Conference PaperPDF Available

A New Scheme for Demand Side Management in Future Smart Grid Networks

Authors:
  • COMSATS University Islamabad, Abbottabad Campus
  • university of management and technology

Abstract and Figures

This paper presents a new energy consumption scheduling scheme to enable Demand Side Management (DSM) in future Smart Grid Networks (SGNs). Electrical grid has been facing important challenges regarding quality and quantity to meet the increasing requirements of consumers. Environment friendly and economical generation along with efficient consumption through effective DSM in future SGNs will help in addressing most of these challenges because of integration of advanced information and commu- nication technologies. In this work, we propose an autonomous energy scheduling scheme for household appliances in real-time to achieve minimum consumption cost and reduction in peak load. We assume that every user is equipped with smart meter which has an Energy Consumption Controlling (ECC) unit. Every ECC unit is connected with its neighbours through local area network to share power consumption information. ECC units run a distributed algorithm to minimize the peak load by transferring the shiftable loads from peak hours to off-peak hours. This ultimately minimizes the total energy consumption cost. Simulation results confirm that our proposed algorithm significantly reduces the peak load and energy consumption cost.
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Procedia Computer Science 32 ( 2014 ) 477 484
Available online at www.sciencedirect.com
1877-0509 © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and Peer-review under responsibility of the Program Chairs.
doi: 10.1016/j.procs.2014.05.450
ScienceDirect
5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014)
A New Scheme for Demand Side Management
in Future Smart Grid Networks
A. Mahmood1, M. N. Ullah1, S. Razzaq2, A. Basit1, U. Mustafa1, M. Naeem1,N.Javaid
1,3,Λ
1EE Dept, COMSATS Institute of Information Technology, Islamabad, Pakistan
2EE Dept, COMSATS Institute of Information Technology, Abbottabad, Pakistan
3CAST, COMSATS Institute of Information Technology, Islamabad, Pakistan
Abstract
This paper presents a new energy consumption scheduling scheme to enable Demand Side Management (DSM) in future Smart
Grid Networks (SGNs). Electrical grid has been facing important challenges regarding quality and quantity to meet the increasing
requirements of consumers. Environment friendly and economical generation along with ecient consumption through eective
DSM in future SGNs will help in addressing most of these challenges because of integration of advanced information and commu-
nication technologies. In this work, we propose an autonomous energy scheduling scheme for household appliances in real-time
to achieve minimum consumption cost and reduction in peak load. We assume that every user is equipped with smart meter which
has an Energy Consumption Controlling (ECC) unit. Every ECC unit is connected with its neighbours through local area network
to share power consumption information. ECC units run a distributed algorithm to minimize the peak load by transferring the
shiftable loads from peak hours to o-peak hours. This ultimately minimizes the total energy consumption cost. Simulation results
confirm that our proposed algorithm significantly reduces the peak load and energy consumption cost.
c
2014 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshuki.
Keywords: Smart grid; Demand side management; Optimal energy consumption; Appliance scheduling; Peak load reduction.
1. Introduction
Energy eciency, reliability, economic constraints and integration of renewable energy resources are important
issues to enhance the stability of power system infrastructure. Increasing population and energy demand have worn
out the traditional grid which has been serving the humanity since decades. Inclusion of a large number of electric
appliances brings instability to the existing grid1. Demand curve of traditional grid is characterized with a steep
peek which is caused by accumulation of heavy loads during peak hours. This situation leads utilities to rely on the
expensive peaker plants in order to fulfil the peak demand. Usually peaker plants are thermal power plants and their
Corresponding author: Nadeem Javaid, Mob: +92-300-5792728, Web: www.njavaid.com
E-mail address: nadeemjavaid@yahoo.com, nadeemjavaid@comsats.edu.pk, nadeem.javaid@univ-paris12.fr
© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and Peer-review under responsibility of the Program Chairs.
478 A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
excessive use results in high emissions of Green House Gases (GHGs). Increasing energy demand and its dependency
upon fossil fuels have raised serious environmental concerns.
Integration of bidirectional communication, networking and advanced control technologies for monitoring and
supervision of power systems will formulate smart grid. This integration will bring more automation, reliability of
electrical services, safety of electrical equipments and ultimately boost the consumers’ comfort level 2. Smart grid
automates the energy management upon the basis of information gathered from energy suppliers and consumers. This
results in improved load management and energy eciency3. Smart grid can integrate distributed power resources
eciently. This locally generated power can be sold to the grid by feed in tariafter fulfilling the local requirements.
Ecient electricity consumption has been proven beneficial economically and socially.
Introduction of new and versatile electrical loads like Plug-in Hybrid Electric Vehicles (PHEVs) etc. has raised the
demand exponentially. Peak load management becomes more significant in context of this increased load. Smart grid
implements AMI for load prediction in residential areas and improves energy eciency4. It will help in reducing the
wide use of thermal power plants to meet the peak demands. This reduction in CO2and GHGs emission is necessary
to address the serious environmental issues.
Smart grid enhances the customers’ satisfaction because of the bidirectional communication which enables the
eective DSM programs. DSM consists of the activities designed for influencing the behaviour of customers regarding
their electricity consumption. It also includes dierent other schemes like installation of load limiters, Direct Load
Control (DLC) etc. Early DSM schemes were introduced in late 1970s5. A major portion of the global power
consumption is reported in buildings which is approximately 40%6.
Various dynamic and eective schemes for autonomous DSM in smart girds have been found in literature. Authors
in5discussed a scheduling model in which a layered structure consisting of three modules for admission control,
load balancing and demand response management is used to control the peak load demand. Run-time scheduling is
used to control the appliances in order to meet the power capacity limit. In 7, backtracking based scheme is used to
schedule the home appliances for local and global peak load reduction. This scheduling model consists of actuation
time, operation length, dead line and consumption profile. Scheduler copies the profile entry of dierent appliances
one by one according to task profile in allocation table. In8, a game-theoretic model based optimization technique
is discussed to schedule the energy consumption of appliances. Game theory is implemented to minimize the peak
load and reduce the energy consumption cost. In this scheme, users are players and their daily use of appliances
are strategies. Optimal performance in terms of energy cost minimization is achieved at Nash equilibrium of energy
scheduling game. Presented model considers a common scenario where a single utility company serves dierent
users. This model systematically manages the appliances schedule and shifts them in order to reduce energy cost.9,
has explained an incentive based energy consumption scheme to reduce the peak load demand and energy cost. In10,
an automatic and optimal energy consumption scheduling scheme is proposed to minimize the PAR and reduce the
waiting time of each household appliance operation. For optimal scheduling of appliances, residential load controller
requires the capability to predict the prices in real time. Vickrey-Clarke-Groves (VCG) mechanism is proposed in 11
to maximize the social welfare i.e. the dierence between aggregate utility function of all users and total energy cost
to the provider.
Present work focuses on an energy consumption scheduling scheme to meet the peak load demand and reduce the
monetary cost. Pricing strategies are also involved in scheduling the appliances in real time. In our proposed scheme,
acentral processing unit is the decision making centre and we call it as Energy Consumption Controlling (ECC)
unit. ECC units are used for scheduling the household appliances. We consider that smart meter of every subscriber
is equipped with an ECC unit. These ECC units are also connected with neighbouring units through Local Area
Network (LAN) to share information about power usage and interact with each other in real time for feasible energy
consumption scheduling. By running a distributed algorithm in each ECC unit, all users automatically interact and
overall system’s performance is improved. By scheduling the appliances, heavy loads are shifted from peak-hours to
o-peak hours according to their energy consumption profile. Peak demand is reduced by controlling the appliances
in dierent ways and consequently the energy cost is reduced significantly.
Remaining sections are sequenced as: section 2 is reserved for power system modeling, section 3 elaborates the
proposed algorithm and section 4 describes the simulation results. Conclusions are drawn in section 5.
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A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
2. Power System Modeling
The power system model given below is based on the work of 9. This model considers a set of consumers, in the
assumed smart grid scenario, who obtain electricity from a single power supplying company as elaborated in Fig.
1. In the power system model, consumer requests are taken as input which are then used to generate an optimum
scheduling of the home appliances as output in order to minimize peak demand and total energy cost. Appliances
operating in o-peak hours cost less as compared to peak hours because of the Time of Use (TOU) pricing which is
easily implementable in smart grid.
Consider all users n N; in this set, each user have set of appliances denoted by An. Every one has 10 appliances.
Fig. 1: Smart grid power architecture.
For each appliance a An, we specify an energy consumption scheduling vector as presented in9:
Zn,a[z1
n,a,····,zH
n,a](1)
Where
zh
n,a: Energy consumption of appliance “a” scheduled for 1 hour from user n.
We also define:
En,a=
H
h=1
zh
n,a(2)
Each user “n” specify his total daily energy consumption of appliance “a”, denoted by En,a, defined by user
according to consumption profile, e.g. En,a=16KWh for a Plug-In Hybrid Electric Vehicle (PHEV) for a daily
driving range of 40 mile. An objective function for daily predetermined energy consumption of user “n”is also
defined. Each appliance is scheduled according to its daily predetermined energy consumption that is:
γn,a
h=βn,a
zh
n,a=En,a(3)
Where, En,a: Predetermined daily energy consumption of appliance “a”.
βn,a: Interval starting time that appliance consumption can be scheduled.
γn,a: Interval end time that appliance can be scheduled.
480 A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
To find the optimal solution, we divide the time span of 24 hours into equal time slots. Let h H is the hour of the
day, then total load at each hour “h” is:
Lh
nN
lh
n(4)
We propose a cost function Ch(Eh) that shows cost of energy in each hour “h” provided by the utility company.
Energy consumption cost is dierent in peak hours and o-peak hours because per unit cost is dierent.
Consumers’ energy demand increase during peak hours. Utility companies run peaker power plants to meet this
peak demand during peak hours. Peaker plants charge higher prices per kilo watt hour. So per unit cost increases in
peak-hours. We consider peak hours between 6:00 PM to 10:00 PM (18 to 22). Electricity price per unit in peak hours
is greater than the per unit price in o-peak hours.
Uhp>Uho(5)
Where
1ho17,23 ho24,18 hp22
Per unit electricity prices are:
Electricity price per unit (Uho)ino-peak hours =Rs. 8.2/KWh
Electricity price per unit (Uhp) in peak hours =Rs. 13.6/KWh
The cost function of o-peak hours and peak hours is given as:
Ch(Eh)=
A
a=1ho
h=1Eh
n,aUhoif opeak hrs
A
a=1hp
h=18 Eh
n,aUhpif peak hrs
(6)
The cost function of 24 hours will be the sum of peak hours and opeak hours and given as:
Ch=Cho+Chp(7)
The loads have been divided into two types, shiftable loads and non-shiftable loads. The shiftable loads are the
appliances whose operation can be shifted to o-peak hours with minimum comfortability loss to the consumer e.g.
washer, dryer, PHEV etc. On the other hand the operation of non-shiftable loads cannot be delayed e.g. refrigerator,
lighting etc. Appliance minimum standby power level is defined by αmin
n,aand maximum power level αmax
n,a. To minimize
the peak load in peak hours, optimal scheduling of appliances can be achieved by solving the following optimization
problem:
minimize
H
h=1
Ch(
N
n=1
A
a=1
(zh
n,a)) (8)
s.t.
En,a=
H
h=1
zh
n,aaAn,nN,
zh
n,a=0hH\Hn,a,
aAn,nN,
Uhp>UhohH.
The above problem minimize the peak load in peak hours and reduce the energy consumption cost.
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A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
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Fig. 2: Flow chart of proposed scheme.
3. Proposed Algorithm
In this section, we propose an energy consumption scheduling algorithm to enable DSM in future smart grid. ECC
units are used for optimal scheduling of appliances. We consider that each user is equipped with smart energy meter.
ECC units are embedded in smart meters for interaction among users and bidirectional communication through LAN.
The aim of this scheme is to decrease the electricity bill of the consumer by shifting the appliances operation from
peak to o-peak hours. The consumer may turn on any appliance at any moment irrespective of the peak hours concern
and ECC unit suggests a convenient start time to the consumer. ECC unit provide feasible schedule to all household
appliances. The proposed algorithm is shown in Fig. 2 and steps are defined as: Step-1: In our proposed algorithm,
the nth user turns on his ith appliance randomly. The request of nth user for ith appliance is generated randomly. The
appliance is not switched ON immediately, in fact the request is sent to ECC unit.
Step-2: When ECC receives the request, it confirms the peak hours condition. If the condition is not satisfied,
appliance “i” is switched ON immediately. Otherwise the algorithm moves to next step. ECC communicates with
smart meter to know about the time of use (ToU) prices. The ToU prices scheme informs the ECC unit about the
corresponding energy consumption prices at that particular moment.
Step-3: During peak hours, ECC unit checks all the standby appliances in home and turns oall, irrespective of their
requests to be switched ON, as it has been reported that a significant amount of energy has been wasting in standby
state of appliances.
Step-4: In peak hours, ECC inquires about nature of the appliance to be switched ON. The appliance may be a
shiftable load e.g. a washer or a non-shiftable load e.g. refrigerator. If the received request belongs to a non-shiftable
load, the appliance is switched ON immediately. Otherwise the load is scheduled to be operated with some delay
according to delay condition. Ultimately the appliance operation is shifted from peak to o-peak hours.
Step-5: In case of a shiftable load in peak hours, ECC reads the power ratings of the corresponding appliance and
compares it with preset threshold value Pmax . For all PiPmax the appliance is directed to start immediately otherwise
the algorithm moves to the next step. If the power ratings of the ith appliance is less then the fixed threshold value
Pmax , the appliance is switched ON immediately without any delay. Otherwise the algorithm moves forward to the
next step.
Step-6: If peak hours condition is satisfied, shiftable appliance having rating greater than the threshold value, the
operation of appliance is shifted from peak to o-peak hours. A delay factor is introduced for the operation of each
appliance cycle. As the appliance operational cycle has been delayed, and it is shifted from peak to o-peak hours, a
delay diis introduced which is equal to the dierence of the scheduled time suggested by ECC unit and the request
482 A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
start time. This delay is inversely proportional with comfortability level of consumer. They are never satisfied with
large delays so we have introduced a threshold value of delay called Dmax.Ifdiis greater than Dma x the appliance is
directed to start immediately otherwise the operation cycle of ith appliance is shifted. This load shifting will help in
reducing the peak demand and hence minimize the electricity bill of the consumer.
Step-7: After each request, ECC schedules the appliances and sends a control message to other users. According to
this scheduling message, other users schedule their appliances.
Step-8: If a control message is received from any other user then user should update its local memory.
4. Simulation Results
We consider the scenario of smart grid system where N=10 users; all users are equipped with ECC units. Each
user has 10 major appliances with shiftable and non-shiftable operation. We include 4 shiftable appliances and 6
non-shiftable appliances. Appliances with shiftable operation i.e., with scheduling constraints of soft energy con-
sumption are called shiftable appliances. We consider 4 shiftable appliances in our scenario i.e., washer (daily usage:
3.4 kWh), dryer (daily usage: 2.5 kWh), dishwasher (daily usage: 7.1 kWh) and PHEV (daily usage: 9.9 kWh). Non-
shiftable appliances are those with non-shiftable operation i.e., with strict energy consumption scheduling conditions.
We consider 6 non-shiftable appliances i.e., Electric stove (daily usage: 4 kWh), entertainment (daily usage: 3 kWh),
refrigerator (daily usage: 2.3 kWh), lighting (daily usage: 2 kWh), air conditioner (daily usage: 1 kWh), fan (daily
usage: 0.04 kWh).
In our scheme, we schedule the appliances from 12:00 AM to next day 12:00 AM. We consider peak hours between
6:00 PM to 10:00 PM. We apply Time of Use (ToU) pricing scheme in our model. Per unit electricity price is dierent
in peak hours and o-peak hours. Electricity price per unit (Uho)ino-peak hours =Rs. 8.2/KWh and electricity
price per unit (Uhp) in peak hours =Rs. 13.6/KWh. All users ON their appliances randomly.
The simulation results of our proposed energy consumption scheduling scheme is quite ecient in terms of reduc-
ing peak load demand, electricity consumption charges with an increase in comfortability level of consumers. Our
proposed technique shifts the household appliances from peak to o-peak hours and peak demand is reduced with
adierent design approach. Our scheme has referred to the well known problem of peak load reduction for grid
stabilty and energy cost saving. Optimal scheduling manages the appliances in such a way that operation of heavy
loads is shifted to o-peak hours. For optimal scheduling, our scheduling scheme tackles this problem by introducing
athreshold value of energy consumption in the peak hours and o-peak hours. If an appliance ratings are higher than
the threshold value; its operation cycle is shifted to opeak hours. As in the future smart grid both power companies
and users can take advantage from economical and environmental aspects of smart pricing models12 . Electricity prices
increased during peak hours and low during o-peak hours. Therefore consumers avoid high price peak hours and
shift their heavy loads to o-peak hours.
Our scheme has referred to the problem of minimizing energy wastage by standby devices in home. Standby power
of an appliance is the power consumed by the appliance when it is not functioning or when switched o. Standby ap-
pliances are consuming 10% of electricity13. Standby devices have been reported to contribute to electricity wastage
and hence it was necessary to tackle the wastage due to standby appliances during the scheduling of household appli-
ances to enable DSM. Furthermore our proposed scheduling scheme contributes more to comfortability level of the
consumer by putting a limit on the delay factor of an appliance. If the appliance cycle is shifted to opeak hours and
the delay goes beyond the limit the cycle is retained and the appliance is switched ON immediately.
4.1. Peak Load Reduction
Fig. 3 shows the energy consumption of users with scheduling and without scheduling in 24 hours. In peak hours,
load increases to 33kwh. When we schedule the appliances according to our proposed scheme, load evenly distribute
over the entire day. Energy consumption reduces to 24%. Fig. 4 shows the percentage load of each user in peak hours
with scheduling and without scheduling of household appliances. Simulation results show that when ECC units are
not implemented in smart meters (without scheduling of household appliances), the percentage load is high. In our
scheme, ECC unit schedules the energy consumption more eciently reduces the peak load to 24%.
483
A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
Fig. 3: Energy consumption with scheduling and without scheduling
Fig. 4: Users’ percentage load in peak hours
Fig. 5: Energy consumption cost with scheduling and without scheduling
4.2. Monetary Cost Minimization
We also minimize the monetary cost by applying our ecient energy consumption scheduling scheme for load
management. Energy consumption cost without scheduling and with scheduling of appliances shown in fig. 5. When
users equipped with ECC units in smart meters and all subscribers utilize the energy consumption in ecient way;
consequently energy cost reduces by 21%. By scheduling of energy consumption, monthly bill of each user also
reduces. Monthly bill reduction of each user is shown in Fig. 6 which shows that with scheduling of appliances each
user pays less to the utility. Ultimately the subscribers might be willing to engage in the intended DSM scheme.
484 A. Mahmood et al. / Procedia Computer Science 32 ( 2014 ) 477 – 484
Fig. 6: Total monthly bill of each user with scheduling and without scheduling
5. Conclusion
In this paper, we proposed an autonomous and distributed energy consumption optimal scheduling scheme in
order to minimize the peak demand and total energy cost. This scheme has evenly distributed consumers’ load over
entire day and balanced the residential load in scenario where dierent users are connected to a single power supply
company. We also focus on the interaction among users for energy consumption information exchange. Simulation
results confirm that our proposed autonomous demand side load management strategy eciently reduce the peak
demand and energy cost. In the future work, our scheme can be extended for integration of renewable energy resources
available at users’ premises and inclusion of feed in tari. Second, it is interesting that our model can be modified for
optimality of user comfortability.
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... In load shifting that reducing of customer demand during peak hour to off peak power in load shifting it does not affect the total consumption of power. Peak clipping [24], it is the load cutting or load reduction in heavy load period. This happened when the shifting or shutting down the consumer equipment [18]. ...
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Chapter
Demand-Side Management (DSM) is one of the methods that tries to understand customer behaviour and put it into a strategy that maintains network stability. Recently, a large number of load scheduling algorithms were developed by various experts, however these methods were not providing accurate results because of their high complexity and utilization of static datasets. To overcome these issues, an improved load scheduling method is proposed in this paper, in which loads are optimized by using the meta-heuristic Grey Wolf Optimization (GWO) algorithm. In addition to this, a real-time dataset is used that is collected from the Chandigarh Region. The information about the total demand felt and met initially is extracted from the available dataset. In addition to this, the minimum hour of electricity that must be provided to the six sectors (AP, PAT, RDS, MGJG, urban and industrial) is also defined. The loads are optimized by the proposed GWO model and later on its performance is evaluated in the MATLAB software. The performance outcomes were delineated by observing the total demand felt by the providers for the month of May, June and July and the total demand met by the proposed scheme. The results proved the efficiency of the proposed GWO model as it was able to provide electricity to every sector as per the demand.KeywordsDemand-side management (DSM)Optimization algorithmRenewable energy resources (RERs)
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Chapter
Utilities in the existing power system network face difficulty in handling loads during peak load conditions that are raised due to the accumulation of loads at the same time. This can be addressed through the effective utilization of available power resources for which load management in demand response is a better option in the future smart grid context. This paper presents an Internet of Things (IoT) based solution for load management at the consumer level with a direct load control (DLC). In the proposed system, residential loads are classified as critical and non-critical, and their time of operation is controlled during peak hours. Additional connected loads in the domestic sector that increase the burden on distribution transformers for which an effective monitoring system is proposed in the paper. Mobile alerts are sent to the consumer as and when peak load occurs on the grid and also when the energy utilization reaches 75% and above the maximum agreed load in order to monitor additional connected loads.
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