Performance evaluation of experimental setups in
home energy management systems in smart grid
W. Naeem1, N. Javaid1,∗, Z. A. Khan2, U. Qasim3,
A. Mahmood1, Z. Mehmood1, A. Ahmad1, F. Ali4, W. Sarwar4
1COMSATS Institute of Information Technology, Islamabad, Pakistan2CIS, Higher Colleges of Technology, Fujairah Campus, UAE
3University of Alberta, Alberta, Canada4Grafton College, Islamabad, Pakistan
∗www.njavaid.com, [email protected], [email protected]
Abstract—Now a days, smart grid is an emerging technologyto increase the optimal use of generated power and reducethe electricity cost. Globally, various schemes in this aspecthave been proposed like DR, DSM, AMI, RTP, TOU, etc. Thispaper presents a survey on some scheduling and optimizationtechniques for home appliances to use the energy efficiently with aminimum cost. The focus of this paper is on experimental resultsand test beds of different schemes to evaluate their performance.This paper also presents critical analysis on the Home EnergyManagement (HEM) scheduling schemes based on their results.
Index Terms: Smart grid, optimization techniques, energymanagement system, DSM, TOU, RTP, IBR.
I. RELATED WORK
In [1], authors focus on techniques for reducing the energy
consumption and peak electricity demands. They also review
the communications and network technologies for HAN, for
interworking the HEM to end points and smart meters. Authors
survey different network technologies as home plug, X10,
Z-wave, EnOcean etc. They also discuss the software and
hardware tools, platforms and test beds for evaluating the
performance of information and communication technologies.
N-Javaid et al. [2] present a review of various HEM
schemes that have been proposed that lead towards efficient
consumption of electricity and reduce the users bill. Different
schemes are discussed in this paper, such as Optimized Based
Residential Energy Management (OREM), In-Home Energy
Management (iHEM), appliance coordination, optimal and
automatic residential energy consumption scheduler. It also
surveys general HEM systems architecture and challenges to
implement this architecture in smart grid.
Authors present a survey in [3] on DR potentials and
benefits in smart grid. Innovative enabling technologies and
systems such as smart meters, energy controllers and commu-
nication systems are described and discussed with reference to
real industrial case studies and research projects. Technologies
such as smart meters, AMI, home energy controllers, EMS,
wired and wireless communication systems are required to
improve the reliability of the power system and lowering the
peak demand. Demand response (DR) offers a broad range
of potential benefits on system operation and reduces overall
plant and capital cost investments and postpones the need for
network upgrades.
Siano, P. et al. [4] mainly focus on DSM and DR, shiftable
load scheduling methods and peak shaving techniques. Using
new techniques, grid capacity, efficiency, reliability, power
quality, sustainability and lowering the carbon foot can be
improved. All of the discussed DSM techniques are based
on load shifting technique. Most of them use scheduling
algorithms to optimize the objective demand profile.
The background and motivation of communication infras-
tructures in smart grid systems is presented in [5]. A smart grid
built on sensing, communications, and control technologies
offers a very promising future for utilities and users. Through a
communication infrastructure, a smart grid can improve power
reliability and quality to eliminate electricity blackout. The
consumers can minimize their expenses on energy by adjusting
their intelligent home appliance operations to avoid the peak
hours and utilize the renewable energy instead.
II. BACKGROUND: ELEVEN HEM TECHNIQUES IN BRIEF
In this section, we briefly discuss the seven selected existing
HEM techniques.
A. Demand Response Management via real time pricing
In [8], a real time pricing technique is presented that
helps to reduce peak to average ratio by managing DR. Two
algorithms are proposed; one is to implement the electricity
consumption scheduling and other is implemented at retailer
side. It supports the consumer economically to maximize the
payoff and helps the retailer to design the real time prices to
maximize the profit.
B. An intelligent home energy management system to improve
demand response
Authors in [9], propose an integrated solution to curtail
the use of energy during peak hours. The presented system
includes ANFIS model to predict and a branch and bound
based appliance scheduler. Predictor predicts the optimal run
time while scheduler schedules and controls the residential
loads to save the huge amount of energy.
C. Modeling for Residential Electricity Optimization in Dy-
namic Pricing Environments
Hubert et al. [10] focus on scheduling home appliances,
home grid components and storing of power in dynamic
pricing environment. It provides solution to optimize the house
hold energy consumption according to users preferences. This
helps the user in increasing the huge savings.
D. An Optimal Power Scheduling Method for Demand Re-
sponse in Home Energy Management System
Authors in [11] describe general architecture of Energy
Management System (EMS) and then present an efficient
scheduling scheme to reduce the electricity cost and Peak-
to-Average Ratio (PAR). If we use Real Time Pricing (RTP)
model, it just only reduces the electricity cost but PAR
becomes high. The proposed scheme uses the RTP model,
combined with Inclined Block Rate (IBR) model. Using this
combined model, the electricity cost as well as PAR is reduced.
E. Genetic-algorithm-based optimization approach for energy
management
An optimized technique to increase the efficiency of hy-
brid system (renewable energy and energy storage) to meet
the controllable Heating, Ventilation and Air Conditioning
(HVAC) load is presented on [12]. In order to minimize the
cost of Photo Voltaic (PV) and wind generation, Genetic-Based
(GA) optimization approach is used. Minimizing cost function
shows minimum PV and wind generation installation.
F. An algorithm for intelligent home energy management and
demand response analysis
Pipattanasomporn et al. [13] present an intelligent HEM
algorithm for DR applications. Proposed HEM algorithm
schedules and effectively controls the appliances to keep the
total household consumption below the demand limit.
G. Demand side management in smart grid using heuristic
optimization
The proposed DSM scheme is a day-ahead load shifting
technique that can be applied at each type of area (residential,
commercial and industrial). Suggested DSM strategy is used
to reduce the energy consumption and provide the benefits not
only to the end user but also to the retailers by reducing PAR.
H. Autonomous Appliance Scheduling for Household Energy
Management
Authors in [15] address the challenge of automation of DR.
This automation is done by accurate load forecasting. For
this purpose, new approach of predicting the hourly energy
demand of appliances, is proposed. Further, depending on
predicted load, load clustering technique and energy pricing
frameworks are also been introduced. This work also presents
the DSM technique to use the energy below the specified limit.
For efficient energy consumption, smart scheduling of home
appliances has also been proposed.
I. Optimal Power scheduling
Zakaria Ziadi et al. in [16] propose an optimized schedul-
ing technique for Distribution Grids (DGs), Battery Energy
Storage Systems (BESSs) and tap changing transformers. The
integration of Control Loads (CLs) has also been presented.
The main purpose of this paper is to reduce the distribution
losses and to prevent the reverse flow of voltage towards
substation. This objective is achieved using Particle Swarm
Optimization (PSO).
J. Appliance Commitment for Household Load Scheduling
Appliance commitment algorithm has been presented in [17]
to optimally schedule the thermostatic appliances to reduce
the energy cost or increase the user comfort. This algorithm
is designed by modeling electric water heater as an example.
Two step scheduling process has also been proposed. First
step is scheduling the day-ahead loads based on forecasted
prices and second step is real time adjustment. This two
step process enables the adjustments in scheduling because
of uncertainty produced by forecast error in prices and con-
sumption. Optimization process is: Step1: Obtain a forecasted
day-ahead price curve. Step2: Estimate the duration of water
heater needs to be on to warm up the water in tank. Step3:
Find the price depending on duration of ON time of water
heater. Step4: Check whether the comfort limits are violated
or not. If violation occurs at instant t1, subdivide the time
interval into [t0, t1] and [t1, 24] Step5: Repeat the process
for new interval until comfort level occurs and then find the
total price. (See the detailed discussion on this mode, given
in paper)
K. Management and Control
Three step control technique is proposed in [18] to manage
the energy production and utilization efficiently. The control
strategy includes local prediction, global planning and real
time scheduling(control). The main motive of this work is to
optimize the efficiency of current grid, support the penetration
of renewable sources and use of energy efficiently.
III. PERFORMANCE EVALUATION OF EXPERIMENTAL
SETUP OF CHOSEN HEM TECHNIQUES
In this section, we discuss the performance of selected HEM
schemes. Simulations are presented to show the performance
of HEM techniques.
A. Demand response management via real-time electricity
price control in smart grids
Authors assume that 100 users are attached with a smart grid
where each user has 4 elastic and 2 semi-elastic appliances.
The time scheduling horizon is H = {1, 2, ..., 12}.Proposed Simulated Annealing based Price Control (SAPC)
algorithm is being used to compute the optimal flat rate price
and optimal real time price. Fig. 1 depicts that real time
pricing scheme reduces the peak to average ratio by 20% as
compared to flat rate pricing scheme. It is clear from figure that
increase in electricity price leads to reduction in total energy
consumption.
Fig. 1. The total energy consumption under different settings of price.
B. An intelligent home energy management system to improve
demand response
The proposed ANFIS model and a branch and bound based
appliance schedular are used for simulation results by using
Time Of Use (TOU) pricing for home energy management
system.
1) Prediction: ANFIS model is used to predict the ap-
pliance switching ON time and its duration of operation.
It is then divided into two models. First model is used to
predict the appliance switching ON time and the other model
predicts its duration of operation. The data of five weeks
was used for training and testing the ANFIS model. Fig. 2
shows the prediction results over one week for two schedulable
appliances; air conditioner and washing machine.
Fig. 2. Prediction result for home appliance usage for (a) air conditioner, and(b) washing machine. Here, blue line and red line represent the patterns forgenerated data and the predicted results, respectively.
The result shows that ANFIS model is useful in predicting
the home appliance usage pattern.
2) Scheduling: Assume that we want to schedule some
home appliances. The peak hours are from 7:30 P.M. to 7:00
A.M., the time between 7:00 A.M. to 2:00 P.M. is normal
and 2:00 P.M. to 7:30 P.M. are off-peak hours. Fig. 3 shows
the graphical representation of power availability and cost of
electricity during different times of the day.
The branch and bound algorithm, applied to above schedul-
ing problem, gives the optimal time of operation for appliances
which are 8:00 A.M., 9:08 A.M. and 10:30 A.M. for dish
washer, washing machine and dish dryer respectively.
Fig. 3. An example of power availability and cost of electricity duringdifferent times of the day used in simulation.
C. Modeling for Residential Electricity Optimization in Dy-
namic Pricing Environments
Different scenarios and their simulation results are discussed
in this section. A simulation scenario consists of home grid
congiguration, number of retailers and a proposed algorithm.
All scenarios are simulated for time horizon D = 24h with
τ′
= 5min and τ = 2.5min. The price signals used are the
actual price signals provided by the retailer.
For flat price scenario, assuming the electricity
provider(µ = 1), reference case is compared with optimized
case. Under optimized case, the objective function C is
$12.16 with PAR 2.07 and under reference case C = $12.38and PAR 4.02. This shows that optimized case outperforms
the reference case.
In scheduling controllable loads, the economic gain varies
from 9.8% to 28.3%. If Energy Storage System (ESS) is
present in house, the proposed method outperforms the ref-
erence case by 21.2% to 37.7%. If consumer has a solar panel
connected to his house, the proposed method improves the
objective function by 34.9% to 86.6%. Similarly, if the option
of selling power to Point of Common Coupling (PCC) is
available the proposed method advances the objective function
by 23.3% to 53.3%.
1) Comparison with perfect forecasts: The proposed
method reduces uncertainty of cost by comparing traditional
non-controllable consumptions with day-ahead power genera-
tion. In fact, there is less probability that the actual value of
cost will be greater than proposed value of algorithm.
The proposed algorithm notably reduces the PAR as com-
pared to Reference case Real-Time pricing (R-RT) and Refer-
ence case Day Ahead pricing (R-DA) because of better control
scheme of HVAC system and pricing mechanism.
D. An Optimal Power Scheduling Method for Demand Re-
sponse in Home Energy Management System
This paper proposes the scheme to reduce the electricity
cost and PAR. The simulations are carried out to present the
performance of proposed scheme for home appliances power
scheduling.
1) Impact Of AOAs: in this paper, authors consider nine
kinds of Automatically Operated Appliances (AOAs) and they
are 16 in numbers.
Electricity cost and Delay time Rate (DTR) are inversely
proportional to each other. The simulation results in fig 4
shows that when the value of DTRavg increases, the value
of electricity cost decreases.
Fig. 4. Trade-off between electricity cost and average delay time rate.
From figure, at DTRavg=0, the major consideration is to
minimize time delay so in this case w1 = 0 and w2 = 0 and
that for minimum electricity cost, w1 = 0 and w2 = 0.Impact of Inclined Block Rate:
In this section, we focus only on minimizing electricity cost.
We consider comparison of electricity cost and PAR with and
without the proposed approach. The fig. 5 leads us to the result
that proposed scheme reduces both the electricity cost and
PAR. By applying this scheme, the average daily electricity
cost of 48.67 cents with PAR=5.22 decreases to 35.97 cent
with PAR=3.37.
Simulation result shows that proposed RTP combined with
IBR is a better way to reduce PAR. The proposed scheme also
performs good in eliminating peak power demand. Two power
consumption profiles are taken to demonstrate the effectiveness
of RTP combined with and without power scheduling based
on RTP are shown in fig 6.
2) Impact Of MOAs: The proposed scheme is also effective
for Manually Operated Appliances (MOAs) operations. Using
this scheme, the average electricity cost reduces from 65.11
cents with PAR=4.26 to 55.01 cents with PAR=3.42 as shown
in fig. 7. These results for MOAs and AOAs show that pro-
posed approach is always effective for residential customers.
E. Genetic-algorithm-based optimization approach for energy
management
Authors examine two scenarios for an energy management
system. The first is simplified and deterministic and other is
stochastic.
1) Scenario I: It corresponds to deterministic wind gener-
ation, PV generation and cooling load for a residential feeder
over a single day. Two different cases are studied using GA-
based optimization.
Fig. 5. The impact of the proposed power scheduling approach on: (a) dailyelectricity cost and (b) PAR.
Fig. 6. The impact of IBR in the proposed approach on PAR.
Case Study 1:
The simulation results in fig. 8 show that in the absence
of load shifting, the modified cooling load is the same as the
cooling load during hours 1− 15. Storage shows the state ofcharge of the storage system at each hour. As shown in figure,
wind generation is always included in the optimal solution
since wind installation cost per megawatt is less than PV
installation cost. The storage system is charged in the early
hours of the day when more energy is available and the load is
low. It releases the stored energy stored when the load exceeds
generation. During peak hours when the wind is low and the
storage is not sufficiently charged, load shifting dispatches the
Fig. 7. The impact of MOAs operations on: (a) daily electricity cost and (b)PAR.
flexible loads to hours when the wind energy is in excess of
the load.
Fig. 8. Simulation result for Case Study I (Scenario I).
For this case study, the optimum PV, wind installation, and
storage capacity are 0.0, 1.9, and 2.92 MWh respectively. Total
installation cost of the system is $4.615× 106 ($3.80× 106 forwind and $0.815× 106 for storage).
Case Study 2:
This case study evaluates the possibility of matching HVAC
load and PV plants, which is particularly important for regions
with poor wind speed profiles and an abundance of solar
energy. The simulation results show that by installing more PV
capacity and storage capacity, the load can be fully supplied
without wind energy generation.
2) Scenario II: It corresponds to stochastic models of wind
generation, PV generation, and cooling load. In this scenario,
two cases are presented.
Case Study 1:
The GA-based optimization problem is solved by con-
sidering the probabilistic PV generation, wind generation,
and cooling load. Fig. 9 shows the cumulative distribution
functions FS(s) of the storage capacity(S) for each Load
Shifting (LS).
Fig. 9. Cumulative distribution of the maximum capacity of the storage systemfor different LS percentages (Scenario II, Case Study I).
Case Study 2:
This case study evaluates the possibility of matching HVAC
load and PV plants for regions with poor wind speed profiles
and an abundance of solar energy. For both S and EE, the
cumulative probabilities increase with LS. Thus, increasing the
LS from 10 to 50% provides the system with more flexibility.
F. An algorithm for intelligent home energy management and
demand response analysis
Simulations show that proposed scheme control and man-
age the appliances operation to keep the total house hold
consumption below the given demand limit. To demonstrate
the efficiency of proposed approach, a house and its different
parameters are considered.
In this paper, authors assume that during peak period (5
P.M.-10 P.M.), a demand limit is imposed on this house.
Different demand limits(8kW, 6kW, 4kW) are considered to
test the efficiency of proposed scheme. Simulations are taken
from [7]. Fig. 5(b)-(d) show the simulated results for 8kW,
6kW and 4kW demand limit respectively, during peak hours.
It shows that proposed HEM effectively controls the ap-
pliances to keep the total household consumption below the
demand limit. However, if demand limit is lower than the total
household consumption then user has to sacrifice the comfort
levels. On the other hand, a low demand limit may result in
creating a high peak during an off-peak demand when DR
ends.
Here it is significant to know the lowest possible demand
limit level that can be assigned to a house before any violation
occur. The lowest possible demand limit for a house depends
on rated power (kW), appliance usage pattern, comfort level
settings, house parameters and duration and start time of
DR event. Authors are interested to find the lowest possible
demand limit before comfort level violation or high load con-
sumption occurs in different scenarios. See table for different
simulation scenarios.
The DR simulations for case 1 and 3 are taken from [7]. For
case 1, no violation occurs at demand limit of 8kW, however,
when the demand limit is below 7kW, high load compensation
is offered at DR event.In case 3, the lowest possible demand
limit must be atleast 8.6kW. If the demand limit is below 8.6
kW, comfort level violation can occur. However, for 9kW no
comfort level violation occurs.
G. Demand side management in smart grid using heuristic
optimization
Simulations are carried out to show that proposed algorithm
has efficiently managed the large number of controllable loads.
It also brings reduction not only in utility bills but also in peak
load demand.
1) Effect On Utility Bills: The simulation results obtained
for residential area is shown in fig. 10. After applying the DSM
strategy, the saving in utility bills for these three types of areas.
The reduction in bills for industrial area is comparatively high
because even small load shifting of high power devices results
in huge saving for customers.
Fig. 10. DSM results of the residential area.
2) Effect On Peak Load Demand: The DSM scheme re-
duces peak load demand for each area. Reduction in peak
load demand not only improves grid sustainability but also
increases cost savings for generation companies. When load
demand reduces, the operating cost of generators reduces and
this increases the reserve generation capacity of system.
H. Autonomous Appliance Scheduling for Household Energy
Management
In this section, evaluation of proposed DSM scheme is pre-
sented. Set of twenty two home appliances is used to simulate
the daily energy consumption of a house. As discussed in [1],
three different pricing frameworks are considered to evaluate
the performance of presented scheme.
Case1-Real Time Pricing: In this case, those customers
are considered who have not installed any microgrid at their
home. Therefore, they totally depend on main power grid to
fulfill their home energy requirements. They can only save
the billing cost by consuming the energy efficiently. Fig. 11
shows the results of unscheduled load energy cost under flat
and dynamic pricing systems.
Fig. 11. Unscheduled load energy cost.
Fig. 12. Scheduled load energy cost.
It is clear from figure 11 that real pricing scheme is costly
than flat rate system by 6.7%. On the other hand, dynamic
tariff scheme is cheaper than flat rate pricing method by 8.54%
during peak hours (fig 12). Under real time pricing, a further
comparison between unscheduled and scheduled loads reveals
that 10.92% savings can be made by scheduling loads.
Case2- Feed In Tariff (FIT): Under FIT plan, utility
company offers a purchase agreement to the producers of
PV energy for all the energy they produce. For simulation,
we assume that a house purchases power from the retailer
at 0.13/kWh while it sells the PV energy to main grid at
0.15/kWh. After calculations, 9.43% of cost saving can be
made under FIT as compared to grid dependent customer.
The authors also considered two scenarios in this case. First
scenario corresponds to household that uses produced PV
power without scheduling its loads. Fig. 13 shows the first
scenario energy cost under FIT plan. It can be seen that energy
bill is 4.162% lower than that of traditional customer (grid
dependent). Second scenario corresponds to household that
uses generated PV energy with scheduling its loads. Fig. 14
shows that energy cost is lower as compared to first scenario
and 5.9% lower than that of grid dependent customer.
Fig. 13. Unscheduled load energy cost under FIT plan.
Fig. 14. Scheduled load energy cost under FIT policy.
Case3- Net Purchase and Net Sale: In this arrangement,
the PV production and household power requirement are com-
pared instantaneously. For simulations, they are compared on
hourly basis. Like previous cases, Energy cost for scheduling
and non-scheduling loads are observed in this case. Fig. 15
shows the load scheduling energy consumption case. Positive
values show the purchased energy while negative values are
for PV sold to grid. The comparison between energy costs of
net sale/net purchase, flat rate and dynamic pricing scheme
is presented in fig. 16. It is clear that net purchase/net sale
pricing is cheaper than dynamic pricing by 8.967% and the
flat pricing plan is higher than that of real time pricing by
9.33%. Fig. 17 shows the energy cost for unscheduled load
consumption under net sale/net purchase plan. It is evident
that this plan still benefits the customer than both others. In
this case, real time pricing is lower than flat rate by 8.607%.
Fig. 15. Unscheduled load energy cost.
Fig. 16. Scheduled load energy cost under net purchase and net sale plan.
Fig. 17. Unscheduled load energy cost under net purchase and net salescheme.
I. Optimal Power scheduling
In order to demonstrate the effectiveness of proposed
scheduling method and effect of integration of CLs in SG,
three cases have been studied which are as follow:
Case1: This case study refers to scheduling of tap positions
of transformers to reduce distribution losses and optimize the
voltage within the possible range. Fig. 18 shows the simulated
results of this case. A voltage rise occurs in the system due to
huge PV generation and a voltage drop at night due to peak
demand as shown in fig. 18a (dashed lines show the maximum
and minimum limits of variables). As tap transformers could
not follow the voltage variations, so they decrease slowly at
day times and then increase at night to cover the voltage drop.
Fig. 18(b) shows that reverse power flow occurs due to PV
generation at day times.
(b)
(a)
Fig. 18. Simulation results of case 1. (a) Node voltages. (b) reactive powerflow at connection point..
Case2: This case study involves the reference schedule
optimization for DGs, BESSs and tap transformers. However,
this study does not include CLs. The simulation results are
taken from [16] (fig. 8). The node voltages in fig. 8(a) are
within the specified range despite of high voltage generation at
day time. Fig. 8(c) shows that tap positions of LRT and SVRs
are optimized which means they are controlling the voltages
and hence reducing the distribution losses. Figs. 8 (d and e)
show the active and reactive powers at connection point. In the
figure, no violation occurs and flow is smooth which indicates
no reverse flow towards substation. This reduces the harm to
substation transformer. Fig. 8(f) shows the active and reactive
power of BESS. It absorbs the excessive power produced
by the DGs, maintaining the node voltage and opposing the
reverse power flow.
Case3: This case study corresponds to optimization of DGs,
BESSs and tap transformers considering CLs, which means
DR is applied to the system in this case. The simulation
results are taken from [16] (fig. 9). The fig. 9a shows the
optimized node voltage. However, voltages in this case are
more concentrated as compared to case 2. This increase in
voltage can affect the substation. The active and reactive
powers of DGs are smooth like in case 2 and no reverse flow
towards substation occurs. Thus, the optimization constraints
are totally satisfied within the applied bounds.
Fig. 19 shows the hourly total power distribution for case
2 and 3. The loss in case 2 is greater than case 3 at night
because of CLs in curtailing the load demand in case3.
Fig. 19. Total power losses of cases 2 and 3.
Authors also considered the Distribution Losses Ratio
(DLR). It can be defined as
DLR =total energy losses
total energy demand in feeder(1)
The DLR of case1 is higher than other cases because no
reference scheduling of DGs, BESSs and tap transformers
is considered. The losses are minimized to 8.74% on case 2
because of proposed optimize scheduling. The DLR is reduced
to 7.57% because of curtailment of loads by CLs. The impact
of CLs also confirms that the capacity of BESS is minimized
by 20% as compared to case2. This increases the free space
at substation and cost savings.
J. Appliance Commitment for Household Load Scheduling
The experimental results are discussed in this section. The
simulation starts at 12:00 am and runs for 24 hours.
Day-ahead Energy Price Forecast: For simulations, fore-
casted price signal pf is obtained by adding white noise ǫ tothe actual price signal pn.
pf = pn + ǫ (2)
The real time pricing (RTP) forecasted price signal and actual
price signal are shown in fig. 20 (Blue line presents actual
price while red line shows RTP forecasted price).
As discussed above, Appliance commitment schedule is a
two step scheduling process:
1) Day-ahead scheduling: Fig. 21 shows the sorted RTP
prices in ascending order to obtain a composite price curve,
wc. The calculated price Pc is $12/MWh and to warm the
inlet water in the tank, duration of ON time, Tc, is 9.18h.
Here, two cases are considered. Case1: When users comfort
is not considered. In this case, EWH operation depends on
spot price. If spot price is less than Pc, EWH is turned on
Fig. 20. The RTP prices (blue: actual; red: day-ahead forecast).
otherwise off. The cost price is $0.248 which is lower than
that of Scheme A (no control) or transactive control (Scheme
B). Case2: When comfort constraints are observed. Fig. 22
shows that hot water temperature increases and decreases from
specified range, violating the user comfort. To satisfy the
comfort constraints, the temperature band must be included.
Scenario1: we assume that upper and lower range of tem-
perature is constant over the complete scheduling time (24
hours)
140 ≤ θn ≤ 160 when0 ≤ tn ≤ 24 (3)
where θn is the temperature of hot water at time tn(◦F). It
is clear from fig. 22 that the upper limit is violated at t= 1.466
h. Thus, the time interval is divided into two: [0, 1.466] and
[1.466, 24]. The whole optimization process is carried out for
this second interval. When it is done, violation of temperature
range is verified. This process repeats until no violation occurs.
The resultant cost price is $0.811 and its results are shown in
fig. 24a.
Scenario2: the temperature range varies for different times
of the day.
{
132 ≤ θn ≤ 150 when 0 ≤ tn ≤ 24
142 ≤ θn ≤ 160 Otherwise(4)
Fig. 24b shows that temperature band is not violated when
temperature decreases at noon. The resultant cost is $0.798
which is lower as compared to scenario 2.
Fig. 21. The composite price curve wc (price threshold, Pc) (blue: day-aheadforecast price; red: sorted day-ahead forecast price).
Fig. 22. Hot water temperature without considering users comfort .
2) Real time adjustment: As the actual and forecasted
prices are different from each other(see fig. 20), thus the sorted
actual and sorted forecasting prices would be different. The
difference in prices might be less, however, the deviation of
temperature would be large because of forecasting errors in
prices. To solve this problem, real time adjustment is done (see
this mode in detail in [3]). Fig.11 depicts that temperature level
is maintained within defined limits after adjustment. However
there is a trade-off between comfort constraints and cost. The
cost payment for the adjustment is $0.831.
K. Management and Control of Domestic Smart Grid Tech-
nology
Two cases are studied to evaluate the methodology. First
scenario corresponds to simulation of group of houses using
real heat demand data to check whether it is applicable
for neighbors. Second scenario corresponds to evaluation of
proposed methodology for a house. A set of 39 houses and
their real heat demand has been simulated using three step
approach.
1) Planning: First of all, prediction is made using a
predictor. A prediction for a household is presented in fig. 25.
It is clear that predictor follows the trend in a good manner.
However, some deviations occur due to human nature and
uncertain use of heat. Once prediction is done, the global
planner schedules the on-off time of generators. Fig. 26 depicts
the scheduling of electricity generators. To reach the the
desire production plan, two sub-plans are made: one, using
the predicted demand other using real heat demand. Figure
Fig. 23. Hot water temp.(◦F) considering user’s comfort.(a)uniform temp.(b)time varying temp.
Fig. 24. Hot water temperatures (blue: real-time adjustment; red: day-aheadschedule)
shows that total heat demand is predicted in a good manner
(2 kWh difference). However, the heat demand prediction is
less accurate.
Fig. 25. Heat demand prediction for a household
Fig. 26. Planning using the three-step methodology for 39 houses.
IV. CONCLUSION
In this paper, we presented the survey of some home energy
management techniques which play a helpful role to consume
the electrical energy efficiently. Optimal consumption of power
helps in reduction of peak load demand and PAR. Reduction
in PAR increases sustainability, efficiency and reliability of
the system. In this paper we have discussed and compared
HEM techniques on the basis of economic gain and peak load
reduction. We overviewed the simulated results and discussed
their performance. Our discussion will hopefully motivate the
future researches to make more efficient and user friendly
home energy management systems.
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