International Journal of Distributed Energy Resources
Volume 7 Number 3 (2011) Pages 245 - 263
Manuscript received: 23. December 2010
THE ROLE OF ELECTRIC HEATING AND DISTRICT
HEATING NETWORKS IN THE INTEGRATION OF
WIND ENERGY TO ISLAND NETWORKS
Simon Gill, Michael J. Dolan, Damien Frame, Graham W. Ault
Electronic and Electrical Engineering Department
University of Strathclyde
Royal College Building, G1 1XW, United Kingdom
Phone (00 44) 141 548 4003
E-mail: [email protected]
Keywords: Distributed Energy, District Heating, Demand Side Management, Heat
Storage, Wind power.
ABSTRACT
Flexible electric heating and heat storage is investigated in terms of managing wind
power on an islanded power-system. A case study is developed consisting of:
power system with operational constraints; firm and non-firm connected wind generation; electric district heating network with heat storage. Simulations proceed by
balancing half-hourly electrical and heat supply and demand over 1 year to determine non-firm generation energy yield. Investigation of dispatch rules demonstrates the importance of managing system flexibility, particularly the heat-store.
Heat store capacity is investigated and small storage tanks are shown to significantly increase wind production, however the marginal increase in wind energy
output drops quickly with increasing storage size. Heat-storage is also shown to
allow successful management of periods with low electrical demand, which can
cause operational difficulties for power networks. Finally a comparison is made
with a system that uses electric boilers as a secondary heat supply. Archival value
is found in the presentation of methods for analysis of combined electrical and heat
networks together with case study results. Clear conclusions are drawn on the economic worth of options for the facilitation of greater renewable energy provision.
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INTRODUCTION
The provision of heat from renewable sources is essential to the European Union’s
2020 targets for renewable energy. The United Kingdom has a target of 15% of
total energy production from renewable sources [1]. Heat demand equates to 42%
of the total energy used and the UK government expects 12% of heat to come from
renewable sources in 2020 compared with only 1% in 2008 [2]. In Scotland, renewable heat production totalled 829GWh in 2009; by 2020 the Scottish Government is aiming for 6420GWh which constitutes an ambitious seven and a half
times increase over eleven years [3].
The majority of the increase in renewable energy production and supply has been
in the electricity sector. For example, UK wind penetration has risen from 809MW
to 2820MW between 2004 and 2008 [4]. As the penetration increases the variability of wind and other renewable resources will lead to difficulties with power system operation [5].
1.1
Electric Boilers, Heat Storage, and District Heating Networks
Electric boilers, powered by renewable electricity generators can provide one
method of generating renewable heat. Incorporating large boilers and heat storage
into district heating networks allows a level of flexibility in the management of
energy, both heat and electricity. Several studies of countries with high penetration
of district heating find electric boilers an economically feasible method of managing the flexible power output of renewable energy generators such as wind power.
Studies of the Danish power system have highlighted electric boilers and heat storage as low cost solutions in terms of managing fluctuating renewable energy
sources. Heat pumps are highlighted as the most effective method of reducing fuel
used in a closed system. However, electric boilers are 5 times cheaper than heat
pumps per MW of heat output and provide an effective low cost option [6].
Another study of the Scandinavian power network looked at the effect of heat
pumps and electric boilers on wind-curtailment, power regulating costs and periods
of low power price. The results show that heat pumps produce significantly higher
system benefit compared with electric boilers for the same installed heat output.
However, the significantly lower cost associated with boilers lead to a higher ratio
of benefits to costs [7]. The development of energy-systems with 100% renewable
generation emphasises the future importance of district heating. For Denmark, a
country with high wind and district heating penetration, scenarios developed suggest district heating system should expanded whilst heat-demand needs to reduced[8, 9]. In a separate study of Ireland, a country with low renewable energy
production and very little district heating, the suggested scenario includes district
heating network to provide heat to 55% of individuals [10].Where heat storage
facilities are included, management of the heat-storage is important to ensure that
wider energy-system goals are achieved. For example, under minimisation of system-costs, heat-storage may lead to an increase in overall CO2 emissions as cheap
coal generation is substituted for expensive gas generation [11].
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Power-system constraints
The constraints imposed on power systems include thermal line limits, voltage
levels and frequency constraints. Flexible demand provides a method to manage
power system with these constraints. A summary of the benefits of demand side
management is provided in [12]. An area highlighted is the likely significant value
of the energy-balancing service that demand-side management can provide. The
ability to store heat for lengthy periods between generation and demand means that
electric boilers connected to heat-storage can provide flexible electrical demand.
The difficulties of operating power systems with large penetrations of wind and
other renewable energy sources are accentuated in the context of small islanded
networks. Stringent constraints may limit the wind capacity that can be given a
firm-connection. For example within the UK, the Orkney Islands have a limit of
26MW of firm generation [13]. In these situations flexible demand and energy
storage is likely to have a high value in terms of assisting operation of the power
system and reducing curtailment of renewable generation [14].
This paper focuses on the role of electric boilers and heat storage in active management of distribution networks with the specific aims of incorporating non-firm
wind connections and management of periods of low electrical-demand. Energy
balance modelling is used, with energy production and demand balanced across the
power and heat networks during each half-hour period of the simulations. Two
success criteria are developed: one relates to the capacity of viable wind that can be
connected, the second links to operation of the electrical-network during periods of
low demand.
2
CASE STUDY SYSTEM
Figure 1 shows the outline of the case study electrical network which operates at
33kV. Existing demand and generation is shaded white; new wind generation and
demand is black. The additional wind capacity can be varied and the additional
load represents an electric boiler for use in the heat distribution network. The existing electrical generation is spread over three generation stations:

Generation station A: Small diesel units which must meet 40% of electrical
demand. This constraint ensures that system voltage remains within specified limits.

Generation station B: Gas turbine generators with maximum generation
constrained by the thermal limitations of the network connection.

Generation station C: A firmly connected wind farm that is allowed to generate at all times without curtailment.
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S. Gill, M.J. Dolan, D. Frame, G.W. Ault
Figure 1: 33kV Case Study Network.
The three generator station constraints, summarised in table 1, are applied at each
half hourly period to designate minimum generation at each station. Station C provides a variable but essentially ‘must-take’ output. Additional generation is provided initially from station B until it reaches its maximum output beyond which
finally station A meets any remaining requirement. Occasionally, during periods of
low electrical-demand, applying the three constraints leads to over generation. In
these circumstances station A is allowed to reduce output to balance generation
with demand.
Table 1: Generation Dispatch Constraints
Generation
Fuel
Constraints
Station A
Diesel
Minimum fraction of demand
Station B
Gas turbine
Maximum output
Station C
3.7MW wind
Firm connection
The district heating network (DHN) modelled in the paper consists of an electric
boiler, heat-storage tanks, a heat-distribution network and a mix of industrial and
domestic heat-demand. The electric boiler directly heats water and converts energy
from electricity to heat with assumed 100% efficiency. Storage tanks allow hot
water to be stored and are assumed to be lossless over the period of storage. Heat
losses in the district heating system are significant and are included in the figures
for overall heat-demand. The electric boiler is assumed to have a power rating
greater than any instantaneous energy-conversion required.
2.1
Objective
Simulations are split into two groups; the objective of the first group is to estimate
the level of viable additional-wind generation in the combined electricity-and-heat
system. Additional wind power is given a ‘non-firm’ connection to the electricity
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network. This allows network operators to curtail generation to ensure that the
electrical network constraints are met. The viability of the wind generation is assessed in term of the capacity factor (CF) achieved by the wind farm. Curtailment
of wind generation by the electrical network results in a reduction of the electrical
energy produced and therefore a reduction in the CF of the wind farm. The CF
provides a proxy for the expected annual income from generated electricity per
MW of installed capacity; if the CF falls too low then developers are unlikely to
build additional capacity due to the reduced returns on investment.
The second group of simulations has the objective of management of periods of
low electrical-demand, defined as below 14MW for the case-study network. This
corresponds to periods during which a small number of generating units are used to
meet demand and therefore system inertia is low.
The simulations study the effect of varying DHN parameters in terms of meeting
the two objectives described. The role of the heat-storage capacity and the method
in which the various system components are dispatched is of particular interest.
2.2
Demand and Generation Profiles
The case study rests on energy balance across electrical-demand, heat-demand,
heat-storage and electrical-generation. Realistic profiles are required for electrical
and heat-demand and for wind generation. Annual data from a UK case study allowed average electrical-demand and wind generation profiles to be extracted with
a half-hour resolution. Operational data from a case study DHN, together with a
generic UK household heat-demand profile [15] allows estimation of similar profiles for heat-demand. The profiles are created as follows:

Existing electrical demand profiles: Produced from operational data from a
comparable power network. Figure 2 shows the profile of a 1 week period
from summer and winter.

Heat-demand profiles: Knowledge extracted from operational data for a
comparable DHN and discussion with DHN operators is combined with a
generic UK profile for household heat-demand to produce a heat-demand.
Monthly average heat demand is shown in figure 3, and daily variations
with a resolution of half an hour are shown in figure 4 for summer and
winter. Spring and summer profile shapes are formed by an equally
weighted average of the summer and winter profiles. The annual half- hour
resolution profile is constructed by using a daily demand profile shape for
the relevant season scaled to give an average matching the monthly average. Four sizes of DHN are simulated in this paper: 1MW, 2MW, 4MW
and 10MW where the size is defined by absolute maximum heat-demand.

Wind Generation: Operational data for a small wind farm in a comparable
island situation is used to produce wind-generation profiles. This generation profile is scaled to match the installed capacity in each simulation. An
example of a period of one week of wind generation production on data is
shown in figure 5.
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S. Gill, M.J. Dolan, D. Frame, G.W. Ault
Figure 2: Example summer and winter week electrical demand, normalised relative to maximum average demand.
Figure 3: Normalised average heat demand by month (Jan - Dec).
Variations in the DHN size and wind-generation capacity are achieved by linear
scaling of the base profiles. For the heat demand profile this assumes that the mix
of industrial to domestic heat demand remains constant as the DHN penetration
increases. The linear scaling of wind generation assumes that new wind capacity
generates with the same profile as existing generation.
The electrical demand profile will be affected by the DHN penetration. Island
households in areas with no mains gas have high levels of electric heating and in
this paper a value of 50% is assumed [16]. Increased DHN penetration will result
in replacement of existing household electric heating and a reduction in the electric
demand profile. A further complication is the inclusion of storage heating leading
to a lag between the electrical and heat demand. In this paper it is assumed that
existing electric-heaters operate instantaneously and are not storage heaters. For an
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increase in DHN demand during each half-hour period the electrical demand for
the same period is reduced by 50% of the DHN demand increase.
Figure 4: Daily heat demand profiles for summer and winter.
Figure 5: Normalised wind generation profile for a 1 week period in case study
network.
2.3
Counterfactuals
Two scenarios are included for comparison: a base-case or business-as-usual scenario and a counterfactual involving development of a non-electric fuelled DHN.
As renewable heating targets become more widespread, this second counterfactual
provides a likely future for island electricity networks as other renewable heat solutions such as biomass are developed. These two cases differ due to the reduction of
the electrical demand profile caused by increased DHN penetration. A non-electric
fuelled DHN is likely to decrease the role wind energy can play on the electricity
network and increase the number of periods of low electrical demand.
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S. Gill, M.J. Dolan, D. Frame, G.W. Ault
SIMULATION METHODOLOGY
This section describes the methodology for the various simulations and defines the
success criteria. Annual simulations are carried out for the period 1st June - 31st
May and have a half-hourly data resolution. Each simulation period requires the
following inputs:

Electrical demand

Heat demand

Firm wind generation output (Generation Station C)

Additional wind generation available

Stored heat
For each period, the simulation first meets the voltage and thermal constraints on
the electrical network by dispatching generation units against a set of rules. Next, a
number of options exist for utilizing additional wind to meet the three demand
groups: electrical, heat and heat-for-storage. Using one of the three dispatch methods described in the following section the simulation attempts to maximise the use
of the additional wind generation and curtails any that cannot be used.
3.1
Method of Dispatching
The link between additional wind generation, heat demand, electrical demand and
heat-for-storage can be managed in a number of ways depending on the goals of
the operators. For example, early charging of the heat-storage tank provides increased security of supply to the DHN. However, a full heat tank reduces flexibility
for the electrical network and increases the likelihood of increased future curtailment of wind. Three dispatch methods are investigated that use additional-wind
generation to meet the three demand groups:

Heat, Store, Electric (HSE): Additional wind generation is first dispatched to meet heat demand and secondly to charge the heat store. Remaining additional wind generation is then made available to meet electrical demand assuming that electrical network constraints have been met.
Otherwise the remaining additional wind generation is curtailed.

Heat, Electric, Store (HES): First dispatch priority is to meet current heat
demand, secondly to meet electrical demand within electrical network constraints, and finally to charge the heat-storage. As before surplus wind generation is curtailed.

Electric, Heat, Store (EHS): First priority dispatch to meet electrical demand up to the limits imposed by electrical network constraints, secondly
to meet current heat-demand and finally to charge the heat store.
In all three methods, any remaining heat demand is met firstly from stored heat and
then from the electrically fired boiler using the other forms of electrical generation.
The existing electrical generation is dispatched as described in section 2 to meet
remaining electrical demand.
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For simulations investigating operation of the electrical network under low demand
conditions a final dispatch rule is added.

If electrical demand drops below 14MW and heat storage capacity is available, the electric-boiler is operated to raise demand back to 14MW. The
heat is used to charge the heat store.
Evaluation of the dispatch methods is carried out for each of the four sizes of DHN.
For each size of DHN the heat-storage capacity is held constant at 100MWh.
3.2
Storage Capacity
The role of heat-storage capacity is investigated through simulations based on a
1MW capacity DHN. Heat-storage connected to the system is varied between 0 and
200MWh and levels of viable wind and ability to raise electrical-demand during
periods of low demand are measured.
3.3
Success Criteria
The primary output of the simulations is the level of viable additional wind generation. Viability is measured in terms of achieved capacity factor and is closely
linked to the level of generation curtailment applied. For a generating unit that is
unconstrained and generates with the base generation profile the capacity factor
achieved is 0.48 in accordance with the annual production profile used. Curtailments of wind generators will cause the CF to fall. A capacity factor of 0.4 or
greater is assumed to be the condition for viability.
Non-firm connection of wind generation is often achieved so that generating units
connected later are curtailed in a manner that ensures that curtailment of earlier
connectors is not increased. This last-in-first-off (LIFO) principle is in operation in
existing active network management schemes. The measure of ‘viable wind’ used
here uses the LIFO principle to find the last unit of capacity achieving capacity
factor of greater than 0.4. Since the resolution of the simulations is 1MW of additional wind capacity, estimates of the actual result of viable additional wind generation are achieved through linear interpolations between simulation results.
The second success criterion involves measurement of low electrical demand periods defined as electrical demand less than 14MW (this accounts for approximately
2% of half-hour periods in the base case). As well as increasing viable additional
wind capacity the dispatchable demand of the electric boiler and heat-store can be
used to reduce periods of low demand. Two measures are used: the absolute lowest
demand level and the fraction of time during which the network operates with electrical-demand lower than 14MW.
4
RESULTS
A total of 117 scenarios are simulated. This breaks down into: 60 investigating
dispatch method, 50 investigating heat-storage capacity and 7 examining low electrical demand. Several sizes of DHNs are modelled with the size defined by the
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peak heat demand. The DHN sizes used in the simulations are 1MW, 2MW, 4MW
and 10MW as measured by the peak heat-demand.
4.1
Dispatch Method
The most effective dispatch method is evaluated in terms of reduction of wind curtailment over the base case and no-electric counterfactual. Figure 6 shows the total
wind power curtailed over the one year period for a system including a 1MW
DHN. The EHS dispatch option is the most successful at limiting wind curtailment,
and the level of curtailment for the counterfactual is slightly higher than for the
base case. Specific values for fractional curtailments for four sizes of DHN are
shown in table 2.
Figure 6: Wind energy curtailed over 1 year under each dispatch method.
Table 2: Fraction of available wind energy curtailed
Dispatch Method
Base
No-Electric
HSE
HES
EHS
Additional Wind Capacity installed
5MW
10MW
15MW
0.0134
0.1254
0.2732
0.0150
0.1325
0.2815
0.0108
0.1160
0.2628
0.0050
0.1107
0.2587
0.0000
0.0854
0.2409
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Figure 7: Capacity factor of last unit under each dispatch method with enlargement of range close to CF=0.4.
In terms of viable wind capacity (as defined by the CF>0.40 condition discussed in
section 3.3) the CF achieved for each additional unit of wind capacity is shown in
figure 7. Linear interpolation between points allows the viable wind using each
dispatch method to be estimated. The results for the four DHN sizes are shown in
table 3.
Table 3: Capacity of non-firm wind achieving CF>0.4
Dispatch
Method
Base
No-Electric
HSE
HES
EHS
Size of district heating network
1MW
7.3
7.2
7.5
7.7
8.3
2MW
7.3
7.1
7.7
8.0
9.0
4MW
7.3
6.7
8.2
8.7
9.6
10MW
7.3
5.8
9.7
10.1
11.0
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The EHS dispatch approach raises the level of viable wind by the greatest amount
for all the DHN capacity options. For example, with a 1MW DHN the EHS dispatch approach leads to an increase in viable wind of approximately 1MW of installed capacity. This is more than twice the increase of the other dispatch options.
The EHS dispatch approach is found to produce the greatest increases in viable
wind generation for all DHN capacity options. As such, simulations carried out in
the following sections use EHS for comparison with the base case and counterfactual unless otherwise stated.
4.2
Heat Store Capacity
The primary aim of heat-storage is to provide thermal energy storage for wind
power that would otherwise be curtailed. The simulations in this section are carried
out to investigate the value of heat-storage capacity in terms of increased levels of
viable wind generation capacity. A 1MW capacity DHN and the EHS dispatch
method are used for the simulations described in this section.
Figure 8: Effect of heat storage capacity on marginal increase in wind generation
output for different additional wind power capacities.
The provision of heat-storage leads to reduced wind power curtailments and therefore a greater annual wind generation for a given additional wind capacity. Figure 8
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illustrates the marginal increase in additional wind generation as heat-storage capacity increases for different additional wind generation capacities (5, 7, 9, 11, 13
and 15MW). Heat-storage capacity can be seen to increase wind generation most in
scenarios with large additional wind capacity. A second trend shows that the marginal effect of increasing heat-storage capacity quickly drops over the fist 50 MWh
of heat storage capacity. The value of additional storage capacity onto existing
large storage tanks is relatively small. This suggests that whilst it may be economically efficient to include small (sub 100MWh) heat-storage tanks in a DHN, larger
storage tanks may not be economic.
The increase in viable wind generation capacity for schemes including heat-storage
is illustrated in figure 9 which shows the capacity factor of the last unit of additional wind for a scenario including a 1MW DHN and with varying capacities of
heat-storage. The increase in viable wind (CF of at least 0.4) produced by inclusion
of 20MWh of heat-storage is approximately 0.5MW, whilst adding a further
180MWh of heat-storage increases viable wind just 0.4MW further.
Figure 9: Capacity factor of the last unit of non-firm wind generation plotted for
different heat storage capacities.
4.3
Support of Low Electrical Demand Periods
Management of periods of low electrical demand is important for small, islanded
power networks since these are often their periods that network operators report
having most difficult in managing. Low system-inertia during such periods due to
small numbers of generating units in operation can lead to increased probability of
large frequency deviations. The problem is exacerbated by the inclusion of some
firmly connected wind generation which has the potential for quick variations in
output, for example if rising wind speed leads to turbines shutting down as wind
exceeds the turbine’s maximum limit.
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The inclusion of dispatchable electrical load, such as the combination of electric
boilers and heat-storage, provides the electrical-network operator with a tool for
increasing electrical demand during specific periods. The availability of this option
depends on the availability of heat-storage capacity during the critical periods. The
simulations in this section address a system with a 1MW DHN including a
100MWh heat-storage tank and use the EHS dispatch method. The final dispatch
rule from section 3.1 is applied which allows operation of the electric-boiler when
electrical demand drops below 14MW. This occurs when there is spare capacity in
the heat-storage tank. If the tank is at full capacity then the electric-boiler cannot
operate.
Table 4 presents the minimum electrical demand achieved throughout the year and
the fraction of time the electrical network operates with demand below 14MW for
various levels of additional wind power. For additional wind power capacity up to
6MW the dispatch rules allow electrical demand to be maintained at 14MW and
above. As additional wind capacity increases the increased use of the heat-storage
tank leads to a lack of capacity available during low electrical demand periods. The
result is that the electrical demand minimum begins to drop back towards that of
the base-case. However, the fraction of time that demand remains low remains very
close to zero.
Table 4: Management of periods of low electrical demand with additional wind
power, heat-storage and EHS dispatch (defined as less than 14MW).
5
Addition Wind
Capacity (MW)
Minimum Demand
Achieved (MW)
Fraction of time below
14MW
5
14
0
6
14
0
7
12.59
0.0007
8
12.43
0.0018
9
12.43
0.0022
10
12.43
0.0027
Base Case
10.98
0.0199
‘no-electric’
10.86
0.0228
DISCUSSION
The success of DHNs and electric boilers in terms of both wind integration and low
demand management comes from effective use of heat-storage.
Of the three dispatch methods discussed, EHS makes the most sparing use of the
storage. HSE dispatch maintains the heat tank at full capacity for the majority of
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the time which reduces the flexibility of the system to minimise wind power curtailment. When using EHS dispatch the heat storage tank has at least 10% capacity
available for 66% of the time; this compares with 96% of the time for HSE dispatch. Maintaining flexibility enables the dispatch method to effectively absorb
wind production.
The availability of heat-storage capacity is also closely linked to the ability of the
electricity network operators to use the electric-boilers to reduce periods of low
electrical demand. To raise electrical demand the heat-production capacity is increased above the current heat demand and this requires the installation of heatstorage. Network operators may consider maintaining an uncharged buffer in the
heat-storage unit to provide heat (and therefore electrical) demand in these situations. This buffer would not be required at all times but only when electrical demand is expected to be low.
Figure 10 shows the ‘buffer-capacity’ required in the heat-storage to raise minimum electrical demand to specific levels. Reaching the target 14MW minimum
electrical demand requires 16MWh buffer-capacity, and a buffer of this size could
be maintained during summer evenings and nights (the periods of lowest electrical
demand).
Figure 10: Maximum heat storage buffer required to raise system electrical demand minimum.
5.1
Electric Boilers as a Secondary Heat Supply
Increasing the capacity of viable wind power involves the reduction of curtailments
to additional wind generation. Most curtailments occur during the summer months
when electrical demand is low so it is important that heat demand occurs throughout this low electrical demand period. The importance of summer heat demand is
investigated in 15 additional simulations in which an electric-boiler forms a seconInternational Journal of Distributed Energy Resources, ISSN 1614-7138, Volume 7 Number 3
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S. Gill, M.J. Dolan, D. Frame, G.W. Ault
dary heat-source for a DHN. The primary source may be waste-incineration, industrial waste-to-heat or bio-mass fired boilers.
In the scenarios investigated, the primary heat supply has a constant output of
6.3MW and a peak DHN heat demand of 12.5MW. A small heat-storage facility
with a capacity of 12MWh allows some smoothing of daily variation. The electricboiler forms a secondary heat supply used to meet heat demand when that heat
demand is greater than 6.3MW. The heat demand profile is formed through scaling
of the original heat demand profiles. Over one year the heat energy output of the
electric-boiler is equal to that of the 1MW DHN simulated above. However, as
daily heat demand during summer months is less than 6.3MW there is no requirement for the electric-boiler during this period.
Figure 11 shows the wind energy curtailed under three scenarios: base-case (Base),
1MW DHN with electric boiler as the primary heat source (Primary), and 12.5MW
DHN with electric-boiler as secondary heat supply (Secondary). At low to medium
wind power penetrations, corresponding to viable levels from the original 1MW
simulations, the level of wind curtailment remains at or close to the base-case level.
Whilst the annual energy converted by the electric-boiler is the same in both situations in the secondary scenario all electric-boiler usage occurs during the winter.
During summer months, when wind curtailment is at its highest there is no flexibility available to manage electrical-demand.
Figure 11: Comparison of curtailed non-firm wind generation with comparable
district heating networks containing electric boilers as primary and secondary
heat-sources.
6
CONCLUSION
This paper investigates district-heating networks linked to the electrical network
with renewable power generation as a method of supplying renewable heat as per
national and international targets for decarbonising energy supplies. The focus of
the paper is on the additional capacity of viable wind generation that can be connected to the electrical network and on the management of the electrical network
during periods of low electrical demand. An energy-balance analysis method to
match supply and demand across the electrical and heat networks is developed and
applied to a real island case study.
International Journal of Distributed Energy Resources, ISSN 1614-7138, Volume 7 Number 3
© 2011 Technology & Science Publishers, Kassel, Germany, http://www.ts-publishers.com
The Role of the Electric Heating and District Heating Networks in the Integration of Wind Energy to
Island Networks
261
Under active network management schemes, wind power generation on an electrical network may be curtailed to avoid breaching constraints on the network. This
leads to a limit on the economically viable capacity of wind generation. Linking a
district-heating network including heat-storage to the electrical network is shown to
raise the level of viable wind power generation in an island case study. Management of the heat-storage facility is found to be important in terms of the increase to
viable wind power capacity. Investigations into three methods of dispatching wind
power generation show that the method that uses the heat-store most sparingly
leads to greatest benefits in terms of increased viable wind-generation. Dispatching
wind power generation to meet current heat and electrical demand and using remaining excess wind energy to charge the heat-store is found to raise the viable
additional wind power capacity twice as much as an approach based on charging
the heat store before using excess to meet electrical demand.
The flexible electrical demand provided by the electric-boiler and heat-store is
shown to successfully reduce periods of low electrical demand which often cause
problems for electricity network operators.
The importance of the flexibility provided by heat-storage has been investigated
specifically as part of a large district-heating network. A final section investigating
electric-boilers used as a secondary source of heat in a district-heating network
finds that the reduced heat-demand almost totally removes increases in viable wind
power generation and the ability to manage low electrical demand periods.
While each of the energy developments could claim to be worthwhile in their own
right (e.g. district heating network development, heat-storage, electric heating,
additional wind power generation) this paper has found articulated some of the
complex relationships between the options and with the operation of the electricity
network. These complexities need to be analysed carefully before ‘system’ developments and new ‘system’ operating approaches are pursued.
Further work may focus on using the methods and models developed to investigate
other forms of energy-storage in electrical and heat systems, and comparisons with
distributed heat-storage, for example on a household level. The impact of some of
the noted assumptions in the modelling approach may also be important. The validity of the findings for the case study to other cases might also be valuable in establishing if the findings on preferred options for electricity and heat system management hold true more widely.
7
ACKNOWLEGMENTS
This work has been supported by Scottish and Southern Electricity (SSE) and Shetland Heat Energy and Power (SHEAP).
8
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International Journal of Distributed Energy Resources, ISSN 1614-7138, Volume 7 Number 3
© 2011 Technology & Science Publishers, Kassel, Germany, http://www.ts-publishers.com