A Review on Demand Response: Pricing, Optimization, and Appliance Scheduling

Conference Paper (PDF Available)inProcedia Computer Science 52(1) · December 2015with 747 Reads
DOI: 10.1016/j.procs.2015.05.141
Conference: 5th International Conference on Sustainable Energy Information Technology (SEIT 2015), London, UK.
The evolution of conventional electric grid into Smart Grid (SG) has enabled utilities as well as consumers to reap fruits due to its time varying price mechanisms. The utilities can acquire benefits by improving stability of grid, lessening blackouts and brownouts, knowing better their consumers power needs and not investing into new infrastructures. On the other hand consumer can also reduce electric bills, gain incentives by installing renewable energy sources and exporting energy to the main grid and attain improved services from utility. Demand Response (DR) is one of the most cost effective and reliable techniques used by utilities for consumers load shifting. In this paper, we are presenting a review of several DR techniques with a specific view on pricing signals, optimization, appliance scheduling used and their benefits. A comprehensive performance comparison is also prepared with the help of multiple criteria of SG paradigm.
Available online at www.sciencedirect.com
Procedia Computer Science 00 (2015) 000–000
6th International Conference on Ambient Systems, Networks and Technologies, ANT 2015 and
the 5th International Conference on Sustainable Energy Information Technology, SEIT 2015
A Review on Demand Response: Pricing,
Optimization, and Appliance Scheduling
Ijaz Hussaina, Sajjad Mohsina, Abdul Basita,
Zahoor Ali Khanb, Umar Qasimc, Nadeem Javaida,
aCOMSATS Institute of Information Technology, Islamabad, 44000, Pakistan
bCIS, Higher Colleges of Technology, Fujairah Campus, Fujairah 4114, UAE
cUniversity of Alberta, Alberta T6G 2J8, Canada
The evolution of conventional electric grid into Smart Grid (SG) has enabled utilities as well as consumers to reap fruits due
to its time varying price mechanisms. The utilities can acquire benefits by improving stability of grid, lessening blackouts and
brownouts, knowing better their consumers power needs and not investing into new infrastructures. On the other hand consumer
can also reduce electric bills, gain incentives by installing renewable energy sources and exporting energy to the main grid and
attain improved services from utility. Demand Response (DR) is one of the most cost eective and reliable techniques used by
utilities for consumers load shifting. In this paper, we are presenting a review of several DR techniques with a specific view on
pricing signals, optimization, appliance scheduling used and their benefits. A comprehensive performance comparison is also
prepared with the help of multiple criteria of SG paradigm.
2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Conference Program Chairs.
Keywords: Smart Grid; Demand Response; Appliance Schedule; Optimization
1. Introduction
Information and Communication Technologies (ICTs) are being used in typical electric grid to enhance it into a
Smart Grid (SG). These ICT services include but not limited to intelligent and autonomous controllers, advanced
software for data management, and two-way communications between power utilities and consumers. Two of the key
objectives in SG are the enhancement of its stability in stressed periods from utility perspective and electricity cost
savings from consumers point of view. To achieve these goals, one of the major concepts is Demand Side Management
(DSM) that includes all activities which target to the alteration of the consumers demand profile, in time and/or shape,
to make it match the supply, while aiming at the ecient incorporation of renewable energy resources. Demand
Response (DR) is a subset of DSM with energy-eciency and energy-conservation programs. The US Department
Corresponding author. Website: www.njavaid.com; Tel.: +92 300 5792728.
E-mail address: nadeemjavaid@comsats.edu.pk; nadeemjavaidqau@gmail.com
1877-0509 c
2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Conference Program Chairs.
2Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000
of Energy defined DR as “a tarior program established to motivate changes in electric use by end-use consumers,
in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower
electricity use at times of high market prices or when grid reliability is jeopardized” [1]. DR is one of the most cost
eective and reliable techniques used by utilities for consumers load shifting. Appliances are scheduled in response to
various time varying price signals in a cheaper time slot to achieve maximum cost savings in the electric bill. As the
research and development of DR is evolving day by day, this review provides a summary with their key characteristics.
Moreover, our contribution complements the existing surveys by presenting: a) an overall objective of each study b)
pricing signal used c) appliance scheduling (AS) type and d) a detailed classification regarding renewable energy and
storage energy used, underlying unwarranted assumptions, uncertainties handled, scalability, forecasting techniques,
communication requirements, maximum possible delay in appliance operation, appliance types and at last but not
least benefits gained by both consumers and utilities.
2. Related Work
In recent years, there has been an extensive research eort on the DR and AS for electricity cost savings, reducing
peak to average ratio and enhancing grid stability while maintaining user comfort. The objectives of [2], are to reduce
consumer energy bills, peak to average power ratio and carbon emissions. Two types of energy management schemes;
Optimization based Residential Energy Management (OREM) and in-Home Energy Management (iHEM) are pro-
posed and compared. In OREM, a Linear Programming (LP) model whose objective is to minimize the total cost of
electricity usage at home with the help of optimal appliance schedules. Aim of the iHEM is to save the electricity
cost while not degrading the consumer comfort too much. The purpose of [3] is to formulate a practical optimization
model for a household to determine the optimal scheduling of home appliances under Time of Use (ToU) electricity
prices. The main contribution of [3], is the consideration of inconvenience level and formulation of the problem as
Mixed Integer Non-Linear Programming (MINLP) rather than Mixed Integer Programming (MIP). It minimizes the
cost with an incentive oered to the consumer during peak times. In [4], the objective is to design a scheduler to
optimize the energy use of an entity for a fixed time horizon so that consumers can obtain the maximum savings in
their monthly electricity bills by knowing future price predictions of electricity. An optimal energy scheduling frame-
work is proposed in which full user preferences and generic electricity pricing schemes are considered. A complete
DSM framework is proposed in [5], that uses two most common DR strategiesAS and power storage to enhance the
consumer benefits. To gain full advantage of the DR, an autonomous scheduler is also proposed in this study that
schedules appliances and power storage devices with the help of Smart Meter (SM) and load aggregator. The main
objectives of [6] are to ensure adaptive learning and add more intelligence in the system to reduce cost, and peak load.
A hybrid intelligent system based on unsupervised learning is proposed to optimize the user comfort with respect to
energy consumption by learning occupancy preferences and patterns. A novel system architecture and control algo-
rithm, called Green Charge (GC) is proposed in [7] that manages renewable energy, Battery Energy Storage (BES)
and grid energy in buildings. It lessens electricity bills by combining on-site renewable generation with energy storage
that stores electric energy during low-cost periods and then use this stored energy during high-cost periods.
In this section, we summarized and organized six latest research results in a novel way that integrates and adds un-
derstanding to the field of DR and made a comparison among them. At the end of this section, we will provide a cogent
summary according to diverse criteria like scheduler type, electricity pricing schemes, optimization problem type, re-
newable energy sources used, uncertainties handled or not, communication requirements, forecasting techniques and
appliance types etc.
3.1. Autonomous Appliance Scheduling for Household Energy management
In [8], a computationally feasible and automated optimization-based residential load control framework is pro-
posed that uses Real Time Pricing (RTP) combined with Inclining Block Rate (IBR). Aim of [8] is to minimize the
households electricity payment and waiting time by optimally scheduling the operation and energy consumption of
Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000 3
each appliance, while maintaining user comfort. Every house is assumed to be equipped with SM that has built
in price predictor and energy scheduler. Real time electricity prices are relayed to SM by utility via a Local Area
Network (LAN). Then, energy consumption scheduling vector of appliances are formulated for complete planning
horizon H. User inputs the appliances start/stop times within the planning horizon, their minimum/maximum power
needs and limit of power in each planning horizon slot with the help of an interface (like In Home Display (IHD),
smart phone or Energy Management System (EMS)). In addition to the above mentioned user constraints, a frustra-
tion based waiting cost is also included in the objective function that increases with waiting time and vice versa. A
multi objective linear optimization problem is formulated that minimizes cost of electricity as well as waiting time
of appliance operation. Now, energy scheduler determines optimal choices of all appliances operation according to
the user provided data. These choices are then implemented on appliance in the form of ON/OFF commands with
specified power levels over wired/wireless Home Area Network (HAN) among appliances and SM. This is the case
when electricity prices are known ahead of time for planning horizon. If electricity prices are partially known for some
planning horizon then price predictor in SM is used to predict the unknown prices. In this situation, the optimization
problems cost minimization objective is further decomposed into two parts; one that is known at that specific time
and the other that is predicted. Energy scheduler also solves and implements this optimization problem in the same
way as in the first case when electricity prices are known ahead of time. In addition authors in [8], also presented
that their proposed optimization-based residential load control framework can be extended with slight modifications
in diverse directions like Appliances with Discrete Energy Consumption Level, Interruptible and Un-interruptible
Residential Load, Availability of Multiple Retail Electricity Sources, Avoiding Load Synchronization, Announcing
the Scheduled Consumption Back to the Utility, Handling Load Reduction Requests, Residential Electricity Storage
and Accommodating Changes in Users Energy Needs. Simulations show that average electricity bill as well as peak
to average ratio reduced 25% and 38% simultaneously. At last but not least, they studied the impact of adopting
IBR, Scheduling Control Parameter, Price Announcement Horizon and Price Prediction and Number of Users on their
proposed framework.
3.2. Appliance Commitment for Household Load Scheduling
The primary objective of [9] is to reduce electricity bills for next 24 hours subject to constraints on user comforts
and meeting the predicted hot water requirements. User comfort in [9] is defined by the limits of hot water tem-
perature and three types of loads are considered: Controllable Thermostatically Controllable Appliances (C-TCAs),
Controllable Non-Thermostatically Controllable Appliances (Non-TCAs) and non-controllable. A novel appliance
commitment algorithm is proposed in [9] that schedules a C-TCA Electric Water Heater (EWH) on the basis of elec-
tricity price and consumption forecasts. Authors in [9] formulated energy consumption scheduling as a nonlinear
optimization problem, however, they transformed it to a set of linear constraints and linear optimization problem.
They solved it with the help of linear-sequential optimization-enhanced, multi loop algorithm. This algorithm is fun-
damentally an exhaustive search algorithm, so the solution is optimal and always solvable. EWH thermal model was
defined with the help of thermal capacitance and thermal resistance. They estimated these parameters from ASHRAE
handbook and statistical regression models. Hot water consumption is predicted from the historical data. In this
optimization problem price forecast, range of thermostat settings, characteristics of electric water heater and demand
for hot water are used to model the objective function and constraints. They used the two step scheduling processday
ahead scheduling and real time adjustments, to find the solution. In day ahead scheduling, on the bases of electricity
price and hot water usage forecasts EWH estimated ON time duration for the next 24 hour period is determined. Price
threshold is found from the sorted (monotonically increasing) electricity price curve where it intercepts with total
EWH ON time. When electricity prices are lower than this threshold value, EWH would be ON otherwise it would
be OFF. Now the constrained optimization problem is solved and compared to the control law of the heater that was
earlier determined without any optimization. If there is no violation of user comfort band then these schedules are as
it is accepted and total payment is calculated. On the other hand, if violations exist then subdivide the time horizon
at point where first violation has occurred. Then repeat this process for the complete 24 hours period. Real time
adjustments are made on the bases of updated information of electricity prices and hot water usage. The two-step ap-
proach provides adjustments for the uncertainties by updating real time prices and hot water usage of a house. If user
gives more flexible limits of temperature then higher savings would be possible. The simulations in [9] revealed that
the algorithm can be optimally used to automatically generate schedules based on dierent cost and comfort settings.
4Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000
The authors in [9] also claimed that appliance commitment problem is better than agent based approaches and their
approach also handles uncertainties which may appear from energy forecast and hot water consumption prediction.
3.3. Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home
The aims of [10] are to minimize the energy expenses of each appliance in Smart Home (SH) with the help of
optimal AS that uses real time energy prices and at the same time conforms to the target trip rate. In [10], they tackle
stochastic characteristics of consumer energy consumption pattern, BES and renewable generation. In addition, Vari-
able Frequency Drive (VFD) concept and limit on the total load demand are salient features included in this work. In
[10], a three step algorithm is proposed and its final solution is found by stochastic optimization. A SM is assumed in
SH that is capable of receiving energy price forecasts from utility and generating schedules along with other tasks for
home appliances. In the first step a LP based deterministic scheduling algorithm is used to minimize the expense of
electricity from grid, Photo Voltaic (PV) and BES. Constraints related to total load, power consumption of appliances
in an interval, BES capacity, solar power limit and its utilization are formulated for first phase of optimization. A
feasible LP based schedule is found in the first step. In the second step, a systematic trip rate driven stochastic oine
scheduling algorithm is proposed to derive the desired energy adaptation variable . The probability that the home
power network trips out during a time interval is defined to be the trip rate. In this phase, operation schedule are gen-
erated for a given set of household appliances with desired trip rate to handle the uncertainties in energy consumption
and runtime of household appliances with the help of some probability distribution function. In this oine scheduling
algorithm, it is assumed that all its inputs are known a priori. As a result, the oine operation schedule is optimum
at that moment. But as the system becomes operative the energy consumed by household appliances and the energy
produced by the solar panels strays from the values utilized to optimize the oine operation schedule. Thus, the
optimality of the oine operation scheduling is lost and the online operation needs fine tuning to compensate for the
optimality loss. So, in the last step the online runtime scheduling is invoked that can eectively handle the uncertainty
in the energy generation from the PV system. Appliance operation scheduling in [10] also speeds up the creation of
the desired operation schedule by exploiting parallelism in the computing process. Simulation results of [10] show
that the proposed energy consumption scheduling scheme achieves up to 41% monetary expenses reduction when
compared to the traditional scheduling scheme that models typical appliance operations in traditional home scenario.
Moreover, execution time of proposed scheduling algorithm in [10] is within 10 seconds, which is fast enough for
household appliance applications.
3.4. An optimal power scheduling method for demand response in home energy management system
The objectives of [11] are to reduce electricity bills and peak to average ratio of demand curve. In [11] a general
architecture for EMS in a HAN is presented and then an ecient AS method is proposed. In [11], they classify
the problem to be a non-linear problem and solve this using Genetic Algorithm in MATLAB. EMS comprises of
Advance Metering Infrastructure (AMI), SM, Home Gateway (HG), Energy Management Controller (EMC), smart
appliances and IHD. AMI is responsible for two way communication, collecting and transmitting consumption data
between SM and utility and relaying price information back to the SM from utility. HG is used to acquire price signals
and control signals from utility company and send load forecasting information to the utility company. In [11] home
appliances are divided into two broad categories; Automatically Operated Appliances (AOAs) and Manually Operated
Appliances (MOAs). AOAs are further classified as interruptible appliances whose operation can be stopped and non-
interruptible appliances whose operation cannot be stopped. As HG receives DR signal from the utility, it creates
optimal schedules of appliance on the basis of information received from user and utility. An IHD is used to input
appliances ON/OFF requests, AOAs length of operation time, appliances start and stop time, operation time interval
and power consumption of appliances. MOAs are not included in this optimization process because their usage cannot
be predicted in advance. In order to generate optimal appliance schedules, the IHD sends all these parameters to HG.
Users always require minimum delay in their appliances start time. An optimization problem is formulated in [11]
based on the parameters entered by user via IHD that optimizes power consumption scheduling matrix and a delay
time rate (DTR). The value of DTR is between 0 and 1. Zero means no delay and 1 means maximum allowable
delay. The price mechanism used in this study is real time price combined with IBR. In this pricing scheme, if a user
consumes more energy than a predefined threshold value then price of the electricity goes higher than normal price.
Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000 5
Table 1. Comparison of DR Techniques based on Selected Quality Criteria
Technique Objectives Scheduler Pricing
Optimization Assumptions Renewable
1 Minimize energy
bill, appliance
waiting time and
Manual RTP com-
bined IBR
LP Future pricing pa-
rameters are known
for the users ahead of
PHEV as a
BES used
2 Minimize energy
bill and user dis-
Automatic RTP Converted-
Temperature band is
uniform, Mean error
of 10% is assumed in
forecasted price
Not used
3 Minimize energy
bill by consumer
Automatic TOU NLP Power consumption
profile in each house
is assumed to be the
Not used
4 Minimize energy
bill and PAR
Manual RTP com-
bined with
Nine kinds of AOAs
and 16 operation per
day for them
Not used
5 Minimize energy
bill and user dis-
Automatic RTP,FIT
and Net
Linear Convex cost func-
tion, PV generation
is able to meet 50%
of its load require-
6 Minimize energy
bill and user dis-
Manual RTP Linear
Solar power is
cheaper than grid
PV and BES
This combined pricing scheme prevents rebound peaks, which otherwise might appear, in opeak periods. Appliances
optimal start timethe only unknown parameter in this optimization problem is determined to reduce energy cost and
DTR. The authors in [11] have shown in their simulation results that real time pricing of electricity combined with
IBR has alleviated rebound peaks in opeak periods. They further deduce that electricity cost and average DTR
formed a pareto optimal frontier where if we try to reduce electricity cost, DTR goes high and vice versa. They also
observed that an average saving of 12.68 cent daily along with reduction in peak to average ratio of 5.22 to 3. 37 is
possible with their devised algorithm [11]. The authors with the help of simulations in [11] also revealed that their
proposed algorithm is still eective in the case of combing AOAs with MOAs.
3.5. Autonomous Appliance Scheduling for Household Energy Management
The goals in [12], are not only to minimize the energy consumption level, but also reduce energy bills and ensure
minimal user discomfort with the help of AS. These goals can be achieved with the help of renewable energy and
DSM techniques. EMSs benefit consumers to lower their electricity bills, as well as utility to reduce their peak power
demands. In [12], energy management savings for a house with standard appliances and PV arrays installed on its
roof-top are presented. The strategy in [12] is to purchase as little energy as possible from grid while export as much
energy as possible to grid. The authors in [12] proposed linear programming based autonomous AS algorithm for a
house with the help of an intelligent Smart Scheduler (SS) and load clustering. They also proposed various energy
pricing frameworks (Real time, Feed in tariand Net sale/net purchase). The basic principal behind SS is that it
calculates and stores the hourly probabilities of appliances in the house for a specific time horizon (an year) on the
basis of historical usage data of appliances while taking into consideration features like day of the week, weather
6Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000
conditions, degree of penetration of the appliances and occupancy level of the house. The SS can estimate the house
hold usage of certain appliances by monitoring hourly probabilities of those appliances. Most preferred ToU for an
appliance is when it has highest probability. SS monitors individual household load consumption and at the same time
confines the appliance aggregate load at a predefined limit. It also ensures that the appliances are scheduled in o-peak
periods so that consumers can achieve maximum reduction in their electricity bills. SS has two way communication
capabilities and can issue commands like start, stop, pause and resume to the appliances. Appliances having similar
ToU probabilities and load profiles are assigned the same cluster. Each cluster has a peak load limit, reaching that,
further appliances operations are not allowed in that time slot. An appliance, that has been disallowed two time slots
consecutively, is given higher priority. In next time slot, these higher priority appliances are scheduled first to reduce
the user discomfort. If at any time slot, a higher priority appliance asks for activation and there is not enough power
capacity with that cluster, then a lower priority appliance needs to be paused. Flexible schedules are automatically
generated for appliances at time slots where they have highest ToU probabilities by incorporating some tolerance
value. SS assigns tolerance value on the basis of priorities of the appliances. It is quite possible that some appliances
may be scheduled in an improper time slot due to poor tolerance value assigned by SS. In that case, a frustration cost
is included in the objective function of the optimization problem to handle the discomfort bore by user.
Simulations in [12] show energy savings for a prosumer with the help of autonomous AS algorithm by considering
dierent pricing signals. The authors in [12] compared their proposed algorithm with the house that has neither
installed any RES nor made schedules and show that their proposed scheduling algorithm is a viable solution to
residential consumers power management.
3.6. Demand Response for Residential Appliances via Customer Reward Scheme
In [13], an incentive based DR scheme for residential distribution system is proposed in which consumers are
rewarded on the bases of how much amount of load they shed and how much improvement in feeder voltages is
caused by them during peak periods. The proposed scheme doesnot depend on cost of electricity consumption. In
[13], first of all a detailed consumer survey is conducted to take their inputs and preferences to participate in the
proposed DR. In the later stages, the results of these surveys are used to design various indices for the load control
algorithm. Five types of indices related to consumer priority, satisfaction, and flexibility are proposed in this research
work. Houses are ranked according to the eects they made on the feeder voltages. Rewards depend on the willingness
of the user to participate in the scheme and are calculated on daily basis. The load control algorithm is implemented
in two hierarchical levels; at the first level the SM (primary controller) regulates the feeder voltage in an acceptable
range and at the second level main controller prevents overloading of the transformer. Everyday at the start of the
time horizon SM sends the appliance state and power data to the main controller that calculates voltage level at each
house. Aggregate power and voltage of the network at each house are checked to insure that they are kept within
standard regulatory limits in every 2 minutes. Oine load flow studies are performed to acquire the appropriate
load adjustments in the case that the power level and/or voltage at each house are violated. The oine load flow is
an iterative process that selects multiple sets of loads for adjustment in that time step. The criteria indices, house
rankings and decision values are calculated in all iterations of the proposed algorithm [13]. The load of that house
is identified and chosen to shed; whose decision variable has maximum value for load adjustment. After shedding
this load the power and voltage levels are re-computed and if violations exist then another load is identified and shed.
This process continues until the power and voltage levels stabilize in the permissible range. All selected appliances
for adjustments are saved and signals are sent at to relevant SMs. If loads are adjustable, then some adjustments
in the parameters of these appliances are made for 15 minutes to reduce the load. On the other hand, if loads are
non-adjustable then these are switched ofor 4 minutes. This process is repeated for whole day and at the end of
the day rewards to the consumers according to the proposed formula in [13] are calculated. The authors made critical
assessment of consumer reward scheme according to their designed criteria indices, evaluation of cost coecient,
implementation and operation of this scheme, its scalability and prevention from consumers misuse of the scheme.
They also presented in [13] that this scheme can eectively shave the network peak for several years, before the feeder
transformer needs to be upgraded.
Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000 7
4. Comparison and Analysis of All Techniques
A comparison of six DR techniques with respect to quality criteria is shown in table I. Overall, there is no single
criterion available that can evaluate the best technique among them. So, we took multiple criteria to assess and
compare these techniques.
Reduction in energy bills is the most common objective of these techniques [1-6] whereas user comfort is just
behind this objective. Minimization of peak to average ratio is the goal of [3-4] that also brings stability in the smart
grid. The least common objectives are greenhouse gas emissions and customer direct incentives. Automatic schedulers
are proposed in technique [1-2], while manual schedulers are suggested in [3-6]. A variety of pricing schemes are
designed and used in these techniques. A vast majority utilizes real time pricing [1-5] and time of use pricing [6].
Net sale and net purchase [1] and feed in tari[1, 4] are the least popular price structures in these techniques. Nearly
all of the techniques formed are linear or its derivative form [1-2,4]. PV or BES is able to supply 50% of the total
needs of the house load as supposed in [5]. A vast majority included and handled electricity prices and consumptions
forecasting. Communication infrastructure is a basic requirement of these DR techniques.Almost all studies have
used three types of appliances i.e deferrable and interruptible, deferrable and non-interruptible, non-deferrable and
non-interruptible. Finally, Table II classifies all the survey papers according to the scalability of the proposed method,
Table 2. Comparison of DR Techniques based on Selected Quality Criteria
TechniqueUsers Forecasting Communication Requirement Max Delay Benefits
1 Multiple
price pre-
diction at
the user
SM with en-
ergy scheduler
and price pre-
User defined 25% reduc-
tion in EC
and 38%
reduction in
2 Single
Energy and
hot water
Signal to con-
trol appliance
Modeling of
of water
may low
Over 20% in
3 Multiple
survey is
SM and main
controller are
survey prior to
4 minutes
or switch
ofor 15
Shave the
network peak
for almost 11
4 Multiple
Not used Too much
tion involved
HEMS User defined 26.06% in EC
and 35.44%
reduction in
5 Single
Energy and
Load FC
Smart sched-
uler uses 2
way commu-
TOU probabil-
ities of appli-
2 time
10.92% in EC
6 Single
Energy FC SM is used to
VFD and ca-
pacity limited
energy drives
Instead of
delay new
trip rate
24% to 44.1%
in EC
forecasting technique used, level of communication requirement, other peculiar requirements, maximum possible
delay in the appliance operation, categorization of appliance and benefits gained.
8Ijaz Hussain et al. /Procedia Computer Science 00 (2015) 000–000
5. Conclusion
In this paper, we have presented a survey of recently published research in the domain of DR. We provide an
extensive review on pricing signals and AS schemes used with respect to multiple criteria. The maximum electricity
cost saving (24 % to 44.1%) was achieved in [6], while 38% reduction in peak to average ratio was possible in [1].
The simulation results of [3], showed that network peak can be shaved for almost 11 years that benefits utility by not
requiring any update in their infrastructure.
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13. Vivekananthan, C.; Mishra, Y.; Ledwich, G.; Fangxing Li, “Demand Response for Residential Appliances via Customer Reward Scheme,”
Smart Grid, IEEE Transactions on , vol.5, no.2, pp.809,820, March 2014. doi: 10.1109/TSG.2014.2298514.
  • ... In the research field, some authors tackle DR from the remuneration, complementary programs and inclusion of Distributed Energy Resources (DER) [10]. In [11] the analysis focuses on the economic trends of DR programs while [12][13][14], focus on the planning of networks based on the DR resources. Finally, authors such as [12,15,16] work on DR programs in smart grids. ...
    ... Stability in the network [14] The smart network recurs to the demand management which includes all the activities directed towards the modification of the voltage profile. ...
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    This paper provides a review and analysis of trends related to demand response (DR). The authors have considered six different topics for the analysis of DR trends: Users, Network Services, Markets, Complementary Programs and Distributed Energy Resources (DER). A brief summary of the consulted articles is included and the behavior of the different DR trend-related topics is shown up to the year 2017 and their projections for 2020. As a result, the characterization of the main DR topics is obtained as well as its current and future trends. Based on the results of the study, it is concluded that the topic of complementary programs is a trendsetter for current trends and it is expected that there is a future change of focus towards the users and new services.
  • ... In direct-load control programs, utility companies or aggregators cycle home appliances, such as air condi- tioners and water heaters, on and off during peak demand periods in exchange for a financial incentive and lower electric bills for the con- sumer. A review of existing residential DR techniques with a specific view on pricing signals and optimization solvers was provided in [16] and the performance of these techniques was compared using multiple criteria. In [17], the residential appliances were categorized into dif- ferent types based on their distinct spatial and temporal operation characteristics, and optimization methods were explored to decide the optimal scheduling of residential appliances for DR using MIP. ...
    ... Because of the higher availability and reliability of the DR resources enabled by a home battery system, utilities are likely to provide greater incentives to offset the initial capital cost of the battery. Prior residential DR research has used either building loads [16,17], battery storage in the form of sta- tionary storage or electric vehicles [5,19], or both [8,12,20,21]. How- ever, if not properly controlled, home battery systems can increase the overall energy consumption and electricity system emissions resulting ampere-hour throughput of a battery from the round-trip energy losses [22]. ...
  • ... Other studies suggest that time-based programs are more suited for residential con- sumers, while incentive-based programs are more appropriate for in- dustrial consumers [30]. In brief, the advantages of DR approaches are [5,6,31,32]: ...
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  • ... Therefore, DSM becomes more beneficial than supply-side management as electricity demands continue to grow at a rate that exceeds the expansion rate of power systems. Many studies have focused on the load control techniques of DSM [6], the roles of DSM in the electricity market [7], the economic benefits of DSM [8], the impacts of DSM on the industrial and residential sectors [9,10], the interactions of DSM with other smart grid technologies [11], the business models of DSM [12], the impacts of DSM on power system reliability [13], the optimization techniques of DSM [14,15] and the load forecasting and dynamic pricing schemes of DSM [16]. Moreover, DSM has been implemented with promising outcomes in various countries, such as the UK [17], China [18], North America [19], Kuwait [20] and Turkey [21]. ...
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