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Residential Energy Consumption Controlling Techniques to Enable Autonomous Demand Side Management in Future Smart Grid Communications

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This paper presents an overview of home appliances scheduling techniques to implement demand side management in smart grid. Increasing demand of consumers have affected the power system badly as power generation system faces a number of challenges both in quality and quantity. Economical generation and efficient consumption can solve this problem in future smart grid as it is integrated with information and communication technologies. Smart grid has opportunities to employ different pricing schemes which help also in increasing the efficiency of appliances scheduling techniques. Optimal energy consumption scheduling minimizes the energy consumption cost and reduces the Peak-to-Average Ratio (PAR) as well as peak load demand. In this work, we discuss different energy consumption scheduling schemes that schedule the household appliances in real-time to achieve minimum energy consumption cost and reduce peak demand to shape the load curve.
Power scheduler operation Presented scheme designs a power scheduler that is capable of minimizing peak load in buildings. This model consists of actuation time, operation length, dead line and consumption profile. The proposed task model schedules the appliances in real time. Electric devices are designed as tasks having starting time, execution time and deadlines. Task T i can be designed by following parameters: F i shows that whether T i has preemptive operation or non-preemptive. A i is activation time of task, D i is deadline and U i illustrates the operation length. Operation of non-preemptive task can start from its activation time A i to the latest start time (D i-U i ). For preemptive task, device activation time must be picked from U i out of (D i-A i ) time slots. Backtracking optimization technique is used for scheduling the appliances. Backtracking additionally frame a search tree on the allocation table. Scheduler copies the profile entry of different appliances one by one according to task profile to the allocation table. This potential search tree consists of all feasible solutions including worthless solutions. At each intervening node, which passes to a feasible solution, it checks whether the node can guide to a feasible solution. If it cannot, remaining sub tree is reduced. Otherwise iteration proceeds to the next level. By this phenomenon, scheduler searches the feasible time slots to schedule the appliances. Optimal scheduling of appliances reduces the peak load curve and also reduces the energy consumption cost. This model reduces the peak load up to 23.1%.
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Residential Energy Consumption Controlling
Techniques to Enable Autonomous Demand Side
Management in Future Smart Grid Communications
M. N. Ullah1,N.Javaid
1,2,I.Khan
1,A.Mahmood
2,M.U.Farooq
2
1Dept of Electrical Engineering, COMSATS Institute of IT, Islamabad, Pakistan.
2CAST, COMSATS Institute of IT, Islamabad, Pakistan.
Abstract—This paper presents an overview of home appliances
scheduling techniques to implement demand side management in
smart grid. Increasing demand of consumers have affected the
power system badly as power generation system faces a number
of challenges both in quality and quantity. Economical generation
and efficient consumption can solve this problem in future smart
grid as it is integrated with information and communication
technologies. Smart grid has opportunities to employ different
pricing schemes which help also in increasing the efficiency of
appliances scheduling techniques. Optimal energy consumption
scheduling minimizes the energy consumption cost and reduces
the Peak-to-Average Ratio (PAR) as well as peak load demand.
In this work, we discuss different energy consumption scheduling
schemes that schedule the household appliances in real-time to
achieve minimum energy consumption cost and reduce peak
demand to shape the load curve.
Index Terms—Demand side management, optimal energy con-
sumption scheduling, peak load demand, smart grid.
I. Introduction
A system that implements communication and information
technology in electrical grid is known as smart grid. Smart grid
system gathers information about the activities of electrical
energy suppliers and users. Smart grid improves the cus-
tomers’ load utilization by deploying the communication based
monitoring and controlling architectures [1]. With the addition
of different types of new loads e.g. Plug-in Hybrid Electric
Vehicles (PHEVs), the normal residential load has potentially
increased. Hence it is very important to develop new methods
for peak load reduction. There are some environmental issues
related to current power systems. Some countries widely use
the oil and coal fired power plants to meet the peak demands,
as a result a huge amount of CO2and green house gases is
emitted. Smart grid enables Demand Side Management (DSM)
to overcome these problems. DSM was proposed in the late
1970s [2]. DSM monitors, plans and implements those utility
functionalities that are designed to encourage low consumption
by the consumers during peak hours in order to shape the
utility load curve. DSM programs are implemented to exploit
better utilization of current available generating power capacity
without installing new power generation infrastructure [3].
DSM programs facilitate users to shift loads from peak hours
to off peak hours to reduce the peak load [4]. Worldwide
energy utilization in buildings is approximately 40% of global
power consumption [5]. Currently, consumption of electricity
in buildings is not efficient and is leading towards the wastage
of billions of dollars and huge amount of green house gas
emission. Better utilization of energy consumption in buildings
is an important issue. Heavy loads run in the peak hours
due to which utility load curve potentially goes high. DSM
controls the residential loads by shifting the load from peak
hours to off-peak hours in order to reduce the peak load
curve and improve energy efficiency by scheduling the energy
consumption.
In previous literature, various load management techniques
have been discussed to enable autonomous DSM in future
smart grid. Incentive based energy consumption controlling
schemes are discussed in [6], [7], [8], have explained a Direct
Load Control (DLC) scheme for residential load control to
enable demand side management. In [9], a home energy
consumption scheduling technique is elaborated which uses
Energy Management Controllers (EMCs) for scheduling the
appliances. [10], has presented a priority based scheduling
scheme, in which the appliance that has higher priority accord-
ing to load curve, switched ON first without any restriction.
Low priority devices are switched ON with some delay.
Different home energy management schemes in future smart
grid are discussed in [11]. The home energy management
schemes are combined with different pricing schemes in order
to make the schemes more efficient e.g. a day ahead pricing
has been used in energy management scheme to minimize the
electricity charges of a consumer [12].
Our work focus on different energy consumption scheduling
schemes to meet the peak load demand and to reduce the
monetary cost. These residential load controlling schemes
are based on optimization and different scheduling criteria.
Scheduling of appliances involves different pricing schemes.
By scheduling the appliances, heavy loads are shifted from
peak-hours to off-peak hours according to their energy con-
sumption profile. In this way, peak-hours load is reduced by
controlling the appliances.
Rest of the paper is organized as follows: Section II de-
scribes different scheduling schemes for demand side man-
agement. In Section III, we conclude this paper.
II. Different Scheduling Schemes for DSM
Efficiency of power consumption is an important factor.
Now a days customers expectations are increasing both in
2013 Eighth International Conference on Broadband, Wireless Computing, Communication and Applications
978-0-7695-5093-0/13 $31.00 © 2013 IEEE
DOI 10.1109/BWCCA.2013.94
545
2013 Eighth International Conference on Broadband, Wireless Computing, Communication and Applications
978-0-7695-5093-0/13 $31.00 © 2013 IEEE
DOI 10.1109/BWCCA.2013.94
545
quality and quantity. Due to limited energy assets and ex-
pensive process of integrating new energy resources, there
is an important need to improve our system power utiliza-
tion. Energy efficiency refers to using minimum energy to
provide the same or improved level of service to the energy
consumer in an economically efficient way. It includes using
minimum energy at any time, including during peak periods.
Smart grid implements advance metering infrastructure for
the load prediction in residential areas and improves energy
efficiency [13]. To achieve high reliability in electric grid
utility companies need to reduce the peak load demand. To
avoid the peak condition in the grid, domestic and industrial
devices such as air conditioners, refrigerators and heater are
shifted to off peak hours.
In future smart grid, both power companies and users can
take advantage from economical and environmental aspects of
smart pricing models [3]. Consumer’s 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. Communication and
metering technologies enable smart devices in homes to reduce
peak load demand during high cost peak hours. Electricity
prices are high during peak hours and low during off peak
hours. Therefore consumers avoid high price peak hours and
shift their heavy loads to off peak hours. General approach
for the management of energy consumption in buildings is
shifting consumption and reducing consumption. Smart grid
applies DSM to cope with energy consumption cost. Energy
consumption cost can be minimized in homes by shifting the
high load from peak hours to off peak hours.
In previous literature, following techniques have been pro-
posed to minimize the peak load in peak hours and monetary
cost.
A. An Autonomous Three Layered Structure Model for
DSM
In [2], a scheduling model has been presented in which a
layered structure consisting of three modules for Admission
Control (AC), Load Balancer (LB) and Demand Response
Manager (DRM) are used to control the peak load demand.
The model of this scheme is shown in Fig.1[2]. Loads are
divided into three different categories based on their load
characteristics for the minimization of peak load demand.
Baseline load is the power consumption of those appliances
that must be run immediately at any time. Baseline load
includes lighting, cooking rang and networking devices etc.
Burst load is the load that is ON for a fixed duration
and starts and stops within the given dead line e.g., washing
machine, dish washer and dryer.
Regular load is related to appliances that are always in
running state during a long period, such as refrigerator, water
heater and Heating Ventilation and Air Conditioning) (HVAC)
of house.
The key role of run-time scheduling of appliances is to meet
the power capacity limit while satisfying different capacity cri-
teria e.g. comfort level of domestic users. Present architecture
controls the appliances using online scheduling approach in
the run-time manner.
Fig. 1. Three layered architecture for load management on demand side
In previous literature, many algorithms have been used for
run time scheduling, most popular are Least Slack Time (LST),
Earliest Deadline First (EDF), Bratley and Spring. Spring
algorithm is used for scheduling in AC module. AC manages
the requests at run time coming from smart appliances and
information received from smart meter. AC module is time
triggered and performs adequate load scheduling. It accepts
some requests based on priority (available capacity and power
request) and rejects the rest. The appliances, whose requests
are accepted by AC, start their operation immediately while
rejected requests of appliances are forwarded to LB. The
search space for scheduling the appliances in different time
slots is driven by heuristic function H. At each stage of search
space, the heuristic function H is applied to tasks one by one
that has to be scheduled. The task with least value specified
by heuristic function called heuristic value is elected to further
extend the current schedule. In this case the heuristic value is
scaled between 0 & 1, which represents the priority of the task.
For example, appliances such as water heater and refrigerator
will have 1 heuristic value when the desired temperature is at-
tained and 0 when the temperature is within the specified limit
called comfort zone. LB schedules the tasks whose requests
are rejected from AC. LB evenly distributes the electrical load
of appliances over a time frame and schedules the requests
that have been refused by AC. To minimize the total energy
cost, a Mixed Integer Programming (MIP) problem is solved.
Therefore LB minimizes the cost function analogous to energy
price. In different circumstances of considered appliances,
LB establishes an appropriate schedule that would evenly
distribute the appliances load over a time horizon. In such
a way, AC and LB schedule the appliances on run time with
respect to limited capacity constraints and overall peak load
and energy consumption cost is minimized.
B. Backtracking-based Technique for Load Control
A task model is presented in [14] to schedule the home
appliances for reducing the local peak load as well as the
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global peak for effective DSM. The task model is shown in
Fig.2 [14].
Fig. 2. Power scheduler operation
Presented scheme designs a power scheduler that is capable
of minimizing peak load in buildings. This model consists of
actuation time, operation length, dead line and consumption
profile.
The proposed task model schedules the appliances in real
time. Electric devices are designed as tasks having starting
time, execution time and deadlines. Task Tican be designed by
following parameters: Fishows that whether Tihas preemptive
operation or non-preemptive. Aiis activation time of task, Di
is deadline and Uiillustrates the operation length. Operation of
non-preemptive task can start from its activation time Aito the
latest start time (Di-Ui). For preemptive task, device activation
time must be picked from Uiout of (Di-Ai) time slots.
Backtracking optimization technique is used for scheduling
the appliances. Backtracking additionally frame a search tree
on the allocation table. Scheduler copies the profile entry
of different appliances one by one according to task profile
to the allocation table. This potential search tree consists of
all feasible solutions including worthless solutions. At each
intervening node, which passes to a feasible solution, it checks
whether the node can guide to a feasible solution. If it cannot,
remaining sub tree is reduced. Otherwise iteration proceeds
to the next level. By this phenomenon, scheduler searches
the feasible time slots to schedule the appliances. Optimal
scheduling of appliances reduces the peak load curve and also
reduces the energy consumption cost. This model reduces the
peak load up to 23.1%.
C. Game-Theoretic Based DSM
In [15], a game-theoretic model based optimization tech-
nique is discussed to schedule the energy consumption of
appliances. In this model users are players and their strate-
gies are their daily scheduling loads. Optimal performance
in terms of energy cost minimization is achieved at Nash
equilibrium of energy scheduling game. The model considers
a common scenario where a single utility company serves
different users. This model systematically manages the appli-
ance schedule and shifts them in order to reduce energy cost.
Energy Consumption Scheduler (ECS) is deployed in smart
meters for scheduling the household appliances. ECS uses a
distributed algorithm to find the feasible schedule for each
user. A smart power system with only one energy source and
multiple consumers has been assumed. This scheme considers
two types of appliances, shiftable and non-shiftable. Shiftable
appliances e.g., PHEVs, washing machine and dryer. Non-
shiftable appliances are those that are always in operating
state for a long time period e.g. fridge and lights. ECS only
schedules the shiftable appliances. The operation of ECS is
presented in Fig.3 [15]. In this technique, scheduler manages
Fig. 3. Model with ECS devises deployment
and shifts the appliances energy consumption for appropriate
scheduling. Consider each user nN,letAndenote set
of appliances. An energy consumption scheduling vector for
appliance aAncan be defined as:
Xn,a [x1
n,a,····,x
H
n,a](1)
Where
xh
n,a : Energy consumption of appliance a scheduled for
1hour from user n.
ECS function decides optimal choice for energy
consumption vector Xn,a. Each appliance is scheduled
according to its daily predetermined energy consumption that
is: βn,a
h=αn,a
xh
n,a =En,a (2)
And
xh
n,a =0,hH\Hn,a (3)
Where
En,a: Predetermined daily energy consumption of
appliance a.
αn,a: Interval starting time that appliance consumption can
be scheduled.
βn,a: Interval end time that appliance can be scheduled.
Hn,a [αn,a,····
n,a]
Appliance minimum standby power level is defined
by γmin
n,a and maximum power level by γmax
n,a . Finally feasible
scheduling set for the appliances of user n is acquired as
follows:
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χn={Xn|
βn,a
h=αn,a
xh
n,a =En,a,x
h
n,a =0,hH\Hn,a,
γmin
n,a xh
n,a γmax
n,a ,hHn,a}
(4)
This feasible schedule is only valid for xnχn. Simulation
results show that with the deployment of ECS function in
smart meters, PAR reduces up to 17% and cost reduces up
to 18% as presented in Fig.4 and Fig.5 [15].
Fig. 4. When ECS not deployed (PAR is 2.1 and the total daily cost is
$44.77)
Fig. 5. When ECS not deployed (PAR is 1.8 and the total daily cost is
$37.90)
D. ECS Device Based Scheduling
In [16], an energy consumption scheduling (ECS) device
based scheduling technique is discussed. In this technique,
authors consider a scenario of power system where an energy
source (e.g., a generator is connected to electric grid) is shared
by different users. Each user is equipped with an ECS device in
the smart meter shown in Fig.8 [16]. Assume that each user
Fig. 6. Smart grid system model with N load subscribers
is facilitated with smart meter. In this scheme, deployment
of ECS devices in smart meters enables autonomous DSM.
ECS devices are equipped in smart meters connected with
each other. These devices are also connected with power grid
and local area network to communicate with the smart grid
infrastructure. Distributed algorithm is used to schedule the
optimal energy consumption for each subscriber. According
to individual energy needs of all subscribers ECS devices
schedule the energy consumption of household appliances.
ECS devices are interrelating automatically by running an
algorithm to find an optimal schedule for energy consumption
of each subscriber. This algorithm reduces the total energy
cost and shape the load curve in peak hours. By subscribing
this algorithm in smart meters, all users pay minimum amount
of utility bills to the utility company as shown in Fig.6 [16].
A new pricing scheme is also introduced for this model
which is developed from game theoretic model, to reduce
the total cost. Simulation results are demonstrated in Fig.7
and Fig.8 [16]. Simulation results show that ECS devices
efficiently schedule the appliances energy consumption in the
whole day.
Fig. 7. Daily cost $86.47 (ECS devices are not used)
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Fig. 8. Daily cost $53.81 (ECS devices are deployed)
E. An Optimal and Autonomous Residential Load Control
Scheme
Smart pricing models in future smart grid can potentially
benefit both users and utility companies regarding the econom-
ical and environmental advantages. In [17], an automatic and
optimal energy consumption scheduling scheme is discussed
to minimize the PAR and reduce the waiting time of each
appliance operation in household. For optimal scheduling of
appliances, residential load controller requires the capability
to predict the prices in real time. In this scenario, real-time
pricing and inclining block rates are combined to balance
the load and minimize PAR. An ECS device is deployed
in residential smart meters to control the load of household
appliances. Fig.9 [17], shows the function of smart meter in
this scheme.
Fig. 9. Smart meter operation in this scheme
When load demand is high in peak hours, a request is sent
by smart grid to smart meters to reduce the load. In this
case, scheduler takes action and increases the upcoming prices
of next 2 or 3 hours by optimization technique. Therefore,
some portion of load automatically suspends and reduces the
total load. Price predictor and energy scheduler are two main
units to control the residential load. Price predictor estimates
the upcoming prices and allows scheduler to schedule the
appliances according to user’s need.
F. Vickrey-Clarke-Groves (VCG) Mechanism Based DSM
Vickrey-Clarke-Groves (VCG) mechanism in [18], maxi-
mizes the social welfare i.e. the difference between aggregate
utility function of all users and total energy cost. Authors con-
sider that each user deployed Energy Consumption Controller
(ECC) device in its smart meter for scheduling the household
appliances. ECC schedules the household appliances on run-
time. VCG mechanism develops the DSM programs to enable
efficient energy consumption among all users. In this scheme,
each user provides its energy demand to the utility. By
deploying a centralized mechanism in ECC device, the energy
provider estimates each users optimal energy consumption
level and declares particular electricity payment for each user.
In this way, VCG mechanism reduces the energy cost. Load
demand can be divided into two types i.e. must-run loads and
controllable loads. Must-run loads e.g. a refrigerator that is al-
ways in ON state during the whole day. The controllable loads
are the appliances where operation can be stopped, shifted and
accommodated in different time slots according to demand.
For optimal solution, we evolve an optimization problem to
reduce the total energy cost charged on energy provider while
maximize aggregate utility functions of all users. The solution
of following optimization problem provides an efficient energy
consumption schedule for user’s energy consumption in order
to reduce the cost.
maximize
xnXn,nN
nN
Un(
kK
xk
n)
kK
Ck(
nN
xk
n)(5)
Where
XnPower consumption vector of user n
Un(·)Utility function of user n
Ck(·)C(Lk)=akLk2+bkLk+Ck
Ck(Lk)Cost function of Lkenergy units offered by utility
in each time slot k.
G. A Scheme for Tackling Load Uncertainty
In [19], an optimization based residential load controlling
algorithm is proposed that tackles the load irregularity to
reduce energy cost in real-time. In algorithm, it is assumed
that each user is facilitated with smart meter. Each smart meter
is equipped with ECC unit. ECC unit schedules and manages
the household energy consumption. In this scenario both real-
time and inclining block rate prices strategies are combined.
Proposed algorithm is formulated as an optimization problem.
Appliances are divided into following categories must run and
controllable loads. Must-run loads start operation immediately
at any time e.g. Personal Computer (PC), TV. These loads start
operation without the interruption of ECC unit. Controllable
appliances operation can be interrupted or delayed.
We separate the operation cycle of appliance into time
slots denoted by T. Each time slot activate with the phase
of admission control. For the starting of appliance operation,
admission control sends an admission request to ECC unit.
Once request is endured, appliance state changes from sleep to
awake. Appliance request acceptance depends on its operation
schedule specified by ECC unit. ECC unit implements a
centralized algorithm and determines the optimal appliances
schedule in each time slot. This centralized algorithm is
based on an optimization problem formulated for appliances
schedule under different constraints.
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TAB L E I
Comparison of different Energy consumption controlling schemes
Scheme Name Method
Load
Mini-
mization
Cost
Mini-
mization
Scheduling Pricing Coverage
A Model for Autonomous DSM
Load control
scheduling
model
66.66% NA Real-
time CPP,TOU,RPP Local
Backtracking Based Technique Back-tracking
based scheduling 23.1% NA Real-
time RTP Local
Game Theoretic Model
Game theoretic
pricing &
scheduling
17% 18% Run
time
Proportional to
daily load &
generation cost
Neighbor-hood
ECS Device Based Scheme Energy consump-
tion scheduling 38% 37% Run
time
Proportional to
daily load &
generation cost
Neighbor-hood
Optimal Residential Load Control Scheme LP-Based
optimization 22% 10-25% Run
time RTP Local
Vickrey-Clarke Groves Mechanism Scheduling and
Optimization 38% 37.8% Run
time
VCG pricing
scheme Neighbor-hood
Scheme for Tackling the Load Uncertainty
Optimization
based algorithm
and scheduling
25.5% NA Real
time RTP & I BR Neighbor-hood
III. Conclusion
In this paper, we have compared different residential load
controlling techniques in the smart grid. Residential load
controlling techniques are employed for efficient consumption
of electricity in residential buildings like homes and offices.
In current power grid, the load demand curve shows that there
is huge difference between the demand of peak hours and
off-peak hours. The utilities want the load curve to be nearly
smooth to avoid the operation of peaker plants. The load con-
trolling techniques shift some specific load from peak hours to
off-peak hours and hence helps in making the demand curve
smooth. These techniques reduce the energy consumption cost
and minimize PAR as well as peak load. Consumer should
also be encouraged to schedule the appliances according to
schemes discussed in the paper. Table I shows the comparison
of different schemes. Scheme I reduces the peak load up to
66.66%. So this model is more efficient. ECS device based
scheme and VCG mechanism minimize the cost up to 37%.
REFERENCES
[1] Javaid, Nadeem, et al. “Monitoring and Controlling Power using Zigbee
Communications.” Broadband, Wireless Computing, Communication
and Applications (BWCCA), 2012 Seventh International Conference on.
IEEE, (2012).
[2] Costanzo, Giuseppe Tommaso, et al. “A system architecture for au-
tonomous demand side load management in smart buildings.” (2012):
1-9.
[3] Photovoltaics, Dispersed Generation, and Energy Storage. “IEEE Guide
for Smart Grid Interoperability of Energy Technology and Information
Technology Operation with the Electric Power System (EPS), End-Use
Applications, and Loads.” (2011).
[4] Martins, Rodrigo, and Felipe Meneguzzi. “A Smart Home model to
Demand Side Management.” (2013).
[5] M. N. Ullah, A. Mahmood, S.Razzaq, M. Ilahi, R.D. Khan, N. Javaid, “A
Survey of Different Residential Energy Consumption Controlling Tech-
niques for Autonomous DSM in Future Smart Grid Communications”,
J. Basic. Appl. Sci. Res., 3(3)1207-1214, (2013).
[6] Caron, Stphane, and George Kesidis. “Incentive-based energy consump-
tion scheduling algorithms for the smart grid.” Smart Grid Communica-
tions (SmartGridComm), 2010 First IEEE International Conference on.
IEEE, (2010).
[7] Ruiz, Nerea, Iigo Cobelo, and Jos Oyarzabal. “A direct load control
model for virtual power plant management.” Power Systems, IEEE
Transactions on 24.2 (2009): 959-966.
[8] Wu, Qiuwei, Peng Wang, and Lalit Goel. “Direct load control (DLC)
considering nodal interrupted energy assessment rate (NIEAR) in re-
structured power systems.” Power Systems, IEEE Transactions on 25.3
(2010): 1449-1456.
[9] Costanzo, Giuseppe T., Jan Kheir, and Guchuan Zhu. “Peak-load shaving
in smart homes via online scheduling.” Industrial Electronics (ISIE),
2011 IEEE International Symposium on. IEEE, (2011).
[10] Rossello Busquet, Ana, et al. “Reducing Electricity Demand Peaks by
Scheduling Home Appliances Usage.” (2011): 156-163.
[11] I. Khan, A. Mahmood, N. Javaid, S.Razzaq, R.D. Khan, M. Ilahi, “Home
Energy Management Systems in Future Smart Grids”, J. Basic. Appl.
Sci. Res., 3(3)1224-1231, (2013).
[12] F. Baig, A. Mahmood, N. Javaid, S. Razzaq, N. Khan, and Z. Saleem,
“Smart home energy management system for monitoring and scheduling
of home appliances using zigbee,” (2013).
[13] Anas, M., et al. “Minimizing Electricity Theft using Smart Meters in
AMI.” P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC),
2012 Seventh International Conference on. IEEE, (2012).
[14] Lee, Junghoon, et al. “Energy consumption scheduler for demand
response systems in the smart grid.” Journal of Information Science
and Engineering 28.5 (2012): 955-969.
[15] Mohsenian-Rad, A., et al. “Autonomous demand-side management based
on game-theoretic energy consumption scheduling for the future smart
grid.” Smart Grid, IEEE Transactions on 1.3 (2010): 320-331.
[16] Mohsenian-Rad, A-H., et al. “Optimal and autonomous incentive-based
energy consumption scheduling algorithm for smart grid.” Innovative
Smart Grid Technologies (ISGT), 2010. IEEE, (2010).
[17] Mohsenian-Rad, A-H., and Alberto Leon-Garcia. “Optimal residential
load control with price prediction in real-time electricity pricing envi-
ronments.” Smart Grid, IEEE Transactions on 1.2 (2010): 120-133.
[18] Samadi, Pedram, et al. “Advanced demand side management for the fu-
ture smart grid using mechanism design.” Smart Grid, IEEE Transactions
on 3.3 (2012): 1170-1180.
[19] Samadi, Pedram, et al. “Tackling “the” “Load” Uncertainty Challenges
for Energy Consumption Scheduling in Smart Grid.” (2013): 1-10.
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... The control center can place bids in the market such as some loads from the peak demand which will be shifted or shed. Any profits made through this load, DSM will be paid back to customers of the group [5,6]. ...
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Recently, Demand Side Management (DSM) has played an important role in the smart grid through the optimization of residential load consumption. Smart DSM is a very important tool that permits customers to take right decisions for their energy consumption, it also helps the energy utilities to decrease the over load demand and reshape the load curve. This paper proposes an optimized DSM technique based on smart metering to minimize load consumption, especially during load peaks. Bat Algorithm technique is proposed to optimize the minimum consumption during peak hours according to load type for three consumers' types whom are classified based on their lifestyles. A control algorithm is applied to the proposed system to achieve load shifting according to the optimization results.
... In a system that whereby the enterprise of power production is separated from that of distribution, the distribution enterprise becomes totally responsible for the implementation of Demand-Side Management (DSM) because large scale electric consumers are directly involved [9,10]. In the proposed system, it is assumed that the energy which is produced is insufficient, and there is need for the demand side to reduce the load for a given period of time during the year [11]. In some studies, the researchers proposed a strategy for energy control that is pricing-based; their proposed strategies are aimed at eliminating the peak load for smart grid. ...
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In the proposed system, a strategy for the control of energy consumption by home appliances is provided. The statistics of previous energy production and consumption of a case study city are used in providing the strategy. In the design of the proposed system, home appliances are categorized into three levels of priority, even though it can be more than three. In this article the control of energy consumption is achieved using a real time energy consumption manager (ECM) based on stored data without the need for real time communication. The system which is proposed here is affordable and simple. Also, it does not require grid upgrade or power line communication through the grid.
... In a system that whereby the enterprise of power production is separated from that of distribution, the distribution enterprise becomes totally responsible for the implementation of Demand-Side Management (DSM) because large scale electric consumers are directly involved [9,10]. In the proposed system, it is assumed that the energy which is produced is insufficient, and there is need for the demand side to reduce the load for a given period of time during the year [11]. In some studies, the researchers proposed a strategy for energy control that is pricing-based; their proposed strategies are aimed at eliminating the peak load for smart grid. ...
Article
Full-text available
In the proposed system, a strategy for the control of energy consumption by home appliances is provided. The statistics of previous energy production and consumption of a case study city are used in providing the strategy. In the design of the proposed system, home appliances are categorized into three levels of priority, even though it can be more than three. In this article the control of energy consumption is achieved using a real time energy consumption manager (ECM) based on stored data without the need for real time communication. The system which is proposed here is affordable and simple. Also, it does not require grid upgrade or power line communication through the grid.
Chapter
Nowadays utilities are offering an incentive for direct control over the loads present on the consumer side to implement demand-side management, where utilities have greater control over consumer-side consumption but it lacks the application of suitable criteria and objectives independently. This paper aims to focus on the problem of power and energy management in the consumption side of the grid by decision-making and load management as per the dynamic price of electricity. It discusses the load shifting technique to handle a large number of controllable devices present on the distribution side using a heuristic-based evolutionary algorithm to handle complex operations and constraints in MATLAB/Simulink. As per the survey conducted in a residential area of Karnal City, Haryana, simulation studies are carried out for different types of consumers with a variety of control loads offering energy management in the distribution of loads as well as energy dynamic pricing.
Thesis
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The electric power grid is a complex interconnected network designed to deliver electricity from suppliers to consumers. It is the largest human-made infrastructure ever built, and it is at the center of our daily life. The complexity of the electric grid comes from the number of components interacting directly or indirectly in the network, such as generators, consumers, transformers, circuit breakers, relays, transmission lines, retailers, nature and the environment. The electric grid can be seen as the circulatory system of the society. The electricity (blood) is produced at the generators (heart) which pushes it (pumps) into the transmission lines (arteries and veins), which transport and distribute it to the end users or consumers (organs). The modern electric network increasingly combines new types of generation units powered by renewable energy sources (wind, water, and solar), which are all dependent of the weather and therefore unpredictable and fluctuating. As an important and essential part of our lives the electric network should be well understood and analyzed, in order to make it reliable, robust and safe for the users. It is well known that instabilities in the electric grid can arise through frequency or voltage unbalances, or due to a line breaking. Much research has been devoted to the modeling and control of the stability of the grid. Most of it it's focused on the frequency and voltage angle stabilization using diverse algorithms and control methods. Other works built up methods for understanding and analyzing the propagation of blackouts in the electric network. The main purpose of this thesis is first to understand the dynamics of the grid in presence of decentralized frequency control directly included in the electric devices. We first consider the case of a single power plant and then extend the study to the case of a network. Second, this thesis aims to analyze the propagation of either blackouts or line outages in a situation where the electric consumption is controlled. To these ends, we model a realistic electric power grid using mathematical tools and computational methods. vii The first step to model the power grid is the modeling of the power plant, which roughly speaking is composed of a generator and frequency and voltage regulators. The power plant model is based on the second Newton's law for a rotating body. The obtained equation, also known as swing equation, gives the time variation of the frequency of the grid as a result of the unbalance between the mechanical and the electrical power. The swing equation is then combined with the frequency regulator model. The frequency regulation considered here is composed by the load frequency control (LFC) which is the primary control and the automatic generation control (AGC), also referred to as secondary control. It is shown that after any deviation the frequency is brought back to its reference value thanks to the LFC and the AGC. The voltage regulation is not considered in this thesis and the voltage is assumed to be always constant. Second, we propose a very simple stochastic demand model able to reproduce the main statistical properties of real demand fluctuations. This stochastic model corresponds exactly to a Markov process for a system composed of N particles, each one making transitions between two states (on, off) with a certain rate p. The frequency fluctuations arising from such model adjusting only the switching rate p are close enough to the real frequency fluctuations measured in the Balearic island grid as to validate the model. The power grid frequency control is a demanding task requiring expensive idle power plants to adapt the supply to the fluctuating demand. An alternative approach is controlling the demand side in such a way that certain appliances modify their operation to adapt to the power availability. This is especially important to enable a high penetration of renewable energy sources. A number of methods to manage the demand side have been proposed. In this work we focus on dynamic demand control (DDC), where smart appliances can delay their switchings depending on the frequency of the system. We first introduce DDC in the proposed simple model to study its effects on the frequency of the power grid. We find that DDC can reduce small and medium-size fluctuations but it can also increase the probability of observing large frequency peaks due to the necessity of recovering pending tasks. Although these events (large frequency peaks) are very rare they can potentially trigger a failure of the system, and therefore strategies to avoid them have to be addressed. We then introduce a new method including communication among DDC devices belonging to a given group, such that they can coordinate opposite actions to keep the group demand more stable. We show that for this method the amount of pending tasks decreases by a factor 10 while large frequency fluctuations are significantly reduced or even completely avoided. Extending the study to the case of a simple network, we show that in addition to the reduction of the frequency fluctuations observed in each node, DDC smooths out the fluctuations of the phase differences between generators. Furthermore, we show that in the case of a sudden breaking of a line, smart devices adapt their consumption according to the outage in the network. This is not the case in the viii uncontrolled network. Regarding the effects of control on a network, we have also studied how secondary control may help to prevent an effect called Braess' paradox. Nowadays, the integration of renewable energy sources requires grid extensions and sophisticated control actions on different time scales to cope with short-term fluctuations and a long-term power imbalance. Braess' paradox constitutes a counterintuitive collective phenomenon that occurs if adding a new transmission line to a network increases loads on some other lines, effectively reducing the system's performance and potentially even entirely halting its operating state. Combining simple analytical considerations with numerical investigations on a simple network, we study the dynamical consequences of secondary control in a AC power grid model. We show that control applied to all nodes provides dynamical stability to the system and cures Braess' paradox, while control applied only to generator nodes has a limited efficiency which depends on the grid topology. Our results highlight the importance of demand control in conjunction with grid topology for stable operation and reveal a new functional benefit of secondary control. Finally we address the issue of cascading failures in a realistic model for the electricity dispatch in the power grid. The components of the power grid are interconnected in such a way that any failure can propagate, affecting neighboring elements of the network if nothing is done to isolate the faulty element or the affected region. The understanding of cascading failures constitutes an important challenge in the electric network control community, and it is primordial for the grid's safety and the economic development of a society. Several models have been proposed to address this problem. Among them, the ORNL-Pserc-Alaska (OPA) model, which is a model proposed by researchers and engineers from Oak Ridge National Laboratory (ORNL), Power System Engineering Research Center of Wisconsin University (PSerc), and Alaska University (Alaska) to study and understand cascading failures and blackouts. We study here the cascading failures on a small network of hundred nodes using the OPA model in which we introduce power fluctuations and DDC. We observe that the complementary cumulative distribution function of the blackout sizes has a tail showing a power law characteristic, both with and without DDC. We also observe a reduction in the number of overloaded lines and blackouts in presence of DDC as compared to the case without control. DDC shifts blackouts from peak hours to valley time, where pending tasks are recovered. Although the number of blackouts is reduced, the probability of observing a large one at valley time is higher than without control, a similar phenomena to that observed in the case of the AC network. ix
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