heating demand as frequency controlled regulation power

www.ijep.org International Journal of Energy and Power (IJEP) Volume 3 Issue 2, May 2014

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Heating Demand as Frequency Controlled

Regulation Power Claus Nygaard Rasmussen*1, Christian Brandt Rasmussen2, Salih Palivan3, Robin Bogø4

Department of Electrical Engineering, Technical University of Denmark

Elektrovej, bld. 325. DK‐2800 Kgs. Lyngby, Denmark

*[email protected]; [email protected]; [email protected]; [email protected]

Received 6 January 2014; Accepted 16 January 2014; Published 19 May 2014

© 2014 Science and Engineering Publishing Company

Abstract

This paper examines the possibility of using heating demand

as a source of Demand as Frequency controlled Reserve (DFR)

in the electric power system, with focus on heat pumps in

conjunction with thermal storage. The potential regulation

capacity is estimated and control strategies are discussed.

The behaviour of a large number of aggregated on/off

regulated heating units with thermal storage is investigated

and it is found that their combined response can approach

that of a linear proportional control if the operational

timescale (given as storage capacity divided by maximum

heat consumption) is sufficiently large. The laboratory

implementation and testing of DFR with various control

algorithms is presented and possible scenarios for practical

implementation are discussed.

The power system impact of DFR with linear proportional

control is assessed via simulations in MATLAB/Simulink and

it is found that DFR can have a positive impact if the

regulation constant and system delay does not exceed certain

limits; otherwise there is a risk of self‐imposed oscillations. It

is found that with the right form of control, DFR can assist in

ensuring stability in systems with high penetration of

renewable energy sources, by providing a large fraction of

the required regulation capacity.

Keywords

Renewable Energy; System Integration; Thermal Storage; Heat

Pumps; Frequency Control

Introduction

In Denmark there are plans for increasing the amount

of wind power in the electric power system to 50%

within the coming decade, and other countries have

similar plans for large scale introduction of renewable

energy. The stochastic nature of wind‐ and solar power

poses a problem because the fast fluctuating power

from those sources must be continuously matched to

the load demand in order to maintain system stability.

Conventional dispatchable generation units are often

not capable of regulating their power outputs fast

enough and the amount of dispatchable generation

capacity will decrease as the share of stochastic

renewable sources increase.

The solution to this problem will most likely be a

combination of several measures. Increased

transmission capacity can reduce the fluctuations

through aggregation and energy storage units or

generation units with high ramp rates can step in to

close gabs between load and demand.

Another interesting approach is to introduce demand

response by adjusting the load according to the

available generation. Demand response can turn load

into an additional regulation reserve and thereby

potentially help solving the stability problem.

Demand response can be obtained either by direct or

indirect control of loads. Indirect control can be based

on a price signal that loads adjust to or a locally

measurable parameter such as power flow, voltage or

frequency. The idea of using load demand as

frequency controlled regulation reserve is attractive

because the regulation signal is available everywhere

and frequency controlled regulation is already an

essential part of the power system. The concept of

Demand as Frequency controlled Reserve (DFR) has been

studied in relation to partially flexible electric demand

such as household appliances, and in particular

refrigerators and freezers(Xu, Østergaard and Togeby

2011),(Nyeng, Miertz and Østergaard 2010)but such

loads suffer from a lack of freedom to perform real‐

time adjustments of power consumption due to small

buffering capacity and user‐imposed restrictions.

International Journal of Energy and Power (IJEP) Volume 3 Issue 2, May 2014 www.ijep.org

23

In this paper we evaluate frequency controlled

demand as a tool to help solve the power system

stability problem arising due to increased penetration

of stochastic renewable power sources. The Danish

power system is used as an example with focus on the

use of flexibility related to heating with heat pumps.

Heating as DFR is of particular interest in countries

with a large heating demand, and the flexibility of

thermal loads is usually large due to the presence of

significant thermal storage capacity in buildings,

heating systems and storage tanks. The ability of heat

pumps to move highly needed flexibility from the

heating system into the power system makes them

very interesting and DFR is one of the ways to facilitate

this link.

The work presented here is related to the Strategic

Platform for Innovation and Research in Intelligent

power (iPower) which is a national Danish research

project funded by the council for Strategic research and

the council for Technology and Innovation.

Heat Pumps as Regulation Power

The annual electric power consumption in Denmark is

36 TWh and the heating demand is approximately 55

TWh(Dyrelund 2009)corresponding to an average

electric load of 4.1 GW and an average thermal load of

6.3 GW. The heating demand is therefore

approximately 1.5times the electric load – on average.

A large part of the heating demand in Denmark is

covered by district heating and this will likely continue

to be so in the coming decades. But a fraction of the

district heating could also be covered by heat pumps in

order to facilitate the use of cheap excess electricity

from wind power production. According to(Dyrelund

2009) the amount of heating covered by domestic heat

pumps will increase to 7 TWh in 2050, while the

amount of district heating supplied by heat pumps will

increase to approximately 4 TWh. A total of 11 TWh or

20% of the heat demand will thus be covered by heat

pumps and the average heat supplied by heat pumps

will therefore be 1.26 GW. The ability of heat pumps to

act as a link between the heating system and the

electric power system might encourage policy makers

to promote the use of heat pumps and this estimate of

future heat pump penetration could therefore be

regarded as being conservative.

The ratio between moved heat and electric power

consumption of a heat pump is known as its

Coefficient of Performance (COP). The COP decreases

with increasing temperature difference. For domestic

heat pumps working between an ambient temperature

of Ta = 5⁰C and a hot water tank with Th = 50⁰C the COP

value is typically in the range of 3 to 4. Table 1 shows

values leading to a conservative estimate of the future

amount of heat‐based DFR.

TABLE 1 ESTIMATED FUTURE AMOUNT OF DFR BASED ON HEAT PUMPS

Parameter Average

value

Total heat demand [GW] 6.3

Share of demand covered by heat pumps [%] 20

COP of heat pumps 3.5

Electric consumption by heat pumps [MW] 360

Electric consumption by heat pumps [p.u.] 0.09

The estimated average electricity consumed by heat

pumps amounts to 360 MW. This could be compared

to the consumption by refrigerators and freezers,

which has been estimated to approximately 90 MW in

the eastern part of Denmark (Xu, Østergaard and

Togeby 2011) ‐ corresponding to approximately 240

MW in Denmark as a whole.

An operational timescale(τs) for an energy storage unit

or energy buffer can be defined as its maximum energy

storage capacity (Emax) divided by its maximum power

consumption (Pmax).

eq.1

Refrigerators and freezers have much smaller energy

storage capacity than typical domestic or centralised

heating facilities and therefore shorter operational

timescales. Parameter values from (Nyeng, Miertz and

Østergaard 2010)have been used to estimate the

timescale of are frigerator to approximately 20 minutes.

The storage timescale related to heating with heat

pumps is determined by the thermal storage capacity

of the house itself and/or the capacity of a designated

storage unit (typically a hot‐water tank) placed in

conjunction with the heat pump. The storage capacity

of an average Danish house was found to be

approximately 13.3 kWh/K (Bogø 2013). With an

allowed temperature span of 2⁰C and a nominal

thermal load of 6.3kW this leads to a storage timescale

of 4.2 hours.

District heating facilities may have much larger storage

capacities and correspondingly longer storage

timescales. As an example, the heating facility at

Marstal in Denmark is equipped with a 1.5 MWel heat

pump and a storage tank of 2100 m3 (Fjernvarme 2014).

The storage timescale of this facility can be estimated

to ~14 hours by assuming a heat pump COP of 3.5 and

a temperature span of 30⁰C.


24

Seasonal Variations

Heat consumption is subject to very large seasonal

variations which strongly affect the ability of heat

pumps to act as DFR. The amount of electricity that

heat pumps consume is influenced by the inside‐to‐

outside temperature difference, both directly and via

the heat pump COP. Figure 1 shows the relative

monthly consumption of electricity by heat pumps.

FIG. 1SEASONAL VARIATIONS IN ELECTRICITY CONSUMED BY

HEAT PUMPS IN A 2050 SCENARIO, IN UNITS OF

AVERAGE(360MW) CONSUMPTION(BOGØ 2013)

Heating with heat pumps cannot be seen as a stable

source ofDFR on a seasonal scale. During the summer

period some heat pumps will continue to supply hot

water or provide air‐conditioning while others remain

completely turned off. The active heat pumps will

maintain power flexibility but the average electric

power consumption dictates the overall amount of

available DFR.During summer, DFR based on heat

pumps will therefore mainly have to be used as

primary regulation on short timescales.

The Need for Frequency Controlled Regulation

It is relevant to make a distinction between two types

of service that DFR potentially could supply.

Type 1 is Frequency controlled Disturbance Reserve

(FDR), where demand acts as stand‐by reserve in case

of disturbances that displaces the grid frequency by

more than ±0.1 Hz. The timescale of this type of

operation would be similar to that of primary (and to

some extend secondary) regulation power, i.e. up to 15

minutes or slightly longer. The secondary generation

reserve must according to rules respond fully within 15

minutes (Bogø 2013) and DFR will therefore not be

required to respond beyond this timescale. But the

response will typically have to be fast (<1 second) and

with high power.

Type 2 is Frequency controlled Normal operation

Reserve (FNR), where demand supplies frequency

controlled regulation power while the frequency

remains within 50 ±0.1 Hz. This operation would be

continuous and therefore supplement primary,

secondary as well as tertiary regulation power. FNR

requires a relatively long storage timescale of

operation (large energy storage capacity) but less

power and not necessarily a fast response. This type of

DFR can reduce the need for continuous regulation

from generators but the required amount of frequency

controlled regulation is usually rather small(Hirth and

Ziegenhagen 2013). The issue of how response time

and operational timescale influence the effect that DFR

has on the power system will be discussed in the next

section.

Presently, the average amount of FNR in Denmark is

23MW (≈0.006 p.u.)(Bogø 2013), and heat pumps

should be able to supply this amount during most of

the time. An increasing amount of wind power in the

system will result in larger power fluctuations on short

timescales. The present standard deviations of ramp

rates in the Danish power system are3.5 MW pr.

minute for load power and 2 MW pr. minute for wind

power(Bogø 2013), resulting in a total standard

deviation of ramp rates in regulation power of 4 MW

pr. minute.

An increase in wind penetration from 21 % to 50 %

would cause the standard deviation of ramp rates in

regulation power to increase to 5.7 MW/min. It seems

reasonable to expect a similar increase in the need for

frequency controlled normal operation reserve (FNR),

that is, up to approximately 33 MW. But this is also a

quite modest amount that could easily be covered by

the projected future amount of DFR.

Fast fluctuations on a timescale from seconds up to

minutes are handled by frequency controlled primary

and secondary regulation, while variations on longer

timescales are handled by manual regulations reserves

(tertiary regulation). Based on data from (Energinet.dk

2013) the average manual regulation reserve in

Denmark can be estimated to approximately 200 MW.

Regulation is therefore primarily manual, taking place

on a longer (>15 minute) timescale with the aim of

closing the gap between day‐ahead planed production

and the actual need for production.

Most types of DFR could possibly perform both types

of frequency control (FDR and FNR). However, if a

large amount of DFR is active there is a risk of over‐

activation, depending on the sensitivity and reaction

time of the DFR controls. This issue will be discussed

in the next section.


25

TABLE 2 EXAMPLE OF LINEAR DFR CONTROL BASED ON FREQUENCY AND TEMPERATURE

f ↓ T → T < Tmin Tmin < T < T0 T0 < T < Tmax Tmax < T

f < fmin P0

, ·

Pmin

fmin < f < f0 ·

f0 < f < fmax · , ·

P0

fmax < f Pmax

Control Options

Frequency control can be performed in several ways,

depending on the type of load and the desired effect on

the power system. The focus here is on thermal loads,

which may be divided into resistive loads and heat

pumps.

One way of controlling heat loads is indirectly via the

thermostat set‐points as proposed by (Xu, Østergaard

and Togeby 2011),(Nyeng, Miertz and Østergaard

2010)and others. In this approach the thermostat set‐

points (Tmin and Tmax) become functions of frequency as

they are shifted up‐wards when the frequency

increases and vice versa. While being simple and

relatively easy to implement, this approach will result

in a partly delayed response that might have

unwanted effects. Other ways of introducing DFR in a

more direct manner will be considered and discussed

in this section.

Linear Proportional Control

Resistive loads could be subjected to linear

proportional control similar to the droop control used

by generators. But there is an important difference

between a generator and a heat‐load DFR. The

generator is capable of delivering continuous

regulation power while the heat‐load is limited by the

energy capacity of its thermal storage. The primary

objective of a heating system is to keep the temperature

within predefined limits and this puts boundaries on

its ability to respond to frequency changes.

Operation schemes that respond to both temperature

and frequency has been suggested by (Bogø 2013) and

(Pehlivan 2013). In the approach suggested in (Bogø

2013) a set of temperature boundaries (Tmin and Tmax)

and frequency limits (fmin and fmax) defines the control.

The power (P0) which is required to maintain the

temperature of the household or water tank at a

constant level is assumed to remain approximately

constant on the timescale of DFR operation. The

electric power consumption (P) varies between Pmin

and Pmax as defined in table 2. Figure 2 shows P(T,f) for

a DFR unit with selected parameters.

This control scheme prevents abrupt changes in power

consumption as the temperature or frequency

approaches the boundaries, and the combined effect of

many DFR units will be to counteract frequency

deviations from f0 (50 Hz) and stabilise the system.

The size of the combined DFR response from many

units will be determined by the control parameters and

obviously the number of active units. The control

response is semi‐proportional and the control ‘constant’

(Kp) depends on the number (N) of active units and the

current status of each unit.

, · , ∑ , eq. 2

Fully proportional control cannot be obtained because

of non‐linear behaviour of units that are either on the

temperature‐ or frequency boundaries or outside these

boundaries.

FIG. 2 EXAMPLE OF POWER AS FUNCTION OF TEMPERATURE

AND FREQUENCY FOR A DFR UNIT WITH LINEAR CONTROL

(BOGØ 2013)

The response from linear DFR seems straightforward

and easy to predict. It will act in parallel with

frequency controlled regulation from generators and

reduce the need for their activation. But there is a

problematic issue related to the possibility of over‐


26

reaction, an issue which relates to reaction time and

sampling rate of the DFR control.

Power System Frequency Variations

In order to evaluate the influence of reaction time it is

relevant to look at the timescales on which the power

system frequency varies. Figure 3 shows a plot of

frequency data from the Danish power system during

a 10 hour period (top) and a zoom of 10 minutes

(bottom). The plot shows variations on two different

timescales; a short timescale with a period of ≈1 minute

and a long timescale with a period of 1‐2 hours.

The standard deviation of the measured frequency is

42 mHz and the amplitude of the fast variations is

approximately 40 mHz. From the relation between

frequency and rotational energy in the system it is

possible to estimate the power variations that these

fluctuations correspond to.

FIG.3 MEASURED FREQUENCY VARIATIONS OVER TIME SPANS

OF 10 HOURS (TOP) AND 10 MINUTES (BOTTOM)

The power imbalance can be expressed as:

∆·

· eq.3

With H ≈ 5 seconds being the inertial time constant of

the system and f0 = 50 Hz. A frequency change rate of

40 mHz in 30 seconds corresponds to small power

imbalances of ∆P ≈ 2.7∙10‐4 p.u. or 1.1 MW.

DFR can enforce or generate power‐ and frequency

fluctuations if it reacts too slowly or if the proportional

constant is too high. A short reaction time for DFR (e.g.

a second or less) will tend to reduce the problem

because the frequency will have little time to change

before DFR reacts to it. A fast response could be

implemented for linear resistive loads but the response

time will always be larger than the sampling time and

a sampling rate of one second or less therefore appears

to be necessary.

A more sophisticated PID control could also be

employed. Self‐induced fluctuations could be

dampened by a differential term which will reduce

power change rates. But this approach does not seem

attractive due to high complexity and lack of

knowledge about the system. In praxis it will not be

possible to set the regulation constants to optimal

values.

Longer reaction time obtained with an integral

regulator could to some extend be useful, but this will

prevent a fast response to fast disturbances and FDR

operation would not be possible. In Figure 3 a 5‐

minute moving average of the frequency has been

included. Control based on such an average could be

used for units with delayed response to prevent self‐

induced frequency oscillations. The unit would

respond to general trends rather than fast variations

and this would likely be a better approach to control of

DFR as normal reserve. But abrupt changes in

frequency due to grid disturbances would not result in

an immediate response from such a unit and it is

especially the ability of DFR to respond faster than

most generators that makes it an interesting addition to

a power system with a large fraction of fast fluctuating

power sources.

A possible compromise that would allow DFR to act

both as FNR and FDR would be to react slowly to

small deviations from f0 and faster to large deviations

from f0. In this way FNR could be performed with an

integral regulator or similar, and DFR would be

activated in case of larger disturbances. One example

of such a regulation could be:

·1

, | | ∆

· , | | ∆ eq. 4

Where K1 and K2 are regulation constants and τ is an

appropriate time constant of e.g. 5 minutes. Fast

regulation is activated for frequency deviations above

∆fmax which could be set to 100mHz.

On/off Control

Heat pumps can typically not be regulated with linear

control only. Some heat pumps are entirely on/off

regulated while others can be linearly controlled

within a certain power range. With such constrains the

previously described linear control scheme cannot be

utilised.


27

On/off regulation has been described in e.g.(Biegel, et

al. 2013). The most simple on/off regulation turn the

appliance on when the frequency is above 50 Hz and

off when the frequency is below 50 Hz. With the fast

frequency variations show in figure 3 an aggregation

of loads with such regulators would constantly switch

between on and off and thereby generate large power

fluctuations which would destabilise the system.

Simulations have shown that when the amount of

active DFR exceeds a critical level, synchronised on/off

regulated loads will tend to increase frequency

variations rather than reduce them.

One way to improve the control properties of on/off

regulated units is to introduce a frequency hysteresis

equivalent to the temperature hysteresis normally used

for on/off regulation and thereby prevent fast

fluctuations around 50 Hz. Frequency‐ and

temperature control can be combined to obtain a

hysteresis area as shown in figure 4. Units with

temperature and frequency corresponding to the red

area are off and units in the green area are on. In the

‘maintain state’ area units are in the state in which they

entered that area. This means that only units with

storage temperatures within a certain temperature

range will react instantly to a change in frequency and

the fraction of units that react instantly will be

proportional to the size of the frequency change. In this

way the aggregated response from many units

becomes less abrupt and fast fluctuations between the

two states are prevented.

Assuming that this type of control is implemented on a

large number of heating units with more or less

randomly distributed power levels, thermal storage

capacities, temperature limits and heat loads, what will

then be their aggregated effect on the power system? If

the temperatures of all units have been fully

randomised, then a linear relation will exist between

frequency and the fraction of units in the on‐state. This

means that the aggregated response from a large

number of randomly distributed units will be linear.

But a fully random distribution of temperatures cannot

be expected. There is a risk of synchronisation because

the units will be exposed to similar variations in

ambient temperature and to the exact same frequency

variations. A fraction of units will also be at, or outside,

the temperature limits. This further reduces the extent

to which unit temperatures will be randomly

distributed and thereby producing a linear aggregated

response.

FIG.4 AN EXAMPLE OF DOUBLE HYSTERESIS ON/OFF CONTROL

BASED ON FREQUENCY AND TEMPERATURE

Probabilistic Control

Simulations of the aggregated response from many

on/off regulated units have been made in order to

determine to which extend they can be assumed to

produce a linear response. Such simulations can only

give an indication of the combined behaviour because

the input parameters can be varied over a wide range.

The simulation results show that the timescale of

operation has a strong influence on the linearity of the

aggregated response from on/off controlled units. A

short timescale allows for little freedom of operation

with unit states that are determined by temperature

rather than frequency and this leads to a small amount

of correlation between frequency and DFR power. A

long timescale on the other hand, will result in more

operational freedom and improved linearity.

A total of 100 heat pumps with thermal storage were

modelled, each with a nominal electric power of 2 kW

and a COP of 3.5.In the reference case the average

thermal storage capacity was equal to that of an

average Danish household (Mieritz 2010) and the

temperature span of operation was between 18⁰C and

22⁰C. This results in a timescale of operation of 4.2

hours.

In order to create a semi‐random distribution of unit

states the input parameters where randomly

distributed within certain intervals and the initial

temperatures were randomly distributed within the

allowed temperature span. The simulations ran over 18

hours with a time resolution of approximately 0.3s and

the results from two simulation cases are shown in

figure 5as the relation between frequency and


28

aggregated power consumption.

FIG.5AGGREGATED RESPONSE FROM 100 ON/OFF REGULATED

UNITS WITH AVERAGE (TOP) AND LONG (BOTTOM)

TIMESCALE OF OPERATION

The top graph shows the reference case in which the

timescale of operation is equal to that of an average

household (4.2 hours). There is a modest correlation

between frequency and power, and a linear fit gives a

coefficient of determination (R2) equal to 0.63.

In the bottom graph the timescale has been increased

to 42 hours and heat pumps are therefore free to

operate in relation to frequency. This result in a much

more linear relation between frequency and power and

a linear fit gives a coefficient of determination (R2)

equal to 0.93.

A long operational timescale can be obtained either by

increasing the thermal storage capacity, by increasing

the allowed temperature span of operation or, by

reducing the heat consumption and thereby the

required nominal power of the heat pump. Future

buildings will most likely have long thermal timescales

due to improved thermal insulation and build‐in

thermal storage such as phase‐change materials. An

example of an existing modern household with very

long thermal timescale is the Energy Flex house

operated by the Danish Technological Institute(Ravn

og Grimmig 2011).

Another issue that needs to be considered is delayed

response from individual units. The ‘maintain state’

area in figure 4 will prevent units from fast toggling

between on and off, but in order to protect heat pumps

it might in any case be necessary to add a constrain

saying that it must remain in a given state for a certain

amount of time. This limits the change‐rate of the

combined response but it also tends to prevent self‐

induced oscillations in case of over‐reaction.

Voltage Controlled Reserve

Flexible loads such as heat pumps could also act as

Demand as Voltage controlled Reserve (DVR). This type of

control can be seen as a counterpart to DFR but its

effect on the power system is different. Voltage can be

viewed is a local parameter while frequency is a

system‐level parameter and this has several

implications on the way that DVR affects the system.

Loads cause voltage drops in the local system and

proportional DVR control will be a stabilising factor.

But a few relatively large loads may have a large local

effect, especially if they are placed in areas with ‘weak’

connections. This increases the risk of over‐reaction

and self‐induced oscillations but it also increases the

possibilities for active use of loads in relation to system

stability etc.

The simplest way of implementing DVR is with linear

proportional control as stated in equation 5.

· eq.5

Where V0 is a reference voltage (e.g. 230 V) and K is a

regulation constant which must depend on the size

and placement of the actual DVR‐controlled unit.

Voltage is also influenced by the reactive power

consumption and this makes voltage control more

complicated.

A detailed evaluation of the effects of voltage control

must be done for a specific network and such an

evaluation is beyond the scope of this work. But DVR

is a very interesting control option for e.g. low voltage

grids with large penetration of photovoltaic generation

and/or electric vehicles.

Experimental Testing

Testing of the DFR (and DVR) concepts have been

made with the set‐up sketched in figure 6. The central

component in the testing is the DFR smart‐box(P. J.

Douglass, R. Garcia‐Valle, et al. 2012), (P. J. Douglass,

et al. 2011)which has been developed at the Technical

University of Denmark, Centre for Electric Power and

Energy (CEE). The DFR smart‐box records voltage and

frequency and usually controls a relay or a thermostat.

In this set‐up the DFR box controls a Pulse Width

Modulation (PWM) circuit which can operate either

with a high switching frequency to provide linear

regulation or as a relay to provide on/off regulation.

The PWM circuit, which is shown in figure 7, uses an

optic coupling to provide galvanic insulation between

the DFR box and the AC line‐voltage.


29

The thermal system consists of an electric heater and a

water‐tank with a circulation pump to ensure a

uniform temperature distribution. The water

temperature measurement is returned to the computer

via the DFR‐box and the computer also receives

voltage‐ and frequency data.

A display‐ and control user interface has been

implemented in MATLAB(Klingest 2012) and this

interface has been further developed to allow the user

to test various control algorithms (Pehlivan 2013).The

user controls the system via the computer but it is

possible to store the control software as embedded

MATLAB on a Digital Signal Processor (DSP) which

can then be included in the DFR smart‐box. In this way

it is possible to implement a chosen control algorithm

via the DFR smart‐box.

FIG. 6 SKETCH OF EXPERIMENTAL SET‐UP

FIG. 7 PWM CIRCUIT FOR CONTROL OF THERMAL LOAD

(PEHLIVAN 2013)

The tests have provided ‘proof of concept’ for several

control algorithms but the actual implementation of

DFR will be a trade‐off between control options and

ease of implementation. Various ways of practical

implementation will be discussed in the last section of

this paper.

Power System Impact

The objective of introducing DFR is to increase power

system stability and provide additional regulation

power. In order to evaluate the extent to which it is

able to do this, afew relatively simple power system

models have been made and tested. The models were

implemented in MATLAB/Simulink assuming a

system like the one sketched in figure 8. In order to

limit complexity it was decided to model the DFR

response as being linear and the decision to do so was

based on the results obtained from simulation of

aggregated on/off controlled units.DFR can therefore

in this simulation be viewed as operating in parallel

with frequency controlled generators, but with a

smaller time‐delay.

The issue of response time is a trade‐off; between a fast

response with a corresponding risk of over‐reaction

and self‐induced oscillations, and a slower response,

that does not fully utilise the benefits of DFR as

disturbance reserve but prevents oscillations.

FIG. 8 SKETCH OF A SIMPLIFIED POWER

SYSTEM CONTAINING DFR

DFR acting as Disturbance Reserve

The effect of DFR as disturbance reserve is illustrated

by figure 9. A load step of 0.6 % will, according to

equation 3, cause the frequency to drop at a rate of

approximately 25mHz/s. The frequency will drop until

a sufficient amount of controllable generation has been

activated, but the limited generation ramp rate delays

this activation and the frequency drops by 112mHz

before it starts to increase again.

FIG. 9 SIMULATED RESPONSE TO A LOAD

STEP OF 0.6 % (BOGØ 2013)


30

The situation is different when fast‐acting DFR is

present. The power imbalance is matched within a few

seconds, the frequency only drops by approximately

6mHz and the stationary error is also smaller. This

indicates that DFR has a large potential as disturbance

reserve in systems with small amounts of controllable

generation.

DFR as Frequency Controlled Normal Reserve

In order to investigate the situation when DFR is acting

as frequency controlled normal reserve (FNR) two time

series (of wind power and load consumption) are

needed as input data. Data from 2013, available on the

homepage of the Danish transmission system operator

with a temporal resolutionof5 minutes were used.

When DFR acts as FNR the effect is to move part of the

actual regulation from generators to DFR. Since DFR

and generators act in parallel, the amount of regulation

supplied by each part will depend on the relative

amounts of active control power and their regulation

constants. When the amount of regulation supplied by

generators decrease, their ramp rates also decrease.

Figure 10 shows the simulated influence of DFR on

generator ramp rates and system frequency. With the

introduction of 0.1 p.u. of DFR the standard deviation

of generator ramp rates is reduced from1.4∙10‐3p.u. pr.

minute to 1.2∙10‐3 p.upr. minute and large ramp rates

are almost eliminated.

FIG.10 SIMULATED EFFECT OF DFR ON GENERATOR RAMP

RATES AND FREQUENCY IN A SYSTEM WITH 21 % WIND

PENETRATION

The introduction of DFR also results in a reduction in

standard deviation of the frequency due to the extra

regulation capacity which results in a stronger

regulation response. Lower ramp rates allow for less

utilisation of controllable generation but it is more

difficult to evaluate the benefits of smaller frequency

deviations.

The amount of DFR and the length of its response

delay turn out to be critical parameters. If DFR

responds too strongly to a frequency change, or with

too much delay, then self‐induced oscillations may

occur and the presence of DFR tends to have a negative

effect on the system, with larger frequency deviations

and increased generator ramp rates.

DFR in Conjunction with Scheduled Power

Another way to evaluate DFR is to assume that

conventional generation from e.g. combined heat and

power plants (CHP) only provides scheduled power

and DFR therefore has to cover the entire need for

regulation. Figure 11 shows an example of simulated

system balance over a period of 120 hours with wind

power data multiplied by a factor of approximately 2.5

to reach a wind penetration of 50 %. The conventional

(CHP) power follows an hourly schedule with small

adjustments to remove stationary frequency errors.

The maximum ramp rate of conventional power is 2%

pr. Minute in this simulation and the DFR control is

proportional with a regulation constant of KDFR = 1 p.u.

/Hz. The maximum ramp rate of conventional

regulation is essential for the outcome of the

simulation because it determines how much regulation

that must be provided by DFR. A maximum of 2% pr.

minute is a rough estimate based on data from

(Suwannarat 2007).

When load exceeds wind power DFR provides the

necessary regulation power to close the gap between

load and scheduled generation.

DFR is capable of keeping the frequency deviations

within 0.1 Hz. The maximum DFR power is 0.1 p.u.

but during 96% of the time it remains within 0.03

p.u.This means that even without intra‐hour regulation

it is possible to supply the required regulation and

maintain system stability with 0.1 p.u. of DFR.

The energy buffering that DFR needs to perform is

found as the integral of the DFR power. Figure 11

shows that this power integral remains below 1.5

p.u.∙ minute. The amount of DFR in the system is equal

to 0.1 p.u. and the required energy buffer therefore

corresponds to nominal DFR power for 15 minutes.

This is small compared to the earlier estimated

domestic heating timescale of 4.2 hours.


31

FIG.11 RESULT OF POWER SYSTEM SIMULATION WITH 50% WIND PENETRATION AND DFR REGULATION

Implementationof DFR

In the previous sections we have discussed the

properties and effects of DFR but unsolved issues

remain with regard to practical implementation. The

technical issues relate e.g. to the type of control, where

the control hardware should be physically placed and

how the input parameters should be chosen. There are

also economic aspects to consider as well as the role of

DFR seen in relation to e.g. price signal control or

direct control of load demand.

There are several possible scenarios with regard to

implementation of DFR; a selection is listed below:

1) Control hardware with imbedded software is built

into the appliance (e.g. a heat pump) at the

manufacturer. A few control parameters may be set by

the user but most of them are fixed. There is no

payment to the consumer for DFR services but a

discount could be given to customers who choose this

option. The discount could be paid for by the

transmission system operator (TSO) and added to the

cost of electricity or paid for by savings in payment for

frequency controlled reserve supplied by generators.

2) DFR control hardware and software is sold as add‐

ons for existing appliances. This option is difficult and

not likely to be used because the individual add‐on has

to work with the control that is already built into the

appliance. This means that e.g. thermostat set‐points

must be changed by the add‐on and it must therefore

be custom‐made to the appliance control. But for

simple resistive loads the add‐on option could be

attractive and easy to use. The cost of such add‐ons

could be returned to the consumers by the TSO or DSO

via an electricity price reduction for customers who

purchase DFR add‐ons.

3) DFR control could be one of the features of smart‐

grid control boxes or advanced smart‐meters installed

in private homes and industry. Possibly the DSO or

TSO would have the option to set certain critical

control parameters in order to be able to adjust the

strength of the DFR response. The main feature of such

a control box would likely be control based on

electricity prices and price forecasts, with DFR as a

possible supplement. This option seems attractive since

DFR in itself will likely not be attractive enough to

make costumers interested.

4) DFR is implemented only in relation to large central

loads and generation, such as, district heating plants,

combined heat‐and‐power plants, large public or

residential buildings and large industry. This is a

realistic scenario since it can be done with minor

changes to the present praxis. Larger loads (e.g. above

100kW) will in this scenario be able to sell frequency

controlled regulation capacity to the TSO and provide

it with their own equipment.

5) Another possibility is that the need for frequency

controlled regulation will not become large enough to

make DFR relevant. The need for fast regulation will

increase but the amount of conventional generation

will remain large enough to cover the relatively


32

modest needs for decades to come. Even if the amount

of conventional generation is reduced dramatically a

large fraction of the regulation requirement could most

likely be obtained via electricity‐price based control as

part of a smart‐grid.

Conclusions

The analysis presented here shows that heat pumps

with thermal energy storage in a likely future (year

2050) scenario will constitute at least 0.09 p.u. and

thereby have sufficient regulation capacity to be able to

supply the full need for frequency controlled

regulation in the Danish power system, and even be

able to cover a substantial part of the inter‐hour

regulation requirement. The simulations have

indicated that the concept of Demand as Frequency

controlled Reserve (DFR) is useful and relevant but

there are potential complications. There is a danger of

overreaction and self‐induced oscillations if the DFR

response is too strong or too slow compared to the

system response. If managed the right way, DFR is

found to have a positive impact on the power system,

with smaller ramp rates of conventional generation

and smaller frequency deviations.

Simulations have also indicated that the combined

response from a large number of on/off regulated DFR

heating units may approach a proportional response if

the individual units are in random temperature states,

but large thermal energy storage capacity is required to

ensure this.

Experimental testing of a range of control algorithms

has shown how the concept can be implemented in

conjunction with thermostat regulation to maintain

user satisfaction and supply frequency regulation at

the same time. The exact choice of control algorithm

must be based on a range of technical requirements,

user requirements and power system requirements.

The issue of practical implementation of DFR remains

open. There is currently no obvious way for system

operators to measure and pay individual consumers

for this service and it is unclear how regulation

parameters should be controlled to ensure a positive

impact on the system. A possible first step could be to

expand the current market for frequency controlled

regulation power to include large flexible loads. Later

on, DFR control could be built into flexible devices,

primarily heat pumps and electric vehicle chargers and

ultimately it could perhaps become a short‐timescale

supplement to price control, as one of several features

of smart‐homes and smart‐grids.

ACKNOWLEDGEMENT

Part of this work has been financed by the

Danishipower project. The authors would like to thank

the council for Strategic research and the council for

Technology and Innovation for their support to this

project.

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Claus Nygaard Rasmussen received a M.Sc. (Eng.) and a

Ph.D. from the Technical University of Denmark in 1997 and

2004. He is now Associate Professor at the Centre for Electric

Power and Energy (CEE) at the Technical University of

Denmark.

Christian Brandt Rasmussenreceived a B.Sc. (Eng.) from the

Technical University of Denmark in 2008. He currently works

as Development Engineer and Project Manager at the Centre

for Electric Power and Energy (CEE) at the Technical

University of Denmark.

Salih Palivan received a M.Sc. (Eng.) from the Technical

University of Denmark in 2013.

Robin Bogø received a M.Sc. (Eng.) from the Technical

University of Denmark in 2013. He is currently employed at

the Danish National Railways.

heating demand as frequency controlled regulation power

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electric power system

storage capacity

electricpower system

power outputs fastenough

combined response

additional regulation

linear proportional

positive impact