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Cost and Load Reduction using

Heuristic Algorithms in Smart Grid

Zafar Iqbal1, Nadeem Javaid2,∗, Mobushir Riaz Khan1,

Imran Ahmed3, Zahoor Ali Khan4,5, Umar Qasim6

1Pir Mehr Ali Shah Arid Agriculture University, Rawalpind 46000, Pakistan

2COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

3Institute of Management Sciences (IMS), Peshawar 25000, Pakistan

4Faculty of Engineering, Dalhousie University, Halifax, NS, B3J 4R2 Canada

5Higher Colleges of Technology, Fujairah 4114, United Arab Emirates

6University of Alberta Edmonton, Alberta T6G 2J8, Canada

∗Corresponding author: nadeemjavaidqau@gmail.com, www.njavaid.com

Abstract—Due to smart grid applications the consumers and

producers are able to meet the demand of each others and thus

take part in demand side management and demand response

program. Hence smart grid leads to optimization of energy

consumption and reduce high cost in today extensive demand

of energy. In this research work we are reducing electricity

consumption cost and load consumption using scheduling the

appliances. The twenty appliances are used to schedule their

energy consumption and load using heuristics techniques i.e.

binary particle optimization, genetic algorithm and wind driven

optimization, using the same data set for each technique and

their results are compared with each other in order to ﬁnd which

technique do better optimization. Simulations are performed in

matlab to show the cost and load reduction by the above three

techniques and validate the experiment. The simulation results

show that binary particle swarm optimization perform better

than the other two techniques and wind driven optimization is

better than genetic algorithm but not able to perform as binary

particle swarm optimization, similarly genetic algorithm is least

efﬁcient as compared to both methods. Our research work is

beneﬁcial to meet the demand side management and help in

reducing electricity cost and load for consumers.

Keywords:-Appliance Scheduling, Demand Side Manage-

ment, Demand Response Program, Cost Reduction, Load

Reduction, Binary Particle Swarm Optimization, Wind Driven

Optimization, Genetic Algorithm, Smart Grid.

I. INTRODUCTION

The world is now moving toward a high technological

era due to advancement in information and communication

technologies. Among these technologies, Smart Grid (SG) is

the advanced form of traditional grid due to incorporation of

two way communication network to facilitate both end users

and utility. SG is helpful in providing intelligent monitoring,

control system, communication and self-healing services to

consumers [1].

Smart grid have some general features which include; (1)

Reliability, SG has the ability to cope with faults and provides

self-healing mechanisms which shows its reliability and less

vulnerability to some natural disaster or intruder attacks,

(2) Flexibility in network topology, its network topology is

very ﬂexible and it allow bidirectional information ﬂow, (3)

Efﬁciency, it provides ﬂexibility in implementation of different

demand side management and demand response programs

to manage end user energy demand, (4) Peak curtailment,

as the prices of electricity changes throughout the day due

to energy demand variations and there are chances of high

peaks during low pricing hours which can disturb the stability

of electric grid. Through the implementation of different

energy management techniques, load can be scheduled in

order to avoid high peaks, (5) Stainability, it has the ability

to sustain even if greater amount of other distributed power

sources penetrate like solar power, wind power etc, (6) Market

enabling, through mart metering infrastructure, smart grid

provides greater ﬂexibility to suppliers and consumers to

sell extra energy back to grid, (7) Demand response, smart

grid provides support to consumers in the form of demand

response programs, where supplier or generators can estimate

the end user energy demands at particular time slots to manage

electricity generation. (8) Platform for advance services i.e. it

also provides some advance services like ﬁre monitoring and

alarm which can shut off power system and emergency call

service [2].

Now a days energy crises are everywhere and the expensive

energy production mechanisms are harder to use in a man-

ageable way. Along with energy demand management, SG

minimizes the emission of greenhouse gases and make the

chances of global warming less to occur. In this work we con-

sider twenty appliances, their power rating, price signal, and

length of operation time to evaluate the energy consumption

and electricity bill. We have done cost and load optimization

in this work,and have used three evolutionary and heuristic

techniques to calculate cost and load for these appliances.

These techniques are binary particle swarm optimization,

genetic algorithms and wind driven optimization.

We have evaluated the performance of each technique and

compare their values with each other in order to ﬁnd which

techniques perform better in term of cost and load optimiza-

tion. For this purpose to calculate the exact values and ﬁnd

accurate comparison among these techniques we have taken

the same power rating, same price signal, same time slots,

length of operation time, same appliances bits pattern,same

number of appliances and same values for all the parameters

for all these twenty appliances and feed them to these three

techniques one by one and generated the results and then these

results are compared as shown in a table V and VI, in the form

of scheduled and unscheduled cost and load, their total cost,

total load reduction, their total percentage cost and percentage

load reduction. If we see the table V and VI it is shown that

Binary particle swarm optimization perform better than other

two techniques.

The rest of the paper is organize as follows.sectiob II

describes the existing work,motivation of idea is in section

III and section IV compare the optimization techniques used

in this paper.Cost and load optimization is described in section

V,and section Vi discusses the Simulation results,and ﬁnally

section VII provides conclusion.

II. RE LATE D WOR K

Some of the existing work related to our work are listed

below. In [3] the authors are using binary particle swarm

optimization for solving the optimization problem and re-

ducing electricity bill. The authors proposing home energy

management system with RES, without RES, with smart

scheduler, without smart scheduler,with smart scheduler plus

RES and conventional system. The consumers are classiﬁed

into three classes i.e. conventional users, smart users and

prosumers, scheduling is performed via BPSO technique to

reduce cost and load. In [4] the authors divide the appliances

into categories and proposing an optimal scheduling algorithm

and using binary particle swarm optimization technique to

reduce electricity cost. They consider supplier and consumer

scenario also with renewable energy resources. They consider

the personal habits and characteristics of appliances and draw

a table of time of use electricity prices for appliances operation

with the objective to reduce cost. When the power demand is

high or low the supplier will get a signal and will ask the

consumers to on/off some appliances or switch off some high

power consuming appliances so they will be able to achieve

load shifting and thus avoid peak hours cost.In [5] the authors

use particle swarm optimization for scheduling the operation

of distributed energy resources. They use decision support tool

to optimize energy services of residential end users. In [6]

the authors used PSO and DER for scheduling the energy

services, they use different cases for scheduling and optimizes

cost. They also use case study and decision support tool to

optimize energy services to smart homes.In [7] the authors

present a scheduling strategy for cost optimization and de-

mand response management using particle swarm optimization

technique to simulate their different scheduling scenarios. In

[8] the authors use non cooperative game theoretic model for

cost optimization using scheduling during power outages. In

[9] the authors use binary particle swarm optimization for

home energy management and appliance scheduling. They

have taken six appliances for their test system and simulated

them via matlab. They generated load and cost plots for sched-

uled and unscheduled cases. In [10] the authors use particle

swarm optimization and binary particle swarm optimization for

scheduling of different scenario as shown in Table.1,in their

work to optimize electricity cost and reduce load consumption.

They schedule DER with no battery storage, with battery

storage, with net feed-in tariff and no battery storage, with net

feed-in tariff and battery storage. They also used case study

for energy service provisioning in a smart home, and thus via

scheduling they achieve cost and load optimization. In [11]

the authors use APSO a variant of PSO to schedule the Power

consumption or charging of two PHEV-1 and PHEV-2.They

have taken some scenarios for scheduling from Monday to

Friday and shift the Peak load demand to off peak hours

and thus reducing total energy consumption cost. They are

using grid power, solar power for charging and ToU pricing

scheme. They have calculated hourly unit power price, hourly

solar power generation, hourly regular power demand, hourly

power demand by PHEV-1, hourly power demand by PHEV-2,

hourly combined power demand, hourly grid power demand,

hourly grid power price through simulation and thus show

optimization in cost via scheduling. In [12] the authors use

genetic algorithm to optimize cost and scheduled appliances

in a smart home. They schedule their appliances in the home

for the purpose of reducing electricity expenses and to reduce

Peak to Average Ratio (PAR).They have introduced general

architecture of energy management system in a smart home

and then propose an efﬁcient scheduling method for home

power usage. They have used RTP and IBR pricing model, by

using this combine model their proposed scheduling strategy

has efﬁciently reduced cost and peak to average ratio. In [13]

the authors use genetic algorithm to ﬁnd the optimum schedule

arrangement for all the energy consumption by appliances in a

smart home to reduce the electricity cost. They used intelligent

task scheduling module to minimize the entire energy expense

in a smart home, if the module could schedule the appliances

start time. They used the genetic algorithm approach for

minimizing the residential total electricity cost in demand

response services. Their approach consider task or appliance

on /off time constraints and the circuit maximum load con-

straints. Further they compared their approach with other two

techniques i.e. SA and greedy method and showed via sim-

ulation that their technique has obtained optimal scheduling

solution for residential customers as well as satisfying the

equipments operation time constraints and the entire power

load constraints. Hence their simulations prove that genetic

algorithm can efﬁciently reduce electricity cost for residential

customers.In [14] the authors in this work proposes NSGA-2

a genetic based algorithm to solve the optimization problem.

The optimization problem is to reduce the total energy cost and

peak to average ratio of the total energy demand. They have

taken strict energy consumption scheduling and soft energy

consumption scheduling for appliances and generate plots for

scheduled energy consumption and their corresponding cost

without optimization and scheduled energy consumption and

corresponding cost with optimization and thus reduces total

consumption cost and peak to average ratio. In [15] the authors

used genetic algorithms for the retailers to maximize the

proﬁt. They have scheduled the appliances and the retailer use

the appliance scheduling information to maximize its proﬁt

by solving the proﬁt maximization problem. They have also

used other techniques besides i.e. Stackelberg Game Approach

and linear programming approach besides genetic algorithm

maximize proﬁt and reduce cost and peak to average ratio.

They have divided 24 hours time slot in to three groups i.e.

on-peak hours (5 PM-12 PM), mid-peak hours (8 AM-5 PM)

and off-peak hours (12 PM-8 AM).

III. MOTI VATI ON

Energy crises and problems are everywhere in the world

and increasing day by day, Hence experts, scientists and

researcher are constantly trying to develop and explore new

ways of energy production and safe utilization. Researchers

and scientists are trying to develop such ways to minimize

Energy wastage and also provide less expensive and smart

energy resources with a minimum customer cost. Hence costly

energy production and costly distribution to consumers as well

as wastage of this costly energy is a big problem these days for

every country unless they did not formulate a special strategy

or design a special solution to mitigate these issues. Hence

smart grid is one such solution of the many existing solutions

for providing less expensive energy to consumers, preventing

wastage of energy and smart utilization and smart distribution

of energy is provided. In short SG means computerizing

the electric grid or conventional power grid into smart one,

so it can perform smart functions like minimize wastage of

energy during distribution to consumers, reduce electricity

cost, maximize user comfort, minimize user frustration level.

The customers are always novice users and are not aware of

demand side management or DR program so they are unable

to use energy according to their use and thus wastage of

energy occurs and they always pay more cost what they have

used. Hence by the use of smart grid the above issue can be

solved or minimize to a maximum extent. In this work we are

taking the similar motivation of electricity cost, electricity bill

reduction and load reduction. We are using three biologically

inspired evolutionary techniques in our work for electricity

cost reduction and load optimization and reduction. i.e. binary

particle swarm optimization (BPSO), genetic algorithms (GA),

and wind driven optimization (WDO).These three optimization

techniques are used to optimize or reduce electricity bill and

electricity load i.e. to generate scheduled and unscheduled

cost, scheduled and unscheduled Load. For this purpose simi-

lar data set i.e. total twenty appliances which consume wattage

above ﬁve hundreds watts, having same price signal, same

power rating, and having the same time slots and length of

operation time (LOT) are taken for each of the above three

techniques mentioned in Table.2, to generate scheduled and

unscheduled cost and load results, ToU pricing, and these

results are then compared as shown in Table.2. And explained

and analyzed in section simulation results and discussion.

Time (hours)

0 5 10 15 20

Cost (Rs)

5

10

15

20

25

30

ToU Pricing

Fig. 1. TOU Pricing Signal

IV. COS T AN D LOAD OPTIMIZATION

Cost Reduction:-The purpose of our work is to achieve cost

and load optimization using evolutionary techniques. These

techniques have reduced the cost and load successfully.

Objectives:-The objective of our work is to reduce electricity

bill and load consumption and as the energy these days are

very costly and if we switch on our appliances during high

peak hours then it will add extra cost to our bill. We have

used three evolutionary techniques to reduce electricity cost

using the same data set for all appliances. The efﬁciency of

the three techniques are described below.

1. Most Efﬁcient Technique.Binary particle swarm opti-

mization is and evolutionary and heuristics technique which is

used in many ﬁelds for optimization. Kennedy and Ebarhart

in 1995 developed the particle swam algorithm by studying

the social and cognitive behavior of ants. The individuals or

objects called particles are ﬂown through a multidimensional

search space [16]. The scheduled cost and unscheduled cost

generated by BPSO for twenty appliances are Rs 1454.8 and

Rs 2341.6 respectively, and the total reduction in cost Rs 886.8

and total percentage reduction in cost is 37.88% as shown in

Table I. Hence BPSO cost reduction is more than the other

two techniques so it show best performance as compared to

other two techniques.

2. Efﬁcient Technique. The wind driven optimization was

initially developed by Dr. Zikri Bayraktar during his graduate

studies at the Pennsylvania State University. The WDO is a

novel nature-inspired global optimization algorithm based on

atmospheric motion. It is a population based iterative heuris-

tic global optimization algorithm for multi-dimensional and

multi-model problems having ability to implement constraints

on the search domain. A population of very small air particles

navigate over an N-dimensional search space using second

law of motion, which is also used to explain the motion

of air parcels within the earth atmosphere.WDO add extra

TABLE I

COS T REDUCTION

Cost Category BPSO WDO GA

Scheduled Cost Rs 1454.8 Rs 1827.8 Rs 2077.8

Unscheduled Cost Rs 2341.6 Rs 2288.1 Rs 2341.6

Reduction in Cost Rs 886.8 Rs 460.3 Rs 263.8

%age Reduction in Cost 37.88% 20.2% 11.26%

term in velocity update equation i.e. gravitation and Coriolis

forces which provides robustness and extra degree of freedom

to ﬁne tune the optimization. They claim that WDO can

perform better than PSO and that it is well-suited for problems

with both discrete and continuous-value parameters [17]. The

scheduled and unscheduled cost by WDO in our work is Rs

1827.8 and Rs 2288.1 and the total reduction in cost is Rs

460.3 and the percentage reduction in cost is 20.2%, which

show enough reduction in cost by WDO and as they claimed

but our simulation via Matlab show that WDO cost reduction

is less than BPSO as BPSO is 37.88% and WDO is 20.2%.

Hence in our scenario WDO is least efﬁcient than BPSO as

shown in Table I.

3. Least Efﬁcient Technique.Genetic algorithms were ini-

tially developed by John Holland in early 1970.Genetic al-

gorithm were developed to show some behavioral processes

observed in natural evolution. The idea with GA is to use

this power of evolution to solve optimization problems. we

have used genetic algorithm to optimize cost and load using

scheduling. We have taken twenty appliances and calculate

their scheduled and unscheduled cost. The scheduled and

unscheduled cost are Rs 2077.8 and Rs 2341.6 respectively,

and their difference is Rs 263.8 which is actually reduction in

cast after we run the scheduled load. The percentage reduction

in cast by GA is 11.26% which in our scenario is less from

both BPSO and WDO as shown in Table V below. Hence GA

in our scenario is least efﬁcient in cost reduction as compared

to two other techniques.

TABLE II

NONLINEAR MOD EL RES ULTS

Case Method#1 Method#2 Method#3

1 50 837 970

2 47 877 230

3 31 25 415

4 35 144 2356

5 45 300 556

Load Reduction:-The second goal was to reduce or balance

load consumption by consumers and end users so that the

peak to average ratio could be maintained and there should

be balance load on the utility company and the consumers

will not have any problem from utility side, and demand side

management purpose will be achieved.

Objectives:-In this work our objectives was to optimize cost

and load consumption using below three evolutionary algo-

rithms and then compare the resulted values with each other to

ﬁnd out which techniques perform best. If the load distribution

TABLE III

LOAD REDUCTION

Load Category BPSO WDO GA

Scheduled Load 118.0 kwh 148.2 kwh 153.2 kwh

Unscheduled Load 153.2 kwh 148.2 kwh 153.2 kwh

Reduction in Load 35.2 kwh 00.0 kwh 00.0 kwh

%age Reduction in Load 22.98% 00.0% 00.0 kwh

is uniform and consumption is balanced by consumers there

will be no high peaks and hence the peak to average ratio will

be small.It is the requirements of demand side management

and demand response program that peaks should be balanced

and the PAR should be less.

1. Most Efﬁcient Techniques.As binary particle swarm op-

timization is explained above. We have used BPSO for load

reduction. The scheduled and unscheduled load by BPSO are

118.0 Kwh and 153.2 kWh respectively and their difference is

35.2 kWh which is actually reduction in load. The percentage

reduction in load by BPSO is 22.98% which show that BPSO

is better than the other two techniques, as shown in Table II.

2. Efﬁcient Techniques. (WDO) wind driven optimization as

explained above are used for load reduction. The scheduled

and unscheduled load by wind driven optimization are 148.2

kWh and 148.2 kWh respectively and their difference is 00.0

Kwh which is actually load reduction. The percentage load

reduction by wind driven optimization is 00.0%. Hence wind

driven optimization is better than GA but not than BPSO in

our case as shown in Table II.

3. Least Efﬁcient Techniques. Genetic algorithm as ex-

plained above is used for load optimization in our work. The

scheduled and unscheduled load by genetic algorithm is 153.2

kWh and 153.2kwh respectively and their difference is 00.0%

which is reduction in load. The percentage reduction in load

is also 00.0% which is similar to wind driven optimization.

Hence in this case the genetic algorithm shows least perfor-

mance as shown in Table II.

V. SIMULATION RESU LTS AND DISCUSSION

In this paper the three techniques discussed above are used

for which plots are explained and analyzed below.

The ToU pricing scheme plot is shown in ﬁg.3 which show

cost in rupees and time slots in hours. The time slots curve

ranging from 10 to 20 for cost and 0 to 25 on x-axis for time

slots. The time of use pricing scheme is dynamic and its price

depends on time slots.It usually includes pricing depending on

very low peak hours, low peak hours, very high peak hours,

high peak hours, medium peak hours, and shoulder peak hours.

ﬁg.4 show bar graph for scheduled and unscheduled load

and cost. The red bar show unscheduled load and unscheduled

cost which are 153.2 kWh and Rs 2341.6 and the blue bar

show scheduled load and scheduled cost which are 118.0

Kwh and Rs 1454.8 respectively as shown in Table V and

VI.Hence the difference between scheduled and unscheduled

load and scheduled and unscheduled cost is Rs 886.8 and 35.3

kwh respectively as shown in ﬁg.4 and Table V and VI,which

show a great reduction in load and cost as evident from the

Time(hours)

0 5 10 15 20 25

Cost (Rs)

0

50

100

150

200

250

300

350

ScheduledCost

UnscheduledCost

(a) Scheduled and Unscheduled Cost of BPSO

Time(hours)

0 5 10 15 20 25

Energy consumption (KWh)

0

5

10

15

20

25

30

35

Scheduledload

Unscheduledload

(b) Scheduled and Unscheduled Energy Consumption of BPSO

Fig. 2. Electricity cost and energy consumption comparison of BPSO algorithm

bar graph. This reduction in load and cost after scheduling

is much greater than the other two techniques as shown in

Table V and VI and ﬁgure 4. Percentage reduction in cost is

37.88% and percentage reduction in load is 22.98% as can

be seen in bar graph and Table V and VI.37.88% reduction

in cost and 22.98% reduction in load for BPSO show that

this optimization techniques perform much better than the

other two techniques as can be seen in Table V and VI. And

ﬁg.1,ﬁg.2,ﬁg.3,ﬁg.4. The second optimization techniques used

in our work is genetic algorithm.ﬁg.5 show the scheduled

and unscheduled cost curve. The red line is scheduled cost

and blue line is unscheduled cost.The time slots are taken in

hours and electricity cost are taken in rupees. The blue line or

unscheduled cost have only one sharp peak which show the

peak or maximum load at that time slot. The total scheduled

and unscheduled cost are Rs 2077.8 and Rs 2341.6 which

shows that unscheduled cost is much more than scheduled

cost and the difference between scheduled and unscheduled

cost is Rs 263.8 which is actually total reduction in cost. The

percentage reduction in cost is 11.26% by GA which is less

than 1/3 of the BPSO as shown in Table .V and VI.It show

that for our same data set and parameters GA perform very

poor as compared with BPSO as the percentage cost reduction

can be seen in Table V.

In ﬁg.6 scheduled and unscheduled load curve are

shown.The red line show scheduled load and blue line show

unscheduled load. energy consumption is shown in Kwh and

time slots in hours. The blue line i.e. unscheduled load show

only one sharp peak and the rest of the curve are smooth.

The total scheduled and unscheduled load calculated by GA

are 153.2 kWh and 153.2 kWh respectively which are similar

and hence their difference will be 00.0 kWh and so the total

reduction in cost will also be Rs 00.0 and the percentage

reduction in load is also zero as shown in table VI.Hence GA

total load in our scenario is zero as compared to BPSO. ﬁg.7

show the ToU pricing scheme for GA, which show similar

curve as for BPSO.The cost are shown in rupees and time

slots are shown in hours and the curve ranging from 15 to 20

on y-axis and from 0 to 25 on the x-axis which show total 24

hours time slots. ﬁg.8 shows the bar graph of scheduled and

unscheduled load and cost for genetic algorithm. The cost are

shown as Rs/hour, the red bar show scheduled load and cost

and blue bar show unscheduled load and cost. The scheduled

load and cost of GA are 153.2 kWh and Rs 2077.8 respectively

while unscheduled load and cost are 153.2 kwh and Rs 2341.6

respectively, which show that scheduled and unscheduled load

of GA are same while cost have Rs 263.8 difference which

is actually total reduction in cost by GA which is less than

1/3 of the cost reduction by BPSO.Its percentage reduction in

cost is 11.26% and load is zero percent, which is signiﬁcantly

less as compared to 37.88% of BPSO as shown in Table .V

and VI and ﬁg.8.

ﬁg.9 show scheduled and unscheduled cost curve. Time slots

is shown in hours and cost in rupees. scheduled cost is shown

by blue line and unscheduled cost by red line. The peaks are

shown as in ﬁg.9.The scheduled and unscheduled cost are Rs

1827.8 and Rs 2288.1 respectively and their difference is Rs

460.3 which is actually total reduction in cost by WDO.The

percentage reduction in cost by WDO is 20.2%,which is less

than BPSO but better than GA.Hence WDO perform better

than GA for cost reduction.

ﬁg.10 show load curve for scheduled and unscheduled load.

energy consumption is shown in watts and time slots are shown

in hours. Both the curve have very low peaks. The red line

show unscheduled load and black line show scheduled load.

The scheduled and unscheduled load for WDO are 148.2 kWh

and 148.2 kWh respectively which are similar hence their

difference is zero and same is reduction in load.WDO reduce

cost 20% but load reduction is zero percent which is similar

to GA. ﬁg.11 show the plot of ToU pricing scheme for WDO

which show time slots in hours and cost in cents/kwh.The Total

time slots are 24 i.e. from 0 to 25.The wind driven optimization

ToU signal is similar to BPSO and GA and varies between Rs

Time(hours)

0 5 10 15 20 25

Electricity Cost (Rs)

0

50

100

150

200

250

300

350

400

450

ScheduledCost

UnscheduledCost

(a) Scheduled and Unscheduled Cost of GA

Time(hours)

0 5 10 15 20 25

Energy consumption (KWh)

0

5

10

15

20

25

30

ScheduledLoad

UnscheduledLoad

(b) Scheduled and Unscheduled Energy Consumption of GA

Fig. 3. Electricity cost and energy consumption comparison of GA

Time (Hours)

0 5 10 15 20 25

Electricity Cost (Cents)

0

50

100

150

200

250

300

UnscheduledCost

ScheduledCost

(a) Scheduled and Unscheduled Cost of WDO

Time (Hours)

2 4 6 8 10 12 14 16 18 20 22 24

Energy Consumption (Watt)

0

5

10

15

20

25

30

UnscheduledLoad

ScheduledLoad

(b) Scheduled and Unscheduled Energy Consumption of WDO

Fig. 4. Electricity cost and energy consumption comparison of WDO

10 and Rs 20. ﬁg.12 show the bar graph of scheduled and

unscheduled load and cost. The cost are shown in rupees. The

red bar show unscheduled load and blue bar show scheduled

load. scheduled and unscheduled cost by WDO are Rs 1827.8

and Rs 2288.1 respectively which show the difference of Rs

460.3 which is reduction in cost and the percentage reduction

is 20.2%,as shown in bar graph and Table.V and VI.The

scheduled and unscheduled load by WDO are 148.2 kwh and

148.2 kwh which is same.

All simulation results and discussion show that BPSO is

best than GA and WDO in cost and load reduction while GA

perform much poor than the other two techniques as seen in

all the ﬁgures and Table V and VI.

VI. CONCLUSION

In this work we have used evolutionary and heuristics

techniques i.e. binary particle swarm optimization, genetic

algorithm and wind driven optimization for cost and load

optimization, using twenty appliances and using the same

data set i.e. power rating, electricity price signal, time slots

and length of operation time for the three techniques and

obtained different results by each, as the energy optimization

is a very critical issue these days, our purpose of this paper

is to minimize and reduce expensive energy cost and balances

the load consumption by residential consumers. We have also

compared the results of scheduled and unscheduled cost, load,

total reduction in cost, load and their percentage reduction

in cost and load by these techniques and found that which

techniques perform best for cost and load optimization. Our

simulation results and table V and VI show the total reduction

in cost and load and also show that BPSO perform the best as

compared to other two techniques. The %age cost reduction

by BPSO is 37.88%, WDO 20.2% and GA is 11.26%.our

simulation results show that we have achieved cost reduction,

and BPSO is best as its reduction is 37.88%. In future we

intend to use these techniques for more optimized values to

obtain and also reduce PAR value, user comfort maximization

and energy minimization.

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