Improving Efficiency of Power Systems by Demand Side Management Method

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 1, February 2017, pp. 100~106
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i1.pp100-106  100
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Improving Efficiency of Power Systems by Demand Side
Management Method
Tibbie Pon Symon V. A.1
, I. Jacob Ragland2
1
Noorul Islam University, Tamil Nadu 629180, India
2
VIT, Vellore, Tamil Nadu, India
Article Info ABSTRACT
Article history:
Received Oct 22, 2016
Revised Jan 7, 2017
Accepted Jan 21, 2017
In the smart grid infrastructure based power systems, it is necessary to
consider the demand side management to enhance the energy reduction and
system control. In many countries the resources are very less so the available
resources have to be used in an efficient manner without any loss. The total
loss cannot be avoided but it can be reduced. In the proposed system, the
Particle Swarm Optimization (PSO) technique is used to distribute the power
in the smart grid. Here, the grids are arranged in such a way that the losses in
it are reduced. The load connected to the grid is rearranged according to their
use. It uses a new and stochastic scheduling technique to handle the
uncertainties in the power system. Solar and wind power are taken in account
for twenty four hours and the values are given to the PSO algorithm. The
experiment was conducted by MATLAB and the results show that the
efficiency level of wind and solar power systems was increased by an
appreciable level. The proposed technique is compared with the normal
system without using Demand Side Management (DSM) and it shows that
the proposed system gives better results than the existing systems.
Keyword:
Demand side management
Home area network
Deregulated power system
Smart grid
Particle swarm optimization
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Tibbie Pon Symon V. A.,
Departement of Electrical and Electronics Engineering,
Noorul Islam University,
Tamil Nadu -629180, India.
Email: tibbiesymon@gmail.com
1. INTRODUCTION
The complexity of the electrical grid and electric power system infrastructure is growing with the
increase of load demands. In many countries, the utility grids are source defined centralized power
distribution systems [1]. The smart grid is a developing area and lot of research and experiments can be done
in the paper design and also in the laboratories. [2], [3]. The main problem faced by the power system is that
the loss in the distribution system, because the load is not same. Some of the load may be inductive,
capacitive, purely resistive or the combination of these loads. The power consumption of load will differ
based on the requirement of an application. Faults in the connected load can also cause loss in the power
system. The generation and distribution system is challenged due the dynamic increase in the energy demand
and the spontaneous rise in the global emission of green home gases. The grid is a complex operator
consisting of different input loads like solar, thermal, wind etc., connected together.
The losses in the grid occur due to in mainly due to the improper arrangement of the infrastructure
in the distribution side. The main reason of losses in the power grid is due to different consumers. The power
taken by the consumer is usually more in the peak time and some of the grids may take more than the rated
value. One of the reasons for the power loss is due to the consumers who are not having well awareness
about the importance of energy. The consumer habits can be changed by awareness-raising programs which
are often conducted by government or activist groups. Simple things to be done are turning off lights in

IJECE ISSN: 2088-8708 
Improving Efficiency of Power Systems by Demand Side Management Method (Tibbie Pon Symon V. A.)
101
disused rooms or switching off the television at night and task settings such as laundry for non-peak hours.
New technologies will be added in the power grid for meeting the demands in future. In the power stations
the latest and more flexible grid management methods will meet the increasing proportion of the renewable
energy sources with conventional infrastructures associated.
Reducing the resource consumption is one of the important parameters. Day by day, the natural
resources are reducing but the renewable energy is still available and that should be used in an efficient
manner. Many countries are now using the renewable energy resources like wind and solar energy more
efficiently. The major disadvantage of this renewable energy is that the wind will not be there for long time
and the sun will not shine for the whole day. Most of the developed countries use these noise and pollution
free renewable energy resources.
The high demand during peak time is managed by the DSM by activating the supplementary power
generation source [4], [5]. The reliability of the grid is increased by setting a threshold value for each
consumer under a grid. If the reliability of the grid is increased then the reliability of the whole power system
will also be increased. When the supplementary power is given for the high power consumers it will not
affect the low power consumers. But, it leads additional maintenance and care for the newly added
supplementary power supply. It also leads to the power loss and money loss [6]. The supplementary power
supply needs the additional power suppliers or generating plants that are infrequently turned on. When the
power is generated from micro turbines or gases or to be imported from other countries then the cost will be
increased.
The smart grid allows the consumers to elegantly consume electricity both by selecting the preferred
suppliers and scheduling the operations of each appliance according to the various conditions. Now DSM is
one of the important concerns to meet the consumer requirements as well as to achieve the system goals such
as peak load reduction, power saving, energy efficiency, time saving, low cost etc. [7], [8]. Now a day the
load increasing is in a spontaneous manner. Even the auto motive vehicles and trains are also be changed as
electrically operated. The power demand is increased due to the Plug in hybrid electric vehicles (PHEV) that
are introducing in the market.
In some cases, the efficient delivery will not be possible due to the complex network. But this can be
achieved by using more advanced devices and technologies. Another way is by scheduling the power among
the grids as well as the distribution system. But, this type of scheduling will make much disturbance for the
consumers. To avoid these problems, a Particle Swam Optimization (PSO) technique is used instead of
power scheduling. PSO is applied in widely in the area for problem solving. This PSO algorithm is based on
the activities of a group of animals which does not have any organizer in their group. Here, the group of
animals will not have any leader and they will randomly find out their food by following a member of their
group with the nearby location from the food. This is a type of potential solution for the problem. The
members of the group attain the best condition concurrently by making the communication between the
group members who already have a better condition. These members will intimate it to the group then they
will also go to that place. This process will be repeated until the best solution or a food source is found
out [9], [10].
2. PROPOSED TECHNIQUE
In the proposed PSO system, the solar power and the wind power are considered. These values are
taken by assuming that the consumers consume their maximum connected capacity. Furthermore, in this
proposed PSO based system, the automatic scheduling solution result within ten seconds which is fast enough
for the smart grid to schedule the power [11]. The architecture of the proposed PSO technique is depicted in
Figure 1. It uses micro generation model which consists of various sources like wind turbines, solar cells and
batteries [12-15]. This combination of the different sources together leads to a new design of Hybrid
Renewable Energy System (HRES) [16]. The PSO is connected with the power grid and the consumers.
Here, PSO is taken as a control system tool to optimize the power scheduling in the smart grid. The generated
power for each hour in a day is calculated and given as input to the PSO. The excess power is used to charge
the battery. If the consumers need more power at peak time the battery will act as the supplementary source.
In this HRES a battery is used to meet either the high power demand or when the renewable energy
sources are not available [17]. The periodic nature of renewable energy makes the fluctuations that are
unpredictable and appears in the power output [18]. Some of the storage techniques like batteries,
compressed air, fly wheels and capacitors are used to solve these problems [19]. The battery can be recharged
by taking the energy from different power generations and renewable sources. Therefore a balanced
optimization technique is needed to meet the stability of the utilization of energy from renewable energy
sources and recharging the battery source.

 ISSN: 2088-8708
IJECE Vol. 7, No. 1, February 2017 : 100 – 106
102
The flowchart of the proposed work is given in Figure 2. Once all the required parameters like cost
rate and generated power from the solar and wind for each hours in a day are collected, they will be given as
the input to the PSO algorithm. Then the cost and power consumption by the consumers for 24 hours are
calculated and displayed. The iterations are repeated and the best value will be selected as the threshold by
the algorithm for each hour. If the threshold value is obtained during early stages of iterations then the
iteration will be stopped in that hour.
Figure 1. Architecture of HRES
Figure 2. Flowchart of the Proposed Work
3. RESEARCH METHOD
The proposed technique is designed on the basis of the demand side management (DSM). It will
either shift requirement from peak time to off – peak time or shrink the electricity peak demand [20]. The
used tool can also monitor all the consumers as well as appliances connected in the houses.
Start
Find out Cost/Power
consumption
Display Results
Select all Required Parameters
Apply PSO Technique
Is threshold
Stop
No
Yes
Solar Panel
Wind Energy
Battery
Power Grid
ConsumersPSO
Renewable Energy
Sources

IJECE ISSN: 2088-8708 
103
The problem solving in the PSO algorithm are done as follows:
Consider a function F(x), where
F(x) = (E – (( 1 – i) * (PV+WE)))*CR (1)
Here, i represents the optimization parameter
E= total power consumed by the appliances
CR= cost rate
PV=solar energy
WE= wind energy
The amount of energy to charge the battery at any instant is given in the following equaion
i* (PV+WE) (2)
The difference between maximum capacity of the battery and the amount available for the given
instant during the recharge
G(x) = Eb – (Eba + i * (PV+WE)) (3)
Eb = the maximum battery capacity
Eba = the maximum energy during the calculation
At any instance, the value of „ i ‟ is a percentage value and it lies between the limits 0 ≤ i ≤ 1
During the recharging G(x) ≥ 0
i.e Eb – (Eba + i * (PV+WE)) ≥ 0 (4)
4. RESULTS AND DISCUSSION
The values of energy produced in each hour of a day for wind as well as in solar plant are given in
Table 1 and Table 2 MATLAB is used for the simulation. Table 1 gives the values for wind energy and
Table 2 is for solar energy.
Table 1. Wind Energy from Wind Farm
Hour of Duty in a day
Generated power
(W/m2)
1 45.1
2 16.7
3 5.7
4 34.6
5 20.8
6 27.2
7 36.8
8 12.2
9 6.5
10 37.6
11 3.00
12 31.6
13 19.7
14 44.2
15 11.3
16 11.4
17 13.7
18 27.6
19 34.9
20 20.9
21 31.1
22 3.24
23 35.1
24 2
Table 2. Solar Energy from Solar Plant
Hour of Duty in a day
Generated power
(W/m2)
1 0
2 0
3 0
4 0
5 0
6 0
7 57
8 215
9 346
10 703
11 956
12 1045
13 988
14 948
15 898
16 788
17 580
18 351
19 76
20 0
21 0
22 0
23 0
24 0
The PSO algorithm will go for a number of iterations and it will give out the best result. The values
taken from the wind and the solar plants are entered into the PSO algorithm and it will calculate the better

 ISSN: 2088-8708
104
one from the function Equation 1, where „i‟ is the optimization parameter. After the iterations are over the
efficiency of smart grid and the connected load is calculated. The input which is given to the PSO is given in
Figure 3 and the efficiency output generated from the PSO is given in Figure 4.
Figure 3. Input to the PSO Figure 4. Efficiency output by PSO
The overall performance output from the PSO is given in Figure 5. The proposed work with DSM is
compared with the existing one without DSM and the values are given in Table 3. It gives the performance
output for the 24 hours for wind and solar with DSM and without DSM. The PSO output for the Table 3 is
given in Figure 6.
Figure 5. Overall Performance Output from PSO
Figure 6. Comparison of proposed and normal system

IJECE ISSN: 2088-8708 
105
Table 3. Values with DSM and without DSM
Hours in a
day
Wind without DSM
(KW)
Wind with
DSM(KW)
Solar without DSM
(KW)
Solar with
DSM(KW)
1 21 .5705 19.9571 0 0
2 1.1076 0.8703 0 0
3 0.0480 0.0434 0 0
4 9.5087 8.6407 0 0
5 2.0718 1.8670 0 0
6 4.7006 1.3426 0 0
7 11.6409 10.8961 0.056 0.0524
8 0.4242 0.3929 0.214 0.1982
9 0.0604 0.0534 0.356 0.2881
10 12.4168 11.4552 0.703 0.0824
11 0.0063 0.0052 0.955 0.8601
12 7.3707 6.6971 1.044 0.9486
13 1.7855 1.7056 0.989 0.9445
14 20.1703 13.2330 0.949 0.6226
15 0.3370 0.1595 0.899 0.4255
16 0.3461 0.2950 0.789 0.6726
17 0.6006 0.5520 0.58 0.05340
18 4.9110 4.5134 0.352 0.3235
18 9.9294 1.015 0.073 0.0075
20 2.1325 1.9208 0 0
21 7.0262 6.5047 0 0
22 0.0079 0.0074 0 0
23 10.1010 8.4190 0 0
24 0.0019 0.0017 0 0
5. CONCLUSION
In the proposed work, PSO technique is used for the development and testing of the consumer based
DSM. The smart grid can be operated in more efficient and reliable way and the power losses can be reduced.
It gives an excellent performance in the system as well as possible benefits for DSM. It also allows the
consumer to select the appliances and manage the resources in an optimized manner. The results from the
PSO simulation achieve an average of 28% of cost saving for the end-user. These results lead to considerable
annual savings for the consumers. This PSO totally reshuffle the grid system by choosing the most efficient
way and gives accurate result. It is used to extend and improve the research in the power system as well as
the grids. This work can be modified in the future by comparing the results with the cuckoo search algorithm
or other algorithms like fire fly, bat, or ant colony.
REFERENCES
[1] N. Gudi, et al., “A Demand Side Management Based Simulation Platform Incorporating Heuristic Optimization for
Management of Household Appliances,” Electrical Power and Energy System, Science Direct, vol. 43. pp. 185-
193, 2012.
[2] “The Smart Grid: An Introduction,” 2008. http://www.oe.energy.gov/SmartGrid Introduction.htm.
[3] D. Zang, et al., “Efficient Energy Consumption and Operation Management in a Smart Building with Microgrid,”
Energy Conservation and Management, vol. 74, pp. 209-222, 2013.
[4] Clastres, “Smart Grids: Another Step towards Competition, Energy Security and Climate Change Objectives,”
Energy Policy, vol. 5, pp. 399-408, 2011.
[5] N. Gudi and L. Wang, “A demand side management based simulation platform incorporating heuristic optimization
for management of household appliances,” Electrial power energy systems, vol. 43, pp. 185-193, 2012.
[6] P. Bunnoon, “Fault Detection Approaches to Power System: State-of-the-ArtArticle Reviews for Searching a New
Approach in the Future,” International Journal of Electrical and Computer Engineering, vol/issue: 3(4), pp. 553-
560, 2013.
[7] J. M. Armas and S. Suryanarayanan, “A Heuristic Technique for Scheduling a Customer Driven Residential
Distributed Energy Resource Installation,” Proc. 15th
International Conference on Intelligent Systems
Applications to Power Systems, pp. 1-7, 2009.
[8] Rand D. and Dell R., “Energy Storage: A Key Technology for Global Energy Sustainability,” Journal of Power
Source, vol/issue: 100(1), pp. 2-17, 2001.
[9] D. Palupirini, et al., “Particle Swam Optimization: Technique, System and Challenges,” International Journal of
Computer Application, vol. 4, 2011.
[10] Valle Y. D., et al., “Particle swarm optimization: basic concepts, variants and applications in power systems,” IEEE
Transactions on Evol. Computing, vol/issue: 12(2), pp. 171–195, 2008.
[11] M. Khanabadi, et al., “Optimal Transmission Switching Considering Voltage Security and N-1 Contigency
Analysis,” IEEE Transactions on Power Systems, vol/issue: 28(1), 2013.

 ISSN: 2088-8708
106
[12] Watson J., “Co-provision in Sustainable Energy Systems: The case of Microgeneration,” Energy Policy, vol/issue:
32(17), pp. 1981-90, 2004.
[13] H. Shahinzadeh, et al., “Applications of Particle Swarm Optimization Algorithm toSolving the Economic Load
Dispatch of Units in Power Systems with Valve-Point Effects,” International Journal of Electrical and Computer
Engineering, vol/issue: 4(6), pp. 858-867, 2014.
[14] J. J. Jamian, et al., “Implimentation of Evolutionary Particle Swarm Optimization in Distributed Generation
Sizing,” International Journal of Electrical and Computer Engineering, vol/issue: 2(1), pp. 137-146, 2012.
[15] F. Yusivar, et al., “Single Phase PV Grid-Connected in Smart Household Energy System with Anticipation on Fault
Conditions,” International Journal of Power Electronics and Drive System (IJPEDS), vol/issue: 4(1), pp. 100~111,
2014.
[16] Deshmukh M. K. and Deshmukh S. S., “Modeling of Hybrid Renewable Energy Systems,” Renewable Sustain
Energy, vol/issue: 12(1), pp. 235-49, 2008.
[17] Hajizadeh A. and Golkar M. A., “Intelligent Power Management Strategy of Hybrid Distributed Generation
System,” Electrical Power Energy System, vol. 29, pp. 783-95, 2007.
[18] Anderson D. and Leach M., “Harvesting and redistributing renewable energy: On the role of gas and electricity
grids to overcome intermittency through the generation and storage of hydrogen,” Energy Policy, vol/issue: 32(17),
pp. 1603– 13.
[19] Dell R. and Rand D., “Energy storage: A key technology for global energy sustainability,” Journal of Power
Source, vol/issue: 100(1), pp. 2–17, 2001.
[20] National solar radiation data base, 2010. http://rredc.nrel.gov/solar/old_data/ nsrdb/.
BIOGRAPHIES OF AUTHORS
Tibbie Pon Symon.V.A received a Bachelor of Technology in Electrial and Electronics
Engineering from Kannur University, Kerala, India in 2001, a Master of Engineering in Power
Electronics from Karunya University, India. Now he is working as an Assistant professor in the
department of Electrical and Electronics Engineering at Noorul Isalm University and doing PhD
in Noorul Islam University, India, Tamil Nadu, India. His area of interest is Power System and
Power Electronics.
Dr. I. Jacob Raglend received his Bachelors degree in Electrical Engineering from The Indian
Engineering College and the Masters degree in Power Systems Engineering from Annamalai
University with first class in 2000 and 2001 respectively. He has done his Ph.D. degree in the
Department of Electrical and Electronics Engineering, Indian Institute of Technology, Roorkee,
India in the year 2007. Presently he is working as a Professor in the School of Electrical
Engineering, Vellore Institute of Technology (VIT) India. He taught course in Basic Electrical
Engineering, Power Systems, Artificial Intelligence and Soft Computing Techniques. His field of
interest is Unit Commitment, Economic Dispatch, Smart Grid, Power System Restructuring and
Deregulation, Artificial Intelligence Applications to Power System and FACTS.

Improving Efficiency of Power Systems by Demand Side Management Method

More Related Content

What's hot (18)

Similar to Improving Efficiency of Power Systems by Demand Side Management Method (20)

More from Yayah Zakaria (20)

Recently uploaded (20)

Improving Efficiency of Power Systems by Demand Side Management Method