT-Battery system
❑WT-Battery system results
Hybrid
system
JLBO
LPSPmax (%) LPSP (%) Npv Nwt Nb TAC ($)
WT
-
Battery
0
0
N/A
56
4246
147730
0.3
0
N/A
55
4072
142150
1
0.8744
N/A
54
3954
138250
2
0.8744
N/A
54
3954
138250
5
0.8744
N/A
54
3954
138250
Results and discussions (19/27)
Fig. 7.34: Hourly produced power and energy
storage level of WT-Battery system by JLBO
algorithm during a year at different LPSPmax
values
55

(a) Convergence of JLBO algorithm
for PV-WT-Battery system
(b) Convergence of JLBO algorithm
for PV-Battery system (c) Convergence of JLBO algorithm
for WT-Battery system
Table 7.18: JLBO results for the proposed hybrid systems
❑Convergence results
Hybrid systems
JLBO results at various LPSPmax values
TAC ($) at 0% TAC ($) at 0.3% TAC ($) at 1% TAC ($) at 2% TAC ($) at 5%
PV
-WT-Battery
66542
60752
50247
43046
34464
PV
-Battery
88853
82790
76052
50424
39409
WT
-Battery
147730
142150
138250
138250
138250
Results and discussions (20/27)
Fig. 7.35: Convergence process of JLBO algorithm for optimum results at different LPSPmax values
56

TLBO EDE SSA EESA
LPSP
max
(%)
LPSP
(%) Npv Nwt Nb TAC
($) LPSP
(%) Npv Nwt Nb TAC
($) LPSP
(%) Npv Nwt Nb TAC ($) LPSP
(%) Npv Nwt Nb TAC
($)
0 0 111 17 1753 64430 0126 14 1837 66621 0 112 17 1794 65710 0111 17 1753 64430
0.5 0.2762 113 16 1688 62220 0.3641 122 14 1711 62640 0.4778 136 11 1771 64061 0.3779 117 15 1685 61970
1 0.6543 116 15 1654 60990 0.8524 133 11 1673 60971 0.9725 132 11 1640 59931 0.9645 127 12 1612 59200
3 2.7274 122 12 1461 54420 2.9859 142 7 1539 55964 1.6359 125 12 1552 57300 2.8168 126 11 1458 54171
Average rank 60515 61549 61750 59943
Final rank 2 3 4 1
Results and discussions (21/27)
Table 7.19: Summary of TLBO, EDE, SSA, and EESA results for the PV-WT-Battery hybrid system at various
LPSPmax values
❑Summary of TLBO, EDE, SSA, and EESA results
❖PV-WT-FC scenario results
57
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

TLBO EDE SSA EESA
LPSP
max
(%)
LPSP
(%) Npv Nwt Nb TAC ($) LPSP
(%) Npv Nwt Nb TAC ($) LPSP
(%) Npv Nwt Nb TAC ($) LPSP
(%) Npv Nwt Nb TAC ($)
0 0 199 0 3150 104640 0 199 0 3150 104640 0 200 0 3200 106200 0 199 0 3150 104640
0.5 0.4932 194 02898 96840 0.4932 194 02898 96840 0.4932 194 02898 96840 0.4932 194 02898 96840
1 0.8820 191 0 2746 92120 0.7526 192 0 2797 93700 0.6160 193 0 2847 95250 0.8820 191 0 2746 92120
32.8942 178 02090 71790 2.8942 178 02090 71790 2.7459 179 02141 73370 2.8942 178 02090 71790
Average rank 91348 91743 92915 91348
Final rank 1 2 3 1
Results and discussions (22/27)
TLBO / EDE / SSA/ EESA
LPSPmax (%) LPSP
(%) Npv Nwt Nb TAC ($)
0 & 0.5 0 0 50 3552 124750
1 & 3 0.5503 0 49 3362 118690
Average rank 121720
Final rank 1
Table 7.20: Summary of TLBO, EDE, SSA, and EESA results for the PV-Battery hybrid system at various LPSPmax values
Table 7.21: Summary of TLBO, EDE, SSA, and EESA results for the WT-Battery hybrid system at various LPSPmax values
❖PV-Battery scenario results
❖WT-Battery scenario results
58

Results and discussions (23/27)
Hybrid System
Index
Jaya
TAC ($)
TLBO
TAC ($)
TACMA
TAC ($)
PV
-WT-FC-DG
Mean
27942
27948
27937
Std.
14.7238
18.8236
4.6662
Best
27933
27933
27933
Worst
27980
27986
27944
PV
-FC-DG
Mean
30329
30322
30324
Std.
7.9609
3.8895
1.7544
Best
30320
30320
30320
Worst
30343
30333
28498
WT
-FC-DG
Mean
28498
28498
28498
Std.
0
0
0
Best
28498
28498
28498
Worst
28498
28498
28498
Table 7.22: Summary of mean, standard deviation, best
performance, and worst performance of the algorithms over 10
independent runs for the hybrid systems
❑Summary of Jaya, TLBO, and TACMA results
❖Three scenarios
➢PV-WT-FC-DG scenario
➢PV-FC-DG scenario
➢WT-FC-DG scenario
59
Similar
Results

Results and discussions (24/27)
Fig. 7.36: Hourly PVs’ power, WTs’ power, energy stored in H2 tanks, and produced DG’s
power during a year (8760 h) of PV-WT-FC-DG hybrid system
(a) Produced PVs’ power of PV-WT-FC-DG system (b) Produced WTs’ power of PV-WT-FC-DG system
(c) Energy stored in H2 tanks of PV-WT-FC-DG system (d) Produced DG’s power of PV-WT-FC-DG system
Hybrid System
PV
-WT-
FC
-DG
Npv
47
Nwt
6
Nt
26
DG
1
RF (%)
95.8239
PV cost ($)
814.6227
WT cost ($)
1468.50
FC cost ($)
6019.50
Electrolyser cost ($)
6019.50
Hydrogen tank cost
($)
1460.40
DG cost ($)
5316.40
Fuel cost ($)
4989.2
Inv/Conv. Cost ($)
1845.10
TAC cost ($)
27933
Table 7.23: PV-WT-FC-DG
PV-WT-FC-DG scenario
60
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
1
2
3
4
5
6
PVs (kW)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
1
2
3
4
5
6
WTs (kW)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
1
2
3
4
5
6
7
8
Energy storage tanks (kWh)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
0.5
1
1.5
2
2.5
3
Diesel power (kW)

Results and discussions (25/27)
Fig. 7.37: Hourly PVs’ power, energy stored in H2 tanks, and DG’s
power during a year (8760 h) of PV-FC-DG hybrid system
(a) Produced PVs’ power of PV-FC-DG system (b) Energy stored in H2 tanks of PV-FC-DG system
(c) Produced DG’s power of PV-FC-DG system
Hybrid System
PV
-FC-
DG
Npv
140
Nwt
N/A
Nt
85
DG
1
RF (%)
98.8915
PV cost ($)
2426.50
WT cost ($)
N/A
FC cost ($)
6019.50
Electrolyser cost ($)
6019.50
Hydrogen tank cost ($)
4774.40
DG cost ($)
4931.30
Fuel cost ($)
2663.90
Inv/Conv. Cost ($)
3485.10
TAC cost ($)
30320
Table 7.24: PV-FC-DG
PV-FC-DG scenario
61
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
5
10
15
20
PVs (kW)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
5
10
15
20
25
30
Energy storage tanks (kWh)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
0.5
1
1.5
2
2.5
3
Diesel power (kW)

Results and discussions (26/27)
Fig. 7.38: Hourly WTs’ power, energy stored in H2 tanks, and DG’s
power during a year (8760 h) of WT-FC-DG hybrid system
(a) Produced WTs’ power of WT-FC-DG system (b) Energy stored in H2 tanks of WT-FC-DG system
(c)Produced DG’s power of WT-FC-DG system
Hybrid System
WT
-FC-
DG
Npv
N/A
Nwt
8
Nt
9
DG
1
RF (%)
88.2118
PV cost ($)
N/A
WT cost ($)
1958.10
FC cost ($)
6019.50
Electrolyser cost ($)
6019.50
Hydrogen tank cost ($)
505.53
DG cost ($)
5653.70
Fuel cost ($)
7111.40
Inv/Conv. Cost ($)
1230
TAC cost ($)
28498
Table 7.25: WT-FC-DG
WT-FC-DG scenario
62
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
1
2
3
4
5
6
7
8
WTs (kW)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
0.5
1
1.5
2
2.5
3
Energy storage tanks (kWh)
11001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
0
0.5
1
1.5
2
2.5
3
Diesel power (kW)

Results and discussions (27/27)
Hybrid System
PV
-WT-
FC
-DG
PV
-FC-
DG
WT
-FC-
DG
Npv
47
140
N/A
Nwt
6
N/A
8
Nt
26
85
9
DG
1
1
1
RF (%)
95.8239
98.8915
88.2118
PV cost ($)
814.6227
2426.50
N/A
WT cost ($)
1468.50
N/A
1958.10
FC cost ($)
6019.50
6019.50
6019.50
Electrolyser cost ($)
6019.50
6019.50
6019.50
Hydrogen tank cost ($)
1460.40
4774.40
505.53
DG cost ($)
5316.40
4931.30
5653.70
Fuel cost ($)
4989.2
2663.90
7111.40
Inv/Conv. Cost ($)
1845.10
3485.10
1230
TAC cost ($)
27933
30320
28498
Table 7.26: Summary of the results for the hybrid
systems achieved by TACMA
Hybrid Systems
PV
-WT-
FC
-DG
PV
-FC-
DG
WT
-FC-
DG
Fuel consumption
by DG in a year (l)
1931.1
1039.6
2735.1
CO2
(Kg)
6082.97
3274.74
8615.57
SO2
(Kg)
77.24
41.58
109.40
NO2
(Kg)
115.87
62.38
164.11
Table 7.27: Emission comparisons of proposed
hybrid systems
❑Summary of TACMA results
63

8. Conclusions and future work
64
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

❑Absolute comfort
❖Proposed a new absolute comfort performance metric derived from TB and DB priorities
❖Achieved high absolute comfort values with the increase in consumer’s budget
❑Priority-induced DSM strategy
❖Presented trade-off under different priority values
❖Lowered rebound peaks by using knapsack capacity limit
❑GC mode ❑SA mode
➢System unit sizing for Hawksbay, Pakistan and Rafsanjan,
Iran
➢Reliability of the system is ensured using maximum LPSP
(LPSPmax) value
➢Emission considered for PV-WT-FC-DG system
➢Proposed an OHEMS
➢Appliances classification into shiftable
and non-shiftable categories
➢Cost and PAR reduction
Conclusions and future work (1/2)
RES and ESS incorporation
65

❑Consider large number of appliances including commercial and industrial sectors
❑Cost due to to energy dissipation, vampire loads or other standby power are not
precisely taken into account, and need to be optimized
❑Electric vehicles as an ESS can influence hugely on energy management
(considering of hybrid ESS)
❑Energy trading between prosumers and utility
❑Multi-objective optimization considering various trade-off solutions
Conclusions and future work (2/2)
66
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

PUBLICATIONS
Journal Publication
1. Asif Khan, Nadeem Javaid and Majid Iqbal Khan, "Time and device based priority induced comfort management in smart
home within the consumer budget limitation", Sustainable Cities and Society, Volume: 41, Pages: 538-555, Published:
August 2018, ISSN: 2210-6707, DOI: https://dx.doi.org/10.1016/j.scs.2018.05.053.
Download from Sustainable Cities and Society. (IF= 4.624, Q1
2. Asif Khan, Nadeem Javaid, Adnan Ahmad, Mariam Akbar, Zahoor Ali Khan and Manzoor Ilahi, "A priority-induced
demand side management system to mitigate rebound peaks using multiple knapsack", Journal of Ambient Intelligence and
Humanized Computing, Volume: 10, Issue: 4, Special Issue: SI, Pages: 1655-1678, Published: April 2019, ISSN: 1868-
5137, DOI: https://dx.doi.org/10.1007/s12652-018-0761-z.
Download from Journal of Ambient Intelligence and Humanized Computing. (IF= 1.91, Q3)
3. Adnan Ahmad, Asif Khan, Nadeem Javaid, Hafiz Majid Hussain, Wadood Abdul, Ahmad Almogren, Atif Alamri and
Iftikhar Azim Niaz, "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage
Resources", Energies, Volume: 10, Issue: 4, Article Number: 549, Pages: 1-35, Published: April 2017, ISSN: 1996-1073,
DOI: https://dx.doi.org/10.3390/en10040549.
Download from Energies. (IF=2.707, Q2)
4. Asif Khan and Nadeem Javaid. “Optimal sizing of stand-alone PV-WT-Battery system with LPSP concept by using hybrid
Jaya and teaching learning-based optimization.“Submitted in Journal of Engineering, (2018). (IF=4.568)
67

PUBLICATIONS
68
Journal Publication
5. Bilal Hussain, Nadeem Javaid, Qadeer Ul Hasan, Sakeena Javaid, Asif Khan and Shahzad A. Malik, "An Inventive
Method for Eco-Efficient Operation of Home Energy Management Systems", Energies, Volume: 11, issue: 11, Article
Number: 3091, Pages: 1-40, Published: November 2018, ISSN: 1996-1073, DOI: https://dx.doi.org/10.3390/en11113091.
Download from Energies. (IF= 2.707, Q2)
6. Bilal Hussain, Asif Khan, Nadeem Javaid, Qadeer-ul-Hasan, Shahzad A. Malik, Omar Ahmad, Amir Hanif Dar and
Ahmad Kazmi, "A Weighted-Sum PSO Algorithm for HEMS: A New Approach for the Design and Diversified
Performance Analysis", Electronics, Volume: 8, Issue: 2, Article Number: 180, Pages: 1-40, Published: February 2019,
EISSN 2079-9292. DOI: https://doi.org/10.3390/electronics8020180.
Download from Electronics. (IF= 1.764, Q3)
7. Asif Khan, Turki Ali Alghamdi, Zahoor Ali Khan, Aisha Fatima, Samia Abid, Adia Khalid, and Nadeem Javaid (2019).
“Enhanced evolutionary sizing algorithms for optimal sizing of a stand-alone PV-WT-Battery hybrid system." Submitted
in Applied sciences. (IF=2.217).
8. Asif Khan and Nadeem Javaid (2019). “TACMA: total annual cost minimization algorithm for optimal sizing of hybrid
energy systems." Submitted in Journal of Ambient Intelligence and Humanized Computing. (IF= 1.910).

PUBLICATIONS
Conference proceeding
1. Asif Khan, Nadeem Javaid, Muhammad Nadeem Iqbal, Naveed Anwar, Inzimam-ul-Haq and Faraz Ahmad, "Time
and device based priority induced demand side load management in smart home with consumer budget limit", in 32nd
International Conference on Advanced Information Networking and Applications (AINA), 2018, pp: 874-881. DOI:
10.1109/AINA.2018.00129. Download from IEEEXplore.
2. Asif Khan, Nadeem Javaid, Adnan Ahmed, Saqib kazmi, Hafiz Majid Hussain and Zahoor Ali Khan, "Efficient
Utilization of HEM Controller Using Heuristic Optimization Techniques", 5th International Conference on Emerging
Internetworking, Data and Web Technologies (EIDWT), 2017, pp: 573-584.DOI: https://doi.org/10.1007/978-3-319-
59463-7_57. Download from Springerlink.
3. Asif Khan, Nadeem Javiad and Sakeena Javaid, "Optimum unit sizing of stand-alone PV-WT-Battery hybrid system
components using Jaya", in the 21st International Multitopic Conference (INMIC), 2018, pp: 1-10. DOI:
https://doi.org/10.1109/INMIC.2018.8595678. Download from IEEEXplore.
4. Asif Khan, Nadeem Javaid and Asma Rafique, "Optimum unit sizing of a stand-alone hybrid PV-WT-FC system using
Jaya algorithm", International Conference on Cyber Security and Computer Science (ICONCS), 2018, pp: 216-
222. Download from ICONCS.
69

PUBLICATIONS
70
Conference proceeding
5. Saman Zahoor, Nadeem Javaid, Anila Yasmeen, Isra Shafi, Asif Khan and Zahoor Ali Khan, "Optimized Energy
Management Strategy for Home and Office", in 6th International Conference on Emerging Internet, Data & Web
Technologies (EIDWT), 2018, pp: 237-246, ISBN: 978-3-319-75928-9. DOI: https://doi.org/10.1007/978-3-319-
75928-9_21. Download from Springerlink.
6. Anila Yasmeen, Nadeem Javaid, Itrat Fatima, Zunaira Nadeem, Asif Khan and Zahoor Ali Khan, "A Metaheuristic
Scheduling of Home Energy Management System", in 6th International Conference on Emerging Internet, Data &
Web Technologies (EIDWT), 2018, pp:214-224, ISBN: 978-3-319-75928-9. DOI: https://doi.org/10.1007/978-3-319-
75928-9_19. Download from Springerlink.
7. Saman Zahoor, Nadeem Javaid, Asif Khan, Bibi Ruqia, Fatima j. Muhammad and Maida Zahid, "A Cloud-Fog-Based
Smart Grid Model for Efficient Resource Utilization", in 14th IEEE International Wireless Communications and
Mobile Computing Conference (IWCMC), 2018,pp: 1154-110, ISBN: 2376-6506.DOI:
10.1109/IWCMC.2018.8450506. Download from IEEEXplore.
8. Asif Khan and Nadeem Javaid, "Optimum sizing of PV-WT-FC-DG hybrid energy system using teaching learning-
based optimization", accepted in 17th International Conference on Frontiers of Information Technology (FIT 2019),
ISSN: 2334-3141.

PUBLICATIONS
71
Conference proceeding
9. Adnan Ahmed, Awais Manzoor, Asif Khan, Adnan Zeb, Hussain Ahmad Madni, Umar Qasim, Zahoor Ali Khan and
Nadeem Javaid, "Performance Measurement of Energy Management Controller Using Heuristic Techniques", The
11th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), 2017, pp: 181-188.
DOI: https://doi.org/10.1007/978-3-319-61566-0_17. Download from Springerlink.
10. Saqib Kazmi, Hafiz Majid Hussain, Asif Khan, Manzoor Ahmad, Umar Qasim, Zahoor Ali Khan and Nadeem Javaid,
"Balancing Demand and Supply of Energy for Smart Homes", The 11th International Conference on Complex,
Intelligent and Software Intensive Systems (CISIS), 2017,pp:1000-1008.DOI:https://doi.org/10.1007/978-3-319-
61566-0_94. Download from Springerlink.
11. Ghulam Hafeez, Rabiya Khalid, Abdul Wahab Khan, Malik Ali Judge, Zafar Iqbal, Rasool Bukhsh, Asif Khan and
Nadeem Javaid, "Optimal residential load scheduling under utility and rooftop photovoltaic units", 12th International
Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2017,pp:142-153, ISBN: 978-3-319-
69835-9. DOI:10.1007/978-3-319-69835-9_13. Download from Springerlink.
12. Asif Khan, Nadeem Javaid, Sajjad Khan, Tanzila Saba, Wahab Khan, and Norin Abdul Sattar. “Enhanced Differential
Evolutionary Algorithm for Optimal Sizing of Stand-alone PV-WT-Battery System considering Loss of Power Supply
Probability Concept." Submitted in IEEE GCC Conference and Exhibition, Kuwait, 2019.

THANK YOU!
72
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019

73
“Efficient Utilization of Energy Employing Meta-heuristic Techniques with the Incorporation of
Green Energy Resources in Smart Cities ”
PhD Final Defense Presentation by Asif Khan on October 11, 2019