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. 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