Demand Side Response System Frequency Control
using Temperature Controlled Devices: Potentials
and Requirements in Germany by 2020
Lea Wagner, Eckehard Tröster
Energynautics GmbH, Robert-Bosch-Straße 7, 64293 Darmstadt, Germany
Email: l.wagner@energynautics.com
Abstract—In power systems with high share of convertercoupled supply devices, such as modern wind turbines or
photovoltaic systems, the inherent inertia decreases. As a result,
system frequency can become more vulnerable to active power
imbalances. In the course of the German Energiewende the
question arises, whether Germany can balance its share of
the reference power plant outage, as defined in the ENTSOE Operation Handbook, without relying on imported inertial
response. Demand Side Response System Frequency Control
(DSRSFC) can be used to replace the inertia of synchronous
generators. In this report the potential of the German domestic
sector for DSRSFC is analysed and compared to the demand
for frequency response. It is shown that the potential is many
times higher than the demand and the selection of the used
technology is key to socio-economic efficiency.
cut
D
i
L
la
P
PR
r
ref
s
SF C
I. I NDICES
time averaged
cutoff
Demand
technology i
Load
linear activation
Potential
Primary Response
rated
reference loss
switch
Demand Side Response System Frequency
Control
II. I NTRODUCTION
The angular speed of the rotor of synchronous generators
is electromechanically coupled to system frequency. After a
sudden loss of generation the rotational energy stored in the
inertial masses provides the lacking power (inertial reserve).
With an increasing proportion of converter-coupled energy
sources (such as modern wind turbines and photovoltaic) the
inertial response will decrease. Hence, system frequency will
become more vulnerable to power imbalances and higher
values of the rate of change of frequency will be attained.
The European Network of Transmission System Operator
for Electricity has addressed this drawback in its grid code
draft “Demand Connection Code” [1]. In this draft Demand
Side Response System Frequency Control (DSRSFC), a very
fast load shedding measure reacting to frequency deviations,
is defined. The intent of this report is to analyse the potential
of DSRSFC to replace inertial response.
The following specifications of DSRSFC are made in the
Demand Connection Code [1]:
In order to make an automatic load shedding without
consumer inconvenience possible, devices with an inherent heat storage shall be used.
• The Transmission System Operators are supposed to
publish a list with suitable temperature controlled devices.
• The devices should react to frequency deviations exceeding a deadband by load shedding.
• The frequency measurement shall be updated at least
every 0.2 s.
• The reconnnection should take place within 5 min after
the stabilisation of system frequency.
The objectives of this report are as follows: First, the
suitability of common domestic appliances for DSRSFC is
reviewed in Section III. Second, the required amount of
shedded load is estimated (Section IV). Third, the potential
and the demand are compared in Section V.
•
III. T HEORETIC P OTENTIAL OF THE DOMESTIC SECTOR
The identification of the DSRSFC potential of the German
domestic sector can be achieved by the assessment of the
time-resolved power consumption of temperature controlled,
domestic devices.
The considered technologies are displayed in the first
column of Table I. The process cooling of food retailing is
not a domestic appliance but was included for comparison.
Accounting for the assumptions, which were made during
data analysis, the potentials were determined for a best
guess, representing the expected potential, and a worst case
scenario, estimating the mimimal potential.
A. Method
Data analysis was conducted based on the following steps:
1) Identification of potential: Data provided in [2], [3],
[4], [5], [6] was compiled (see Table I, column 3). The
available records were time averaged and in most cases for
load shifting of at least one hour. Because of the latter, the
used data were most likely an underestimation of the real
DSRSFC potential. In the next steps time resolution and
extrapolation was conducted.
2) Time resolution: Time categories consisting of a yearly
(month), a weekly (work day, Saturday, Sunday) and a time
of day (day, night) breakdown were defined.
Best guess scenario: For the resolution of the monthly
electricity consumption related to heating, heating degree
days1 from [7] were used. It was presumed that the cooling
1 Heating
degree days are a measure of heating.
TABLE I: Characteristics and used values of the considered
temperature controlled devices. PSF C,P,,i = time-averaged
DSRSFC potential of technology i, df = development factor,
d – n = day or night, bg = best guess scenario, wc = worst
case scenario.
3
circulation
pump
bg
wc
1,716
X
-
heat pump
bg
wc
319
electric water
heating
bg
wc
fridge
PSF C,P,,i
[MW]
2,633
4
5
dependency
month
day
d
X
-
6
7
df
–n
-
0.74
0.37
-
X
-
0.83
0.76
X
-
-
X
X
3.00
1.33
718
-
X
X
X
0.98
0.98
bg
wc
1,117
-
-
X
X
1.00
0.68
freezer
bg
wc
1,237
-
-
X
X
0.88
0.80
process cooling
(food retailing)
bg
wc
678
-
X
X
X
X
1.33
1.00
devices are located in a room at constant temperature and the
consumption of warm water is continuous during the course
of the year. A dependency on weekdays was considered
only for the consumption of warm water and the process
cooling of food retailing. Load curves from [8] were used
to estimate the proportion of power consumed during night
and day time. Night storage heating and the considered
electrical water heating devices are equipped with a storage
capacity, which is capable of storing the demand of at
least 24 h. Thus, the time of electricity demand and heat
supply are independent within a period not exceeding 24 h.
Consequently, no dependency of the power consumption of
night storage and water heating on the time of the day was
considered.
Worst case scenario: It was assumed that during the whole
year several days with an average temperature above the
heating limit (15 ◦ C, [7]) can occur. Hence, in the worst
case there is no electricity consumption of heating devices.
For the determination of the minimum potential of the nonheating devices the minimum of the respective load curves
was chosen.
An overview of the assumed dependencies can be found
in Table I, columns 4-6.
3) Extrapolation: The extrapolation to 2020 was conPSF C,2020
ducted with development factors, df = PSF
, which
C,today
were deduced from [9], [10], [4], [6]. For the worst case
scenario the lowest development factor was chosen, for the
best guess scenario the value with the highest congruence in
the literature (see Table I, column 7).
B. Results
The resulting data set defines the time-resolved DSRSFC
potential of each considered technology. In Figure 1 the
aggregated DSRSFC potential is presented.
The maximum potential is available at a Saturday in
January and amounts to 13.6 GW. The minimim of 3.9 GW
occurs in July or August during weekend nights. The dependency of the accumulated potential on the time of the year
12,000
10,000
8,000
6,000
4,000
2,000
0
(a) Best Guess Scenario
3,000
2,500
2,000
∑ PSFC,P,i [MW]
night storage
heating
2
scenario
bg
wc
work day - day
work day - night
Saturday - day
Sunday - day
weekend - night
14,000
∑ PSFC,P,i [MW]
column
technology
16,000
1,500
1,000
500
0
Workday Saturday
Sunday
Night
(b) Worst Case Scenario
Fig. 1: Time-resolved, accumulated DSRSFC Potential.
is lower for night times. The daytime potential exceeds the
nighttime potential by approximately one third. Because of
the vanishing potential of the heating devices the aggregated
minimum potential is not dependent on the time of the
year. Hence, this dependency is not considered in Figure 1
(b). The minimum potential adds up to 1.6 GW (night) –
2.6 GW (workday), accounting for 20 – 50% of the expected
potential, depending on the time of the year.
C. DSRSFC Potential per Device
Assuming that the investment costs for enabling a device
to DSRSFC are equal for every type of technology i, the
DSRSFC potential per device PSF C,device,i is a pointer for
the unlocked DSRSFC potential per investment costs. The
expected DSRSFC potential per device can be deduced from
the findings exposed in Table I column 3 (PSF C,P,,i ),
market penetration data mi [2] and the number of households
nHH [6].
PSF C,P,,i
PSF C,device,i =
.
(1)
mi nHH
In order to determine the minimum DSRSFC potential per
device the minimum of the timely resolved data per technology presented in Section III-A2 was chosen and equation (1)
applied. Note that the potential per device relates to 2005–
2012.
Figure 2 shows that a domestic cooling device provides
a low but reliable potential (bg: 24–42 W, wc: 18–37 W),
while the potential of a heating device is middle to large
(bg: 46–2,051 W) though not always available. It becomes
evident that reserves of process cooling of food retailing are
100,000
PSFC,device,i [W]
10,000
1,000
100
10
1
fridge
freezer
circulation heat pump
pump
water
heating
night
storage
heating
process
cooling
(a) Best Guess Scenario
100,000
PSFC,device,i [W]
10,000
1,000
100
10
1
fridge
freezer
process
cooling
(b) Worst Case Scenario
Fig. 2: DSRSFC potential per device.
the best choice to provide DSRSFC: The potential per device
is large and secured (bg: 12,237 W, wc: 3,059 W).
IV. R EQUIREMENTS FOR STABILISING S YSTEM
F REQUENCY
For the assessment of the required DSRSFC potential
solely the active power balance was considered. System
frequency serves as an indicator for this equilibrum. As
system frequency is a global quantity in a synchronous zone
the corresponding network can be modelled as one node.
A recent study [11] investigates the effect of decreasing
inertial response, quantified by the acceleration time constant T , on system frequency. It was shown that reduced
inertial response only becomes grave once the acceleration
time constant T drops below 1 s. Today typical values of the
time constant in the transmission grid of Continental Europe
(CE) are 10 s and higher [12]. It is very unlikely that critical
T values will occur in CE by 2020, but solely regarding
Germany T ≤ 1 s is reasonable in a low load situation
with high wind penetration. Since the German Energiewende
should not be undertaken at the expense of the other nations,
measures to replace inertial response, such as DSRSFC, must
be investigated.
The developed one node model was justified by and tuned
to the simulations presented in [11] (Section IV-B). The
tuning was conducted within the scope of CE while the
DSRSFC requirements were determined for Germany (Section IV-C). This transfer included the following assumptions:
• Apart from scaling issues the size of the grid is nonrelevant for the course of frequency.
• German primary response resembles the CE ones.
The theoretic behavior of system frequency after a sudden
generation loss is explained in the following Section IV-A.
A. Theory of the course of frequency
After a sudden loss of generation ∆P the course of
frequency can be described by equation (2).
∆P
f˙
k
PP R
=T
− (fr − f ) −
PL
fr
fr
PL
(2)
The inertial response (first term, right hand side) is mainly
responsible for the rate of change of frequency, f˙, immediately after the loss. The load dependency on system
frequency, k, (due to rotating machines, etc.) is described by
the second term and accounts for the self-regulating effect.
The third term depicts the effect of the primary response
PP R . PL is the total load of the system and fr the rated
frequency, 50 Hz in this case. A detailed derivation of the
correlations can be found in [13]. Note that all of these
parameters except fr are time dependent on longer time
scales. As at most the first minute after a sudden loss of
generation is considered, only the dependency of f and PP R
are relevant. For PP R a real proportional control according
to equation (3) was assumed (Parameter TP R and KP R ).
ṖP R + TP R PP R = KP R (fr − f )
(3)
B. Method
The simulations were performed with DIgSILENT PowerFactory. The one node model consists of two synchronous
generators and two loads connected to a busbar. One generator represents a failing power station and the other the rest
of the supply devices. They supply the power ∆Pref and
(a)
100
(b)
(c)
PSFC,D (T) [MW]
80
60
40
20
0
0
0.2
0.4
0.6
T [s]
0.8
1
1.2
Fig. 4: Dependency of the DSRSFC demand on the acceleration time constant T . The demand was activated according
to (a) a frequency sensing switch with fcut = 49.6 Hz, (b)
linear activation without time delay, (c) linear activation with
∆t = 0.2 s.
Fig. 3: System frequency after a generation loss of 3 GW
with a total load of 150 GW and varying acceleration time
constants T . Simulation results of [11] (black, thick lines) in
comparison with the results of the developed model (grey,
thin lines).
PL − ∆Pref , respectively. The loads, which are qualified
for DSRSFC, consume PSF C,D , while the non-dispatchable
loads consume PL − PSF C,D . The dynamic behaviour of
system frequency can be described by equations (2) and (3).
The parameter KP R of the primary response was determined according to the ENTSO-E Operation Handbook [14].
In an iterative process the time constant TP R was ascertained
in such a way that the simulation results were in accordance
with [11] (see Figure 3). (The difference in the settling
frequencies is irrelevant, as only the frequency course until
its nadir is of interest in this paper.) A sudden loss of a
specific amount of power injection is particularly critical in
low load situations. Thus, the reference loss ∆Pref,CE =
3 GW [14] was simulated by the Transmission System
Operators in a low load situation (PL,CE = 150 GW) with
varying acceleration time constants [11].
In order to determine the DSRSFC demand of Germany,
the total load was rescaled to PL,Germany = 30 GW, which
represents a common low load situation of Germany. Thus,
the generation loss ∆Pref,Germany , which Germany has to
balance by itself, amounts to:
PL,Germany
30 GW
· ∆Pref,CE =
· 3 GW = 0.6 GW
PL,CE
150 GW
In order to draw a worst case and to account for the
establishment of converter-coupled loads the parameter k
was fixed to the low value 1 [13]. The power PSF C,D was
determined so that the frequency nadir ∆fnadir scratches
the maximum allowed dynamic deviation ∆fnadir,max =
800 mHz as prescribed in the ENTSO-E Operation Handbook [14]. Measurement inaccuracies are ∆PSF C,D =
±1 MW and ∆(∆fnadir ) = ±1 mHz.
There were two ways of activation of DSRSFC examined:
As a first approach the DSRSFC load was equipped with
a frequency sensing switch. The load was completely shed
if system frequency falls below the cutoff frequency fcut .
Varied parameters included the acceleration time constant T
and the cutoff frequency fcut .
In a more realistic approach a linear dependency of
the activated DSRSFC potential on the frequency deviation
was assumed. This linear model reflects a high number of
DSRSFC loads each including a cutoff frequency, which is
randomly chosen within a specific range (central limit theorem). The frequency fmax , where the activation of DSRSFC
begins, was selected to be 49.9 Hz and the frequency fmin ,
where DSRSFC is fully activated, was set to 49.5 Hz. It is
expected that the linear activation corresponds to activation
by a switch, if the DSRSFC potentials activated from fmin
to fcut and fcut to fmax are equal. Thus, because
1
√ (fmax − fmin ) + fmin = 49.617 Hz
2
holds the linearly activated DSRSFC demand is expected to
correlate with the DSRSFC demand activated by a switch
with fcut = 49.617 Hz. The investigations were performed
under varying acceleration time constants and linear activation without time delay and with ∆t = 0.2 s .
C. Results
According to the expectation (Section IV-B) the DSRSFC
demand activated by a switch (fcut = 49.6 Hz) is similar
to linearly activated DSRSFC demand (Figure 4, (a) and
(b)). The demand decreases with increasing acceleration time
constant and approaches zero near T = 1.1 s. Interestingly
it does not exceed 110 MW even for very low acceleration
time constants (e.g. T = 0.1 s).
If the DSRSFC potential is linearly activated and additionally a time delay of 0.2 s included, the DSRSFC demand is
slightly increased compared to an activation without time
delay. The impact is higher for lower acceleration time
constants.
The dependency of the DSRSFC demand on the cutoff
frequency is presented in Figure 5. It is largely independent
of the acceleration time constant. As an example T = 0.5 s
was chosen. The selection of the cutoff frequency impacts
the DSRSFC demand only by several MW. Thus, Figure 4
gives overall evidence about the DSRSFC demand.
PSFC,D,s (fcut, T = 0.5 s) [MW]
70
66
62
58
54
50
49.2
49.3
49.4
49.5
49.6
49.7
49.8
49.9
50.0
fcut [Hz]
Fig. 5: Example of the correlation between DSRSFC demand
PSF C,D,s (fcut , T = 0.5 s) and the cutoff frequency fcut .
The maximum measured DSRSFC demand amounts to
PSF C,D,s (fcut = 49.25 Hz, T = 0.1 s) = 106 MW.
In systems with T = 0.1 s the maximum dynamic
deviation fnadir,max = 800 mHz is exceeded 159 ms after
the failure. Thus, if the time delay amounts to ∆t = 0.2 s,
no DSRSFC can be activated within this period. By means
of DSRSFC only systems with acceleration time constants
≥ 0.167 s can be stabilised.
V. C ONCLUSION
The German domestic sector provides a huge DSRSFC
potential of up to 13.6 GW. Even worst circumstances allow
for a DSRSFC potential of 1.6 GW, if all relevant devices
are appropriately equipped. The required DSRSFC demand
is comparatively small: It does not exceed 110 MW. From a
socio-economic point of view the most efficient technology
appears to be the process cooling of food retailing (see
Section III). It was estimated that during workdays and
Saturdays the power demand of these devices sum up to 928
MW; on Sundays and during nighttime the average power
demand is 384 MW. In the worst case scenario the power demand is 697 MW and 174 MW on workdays and Saturdays
and on Sundays and during the nights, respectively. Thus, if
all cooling devices of the food retailing sector were equipped
with frequency sensing relays, the DSRSFC demand could
be easily covered, even if one third of the powered devices
cannot be switched off, because (for example) the internal
temperature is too close to the upper temperature range
setting.
In real power systems the limitation of DSRSFC to
systems with T ≥ 0.167 s is not relevant, because also in a
100 % renewable energy scenario there is synchronous generation such as hydro power or biomass power stations [15],
which provide enough inertia to ensure that T ≥ 0.167 s.
Future investigations of the DSRSFC demand should take
voltage issues into account and consider the effect of the
spatial distribution of generators and loads.
ACKNOWLEDGMENT
The authors would like to thank Nis Martensen for enlightening discussions.
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