Primjena teorije portfelja u procjeni vjetropotencijala

Priprema projekta vjetroelektrane obuhvaća mnogo međusobno povezanih koraka, a jedan od najvažnijih je svakako pravilna procjena vjetropotencijala. Procjena vjetropotencijala tipično se temelji na mjerenju karakteristika vjetra korištenjem mjernog stupa, te modeliranjem unutar nekog softverskog/matematičkog modela. U situacijama kada na lokaciji potencijalne vjetroelektrane postoji nekoliko mjernih stupova i mjernih uređaja, često se postavlja pitanje kako optimalno iskoristiti te podatke. U ovom radu prikazana je metoda korištenja takvih podataka kroz primjenu teorije portfelja, etablirane u ekonomskoj teoriji, ali i u mnogim drugim znanstvenim disciplinama. Metoda je vrlo fleksibilna u smislu ulaznih podataka i softverskih/matematičkih modela, a rezultati dobiveni unutar rada pokazuju da je njezinom primjenom moguće povećati točnost i smanjiti nesigurnost procjene vjetropotencijala. Prikazana metoda otvara širok prostor za daljnja istraživanja i poboljšanja, a sve u svrhu boljih rezultata procjene vjetropotencijala i u konačnici kvalitetnije pripremljenog projekta vjetroelektrane. Development of a wind farm project includes a lot of interconnected steps, with one of the most important ones being a proper energy yield assesment. Wind energy yield assesment is typically based on wind measurements on a measurement mast that are later modeled in one of the software/mathematical models. In cases where there are multiple wind measurements on the potential wind farm site, a question arises on how to optimally use all the available data. This paper shows a method of using such data through the application of the portfolio theory, a well established theory in economics, but also in other scientific disciplines. The method shown is very flexible in terms of input data and software/mathematical models, and the results of its application show that it is possible to increase accuracy and reduce uncertainty of energy yield assesment. The method opens up a wide space for further research and improvements, all with the objective of achieving better results of energy yield assesment and finally, better prepared wind project. The objective of the research was to improve wind potential assessment by using portfolio theory in cases when there are more wind measurements on a prospective wind farm site, as well as to increase the efficiency of using the avaliable wind measurement data. The basic hypothesis was that by using more than one measurement it is possible to increase the accuracy and reduce uncertainty of wind potential assessment, compared to using only one measurement for the entire location. Also, by using available measurement from nearby measurement masts, it is possible to assess the potential on a reference position. The paper describes the method of using portfolio theory for wind potential assessment, that can be used to upgrade the standard method. The method can be used when there are multiple measurements on site, it is flexible enough to be used with new measurement technologies (i.e. SODAR, LIDAR), as well as new wind flow models (i.e. CFD models). The method is realized through three key components: i) standard wind potential assessment procedure, ii) application of portfolio theory and iii) analysis and interpretation of results. The fist component represents the standard method of wind potential assessment that is usually done for any planned wind farm project. It consists of preparation and processing of measurement data, preparation and processing of orography and roughness data, and application of mathematical model for wind potential assessment – in this paper EMD's WindPro with integrated WAsP. In standard method, this procedure is done once, using mostly one prepared set of measuremet data (the data set can be a combination of several measurement sets, but in the end it is only one input data set). This paper offers a different apporach – each measurement set is treated separately and independently of the remaining measurements. In other words, if there are multiple measurement devices on a measurement mast on different heights, easch measurement is treated separately. Consequently, there are multiple results of wind potential assessment, each based on its own data set. For each calculated assessment, uncertainty of the assessment is calculated separately, as described in detail in the paper. Calculated wind potential and uncertainty represent a pair of input data that will be used for applying portfolio theory. Correlation of measurements, required for using portfolio theory, is also calculated. The second component of the descirbed method is based on applying the laws of portfolio theory to calculated input data with given shares (in this paper in steps of 10%). The paper first shows combinations of two measurements in different heights and positions, and later describes the application to multiple data sets. The results show that it is possible to reach better accuracy and lower uncertainty by using the proposed method. The third component of the method is analysis and interpretation of results. The results must be critically observed and related to reality, so that proper conclusions can be drawn. In this paper there are several analyses done: the influence of uncertainty, reference measurement in shorter period, lack of reference measurement, connection of wind speed (m/s) and production (kWh) and using portfolios with minimal error and uncertainty. The latter shows very promising results for further research. The reults show that when we use shorter period measurements for calculating optimal shares (the shares where both error and uncertainty are equal to zero), we can transfer those shares to longer term meaurements and get very good results. In this case it is recommended to have at least one measurement on a proposed wind generator site, even in a shorter period, as the quality of results increases dramatically. The analyis has also shown that it is possible to apply the method for vertical extrapolation, instead of logarithmic law. Also, some „common truths“ are questioned here. For example, this paper showed that the measurement closer to the reference does not need to be more accurate than a measurement further away. In this case, measurement four kilometers away was more accurate than the one two kilometers away. This paper only opens up the topic of applying portfolio theory in wind potential assessment. There is a wide space for further application and research, that would enhance this method, but also the standard method of wind potential assessment.