Conference PaperPDF Available

A Survey of 'User Comfort' in Home Energy Management Systems in Smart Grid


This survey Paper is an ingathering of a comprehen-sive study of user comfort in smart grid. It is a compounding of different arenas of engineering like communication, electronics, management and power. Many techniques, like demand response, demand side management, real time pricing, time of use, home energy management, etc, are explained according to their role in Smart Homes (SHs). This is the short discussion about basic units of SH and their interrelationships. These techniques help to improve electricity consumption. In this survey paper, we focus on user comfort; reduction in bill payments, scheduling methods, necessary addition of renewable energy sources, etc. Thus, the user suffers minimum delay in appliance operation and retailer gets minimum peak-to-average ratio. This is a result of evolutionary enhancement of the previous electricity networks schemes towards an improved and maintainable energy schemes. Keywords Smart Grid, Demand side management, smart home, renewable energy resources, demand response I. INTRODUCTION Smart Grid (SG) is a vision of the coming times power structures along with innovative sensing equipments, control methods and communication schemes at distribution points as well as transmission level. So that electricity supply should be smart and user friendly. In other words, we can say that, The included physical power system on SG and its information methods that attach various tools and effects recital to convert into a customer service program. The smart power system will probably contain some novel technologies in communication, broadcast organization, advanced metering, automation, distributed storage, guard, and surety to tolerate a considerable increment in the dependability and validity of the power network, which will in turn lower the energy costs and user ease. In the present era, environmental businesses, oil shortage, and economic facets of consuming the available energy re-sources. These things motivated nations, govt., technology suppliers, and academic societies to supernumerary conven-tional resources by Renewable Energy Resources (RERs). For this purpose SG and Home Energy Management(HEM) and industrial and commercial energy management are necessary. In society, this Real Time Prices (RTP) is provided by the retailer and industrial and commercial area. In residential area,
A survey of ‘user comfort’ in home energy
management systems in smart grid
A. Mahmood1, A. Ahmad1, H. T. Javed2, Z. Mehmood1,
Z. A. Khan3, U. Qasim4, N. Javaid1,
1COMSATS Institute of Information Technology, Islamabad, Pakistan
2National University, FAST, Islamabad, Pakistan
3CIS, Higher Colleges of Technology, Fujairah Campus, UAE
4University of Alberta, Alberta, Canada,,
Abstract—This survey Paper is an ingathering of a comprehen-
sive study of user comfort in smart grid. It is a compounding of
different arenas of engineering like communication, electronics,
management and power. Many techniques, like demand response,
demand side management, real time pricing, time of use, home
energy management, etc, are explained according to their role
in Smart Homes (SHs). This is the short discussion about basic
units of SH and their interrelationships. These techniques help
to improve electricity consumption. In this survey paper, we
focus on user comfort; reduction in bill payments, scheduling
methods, necessary addition of renewable energy sources, etc.
Thus, the user suffers minimum delay in appliance operation
and retailer gets minimum peak-to-average ratio. This is a result
of evolutionary enhancement of the previous electricity networks
schemes towards an improved and maintainable energy schemes.
Keywords Smart Grid, Demand side management, smart home,
renewable energy resources, demand response
Smart Grid (SG) is a vision of the coming times power
structures along with innovative sensing equipments, control
methods and communication schemes at distribution points as
well as transmission level. So that electricity supply should
be smart and user friendly. In other words, we can say
that, The included physical power system on SG and its
information methods that attach various tools and effects
recital to convert into a customer service program. The smart
power system will probably contain some novel technologies
in communication, broadcast organization, advanced metering,
automation, distributed storage, guard, and surety to tolerate
a considerable increment in the dependability and validity of
the power network, which will in turn lower the energy costs
and user ease.
In the present era, environmental businesses, oil shortage,
and economic facets of consuming the available energy re-
sources. These things motivated nations, govt., technology
suppliers, and academic societies to supernumerary conven-
tional resources by Renewable Energy Resources (RERs). For
this purpose SG and Home Energy Management(HEM) and
industrial and commercial energy management are necessary.
In society, this Real Time Prices (RTP) is provided by the
retailer and industrial and commercial area. In residential area,
every house is equipped with smart meter which continuously
communicates with the SG through the internet. Home energy
management controller: Which schedule all the appliances of
the house, manages load according to the RTP and reduces
bill payment as well as peak-to-average ratio.
Now a days researchers are working on this hot topic of SG.
They are trying to improve the algorithm. So that retailer gets
profit as well as user reduces his bill payments and maintain
comfort zone. All this happens by following these techniques:
scheduling load, by using renewable energy like solar panel
and wind machine, having a genset, etc.
Keeping in mind the user comfort, we bear to look at these
points. Firstly, we have to schedule the appliances according
to provided Price vector (P) given by a retailer. So that User
(U) maximizes payoff and get zero delay or minimum time
lag in operation of appliances in Time slot (T). There is a
trade off between quality of usage and payment. Both are
considered under user benefit. Here a lot of Demand Side
Management (DSM) schemes and algorithms presented in
the literature and history of SG. Many of them are system
specific strategies and a part of them not valid to practical
structures that have a vast range of independent devices. Many
techniques we are using here, were manufactured by using
dynamic and liner programming. However these programming
techniques are not enough to control a number of controllable
devices from different conditions of gimmicks and gimmick
have many computational methods and heuristics. The DSM
techniques main objective is to reduce the system peak load
demand and operational cost. Although service programs are
going on and offering many reasons to represent user for direct
control on the selected loads by assemblage the users loads,
many procedures are working in the literature which does not
see both the criteria and objectives independently. Hence, it
is difficult to use these systems for DSM of future SG which
has a goal to provide the user with full hold over their energy
utilization. So we bear to design algorithms and methods of
scheduling appliances to get satisfied answers. In order to do
this we go through several papers and compare them with
regard to user comfort and payments.
In [1] authors provide comprehensive survey which deals
with renewable energy that requires experienced preparation
and operation scheduling as well as state of art technologies.
There are many ways for handling RERs. One of them
is Demand Response (DR). The resent DR’s definition and
classification are analyzed in the current work. However, a
comprehensive, valuable and cost calculation of DR is also
described in this paper. Here a comprehensive review of latest
classifications, modulation and DR definitions are also present,
along with expenditure and benefits.
Authors in [2] survey that Advanced Meter Infrastructure
(AMI) is responsible for gathering all the data from loads
and customers. AMI is also responsible for implementing
commands and control signals to perform necessary control
actions as well as DSM. These advancements face many
challenges and require expensive tools. AMI is one such tool
and performes communication and data handling. AMI is an
infrastructure that can perform real time data acquisition from
consumers then transmit the data and return the executive com-
mands to the loads. The acquired data could also be utilized for
consumption regulation, at both consumer and providers ends.
We can also use mobile Applications(apps). Furthermore, AMI
give excess to the users to better control their consumption
pattern. It also offers higher power quality and stability. Author
shows that AMI is a relatively new concept which needs
improvements in the areas of communication, data analysis
and control schemes.
Fadlullah Z.M et al.[3] old grid has to be converted into
smart grid with the combination of IT. SG offers the integra-
tion of electrical power Eng. Schemes along with network
communication. Use of game theory, provide solution of
different problems, starting from distribution load management
till the storage in the Grids. Many researcher have different
opinion for the game theory. Nash equation solved many resent
issues happening in the SG. Now in future, the game theory
must have to focus on the design of making SG and its
communication protocols.
Authora in [4] observe increase in computer use and elec-
tricity prices. Energy consumption and speed are parameters
of comparison. Hardware is too improved, now we have to
focus on the software. Processors are made which can tradeoff
between the power and efficiency and these processors take
decision at run time. The surveyed algorithm depends upon,
what was happening in hardware and in applications and
suggest the ideal and basic strategy on given information.
Hardware does not provide path to measure power of every
application. If software is efficient and according to hardware
then it will help. A solution that has all these components
interacting in synergy to achieve system-wide efficiency goals
will most certainly be better than solutions built into any single
component in isolation.
In [5] authors discuss that a sensors, scheduler, multimedia
devices controller, systems and communication protocols ba-
sics of a SH. Many algorithms from various papers and their
importance are described exactly to their importance in SH.
It look like that in home intelligent controller is working on
dispersed manner. This dispersed intelligent may be practiced
in the form of SH. Most of controllers based on visual, haptic
perceptions and auditory. Previously, people have dotes on
information security so system must be scoured.
P.G.D Silva et al. [6] says that lack of understanding of the
real-world, needs of the users as well as the impact of new
technologies and tools may lead to the underestimation or the
abandonment of innovative approaches.A survey is presented
that describes these aspects, and especially on the goal of
providing new value-added energy services to end-users. This
will bring new opportunities as well as challenges for all
stakeholder. Combined with the deployment of new infrastruc-
ture, such as smart meters, and the increasing importance and
penetration of distributed renewable generation, new services
and tools will be created to ease the new level of engagement
of customers with system.
Authors in [7] observe that the instalment of energy storage
devices or batters in the home is one of the best way of
saving energy and reduce dependence on fossil fuels in the
near coming times of SG. If the storage devices are not fully
charge and user are using electricity from the Grid, thats
means a high demand required. And more energy required.
So results in the more emission of carbon from production
unit. And more bad one is to black out. To solve these
issues he provide a NGB(novel agent based) storage system
management schemes, and allow storage system to move
towards profit.
We considered a SG with two type of participants one is user
and other is retailer. In this paper we surveyed seven papers on
Home Energy Management of Smart Grid. We conceder user
in survey: techniques and algorithms to reduce payment and
appliance operation delay as well as scheduling of appliances
through HEM with necessary advancement to improve these
Demand response management via real-time electricity
price control in smart grids (DRM) [8] In this paper, every
user is equipped with a smart meter and retailer sets real-time
electricity prices and informs user via Local Area Network
(LAN) Every scheduler in smart meter optimally computes
and distributes energy consumption according to the price for
upcoming scheduling horizon, H={1, ..., h}, for the set of
users U={1, ..., u}. By keeping in view the user Ucomfort,
authors divide house appliances in to three categories: Au,Bu
and Cu.
Auare back ground or inelastic appliances. There is no
flexibility to adjust them like refrigerator, electric kettle and
lighting, etc. Secondly Buare elastic appliances based on high
quality of usage and more energy consumption permitted time
like fan, iron, etc. Thirdly Cuis semi-inelastic appliance which
consumes fixed total energy with in a preferred time period
and user can control/chose when the energy is consumed in
preferred time period like dryer dishwasher hybrid vehicle, etc.
Energy consumption of user appliances auin Hhorizon and
scheduling vector eau.
eau={eau,1, ..., eau, H}.
Price vector P. Let Phis the price for hthtime slot.
A time slot can be one hour for H= 24 hours. Retailer
transmits the prices and each user computes its original eau.
In back ground appliances, each auAuand works in a
period HauH, during which auconsumes rau. where rau
is energy per time slot.
eau,h =rau,h hHau
When a fixed amount of energy is reserved for the Au,
no mater peak load hours or not then Buand Cumust
schedule properly to avoid further overloading and peaks.
For each appliances auBu, user Uobtains different
level of satisfaction for same amount of energy consumed.
Suppose that satisfaction is measured by time dependent
quality of usage function Uau,h (eau,h )which depends on
who, when and how much energy is consumed. Uau,h (eau,h )
may be equal to zero during undesirable operation hours. It
is reasonable to assume that Uau,h (eau,h)is non-decreasing
concave function of eau,h at any time slot hH. Besides
auBu, the consumption per time slot is given as:
0eau,h rmax
uis maximum energy that can consumed in the time slot
when application auis working. As a semi-inelastic appliance
auCu, working in a period Hau H, consumes total
eau,h =Eau(2)
Here the period H
auis consecutive period starts from αau
Hand ends on βauH.H
au={αau,au+ 1, ., βau}. The
choice of αauand βaudepends on user Uhabit and comfort.
So energy consumed in this slot is given as:
0eau,h rmax
Here the other eau,h = 0 for h /H
auand no operation is
needed outside the operation time period, H
au. The load can
be shifted to reduce price for user and comfort level never
decreases on total energy consumption, limit is Cmax
ucan be
set by the retailer.
eau,h Cmax
User Uhas two goals, firstly meet all demands and maximize
overall satisfaction given in this equation:
Uau,h(eau,h ),(5)
Second one is to minimize bills, obtained as:
P h(X
So, we have to formulate in between way.
P1: Maximize
Uau,h(eau,h )(7a)
P h(X
subject to : 0 eau,h rmax
0eau,h rmax
eau,h =Eau, auCu(7e)
eau,h Cmax
variable eau, auBu, Cu(7g)
Where Eu,h is total energy consumed by background
Eu,h =X
rau,h (8)
Through solving P1 each user can independently determine
its optimal energy usage based on the prices forecasted by the
retailer. By implementation of this P1 we maximizes the user
comfort and reduced the bill pay and user has the authority to
adjust its performance in the Hhorizon.
An intelligent home energy management system to improve
demand response (IMEM) [9].
The focus of this paper is on the load forecasting in the
home as well as in a neighborhood and presenting a novel
appliance scheduling scheme which uses TOU or differential
Daily activities ofa residential user are characterized by
a list of tasks to be scheduled at preferred time intervals.
Some of these tasks are persistent, as they consume electricity
throughout the day (e.g., refrigerator), while others may be
scheduled within a defined time interval (e.g., washer/dryer
or oven). In this paper, the demand-side energy management
problem is considered as the scheduling of a customers daily
tasks according to user-specified constraints and the TOU
pricing offered by the utility company to achieve cost sav-
ings, user comfort and peak demand reduction. An intelligent
power management application is discussed for controlling
appliances in the home as well as for gathering data about the
past usage schedules of the appliances. A branch and bound
algorithm is formulated to schedule the appliances as per the
customers usage preference and comfort. A self-organizing
home energy management network is developed to control
appliances remotely. The appliance controllers developed in
this project offer a zero configuration appliance network with
no user configuration. In this way user gets maximum comfort
in Minimum prices.
The proposed system provides continuous interaction
between the residential customer and the utility company by
employing an adaptive neural-fuzzy learning algorithm. The
solution presented in this paper would enable the utility to
predict and tailor the electricity demand in multiple dwelling
units in a given residential community:
a) By providing suitable incentives (such as differential or
TOU pricing) to customers,
b) By scheduling and controlling appliances to smoothen the
The residential customer is offered the following
i) Improved energy efficiency for electricity usage resulting
in cost savings;
ii) Maximum usage of solar power locally within the home
by shifting operation of certain appliances to times when
solar power is available;
iii) Maximum user comfort by learning from user inputs,
usage patterns, and weather conditions;
iv) Effective customer education and interaction;
Information can be provided to the customer about the daily,
weekly, and monthly energy consumption patterns and advice
on energy savings to meet the customers monthly energy
The proposed energy management solution learns and
adapts to the residential energy usage patterns. The adaptive
neuro-fuzzy learning algorithm developed in this study makes
DR decisions based on the following factors:
1) Peak load forecast,
2) Differential electricity prices,
3) Customers usage patterns and energy budget,
4) Social and environmental factors,
5) Available solar power.
An application in the network receives all its schedules
through gateway and appliances respond through same gate-
The users input is limited to scheduling appliances to turn
on/off at particular times or complete a task within a specified
duration. The user also provides one time information
related to dwelling unit type and size, installed solar PV,
thermal power generation capacity, and target monthly energy
budget (presumably depending on household income). The
MC continuously collects data regarding usage patterns of
appliances from the users interaction with the system. The
customers usage patterns take into account the history of
energy demand considering the following parameters:
i) Time and season (time of day, day of week, month of year
ii) Weather including the effects of persistent extreme weather.
The MC interacts with the user and generates appliance
schedules, allows the user to edit and add his/her own sched-
ules, delivers schedules to the respective appliances, monitors
and logs the operation status of the appliances over a period
of time and generates data for the predictor algorithm. It
provides affinal schedule to the respective appliances through
a home energy network gateway and monitors the state of
each appliance in the network, energy management and DR
are directly related to the user comfort.
Modeling for Residential Electricity Optimization in Dy-
namic Pricing Environments (MREODP) [10]
Energy user is equipped with a smart meter and a small-
scale energy generation and storage unit. These include solar
photovoltaic modules, as mall wind turbine, a stand alone
genset and an energy storage system. The generation and
storage of energy are directly related with user comfort and
bill payment. Every user can sell electricity, when energy is
spare in peak hours.
By keeping in view user comfort and bill payments author
divide home appliances into two categories: controllable and
non-controllable appliances.
Controllable appliances include those domestic appliances
for which user wants zero delay and turn ON and OFF
immediately like TV, microwave oven, lights etc. These are
fully separated from controller.
Non-controllable appliances are air conditioner, freezer
and heater etc. These are flexible loads according to user
preferences and comfort. The electrical system considered
operates over time under several constraints related to physical,
modeling, comfort and electricity markets considerations.
First of all, storage system is constrained by its maximum
capacity, and its charging and discharging rates. User can
utilize this energy in peak hours for user comfort zone. In this
paper, weather condition or forecast is also used for prediction
of weather in coming days. So we will be ready for energy
storage. This paper also focus as on modeling constraints for
user comfort. Appliances should be scheduled over time. So
user gets maximum comfort and payoff.
Comfort Preference Constraints:
The consumers comfort preferences on Trover Dare
transposed into two limit functions Tr,min defined on Dand
discredited according to the subdivision d
i[1, N ]Tr,min
Trthe room temperature, Dis te time horizon and dis
interval in that time horizon.δL
jappliance scheduling factor
and δL
j[1,0] Additionally, based on the consumers comfort
preferences regarding each controllable, non-interruptible
load L, a set of positions Lover which load Lcannot be
functioning is defined:
jL, δL
j= 0 (10)
The prices provided by retailer also effect user comfort and
bill payments.
An Optimal Power Scheduling Method for Demand Re-
sponse in Home Energy Management System(OPSMDR) [11]
Author schedule the power usage for both interruptible and
non-interruptible loads, so electricity cost is reduced. Real
objective of deploying the energy management system (EMS)
in home is to minimize the expanses and peak-to-average
ratio, to manage user comfort and to ensure user security.
This system is based on the smart meters, Home gateway(HG)
and In-Home-Display (IHD) etc, connected through wireless
When HG receives DR information and profile of Real ime
Electricity Prices (RTEP) from the utility company, the EMC
can decide on power scheduling for all interruptible devices
in home. Residential users want to schedule each appliance
between its comfort zone. During the time interval from start
to end appliances are scheduled by EMC via HG.
RTP has much more higher flexibility than Time of Use
Prices (TOUP) and Critical Peak Prices (CPP). So we can
concentrate on appliance scheduling. They have to operate
on low Energy Prices (EP) time. A resident wants to reduce
his/her electricity cost and plans to run most of appliances at
night due to low price time. It reduce user bills.
In this paper, authors set two electricity price levels in
Inclined Block Rates (IBR), and the EP changes every hour.
The EP function is given as:
prch(sh) = ah, if 0shch
bh, if sh> ch
Where, shdenotes the total power consumption in the home
during the hour hth ,ahis the Real Time Electricity Prices
(RTEP) during the hth hour in a day, bhis the second
electricity price level that should be greater than ahand chis
the threshold of power consumption at hour hof IBR. When
the power consumption shis less than or equal to the threshold
ch, the EP would be ah. Otherwise, the EP would be bh, and
the unit is cents/kWh.
Considering that one hour has been divided into five time
slots, authors should make a modification in the EP function.
By dividing shand chby 5, we obtain the total power
consumption value ˆsuand the IBR threshold ˆcuin every
12-minute, respectively. Then, the EP function is altered as:
pˆrchsh) = ˆah, if 0ˆshˆch
bh, if ˆsh>ˆch
After modification, the only difference from the afore men-
tioned function is its format, i.e., the number of variables
becomes 120 instead of 24 due to the time division, whereas
the EP values would be the same as before.
If ˆ
buis a constant value greater than ˆau, the EP would be
fixed at ˆ
buwhenever the total power consumption exceeds
the threshold. This would be the case that if there is a time
interval where the total power consumption exceeds the
threshold, which can happen at any time during the day. If
this time interval arises at the low price time, that would be
acceptable. However, if the corresponding time interval occurs
during the highest price time, the entire power system seems
to be overloaded such that it may damage the system and
yield to a blackout. Therefore, in this paper, author assume that
Where λis a positive value. In the IBR, the second price level
buis changed with ˆau, which means when the normal EP ˆauis
the highest in the day, then ˆ
buis also the highest. In this case,
the circumstance mentioned before would not arise. However
it seems unrealistic to utilize this price function because it is
impossible to obtain the entire EP function ahead of the day.
Genetic-algorithm-based optimization approach for energy
management (GABOAEM) [12]
The better energy manager is the one which is comfortable
to the user. The renewable energy sources like solar panel and
wind turbine also increase user comfort level. In this paper,
authors focus on renewable energy sources and decrease the
demand of fossil fuel by retailer to minimize payment of user.
Time elastic loads are deferrable for some minutes or hours
in order to get little cost or no cost. Their energy utilization
can be agreed according to renewable power supply variation.
So this type of hybrid system is dependent on the renewable
energy generation units, self-discharge units and round trip
efficiencies of energy storage units and on sizing of the system
The design, simulation, and optimization of hybrid power
systems have been the subjects of several studies. Operating
concepts, performance evaluation, and economic analysis of
such systems were investigated where the component sizing
is either arbitrary or based on practical and experimental es-
timates, with no attempt at optimizing their parameter values.
There are variety of heuristic optimization techniques for the
optimal design and operation of the hybrid systems. However,
none of these investigations focused on demand flexibility and
Heavy Air Conditioner (HVAC) loads, in particular, or on
matching HVAC loads with renewable energy sources without
the need for supplementary conventional generations.
The proposed approach is fuzzy C-Means (FCM) based
to obtain clusters for characterizing secondary variables. The
objective function of the FCM is to minimize the distance
from any given data point to a cluster center weighted by that
data points membership grade. An iterative algorithm is used
to update the cluster centers and the membership grades for
each data point, which moves the cluster centers to the most
appropriate location within the data sets. The elbow method
is used to determine the number of clusters. Accordingly, the
days are grouped into 10 clusters with similar periods wind
speed, solar irradiance and load data. Maximum-likelihood
estimates of the distribution parameters are calculated for the
historical hourly data within a cluster. Each cluster is then
represented by three sets of 24 individual Probability Density
Function (PDF)s over the 24 hperiod. Through PDF authors
predict load for coming days and hours by observing the
consumed load by users This is directly related to the users
comfort and payment. If they predicted well, then peak-to-
average ratio remains constant and retailer remains in average
zone with best prices and user gets better bill and minimum
delay in appliances operation.
By getting previous data for wind and Photo Voltaic (PV)
generation and cooling load, authors first normalize both.
Then we assign the scaling parameters αpv and αw to obtain
minimum cost function
MinCost =M in{Cpv .αpv +Cw.αw +Cs.S +Cp.P }(12)
Cpv Installation cost of PV
CwInstallation cost of wind
CsInstallation cost of the storage system
CpCost of power rating of the storage system
SMaximum capacity of the storage system
PMaximum power rating of the storage system
αpv Installed capacity of PV
αw Installed capacity of wind
Load shifting can provide some flexibility. Since the hourly
generation of wind farms and solar plants is not strictly under
human control, load shifting can be useful during periods
of insufficient generation. Implementing smart load control
can reduce the mismatch between renewable generation and
HVAC load. Load shifting is expressed as:
t=LtLs,t (13a)
t+1 =Lt+1 Ls,t (13b)
Ltis load demand at hour t,Ls,t Shifted load demand at
time tand L
tmodified HVAC loads at hour tThe shifted
load is positive and is less than the specific (δ) percentage of
the load at each time t.
0Ls,t δ.Lt(14)
δpercentage of HVAC load to be shifted to the next hour
t. The fitness function is then evaluated, and a confidence
coefficient (γ) is allocated to the probabilistic constraints
the constraints are checked for violation. If there is any
violation, the violated constraints are assigned a large penalty
factor (v)and combined with the fitness function fto give:
f(x, u) = f(x, u) + v[h(x, u)]2(16)
The fitness function of the Genetic-Algorithm (GA) is a
weighted sum of the hybrid system installation costs for all
of the clusters and is given by
F itnessF unction =Min{
cis the cluster number. The weighting coefficient for each
cluster wcis defined as the ratio of the number of days
within that cluster to the total number of days (365 10days).
Minimizing the installation cost of the hybrid system selects
the optimum PV, wind generation, and storage capacity to
supply the HVAC load at the desired confidence level without
the need for supplementary generation.
An algorithm for intelligent home energy management and
demand response analysis (AIHEMDR) [13]
DR is the basic terminology used for the benefit of both
user and retailer. Here authors discuss DR very deeply. In the
context of a residential home, three types of DR automation
levels exist:
a) Manual DR
b) Semi-automated DR
c) Fully automated DR
Fully automated DR is famous automation type that can be
achieved by a Home Energy Management (HEM) system. An
HEM system is accountable for checking and handling the
process of in-home appliances, and providing load shifting
and shedding according to a specified set of requirements.
The first step before the proposed HEM algorithm can
operate is, for a homeowner to set their load priority and com-
fort preference. The specified demand limit is the important
factor to determine the status of appliances in the algorithm.
Any violation in the demand limit will result in turning
OFF selected appliances according to their priority. Customer
preference settings are allowed to be violated from the least
important loads to the most important ones to guarantee the
requested demand limit.
Author present an example in which HEM controls the
clothes dryer by turning OFF its heating coils, while leaving
the motor part running. This is to ensure that the clothes dryer
can resume its operation without homeowner intervention. If
the demand limit is imposed on this house and the clothes
dryer is ON, the HEM is allowed to control the clothes dryer
as needed according to the preset load priority. The clothes
dryer ON time limit, 30 min, can be specified to ensure that the
clothes dryer operates for at least a certain duration before it
can be tuned OFF. The heating coil OFF time limit can also be
specified to prevent excessive heat loss during the clothes dryer
operation. However, these comfort level settings are allowed
to be violated if any loads of higher priority need to operate
to maintain the preset comfort ranges.
If there is a comfort level violation, the HEM decides on the
status of each appliance based on the requested demand limit
level. The HEM sends control signals to change the selected
appliance status. The total household power consumption is
compared with the requested demand limit. If the household
consumption is lower than the demand limit and there is no
comfort level violation, no action is taken. However, with
the comfort level violation of appliance (APP) (Sapp), the
HEM will force to turn ON the selected appliance to minimize
the comfort level violation. If the household consumption is
greater than the demand limit and there is no comfort level
violation, the HEM will turn OFF the lowest priority loads, in
this case starting with the electric vehicle, the clothes dryer and
the space cooling unit, to keep the total power consumption
below the demand limit. If the household consumption is
greater than the demand limit and there is a comfort level
violation of the appliance APP, then the HEM will compare the
priority of all ON appliances with the priority of this appliance,
starting from the lowest priority loads to the highest one. If
the priority of the appliance APP is greater than any other
appliance which is ON, the HEM will shut OFF the lower
priority loads until the appliance APP can be turned ON and
the total power consumption is below the demand limit.
Demand side management in smart grid using heuristic
optimization (DSMSGH) [14] DSM is one of the important
function in a SG that allows customers to make decision
regarding their energy consumption and comfort zone.
In this paper authors propose load shifting technique for
DSM strategy based on load shifting techniques and algo-
rithms. Authors discus retailer side and their energy manage-
ment using mathematical functions and problems. All these
factors are directly attached with user comfort. Mismanage-
ment of energy leads to black out and high rates. So load
shifting of user side in the responde of retailer side, is the
beauty of smart grid.
The transformation of today’s grid towards SG opens new
perspectives on DSM. First, a significant part of the generation
in smart grid is expected to come from renewable energy
resources such as wind and solar. The unpredictability of these
renewable energy sources makes power dispatch functions
in a smart grid challenging. Such a scenario necessitates
the use of load control methodologies. Next, the operation
of SG requires a two way communication between central
controller and various system elements. The designed DSM
system should therefore be able to handle the communication
infrastructure between the central controller and controllable
loads. It maximizes the economical benefits for user as well
as reduce peak loads.
Objective of the DSM could be maximizing the use of
renewable energy resources, maximizing the economic benefit,
minimizing the power imported from the main distribution
grid, or reducing the peak load demand. Smart grid manager
designs an objective load curve according to the objective of
the DSM. The proposed optimization algorithm aims to bring
the final load curve as close to the objective load curve as
possible such that the desired objective of the DSM strategy
is achieved.
Proposed load shifting technique is mathematically
formulated as follows:
(P Load(t)Objective(t))2(18)
Where, objective(t)is the value of the objective curve at
time tand P Load(t)is the actual consumption at time t.
The P Load(t)is given as:
P Load(t) = F orecast(t) + Connect(t)Disconnect(t)
where F orecast(t)is the forecasted consumption at time t,
where Connect(t)and Disconnect(t)are the amount of loads
connected and disconnected at time t, respectively during the
load shifting.
Due to load shifting user gets low prices and can adjust its
appliances. In this way user comfort increases.
Optimal Power Scheduling for Smart Grids Considering
Controllable Loads and High Penetration of Photovoltaic Gen-
eration (OPSSG) [15] is based on Distributed Generation(DG)
especially when DG is based on RER’s. In DG when voltage
injected back into power line then voltage deviation take place.
This deviation is about 2 volts. Controllable loads (CL’s)
also help in promoting the SG. Therefor this paper proposes
a decision technique of an optimal reference scheduling for
DG’s, Battery Energy Storage System (BESS), CL’s and tap
Aim of this paper is to achieve loss reduction in distribu-
tion system by voltage control and power flow smoothing.
Optimization performs on the base of predicted data of load
demand and DG generation. When DG generation is trans-
mitted to the grid station then it will result in increase of
voltage. SO author provide us a method of limiting the range
of voltage deviation. Regarding the PV generation, effects on
the distribution losses reduced because DG transition is not
for long ranges.
Grid provide us the active power but PV generator provide
us the reactive power. So, we can not simply inject the reactive
power in the power line of grid station. First convert the
reactive power into active power. Author provide a maximum
power point tracking(MPPT) function assumed to be already
into the PV unit QDGKis reactive power PDGKis active
BESS is introduced at the connection poiont to smooth
the power flow and prevent from reversing into substation.
In order to change the levels of voltage tap tranxsformers
are used. In this paper,s CL’S are those which can adjust in
different time slots by scheduler. Author provide a function
for CL’s
CL =1
f(P owerDeman d)(21)
Where CL is the maximum allowable increase or decrease
in power consumed by CL’s and f for function. So, the problem
formulation is explain as main objective. In order to minimize
total distribution losses, while satisfying the constraints related
to voltage steps, power and even physical limitation like tap
changing of BESS, DG’s, tap changing transformers and CL’s.
SG is an open and vast research area, full of potential in
research side. Which is rising rapidly because of industrial,
commercial, government, residential users and retailer require-
ments. This work shows a worldwide overview of SH. It also
discusses the importance and labialization of SH components.
The taxonomy of protocols, devices, methods, media, algo-
rithms, and utilities provide an informed comparison between
the corresponding technologies. These techniques are covering
many aspects of SG power distributions, like TOU, RTP,
HAM, DR, DSM, etc. However, we focused on the user
comfort analysis. It helps the user to schedule the appliances
in order to distribute load and reduce peak-to-average ratio,
so, user gets minimum bill and minimum or zero appliances
operation delay. The purpose of this paper is to gather this data
together in a single place and point out the importance and
necessary uptakes to improve the performance in residential
user side.
[1] Aghaei, J., Alizadeh, M. I. (2013). Demand response in smart electricity
grids equipped with renewable energy sources: A review. Renewable and
Sustainable Energy Reviews, 18, 64-72.
[2] Rashed Mohassel, R., Fung, A., Mohammadi, F., Raahemifar, K. (2014).
A survey on Advanced Metering Infrastructure. International Journal of
Electrical Power and Energy Systems, 63, 473-484.
[3] Fadlullah, Z. M., Nozaki, Y., Takeuchi, A., Kato, N. (2011, November). A
survey of game theoretic approaches in smart grid. In Wireless Commu-
nications and Signal Processing (WCSP), 2011 International Conference
on (pp. 1-4). IEEE.
[4] Zhuravlev, S., Saez, J. C., Blagodurov, S., Fedorova, A., Prieto, M.
(2013). Survey of energy-cognizant scheduling techniques. Parallel and
Distributed Systems, IEEE Transactions on, 24(7), 1447-1464.
[5] Alam, M. R., Reaz, M. B. I., Ali, M. A. M. (2012). A review of smart
homesPast, present, and future. Systems, Man, and Cybernetics, Part C:
Applications and Reviews, IEEE Transactions on, 42(6), 1190-1203.
[6] Silva, P. G. D., Karnouskos, S., Ilic, D. (2012, October). A survey towards
understanding residential prosumers in smart grid neighbourhoods. In
Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES
International Conference and Exhibition on (pp. 1-8). IEEE.
[7] Vytelingum, P., Voice, T. D., Ramchurn, S. D., Rogers, A., Jennings, N.
R. (2010, May). Agent-based micro-storage management for the smart
grid. In Proceedings of the 9th International Conference on Autonomous
Agents and Multiagent Systems: volume 1-Volume 1 (pp. 39-46). Inter-
national Foundation for Autonomous Agents and Multiagent Systems.
[8] Qian, L. P., Zhang, Y. J. A., Huang, J., Wu, Y. (2013). Demand response
management via real-time electricity price control in smart grids. Selected
Areas in Communications, IEEE Journal on, 31(7), 1268-1280.
[9] Ozturk, Y., Senthilkumar, D., Kumar, S., Lee, G. (2013). An intelligent
home energy management system to improve demand response. Smart
Grid, IEEE Transactions on, 4(2), 694-701.
[10] Hubert, T., Grijalva, S. (2012). Modeling for Residential Electricity
Optimization in Dynamic Pricing Environments. Smart Grid, IEEE Trans-
actions on, 3(4), 2224-2231.Ozturk,
[11] Zhao, Z., Lee, W. C., Shin, Y., Song, K. B. (2013). An Optimal Power
Scheduling Method for Demand Response in Home Energy Management
System. Smart Grid, IEEE Transactions on, 4(3), 1391-1400.
[12] Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M. S., Baghzouz,
Y. (2013). Genetic-algorithm-based optimization approach for energy
management.Power Delivery, IEEE Transactions on, 28(1), 162-170.
[13] Pipattanasomporn, M., Kuzlu, M., Rahman, S. (2012). An algorithm
for intelligent home energy management and demand response analysis.
Smart Grid, IEEE Transactions on, 3(4), 2166-2173.
[14] Logenthiran, Thillainathan, Dipti Srinivasan, and Tan Zong Shun. ”De-
mand side management in smart grid using heuristic optimization.” Smart
Grid, IEEE Transactions on 3.3 (2012): 1244-1252.
[15] Ziadi, Z., Taira, S., Oshiro, M., Funabashi, T. Optimal Power Scheduling
for Smart Grids Considering Controllable Loads and High Penetration of
Photovoltaic Generation.
... To reduce energy consumption and carbon emission, several works had proposed various energy management systems [1,2] for smart grids [3] or smart homes [4]. The main purposes of original energy management systems are to reduce the energy consumption, peak load, and electricity cost. ...
... On the contrary, delaying the starting time of an automatic washing machine, the user comfort may be rarely degraded. Hence, related problems such as the user comfort, reduction in electricity bill, electrical appliance scheduling, and required addition of renewable energy sources (RES) had been surveyed in [3]. Undoubtedly, a modern home energy management system (HEMS) must not only decrease the power consumption and electricity bill, but also preserve the user comfort. ...
Full-text available
To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user's QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user's QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user's QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user's QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user's QoE.
... (b) Application of recommendation system to intelligent services and e-commerce: Intelligent energy management services based on actionable recommendation and analytics have been seen to give consumers the motivation for energy conservation, and identify problems in their energy behaviors (Shin and Hwang, 2012). By suggesting information about suitable products to consumers, recommender systems have emerged as the cornerstone of e-commerce platforms that provide services associated with competition (Li et al., 2016;Mahmood et al., 2015). One of the major advantages of intelligent techniques in home energy management is the ability to aid reduction in power consumption and support DSM (Ayres et al., 2018). ...
Full-text available
Innovative solutions targeting improvements in the behavior of energy consumers will be required to achieve desired efficiency in the use of energy. Among other measures for stimulating consumers’ behavior changes based on attention triggers, personalized recommendations are essential to enhance sustainable progress towards energy efficiency. In light of this challenge, the current study focuses on innovative energy services that are based on intelligent recommendation systems and digital twins. We review several trends associated with the modeling and diffusion of energy services, taking into account the positive interrelationships existing between recommendation provisions and demand-side consumer energy behavior. This is achieved by means of a content analysis of the state-of-the-art works, focusing on the IEEE Xplore and Scopus databases. Based on this review, we present new empirical evidence to validate data-driven twin technologies as novel ways of implementing consumer-oriented demand-side management via sophisticated abstraction of consumers energy behaviors, and identify various barriers associated with the adoption of energy services, especially as they relate to the implementation and overall adoption of the digital-twins concept. Lastly, we use the review to summarize a coherent policy recommendations related to the wide-spread adoption of the digital-twins concept, and demand-side management solutions in general.
... To provide a suitable solution for end-users' representation capable of managing energy resources, it is needed to integrate energy management solutions, such as demand side management [59], active participation in demand response programs [60], energy forecasting [61], scheduling algorithms [62] and learning algorithms [63]. Energy management related algorithms are seen in µGIM platform as Energy Strategies. ...
Microgrids bring advantages to end-users and to the smart grid environment. However, adequate management software, enabling bringing to the field new energy management concepts, is not available yet. Small, single-tasked, software is usually proposed and tested while a clear overall system architecture for microgrid management required to take full advantage of the microgrids’ potential. Previous publications usually focus on energy-related problems and do not provide an efficient and viable solution for players’ representation and microgrid operation. This paper proposes a complete architecture for a microgrid management system based on a multi-agent approach – µGIM – allowing the easy implementation of different energy strategies. The µGIM agents can independently manage local resources while able to collaborate and/or compete with other agents. Designed to run in single-board computers, µGIM agents are light-weighted and easily deployed in buildings. To demonstrate these capabilities, the paper details and presents a microgrid deployment using µGIM solution.
... The parameters to be considered for human comfort were shown in [7]- [9]. The author in [10] presented three core elements to consider for the adoption of this technology in smart buildings: Heating, ventilation and air conditioning (HVAC) modelling and controlling [11], [12], monitoring and prediction of electrical energy consumption and demand response through Smart Grid integration. ...
... 29 This system can also optimize the operational schedule of 30 home appliances and simultaneously manage the distributed 31 energy resources and storage [5]. The overall architecture of a 32 typical HEM with demand response (DR) is shown in Fig. 1. 33 A smart meter is commonly installed at home and constantly 34 in communication with a smart grid via the Internet, which 35 links customers and utilities [6]. 36 The system consists of a personal computer (PC), which 37 acts as a control center with software and communica-38 tion protocols, such as WiFi, ZigBee, Bluetooth, and KNX. ...
Full-text available
The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.
... Building energy management systems (BEMS) are one solution used by residential building operators to efficiently balance the electricity demand and electricity supply of buildings and to minimize the electricity cost, while maintaining the occupant's comfort [5,7]. Recent research has been conducted to study the implementation of BEMSs in residential buildings [8][9][10][11][12][13]. However, due to the dynamic nature of occupants' behavior, the electricity consumption may be different over time [14][15][16]. ...
Full-text available
Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.
... These services, together with Vehicle-2-Grid (V2G) operations and the intelligent use of the ESS, allow to reduce the stress caused by the MG to the connected distribution grid in order to get incentives, avoid penalties, reduce both the consumptions and the operational costs, which strictly depend on the energy price policies adopted by the distribution grid. Concerning this topic, in (Kirschen, 2003;Amer et al., 2014) are discussed the development of new energy policies which will involve the costumer to assume an active role in the energy market by means of the application of DR services. In this work, a procedure based on computational intelligence techniques for the data driven synthesis of an EMS is proposed. ...
Conference Paper
Microgrids (MGs) play a crucial role for the development of Smart Grids. They are conceived to intelligently integrate the generation from Distributed Energy Resources, to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well. In this work it is proposed a novel synthesis procedure for modelling an Adaptive Neuro-Fuzzy Inference System (ANFIS) featured by multivariate Gaussian Membership Functions (MFs) and first order Takagi-Sugeno rules. The Fuzzy Rule Base is the core inference engine of an Energy Management System (EMS) for a grid-connected MG equipped with a photovoltaic power plant, an aggregated load and an Energy Storage System (ESS). The EMS is designed to operate in real time by defining the ESS energy flow in order to maximize the revenues generated by the energy trade with the distribution grid. The ANFIS EMS is synthesized through a data driven approach that relies on a clustering algorithm which defines the MFs and the rule consequent hyperplanes. Moreover, three clustering algorithms are investigated. Results show that the adoption of k-medoids based on Mahalanobis (dis)similarity measure is more efficient with respect to the k-means, although affected by some variety in clusters composition.
Home energy management systems can be defined as systems responsible for monitoring and managing electricity demand to optimize energy consumption. According to a report by the US Energy Information Administration, by 2030, the gap between electricity generation and consumption will reach more than 2 billion GWH, of which 30% of the demand is for the domestic sector, where energy management systems play an essential role in bridging the gap between production and demand. With the expansion of smart homes and companies that work in this field, many activities have been done so far to improve consumption patterns, demand response, monitoring, and control of high-consumption equipment. Home energy management systems consist of five parts: monitoring, control, management, logging, and fault detection. All these components must be directly or indirectly connected to the power company, renewable energy sources, and all household appliances (controllable or uncontrollable). This chapter comprehensively describes home energy management systems and reviews all the existing infrastructure and technologies to achieve the mentioned goals.KeywordsHome Energy Management Systems (HEMS)Demand Response (DR)Smart gridsSmart homeIntegrated wireless technology
Full-text available
This survey paper is an excerpt of a more comprehensive study on Smart Grid (SG) and the role of Advanced Metering Infrastructure (AMI) in SG. The survey was carried out as part of a feasibility study for creation of a Net-Zero community in a city in Ontario, Canada. SG is not a single technology; rather it is a combination of different areas of engineering, communication and management. This paper introduces AMI technology and its current status, as the foundation of SG, which is responsible for collecting all the data and information from loads and consumers. AMI is also responsible for implementing control signals and commands to perform necessary control actions as well as Demand Side Management (DSM). In this paper we introduce SG and its features, establish the relation between SG and AMI, explain the three main subsystems of AMI and discuss related security issues.
Full-text available
A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.
Conference Paper
Full-text available
The smart grid and its promises have sparked world-wide research and demonstration projects, some of which focus on how to empower its users with better tools to monitor, understand, and manage their energy behaviour. It is often seen, however, that the providers of the tools may be driven by a technology push, and hence make assumptions that may fall a short of users' expectations. The lack of understanding on the real-world needs of the users as well as the impact of new technologies and tools may lead to the underestimation or the abandonment of innovative approaches. The issues of how the future smart city residential prosumer will benefit from the future smart grid services and what trade-offs s/he will be willing to make are hot topics. Here, a survey is presented that focuses on these aspects, and especially on the goal of providing new value-added energy services to end-users. An analysis of the survey results and key messages identifies research gaps and proposes promising directions that may be followed when designing applications and services for the residential smart grid prosumers.
Conference Paper
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
Dealing with Renewable Energy Resources (RERs) requires sophisticated planning and operation scheduling along with state of art technologies. Among many possible ways for handling RERs, Demand Response (DR) is investigated in the current review. Because of every other year modifications in DR definition and classification announced by Federal Energy Regulatory Commission (FERC), the latest DR definition and classification are scrutinized in the present work. Moreover, a complete benefit and cost assessment of DR is added in the paper. Measurement and evolution methods along with the effects of DR in electricity prices are discussed. Next comes DR literature review of the recent papers majorly published after 2008. Eventually, successful DR implementations, around the world, are analyzed.
The distributed generator (DG) has a huge economical and environmental potential, especially if it is based on renewable energy sources (RESs). It is very important for the future development of smart grids. However, high penetration of DGs into distribution systems can cause voltage deviations beyond the statutory range, and reverse power flow toward the substation transformer. Consequently, it can increase the distribution system losses if it is not well supervised. Thus, in order to meet smart grid objectives, DGs have to be controlled in coordination with other power resources existing in the distribution system. Controllable loads (CLs) can also help in promoting smart grids through demand response (DR) application. Therefore, this paper proposes a decision technique of an optimal reference schedule for DGs, battery energy storage system (BESS), CLs, and tap changing transformers. The main objective of the proposed optimization problem is to achieve loss reduction in the distribution system. However, other aims such as voltage control and power flow smoothing have been achieved. The optimization is performed based on predicted values of load demand and DG generation. Simulations are conducted for one operation day to illustrate the optimality of the proposed scheduling method and to assess the impact of CLs in smart grids.
Conference Paper
The concept of smart grid to transform the age old power grid into a smart and intelligent electric power distribution system is, currently, a hot research topic. Smart grid offers the merging of electrical power engineering technologies with network communications. Game theory has featured as an interesting technique, adopted by many researchers, to establish effective smart grid communications. The use of game theory, to date, has offered solutions to various decision making problems, ranging from distributed load management to micro storage management in smart grid. Interestingly, different researchers have different objectives or problem scopes for adopting game theory in smart grid. Nevertheless, all the game theoretic approaches for making effective smart grid solutions have a common aspect, namely the Nash equilibrium, arriving at which may lead to an optimal solution to the relevant problem. In this paper, we survey a number of game theory-based applications to solve different problems in smart grid. This survey reveals that game theory can be apparently simple yet become an effective technique to facilitate intelligent decision making in smart grid frameworks.
Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs which offer incentives to customers who curtail their energy use during times of peak demand. In this paper, we propose an integrated solution to predict and re-engineer the electricity demand (e.g., peak load reduction and shift) in a locality at a given day/time. The system presented in this paper expands DR to residential loads by dynamically scheduling and controlling appliances in each dwelling unit. A decision-support system is developed to forecast electricity demand in the home and enable the user to save energy by recommending optimal run time schedules for appliances, given user constraints and TOU pricing from the utility company. The schedule is communicated to the smart appliances over a self-organizing home energy network and executed by the appliance control interfaces developed in this study. A predictor is developed to predict, based on the user's life style and other social/environmental factors, the potential schedules for appliance run times. An aggregator is used to accumulate predicted demand from residential customers.
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
This paper proposes a real-time pricing scheme that reduces the peak-to-average load ratio through demand response management in smart grid systems. The proposed scheme solves a two-stage optimization problem. On one hand, each user reacts to prices announced by the retailer and maximizes its payoff, which is the difference between its quality-of-usage and the payment to the retailer. On the other hand, the retailer designs the real-time prices in response to the forecasted user reactions to maximize its profit. In particular, each user computes its optimal energy consumption either in closed forms or through an efficient iterative algorithm as a function of the prices. At the retailer side, we develop a Simulated-Annealing-based Price Control (SAPC) algorithm to solve the non-convex price optimization problem. In terms of practical implementation, the users and the retailer interact with each other via a limited number of message exchanges to find the optimal prices. By doing so, the retailer can overcome the uncertainty of users' responses, and users can determine their energy usage based on the actual prices to be used. Our simulation results show that the proposed real-time pricing scheme can effectively shave the energy usage peaks, reduce the retailer's cost, and improve the payoffs of the users.