Available Active Power Estimation for the Provision of Control Reserve by Wind Turbines Summary Keywords: 1. Introduction 2.

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1 Available Active Power Estimation for the Provision of Control Reserve by Wind Turbines Dominik Schneider, Kristie Kaminski Küster, Malte Siefert, Markus Speckmann Fraunhofer IWES, Kassel, Germany Koenigstor 59, Kassel, Tel.: , Fax: Summary The available active power (AAP) is equivalent to the power a wind farm could produce if it had not been curtailed. The AAP is relevant information in the case of congestions management, down-regulation due to negative prices at the spot market or for the provision of control reserve. Until now only few methods for the estimation of the AAP are available. In this paper five different methods are described and compared. Therefore four criteria are defined: accuracy under normal operation, accuracy under curtailment, consideration of changes in the wind farm s composition and costs. For the test 10-minutes averaged data from a wind farm in Brandenburg (Eastern Germany) that were measured over 21 months was used. Four of the five methods show high accuracy under normal operation, only the last measured value method is less accurate. Under curtailment the accuracy of the adjusted power curve method and the site-specific power curve decreases with increasing curtailment levels whereas the reference wind turbine method s and physical model s accuracy stays the same. Under additional consideration of the two other criteria the physical model seems to be most suitable for the estimation of the AAP. Further research will test the most promising methods also with three seconds data which is crucial for the provision of control reserve by wind farms. The provision of control reserve by wind farms using the AAP will be shown in a field test in the middle of Keywords: wind integration, control reserve, wind farms, available active power, wake model, nacelle wind speed correction 1. Introduction By the first half of 2012 the share of electricity from renewable energy sources in Germany has reached 25%. Wind energy covered 9.2% of the German electricity consumption. This leads to great challenges to the electricity system. Congestion management, down-regulation in cases of negative prices at the spot market and the provision of control reserve by wind turbines will become more important in the near future. In all of these three cases it is relevant to know what power the wind farm could produce without any curtailment. This power is called the available active power (AAP) or possible power. Until now there are only few methods for the estimation of the AAP available. The German federal network agency [1] for example proposes two methods to calculate the compensation for wind farm owners in the case of curtailments due to grid congestions (last measured value method and adjusted power curve method). Most manufacturers simply use the nacelle anemometer wind speed (NWS) and a power curve to estimate the AAP (comparable to site-specific power curve method). Eisen et al. [2] implemented and tested a method that is similar to our physical model. In this paper we compare all of these four methods plus one that is called reference wind turbine method. 2. Requirements At the moment wind farms in Germany cannot provide control reserve as there is no approved method how to prove the delivery of control reserve. One method could be the one shown in Figure 1 for the case of negative control reserve [3]. During the provision phase the wind farm feeds in its AAP. During the delivery of control reserve the feed in is the AAP minus the requested control reserve power.

2 Normalised power P Provision of negative control reserve Delivery of negative control reserve Figure 1: Proof of the delivery of negative control reserve using the AAP To use the AAP for the provision of control reserve a method is needed that estimates the AAP with a high accuracy and is as cheap as possible. Therefore we have defined the following criteria to compare the different methods. As mentioned the AAP can also be used for other purposes but the requirements for the provision of control reserve are probably the highest. 2.1 Accuracy under normal operation The accuracy of the estimated AAP of most methods can only be calculated exactly under normal operation, that means without any curtailment, as the the real AAP has to be measured. For this reason the different methods are compared with this criterion although it does not make sense to estimate the AAP if it could be measured at the same time. 2.2 Accuracy under curtailment More important is the accuracy of the different methods under curtailment of the wind farm. Here it is essential to consider wake effects inside the wind farm to get an exact estimation of the AAP. The accuracy of the AAP has to be that high that in the case of the delivery of control reserve the increase or decrease of power is clearly visible. Additionally the accuracy has to be similar to the deviations of conventional power plants from their schedules assuming that wind turbines are able to control their output exactly. That means a normalised root mean square error (nrmse) of about 1.5%. 2.3 Consideration of changes in composition of wind farm AAP Forecast Feed in t [min.] This aspect assesses the consideration of changes in the composition of the wind farm. This might be due to the failure of single wind turbines or the exchange, removal or installation of wind turbines. 2.4 Costs Costs play an important role for the feasibility of the estimation method. Costs include for example the acquisition and installation of additional equipment. Furthermore the costs are influenced if the method does not allow to curtail all wind turbines. 3. Methods To find the best method for the estimation of the AAP we have chosen five different methods. Some of them like the two methods defined by the German federal network agency existed already and serve as a comparison to the methods that have been developed during our project. All methods will be described with regard to their core idea, needed input data and algorithm. 3.1 Last measured value The first method described by the German federal network agency (Pauschales Verfahren) is a very simple method. It assumes that the AAP stays constant over the whole curtailment period and so the AAP is estimated as the last measured feed in before the curtailment (Figure 2). 80% 70% 60% 50% 40% 30% 20% 10% 0% Time [h] Figure 2: Schematic representation of the last measured value method for a curtailment of six hours The only needed input data is the feed in before the curtailment. 3.2 Adjusted power curve AAP measured AAP estimated The second method described by the German federal network agency (Spitzabrechnungsverfahren) uses the nacelle anemometer wind speed (NWS) and the wind turbine s specific power curve. The theoretical power calculated in this way is then corrected by a factor that is calculated by the theoretical and measured power averaged over 60 minutes before the curtailment.

3 Power [kw] For this method the NWS and the power curve of every turbine is required. For the calculation of the correction factor the averaged power and the averaged NWS over the last hour before the curtailment is needed. The averaged NWS of the hour before the curtailment and the power curve are used to calculate the averaged theoretical power P before,theo. Then the correction factor is calculated by dividing the averaged measured power P before,meas by P before,theo. During the curtailment the AAP is calculated as follows: Where P theo is calculated by the measured NWS and the power curve. 3.3 Site-specific power curve This method uses historical data to calculate a site-specific power curve for each wind turbine. This power curve is used during curtailment to calculate the AAP. To calculate the site-specific power curve historical data of the measured NWS and produced power is required. To ensure proper results the period during that the historical data was measured should be as long as possible. During the curtailment the current NWS is needed Measured Values Power Curve NWS [m/s] Figure 3: Measured values and power curve of single wind turbine To get the site-specific power curve that represents the relation between NWS and produced power we used the local regression model method LOESS (Figure 3). During the curtailment the AAP can be estimated simply by the measured NWS and the power curve. 3.4 Reference wind turbine The reference wind turbine method uses the measured NWS and produced power of one reference wind turbine that is not curtailed and the wind direction to estimate the AAP of the other wind farm s turbines. The reference wind turbine is selected depending on the wind direction so that this turbine is not affected by wake effects of the other turbines. This method requires historical data of the NWS and the produced power from every wind turbine and the wind direction. Again, the more historical data is available the better the results are. During the curtailment the measured NWS and produced power of the reference wind turbine and the averaged wind direction is needed. With the help of artificial neural networks the relation between measured NWS and produced power of each wind turbine plus the averaged wind direction and the produced power of every other wind turbine is derived. During the curtailment these relations are used to estimate the AAP of every other wind turbine based on the measurements of NWS and produced power of the reference wind turbine. Then the AAP of all wind turbines is summarized to get the AAP of the whole wind farm. 3.5 Physical model The physical model tries to reflect the physical effects within the wind farm in order to consider the wake effects caused by the rotor influencing the NWS measurement and the wake effects caused by other wind turbines. The idea is basically the same as in Eisen [2] but the implemented NWS correction and the wake model are based on our own work. Our model is simpler, e.g. no detailed wind turbine model is needed. For this method static data like wind turbines positions, hub heights, rotor diameters, and power curves are needed. Additionally historical time series of measured NWS and produced power of every wind turbine are required to correct the NWS measurement. Figure 4 illustrates the proceeding of this method. First the measured NWS is corrected to get the wind speed in front of the rotor. This is done with a correction that is parameterised by comparing the manufacturer power curve with the power curve calculated with the historical data. Then the corrected wind speed is used as input for the inverse wake model of the

4 nrmse wind farm with the current c T -Values that result from the curtailment. Wind speed in front of wind farm Inverse wake model (current C T values) Wind speed in front of wind turbine Figure 4: Proceeding of the physical model (following [2]) The result is the free wind speed in front of the wind farm that is then used as input for the wake model that is now parameterised with the c T -values for normal operation. The resulting wind speed in front of each wind turbine is then used to estimate the AAP via the power curves. 4. Test wind farm To test the described methods we used historical data from a wind farm located in Brandenburg, Eastern Germany. The wind farm consists of 18 wind turbines from the same manufacturer and is not affected by other wind farms. The nominal power of the wind farm is 36.9 MW. The available data consists of wind speed, produced power and wind direction time series plus the static data like positions, rotor diameters, hub heights, nominal power, power curves, etc. The time series data was averaged over 10 minutes each. The historical data was recorded from 1 st January 2011 to 25 th September For the simulations we used only valid data. That means that only data from times when the whole wind farm ran under optimal conditions (no errors, no curtailments, etc.) is used. In future we will test the best methods also with data with a resolution of 3 seconds as this a requirement for the provision of control reserve. 5. Results Anemometer correction Measured wind speed (during curtailment) Estimated wind speed (IF no curtailment was taking place) Wake model (C T values for normal operation) The different methods will be evaluated regarding the four requirements defined in chapter Accuracy under normal operation The accuracy of the different methods during normal operation, which means without any curtailments, is measured as the normalized root mean square error (nrmse): As the accuracy of the last measured value method and the adjusted power curve method depend on the duration of the curtailment the accuracy of all methods is calculated in relation of this duration (Figure 5). 25% 20% 15% 10% 5% 0% curtailment duration [h] Figure 5: Accuracy of the different methods depending on the duration of the curtailment during normal operation During normal operation the site-specific power curve method delivers the best results with an nrmse of 1.39%. Due to more calculation steps the physical model has a slightly higher nrmse of 1.68%. The reference wind turbine method shows an nrmse of 2.36%. The last measured value method and the adjusted power curve method deliver proper results for very short curtailment durations but get more and more inaccurate with longer curtailment durations (Figure 5). This effect is due to the changing conditions (wind speed, wind direction, turbulence intensity, etc.). For a curtailment duration of four hours the adjusted power curve method and the last measurement method show an RMSE of 2.89% respectively 15.46%. 5.2 Accuracy under curtailment Last measured value Adjusted power curve Site-specific power curve Reference wind turbine Physical model Another important aspect is the accuracy of the different methods during the curtailment. As the wind turbine extracts less energy from the wind during the curtailment the measured NWS of the curtailed wind turbine and of all downwind wind turbines speeds up relatively to the NWS under normal operation. The physical model considers this by the different c T - values. This is why we use the physical model as reference to calculate the wind speeds that would have been measured by

5 nrmse the nacelle anemometers during the curtailment. Figure 6 shows the accuracy of the different methods for curtailments of 0%, 10%, 25% and 50% of the wind farm s nominal power. 25% 20% 15% 10% 5% 0% last measured value* adjusted power curve* site-specific power curve reference wind turbine Figure 6: Accuracy of the different methods for curtailments of 0%, 10%, 25% and 50% of the wind farm s nominal power As the physical model serves as reference its accuracy is the same for all curtailment levels. This is the same with the last measured value method because this method does not use the NWS to estimate the AAP. The accuracy of the reference wind turbine method is also the same for different curtailment levels because the reference wind turbine s measured NWS is not influenced by any changes compared to normal operation. The accuracy of the sitespecific power curve method and the adjusted power curve method decreases with increasing curtailment levels as they overestimate the AAP due to the speed up of the measured NWS. For example the nrmse increases from 1.40% under normal operation to 22.28% under a curtailment of 50% in the case of the sitespecific power curve method. 5.3 Consideration of changes in composition of wind farm 0% curtailment 10% curtailment 25% curtailment 50% curtailment physical model As all methods first calculate the AAP of every single wind turbine failures can simply be considered by setting the downwind turbines AAP to zero. The methods differ in the consideration of changes in the wakes caused by the failure. The last measurement value method does not consider these effects. The adjusted power curve method and the site-specific power curve method consider these effects to some extend as the NWS of downwind wind turbines speeds up. The reference wind turbine method does generally not take the wake effects into account unless there is enough historical data for this specific constellation to train an artificial neural network for this specific situation which is very improbable. If the reference wind turbine itself is down another wind turbine has to be used as reference. This is only possible if there is any other wind turbine that is not affected by other wind turbines wakes under the current wind direction. The physical model is able to take all wake effects into account and will return the best results of all the methods in the case of a wind turbine failure. If wind turbines are exchanged, removed or additionally installed the last measured value method and the adjusted power curve method can easily estimate the AAP of the new composed wind farm as they do not need long term historical data. All other methods rely on historical data. The site specific power curve method and the physical model need historical data to calculate the wind turbines power curve based on the NWS. This data should preferably cover all wind speeds. The reference wind turbine method requires more data, preferably for every wind speed from every wind direction. That often means data from several month as some wind directions can be found very rarely. 5.4 Costs All tested methods do not require any installation of additional hardware that would involve costs. They are all based on standard data that is measured anyway during the operation of a wind farm. Although the algorithms of the different methods differ in regard to their complexity they just have to be developed once and maybe adopted to the specific wind farm. This means there are no significant differences regarding the costs. On the other hand costs can be involved if the method affects the operation of the wind farm. This is the case with the reference wind turbine method. As the reference wind turbine cannot be curtailed it cannot be used for the delivery of control reserve that leads to a lower offer and revenue. 5.5 Summary Table 1 shows a comparison of the different methods with the criteria defined in chapter 2. The site-specific power curve method, the reference wind turbine method as well as the physical model show high accuracy

6 Accuracy under normal operation Accuracy under curtailment Consideration of changes in wind farm composition Costs under normal operation. But the accuracy of the site-specific power curve method decreases dramatically under curtailment whereas it stays at the same level for the two other methods. That means that only the reference wind turbine method and the physical model estimate the AAP accurately in those situations it is needed. Last measured value Adjusted power curve Sitespecific power curve Reference wind turbine Physical model o o -- + o o o o o Table 1: Comparison of the different methods with the criteria defined in chapter 2 Under consideration of the advantages of the physical model regarding the consideration of changes in a wind farm s composition, the costs and the slightly higher accuracy compared to the reference wind turbine method the physical model is the best method to estimate the AAP of a wind farm. The nrmse of about 1.7% is similar to the deviations of conventional power plants from their schedules which is about 1.5%. It has to be taken into account that the shown results are only valid for averaged values over ten minutes. When data with a resolution of three seconds is available the most promising methods will be tested again. 6. Conclusion and Outlook We have compared five different methods for the estimation of the available active power of wind farms with four criteria based on averaged data over ten minutes. The physical model seems to be the most suitable method for this application. It shows high accuracy under normal operation as well as under curtailment, can cope with changed compositions within the wind farm and does not cost more than the other methods. For the provision of control reserve the available active power has to be calculated every three seconds. Therefore we will test the most promising methods again with three seconds data. In addition we will improve the best methods to further increase their accuracy. In the middle of 2013 we will use this method as part of a field test in the project Regelenergie durch Windkraftanlagen to provide control reserve. Acknowledgment This paper is based on the project Regelenergie durch Windkraftanlagen (promotion index ) which is founded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety. The responsibility for the content of this paper is up to the authors. References [1] Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahn. Leitfaden zum EEG-Einspeisemanagement [2] Eisen S, Sørensen, PE, Donovan M, Hansen K. Real Time Estimation of Possible Power for Wind Plant control. Nordic wind power conference [3] Speckmann M, Baier A. Provision of Frequency Control by Wind Farms. Wind Integration Workshop, Aarhus, 2011.

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