Demand Side management of smart grid using IoT

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1776
Demand Side management of smart grid using IoT
Saniya Momin1, Dr.S.S. Deshpande2
1Dept. of Electronics Engineering, Walchand College of Engineering, Sangli, Maharashtra
2Prof. Dept. of Electronics Engineering, Walchand College of Engineering, Sangli, Maharashtra
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Abstract – This paper focuses on demand side
management (DSM) using smart grid using IoT
technology. DSM is an important function in smart to
manage electricity. DSM attains energy conservation and
utilization, energy usage efficiency enrichment and saves
cost through information technology. The main part of our
work is fabrication of cost effective based on Demand side
management using IoT technology smart grid which
displays readings on OLED, Instant Power consumption
from each user, sends and receive information from grid
through cloud computing. The power consumption
continuously monitored and recorded through webserver.
A specific load will be provided to each user, if the load
exceeds the particular limit, then it will record the
readings on webserver and electricity for that specific user
will be cut off. Domestic level Energy Management unit is
designed for easy monitoring and control of house hold
appliances.
Key words: Smart Grid, Demand Side Management
(DSM), Cloud Computing.
1. Introduction
Electricity is a vital element in today’s economic
development and national growth of the country. Over
the past numerous decades, electric energy structure has
encountered stressed circumstances because of ever
growing energy demand, inefficient use of electric
energy generation and transmission resources.
Transmission line outage were a common motive of
stress condition, which might be viable to occur
throughout peak hours. This kind of events which will be
the reason to supply limit situation where load shedding,
cascading failures and blackouts are possible. Emerging
challenges, such as old infrastructure, increase in
demand and growing carbon emission are driving
conventional power grid towards smart grid.[1]
1.1 Smart Grid
The smart grid is the technology which will incorporates
information and communications methods will change
the infrastructure, electricity generation, transmission
and consumption. Today’s centralised power system is
smart grid like centralised electricity grid which will
generate, distribute and regulate flow of electricity to
each user.[2]
The Feedback loop of communication in fig-1 is a
strength of smart grid.
Fig-1- Smart grid
1.2 Demand Side Management
Demand- side Management (DSM) is an arising field in
the energy industry. mileage companies are decreasingly
espousing it as part of their sweats to control their costs
and reduce the impact of energy dearth’s on their
nethermost lines. DSM is also effective in controlling the
implicit adverse goods of power cuts during peak hours
of the day and in case of high charges or traffic of electric
grids. These factors can lead to advanced costs and
vexation to the end- druggies, especially during
weekends and at out- peak hours.
The complications in smart grid are partly tackled
through demand side management (DSM). This method
will bring down the pressure on grid along with
electricity bills. DSM resource to the users to make
desire concerning use of electricity that is when and how
much to use. DSM has some regions of impact which are
load shifting and load reduction.

Implementing DSM algorithm substantially relies on
two-way communication among the utility and end user.
The end user willingness to reduce/shift their power
consumption. Power generation can be averted through
shifting power usage from peak hours to off-peak hours
so that cost of production gets reduced. [3-4]
There are six types of DSM technique methods for
different users for load shaping and they are load
shifting, valley filling, strategic conversation, peak
clipping, strategic load growth and flexible load shape.
Load shifting is nothing but the load shift from on peak
to off peak hours and the advantage of load shifting is to
lower the cost and peak demand. [5]
Electricity charge relates at the power consumption of
the users. In electricity market, The DSM has the
essential role. As demands increases the cost of
electricity also increase. The increase in electricity cost
will change the whole users in power system. By
lowering the peak to average ratio, DSM regulates the
power cost in electricity market. [6]
A DSM approach for domestic level users based on
totally on load shifting approach is proposed here. The
idea of behind this method is primarily based on users
load precedence and comfort preferences. DSM allows in
managing and controlling power consumption on the
basis of electricity supply. [7]
1.3 Cloud Computing
Discussing about Cloud computing, it is nothing but the
computer system resource which is available on demand
of user. It is mainly used for data storage and power
computing. They are distributed over different locations
and each location has its own data centre. Cloud
computing which consists Storage, server, databases,
networking software which works over the internet. [8]
To use Clouds, first determine what kind of cloud needs
to be used so there are three types of clouds according to
their architecture and services that is public cloud,
private cloud and Hybrid cloud and their services are
like IaaS (Infrastructure as s service), PaaS (Platform as a
service) and SaaS (Software as a service). [9-10]
Fig-2- Cloud services model
2. Related Work
We have studied several research paper Based on
Demand side managements in Electricity and smart grid
and analysed different aspects.
A research paper based on a demand side management
in smart grid based on ECC unit, we have pointed that
ECC unit runs on a distributed algorithm which reduces
peak load in by transferring shiftable loads from off-peak
hours with on-peak hours [1].
Another research paper, we have studied about
Intelligent demand side management in smart grid. This
model is based on GUI (Graphical User Interface). GUI
will be used to monitor, power consumption and
calculate instantaneous cost of users [2].
Some research paper shows their impact on aspect such
as overcome electric power stress through demand side
management. This paper focuses the fabrication of the
cost based on smart meters which displays reading on
LCD and monitor power consumption then sends and
receives information from grid using GSM module [3].
Demonstrated new scheme to stabilize power usage in
smart grid. As Demand response management in smart
grid is imperative which gives proper shape to the total
load in demand side [4].
During this paper, we got to know few more benefits of
smart grid and involvement of government in some
countries. We also studied advance smart meters which
record energy usage and provide it to the users [5].

In this paper, we studied about the path of the smart grid
which involves challenges in smart grid and solution
upon it which tells how to reduce cost of the
consumption and stabilize the readings [6].
The part of this paper aims to provide energy systems so
that researcher decision makers will get proper guidance
to underlying drivers of consumers acceptance of the
Smart grid and some steps for smart grid [7].
The Consumers can choose on-peaks hours for maximum
use of power on residential purpose in way to reduce
energy bills so that they can use power in those peak
hours [8].
In the research paper on cloud computing tells that first
cloud services were introduces by Amazon in 2006.
Cloud computing it the life time dream of computing as a
utility which has the capacity to convert a huge part of
the IT company and making software even more
attractive as a service and shaping the IT hardware is
designed and purchased. Inventors with new invention
ideas for new internet services no longer bear the huge
capital expenses in tackle to emplace their service or
mortal expenditure to operate it.
Cloud working out is the delivery of computing services
such as servers, storage, database, networking, software,
analytics, intelligence, and more, over the Cloud
(Internet). The well-known Cloud services are Amazon
web services, Google cloud platform and Microsoft Azure
[9].
It aims to make and read sophisticated service terrain
with important computing capabilities through an array
of fairly low-cost computing reality, and using the
advanced deployment models like SaaS (Software as a
Service), PaaS (Platform as a Service), IaaS
(Infrastructure as a Service), HaaS (Hardware as a
Service) to distribute the important computing capacity
to end-users. This paper will explore the background and
service models and also presents the being exploration
issues and counteraccusations in cloud computing such
as security, reliability, sequestration, and so on [10].
3. Proposed Methodology
The idea behind this system is to design and implement
the demand side management system with user friendly
interface and control the functionalities for energy
provider and load controllers that collects energy
utilization data from the user and perform control based
on webserver by using Energy sensor. The residential
users do not have time to perform Demand response
manually. So, the IoT based system plays a vital role in
executing the automated demand response within an
area. Each user’s loads are used and the corresponding
priority is adjusted based on the priority of the loads.
Each load is connected through relays. Load curtailment
request will be received by the relays, and they ensure
the total power consumption below the specified
demand limit. The proposed DSM system allows the
home owner to use their loads when needed as long as
the total domestic power consumption remains under
the specified limit.
In proposed system, the Energy sensor, Webserver and
data base view will be used to monitor and control
appliances status and power consumption. To control
the user’s energy usage, Time of Use based pricing
scheme is used here. The power consumed in the off-
peak time costs less as compared to the peak hours.
Power consumption cost per unit in peak hours is
greater than the per unit price in off-peak hours.
The request power signal from each user is send through
communication module. This communication module is
used to fetch power consumption data from all the users
and provide an interface for users to regain appliances
status and review their power consumption. The
Webserver contains data to monitor and control the
power consumption.
Fig- 3- Block Diagram
The main purpose of the system is to design and develop
Demand side management system which will be user
friendly and control energy provider and controller. The
Electricity from Energy corporation will send to
individual N Users according to their need and peak
time. Energy sensor will measure Voltage, current and
power being consumed by each user. User interface is
used to Monitor and control user’s status and power
consumption.

The PZEM-004T Energy sensor will give voltage, Current
and power value and ESP-32 Microcontroller will
monitor Voltage and current and sends and received
information on the Grid and to display data for each user
OLED is used. The power transmission and consumption
records for each user will be collected using Cloud
computing. The request signal from user is send through
communication module. This will collect electrical
power consumption data from all the loads and
providing an interface for users to regain appliances
status and review their power consumption.
4. Algorithm
Demand Side Management Strategy developed to meet
the capacity provided by the utility. The flowchart of the
proposed strategy as shown in fig -3 First read the
voltage, current and power of each user from Energy
sensor and monitor it with the help of microcontroller
and send it to the webserver’s Data base. Then calculate
the total power (P) of each user. The power consumption
limit is given as decision value (Z). Then take the
difference between decision value and absolute power.
According to Value load control differences are possible.
The difference is above decision value then relays will
turn of off along with that supply to extension will be cut
off so that appliances value will not reach till energy
sensor that means when value exceeds decision value
the whole user will stop working because it is consuming
more energy than required.
Fig-4- Flowchart
5. Result and Discussion
The Hardware result is discussed here as the input to
each user will be provided with domestic level power.
Energy sensor will calculate five parameters like voltage,
Current, Power, Frequency and power factor for each
user respectively.
The energy providers have some limit value which is
taken as the decision value. With the help of ESP32
microcontroller and webserver total power will be
calculated and get compared with decision value. When
this decision value (Z) is less than the total power
consumption of each user, then curtailment is done to
Start
Read V, I and P of each user
from microcontroller
Send Data to Web
server
Separate V, I and P
Calculate the Total Power of each
user and instantaneous terrif
Define power limit as
decision value
Calculate the difference between
the decision value (Z) and the total
power (P)
If
Z>P
Permits all loads Cut the loads to meet
the limit
End

meet the decision value and that will be stored on
webserver.
Fig 5- Hardware Implementation
Here two cases are considered.
Case 1: When Z < P
If power consumption by one of the users is greater
than the decision value that times curtailment takes
place.
Case 2: When Z > P
If power consumption by one of the users is lesser than
the
decision value and if the decision value is greater, then
there will be no curtailment will take place. The system
allows all the loads.
6. Conclusion
The Demand side Management system with demand
response plays a most vital role in effectively managing
the wastage of power on the consumer side. This paper
presents a better way to manage the power consumption
of residential users. This Demand Side Management
system for demand response applications might
effectively manage and control the operation of assorted
appliances to manage overall consumption below a
threshold.
The proposed DSM takes under consideration each load
priority and user preferences with IoT technology which
is used for this method. Thus, the work provides an
affordable, flexible, user-friendly, and very secure design
for implementing a Demand side Management System.
Hardware results indicate the effectiveness of the
projected DSM Strategy and also the profit in their
electricity bills.
7. REFERENCES
[1] A. Mahmood, M. N. Ullah, S. Razzaq, A. Basit, U.
Mustafa1, M. Naeem, N. Javaid, “A New Scheme for
Demand Side Management in Future Smart Grid
Networks” 5th International Conference on Ambient
Systems, Networks and Technologies (ANT-2014).
[2] Anjana S. P, Angel T. S, “Intelligent Demand Side
Management for Residential Users in a Smart Micro-grid”
2017 IEEE International Conference on Technological
Advancements in Power and Energy (TAP Energy).
[3] Misbah Rani1, Fareeha Ramzan1, Atif Javed1, Adil
Farooq2, Tahir Nadeem Malik1, “Smart Grid
Implementation to Overcome Electric Power System
Stress Conditions through Demand Response in
Pakistan” 2016 IEEE.
[4] J. Matsumoto and Z. Wende, "New Demand Response
in Architecture for Stabilization of Power Quality in
Smart Grid," in IEEE International Conference on
Information, Communication and Signal Processing,
2013, pp.1–5.
[5] G. De Smedt and M. Adonis, “Smart Meter for
Renewable Energy Microgrid Island” April 2014,
ResearchGate publication.
[6] H. Farhangi, “The path of the smart grid,” IEEE Power
Energy Mag., vol. 8, no. 1, pp. 18–28, Jan. 2010.
[7] Brandon Davito, Humayun Tai, and Robert Uhlaner,
“The smart grid and the promise of demand side
management.” McKinsey& Company Publishers, pp. 38-
44, Dec. 2009.
[8] Yi Liu, Chau Yuen, Shisheng Huang, Naveed Ul
Hassan, Xiumin Wang, ShengliXie, “Peak-to-Average
Ratio Constrained Demand-Side Management with
Consumer’s Preference in Residential Smart Grid,” IEEE
Journal on selected topics in Signal processing, vol.8,
no.6, pp.1084-1097, Dec. 2014.
[9] Mrs. Ashwini Sheth, Mr. Sachin Bhosale, Mr. Harshad
Kadam, “Research Paper on Cloud computing”
Contemporary Research in India (ISSN 2231-2137):
SPECIAL ISSUE: APRIL, 2021.
[10] M. Rajendra Prasad, R. Lakshman Naik, V. Bapuji,
“Cloud Computing: Research Issues and Implications”
International Journal of Cloud Computing and Services
Science (IJ-CLOSER) Vol.2, No.2, April 2013, pp. 134~140
ISSN: 2089-3337

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