Responding to the challenges of wind variability
M. L. Kubik1*, P. J. Coker2, Mark Miller3 and J.F.Barlow4
1,4
2
Technologies for Sustainable Built Environments, University of Reading, United Kingdom
School of Construction Management and Engineering, University of Reading, United Kingdom
3
AES Kilroot, Carrickfergus, County Antrim, United Kingdom
* Corresponding author: [email protected]
ABSTRACT
The variability of energy supplied from renewable resources is commonly identified as one of
the major challenges of integrating renewable energy with existing power systems. In
Northern Ireland, with a 40% renewable energy target by 2020, this is a particularly
challenging proposition. No existing large-scale electricity grid is able to operate without
some minimum level of conventional generation, which is required for both system security
and to maintain power quality. This minimum stable generation level restricts the amount of
wind energy that can be used to satisfy system demand, any excess of which must be wasted
(‘curtailed’), at cost, if it cannot be stored. A further system operator concern is the
occurrence of low probability, high swings in wind generation that exceed the operational
characteristics of conventional units, requiring, for example, fast acting peaking plant to
respond to a sudden drop in wind power. As wind power increasingly displaces conventional
generation from operating, the capacity of this generation to respond to wind variability
diminishes.
The purpose of this paper is to overview a research project investigating both these complex
issues using the existing conventional generation mix. The challenges are quantified and
important factors in order to reduce the system impact of curtailment and improve capability
are proposed. Scenarios that lessen the impact of wind curtailment are identified to also
reduce system capabilities to manage wind ramping. Potential strategies to respond to the
seemingly contradictory requirements of these issues are outlined.
Keywords:
Northern Ireland; variability; intermittency; renewables; conventional generation.
1. INTRODUCTION
The pressing need to avert catastrophic climate change, a depletion of fossil fuel resources
and a desire to improve energy security is driving a global transformation in the way we
produce energy. In Northern Ireland, an electricity sector renewable energy target of 40% by
2020 has been set by the UK government and approved by the Northern Ireland Assembly
(EirGrid & SONI 2010). This target is to be met by a large expansion in generation from wind
energy. However, wind is an inherently variable renewable resource and this presents
challenges for system balancing (Laughton 2007; Milborrow 2009; Gross et al. 2006). Some
concerns about the variability of wind supply are recognised, in particular their interaction
with a dwindling mix of conventional fossil fuel generation.
The challenges of renewable resource variability and the importance of conventional
generation for at least the medium term in electricity systems has been introduced in Kubik et
al. (2012). No existing large-scale electricity grid is able to operate without some minimum
level of conventional generation, which is required to maintain power quality and to provide
system security against power plant trips. This minimum stable generation level restricts the
amount of wind energy that can be used to satisfy system demand, any excess of which must
be curtailed, at cost, if it cannot be stored. In Northern Ireland, the relative isolation of the
system means a minimum of three conventional units must be kept synchronised on the grid
to maintain system security. A further system operator concern is the occurrence of low
probability, high swings in wind generation that exceed the operational characteristics of
conventional units, requiring, for example, fast acting peaking plant to respond to a sudden
drop in wind power. As wind power increasingly displaces conventional generation from
operating, the capacity of this generation to respond to wind variability diminishes.
This paper overviews a research project investigating both these complex issues and identifies
a number of strategies to respond to the challenge of wind variability. First, a brief overview
of the existing status of research into these challenges is presented in Section 2. The
overarching research method to quantify these issues is then introduced in Section 3. Key
results are presented in Section 4 and how the seemingly contradictory requirements may be
resolved are discussed in Section 5.
2. LITERATURE REVIEW
2.1. Wind curtailment
The issue of wind curtailment has been examined by a diverse collective of authors, using a
variety of modelling approaches and tools. A number of studies identify wind curtailment as
an issue and call for more power plant flexibility (De Vos et al. 2013; Denholm & Hand 2011;
Hong et al. 2012). However, the calls for additional flexibility place a heavy emphasis on
new generation and do not consider what can be achieved with the existing thermal generator
mix. Another collection of literature describes the challenge of wind curtailment from a
transmission system perspective (Burke & O’Malley 2009; Kalantari & Galiana 2010; Gu et
al. 2011; Daneshi et al. 2011), however curtailment is recognised to occur for reasons other
than transmission constraints; particularly for unit commitment or load balancing reasons
under high wind penetrations (Burke & O’Malley 2011). This is something that warrants
further investigation particularly in Northern Ireland due to its high wind target.
2.2. Wind variability
Although studies in more recent years have begun to turn their attention to the analysis of
growing penetrations of wind energy, an area that lacks detailed attention is a focus on
extremes; namely, how often low and high generation events occur and last, and particularly
the transitional behaviour of wind; how quickly output from wind generation can change.
Attempts to identify the rate of change of wind power for both ramp-up and ramp-down
events have been made in a number of studies (Milborrow 2001; Milborrow 2007; Ummels et
al. 2007; ILEX & Strbac 2002), however the emphasis has been on the ability of wind to
contribute to energy supply, showing that most changes are well within system capabilities.
Foley et al (2013) similarly recognise the impact of increasing wind ramp-ups and rampdowns on the Irish grid has not received adequate attention. In Northern Ireland, where wind
generation in 2020 is expected to make up 57% of installed generator capacity (EirGrid &
SONI 2010), it is extremely important to understand whether there is sufficient ramping
capacity in the conventional generation mix, particularly during high wind periods when only
a minimal amount of conventional generation will be online for system security purposes.
3. METHOD
The analysis in this paper used an extensive thirty-two year wind simulation dataset to
represent variability, producing onshore and offshore wind parameters Won and Woff.
Justification for this approach and details of this how it was validated may be found in Kubik
(2013). Unit specific parameters for conventional generation in Northern Ireland in 2020
(including minimum stable generation Gmsg, maximum generation Gmax, and ramp up and
down rates Ru and Rd) were used to characterise the plant mix. These inputs provided enough
data to analyse wind variability, as illustrated in Figure 1.
Input parameters*
Demand
time series D
Plant mix scenario
(GU characteristics
Gmsg, Gmax, Ru, Rd)
North-South
Interconnector Ins
Onshore wind
time series Won
Moyle
Interconnector Im
Offshore wind
time series Woff
Curtailment analysis
For each time step, balance generation:
Gb = D – Won – Woff – Ins – Im – Gmsg
Then curtailment requirement:
If (Gb < 0), Then Gc = |Gb|
Else Gc = 0
Quantify
curtailment
Calculate
costs
Calculate
emissions
Investigate modifications and relative
benefits for scenario
Wind variability analysis
Determine probability distribution of wind
swings at different timescales.
Wt = Woff +Won
Compare dWt/dt against Ru and Rd.
Quantify
events
Calculate costs
Investigate modifications and
relative benefits for scenario
*Blue dashed line indicates model input parameters that are used in the analysis of curtailment only. Red dotted line
indicates model inputs used in both curtailment and wind variability analysis.
Figure 1 - Modelling approach to analyse wind curtailment and swings in wind generation
Wind curtailment levels were determined by introducing additional parameters of simulated
2020 system demand (D), and the interaction of the system with neighbouring electricity
networks by adding Northern Ireland’s interconnector links to Great Britain (the Moyle
interconnector, IM), and the tieline to the Republic of Ireland (the north-south interconnector,
INS). The balance of this combination of parameters was used to determine required wind
curtailment balancing Gc for any given half-hourly period as indicated in Figure 1.
The modelling process was scenario driven, using both historic and simulated 2020 input data
to inform how renewable resource variability impacts will change. Twenty-eight power plant
minimum unit combinations were considered and the most valuable combinations were
identified. A complete description of these variables, data validation and the method of
calculating costs and emissions are not reproduced in this paper due to the limit on space
requirements, but may be found in Kubik (2013). It is similarly not practical to discuss all the
scenarios and unit combinations in detail, however six significant scenarios have been
selected for further discussion, identified in Table 1.
Table 1 – Summary of important scenarios identified in the analysis
Scenario
Description
A
B
C
D
E
F
‘Business as usual’ maintaining the existing 3 unit security constraint
Poorest emission saving 3 unit combination.
Lowest curtailment achievable with existing 3 unit security constraint
Lowest curtailment 2 unit combination achievable including CPSi
Largest emission saving 2 unit combination.
Lowest curtailment 2 unit combination achievable without CPS
MSG
(MW)
466
388
350
305
176
90
1hr ramping
range (MW)
423
444
594
369
172
450
i. CPS - Coolkeeragh power station: 400 MW combined cycle gas turbine power plant.
4. RESULTS AND DISCUSSION
4.1. Wind curtailment
400
8.00%
350
7.00%
300
6.00%
250
5.00%
200
4.00%
150
3.00%
100
2.00%
50
1.00%
0
% wind curtailed
Curtailment required (GWh)
The results of the wind curtailment analysis are shown in Figure 2. This identifies that under
business as usual conditions (Scenario A), the level of wind curtailment in 2020 would be
expected to be 360 GWh, the equivalent of 7.5% of all wind generation output. Required
wind curtailment and its associated impacts progressively reduces through the scenarios
considered as the minimum stable generation level is lower. The lowest wind curtailment
level achievable (Scenario F) would save 287 GWh of wind from curtailment.
0.00%
A
B
C
D
E
F
Figure 2 – Expected wind curtailment levels in 2020 under generator unit scenarios defined in
Table 1
The correlation between wind curtailment and the carbon emissions avoided by reducing
conventional generation on the system is shown in Figure 3 for all 28 scenarios. This
indicates that a relationship between lower curtailment and reduced emissions exists, but the
actual benefit of any given curtailment level is strongly influenced by the constituent plant
mix. Scenario B for example, identifies that although the plant mix tested reduced wind
curtailment, it would increase overall system emissions due to the lower efficiency of
operating Kilroot’s units on coal. Scenario E demonstrates that the greatest emissions saving
(202 ktCO2eq) is not actually achieved through the scenario with the largest reduction in wind
curtailment (Scenario F).
250
E
150
F
D
100
C
50
350
300
B
Emissions avoided (ktCO2eq)
200
R² = 0.4529
0
250
200
150
100
Required curtailment of wind (GWh)
50
0
-50
Figure 3 - Correlation between wind curtailment required to balance system and the carbon
emissions avoided in achieving this relative to 'business as usual' conditions (scenario A),
with Table 1 scenarios annotated on the graph
4.2. Wind variability
The characteristics of wind variability in Northern Ireland with a 2020 installed capacity of
wind are shown in Figure 4, based upon the analysis of a 32-year simulated wind generation
data set as outlined in the method section. The results reveal that the changes in wind
generation (with respect to a total installed wind capacity of 1630MW) remain less than
approximately ±5% for 99% of the time, consistent with the findings discussed in Section 2.2.
However, infrequent large swings in wind generation occur with high levels of volatility. At a
4-hourly time step, these could be as large as ±70% of installed wind capacity, ±40% at a 1hourly time step, and ±15% at a 5-minute time step.
The impact of wind variability is mapped against the capabilities of conventional generation
in Figure 5 for 4-hour time steps, as these are shown in Figure 4 to be the most problematic.
All scenarios are shown to be susceptible to some ramping events, where up/down wind
ramping exceeds plant ramping capabilities to respond. Upward wind ramping, requiring a
ramp down of conventional plant output to balance, is much more frequently problematic.
This can be managed however by curtailing the ramp rate of wind generation, albeit at cost to
the system. Wind generation drop off, requiring a ramp up of conventional plant, is much less
frequently an event. This is because wind drop off can be supported by OCGT peaking
generation in almost all cases. The running of peaking generation is however costly to the
system.
Scenario E identifies that lowers curtailment and reduced emissions can reduce ramping
capability and suffer from more unmanageable events; however, this can be influenced by
plant modifications, as Scenario F demonstrates with low curtailment and emissions as well as
a similar response capability to ramping events as Scenario A which includes 3 units.
100.0000%
4hr NI 2020
10.0000%
1hr NI 2020
5min NI 2020
Time (%)
1.0000%
0.1000%
0.0100%
0.0010%
0.0001%
-80.0%
-60.0%
-40.0%
-20.0%
0.0%
20.0%
40.0%
60.0%
80.0%
Change (% capacity)
Figure 4 - Characteristics of wind variability in Northern Ireland in 2020 at three timescales
showing the frequency of changes in output (2020 installed wind capacity is 1630 MW)
700
Upward wind ramping event
Av. hourly 'events' per year
600
Downward wind ramping event
500
OCGT required
400
300
200
100
0
A
B
C
D
E
F
Figure 5 - Quantification of the number of wind ramping events for each plant scenario in
Table 1 for 4-hrly swings in wind generation output, averaged for a standard year
5. RECOMMENDATIONS
The results have identified that changes to the existing generation mix can reduce wind
curtailment and associated emissions and costs to the system. The existing three-unit security
constraint needs to be relaxed for the largest savings to be achieved (Scenarios D, E and F).
The existing system security constraints (Scenario B) and certain high minimum generation
units (Scenario C) were identified to be barriers to achieving the lowest wind curtailment and
emissions levels.
Appropriate market incentives are required to encourage operational improvements where
conventional units are required to run from a system security perspective. Under existing
constrained running rules, such units have an actual disincentive to provide flexibility.
Potential modification options for Northern Ireland that would significantly reduce wind
curtailment, such as running Kilroot power station flexibly on minimum oil generation, are
restricted by market rules at present. The redesign of the Irish SEM to comply with the
European Target Model for integrated electricity markets in 2016 presents an opportunity to
address these factors.
The analysis of wind variability identified that ramping events occur under all scenarios,
including the present day arrangements (Scenario A). These require either restriction of the
upward wind ramp rate or the firing of OCGT generation to manage, both with associated
costs. A small number of wind drop off events would remain unmanageable even with these
strategic responses, requiring electricity to be imported or further generation capacity to be
constructed in Northern Ireland.
The frequency of events occurring was identified to be higher under some of the two unit
scenarios (e.g. Scenario E). This is due to a lower combined ramp rate and ramp range
capability from the conventional plant mix. As these same scenarios were identified to be the
most beneficial to reducing system carbon emissions and curtailment the most, this creates an
apparently paradoxical set of requirements for managing wind curtailment and variability
issues. A number of solutions to these contradictory requirements are possible:



Plant modification – changes to the operating regime of the plant mix identify that
minimum generation may be reduced without adversely affecting ramping capability.
This is demonstrated was in Scenario F, which would modify Kilroot power station to
run on heavy fuel oil during times of high wind generation. However, such a
modification would come with a cost to the system, due to the necessary
improvements and the high commodity price of heavy fuel oil.
Battery storage – the analysis was extended to include a 100 MW unit of battery
storage in Kubik (2013). This identified storage would significantly improve ramping
capabilities particularly for two unit combinations with lower plant flexibility.
Storage would also avoid the costly running of open cycle peaking generation, saving
an average of £2.3m per annum in the lowest emission scenario (E).
Interconnection – interconnection has a potentially important role in managing
variability. If Northern Ireland’s security constraints can be relaxed by reducing the
supply bottleneck to the Republic of Ireland, this should improve ramping capability
by drawing upon the generation south of the border. Further work is required to
identify the extent to which interconnector capacity can be relied upon however.
6. CONCLUSIONS
Simulation of the Northern Ireland system has demonstrated that power plant modifications
can reduce curtailment levels and increase system resilience to variability, as well as reducing
associated CO2 emissions and costs. Importantly, this can be done without capital spending
on new plant, making it a valuable interim solution. Twenty-eight unit combinations were
ranked in this regard, and the analysis identified that curtailment could be reduced from
business as usual levels by 287 GWh (Scenario F) or greenhouse gas emissions by 202
ktCO2eq (Scenario E). The existing three-unit security constraint needs to be relaxed for the
largest savings to be achieved. The high minimum generation of some of the Northern
Ireland units was identified as a barrier to avoiding wind curtailment; Coolkeeragh power
station was particularly significant in this regard. A weak (0.45) positive correlation between
lower wind curtailment and greater emissions savings was established, inferring that
individual power plant efficiency characteristics are an important factor in determining the
carbon benefit of reducing wind curtailment. The lowest curtailment scenario does not infer
the largest reduction in emissions.
The characteristics of wind variability in Northern Ireland with a 1630 MW installed capacity
of wind generation were quantified. The results reveal that the changes in wind generation
remain less than approximately ±5% for 99% of the time, but that infrequent large swings in
wind generation occur with high levels of volatility. At a 4-hourly time step, these could be as
large as ±70% of installed wind capacity. Mapping these against plant capabilities in the
same set of scenarios as considered for curtailment identified that the existing generation mix
is insufficient. The restriction of the upward wind ramp rate to and the firing of OCGT
generation are required to manage more events. Both come with associated balancing costs.
However, the research approach used in this paper assumed perfect prediction of the wind
generation; further work is needed to account for the uncertainty in forecasting.
Analysis of system capability has identified that scenarios where wind curtailment can be
reduced also tend to reduce conventional generation’s ability to respond to wind ramping
events. This was particularly the case for the lowest emission scenario, where Ballylumford
C’s 100MW gas turbine is used (Scenario E). Plant modification (as in Scenario F), battery
storage and interconnection were all suggested to have a strategic role in overcome this
seemingly contradictory set of requirements.
ACKNOLWEDGEMENTS
The authors would like to acknowledge the EPSRC for their funding, without which this
research could not have been conducted. They also extend their thanks to the System
Operator for Northern Ireland, National Grid and NASA, all of whom provided data to carry
out the analysis.
REFERENCES
Burke, D.J. & O’Malley, M.J., 2011. Factors Influencing Wind Energy Curtailment. IEEE
Transactions on Sustainable Energy, 2(2), pp.185 –193.
Burke, D.J. & O’Malley, M.J., 2009. Transmission connected wind curtailment with increasing wind
capacity connection. In IEEE Power Energy Society General Meeting, 2009. PES ’09. IEEE
Power Energy Society General Meeting, 2009. PES ’09. pp. 1 –5.
Daneshi, Srivastava, A.K. & Daneshi, A., 2011. Security constrained unit commitment with phase
shifter and wind generation. In 2011 IEEE Power and Energy Society General Meeting. 2011
IEEE Power and Energy Society General Meeting. pp. 1 –7.
Denholm, P. & Hand, M., 2011. Grid flexibility and storage required to achieve very high penetration
of variable renewable electricity. Energy Policy, 39(3), pp.1817–1830.
EirGrid & SONI, 2010. All Island Generation Capacity Statement 2011-2020, Available at:
http://www.soni.ltd.uk [Accessed June 1, 2011].
Foley, A.M. et al., 2013. Addressing the technical and market challenges to high wind power
integration in Ireland. Renewable and Sustainable Energy Reviews, 19(0), pp.692–703.
Gross, R. et al., 2006. The Costs and Impacts of Intermittency, UKERC.
Gu et al., 2011. Congestion-induced wind curtailment: Sensitivity analysis and case studies. In North
American Power Symposium (NAPS), 2011. North American Power Symposium (NAPS),
2011. pp. 1 –7.
Hong, L., Lund, H. & Möller, B., 2012. The importance of flexible power plant operation for Jiangsu’s
wind integration. Energy, 41(1), pp.499–507.
ILEX & Strbac, G., 2002. Qualifying the system costs of additional renewables in 2020, UMIST.
Available at: http://www.dti.gov.uk/files/file21352.pdf.
Kalantari, A. & Galiana, F.D., 2010. The impact of wind power variability and curtailment on ramping
requirements. In Transmission and Distribution Conference and Exposition: Latin America (T
D-LA), 2010 IEEE/PES. Transmission and Distribution Conference and Exposition: Latin
America (T D-LA), 2010 IEEE/PES. pp. 133 –138.
Kubik, M.L., Coker, P.J., et al., 2013. A study into the accuracy of using meteorological wind data to
estimate turbine generation output. Renewable Energy Journal, 57, pp.558–561.
Kubik, M.L., Brayshaw, D.J., et al., 2013. Exploring the role of reanalysis data in simulating regional
wind generation variability over Northern Ireland. Renewable Energy, 57, pp.558–561.
Kubik, M.L., 2013. The impact of high levels of renewable generation on conventional fossil fuel
based electricity markets. The University of Reading.
Kubik, M.L., Coker, P.J. & Hunt, C., 2012. The role of conventional generation in managing
variability. Energy Policy, 50, pp.253–261.
Laughton, M., 2007. Variable renewables and the grid: an overview. In G. Boyle, ed. Renewable
electricity and the grid: the challenge of variability. Earthscan, pp. 1–30.
Milborrow, D., 2009. Managing Variability, WWF-UK, RSPB, Greenpeace UK and Friends of the
Earth EWNI. Available at: http://www.greenpeace.org.uk/files/pdfs/climate/wind-powermanaging-variability.pdf.
Milborrow, D., 2001. Penalties for intermittent sources of energy, Cabinet Office: PIU Working Paper
on Penalties. Available at: www.cabinetoffice.gov.uk/strategy/downloads/files/Milborrow.pdf
[Accessed January 22, 2013].
Milborrow, D., 2007. Wind power on the grid. In G. Boyle, ed. Renewable electricity and the grid: the
challenge of variability. Earthscan, pp. 31–54.
Ummels, B.C. et al., 2007. Impacts of Wind Power on Thermal Generation Unit Commitment and
Dispatch. IEEE Transactions on Energy Conversion, 22(1), pp.44 –51.
De Vos, K. et al., 2013. Revision of reserve requirements following wind power integration in island
power systems. Renewable Energy, 50(0), pp.268–279.