ONLINE-MONITORING AND PREDICTION OF WIND POWER IN GERMAN TRANSMISSION SYSTEM OPERATION CENTRES Dipl.-Ing. Kurt Rohrig Institut für Solare Energieversorgungstechnik e. V. Königstor 59 D-34119 Kassel, Germany Tel. ++49 561 7294-328, Fax ++49 561 7294-260 Email:
[email protected] Co-Authors: Dipl.-Ing. B. Ernst, Dr.-Ing. M. Hoppe-Kilpper, Dipl.-Ing. F. Schlögl
Dipl.-Ing. Kurt Rohrig is an mechanical engineer with a diploma in numerical mechanics from the University Kassel. He has worked with ISET since 1991 and is head of information and prediction systems in the R&D division information and energy economy. Mr. Rohrig has more than 12 years experience in wind power projects. He is the scientist-in-charge for projects handling the online monitoring and prediction of wind power for large supply areas – operated in co-operation with large power transmission utilities. Mr. Rohrig has contributed to numerous publications in the field of wind energy integration in the electrical energy supply.
Abstract 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. Normally, they are able to absorb a certain amount of unregulated and fluctuating production from renewable energy sources (RES), especially wind power. For systems with a high penetration of wind power, the most significant difference is that in addition to forecasts of the consumption, predictions of the unregulated wind power production are also to be prepared. Such predictions are necessary for the Transmission System Operator (TSO) and for players on the power market that owns significant wind power production sites as well. By July 2003, more than 14000 Wind Turbines (WTs) with a capacity of 12,850 MW have been installed in Germany [1]. Windgenerated power provides a noticeable percentage of the total electrical power consumed. This indicates that wind is a significant factor in electricity supply, and in balancing consumer demand with power production. In the control areas of the German Transmission System Operators (TSO) E.ON Netz and Vattenfall Europe Transmission GmbH more than 100 % of the electricity consumption has been covered by wind power at times. This aspect will be distinctly strengthened when the offshore potential in the North and Baltic Seas is developed to the extent that is predicted in many scenarios. 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 TSOs E.ON Netz, RWE Net and Vattenfall Europe Transmission, ISET has developed a new planning tool to support large scale wind power integration into the electrical energy supply system – the Wind Power Management System WPMS. This tool provides the current level of wind power generation (online-monitoring) as well as the short-term prediction from 1 hour up to 72 hours.
Problem The operation of electrical networks is based on a strong planning of power generation. Conventional power generating units (coal fired, gas fired, nuclear, hydro, gas turbines etc.) are operated at any time in a certain power mode. System faults with corresponding outages of generators are covered by the system spinning reserve. The growing amount of unregulated and fluctuating production from renewable energy sources (RES), especially wind power creates new conditions in system operation and control. In the meantime, the installed wind power in particular grid areas (and control zones) has already achieved such a scale that problems in grid control and grid operation management can occur in strong wind periods, caused by power fluctuations. Phases have already occurred in which the total grid load was covered by wind energy, for the complete control area of Vattenfall Europe Transmission, and grid stability could only be maintained through interaction with other control zones. This aspect is particularly significant in conjunction with the erection of larger offshore wind farms, which supply power in the range of several hundred MW over one connection point. In Europe, the Transmission System Operators 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 customer-supplying 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 for 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. The basis for grid operation and the schedule of conventional power plants is the so-called load schedule, i.e. the amount and temporal course of the power consumption for the near future. This schedule is today determined with modern, computer-aided prediction systems, but also with conventional methods. The power generated from wind and other renewable energy sources is perceived as negative consumption in the system. The measurable amount of load in the system is then the difference between the total consumption and wind power generation. In power plant scheduling, the amount and course of wind power for the following day are the most difficult variables to determine. 20000 wind
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Figure 1: Load profile of E.ON Netz in 05/2003 including wind generation st
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Figure 1 shows a typical load profile of the E.ON Netz area from 1 – 7 May, 2003. 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 approach for obtaining basis data for generation schedule and grid balance can be considered to be the online acquisition of the power output of all WTs operated in a supply area. However, due the very widespread installed WTs in Germany it is hardly realistic to equip all with monitoring systems. 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 wind power production 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 8/2003), 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 50 representative wind farms with a concerning capacity of 1850 MW into the total wind power production of the entire control zone and five grid regions. 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].
Figure 2: Online Acquisition and Projection The current wind power production is calculated by extensive equation systems and parameters, which consider various conditions, such as the spatial distribution of WTs or environmental influences. The
observed time series from the selected wind farms are thereby transmitted online to the control center. This model has been successfully utilised since mid 1999 in the load dispatcher of E.ON Netz GmbH for the online recording of current wind feed-in. Since January 2003, the online model has also been operated by the load dispatcher of the RWE Net and is now being adapted for Vattenfall Europe Transmission GmbH.
Day-ahead forecast Supported by the Federal Ministry for Economics and Technology, and in co-operation with E.ON Netz, Lahmeyer International (LI) and the Fördergesellschaft Windenergie (FGW), a numerical model for wind power prediction has been developed by ISET [4]. This model is based on three essential foundations: · · ·
predictions of wind speed and direction from the Deutschen Wetterdienst (DWD) for selected representative locations, determination of the corresponding wind power, with the help of Artificial Neural Networks (ANN), extrapolation of the wind power on the total feed-in in the control area with the transformation model (online model).
The prediction model delivers the temporal course of the expected wind power for the E.ON control zone for up to 72 hours in advance. To achieve this, an assortment of the representative wind farms, or wind farm groups, were determined. For these locations, the DWD provides routine time series of predicted meteorological parameters in 1-hour intervals for a forecast period of up to 72 hours and a spatial dispersion of 7 km. The high-resouted forecasts are achieved by the non-hydrostatic grid point model (local model LM). The horizontal resolution amounts to 7 x 7 km by 35 vertical model layers and approx. 106,000 grid points per layer. The model runs twice a day starting at midnight respectively at noon (UTC). Its first model output is available at 7.00 am (MET), and includes 72 intervals per location from 12.00 midnight of the current day. The second output is provided at 7.00 pm (MET) and includes the time period from 12.00 noon of the current day up to 72 hours.
Figure 3: ANN Input. (vx, vy – wind speed in x and y direction; p – air pressure; T - temperature; H – humidity; C – clouds; PWF – wind farm power)
The corresponding power of the wind farm is calculated by Artificial Neural Networks (ANNs). Artificial Neural Networks are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. The ANNs are trained with predicted meteorological parameters and measured power data from the past, in order to learn the relation between weather data and wind farm power output. This method is superior to other procedures, which calculate the relation between wind speed and power by the use of power curves of individual plants, as the actual relation between wind speed (and other meteorological parameters) and wind farm power output is dependent on a multitude of local influences and is therefore very complex, i.e. physically difficult to describe. A further advantage of artificial neural networks over other calculation procedures is the “learning” of relations and “conjecturing” of results, also in the case of incomplete or contradictory input data [5]. In the framework of a further project, funded by the Federal Ministry for Economics and Technology, and in co-operation with E.ON Netz, Vattenfall Europe Transmission, DWD and AKTIF Technology, besides the qualitative improvement, especially for the use in complex terrain, the expansion of the model to further supply areas or control areas has been realised. The extended model provides wind power predictions for the control areas of E.ON Netz GmbH and Vattenfall Europe Transmission GmbH, as well as for the complete German grid. The core of the model is the transformation model, which enables the transmission of wind power forecasts of a reference measurement network to chosen grid areas in Germany. The reference measurement network was extended from 16 to 50 locations. A further aim of the project is the improvement of local forecasts, in order to avoid the overloading of transmission lines and to support new control strategies for offshore wind farms. The transformation algorithm allows the conversion of the reference forecasts to every chosen sub-area in Germany. The model has its particular advantages in precision, short computing time and low operating costs, as only a low number of forecast and measurement locations are necessary. As the model consists of a cluster of single wind farm prediction modules, which output is scaled up to the expected wind power for the entire control zone, it can also be used for single wind farms or wind farm groups. The environments of the reference wind farms are quite different – coastal areas as well as complex terrain. The prediction error (root mean square error RMSE in % of the rated power) of the dayahead forecasts (24 – 48 hours) ranges from 9 % to 19 % for single wind farms and amounts to 9 % for the control zones (E.ON Netz and RWE Net).
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Figure 4: Frequency distribution of the single forecast error for the entire control zone of E.ON Netz
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Figure 4 shows the frequency distribution of the single forecast error (Ppredicted – Pmeasured) for the control zone of E.ON Netz for a period of 680 days. The light blue bars depict the prediction error based on wind speed and direction, the dark blue bars depict the prediction errors based on additional meteorological parameters air pressure, temperature, humidity and cloud coverage. 86 % (85 %) of the forecast errors are in the range of +/- 10 % of the installed capacity.
Short Term Prediction Most of the NWP models provide forecasts of meteorological data only several times a day for a specific period. This fact leads to a disadvantage for all prediction models that are based only upon these data, like most of the physical models. The ability of ANNs to enable short-term predictions of the power of WTs was investigated in the past by several institutes [6], [7]. This ability is used to evaluate deviations between the current values and the prediction of the wind farm power and to adjust the predictions for the next 1 to 8 hours to the current situation. The shorter the prediction horizon the greater is the dependence of the feed-in on local influences rather than global weather conditions. From there, the accuracy also decreases with the reduction in the spatial spreading of the prediction area. Furthermore, current changes in the weather conditions can not be taken into account by models that are based purely on numerical weather forecasts. As the local weather conditions in the near past (and present) are indirectly recorded over the measured power output of the wind farm, the predictions for short time horizons can be significantly improved by the inclusion of this information. Compared to the results from [6], [7] the short-term prediction is not exclusively based on information from the present and near past, but on the comparison of predicted meteorological parameters and measured power data. For this, the meteorological input parameters are used together with measured power data from the near past. Through the comparison of these time series from the near past, deviations of the temporal course can be recognised and corrected. 3000 Online Forecast (D-1) Update 08:00 Update 10:00
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Figure 5: Real and predicted course of wind generation Figure 5 shows the real and the predicted course of the wind power production. The deviation of the dayahead forecast is corrected by the short-term prediction module. A great advantage of this method is, that the expected wind generation can be newly calculated at any time, i.e. calibrated with current measurement data.
coast inland control zone hours RMSE correlation RMSE correlation RMSE correlation 1 4,8% 0,966 6,2% 0,953 2,6% 0,991 2 6,4% 0,936 8,7% 0,908 4,1% 0,977 3 7,5% 0,914 10,2% 0,867 5,1% 0,964 4 8,1% 0,898 11,3% 0,833 5,9% 0,952 5 8,4% 0,888 12,0% 0,809 6,4% 0,942 6 8,8% 0,877 12,5% 0,790 6,9% 0,933 7 9,0% 0,870 13,0% 0,773 7,2% 0,925 8 9,3% 0,860 13,3% 0,764 7,7% 0,917
Table 1: prediction errors of short-term forecasts Table 1 shows the accuracy (standardized RMSE and correlation) of the 1 – 8 hour forecasts for a coastal wind farm in comparison to an inland wind farm and the entire control zone of E.ON Netz. The coastal wind farm consists of 111 WTs with an associated power of 80.8 MW and the inland wind farm consists of 12 WTs / 7.9 MW. The number of WTs in the E.ON Netz control zone amounts to 6850 with an associated power of 5.7 GW. The one hour persistence shows an error (RMSE) of 3.2 % for the E.ON Netz control zone.
Further application In frame of the European project DISPower [8], the short-term prediction model has been adapted to provide 2-hour forecasts for 6 wind farms in the UK. For each wind farm the UK Met Office provides data of four or five grid points of their numerical weather prediction (NWP) model.
Figure 6: Locations and grid points of the UK wind farms The model outputs resulted in an avoided energy lack (caused by negative forecast error) of 2,7 % of the total energy production and an avoided energy surplus (caused by positive forecast error) of 3,3 % compared to the persistence.
Farm Forecast Bears Down Bryn Titli Carno Kirkby Moor Llyn Alaw Taff Ely
Nom. Power [MW] 9,6 9,9 33,6 4,8 20,4 9
Lack Surplus RMSE Correl. [MWh] [MWh] 11,8% 13,1% 9,8% 12,9% 9,7% 13,9%
0,921 0,913 0,922 0,913 0,947 0,909
845 1650 4594 836 2966 1643
802 1797 4592 899 2801 1241
Table 2: Results of short-term prediction of UK wind farms. Table 2 shows the results of the short-term prediction of the six UK wind farms. All 2-hour forecasts feature a 2 % improvement of the standardized RMSE compared to the persistence. Thus, the short-term prediction model is not only usable for German TSOs but also provides important data for energy bidding within the UK electricity trading system.
Wind Power Management System WPMS One fundamental part of modern prediction and energy management systems is the breakdown of all needed information as well as the compatibility to the information and communication technology (ICT) of the user. Thus, the computer models for online-monitoring and prediction of wind power, developed at ISET, have been merged to the Wind Power Management System WPMS. This system is easily adaptable to any ICT environment via unique interfaces. The input data, i.e. the predicted meteorological parameters and the measured power data, are converted in XML by a pre-processor and sent to the kernel, which consists of the transformation and prediction modules. The kernel carries out the following operations: · the determination of the current wind power generation for control zones and sub-regions · the day-head forecast of the wind power generation for wind farms, control zones and sub-regions · the short-term prediction of the wind power generation for wind farms, control zones and subregions.
Figure 7: Graphical user interface of WPMS
Figure 7 shows the graphical user interface of WPMS. The left frame shows the control zone of RWE Net and the associated wind capacity. The size of the squares depicts the installed capacity and the colour (from blue to red) the current wind generation. The upper right frame shows the run of the day-ahead forecast (green), the current wind generation (blue), the 1-8 hour forecast (yellow) and the difference between predicted and measured wind power (red). The lower right frame shows the current power and status of the representative wind farms. A post-processor is optional and converts the kernel output into special formats for further processing. Beside the wind generation data for the entire control zone, the current wind power fed-in as well as the short-term and day-ahead forecasts are computed for defined sub-regions in order to detect early and avoid possible bottle-necks and overloads of grids.
Outlook According to Federal Government’s long-term planning, wind turbines with a total rated capacity of up to 25 GW shall be erected in the North and Baltic Seas, which would cover around 15 % of the German electricity consumption. For the integration of this immense electrical power into the German energy supply system, the wind turbines already installed on land have to be considered. This large amount of intermittent generation has growing influence on the load and security of the electrical network, the operation of thermal power plants, the electricity trading and on the overall efficiency of the German electrical supply system. Large (offshore) wind farms will therefore require access from a central operational control centre in order to coordinate and control the operation of many individual wind turbines with correspondingly defined demands. This task and responsibility will presumably be assigned to wind farm operators, whereby the predetermined desired values (e.g. for active and reactive power output) must be defined in consultation with the grid operator, responsible for control. As the current operational data of large wind farms will be joined together by the grid operator and desired values are given, a type of control centre for clusters of large wind farms is established, which increasingly takes on the character of conventional power plants. The utilisation of operation management, to be newly developed, would provide a multitude of new applications to enable simple, flexible and uninterruptible reaction to the demands of participants (wind farm operators, TSOs and electricity traders). These operation control centres must facilitate energy and power control, as well as the provision of reactive power, in order to maintain wind farms comparable to conventional power plants. Further development, modification and adapting of online-monitoring and prediction models is necessary for the operational control of large on- and offshore wind farms. Besides prediction of the temporal course of the total power of all wind turbines for the following days, short-term highly resolved predictions (from 15 minutes up to 24 hours) for individual wind farms, and wind farm clusters, are the basis for secure grid and system control. The operational control of large on- and offshore wind farms places new increased demands on wind power prediction.
Figure 8: Structure for the control of large wind farms and wind farm clusters on- and offshore
With the combination of the models for online monitoring and prediction of the wind power feed-in, a systematic solution for the integration of wind energy has been developed by ISET, which is now operated, or is being implemented, by electricity transmission companies with high penetration of wind energy. In conclusion, it can be established that a precise model for wind power prediction, which can be adapted to arbitrary locations, in combination with the model for online monitoring, significantly reduces obstacles for the acceptance of wind energy use from the perspective of energy suppliers and grid operators and, therewith, further consolidates the position of renewable energies in the electrical energy supply.
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
B. Ernst, K. Rohrig: Online Monitoring and Prediction of Wind Power in German Transmission System Operation Centres, First IEA Joint Action Symposium on Wind Forecasting Techniques, Norrköping 2002
5
R. Brause: Neuronale Netze, Teubner Stuttgart 1991
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M. Menze, Leistungsprognose von Windenergieanlagen mit Neuronalen Netzen, Diplomarbeit ISET 1996
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J. O. G. Tande, L. Landberg, A 10 Sec. Forecast of Wind Turbine Output with Neural Networks,European Wind Energy Conference 1993
8
DISPower - Progress report www.dispower.org , ISET 2002
