An aggregated fridge-freezer peak shaving and valley filling control strategy for enhanced grid operations
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An aggregated fridge-freezer peak shaving and valley filling
control strategy for enhanced grid operations
Macarena Martin Almenta, Student Member IEEE
John Morrow, Member IEEE
Robert Best, Member IEEE Aoife Foley, Member IEEE
Brendan Fox School of Mechanical & Aerospace Engineering,
School of Electronics, Electrical Engineering & Computer Queen’s University Belfast, UK
Science, Queen’s University Belfast, UK a.foley@qub.ac.uk
mmartinalmenta01@qub.ac.uk
Abstract - The need for fast response demand side participation operators, electricity retailers or generators to shed load and
(DSP) has never been greater due to increased wind power switch to on-site diesel generators or reduce heating, chiller or
penetration. White domestic goods suppliers are currently air-conditioning loads when required [6]. Residential sector
developing a ‘smart’ chip for a range of domestic appliances load participation in liberalized markets has not been fully
(e.g. refrigeration units, tumble dryers and storage heaters) to developed because smart domestic appliances and smart
support the home as a DSP unit in future power systems. This meters have not been widely deployed. It could be argued that
paper presents an aggregated population-based model of a single this is due to the lack of a retail market structure in the
compressor fridge-freezer. Two scenarios (i.e. energy efficiency residential sector. It is expected that within the context of a
class and size) for valley filling and peak shaving are examined smart grid, consumers will actively participate in the
to quantify and value DSP savings in 2020. The analysis shows electricity market due to the increased availability of pricing
potential peak reductions of 40 MW to 55 MW are achievable in and usage data via smart meter interfaces.
the Single wholesale Electricity Market of Ireland (i.e. the test
A number of studies have examined the opportunity for
system), and valley demand increases of up to 30 MW. The
residential sector schedulable-interruptible load participation.
study also shows the importance of the control strategy start
For example, [7] showed the power system benefits from
time and the staggering of the devices to obtain the desired
implementing direct load control demand response in electric
filling or shaving effect.
hot water heating. Cold appliances (e.g. fridge-freezers, chest-
Index Terms -- demand side participation, domestic load, fridge-
freezers and refrigerators) are also a potential schedulable-
freezer, smart grids. interruptible load candidate due to their thermal storage
capabilities. Most researchers have to date considered
refrigerator loads [8]-[10], analyzing the potential load-
I. INTRODUCTION shifting effect and time of use tariffs to reduce power peaks.
Today’s grids are facing many challenges [1] associated [11] focused on domestic freezers. [12] provided an
with increased wind power penetration (e.g. system balance, examination of frequency response for power systems using
ramping and curtailment). The participation of domestic loads refrigerators. The different operating factors for fridge-
from individual households can possibly reduce stress on the freezers (e.g. the effect of ambient temperature, door opening
power system and offer additional ancillary services to grid and thermostat position [13], the use of different types of
operators [2], [3] to integrate wind power. However, refrigerants [14] or the effect of different types of components
schedulable-interruptible loads [4] should not significantly of the refrigeration system [15] have also been investigated.
affect the consumer experience and performance of a domestic The potential of domestic appliances as responsive loads have
appliance. The objective of load participation in the smart grid been analyzed in a statistical manner [5], [16].
is to use these schedulable-interruptible loads to reduce peak
In this paper an aggregated population-based model of a
demand, mitigate system disturbances, delay or avoid
compressor fridge-freezer is developed. Valley filling and
additional capital investment in new power plants and prevent
peak shaving are examined under two scenarios (i.e. the
excessive use of more expensive or less efficient power plants.
fridge-freezer energy rating and size) to quantify the DSP
Some domestic appliances are considered schedulable-
‘energy’ savings. The hypothesis in this research is that load
interruptible loads making them prime candidates to
participation using smart metering can provide significant
implement load participation [5]. Load participation is
operational, economic and environmental benefits to a power
traditionally known as demand side management (DSM). In
system. The control algorithm is developed in MATLAB and
many power systems the commercial and industrial sectors
an aggregated fridge-freezer load is modelled using datasets
also have special load participation contracts with grid
This work was supported by a Charles Parsons Energy Research Award,
which is funded by Science Foundation Ireland (SFI), Award 06/CP/E002.
978-1-4673-8040-9/15/$31.00 ©2015 IEEEfrom the Single wholesale Electricity Market (SEM) of
Northern Ireland and the Republic of Ireland (i.e. the test
system).
II. METHODOLOGY
In this study a fridge-freezer with only one compressor is
modelled because it is the most widely sold domestic
appliance [17]. In a fridge-freezer, the circulation of the
refrigerant is driven by a compressor, which requires a motor
and thus electrical energy.
A. Description of the model of a single fridge-freezer
The model of a single fridge-freezer developed in [18]
predicts the temperature in each compartment of the fridge-
freezer based on heat transfer equations [19]. The model
inputs are the dimensions of the fridge and freezer
compartments, the compressor power consumption,
coefficient of performance (COP) tested in extreme working
conditions, thermal masses and thermal time constants of the
fridge and freezer. The heat transmitted to the thermal mass
of the device is equal to the convection heat losses as a result
of the temperature difference between the room and the
compartments, minus heat transfer from the refrigerant to the
compartment. The Simulink model developed is shown in
Fig. 1.
B. Description of the aggregate model
The study uses a model to simulate each device and Figure 1. Simulink model of the single compressor fridge-freezer.
aggregate their individual behavior to predict the demand of
the aggregate load. Simulations assuming 1,000 fridge-
freezers have been carried out, considering different
characteristics in each unit depending on the scenario under
study. The time span for cooling is not a fixed parameter [8]-
[10], but depends on the characteristic of the device and the
thermal mass inside each cabin.
Randomness following a standard uniform distribution
has been added to the model to create a more realistic
situation. These are temperature limits of ±0.5°C and overall
heat transfer coefficient of ±0.01W/°C. The thermal mass
varies randomly between 17% and 31% of the capacity of
water of each compartment. The expected percentage of
fridge-freezers in each energy label class is projected to
change in the UK over the coming years [20], as shown in
Fig. 2. Figure 2. Expected percentage of fridge-freezers in the UK [20].
The technical specifications of a fridge-freezer are TABLE I. Technical specifications of medium size fridge-freezer with
summarized in Table I, together with the overall heat transfer different energy efficiency ratings [21].
coefficient (Req). The latter is obtained by running the model
for one unit with the data provided by the manufacturer, Energy label class A+++ A++ A+
allowing for a 5% error in the energy consumption. Model (KG36N/) SB40 AI32 AW22
Energy consumption (kWh/annum) 159 238 281
The expected percentage of small, medium and large size Storage volume fridge (l) 219 219 223
A++ fridge-freezers is estimated from [22] to be 33%, 44% Storage volume freezer (l) 66 66 66
and 23% respectively in 2020. The technical specifications of Temperature rise time (h) 17 14 14
Coefficient of performance (COP) 1.25 1.02 1.02
the fridge-freezers are summarized in Table II, together with
Compressor power (W) 60 70 85
the overall heat transfer coefficients (Req). Req fridge (W/°C ) 1 1.3 1.8
Req freezer (W/°C ) 0.67 0.86 0.86TABLE II: Technical specifications of A++ fridge-freezer of different A. Scenario 1: Energy efficiency class in the year 2020
sizes [23].
In this scenario, fridge-freezers are assumed to be of
Size Small Medium Large medium size but with the 2020 range of energy efficiency
Model (K/) UL15A60GB GV33VL31G GE49BBI30G class shown in Fig. 2.
Energy class A++ A++ A++
Energy consumption Two different modes of control have been studied:
140 219 255
(kWh/annum) 1. Non-staggered. All the devices are given the same
Storage vol. fridge (l) 108 192 301
Storage vol. freezer (l) 15 94 111
valley and peak period starting times in which to
Temp. rise time (h) 10 23 44 operate the control strategy. All the devices are in
COP 1.02 1.3 1.05 different stages of the cooling cycle when the valley
Comp. power (W) 75 80 90 and peak periods are initiated.
Req fridge (W/°C ) 1 1.3 1.4
Req freezer (W/°C ) 0.28 0.87 0.62 2. Staggered. The starting time of the valley and peak
periods for individual devices within the aggregate
are distributed over a period of 60 minutes. Every
C. Valley filling and peak shaving control strategy minute the control strategy of a proportional number
A valley filling and peak shaving control strategy is of devices is modified, i.e. for the simulation of 1000
proposed in this study. This control algorithm is designed to units, 17 units are activated every minute.
simulate a standard fridge-freezer thermostat, which
Fig. 4 shows the effect of the staggered and non-staggered
maintains the freezer temperature between -19°C and -23°C
control modes on the system demand. A valley period of two
and the fridge temperature around 5°C. At chosen periods the
hours and a peak period of 1.5 hour have been chosen, as this
thermostat upper and lower temperature limits are varied
produces the desired effect of modifying system demand
according to the demand profile. Subsequently load
while not giving rise to significant disturbances caused by the
participation increases consumption during valley periods and
devices returning to the standard limits.
reduces consumption during peak periods while maintaining
food safety. During the valley period the thermostat
temperature limits are modified to maintain a freezer
temperature between -23°C and -25°C, while to prevent
freezing the fridge temperature is maintained above 3°C.
During the peak period the limits are modified to maintain the
freezer temperature between -18.5°C and -18°C and fridge
temperature under 7°C.
III. RESULTS AND ANALYSIS
The model is run for a total of 48 hours. During the first
24 hours it is run without the control strategy, but including
the randomization effects of individual appliances within the Figure 4. Proposed modified system demand in Ireland during the valley
aggregate load. During the subsequent 24 hours the valley period.
filling and peak shaving control strategy is engaged. A typical
winter day has been chosen to evaluate the effect of each As shown in Table III, for the non-staggered control mode
control strategy on the system demand. Fig. 3 shows the the system demand in the valley period is increased by 3 MW
system demand on the 10th December 2013 [27]. In 2020 the and is shifted to 04:00. For the staggered control mode the
system demand on a winter working day is assumed to be minimum system demand in the valley period is increased by
10% greater than the equivalent day in 2013. According to 26 MW and is shifted to 03:15. For both cases the amount of
the TSO grid capacity statement demand is forecast to grow stored energy is similar, at 60 and 64 MWh for the non-
2% each year [24]. staggered and staggered modes respectively.
TABLE III. Modified demand for the proposed scenarios and control
modes.
Period Scenario Time of Modified demand
valley/peak (MW)
Valley 1: Non- staggered 04:00 + 3
1: Staggered 03.15 + 26
2: Non-staggered 04:00 + 7
2: Staggered 03:15 + 30
Peak 1: Non-staggered 17:45 - 50
1: Staggered 17:45 - 55
2: Non-staggered 17:45 - 47
2: Staggered 17:45 - 42
Figure 3. System demand in Ireland on a winter working day.Fig. 5 shows the modified system demand during the peak
period. The non-staggered control mode creates a second
peak when the devices revert to the standard thermostat
setting. As shown in Table III, for the non-staggered control
mode the maximum system demand in the peak period is
decreased by 50 MW. The staggered mode results in a peak
reduction of 55 MW.
Figure 7. Proposed modified system demand in Ireland during the valley
period considering the size of the appliance.
As shown in Table III, for the non-staggered control mode
the system demand in the valley period is increased by 7 MW
and is shifted to 03:45. For the staggered control mode the
minimum system demand in the valley period is increased by
30 MW and is shifted to 03:00. For both cases the amount of
Figure 5. Proposed modified system demand in Ireland during the peak stored energy is similar, at 74 MWh for the non-staggered
period and staggered modes. Fig. 8 shows the proposed modified
system demand in Ireland during the peak period. The non-
The peak shaving and valley filling control strategies staggered control mode creates a second peak when the
presented can also be combined in other ways to maximize devices revert to the standard thermostat setting. This is an
their DSP potential. For example in winter the modified undesirable disturbance to the grid. This disturbance is much
valley filling control strategy, maintaining temperature limits observed when using staggered control.
in the freezer between -21°C and -25°C (i.e. overcooling),
may be enabled throughout the day with the exception of the
peak period.
Fig. 6 shows the proposed modified system demand
during the peak period with overcooling. The time when to
send the signal is crucial to obtain the required effect of peak
shaving. The peak period is chosen to be 60 minutes. If the
starting time of the peak period is changed from 17:15 to
16:45 and 16:30 the peak reduction is 27 MW, 67 MW and
29 MW respectively.
Figure 8. Proposed modified system demand in Ireland during the peak
period considering the size of the appliance.
As shown in Table III, for the non-staggered control mode
the maximum system demand in the peak period is decreased
by 47 MW. The staggered mode results in a peak reduction of
43 MW.
IV. DISCUSSION
The potential to shave peak demand and increase
minimum load using the energy storage of a fridge-freezer
Figure 6. Proposed modified system demand in Ireland during the peak
period with overcooling.
has been quantified for a power system (i.e. the SEM in
Ireland). Peak reductions of 40 MW to 55 MW are
achievable, with valley demand increases of up to 30 MW.
B. Scenario 2: Size of the appliance in the year 2020 Pre-cooling can further reduce the peak, but at the expense of
In this scenario, fridge-freezers are assumed to be A++, energy consumption. Both of the control options tested shift
i.e. moderately efficient, and have the size range estimated the time of peak demand, however a non-staggered approach
from [22]. to refrigeration control tends to introduce an undesirable
second peak. The ability to change maximum and minimum
Two different modes of control have been studied, as in
demand relates to the time constant of the fridge-freezer and
Scenario 1. Fig. 7 shows the effect of the staggered and non-
the timescale of the peak or valley to be compensated. This
staggered control modes on the system demand.
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