Grid Integration of Wind Energy in Germany - Towards Managing 25 GW Offshore Wind Power

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1 Fifth International Workshop on Large-Scale Integration of Wind Power and Transmission Networks for Offshore Wind Farms, 7 8 April, Glasgow Power monitoring and Prediction Systems Grid Integration of Wind Energy in Germany - Towards Managing 25 GW Offshore Wind Power Abstract U. Focken 1, M. Lange 1 and B. Graeber 2 1 energy & meteo systems GmbH Marie Curie Str.1, Oldenburg, Germany Phone +49 (0) , Fa +49 (0) matthias.lange@energymeteo.de 2 EnBW Trading GmbH Durlacher Allee 93, Karlsruhe, Germany Phone +49 (0) , Fa +49 (0) b.graeber@enbw.com This work gives an overview how 16 GW of wind power is currently integrated into the German energy supply system. The focus of the presentation lies on reporting practical eperiences concerning the use of wind energy in Germany within the framework of the recently adapted renewable energy act and the newly introduced immediate echange of wind power between the four German grid control areas. Possible concepts of balancing the differences between predicted and actual wind power generation under the new framework are discussed. As one major side-effect of the echange of wind power the demand for monitoring the current energy production of wind farms and for short-term predictions of wind power has significantly increased and opened a broader market for these services. As plans for going offshore are ambitious (25 GW in 2030) wind energy has to be effectively integrated into the German electricity system. This requires new strategies to manage the anticipated large amounts of offshore wind power. Here the main idea is to regulate wind farms similar to power plants and provide the services comparable to conventional power plants. A heuristic estimate indicates that by 2030 the day-ahead balancing power due to wind energy might increase by a factor 3 to 4 compared to the current status. However, due to the good predictability of wind power for the net few hours the actual regulation power will be significantly smaller. Introduction Integrating 16 GW of installed wind power into the energy supply system in Germany is a real challenge already at the present stage. In addition, according to the strategy of the German government up to 25 GW offshore wind power might be installed by the year Hence, the electricity system has to be prepared to embed growing amounts of wind power with all important sectors affected: grid operation, power plant scheduling and energy trading. RWE EnBW e.on Vattenfall Europe Figure 1 The horizontal echange of wind power allows, e.g. that Vattenfall transfers a part of its high share of wind energy to the RWE or EnBW with a lower share.

2 The fluctuating availability of wind energy due to meteorological conditions is one major challenge regarding the integration of such dimensions of wind power into the electricity supply system. There is no doubt that wind energy has to grow up in the sense that in the near future it becomes a more reliable and controllable source of energy and is no longer regarded as a stochastic nuisance. The energy market requires that wind energy has to be predictable on different time scales. This is necessary for trading on an intra-day, day-ahead and longer term basis. From a more technical point of view wind farms have to behave like TSO Installed Relative Relative wind power fraction of fraction of [GW] installed wind power [%] total electricity consumption [%] e.on RWE Vattenfall Europe EnBW Total the German renewable energy act (EEG) in September 2004 a new mechanism was introduced which requires TSO to immediately echange fluctuating wind power between each other in proportion of the electricity consumption in their control area (illustrated in fig. 1). This so-called immediate horizontal wind echange distributes wind power including the fluctuations among TSO in near real-time leading to a more evenhanded procedure regarding the allocation of balancing power and the associated costs. The immediate echange of wind power is based on measurements of the current production at representative wind farms in each control area. The wind power echange of the current 15 min interval is based on the average production over the previous 15 min. These values are distributed between TSO in near real-time (fig. 2). The echange is regarded as temporary until the accounting data from all wind farms is available for final validation and correction schedules. Table 1: Absolute and relative installed wind power and relative electricity consumption in the TSO areas in Germany. Source: VDEW, TSO power plants, i.e. they have to contribute energy on demand and also supply grid services, e.g. regulation power. In addition, the technical effort and the costs due the grid regulation of wind power have to be allocated fair among grid operators. Horizontal Echange of Wind Power In Germany the four large transmission system operators (TSO) are responsible for balancing and distributing wind energy that is fed into the transmission grid either directly at high voltage or via the medium-voltage grid. Wind energy has priority and, hence, the TSO are obliged to incorporate the current production. Due to their geographical location the amount of wind power installed in the TSO areas and the electricity consumption are very divergent (table 1). While e.g. the Vattenfall Europe area contains appro. 38% of wind power the relative electricity consumption is only 19%. fro m RZi EON transmission factors: zone A RZ2 A RZ4 A RZ3 EnBW RZ3 A RZ2 A RZ4 A RZ3 WE RZ 1 _ H _ H Grid control Grid control VET RZ2 H Grid control RWE RZ4 H A RZ 2 A RZ 4 A RZ 2 A RZ 4 Grid control Up to August 2004 each TSO was responsible for regulating the complete share of wind power within their area. An echange of wind energy used to be based on daily or monthly schedules with constant power. But with the amendment of Figure 2: Technical implementation of horizontal echange of wind power between German TSO. The production of the last 15 min interval is determined by an upscaling of measurements at representative wind farms. The values are distributed in near real-time between TSO.

3 Possible concepts for balancing fluctuating wind power generation With the immediate horizontal echange of fluctuating wind power generation between the four control areas in Germany, every TSO is responsible for balancing the difference between planned wind power generation based on forecasts (which can be sold, e.g., on the day ahead market) and the real generation which he receives more or less in real time. There are several concepts for performing this task. One basic distinction is whether the balancing is performed separately from the classical grid regulation or integrated with it. If the balancing is carried out separately, specific reserve capacities allocated to wind balancing have to be provided. The required quality can differ from traditional products on the reserve market like secondary and tertiary reserve due to limited gradients and good short term predictability of wind power generation. Besides contracted reserves the grid operator could partly use the intra-day market to perform the balancing. The integrated approach combines the balancing of wind power forecast errors with traditional balancing tasks for load fluctuations, load forecasts errors, power plant outages and scheduling errors, for which secondary and tertiary reserves are used. As all of these deviations are almost uncorrelated the required additional reserve capacities due to wind power are lower then in the separate balancing approach [Schlecht2005]. The required reserve energy for balancing can be provided with defined reserve qualities, especially secondary and tertiary reserves which are dispatched by the grid operator e.g. on a 15 min or 1 h basis. Alternatively, the balancing is performed within a large power plant system with the most suitable plants at any time. With the latter approach all available fleibility in a large pool can be used considering continuously updated short term wind energy production and load forecasts. From a cost perspective the integrated balancing approach in combination with using the total available fleibility of a large power plant system can be epected to be the best choice. Monitoring and forecasting wind power As one major side-effect of the immediate echange of wind power between TSO the demand for monitoring the current energy production of wind farms and for short-term predictions of wind power has significantly increased. The currently produced wind power is assessed separately in each TSO area using a standardized procedure that is based on online-measurement of representative wind farms. A set of appro. 60 sites is adequately scaled up to give an estimate of the total of about 16,000 wind turbines in Germany. This estimation is considered as the real wind power production and basis for the scheduling and accounting scheme between TSO. The online-estimation works fully automatic in 15 min intervals. Independent of the implemented balancing concept grid operators, energy traders and power plant schedulers require day-ahead information of the anticipated production of wind energy. Thus, wind power predictions with a time horizon of 72 hours have become indispensable instruments for the energy market. The requirement for precise predictions has triggered real competition among the providers of wind power predictions in Germany. They are now under pressure to perform. Users of wind power prediction epect high accuracy and 100% availability. Hence, wind power predictions are no longer a pure research area but also a commercial service for the energy market. Several providers offer operational wind power predictions for the German market and deliver to the large TSO on a daily basis. The most wellknown systems based on a scientific background are the ISET system [Ernst2003], the SOWIE model by Eurowind and Previento [Lange2003] operated by energy & meteo systems. The systems are based on different approaches and use data from different numerical weather models as input. The ISET system is a statistical model where artificial neural networks are trained on the statistical relation between numerical weather prediction and measured power output. This has the advantage that systematic errors are directly removed from the prediction. However, the disadvantage is clearly that the system acts as a black bo such that optimisations require long term testing periods. After each new training

4 session the system can behave differently. Moreover, measured data over several months is necessary to train the network and initialise a prediction. Previento Physical Model: spatial refinement thermal stratification regional upscaling forecast uncertainty predictions are calculated in an hourly resolution. Using a database of the German wind turbines these sites are scaled up under consideration of machine type and hub height to obtain the aggregated power output. In addition to the forecast value Previento can provide the individual uncertainty depending on the prevailing weather situation. The forecast quality of the prediction for the aggregated power output of all German wind farms is currently between 7 8 % root mean square error (rmse) normalized to installed power i.e. an absolute rmse of GW. This is of the order of magnitude of the typical load fluctuations. However, in certain cases where the meteorological situation does not evolve as predicted the forecast errors can be substantial. These phenomena are mainly due to the highly non-linear processes in the atmosphere which are difficult to rule out completely. Hence, the further improvement of wind power predictions is an important issue. Offshore Wind Energy Figure 3: Previento is a wind power prediction system that is based on physical parameterisations of the atmosphere. SOWIE and Previento are so-called physical systems and are based on a meteorological description of the atmosphere. In both systems the coarse resolution of the numerical weather prediction is spatially refined to obtain the wind speed for given sites. However, the systems differ significantly in their refinement methods and the physical parameterisation to obtain the wind speed in hub height. The wind speed is then plugged into the power curve of the wind turbines to calculate the power output. The advantage of physical models is their deterministic parameterisation based on meteorological parameters and the fact that no measured data is needed to produce a prediction. Of course, measured data can optionally be used to improve the forecast accuracy. Figure 3 shows the principal scheme of the prediction system Previento. For a number of appro. 50 representative wind farms separate The plans to erect large offshore wind farms in the North Sea (fig. 4) and the Baltic Sea are ambitious. According to the strategy of the German government 25 GW offshore wind power are intended by 2030 [BMU2002]. Currently applications to build offshore farms reached a level of appro. 60 GW. Fig. 4 shows the area in the North Sea that is accessible for German offshore sites (source: BSH).

5 Challenges Offshore Grid enhancement: Offshore wind power is produced far away from the consumer and today s grid capacity is not sufficient to carry away the anticipated amounts of electricity. A recently published study by the German Energy Agency [Dena2005] showed that up to the year 2015 the additional costs for grid enhancement are eurocent / kwh for an installed capacity of 10 GW Load h offshore wind power. Load Load - wind generation must run generation min konv. Generation Figure 5 Current situation (2005): Load duration curves and minimum generation in Germany. Wind power is regarded as negative load. At the current grid penetration wind energy does not interfere with the minimum conventional generation. Substitution of conventional generation: From an energy economical point of view wind generation will have to meet specific criteria for effectively substituting large shares of the generation. For grid stability and balancing a certain minimum generation has to be met by fleible power plants. Figure 5 depicts schematically the current situation in Germany with load duration curves. The load is partly provided by wind power generation and the remaining load mainly with convention power plants. Part of these plants are must-run plants e.g. combined heat and power plants, or run of river plants. Their electrical generation can hardly be reduced. Another part of the conventional and nuclear generation is required for grid services and for balancing fluctuation generation or load. With increasing wind generation the situation in the year 2030 might look like the one shown in fig 6. If the control possibilities of wind power generation are not improved in comparison to the present situation all wind power generation which falls below the minimum conventional production line would have to be controlled down. Load Load Load - wind generation must run generation min konv. Generation Figure 6 Future projection (2030): Load duration curves and minimum generation in Germany with 23 GW onshore and 25 GW offshore. If wind power cannot be regulated it will interfere with the minimum conventional generation in case of high production (yellow area) and has to be neglected. An intelligent control of wind farms can make this region accessible for wind energy. Generation which falls below the must run generation line will have to be controlled down in any case unless additional storage capacity can be provided. If wind generation can provide controlled, generation and required grid services, such as reactive power, part of the yellow area (fig. 6) between the must run line and the minimum conventional generation line can be met by wind energy and substitute conventional or nuclear generation. Predictability: Of course, the power output of the wind farms offshore has to be known in advance as eactly as possible. However, as the characteristics of the marine atmosphere are only partly known so far, the forecasting techniques used for onshore sites have to be adapted, in particular, the vertical wind profile cannot simply be assumed as logarithmic [Tambke2005b]. First studies [Tambke2005a] show that the prediction accuracy of the day-ahead forecast for a single wind turbine offshore is about 15% rmse normalised to the installed power. This is comparable to high wind onshore sites. Moreover, the offshore wind farms will be highly concentrated in a rather small spatial area, e.g. in the German bight which has a diameter of appro. 200 km. In terms of the prediction of the aggregated power output of the offshore wind h

6 farms this means that there is only a small benefit due to spatial smoothing effects. Spatial smoothing refers to the statistical effect that the relative error of the prediction of the aggregated power production of an ensemble of wind farms is smaller than that of a single wind farm as forecast errors cancel out partly. The error reduction depends of the size of the region that is considered as shown in fig. 7 [Focken2001]. error reduction compared to single site 1 0,8 0,6 0,4 0, diameter of region [km] Fig. 7 The relative prediction error for the aggregated power output of many wind farms in a region decreases for increasing region size as errors are less correlated [Focken2001]. Estimation of balancing power in 2030 In order to estimate the balancing power due to wind energy based on the day-ahead prediction error in the year 2030 we assume that the amount of regulation power is proportional to the prediction error [Schlecht2005]. In 2030 we epect 25 GW offshore and the about same installed capacity onshore giving a total of 50 GW. We first conservatively estimate the best case where we consider a region with the diameter of Germany of appro. 800 km which contains all onshore and offshore wind farms. We assume that the prediction error of a single wind farm is 15% rmse normalized to installed power. According to fig. 7 the reduction factor for this size is Hence, for the aggregated power output of the region we obtain an absolute forecast error of 15%*0.45*50 GW = 3.4 GW which is by factor 2.8 larger compared to today s average forecast error of about 1.2 GW (7 % rmse) due to the currently installed 16.6 GW capacity onshore. Thus, the day-ahead balancing power would also be by a factor 2.8 larger than today. Of course, we have to keep in mind that the distribution of wind farms is far from being uniform as offshore wind power will be concentrated in the North and Baltic Sea. If we treat the German bight as a separate region with 200 km in diameter the error reduction factor due to spatial smoothing would only be 0.75 (fig. 7) which leads to 15%*0.75*25 GW= 2.8 GW for the offshore prediction error. The onshore region would be 15%*0.45*25 GW=1.7 GW. The worst case is a full correlation between these two regions leading to a combined forecast error of 2.8 GW GW = 4.5 GW. Consequently, assuming a linear relation between balancing power and forecast error, the day-ahead balancing power would be 3.8 times larger than the current status. Hence, using this heuristic estimation of the need for balancing power for the net day due to wind energy in the year 2030 the range seems to be between a factor 3 to 4 larger than today. This is quite substantial but one has to note that the shortterm regulation power (e.g. tertiary reserve) that is used to compensate deviations between load and generation is much smaller. This is partly due to the fact that wind power forecasts are very precise on the scale of hours. If wind farms were controllable this even enables wind power to be used for regulation purposes. Of course, the estimation does not consider the epected improvements in day-ahead wind power predictions which are going to significantly reduce the amount of balancing and regulation power due to wind energy. Conclusion The integration of 16 GW of wind power into the German electricity supply system on the level of the TSO and the energy market can be managed adequately under the current scheme. Restrictions apply mainly due to bottlenecks in local medium voltage grids which are currently solved by regulating down certain proportion (800MW) of wind farms. With the amendments in the renewable energy act, in particular, the introduction of the immediate horizontal echange of wind power between TSO a fair solution has been found where all TSO have

7 to manage fluctuations and deal with grid regulation. Hence, the technical effort and the costs due to wind energy are evenly distributed and areas with little wind power have to participate as well. There are several concepts of balancing deviations between epected and actual wind power production. One basic distinction is whether the balancing is performed separately from the classical grid regulation or integrated with it. If the balancing is carried out separately, specific reserve capacities allocated to wind balancing have to be provided. The integrated approach combines the balancing of wind power forecast errors with traditional balancing tasks for load fluctuations, load forecasts errors, power plant outages and scheduling errors, for which secondary and tertiary reserves are used. From a cost perspective the integrated balancing approach in combination with using the total available fleibility of a large power plant system can be epected to be the best choice. Which concept is realised depends on the general company s policies and economical considerations. With growing operational eperience wind energy is more and more established in grid operation as well as trading. To support the decision makers the current and future production of wind energy has to be known. This requires adequate tools to monitor and predict wind power leading to a real competition among providers of such services. Being under pressure to perform triggers new innovations in improving wind power predictions and enhances the level of professionalism in the rather new branch of energy meteorology. Offshore wind power is a rising giant bringing new challenges for efficient grid integration. At high grid penetration wind farms have to provide controlled, generation and required grid services, such as reactive power, in order to substitute conventional generation at a high level. The onshore concepts are to be developed further in order to take the special conditions on the ocean into consideration. This holds, in particular, for wind power predictions. The first studies are promising in the sense that the current numerical weather models have the necessary accuracy to epect useful wind power predictions. A heuristic estimation of the day-ahead balancing power due to 50 GW wind energy in Germany by 2030 indicates an increase of balancing power for the net day by a factor 3 to 4 compared to today s situation. However, the short-term regulation power covered e.g. by the tertiary reserve will, of course, be lower due to the good predictability of wind power over the net few hours. However, there should be no doubt that there is still a considerable way to go for wind energy to become a fully recognized and reliable source of energy. In the long run wind power plants have to be operated similar to power plants providing services for grid operation and trading. This not only a matter of technical innovations such as techniques storage or better predictions. It is also a question of the regulatory framework, in particular, how costs for operating wind farms are refunded to the wind farm owner. References [BMU2002] Strategie der Bundesregierung zur Windenergienutzung auf See, German Ministry of Environment, Berlin (2001) [Dena2005] Energiewirtschaftliche Planung für die Netzintegration von Windenergie in Deutschland an Land und Offshore bis zum Jahr 2020, German Energy Agency dena, Berlin (2005) [Ernst2003] B. Ernst, K. Rohrig: Onlinemonitoring and prediction of wind power in German transmission system operation centres, Proc. IEA Joint Action Symposium on Wind Forecasting Techniques, Norrköpping (2002) [Focken2001] U. Focken, M. Lange, K. Mönnich, H.P. Waldl, H.G. Beyer, A. Luig: Short-term predicton of the aggregated power output of wind farms a statistical analysis of the reduction of the prediction error by spatial smoothing effects. J. Wind Eng. Ind. Aerodyn. 90 (3) (2002) [Lange2002] M. Lange, U. Focken, and D. Heinemann: Previento - Regional Wind Power Prediction with Risk Control. Proceedings of the World

8 Wind Energy Conference, Berlin (2002) [Tambke2005a] J. Tambke, C. Poppinga, U. Gräwe, L. v.bremen, D. Heinemann: Forecasting Wind Power above North and Baltic Sea using Combined and Refined Numerical Weather Predictions, Proc. Workshop on Large-Scale Integration of Wind Power and Transmission Networks for Offshore Wind Farms, Glasgow (2005) [Tambke2005b] J. Tambke, M. Lange, U. Focken, J.-O. Wolff, JAT Bye: Forecasting Offshore Wind Speeds above the North Sea, Wind Energy (8) pp (2005) [Schlecht2005] D. Schlecht: Regelenergiebedarf der Windenergieeinspeisung, Fachtagung Windenergie und Netzintegration, Hannover (2005), _2005_02/2_4_Schlecht.pdf