ONLINE-MONITORING AND PREDICTION OF WIND POWER IN GERMAN TRANSMISSION SYSTEM OPERATION CENTRES
Bernhard Ernst, Kurt Rohrig, René Jursa Institut für Solare Energieversorgungstechnik e. V. Königstor 59, D-34119 Kassel, Germany Tel. ++49 561 7294-328,
[email protected] [email protected] [email protected]
Abstract Normally, electrical systems are able to absorb a certain amount of unregulated and fluctuating production from renewable energy sources (RES), especially wind power. The electrical systems must be designed and operated in order to accommodate the changes in the consumption, a trip of a conventional production unit or a fault on a transmission line. For systems with a high penetration of wind power, the most significant difference is that in addition to forecasts of the consumption, predictions are also to be prepared of the unregulated wind power production. Such predictions are necessary both for the TSO and for the players on the power market that owns significant wind power production sites as well. At the end of 2002, more than 13,500 Wind Turbines (WTs) with an installed capacity of 11,850 MW generated approx. 17,300 GWh and supplied about 3.6 % of the German electricity consumption in 2002 [1]. Wind-generated power now provides a noticeable percentage of the total electrical power consumed, and also exceeds the base load on the network in some utility areas. This indicates that wind is becoming a significant factor in electricity supply, and in balancing consumer demand with power production. Not least in the grid areas of the German TSO’s E.ON Netz and Vattenfall Europe Transmission GmbH more than 100 % of the electricity consumption at times has been covered by wind power. A well-established and scientific analysis of the time response of wind power as well as the accurate determination of the current and expected wind power will lead to an improved integration of wind generation into the electrical power system and reduce CO2 emissions sustainable. In frame of governmental and EC funded projects and in co-operation with the German TSO’s E.ON Netz, RWE Net and Vattenfall Europe Transmission, ISET developed a new planning tool to support large scale wind power integration into the electrical energy supply system – the Wind Power Management System WPMS. WPMS provides the current level of wind power generation (onlinemonitoring) as well as the short-term prediction from 1 hour up to 72 hours.
In Europe, the Transmission System Operators (TSO’s) are responsible for a save grid operation. They have to provide system services like online regulation, planning and estimation of regulation power (load prediction for its grid area in comparison to the sum of the nominated load prediction of the customersupplying market participants), losses etc. The determination of the amount and the sequence of the wind power feed-in for the following day is the most difficult task of the generation schedule. Apart from power station down-time and stochastic load variations, unexpected variations of wind power are the most frequent cause regulation and
compensation power needs. The more accurate the predicted and online monitored wind power production corresponds to the real wind power production, the less regulation power is needed on the present day. 20000 load incl. wind load w/o wind
18000 16000 14000 Megawatts
Introduction
12000 10000 8000 6000 4000 2000 0 0
2
4
6
8
10
12
14
16
18
20
22 Hours
Figure 1: Typical load profile of E.ON Netz in 11/2002
Figure 1 shows a typical daily load profile (Sunday) of the E.ON Netz area at 3rd November, 2002. The yellow area shows the power of the conventional power plants while the blue band shows the wind power. Both areas together is the actual customer’s demand. Online-Monitoring of Wind power Generation The most precise procedure for obtaining basis data for generation schedule and grid balance can be considered to be the online acquisition of the power contribution of all WT’s operated in a supply area. However, due to the very widespread installed WT’s in Germany it is hardly realistic to equip all WT’s with monitoring systems.
Figure 2: Online Acquisition and Projection
Online monitoring requires an evaluation model which allows the observed time series of power output of representative wind farms to be extrapolated to the total feed-in from WT’s of a larger net region or control zone. In co-operation with E.ON Netz, the TSO with the worldwide largest wind capacity (5.7 GW as of December 2002), ISET has successfully developed an online monitoring system, which is able to provide the current wind power generation from all plants distributed over the utility supply area [2]. This model transforms the observed power output from 16 representative wind farms with a concerning capacity of 425 MW into the total wind power input into the grid. The determination of the wind farms and the development of the transformation algorithms are based on the long-term experience of the “250 MW Wind“ program and its extensive stock of measurement data and evaluations [3].
The current wind power production is calculated by extensive equation systems and parameters, which consider various conditions, such as the spatial distribution of WT’s or environmental influences. The observed data from the selected wind farms are thereby transmitted online to the control center. This online model is a basic part of ISET’s Wind Power Management System (WPMS) which consists of three tiers. The 2nd and 3rd tier are based on ISET’s wind power prediction tool.
Short-term prediction In co-operation with E.ON Netz, Lahmeyer International and the Fördergemeinschaft Windenergie, ISET developed a new wind power prediction model, the Advanced Wind Power Prediction Tool (AWPT). This model is effectively based on a hybrid of four proven approaches: § the accurate numerical weather prediction provided by the DWD (German Weather Service) § the transformation of predicted wind data to the location of wind farms using the numerical mesoscale atmospheric model KLIMM [4], [5] § the determination of the accessory wind farm power output, using ANNs § the extrapolation of the predicted power to the total power input into the utilities’ grid by the online-model. The meteorological component of the prediction tool is based on operational weather forecasting. For this purpose basically the routine updates from the numerical weather prediction model for the investigation area, i.e. the Lokal-Modell (LM) of the DWD are used. The LM is the newest generation model of the DWD and is specifically designed for the handling of the typical small-scale circulation patterns in the German inland area providing results in a spatial resolution of 7x7 km2. The LM results are provided in a one hourly sequence, the updates are calculated twice a day. The following output data of the LM are used for the wind power prediction: § the wind velocity at 30 m above ground § the wind direction § air pressure/ temperature § humidity
cloud coverage, which is used for the determination of the atmospheric stability class. For selected, representative wind farm sites, using i.e. the medium points of these sites as the grid points of the LM, the routine forecast updates are evaluated and the concerning wind farm power output is calculated by Artificial Neural Networks (ANN).
Figure 3: ANN inputs
The capability of ANN, for the prediction of the power output of WT’s, was examined by several institutes [6]. The advantages of ANN over standard computing algorithms are that they ‘learn’ from experience, and ‘guess’ or interpolate results, even when their inputs are contradictory or incomplete. Various ANN modules (more than 100) are trained to learn the relationship between variations in the meteorological data and the wind power output, using past wind and power data. By the comparing of the results with observed power data, the optimal configuration of ANN modules is determined. The advantage over other approaches is the determination of the physical coherence by using observed data because the real relationship between meteorological data and wind farm power output can hardly be described sufficiently by physical models. Moreover the addition of further parameters does not require expensive modifications of the model. These trained networks compute the predicted wind power output of the representative wind farms which is used for input of the transformation algorithm of the online model. Therefore the online model allows a prediction of the total wind power feed-in of large utility supply areas, based on only a few locations with predicted wind
speed. This tool represents the 2nd tier of the WPMS and provides the run of the wind power output for the control area or selected subareas. As mentioned above the input to the 2nd tier of the WPMS is the result of DWD’s weather forecasting model LM. The LM is running twice a day starting at noon and at midnight. The computing time is about 7 to 8 hours, so the results are available around 8 a.m. and 8 p.m. respectively. The resolution is 1 hour and the prediction schedule is 72 hours. The day-ahead forecasts, typically used for the load management, are computed from the 8:00 a.m. results of the LM and provide the run of wind power generation for 24 hours of the next day. In Germany the electricity market closes around 3 p.m., so there is no possibility to use the 8 p.m. results of the LM to update the wind power generation forecast for the next day. Over a duration of two years the average error1 between predicted and observed power for the day-ahead prediction without updating is about 8.8 % of the installed capacity. 35% 30% 25% Frequency
§
20% 15% 10% 5% 0% -50
-40
-30
-20
-10
0
10
20
30
40
50
Forecast Error [% of installed power]
Figure 4: Frequency distribution of the forecast error
Figure 4 shows the frequency distribution of the errors for the day-ahead forecast. The bars on the right side stand for over estimations of the wind power, while the bars on the left side show how often the predictions were less than the actual measured power. Most of the forecast errors (85 %) are in a small band of ± 10 % of the installed capacity, large errors (over 20 %) occur very rarely. The most frequent cause of forecast errors is caused by a wrong timing of significant large variations of weather situations. Wind power prediction models which are based only on operational weather forecast are not able to correct these deviations. Thus, another module, 1
Root of the mean squared error (RMSE)
the so called the 3rd tier, uses the predicted wind farm power output, computed by the 2nd tier in combination with measured wind farm power output of the near past to provide topical updates and adjustments of the prediction computed by the 2nd layer. These updates and adjustments of the predicted power output of the next 6 hours are also computed by ANN and can be carried out at any time. Table 1 shows the accuracy of the 3 – 6 hour forecasts in comparison to the persistence model. Shorter forecast times are not listed, but even on a one or two hour forecast the ANN slightly beats the persistence model, especially large changes are predicted better with the ANN. The persistence model estimates that the power output will not change from one time step to the next. Even if this sounds very simple it is not an easy model to beat, because in such a large supply area the power output in fact doesn’t change a lot within a few hours.
Forecast Persistence
WPMS 3
TP [h] 3 4 5 6 3 4 5 6
RMSE [%] Correlation 6.5 8.0 9.4 10.5 5.2 5.7 6.1 6.3
0.92 0.88 0.84 0.80 0.95 0.94 0.93 0.93
Table 1: Forecast errors (RMSE) and correlation between prediction and measurement for different forecast times (TP).
The advantages of this models can be summarized as follows: § the model architecture and the combination of online-monitoring and prediction model allows universal applications § high precision and minimum computation time § easy adaptation to other RES The model is in operation at E.ON since July 2001 to support the grid balance and the generation schedule as well as the horizontal wind energy exchange between the TSOs.
In order to get an estimation about the possible deviation of the predicted wind power from the online monitored wind power, ISET has started to calculate a confidence interval for the wind power forecast. In the time interval from November 2000 until February 2003 every occurred weather situation from the forecast data of the representative wind farms has been analysed. The goal is to recognize common weather situations and to get an idea about the possible forecast error they may implicate. Probably it is not possible to classify every weather situation in terms of the forecast error, but it will be very helpful to do so at the most common situations. Although the ANN of the prediction model use several weather variables like wind, temperature, pressure etc. the classification model only uses the predicted wind data of the 16 representative wind farms. Based on the wind data the weather situation is classified and the average forecast error (RMSE) is assigned to this particular situation. Starting from these RMSE values a confidence interval for the total wind power can be estimated for all combinatorial possible ensembles of forecasted wind data at the representative wind farms. The confidence interval per wind forecast has been estimated from the data of the observed time interval. On the basis of this data it has resulted that one can be 90 % confident that the predicted wind power lie between the limits of the computed interval. In order to check the width of the confidence interval, the frequency of the deviation from the limits of the confidence interval has been computed. Thereby the deviation is measured respectively to the width of the confidence interval for the forecasted wind situation. The frequency distribution of the deviation from the confidence interval shows a strong decay for increasing deviation. This fact is another verification for the stated confidence interval.
Outlook Confidence Interval for the prediction
Currently the next generation of the prediction tool is developed. Partners are E.ON Netz GmbH, Vattenfall Europe Transmission
GmbH, Deutscher Wetterdienst and AKTIF Technology. The project is funded by the BMWA (Federal Ministry of Economics and Labour) implies the further development and extension with § improvement of local forecasts § coverage of three transmission grids: E.ON Netz, Vattenfall Europe Trans-mission and Germany (incl. Sub-areas) § seasonal forecasts § adaptation for offshore wind farms § improvement of the confidence interval Implementations of the new model are planned in 2003. Apart from this, the model is currently adapted for the operation at RWE Net and for the support of a wind farm owner in UK. Thus, in 2003, all German TSO’s with high wind power penetration will use this model which predicts more than 95 % of wind power in Germany.
References [1] Renewable Energy Information System on Internet, REISI http://reisi.iset.uni-kassel.de [2] C. Enßlin, M. Hoppe-Kilpper, W. Kleinkauf, K. Rohrig, Online Monitoring of 1700 MW Wind Capacity in a Utility Supply Area, European Wind Energy Conference 1999 [3] Institut für Solare Energieversorgungstechnik, Wind Energy Report Germany 2002, September 2002. [4] J. Eichhorn et.al., A Three-Dimensional Viscous Topography Mesoscale Model, Contributions to Atmospheric Physics, Vol.70,No.4, November 1997. [5] Roland Ries, Oliver Heil, Einsatz anspruchsvoller Verfahren zur flächendeckenden Windpotentialanalyse im Binnenland, WMEP Jahresauswertung 1996, ISET 1997. [6] J. O. G. Tande, L. Landberg, A 10 Sec. Forecast of Wind Turbine Output with Neural Networks, European Wind Energy Conference 1993 [7] Manfred Menze, Leistungsprognose von Windenergieanlagen mit Neuronalen Netzen, Diplomarbeit ISET 1996
