WIND FORECASTING OPTIONS FOR LARGE SCALE WIND AUTOPRODUCERS WITH ELECTRICITY STORAGE RESEARCH ABBREVIATED OUTCOMES A wind forecasting application study for a large-scale wind autoproducer with electricity storage at Dundalk Institute of Technology, Ireland Daria Bandurowicz The Master Thesis was supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism Project PL0460
PROBLEM STATEMENT Wind power generation is technically and economically viable but wind power plant output depends on wind speed. Such speeds are difficult to predict accurately over daily periods as the wind speed is stochastic in its nature and fluctuates from minute to minute. Within the boundaries of Dundalk Institute of Technology campus accurate short-term wind speed forecasts, load forecasts and the utility charges are among the greatest research interests, which all combined would enable to predict electricity surplus or deficit. In the nearest future, this knowledge will contribute to optimum operation managing the available energy generated by the wind turbine and the capacity of installed flow battery. In this thesis free of charge wind-speed maps provided on-line by the Irish Meteorological Institute were used as input data to compute predicted wind energy. These results were further compared with the real energy generated and registered by the wind turbine s ION meter; all averaged over 3 hours, 24 hours and the fifth day time horizons during period from 3 rd November until 22 nd December 2010. In this paper two methodologies were applied to forecasts the energy and test accuracy of those forecasts: The first consisted of acquiring wind speed maps from Met Eireann, calculating surface roughness lengths thanks to on-site anemometers and projecting those wind speeds to hub height of 60 m; The second was based on finding correlation between Met Eireann wind speeds at 10 m and ION meter readings and then fitting proper polynomial trend line curve. i
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TABLE OF CONTENTS 1 Introduction... 1 1.1 Motivation... 1 1.2 Objectives... 2 2 Research... 5 2.1 Introduction... 5 2.2 Data acquisition... 7 2.3 Energy forecasted vs. energy generated... 12 2.3.1 Forecasts applying method A... 13 2.3.2 Forecasts applying method B... 22 2.4 Results and discussions... 24 2.4.1 Commercial forecasting applications... 28 3 Conclusions and recommendation... 30 v
LIST OF FIGURES Figure 2.1 Wind turbine and battery grid connection at DkIT... 5 Figure 2.2 WT power generated (ION meter) and power imported from grid (ESB meter) 6 Figure 2.3 Campus electricity metering equipment... 7 Figure 2.4 DkIT Vestas V52 measured power curve for 2008... 8 Figure 2.5 Wind maps for 3 hours time horizon (source: Met Eireann)... 9 Figure 2.6 Wind maps for a 5-day time horizon circled is approximate location of Dundalk... 10 Figure 2.7 Dynamical variation of observed wind speed measurements at 10 m... 11 Figure 2.8 Frequency distribution of wind speeds measured at 10 m (3 rd Nov-22 nd Dec). 12 Figure 2.9 DkIT measured power curve as a function of wind speed (with standard deviation)... 14 Figure 2.10 Energy forecasted and measured for 3 hour time slot... 15 Figure 2.11 Energy forecasted and measured applying method A for 3 hour slot selected data points... 16 Figure 2.12 The accuracy range of 3 hour energy generated as wind speeds... 17 Figure 2.13 Energy forecasted and measured for the 24 hours slot (3 rd Nov 22 nd Dec)... 18 Figure 2.14 The accuracy range of 24 hour wind forecast (3 rd Nov 22 nd Dec)... 18 Figure 2.15 Energy measured and forecasted for 5 th day (3 rd Nov -22 nd Dec)... 19 Figure 2.16 The accuracy range of the 5 th day energy forecast (3 rd Nov 22 nd Dec)... 20 Figure 2.17 Forecasted and real energy for 3 h slot method A applied (13 th - 22 nd Dec)... 21 Figure 2.18 The accuracy range of the 3h slot energy forecast method A (13 th -22 nd Dec) 21 Figure 2.19 The correlation of Met Eireann met station at 10 m and ION meter at DkIT for a 3-hour forecast... 22 Figure 2.20 Forecasted and real energy measured for a 3 h slot during 13 th - 22 nd Dec - method B applied... 23 Figure 2.21 Accuracy range of 3 hour forecast (13 th Nov - 22 th Dec) method B applied... 23 Figure 2.22 Real energy and forecasted energy applying method A and B (not all results are displayed)... 24 Figure 2.23 Panorama from the nacelle Crowne Plaza Hotel in the background... 26 Figure 2.24 The weak effect of an obstacle and WTof 60 m hub height (www.windpower.org)... 27 Figure 2.25 Vestas V52 at the DkIT campus... 28 Figure 2.26 3TIER hour ahead forecast... 29 vi
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LIST OF TABLES Table 1 Weather forecast for the next 24 hours (Met Eireann)... 10 Table 2 Method A results for different time horizons... 25 Table 3 Comparison of method A and B (13 th Dec 22 nd Dec)... 25 viii
1 INTRODUCTION 1.1 Motivation Off-shore and on-shore wind power generation is rapidly growing worldwide becoming more and more promising renewable source of electricity generation. Fast growth of largescale distributed renewable energy resources, deregulation of electric power markets, growing electricity consumption and demand impose significant amount of uncertainties on operation of the power system from both resource side and load side The Global Wind Energy Council, which is the trade association based in Brussels, in cooperation with Greenpeace International presented in early February 2010 its 3 rd edition of the Global Wind Energy Outlook for 2010, stating that annual market growth rates were to be at 27% for 2010. According to this report, the most conservative reference scenario (based on existing policies, including assumptions such as growing global electricity demand and continuing electricity and gas market reforms) estimates that 20 GW to 26 GW of new capacity will be added yearly between 2010 and 2020, reaching 41 GW per year in 2030 (GWEC 2010). In the most optimistic scenario the annual growth rates of 120 GW by 2020 were estimated, increasing and stabilizing at around 185 GW by 2030 (GWEC 2010). Wind is a proven source of clean and affordable source of energy, therefore wind resources have to play an important role to meet ambitious goals in terms of environment and energy policies set by the European Union, which established mandatory national target corresponding to a 20% share of renewables in overall Community energy consumption by 2020 and a mandatory 10% minimum target to be achieved by all Member States for the share of renewable energy in transport consumption by 2020. The operation of wind energy based systems highly depends on weather conditions; resulting output of energy generated is variable in time and does not always follow the load demand side. In order to fulfill the requirements energy needs to be stored in periods of scarcity of wind resources. A wide variety of energy storage technologies with diverse properties and attributes are available. Despite certain drawbacks such as limited life cycle, problems with depth of discharge or rapid loss of energy content, batteries are still the most popular solution for energy storage, especially in remote areas where the system is not connected to the grid. New generation flow batteries are promising technology as they are highly rechargeable. In fact they can be recharged to 100% of capacity quite quickly; they are deep discharge capable, meaning that user can deplete the battery charge completely without damaging it. These two characteristics allow using an external power source like a wind or solar farm to charge the batteries during the day and tap the batteries on a daily basis when those sources are offline. Around the word there are a few companies offering flow battery technologies, just to mention some like ZBB Energy Corporation, DuPont or Deeya Energy. Those companies promote their products as useful for off the grid energy storage as well as for smoothing the supply of electricity supplied from renewable sources like wind (ZBB n.d.). Matching supply and demand is especially complicated when it comes to renewable energy sources like wind. The wind does not always blow when needed, which has a direct impact on electricity companies which in turn are forced to keep conventional power stations 1
standing by so that on calm days, or when electricity demand rises users would not suffer electricity shortages. Moreover, increasing percentage of wind power generation in overall energy mix may cause fluctuations in energy output. Transmission system operators (TSOs) struggle to balance supply and demand on national and regional grid systems as they need to predict and manage this variability to avoid further balancing problems. With the increasing level of penetration of wind energy in global energy mix, it is becoming very important to make wind farms operating more like conventional plants and therefore forecast and manage, at short to medium timescales, how much electricity will be produced. Forecasting is already a must in some countries - new constructed wind farms in California, USA are required to use the best possible means available to forecast the output, and send those estimates to Cal ISO (the California independent system operator). In some European countries like Denmark, Germany, Spain, Ireland (for wind farms greater than 30MW), where already a high level of wind generation penetration is present, operators, managers and TSOs are routinely forecasting the output from their wind farms (Parkes et al. 2006). These forecasts are also used to schedule the operation of other plants and are used for trading purposes as well. In other regions it is a commercial choice whether to implement wind power forecasting or not. Nevertheless, knowledge about wind energy forecasting and the ability to forecast the production is of large value for TSOs, utilities, wind energy promoters, market integration of wind energy, regulatory authorities and many more. 1.2 Objectives In this paper the system installed within the campus of Dundalk Institute of Technology (DkIT), Ireland was studied. The wind turbine (WT) Vestas V52 of rated power 850 kw and hub height 60 m has been running for the last five years. This large-scale commercial WT operates as an autoproducer meaning that it can either import or export the electricity to the grid incurring different connection fees and use of system charges. At present DkIT is installing a flow battery 125kW, 500kWh of ZBB manufacturer for primarily research purposes but this also will further reduce annual electricity bills for the campus. As for 2010, this system was the first of its kind in Europe and only the third in the world from this manufacturer. In this study the final goal is to suggest solutions for a wind forecasting technique that would best suit wind/storage system in DkIT and which could be implemented in the future. The scope of this work is to study the wind forecasting techniques that have been developed and are currently used, the on-line wind forecasting resources, and specific products that are currently available on the market. As input data for the forecasting application analysis, free of charge wind speed maps available on the Met Éireann - the Irish Meteorological Service Online website were used. Having computed the WT s forecasted and real energy from the ION meter readings, the accuracy of those forecasts was assessed. So far, at DkIT wind speeds were measured with three anemometers at heights of 5 m, 10 m and 15 m; additionally the wind vane at 10 m was indicating wind directions. Wind speeds measurements and data of power generated by the WT itself were available for this study and were used to represent the WT s measured power curve. Part of the future work 2
at DkIT will include the addition of wind forecasting system to optimise the control of the wind storage system for best economic performance. However, this would be a comprehensive task and due to limited time for this research, this paper aims at preliminary studies concerning wind speed forecasting applications. 3
2 RESEARCH 2.1 Introduction Dundalk Institute of Technology (DkIT), located in the northeast of the Republic of Ireland within the distance ca.7 km from the Irish Sea, commissioned a Danish Vestas V52-850kW wind turbine on its campus in 2005. It operates as an autoproducer supplying campus needs first, sending the electricity excess out to the grid or importing the deficit. It is believed to be the first large commercial wind turbine on a college campus in the world. Figure 2.1 shows how the wind turbine with the flow battery is connected to the grid and supervisory control and acquisition system (SCADA). Figure 2.1 Wind turbine and battery grid connection at DkIT DkIT established a research Centre for Renewable Energy CREDIT, and one of its projects was the development of a large commercial wind turbine on the campus for the purposes of research and training. At present DkIT is installing a flow battery mainly for research purposes but it will also help to further reduce annual electricity bills of the campus. The broad research will take into account a number of factors including electricity prices, wind and load forecasting. 5
At present, the particular electricity supplier Vayu offers only day and night rates: day is from 08:00 to 23:00 and night is from 23:00 to 08:00. The DkIT in March 2010 was facing the following prices: Payment for Exporting Energy Day energy 4.0594 c/kw; Night energy 3.1418 c/kw; Energy Charges Day energy 5.2635 c/kw; Night energy 3.3631 c/kwh. Meanwhile, other suppliers, like ESB, use varying half hour pool prices (ESB n.d.,). If in the future DkIT moved to this electricity supplier, the wind speed and power forecasting (combined with electricity storage) would play extremely important role for DkIT in further bill reduce. If DkIT were provided with reliable short-term wind speed forecasts, it could charge the battery rather than selling the electricity to the grid at low price. In this paper the free on line maps, showing forecasted wind speeds provided by Met Eireann website were used. Those maps were further used as input data in order to compute the accuracy of forecasted energy and check the real energy generated by the WT. Figure 2.2 shows the power generated and measured by two meters: the ION meter measures power generated by WT and the ESB main meter measures how much power campus imports or exports to the grid. Figure 2.2 WT power generated (ION meter) and power imported from grid (ESB meter) The data available from those both meters indicates the mean power generated over 5 minute slots. As DkIT can sell the excess of the electricity to the grid, the negative values of ESB meter indicate that energy is exported and the meter is counting backwards. Sample periods when the electricity was exported to the grid are marked with red circles in above 6
Figure 2.2. It is of great interest for DkIT to foresee such periods of high wind speeds generating excess of the electricity and decide whether to store it or sell to the grid. Wind speed and energy forecasting also could be used for planning maintenance. The WT itself consumes small amount of energy - on average 3.5 kw for internal lightening, yawing mechanism, etc. If DkIT were provided with reliable short-term wind speed forecasts, it could charge the battery rather than sell the electricity to the grid at low price. In the Figure 2.3 the main measuring meters are shown. Figure 2.3 Campus electricity metering equipment 2.2 Data acquisition Wind turbine The WT s performance data as well as wind speeds, measured by the ultrasonic detector mounted on the top of nacelle for almost past 4 years, were provided by CREDIT. The wind speed data and corresponding power generation from WT is averaged over 10 minute periods. One complete year of data was selected in order to create the measured power curve of V52 which can be seen in Figure 2.4. The power curve provided by the manufacturer in brochures is usually obtained in ideal conditions of wind tunnels and clearly the WT in an operational site is not placed ideally in relation to nearby obstacles (this V52-850 kw is located in an urban area). This means that the ideal power curve is not always suitable for assessing the power performance of an operating wind turbine. 7
Figure 2.4 DkIT Vestas V52 measured power curve for 2008 The generation of electricity starts from a minimum wind speed 2.5 m/s and reaches the nominal rated power output at speed of 14 m/s. The bin method was applied to create the power curve. The power curve that characterizes the wind turbine behavior was composed by the link of the different points obtained and therefore, the theoretical power that corresponds to given wind speed can be obtained through the interpolation between the two nearest bins (Llombart et al. 2005). The wind speeds forecast maps The Irish Meteorological Service Online Met Eireann provides on its website free of charge weather forecasts for different time horizons. From this source, the following wind speed data forecasts were acquired for further research: Average wind speeds forecast for the next 3 hours; Average regional (County Louth) wind speeds forecast for the next 24 hours; Wind speeds forecast for the next 5 days the 5 th day forecasts were read for this study. 8
Figure 2.5 represents two weather forecast maps available on Met Eireann website. The information depicted on left panel differs from the right panel as follows: The left panel: Wind is shown in two forms: As wind arrows the head of the arrow shows the direction in which the wind is blowing and the feathers the strength (a short feather is 5 knots, a long feather is 10 knots and a triangle is 50 knots by adding the feathers one can get the wind speeds); As warning regions - where speeds above 22 knots are cultured - the color bands correspond with Beaufort force 6 and higher; The right side wind map shows atmospheric pressure (black) and wind speed as isotachs (orange) - lines of equal wind speed. Those maps show the average wind speed for the next 3 hours and are updated hourly (usually 15 minutes to full hour). Figure 2.5 Wind maps for 3 hours time horizon (source: Met Eireann) Table 1 shows the 24 hours forecast. This forecast is updated by the Irish Meteorological Institute once a day, usually short before midnight. 9
Table 1 Weather forecast for the next 24 hours (Met Eireann) Forecast Co. Louth Weather Wind Temperature Saturday 17 km/h Figure 2.6 shows wind strength in knots (nautical miles per hour; 1 knot 0.514 m/s), which is indicated by the colored areas. The colors correspond to Beaufort forces (gale force, or Beaufort force 8, is equivalent to 34 knots). Wind direction and strength are indicated by the arrows. However, for this map Met Eireann did not provide any additional information on how arrows values should be interpreted. Figure 2.6 Wind maps for a 5-day time horizon circled is approximate location of Dundalk 10
The information presented in all above maps is for 10 m height and was derived from Numerical Weather Prediction (NWP) models used in Met Eireann as forecaster guidance. Information for up to 5 days ahead came from the ECMWF model, presented at 6-hourly intervals. However, the Institute wares users that the official forecast might not always reflect the guidance charts illustrated; the forecasters also consider information from satellite, radar and weather observations, in addition to the application of their own experience (Met Eireann 2010) It is also of vital information that all Met Eireann graphic forecasts are a computergenerated output and may on occasion differ from the text forecast, in which case the text forecast should be given priority. To test the accuracy of those winds speed forecasts and energy generated versus predicted, three time horizons were considered for further study: 3 h forecast, 24 h forecast, and forecasts for the 5 th day. On site wind speed data analysis At DkIT wind speed is measured with cup anemometers at three heights: 5 m, 10 m and 15 m. Figure 2.7 shows wind speed measured at 10 m height and averaged over 10 minute periods one can see how highly variable the wind velocity is on an hourly and daily basis. Figure 2.7 Dynamical variation of observed wind speed measurements at 10 m It is clear that the relationship between wind speed and power output is not linear. In fact the power available from moving air is proportional to the cube of the wind velocity. The Figure 2.8 is a frequency distribution of the wind speeds measured by the anemometer at 10 m during the whole data acquisition, i.e. from 3 rd November till 22 nd December 2010. 11
At calm times, even if the anemometer is not spinning around, the data logger would register 0.3500017 m/s. During the whole period of data acquisition the anemometer mounted at 10 m height did not register wind speeds higher than 13.5 m/s over averaged 10 min periods. Figure 2.8 Frequency distribution of wind speeds measured at 10 m (3 rd Nov-22 nd Dec) The power generated by the WT is proportional to the cube of wind speed, which means that all variations in real wind speeds have a very huge influence of forecasted and real energy output. The wind speed forecasts noted from the Met Eireann website for given time horizons (3 h, 24 h, and 5 th day) assume averaged values. 2.3 Energy forecasted vs. energy generated In order to be able to calculate the forecasted wind energy, free of charge wind speed maps for different time horizons were downloaded from the Met Eireann website from 3 rd November 2010 until 22 nd December 2010. The general observation was done from 3 rd November until 22 th December in 2010. The Met Eireann website was being checked regularly in order to download wind speed maps for desired location - Dundalk, County Louth, Ireland. This Meteorological Institute provides wind speed and weather forecasts for various time horizons out of which a 3-hour, 24-hour and the fifth day time horizon forecasts were studied in this paper. 12
In case of the 24-hour and the 5 th day wind speed forecasts, the corresponding wind energy generation was calculated for 3 rd November 22 nd December, 2010. In case of the shortest 3-hour time horizon wind speed forecasts provided by the Met Eireann, which are of the most interest for DkIT in terms of wind energy storage facility, two methodologies (method A and method B) were applied to forecast, compute and compare the accuracy of wind energy generated: A) The maps of wind speeds at 10 m height were read, a virtual surface roughness length z 0 =0.002 was assumed for the Met Eireann data. The logger wind speeds were projected to the hub height at 60 m taking into consideration the calculated surface roughness lengths of DkIT, which were different for corresponding periods averaged. The Met Eirean forecasts and the field logger forecasts were checked afterwards with the real energy generated by the wind turbine s (ION meter readings). The results are shown for two periods; 3 rd November 12 th December (Method A); 13 th December 22 nd December (Method A repeated). B) Observations and results obtained from 3 rd November until 12 th December were used. The method B was based on finding correlation between Met Eireann wind speeds at 10 m and ION meter readings and then fitting proper polynomial trend line curve. This allowed forecasting the energy from 13 th December until 22 nd December for 3 h slot. The findings were presented and compared for both methods during 13 th Dec 22 nd Dec. Results of 24-hour and 5 th day forecasts are also presented for general information concerning the accuracy of Met Eireann wind speeds forecasts and wind energy generation for DkIT. It was impossible to compare different time horizons forecasts as wind speeds are presented in different manner for different time horizons. However, speaking about the thesis research duration, some missing data points had to be excluded from the research as there were times when the turbine was in pause mode or it was not possible to read the energy generated and registered by ION meter / ESB meter. These facts narrowed already short time and small number of input data available for this study. For 3-hour time horizon, it was possible to gather 144 samples for Method A (3 rd Nov 12 th Dec) and 72 samples for Method B analysis; while for the 24-hour forecast there were 37 samples and for 5 th day forecast 19 samples. 2.3.1 Forecasts applying method A From the data logger mounted at DkIT field, the average wind speeds for 10 minute periods were available, while the WT s ION meter readings provided the average data over 5 minute periods. As wind speeds at DkIT were measured at 3 different heights (5 m, 10 m and 15 m) it was possible to project the wind speed forecasts to hub height of 60 m; and later the supposed WT power generation could be calculated. The previously presented and calculated power curve for 2008 was used a polynomial trend line curve was fitted to this power curve but only the useful part was used, i.e. for speed bins ranging from 2.75 12.5 m/s (Figure 2.9). 13
Figure 2.9 DkIT measured power curve as a function of wind speed (with standard deviation) For each time horizon corresponding calculated and averaged surface roughness length was applied. In the very first steps of research, the same calculated surface roughness length was applied both to the logger and Met Eireann station data. Though, the approach was changed calculated average surface roughness length was applied to the logger for each period averaged as previously but a constant roughness length z 0 =0.002 m to the Met Eirean was assumed. It was done so in first approach as there were attempts to estimate the virtual surface roughness of an unknown location of Met Eireann data station. 14
Figure 2.10 Energy forecasted and measured for 3 hour time slot The forecasted and measured energy from 3 rd Dec until 12 th Dec for the 3 hour forecasting period is presented as bar chart in Figure 2.10. Generally speaking, the ION meter measures the WT output averaging the data over 5 min periods while the anemometer connected to data logger is averaging data over 10 min periods. In Figure 2.10 one can see forecasted energy computed with method A and the real energy generated. At very first glance there appears to be no general pattern there were times of highly overestimated or underestimated forecasts as well as times when forecasts were considerably close to the real values. Beneath in Figure 2.11 some data points were randomly selected to better show the values of forecasted, real and the energy estimated by the logger. 15
Figure 2.11 Energy forecasted and measured applying method A for 3 hour slot selected data points In Figure 2.12 the accuracy of the 3 h slot forecasts is shown. The total number of samples in this time period is 144. The red line in the picture shows prefect forecasts (100% accuracy). Data points situated above this line were the underestimated forecasts i.e. the ratio of measured to estimated forecast was greater than one (measured/estimated > 1). 16
Figure 2.12 The accuracy range of 3 hour energy generated as wind speeds Out of total amount of 144 samples 62 were underestimated, 82 were overestimated and 20 (marked with orange circle in Figure 2.12) were completely missed forecasts (its accuracy was 0%) and there was just one perfect forecast (100% accurate). The two black arrows in above Figure 2.12 indicate the percentage of observations which were in the range ±30% of accuracy. Forecast accuracy within the range ±30% was observed in 37 data samples (25.7%) Apart from the forecasts for the next 3 hours, the Met Eireann provides the forecasts for the next 24 hours and updates this information on a daily basis. This 24 hours forecast was being studied as well and the main findings for the whole period 3 rd Nov 22 nd Dec are commented. In Figure 2.13 the 24 hour energy forecast measured with previously described method A is shown for the whole period (3 rd Nov 22 nd Dec). There are some data points when Met Eireann forecasts exaggerated the estimated energy produced as much as twice. There are also data samples when the logger overestimates the forecasted energy. 17
Figure 2.13 Energy forecasted and measured for the 24 hours slot (3 rd Nov 22 nd Dec) In case of the 24 hour forecast (see Figure 2.14), there were collected 37 data samples, out of which more than a half presented an overestimated energy forecasts. Out of total 37 data points, only 7 (19%) were within the ±30% accuracy range. Figure 2.14 The accuracy range of 24 hour wind forecast (3 rd Nov 22 nd Dec) 18
The third wind and energy forecast horizon considered in this thesis was the forecast for the fifth day only. Met Eireann provides this forecast in 6 hours time slots but for the thesis purposes these values were added and averaged over 24 hours time. The available maps would show the forecast for this time horizon in a 6-hour time slot, so the average wind speed value was calculated for the whole 24 hours. Such forecast could be of interest for Irish wind farms greater than 30 MW, as by law they are obliged to forecast their power output. Forecasts for the next 24 hours would be useful for a dayahead market (spot market). This is a type of physical power market, where supply and demand governs the prices. The spot market is a day-ahead market, where offers for the following day must be done before midnight. For the next 24 hour wind speeds forecasts the data on Met Eireann website was updated shortly before midnight. Figure 2.15 compares the results of forecasted and real energy generated during the fifth day slot. This longest period of forecast considered revealed the fact that during 19 days studied, the Met Eireann data resulted in highly underestimating the real energy generated by the Vestas V52. Figure 2.15 Energy measured and forecasted for 5 th day (3 rd Nov -22 nd Dec) 19
More precise data concerning 5 th day forecast of energy generated is presented in Figure 2.16 1200% 1000% 800% Underestimated Forecast accuracy 5th day slot 3rd Nov-22nd Dec Method A Total N = 19 Underestimated = 16 Overestimated = 3 ACCURACY 600% 400% perfect forecast measured/estimated 200% 0% Overestimated 0% 10% 20% 30% 40% 50% TOTAL N 60% 70% 80% 90% 100% Figure 2.16 The accuracy range of the 5 th day energy forecast (3 rd Nov 22 nd Dec) For this stage of analysis there were only 19 data samples as the 5 th day service forecasts were a new service available on website dated from 17 th November 2010. In most considered cases, having used the wind speed forecasts provided by Met Eireann, computations resulted in underestimating the generation of forecasted energy. Only 15.78% of data samples were included within the range ±30% of accuracy. For the time frame between 13 th Dec and 22 nd Dec two methods were used to predict the energy generated, it was then possible to compare the accuracy of both methods A and B applied. Below the results for method A are commented. Figure 2.17 shows the second part of analysis dated from 13 th December until 22 nd December 2010 while still method A was applied to calculate the forecasted energy for a 3 h slot. For the sake of better visibility for the reader, the logger data is not included in Figure 2.17. 20
Energy 3h slot 13th Dec - 22nd Dec Method A 2000 ENERGY [kwh] 1500 1000 500 Figure 2.17 Forecasted and real energy for 3 h slot method A applied (13 th - 22 nd Dec) In Figure 2.18 the accuracy of 3 h slot forecasts is presented: out of 72 forecasts, 33 were underestimated, 39 were overestimated and within ±30% accuracy range 23.6% of these forecasts were included. Figure 2.18 The accuracy range of the 3h slot energy forecast method A (13 th -22 nd Dec) 21
2.3.2 Forecasts applying method B Due to the installed flow battery system storage capable of charging in merely 4.5 h, free of charge wind speed forecasts for upcoming 3 hours would be of the greatest interest to DkIT. After having studied the turbines performance, the field logger and ION meter data during period from 3 rd November till 12 th December, the knowledge gathered could be used. Observations and results obtained from 3 rd November until 12 th December indeed were used. The method B was based on finding correlation between Met Eireann wind speeds at 10 m and ION meter readings and then fitting proper polynomial trend line curve (Figure 2.19). This allowed forecasting the energy from 13 th December until 22 nd December for 3 h slot. Unfortunately, such a short time of data acquisition did not allow experiencing the presence of high wind speeds. Figure 2.19 The correlation of Met Eireann met station at 10 m and ION meter at DkIT for a 3-hour forecast Figure 2.20 shows the forecasted and real energy calculated for 13 th - 22 nd December 2010. Here method B was applied wind maps were downloaded from the Irish meteorological institute as previously and correlation function used to calculate the forecasted energy associated with wind speeds. 22
Figure 2.20 Forecasted and real energy measured for a 3 h slot during 13 th - 22 nd Dec - method B applied Figure 2.21depicts the accuracy of forecasted energy while applying method B. The total number of data sets was the same as for method B during this period of research. Out of the total number of 72 data sets 41.6 % were within the range ±30% of accuracy. Figure 2.21 Accuracy range of 3 hour forecast (13 th Nov - 22 th Dec) method B applied 23
Figure 2.22 shows the forecasted and real energy measured for the period 13 th Dec untill 22 nd Dec while respectively method A and method B was applied. Figure 2.22 Real energy and forecasted energy applying method A and B (not all results are displayed) As one can see from Figure 2.22 the forecasted and real energy for 3-hour time step was highly variable. There were still some data samples when method A and B gave quite correct forecasts (forecasting period 9, 16, 17, 18 etc.). 2.4 Results and discussions The data acquisition during the general period from 3 rd November until 22 nd December 2010 involved applying two methods A and B for computing the forecasted energy for different time horizons. Table 2 summarizes the outcomes of research while applying A method for different time horizons. 24
Table 2 Method A results for different time horizons No. data sets A method 3h slot (3 rd Nov-12 th Dec) A method 24h slot (3 rd Nov-22 nd Dec) A method 5 th day slot (3 rd Nov-22 nd Dec) Total 144 37 19 Underestimated 62 14 16 Overestimated 82 23 3 ±30% accuracy (% total No.) 37 (25.7%) 7 (19%) 3 (15.78%) It is clearly visible that the longer the period of the forecasts was the less accurate was the amount of the forecasted energy. The A method was used to compute the forecasted energy for different time horizons using wind speed forecasts as input data obtained from differently represented maps. Nevertheless, the 5 th day forecasts were the least accurate as only 15.78% of total data samples were within the ±30% accuracy range; meanwhile the 3 h slot energy forecasts were the most accurate (roughly one quarter was within ±30% accuracy range).table 3 summarizes the outcomes of forecasting using method A and B for the same data samples. Table 3 Comparison of method A and B (13 th Dec 22 nd Dec) No. data sets A Method 3 h forecast B Method 3 h forecast Total 72 72* Underestimated 33 33 Overestimated 39 37 ±30% accuracy (% total No.) *Note: In B method 2 data sets were perfect forecasts 17 (23.6%) 31 (41.6%) After having applied the new approach of method B and calculating the forecasted energy, still quite similar number of underestimated and overestimated forecast is present. Nevertheless, the method B was capable of achieving two almost perfect energy forecasts. Moreover, the method B appears to be more precise as 41.6% of total data samples were within ±30% range of accuracy (compared to 23.6% for method A). The possible sources of inaccuracies, which influence the final results, in this comprehensive study are many - just to start with the fact that energy generated by WT is proportional to the cube of wind speed. If the bin characterized by wind speeds between 5 and 5.25 is observed, and a cubic relationship between the different variables is assumed, in theory the power output associated with the wind speed at the maximum value of the bin is 33.1 % higher than that associated with the wind speed at the minimum value of the bin. The wind speed forecasts were obtained from the website where wind speeds were indicated with a very small accuracy it was presented as arrows, which value could 25
increment by 5 knots (5 kts = 2.57 m/s) while adding the feathers. The same wind speed data was assumed for the whole County Louth and not for a specified location of WT. Furthermore, the maps provided look different for different time horizons studied in this paper and the wind is shown in various ways. In general, the wind speed forecast used as an input data to calculate the energy generated is based on a visual inspection of the maps published. The wind turbines specific localization in an urban area also has an influence on the real wind speeds that hit the rotor s blades surface. One of the reasons of still high erroneous forecasts can be due to high surface roughness length in vicinity of the campus, which surely deflects the wind profile. Figure 2.23 Panorama from the nacelle Crowne Plaza Hotel in the background The Plaza Hotel, situated ca.300 m from the wind turbine, is one of the highest obstacles present from the south side to WT. The Figure 2.24 shows to what extent the remote Plaza Hotel, ca.40 m of height, 10 m of width and within the distance of 300 m, could play a weak effect on the turbine while wind blowing from this direction. 26
Hub height 60 m Obstacle height 40 m width 10 m Figure 2.24 The weak effect of an obstacle and WTof 60 m hub height (www.windpower.org) The wind speed changes with height and the wind speed share depends on the local conditions. There is also a wind direction share over height. Wind turbines therefore experience a wind speed share as well as wind direction share across the rotor, which result in different loads across the rotor. All other obstacles in the vicinity also have an influence on the wind reaching the blades. High trees, bushes and other buildings at the campus also have a shadow effect on the WT. 27
Figure 2.25 Vestas V52 at the DkIT campus Mechanical anemometers are also subject to over speeding and to deviation from the desired cosine response to off-horizontal flow. Such instruments should also be calibrated, which in case of DkIT equipment was not reported. The calculations above show that for real-time generation forecast purposes only close measurement/estimation points could be used. The wind forecasts work on worldwide global models, which in theory are not capable of forecasting local turbulences which cause the 0.5-5 min deviations in the power output. In spite of this fact, based on further measurements quite good energy production estimations can be done. 2.4.1 Commercial forecasting applications In the market there is a growing number of companies, which commercially use the data from NWP models to offer their own wind power forecast applications, just to mention 3TIER, GL Garrad Hassan. As for a single DkIT wind turbine there is a need to know the wind speed at certain height (hub height) rather than the prediction of wind power. Meanwhile, all commercial applications offered the wind speed and wind power forecasting at the same time. If user desired to obtain just the wind speed for DkIT campus, the price offered wouldn t have declined. 3TIER company offered the following in February 2011: 28
- Day ahead wind speed and power forecast 700 $/month; - Hour ahead wind speed and power forecast 1100$/month; - Real time precise (10-minute) wind speed and power forecast 2500$/month. 3TIER both offers downloadable forecast data files for power forecast (CSV, XML, XLS) both with downloadable forecast data files for weather forecast (CSV, XML, XLS). Beneath we can see an example of an hour forecast delivered by 3TIER. Figure 2.26 3TIER hour ahead forecast Another commercial application was offered by the Irish Met Eireann, which surprisingly was able to offer just the wind speed at desired height. The price consulted with the Met Eireann representative was 1,000 /year for the one hour ahead wind speed forecast. The data would be made available as downloadable forecast data files for weather forecast sent by e-mail or accessible on-line. Both 3TIER and Met Eireann form of data could be then be applied to SCADA system already running at DkIT; nevertheless, the price offered by the Irish institute is far more attractive than 3TIER. 29
3 CONCLUSIONS AND RECOMMENDATION In the final comparison of the accuracy assesment of forecasted and real energy, applying two different methodologies, the following can be said the Methodology B returned to be almost two times more accurate in predicting the nergy rather than Methodology A. Nevertheless, there is always the bacis question of correctness and reliability of data obtained from the Met Eirean maps and of their arrangement with the real situation occurring in the DkIT field. The information presented in the free of charge forecasting maps made by Met Eirean is not precise enough in order to be freely used for wind power forectasing at DkIT. In conclusion, this time limited research suggests that, while applying the method B with simultaneous Met Eireann s wind speed forecast maps, one would be able to deliver more precise WT s energy output than using the A method. To confirm these outcomes, further research is recommended over longer period of time - one year at least ideally. The local factors (topography, etc.) influencing the wind shear schould be also roughly estimated Furthermore, in order to obtain more accurate energy forecasts for the DkIT and estimate the economical benefits, the use of commercial wind speed/power forecast, along with Campus energy demand prediction, is strongly recommended. 30
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