10 DEWI MAGAZIN NO. 44, FEBRUARY 2014

Introduction

The first research programs on short term power forecasting have been initiated during the ‘90s [5], but the number of wind forecasting applications has significantly risen during the past decade [6]. Recently, forecasting has become cru­cial due to the increased penetration of Renewable Energy Sources (RES) in the national electrical productions, as re­marked in [1], [2] and [3]. While traditional energy sources may be modulated by energy market mechanisms, RES pro­duction is inherently not programmable. As a consequence, the variability of energy produced by renewable sources (e.g. wind and solar) affects not only the energy grid opera­tion but also the energy market in a more remarkable way than the in past. In some particular situations (e.g. Storm Xaver on December 6th and 7th, 2013) [4], it may happen that the functionality and the safety of the electrical grid is strongly driven by the renewable sources, as showed in Fig. 1.Both in normal operation and during such extreme events, the Transmission System Operators (TSOs) primary goal is the security of the electrical grid transmission, avoiding black-outs or large overproductions. One of the measures

adopted to achieve this goal is to request a forecast of pro­duction schedule (with a timeline of few days) from the power producers. Penalty mechanisms are usually modu­lated according to the forecast error in order to support the provision of reliable forecasts.Forecasting mechanisms are also crucial in the energy mar­ket, where the energy selling price is a function of the en­ergy mix that the market is offering at each moment (please refer to Fig. 1). Energy producers, suppliers and users buy short term energy from the market, and producers may adapt their offer in the daily and intra-daily sessions. Short term production forecasts are therefore becoming an im­portant tool for energy suppliers.The need for forecasting tools also affects manufactures, plant operators and business companies involved in RES, as future wind conditions may have a deep impact on their decisions. As an example, O&M intervention risks can be minimized during low wind events, and the same applies for logistic services (e.g. in offshore platforms, installation or maintenance). In such mixed scenario, many RES play­ers may take advantage of forecasting methods to support decisions.

G. ArtipoliG. Artipoli, F. Durante; DEWI GmbH Italy

ENGLISH

Physical Modeling in Wind Energy Forecasting

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Overview of Forecasting Methods

A recent review [7] outlines the several strategies that have been applied in wind energy forecasting. A very general clas­sification of the methods [8] usually divides the approaches in three classes:

Statistical Approaches1) . They are based on statistical methods like Artificial Neural Networks (ANN) [13], AutoRegressive Moving Average methods (ARMA) and other. They statistically bind the future energy output to multiple input information (e.g.: real­time SCADA data, local measurement data, weather service infor­mation). The power forecast is performed in a single step. Although additional information by meteorologi­cal services can be added as input, on­line measure­ments (real­time wind power, speed, direction) play a major role. It is commonly agreed that their effective­ness is limited to very­short­to­short range (the first 6 hours in lead time).Physical Approaches.2) They use physical modelling to estimate the wind resource, starting from meteoro­logical information and adapting them to the local physical influences. This step is usually performed with mesoscale, Computational Fluid Dynamic (CFD) or lin­

ear models. The conversion from local conditions to wind energy production may be performed through manufacturers’ power curves or statistically. When site off­line data are available, a further Model Output Statistics (MOS) step may be applied to correct the systematic errors of predicted power. This approach is considered to be effective in the short­to­medium range (after 6 hours in lead time)Combined Approaches3) . Results derived from physical modelling are enriched or statistically corrected by real­time plant data. The combined models are able to adapt themselves to the different types of information provided in order to achieve accuracy both in very short and in the short­to medium timescales.

The accuracy of each method strongly depends both on the forecast horizon and the degree of climatology complex­ity, as shown by the results of the ANEMOS project [10]. In complex climates the wind power predictability can be sig­nificantly low. Moreover, statistical or combined approach­es are not always feasible, due to the challenge in including real time data in the forecasting process. As a consequence, physical approaches are a prevalent compromise, since off-line SCADA data are normally available.

Fig. 1: Energy production and prices in the Danish market during Storm Xaver (source: database hosted by EMD International A/S)

12 DEWI MAGAZIN NO. 44, FEBRUARY 2014

DEWI has developed a forecasting system able to produce power forecasts in the short­to­medium term. The system is currently designed following a physical approach where the relationship between synoptic and local wind conditions is described by an intermediate, physical, mesoscale model.Whilst the importance of both general circulation models and statistical methods have been extensively discussed in detail by previous research activities, the effects of me­soscale models as intermediate step between global fore­casts and statistical model is still under debate and ques­tioned. The work presented here focuses on the contribution of mesoscale modeling in three different forecasting systems. Each system has been tested with production data collected at 4 wind farms in southern Italy sited in complex terrain. The wind farms differ regarding site climatology, layout de­sign, model and number of wind turbines.

Methodology

Three forecasting systems are schematically represented in Fig. 2.The first system is based on Global Forecasting System (GFS) data, used as input into a MOS. The GFS is a coupled model [11] (atmosphere, ocean, land/soil and sea/ice models), providing forecasts of the weather conditions over the en­tire globe. The GFS provides forecasts four times a day, with a forecast horizon up to two weeks in lead time and with a temporal resolution of 3 hours. The output meteorological variables are grouped on a grid with a horizontal resolution of 0.5 degrees, and multiple vertical levels. For our pur­poses, the information has been interpolated in space and time, and then tuned by an ANN to reduce local and plant representativeness errors. The conversion between local wind conditions and power output is achieved with a power curve statistically built from operational data (Fig. 3).The second system is based on a physical, mesoscale model initialized with GFS data; mesoscale results are, in turn, pro­

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cessed by an ANN, designed in the same way as the previous system. As final step, the local wind is converted to power with an operational power curve. The applied physical step is performed by the non-hydrostatic, mesoscale model WRF [12]. For our purposes, 3 domains have been centered over central and southern Italy. The inner domain has been de­signed with a horizontal resolution of 5 km. The GFS dataset, the same used for system A, has been provided as boundary condition to the WRF model. WRF results have been saved every 10 minutes and interpolated in space.In the last system, GFS forecasts and WRF outputs have been simultaneously combined in an ANN, designed on purpose. This ANN differs from the previous ones as it both corrects systematic errors of the models and identifies the most proper input for the occurring meteorological situa­tion.Each of the three systems produced 72 hours ahead fore­casts for a period of 12 months (2012­01­01 to 2012­12­31) with a 12 hours frequency cycle (forecast starting at 00Z and 12Z for each day of the year), for a total amount of 703

forecasts over a theoretical maximum number of 732. The missing ones are due to absence of some runs by GFS da­tabase.

Results

Accuracy of the forecasted power has been evaluated with the usual statistical indexes: Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Square Error (NRMSE) [14]. For both metrics, the normalization param­eter is the nominal power of the wind farm. To quantify the impact of the forecasting error over the actual produced energy, a different normalised absolute mean error has been used, and simply defined as Energy Error (EE) in EQ 1, where P* is the plant nominal power, N is the total number of simulations, n is the total number of hourly samples of the simulation, Pji is the actual hourly production and Pji

f is the respective forecasted value. For each wind farm, persis-tence has been used as term of reference for relative com­parisons. Persistence (or naïve predictor, as defined in [7]),

Samples NMAE NRMSE Error Energy NMAE NRMSE Error EnergyPersistence 38.008 17,9% 27,5% 104,3% 0,0% 0,0% 0,0%Forecast Mod A 38.340 10,3% 16,4% 57,8% -7,7% -11,1% -46,5%Forecast Mod B 41.506 11,2% 17,4% 63,8% -6,7% -10,1% -40,6%Forecast Mod C 37.404 8,9% 14,2% 49,8% -9,0% -13,4% -54,5%

Samples NMAE NRMSE Error Energy NMAE NRMSE Error EnergyPersistence 41.147 25,9% 36,9% 101,9% 0,0% 0,0% 0,0%Forecast Mod A 40.293 12,5% 19,8% 49,1% -13,4% -17,1% -52,8%Forecast Mod B 43.777 16,4% 23,9% 65,5% -9,4% -13,0% -36,5%Forecast Mod C 39.267 11,2% 18,1% 43,9% -14,7% -18,8% -58,0%

Samples NMAE NRMSE Error Energy NMAE NRMSE Error EnergyPersistence 34.031 21,8% 32,8% 92,4% 0,0% 0,0% 0,0%Forecast Mod A 35.452 11,2% 17,0% 47,2% -10,6% -15,8% -45,2%Forecast Mod B 38.477 13,8% 21,2% 58,5% -8,0% -11,6% -34,0%Forecast Mod C 34.597 9,9% 15,6% 41,3% -11,8% -17,3% -51,1%

Samples NMAE NRMSE Error Energy NMAE NRMSE Error EnergyPersistence 44.187 18,6% 28,2% 102,5% 0,0% 0,0% 0,0%Forecast Mod A 40.901 9,0% 14,2% 49,1% -9,6% -14,1% -53,4%Forecast Mod B 44.398 10,8% 17,1% 60,0% -7,9% -11,2% -42,5%Forecast Mod C 39.986 7,7% 12,4% 41,8% -10,9% -15,9% -60,6%

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Improvement referred to PersistenceForecast Performance - 00:72 h Ahead

Improvement referred to PersistenceForecast Performance - 00:72 h Ahead

Improvement referred to PersistenceForecast Performance - 00:72 h Ahead

Improvement referred to PersistenceForecast Performance - 00:72 h Ahead

Plant 001 Plant 002 Plant 003 Plant 004Forecast Mod A ‐42,5% -51,7% -48,6% -51,6%Forecast Mod B -37,4% -36,7% -36,7% -41,9%Forecast Mod C -50,3% -56,8% -54,6% -58,6%

NMAE Relative Improvement (over Persistence)

Fig. 4: Results table for all systems, grouped by wind farm. NMAE relative improvement

Fig. 5: NMAE relative improvement

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14 DEWI MAGAZIN NO. 44, FEBRUARY 2014

is a simple forecast, obtained by the assumption that future production would have a constant value, the one measured at the starting moment of the forecast. Results for each system and each plant are shown in Fig. 4. The comparison with persistence has been calculated as absolute differences between indexes for each metrics. In case of NMAE, a relative improvement over persistence has been quantified (Fig. 5), in order to compare system results independently of specific site conditions.

On a 72h forecasting scenario, all systems beat the naïve predictor, independently of the chosen wind farm. In terms of NMAE, the less predictable site was Plant002, con­firmed also by the persistence NMAE index (25.9%): the largest among the wind farm used in the present study. In Plant002, system C is able to decrease NMAE of the naïve predictor by -14.7%, with a relative improvement of 56.8%. The most predictable site has been found to be Plant004, where the NMAE is reduced from 18.7% (Persistence) to

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7.7% (System C), with a relative improvement of 58.6%. In terms of NMAE, Plant001 and Plant003 reach a relative im­provement of 50.3% and 54.6%, respectively. Regardless of the studied wind farm, system B (based on WRF intermediate step) is outperformed by system A (based on GFS inputs), as summarized in Fig. 5. Performances are improved when inputs are combined in system C. This con­sideration is confirmed not only by the forecast aggregate statistics shown in Fig. 4, but also by their hourly distribu­tion as presented in Fig. 6. As a general consideration, the additional use of mesoscale solutions (system C) improved the NMAE performances of about 5%­8%, with respect to solutions obtained without mesoscale data (system A).Furthermore, the system B is outperformed by the other two systems. A qualitative analysis, conducted on time se­ries results, pointed out that the mesoscale model is able to build realistic atmospheric structures not described by global models. However, the representation of these phe­nomena appears in many cases as affected by phase errors (Fig. 7). MOS in system C seems to be able to statistically identify and, in some degree, correct these phase errors leading to better results than those obtained by the single models A and B.

Conclusions

Results show that mesoscale modeling is useful for the prediction of the power output in complex terrain as it in­troduces additional information that is not provided by the global forecasts. A qualitative analysis suggests that small scale phenomena like land­sea breezes and ramp­ups are often realistically predicted by the mesoscale models, but are frequently affected by phase errors that worsen the statistical skill scores. A direct improvement of forecasting performances was only visible when mesoscale modeling time series have been used as input to the ANN, along with GFS time series.

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