International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
Modeling of Electric Water Heater and Air Conditioner for Residential
Demand Response Strategy
Maytham S. Ahmed1,4, a *, Azah Mohamed1,b , Raad Z. Homod2,c, Hussain Shareef3,d, Khairuddin Khalid1,e
1
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti
Kebangsaan Malaysia, 43600 Bnagi, Selangor, Malaysia.
2
Department of Petroleum and Gas Engineering, Basrah University for Gas and Oil , Qarmat Ali Campus, 61004 Basrah, Iraq.
3
Department of Electrical Engineering, United Arab Emirates University, P.O. Box 155511 Al-Ain, UAE.
4
General Directorate of Electrical Energy Production- Basrah, Ministry of Electricity, Iraq.
Thus, fully understanding the behavior and characteristics of
each DR resource is essential for realizing their benefits.
Typically, DR residential sector enabled technologies related
to smart home use smart devices such as smart meters, smart
plugs and sensors that can control intelligently home
appliance [3].
With the expansion of the smart grid concept and
implementing DR control strategy, customers need to measure
power consumption at home and study changes in electricity
consumption and signals from a power utility by using load
models [4]. Load modeling has been widely used in various
studies for the past decades. However, load modeling is still a
challenging area that needs to be fully understood. Many
studies have been conducted to provide and develop precise
load models using new techniques. Load models must be
developed to match simulated behaviors with measured real
data [5]. Residential load represents the largest energy
consumption and therefore the load model scale can change,
starting from the transmission line power grid level to the
home appliance level [6]. The load model that provides power
flow and dynamic performance simulation is divided into two
groups; static load model which depends on steady-state
network representation and considers only voltage-dependent
characteristics, such as power flow and dynamic load model
which considers both voltage-dependent and frequencydependent variation of the load, such as dynamic stability [7,
8].
Load modelling is necessary to evaluate residential DR at the
distribution circuit level and to study customer behaviours [4,
9]. Residential DR is an approach to assist consumers to
alleviate power consumption. According to prior agreements
between utility and customers, the electric utility company
sends rate prices or DR signals to residential customers [10,
11]. To support a residential DR program, thermal storage
loads such as electric water heater (EWH), refrigerator and air
conditioner are normally considered for control due to their
high energy consumption and can be easily changed with
minimal impact on homeowners [12, 13]. Air conditioner and
EWH consume high power compared to other home
appliances such as refrigerator and clothes dryer. In some
countries, it contributes significantly to the electricity peak
load and consume more than 30% of other home appliances
[14]. A large-tank of EWH can be chosen as a perfect load
Abstract
Power consumption of household appliances has become a
growing problem in recent years because of increasing load
density in the residential sector. Improving the efficiency,
reducing energy and use of building integrated renewable
energy resources are the major key for home energy
management. This paper focuses on the development of
simulation models for two appliances, namely, an electric
water heater (EWH) and air conditioning (AC) load for the
purpose residential demand response (DR) applications.
Residential DR refers to a program which offers incentives to
homeowners who curtail their energy use during times of peak
demand. EWH and AC have a great probability in executing
residential DR programs because they consume more energy
compare to other appliances and are frequently used on a daily
basis. Load model designed according to operational and
physical characteristics. Validations were made on the models
against real data measurement and it is found to be an accurate
model with mean average error of 0.0425 and mean square
error of 0.3432 for EWH and mean average error of 0.1568
and mean square error of 0.3915 for AC respectively.
Furthermore, the results give suggest and insight the need for
control strategies to evaluate better performance in residential
DR implementations.
Keywords: Residential demand response, smart appliance,
HVAC loads, EWH loads, home energy management, energy
efficiency.
INTRODUCTION
To support renewable energy potential with the assistance of
information and communication technologies and provide
flexibility to the electricity grid, the smart grid concept has
been presented as the best solution in recent years[1].
Developing smart grid provides benefits to the customers and
to the power grid as well as the electric power supplier[2]. In
a smart grid, a cost-effective way to achieve balance between
load and power generation especially power from variable
renewable energy sources due to their intrinsically intermittent
nature is by means of demand response (DR). By
implementing DR control strategy, it can also help shave peak
load, fill valleys, and provide emergency support to the grid.
9037
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
water flow rate 𝐹𝑙𝑟, 𝑡, temperature of inlet water, 𝑇𝑖𝑛𝑙,
temperature of water tank, 𝑇𝑜𝑢𝑡,𝑡 , ambient temperature, 𝑇𝑎𝑚𝑝
and the set point temperature, 𝑇𝑠𝑒,𝑡 . Moreover, the output
parameters are the energy consumption of the EWH in kW
and temperature of water tank at next time step. The output
water is utilized as an input to the model at next step of time.
Furthermore, additional parameters are needed to be
considered such as the size of tank, the surface area, the
volume and water heater cross-sectional area.
for residential DR because it contributes a significant amount
of energy load, contain thermal storage and consume high
power that coincides with utility peak power periods [15].
Thus, home appliance load modelling is essential to
implement residential DR control strategy and to study
customer behaviours that can be used for home energy
management system (HEMS) [16, 17]. Some research works
have been conducted on the use of residential DR to improve
HEMS in the domestic sector [18, 19].
Previous research works develop load models for providing
balancing service [20, 21]. For HEMS and residential DR
studies, several researchers have focused on physical load
models. In [22], physical-based load model of EWH has been
developed and tested against real data measured at home. In
[23], load modeling concepts and basic definitions are
described. In [25], physical load models based on DR signals
have been developed for controllable load appliances. Other
physical load models have been developed by using survey
and measured data [24] and the load models are validated
online [25]. However, to evaluate the accuracy of the load
model, only few load models have been validated. In this
paper, improved physical modeling of EWH and air
conditioner has been developed based on the physical
characteristics of the appliances. The parameters of the
simulation models along with temperature profile are
determined so as to give accurate values of the room
temperature for air conditioner and the water temperature of
EWH. The temperature variations over time of the EWH are
modeled such that it can determine the use of hot water by the
users considering different scenarios. A comparison is also
made on the physical load models of air conditioner and EWH
against real measured data.
Flow rate
Flr,t
Ambient temp
Tam (°C)
Inlet temp
Tinl (°C)
Set point temp
Tse (°C)
Electric
water
heater
Model
(EWH)
Power
(Pewh)
(Tout,t+1)
DR signal
Sn,ewh
Tank temp
Tout,t (°C)
Figure 1: Flow chart of the EWH load model
Table 2 Shows the characteristics and parameters of the air
conditioner load considered for DR with rated voltage 220–
230 V and rated power at 1.25 kW.
CHARACTERISTICS
OF
ELECTRIC
WATER
HEATER AND AIR CONDITIONER
The characteristics and parameters of the EWH load model
with rated voltage 220–230 V and rated power at 4 kW is
shown in Table 1. To obtain an accurate EWH load model
with the mathematical expressions that can be utilized with
the physical-based model, there is need to determine
relationship between the input and output parameters as
shown in Figure 1.
Table 2: Air conditioner load characteristics
Parameter
Model
Rated power (kW)
Cooling capacity
Air flow volume
Number of people
Air conditioner (AC)
AC-S10CGA (AKIRA)
1.25 kW
10000 Btu/h
420 m3 /h
5
Table 1: Electric water heater load characteristics
Parameter
Electric water heater (EWH)
Rated power 𝑃𝑒𝑤ℎ (kW)
Volume, Voltank
Ambient temperature, 𝑇𝑎𝑚𝑝
4 kW
100 gal/m
As room temperature
Tank base area, 𝐴𝑡𝑎𝑛𝑘
1.30064 m2
To develop a model of the air conditioning units, it is
necessary to determine all parameters that can be used with
the physical-based AC model to obtain an accurate AC load
model, as shown in Figure 2. The parameters are divided to
three categories, air conditioning unit characteristics, home
structures and temperatures set points. The input parameters
of the model are the signal of demand response 𝑆𝑛𝑒𝑤ℎ,𝑡 , room
temperature at time t, 𝑇𝑟,𝑡 , occupant heat gain 𝐻𝑝 , set point
temperature 𝑇𝑠,𝑡 , and outside temperature 𝑇𝑜𝑢𝑡,𝑡 . Furthermore,
the output parameters are the power consumption of the air
conditioner in kW and room temperature at next time step. In
addition, other parameters should be taken into account
including, size of room, the season, the number of people in
the home, number of windows, the area of home, heat gain
rate of a house, cooling load capacity and solar radiation.
The parameters are divided into three categories; electric
water heater characteristics, the use of hot water and
temperatures set points. It is assumed that the water heater is
able to receive signals as DR signal from control center. The
input parameters are the signal of demand response 𝑆𝑛𝑒𝑤ℎ,𝑡 ,
9038
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
Occupant heat
SIMULATION MODELS OF ELECTRIC WATER
HEATER AND AIR CONDITIONER
Simulation Model of Electric Water Heater :
A domestic EWH consists of a thermostat to sense
temperature and switch on / off to heat the water. The data for
the EWH model depends on a storage tank water heater and
many other parameters that contribute to the design of an
efficient physical model.
gain Hp
Tout temp
Tout, t (°C)
Air
condition
load Model
(HVAC)
Set point
Temp (Ts)
(°C)
Power
consumption
Phvac, t
Room temp
Tr,t+1
DR signal
(Sn,hvac)
In the initial condition, one needs to calculate the water
temperature at time (t) of the EWH based on the usage
pattern[26, 27]. First, consider the outlet water temperature of
the tank which is expressed as,
Room temp
Tr,t (°C)
Figure 2: Flow chart of the air conditioning load model
𝑇
𝑜𝑢𝑡,𝑡1
= (
𝑇𝑜𝑢𝑡,𝑡 ∗(𝑉𝑜𝑙𝑡𝑎𝑛𝑘 −𝑉∗𝑑𝑡)+𝑇𝑖𝑛𝑙∗𝐹𝑙𝑟,𝑡 ∗𝑑𝑡
𝑉𝑜𝑙𝑡𝑎𝑛𝑘
)+
𝑑𝑡
60
𝑃
∗ (𝑉𝑜𝑙𝑒𝑤ℎ,𝑡 (
𝑡𝑎𝑛𝑘
3412 𝐵𝑇𝑈
𝑘𝑤ℎ
−
𝐴𝑡𝑎𝑛𝑘 ∗(𝑇𝑜𝑢𝑡,𝑡 −𝑇𝑎𝑚𝑝 )
𝑅𝑡𝑎𝑛𝑘
) ) (1)
where 𝑇𝑖𝑛𝑙 is the inlet water temperature ℃, 𝐹𝑙𝑟,𝑡 is the hot water flow rate at a given interval in m3/s, 𝑇𝑎𝑚𝑝 is the ambient
temperature, 𝑉𝑜𝑙𝑡𝑎𝑛𝑘 is the volume of the tank in m3, 𝐴𝑡𝑎𝑛𝑘 is the surface area of tank, 𝑅𝑡𝑎𝑛𝑘 is the heat resistance of the tank in
(℃. m3.h/btu), 𝑑𝑡 is the duration of the time slot t,
Using Eq. (1) the component models of EWH are simulated in
Matlab as shown in Figure3.
A domestic EWH has a thermostat to sense temperature and
switch on/off to heat the water. The difference between the set
point upper or lower limits of the tank temperature is called
the dead band. If the water tank temperature drops below the
set point lower limit minus the dead band temperature range,
the EWH coils is turned ON (1). However, if the water tank
temperature is raised to its set point upper limit plus the dead
band temperature, the heating coils of EWH is turned OFF
(0). The operation of the EWH depends on the status of the
device, 𝑚𝑒𝑤ℎ which is expressed mathematically as,
Figure 3: MATLAB block for the simulation model of EWH
load model
1,
0,
𝑚𝑒𝑤ℎ = [
𝑚𝑒𝑤ℎ,𝑡−1
𝑇𝑒𝑤ℎ,𝑡 < 𝑇𝑠𝑒,𝑡 − ∆𝑇
𝑇𝑒𝑤ℎ,𝑡 > 𝑇𝑠𝑒,𝑡 + ∆𝑇
𝑇𝑠𝑒,𝑡 − ∆𝑇 ≤ 𝑇𝑒𝑤ℎ,𝑡 ≤ 𝑇𝑠𝑒,𝑡 + ∆𝑇
where 𝑆𝑛𝑒𝑤ℎ,𝑡 is the DR signal which changes the electric
power demand of the EWH, 𝑇𝑠𝑒,𝑡 is the set point temperature,
∆𝑇 is the dead band temperature, ±2℃. The electric power
demand of the EWH load model depends on the DR
signal 𝑆𝑛𝑒𝑤ℎ,𝑡 . During a DR event, the signal which originates
from the revised thermostat set point can be changed by
homeowner.
The amount of EWH power consumed in kW depends on the
] ∗ 𝑆𝑛𝑒𝑤ℎ,𝑡
(2)
thermostat operating in the OFF/ON states and running at its
rated power. The power of EWH at a given time is calculated
by using,
𝑃𝑒𝑤ℎ,𝑡 = 𝑚𝑒𝑤ℎ ∗ 𝑃𝑒𝑤ℎ
(3)
where 𝑚𝑒𝑤ℎ is the status of device, 𝑚𝑒𝑤ℎ =1 device is
switched on, 𝑚𝑒𝑤ℎ = 0 mean the device is switched off
and 𝑃𝑒𝑤ℎ is the EWH rated power in kW.
9039
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
energy that changes the air temperature in the room by 1 ℃,
𝐶ℎ𝑣𝑎𝑐 is the cooling load capacity in (Btu/ ℃), and 𝑚ℎ𝑣𝑎𝑐,𝑡 is
the status of air conditioner in time slot.
Simulation Model of Air Conditioner :
In the initial condition, one needs to derive the mathematical
expressions to obtain an accurate air conditioner load model.
The mathematical air conditioner model is presented as a set
of equations to obtain the specifics of the relationship between
the output and input parameters. To determine the room
temperature at time, t based on the cooling load factor for
glass/corrected cooling, 𝐶𝐿𝐹 ⁄𝐶𝐿𝑇𝐷𝑐 the load temperature
difference [27, 28] is determined as follows:
𝐶ℎ𝑣𝑎𝑐
𝑇𝑟,𝑡+1 = 𝑇𝑟,𝑡 + 𝑑𝑡 (𝑚ℎ𝑣𝑎𝑐,𝑡
𝑑𝑐
+
𝑄𝑡
𝑑𝑐
)
The change in load depends on various parameters, such as
time, day, month, season, number of occupants, and country.
The room temperature is used as input temperature to the air
conditioner. Consider the heat gain rate of a house, 𝑄𝑡 , which
is expressed as,
(4)
where 𝑇𝑟,𝑡 is the room temperature at time t ℃, 𝑄𝑡 is the heat
gain rate of a house, 𝑑𝑡 is the length of time slot, 𝑑𝑐 is the
𝑄𝑡 = 𝑆𝐻𝐺𝐶 + (𝐻𝑝 ∗ 𝑁𝑝 ) + ((
𝐴𝑓𝑙
𝑅𝑓𝑙
+
𝐴𝑤𝑎𝑙
𝑅𝑤𝑎𝑙
+
𝐴𝑐𝑒
𝑅𝑐𝑒
+
𝐴𝑤𝑖𝑛
𝑅𝑤𝑖𝑛
) ∗ (𝑇𝑜𝑢𝑡,𝑡 − 𝑇𝑟,𝑡 ) + (𝑘 ∗ 𝑠 ∗ 𝑉ℎ𝑜𝑠 ) ∗ (𝑇𝑜𝑢𝑡,𝑡 − 𝑇𝑟,𝑡 )) + 𝐴𝑤𝑖𝑛𝑠 + 𝐻𝑆𝑂𝐿𝐴𝑅 (5)
where 𝑆𝐻𝐺𝐶 is the window's solar heat gain coefficient[29], 𝐻𝑝
is the occupant heat gain in (Btu/ h), 𝑁𝑝 is the number of
people inside a room, 𝐴𝑓𝑙, 𝐴𝑤𝑎𝑙𝑙 , 𝐴𝑐𝑒 , 𝐴𝑤𝑖𝑛 ) are the area of
floor, wall, ceiling and window, respectively of a dwelling in
(m2), 𝑅𝑓𝑙 , 𝑅𝑤𝑎𝑙 , 𝑅𝑐𝑒 , 𝐴𝑤𝑖𝑛 are the average thermal resistance
of the floor, wall, ceiling and window, respectively in (℃. m2.
h/ Btu), 𝑇𝑜𝑢𝑡,𝑡 is the outside temperature in ℃ [30], 𝑘 is the
change in room air at any time slot, 𝑠 is the air heat factor in
(Btu /℃. 𝑚3 ), 𝐴𝑤𝑖𝑛_𝑠 is the window area facing south in (m2)
[31] and 𝐻𝑆𝑂𝐿𝐴𝑅 is the solar radiation heat power in (W/m2).
To change the room temperature by 1℃ , the specific heat of
air needs to be specified. The specific heat capacity of air, 𝐶𝑝
is 0.2099/𝑚3 ℃ , and the house volume, 𝑉ℎ𝑜𝑠 in 𝑚3 , is
included in the following equation,
𝑏𝑡𝑢
𝑑𝑐(
℃
) = 𝐶𝑝 (𝑏𝑡𝑢/𝑚3 ℃) ∗ 𝑉ℎ𝑜𝑠 (𝑚3 )
(6)
The component models of air conditioner which depends on
Eq. 4 and 5 were created to describe parts of the system as in
Figure 4.
Figure 4: Matlab block for the simulation model of AC unit load model
plus the dead band temperature, the air conditioner is turned
ON (1). However, if the room temperature is within its
tolerable band then the air conditioner keeps the same status
as described mathematically in Eq. (7).
The differences between the set point of air conditioner and
lower or upper limit of the temperature is called dead band.
The air conditioner is controlled such that if the temperature
of room decreases below a set point minus the dead band
temperature, the air conditioner is turned OFF (0) and if the
temperature of room increases above its maximum set point
9040
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
0,
1,
𝑚ℎ𝑣𝑎𝑐,𝑡−1
𝑇ℎ𝑣𝑎𝑐,𝑡 < 𝑇𝑠,𝑡 − ∆𝑇
𝑇ℎ𝑣𝑎𝑐,𝑡 > 𝑇𝑠,𝑡 + ∆𝑇
𝑇𝑠,𝑡 − ∆𝑇 ≤ 𝑇ℎ𝑣𝑎𝑐,𝑡 ≤ 𝑇𝑠,𝑡 + ∆𝑇
] ∗ 𝑆𝑛ℎ𝑣𝑎𝑐,𝑡
The use of hot water
(a) 3
where 𝑆𝑛ℎ𝑣𝑎𝑐,𝑡 is the DR signal, 𝑇𝑠,𝑡 is the set point
temperature and ∆𝑇 is the dead band temperature of ±2℃.
The amount of air conditioner power consumed in kW at a
given interval, 𝑃ℎ𝑎𝑣𝑐𝑡 can be expressed as
water usage
2.5
Flow rate (gpm)
𝑃ℎ𝑣𝑎𝑐,𝑡 = 𝑚ℎ𝑣𝑎𝑐 ∗ 𝑃ℎ𝑣𝑎𝑐
(7)
(8)
where 𝑚ℎ𝑣𝑎𝑐 is the status of the air conditioner, 𝑚ℎ𝑣𝑎𝑐, =1
means air conditioner is switched on, 𝑚ℎ𝑣𝑎𝑐, = 0 means the air
conditioner is switched off and 𝑃ℎ𝑣𝑎𝑐 is the air conditioner
rated power in kW.
2
1.5
1
0.5
The electric power demand of the AC load model depends on
the DR signal 𝑆𝑛ℎ𝑣𝑎𝑐,𝑡 . During a DR event, this signal which
originates from the revised thermostat set point can be
changed by end user.
0
0
2
4
6
8 10 12 14 16 18 20 22 24
Time in hour
Water Heater
(b) 7
Power consumption (kW)
RESULTS AND DISCUSSION
The simulation results for EWH and AC load models
validated with real data measurement and the performance of
the proposed model are described accordingly.
EWH Simulation Results:
To illustrate the performance of the EWH model, two case
studies are conducted. The first case shows the usage of the
hot water at different times as in Figure 5a. In Figure 5b the
maximum and minimum temperature of water heater setting is
assumed to be 48℃ and 42℃ , respectively and these values
can be changed in the physical model according to the desire
of homeowner. When the hot water is used at 7 am and the
water temperature reaches its minimum allowable set point of
42℃, the EWH will turn on to maintain the water temperature
at its comfortable range. When the hot water is used between
16 pm and 18 pm, the EWH will turn on to maintain
temperature of water in the tank until the temperature reaches
its maximum allowable set point of 48℃ to turn off the EWH.
When the temperature of water in the tank is within 42–48 ℃,
the heater switch status will keep the previous device state as
shown in Figure 5b
55
50
6
45
5
40
4
35
3
30
25
2
20
1
0
0
Temperature,C
𝑚ℎ𝑣𝑎𝑐 = [
15
2
4
6
10
8 10 12 14 16 18 20 22 24
Time in hour
Figure 5: Simulation model of EWH load (a) Flow rate of
the hot in gpm (b) Hot water temperature within 42–48 ℃
with the power consumption pattern
In the second case, it is assumed that the homeowner used the
hot water with different times: at 5 am with 0.3 gpm, at 10 am
with 0.6 gpm and 20 pm with 0.9 gpm as illustrated
in Figure 6a.
In Figure 6b at time 12am the EWH operates to bring the
water temperature to its maximum allowable set point of
48 ℃, and then the EWH will turn off. From Figure 6 (a) and
(b) at time 10 am the use of water heater was very low so, the
EWH keeps off because the temperature of hot water is still in
the range. However, at time 20 pm the home owner used the
hot water more than one hour thus causing the EWH to turn
on to maintain the temperature of water at its comfortable
range.
9041
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
36
The use of hot water
(a) 3
water usage
34
Temperature, C
Flow rate (gpm)
2.5
2
1.5
1
0
0
30
28
2
4
6
24
0
8 10 12 14 16 18 20 22 24
Water heater
(b)7
35
3
30
800
600
25
400
20
200
15
2
4
6
H solar
Solar irradiation (w/m2)
Temperature, C
4
1
8 10 12 14 16 18 20 22 24
1000
40
2
6
55
45
5
4
Figure 7: Outside temperature measured according to
Malaysia weather
50
6
2
Time in hour
Time in hour
Power consumption (kW)
32
26
0.5
0
0
Temp
10
8 10 12 14 16 18 20 22 24
0
0
Time in hour
2
4
6
8 10 12 14 16 18 20 22 24
Time in hour
Figure 6: Simulation model of EWH load (a) Flow rate of the
hot in gpm (b) Hot water temperature with the power
consumption pattern
Figure 8: Variation of solar irradiation data measured in
Bangi, Malaysia
A case study has been conducted to demonstrate the
performance of the air conditioner model. It is assumed that
the home owner set the comfort level of the room temperature
for air conditioner unit between 26℃ and 22℃ . If the
temperature reaches its maximum set point temperature of
26℃, then the air conditioner is turned on. When the room
temperature reaches its minimum set point temperature of
22℃, the air conditioner is turned off to maintain the room
temperature at its comfortable range. When the room
temperature is within 22–26 ℃ , the switch status will keep
the previous device state as shown in Figure 9.
The figures clearly show that proposed EWH model works
well with different scenario to maintain hot water temperature
at different set points and different usage of hot water. In
addition, the model is flexible to reflect any desired hot water
usage that can easily adapt with demand response
applications.
Air Conditioner Simulation Results :
In the simulation, the maximum and minimum temperature of
air conditioner are set at 26℃ and 22 ℃ , respectively and
these values can be changed in the physical model according
to the customer’s desire. Real data were measured to use as
input for the AC load model that include outside temperature
in 15 May 2015 as shown in Figure 7 and solar irradiation (H
solar) as shown in Figureb8 and both measured in Bangi,
Malaysia.
9042
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
Temperature, C
2
20
1.5
1
15
0.5
2
4
6
Power consumption (kW)
2.5
25
10
0
The temperature consists of outdoor and indoor set points.
These data are real data measured by a temperature and
humidity sensor wireless data logger connected inside and
outside residences and should be the same for all residences in
the same neighborhood.
Other data of input model for dwellings that use the air
conditioner model are acquired from houses in Malaysia [32].
A condominium unit with an area of more than 100 square
meters, which is the average size of a single-family home in
Malaysia[33] is considered in the case study. The building
structure is calculated according to ASHRAE[34] which
includes the areas and the heat resistance of windows, ceiling,
walls, and floor of the building. The size of the air conditioner
load and its power consumption and cooling capacities are
developed based on the building floor plan, occupants, room
size, and environment as seen in Table 2. Obtaining all the
input parameters for the model would simplify the demand
aggregation for air conditioner load.
When applying these input parameters to the model, the
model output, which includes power consumption, is
compared with real data measurements. A 1 minute interval
time for 24 hours data is considered in the comparative study.
Data were obtained by using a power quality analyzer to
measure the air conditioner power consumption of households
within 1 minute interval for 24 hours. Figure 11 shows the
measured temperature and humidity outside a room according
to the weather in Malaysia.
3
0
10 12 14 16 18 20 22 24
8
Time in hour
Figure 9: Simulation model of air conditioner power
consumption pattern with room temperature set between 26℃
and 22℃
Model validation of the EWH load and Air Conditioner :
To validate the EWH simulation model, it is compared with
the real data measured in a residential home in Bangi,
Selangor, Malaysia. Initially, the input parameters such as
tank size, volume of the tank, rated power, outdoor
temperature, and set point temperature were applied to the
simulation model. The model output in terms of power
consumption, is then compared with real data power
measurements. A 1 hour data with 1 second interval time is
considered in the comparative study. Comparisons of the
simulation and real data for EWH are illustrated in Figure 10.
37
35
Temperature, C (Tr, To )
1.2
33
0.9
31
setpoint tem.
0.6
Outdoor Temp.
Indoor Temp.
AC Power
0.3
29
5
27
Measurment
Model
25
Power, (kW)
Air-conditioner
30
23
Relative Humidity, %
Power (kW)
3
2
19
21
23
1
3
5
7
9
11 noon 13
15
1.2
Setpoint RH
Outdoor RH 0.9
Indoor RH
AC Power
90
80
0.6
70
60
0.3
50
15
17
19
21
23
1
3
5
7
9
11 noon 13
15
Time (hours) 5/13/2015
1
0
0
17
Power, (kW)
21
15
100
4
5
10
15
20
25
30
35
40
45
50
55
Figure 11: Measured temperature, relative humidity and
power consumption for air conditioner
60
Time (minute)
Figure 10: Simulation and real power consumption pattern of
the EWH
From Figure 11, at 3:00 a.m. the room temperature and
humidity were quite high. However, when the air conditioner
was turned on, the temperature started to decrease until it
reached the set point temperature of 24℃. The figure clearly
shows that the air conditioner unit turns on/off and then
maintains its indoor temperature at the set point temperature.
In addition, humidity was reduced and fluctuated between
40% and 55%, which is a comfortable level for human
comfort zone. The same inputs are used to compare the
temperature and power consumption outputs of the model
with the actual measurements. Comparisons for air
conditioner at 1-minute interval are illustrated in Figure 12 for
a period of 24 hours on May 13, 2015.
Figure 10 indicates similarities between real power
consumption measured in the home and the power
consumption obtained from the simulation model output. The
comparison between real and model of EWH results show that
a small variance between the results indicates that the model
is close to the actual condition with the mean average error
(MAE) of 0.0425, and mean square error (MSE) of 0.3432.
On the other hand, to validate the air conditioner there is a
need to know the input parameters such as air conditioner unit
size, house structure parameters, outdoor temperature, and
thermostat set point. The input physical model of air
conditioner considers three important factors; characteristics
of air conditioner, temperature and building characteristics.
9043
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
Temperature,C
(a) 30
Power (kW)
(b)
Real Temp.
Model Temp.
28
26
24
22
15
1.4
17
19
21
23
1
3
Real power
5
7
9
11
13
15
7
9
11
13
15
Model power
1
0.5
0
15
17
19
21
23
1
3
5
Time in hour
Power (kW)
(C)1.4
Real power
Model power
1
0.5
0
8
9
10
11
12
13
14
15
Time in hours
Figure 12: Real and model air conditioner load validation (a) room temperature pattern (b) power consumption pattern (c)
Zoomed-in view of power consumption
Figure 12(a) shows a comparison of the simulation and realtime results of indoor temperature; a small variance between
the results indicates that the model is close to the actual
condition. Figure 12(b) indicates similarities between real
power consumption measured in the home and the model
output. Figure12(c) shows the zoomed-in view of power
consumption in which it can be noted that it takes more time
to cool the room during the noon and consumes more power
due to temperature outside and solar radiation. The
comparison between air conditioner real power and model
power shows that the MAE is 0.1568, whereas the MSE is
0.3915. The results explained that the performances of the
proposed models are close to the actual condition distribution
circuit load profiles.
residential DR applications. The models were developed at
home appliances level based on the operational and physical
characteristics to reflect any strategies of residential DR.
Simulation results show that the models are accurate and can
consider different scenario and different set points to maintain
hot water temperature for EWH and room temperature for AC
unit. Moreover, the models show similarity with the actual
load profile when compared with real data measurements with
MSE and MAE of 0.3432 and 0.0425 for EWH, respectively
and MSE and MAE of 0.3915 and 0.1568, respectively for
AC. The developed EWH and air conditioner load models can
be used to perform studies related to residential DR for
controlling home appliances using home energy management
system. With the application of residential DR, home power
consumption can be controlled by utilizing the technological
development.
CONCLUSION
In this work, two models have been developed to simulate the
behavior of residential home appliances such as EWH and air
conditioner and to present a methodology to enabled
9044
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
ACKNOWLEDGEMENT
The authors gratefully acknowledge University Kebangsaan
Malaysia for the financial support on the project under the
research grant project DIP-2014-028
[13]
REFERENCES
[1]
S. Rahman, "Smart grid expectations [In My View],"
Power and Energy Magazine, IEEE, vol. 7, pp. 88, 8485, 2009.
[2] T. J. Lui, W. Stirling, and H. O. Marcy, "Get Smart,"
Power and Energy Magazine, IEEE, vol. 8, pp. 66-78,
2010.
[3] M. Amer, A. Naaman, N. M'Sirdi, and A. El-Zonkoly,
"Smart home energy management systems survey," in
Renewable Energies for Developing Countries
(REDEC), 2014 International Conference on, 2014, pp.
167-173.
[4] N. Gudi, L. Wang, and V. Devabhaktuni, "A demand
side
management
based
simulation
platform
incorporating heuristic optimization for management of
household appliances," International Journal of
Electrical Power & Energy Systems, vol. 43, pp. 185193, 2012.
[5] R. Z. Homod, K. S. M. Sahari, H. A. Almurib, and F. H.
Nagi, "Double cooling coil model for non-linear HVAC
system using RLF method," Energy and buildings, vol.
43, pp. 2043-2054, 2011.
[6] T. Overbye, "Effects of load modelling on analysis of
power system voltage stability," International Journal of
Electrical Power & Energy Systems, vol. 16, pp. 329338, 1994.
[7] D. Bargiotas and J. Birdwell, "Residential air
conditioner dynamic model for direct load control,"
Power Delivery, IEEE Transactions on, vol. 3, pp. 21192126, 1988.
[8] M. Sedighizadeh and A. Rezazadeh, "Load Modeling for
power flow and transient stability computer studies at
BAKHTAR network," World academy of science,
engineering and technology, vol. 36, 2007.
[9] C. Walker and J. Pokoski, "Residential load shape
modelling based on customer behavior," Power
Apparatus and Systems, IEEE Transactions on, pp.
1703-1711, 1985.
[10] P. Siano, "Demand response and smart grids—A
survey," Renewable and Sustainable Energy Reviews,
vol. 30, pp. 461-478, 2014.
[11] H. T. Haider, O. H. See, and W. Elmenreich, "A review
of residential demand response of smart grid,"
Renewable and Sustainable Energy Reviews, vol. 59, pp.
166-178, 2016.
[12] E. T. Mayhorn, S. H. Widder, S. A. Parker, R. M. Pratt,
and F. S. Chassin, "Evaluation of the Demand Response
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
9045
Performance of Electric Water Heaters," Pacific
Northwest National Laboratory (PNNL), Richland, WA
(US)2015.
R. Z. Homod, K. S. M. Sahari, F. Nagi, and H. A.
Mohamed, "Modeling of heat and moisture transfer in
building using RLF method," in Research and
Development (SCOReD), 2010 IEEE Student
Conference on, 2010, pp. 287-292.
R. Jia, "Power management of aggregate electric water
heater loads by voltage control," in 2007 IEEE Power
Engineering Society General Meeting, 2007, pp. 1-6.
R. Diao, S. Lu, M. Elizondo, E. Mayhorn, Y. Zhang, and
N. Samaan, "Electric water heater modeling and control
strategies for demand response," in Power and Energy
Society General Meeting, 2012 IEEE, 2012, pp. 1-8.
H. A. Aalami and S. Nojavan, "Energy storage system
and demand response program effects on stochastic
energy procurement of large consumers considering
renewable generation," IET Generation, Transmission &
Distribution, vol. 10, pp. 107-114, 2016.
M. S. Ahmed, A. Mohamed, R. Z. Homod, H. Shareef,
A. H. Sabry, and K. B. Khalid, "Smart plug prototype
for monitoring electrical appliances in Home Energy
Management System," in 2015 IEEE Student
Conference on Research and Development (SCOReD),
2015, pp. 32-36.
B. Atanasiu and P. Bertoldi, "Latest assessment of
residential electricity consumption and efficiency trends
in the European Union," International Journal of Green
Energy, vol. 7, pp. 552-575, 2010.
T. Dergiades and L. Tsoulfidis, "Revisiting residential
demand for electricity in Greece: new evidence from the
ARDL approach to cointegration analysis," Empirical
Economics, vol. 41, pp. 511-531, 2011.
J. Kondoh, N. Lu, and D. J. Hammerstrom, "An
evaluation of the water heater load potential for
providing regulation service," in Power and Energy
Society General Meeting, 2011 IEEE, 2011, pp. 1-8.
A. Sepulveda, L. Paull, W. G. Morsi, H. Li, C. Diduch,
and L. Chang, "A novel demand side management
program using water heaters and particle swarm
optimization," in Electric Power and Energy Conference
(EPEC), 2010 IEEE, 2010, pp. 1-5.
M.-L. Chan, E. N. Marsh, J. Yoon, and G. B. Ackerman,
"Simulation-based load synthesis methodology for
evaluating
load-management
programs,"
Power
Apparatus and Systems, IEEE Transactions on, pp.
1771-1778, 1981.
W. Price, H.-D. Chiang, H. Clark, C. Concordia, D. Lee,
J. Hsu, et al., "Load representation for dynamic
performance analysis," IEEE Transactions on Power
Systems, vol. 8, pp. 472-482, 1993.
R. Yao and K. Steemers, "A method of formulating
energy load profile for domestic buildings in the UK,"
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 16 (2016) pp 9037-9046
© Research India Publications. http://www.ripublication.com
Energy and Buildings, vol. 37, pp. 663-671, 2005.
[25] A. Noureddine, A. Alouani, and A. Chandrasekaran,
"On the maximum likelihood duty cycle of an appliance
and its validation," Power Systems, IEEE Transactions
on, vol. 7, pp. 228-235, 1992.
[26] M. S. Ahmed, H. Shareef, A. Mohamed, J. A. Ali, and
A. H. Mutlag, "Rule Base Home Energy Management
System Considering Residential Demand Response
Application," Applied Mechanics & Materials, vol. 785,
2015.
[27] S. Shao, M. Pipattanasomporn, and S. Rahman,
"Development of physical-based demand responseenabled residential load models," Power Systems, IEEE
Transactions on, vol. 28, pp. 607-614, 2013.
[28] R. Z. Homod, K. S. M. Sahari, H. A. Almurib, and F. H.
Nagi, "RLF and TS fuzzy model identification of indoor
thermal comfort based on PMV/PPD," Building and
Environment, vol. 49, pp. 141-153, 2012.
[29] "Solar Heat Gain Coefficient (SHGC) FAQs,
Department of Energy Building Energy Codes Program.
[Online]. Available:
http://www.energycodes.gov/
support/shgc_faq.stm.."
[30] M. Nikpour, M. Z. Kandar, M. Ghasemi, and H. Fallah,
"Study of the effectiveness of solar heat gain and day
light factors on minimizing electricity use in high rise
buildings," World Academy of Science, Engineering and
Technology, vol. 5, pp. 73-77, 2011.
[31] S. El-Férik, S. A. Hussain, and F. M. Al-Sunni,
"Identification of physically based models of residential
air-conditioners for direct load control management," in
Control Conference, 2004. 5th Asian, 2004, pp. 20792087.
[32] Q. J. Kwong, N. M. Adam, and B. Sahari, "Thermal
comfort assessment and potential for energy efficiency
enhancement in modern tropical buildings: A review,"
Energy and Buildings, vol. 68, pp. 547-557, 2014.
[33] M. Ancrenaz, B. Goossens, O. Gimenez, A. Sawang,
and I. Lackman-Ancrenaz, "Determination of ape
distribution and population size using ground and aerial
surveys: a case study with orang-utans in lower
Kinabatangan, Sabah, Malaysia," Animal Conservation,
vol. 7, pp. 375-385, 2004.
[34] A. Handbook-Fundamentals, "American society of
heating, refrigerating and air-conditioning engineers,"
Inc., NE Atlanta, GA, vol. 30329, 2009.
9046