UTICAJ KRIVE SNAGE VETROGENERATORA NA TEHNO-EKONOMSKE POKAZATELJE SISTEMA ZA NAPAJANJE POTROŠAČA MALE SNAGE

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1 UTICAJ KRIVE SNAGE VETROGENERATORA NA TEHNO-EKONOMSKE POKAZATELJE SISTEMA ZA NAPAJANJE POTROŠAČA MALE SNAGE Vukman Bakić *, and Saša Stojković ** * University of Belgrade, Institute Vinča, Laboratory for Thermal and Energy Research, Belgrade, Serbia ** Faculty of technical sciences, Čačak, Serbia Apstrakt: U radu su prikazani rezultati analize uticaja krive snaga-energija vetrogeratora na tehnoekonomske pokazatelje sistema za napajanje potrošača relativno male snage. Rezultati su svedeni na vetrogenerator naznačene snage 1 kw. Zbog toga, oni važe i za vetrogeneratorski deo hibridnog sistema bilo koje vrste. Osnovni cilj je da se kvantifikuju prednosti vetrogeneratora sa boljom krivom snaga-brzina vetra. Detaljna ekonomska analiza izvedena je korišćenjem Life Cycling Costs (LCC) metoda, kojim se svi troškovi i prihodi u toku životnog veka aktuelizuju, tj., svode na sadašnju vrednost. Tehno-ekonomska analiza sedam sistema sa vetrogeneratorima sa različitim krivama snaga-brzina vetra izvedena je metodom numeričkih simulacija, za šta je korišćen softverski alat HOMER. Naznačene snage vetrogeneratora su 0.4 kw, 0.55 kw, 1 kw, 1.8 kw i 3 kw. Analiza je dinamička, sa satnom dinamikom, a za meteorološke podatke o brzini vetra korišćeni su podaci za Kopaonik i Beograd. Podaci su u vidu tipične meteorološke godine TMY2 ( Typical meteorological year 2 ). Rezultati analize pokazuju da kriva snaga-energija znatno utiče na efikasnost sistema, zbog različite iskoristivosti kinetičke energije vetra. Ključne reči: energetska efikasnost, obnovljivi izvori energije, vetrogenerator, HOMER softver. INFLUENCE OF POWER-WIND VELOCITY CURVE ON TECHNO- ECONOMICAL METRICS OF THE POWER SYSTEM FOR SUPPLY OF SMALL POWER CONSUMERS Abstract: The paper presents the results of analysis of the influence of the power curve upon techno-economical metrics of the power system which supplies electrical energy to the relatively small power consumers. Results are scaled to the rated wind generator power of 1 kw. The results are also valid for the wind generator of any hybrid system. The basic goal is to quantify advantages of systems with better power-wind velocity curve. Detailed economic analysis is done by Life Cycling Costs method (LCC), by which all expenditures and revenues are discounted, i.e. recalculated to present value. The techno-economic analysis of the seven systems with wind generators characterized by different power-wind velocity curve is done using method of numerical simulation by HOMER software tool. Rated powers of the wind generators are 0.4 kw, 0.55 kw, 1 kw, 1.8 kw and 3 kw. The analysis is dynamical, with hourly time step. The meteorological data about wind velocity of Kopaonik and Belgrade are employed. Data are in the so-called TMY2 (Typical meteorological year 2) form. Results of the analysis show that power-wind velocity curve greatly influences the efficacy of the system, due to different utilization of kinetic energy of wind. Keywords: energy efficiency, renewable energy sources, wind generator, HOMER software. 1. INTRODUCTION Energy of wind is used by mankind during 3000 years. Historical review of development of devices for wind energy utilization is shown in detail in [1]. Wind has been exploited in different ways, mainly for grain milling and water pumping. With the advent of the industrial era, wind energy was 1

2 replaced by fossil fuels. The revolution was in 20th sentury, when new designs enabled electricity generation. After the 1970s, wind technology expirienced a huge development. Today there are wind generators of powers 400 W, up to 5-6 MW. Wind energy is inexhaustible and renewable resource, like as solar energy. The basic drawback of wind energy is intermittent and seasonal variability of the wind, what needs complex control systems. Besides, wind turbines and generators designs are quite different. Several types of electrical machines are used for wind generators. Rotor velocity can be constant or variable, as control can be of stall or pitch type [2]. Due to that, regarding high power machines, higher of 0.5 MW, the basic goal is the better utilization of wind energy in all the regimes. However, as far as small power is concerned, stand-alone, as well as connected to grid power systems are developed. They serve for supply homes, cottages, government objects, telecom repeaters, meteorological stations, traffic signalization, and lighting. Due to their small power and accessibility to wider number of users, the basic goal is to design them in such a manner that are as cheap as possible, with simple control. Because of that, curves of power output are different for the generators of small power. The consequence is different electrical energy yield. In this paper the basic goal is to compare curves of electrical power vs. wind speed, regarded the small power wind generators. The power is of power up to 3 kw, and main concern is electrical energy yield of one kilowatt power. Seven generators are analysed, with the goal to calculate some technical and economical parameters. Final goal is to enable better choice of wind generator for supply of meteorological mast s electrical equipment for wind measurement. For analysis, method of numerical simulations is employed, and meteorological data relates to Kopaonik and Belgrade. Those are in the so-called Typical Meteorological Year 2 (TMY2), which are commonly used for analysis of solar power systems. The hourly averaged data are employed. The Life Cycling Costs (LCC) method is used for economic analysis [3,4]. For these type of analysis use of specialised software tools are common, as example [5-7]. In this paper HOMER software tool is employed [6]. The paper is organised in the following way. After Introduction, in Section 2 configuration of the system is presented, as in the Section 3 Simulation model is shown. The results of analysis are given in the Section 4, as Conclusion is in the fifth section. At last, the references are shown. 2. SYSTEM CONFIGURATION In the paper some technical and economical parameters of seven types of WG are analysed. The generators are of different rated power. The simulation model of the power system is simple and is shown in Fig. 1. The model is defined in HOMER software tool environment. Wind generators are analysed one by one. Types of generators and their rated powers are shown in Table 1. In the last column inverters rated powers for all of the generators are presented. Rated power approximately agrees to the rated power of the generator. Such choice of the inverter s power is needed because inverter s rated power influences technical and economical parameters. Figure 1. Wind generator system configuration 2

3 Table 1. Types of wind generators and inverters powers Tip WG Hummer 400 [9] SWAR [10] Whisper 100 [11] Whisper 200 [11] Skystream 3.7 [12] Whisper 500 [11] Westwind [13] Rated power of WG [kw] Rated power of inverter [kw] Six types of wind generators are DC type, as one is AC (Skystream). It is supposed that efficiency of all the generators is 95%. Efficiency is constant and does not depend on inverter s load. That means power which output from the inverter is calculated in HOMER as power 5% lower than power produced by wind generator. It is accepted that all electrical energy produced by wind generator (Westwind 3 kw in Fig. 1) is delivered into utility s grid (Grid in Fig. 1). That is the reason why load (Primary load in Fig. 1) is designated by zero, in order to calculate all the technical and economical parameters consistently, since wind generator s powers are different. Type of generator, inverter power and location (Kopaonik or Belgrade) are changeable in the model. 3. SIMULATION MODEL 3.1. Wind generators Power output vs. wind speed curve is the basic curve which is used for calculation of electrical energy produced by wind generator. During one-year period, output power is calculated in accordance with that curve, which is given by generator s manufacturer. It curve is defined by testing, but for sea-level conditions, i.e. for air density of ρ=1.225 kg/m 3. Figure 2 depicts these curves of Hummer 400, SWAIR (Southwest AIR-X), Whisper 100, and Whisper 200 types of generators. Curves of Skystream, Whisper 500, and Westwind 3 kw are shown in Fig. 3. They are presented separately due to better visibility. Figure 2. Power output vs. wind speed curve for four wind generators 3

4 Figure 3. Power output vs. wind speed curve for three wind generators Figures 2 and 3 depict curves of wind turbines of different rated powers, namely kw. Due to that, their technical parameters, which depend on rated generator power, could not be compared directly. They have to be scaled by dividing by base value. The base value is rated power of wind generator. However, economic parameters could be compared directly, because it is supposed that capital, as well as operating and maintenance costs are proportional to the generator s rated power. Figures 2 and 3 show that electrical energy yield is very much dependent on cut-in wind speed, i.e. wind speed generator starts to generate electrical power at. Also, electrical energy production depends on curve s rate in the zone where the generator works. For example, if we compare two curves of wind generators of same rated power, namely Whisper 500 and Westwind 3 kw (Fig. 3), we can see that at wind speed of 8 m/s Whisper 500 generates 1.7 kw of electrical power, as Westwind produces 0.8 kw. Due to that, the basic goal in the paper is to analyse the influence of power output vs. wind speed curve on electrical energy yield of the wind generator Power system s cost Basic goal of economic analysis is to find out an interaction of technical, climatic, and economic parameters. Such analyses can be done only on case-by-case basis. It is known in advance that generators are chosen to produce electrical energy in conditions with relatively weak wind, related to the rated power of the wind generator. Moreover, it is known that such power systems were not economically viable due to very high costs. Fortunately, during last several years costs are decreased to acceptable level. In this paper costs are used as follows. Wing generators: Capital cost: 2500 $/kw [5] Replacement cost: 90% of capital cost Operation and maintenance cost: 50 $/yr kw (fixed cost by capacity). These costs of wind generators are typical, and it is accepted that it is same for all analysed generators, in order these parameters to be compared. Exact costs are not known, they differ between manufacturers, but they do not differ much from typical costs. Wind generators costs are presented in Table 1. 4

5 Table 2. Wind generators and inverters system costs Tip WG Hummer SWAR Whisper Whisper Skystream Whisper Westwind Capital cost [$] Replacement cost [$] Operational and maintenance cost [$/yr] Inverter Capital cost [$] Replacement cost [$] Operational and maintenance cost [$/yr] Inverters: Capital cost: 400 $/kw Replacement cost: 400 $/kw Operation and maintenance cost: 0. Economic parameters: Annual real interest rate: 6% Project lifetime: 20 years System fixed capital costs (permiting, environment study, grid interconnection: 1600 $, fixed + engineering: 500 $, fixed): 2100 $ System fixed operational and maintenance 0 $/yr. Regarding the electrical energy that is sold to utility, flat rate of 9.20 c /kwh [8] was accepted. Currency on September in Repiblic of Serbia was 1 =1.35 $. This means rate is /kwh= $/kwh. That value is employed in HOMER simulation model. It is supposed that all the electrical energy is to be sold to the utility, without restrictions. Lifetime of all equipment is 20 years, wherefore there is no replacement of the equipment during project analysis period. Detailed economic analysis is done by Life Cycling Costs method, by which all expenditures and revenues are discounted, i.e. recalculated to present value [3,4] Wind resource Meteorological data about wind velocity used in this paper are in the form of Typical Meteorological Year 2 (TMY2), for two places in Republic of Serbia, namely mountain Kopaonik and Serbia s capital Belgrade. Mountain Kopaonik is characterised by greater wind speed in Republic of Serbia, because the meteorological station is placed at altitude of 1711 m, whereas Belgrade is at altitude of 132 m. Belgrade is very similar to majority of places in the state, and its wind speed is unfavourable for employing of wind generators. TMY2 data type is calculated on the several-decades measurements, using of statistics laws [7]. So-called typical months are created and connected in one-year period. In [6] it is pointed out that they are very appropriate for PV analyzing, because the measurements are intended to that purpose. However, wind data are precise, 5

6 but meteorological station in which data are gathered commonly is not in the strong wind area, which is proper for wind generator. Figure 4 shows average monthly wind speeds for Kopaonik. Figure 5 shows same data for Belgrade. Figure 4. Monthly average wind speed - Kopaonik Fifure 5. Monthly average wind speed - Belgrade The average monthly wind speed in Kopaonik (Figure 4) is 3.86 m/s, as in Belgrade it is 2.15 m/s (Figure 5). It is clear that with wind velocity of 2.15 m/s in Belgrade, wind generators of relatively small power and rated speed of m/s could not be efficient. There are two influential factors which should be taken into account if we want to calculate electrical energy produced by wind generators. Those are altitude, due to reduced air density, as well as the fact that the wind speed is measured commonly in station at altitude of 10 m, as hub of the wind generator is located on the greater altitude, dependent of rated power and application of wind generator. The hub height is the height above ground of the hub (the center of the rotor), as altitude is the elevation above sea level. Figures 4 and 5 indicate very great importance of the adequate local meteorological data in the hybrid system planning. In other words, meteorological masts for the wind characterization are erected at the places where the wind is relatively strong. That decision commonly is done by experience, or using measurements of nearby meteorological stations. Due to that reason, adequate wind data should be implemented, if they are available. 4. RESULTS OF NUMERICAL SIMULATIONS 4.1. Wind generators technical and economical indicators Ground-level obstacles such as topographic features and vegetation tend to slow the wind near the surface. Since the effect of these obstacles decreases with height above ground, wind speeds tend to increase with height above ground. The logarithmic or power law profile is commonly used by wind energy engineers. The power law profile with coefficient of α=0.18 is used in this analysis. This wind shear model is depicted in [6]. In this paper basic goal is to choose the wind generator for supply of the meteorological mast s equipment for wind measurements. These masts attain height of m [14], although their heights mostly are 50 m. The wind generator should be placed as high as possible, because the wind speed is stronger. Wind generation should be displaced from equipment for wind measurement enough (for example 10 m) in order not to influence 6

7 measurements results. For meteorological masts low electrical power is demanded. Due to weight and electrical power, Hummer 400 (5.5 kg) or AIR-X (5.8 kg) is appropriate. For other applications (supply of telecom repeaters, houses, cottages, objects, and so on) other wind generators analysed in this paper could be used. Figure 6 depicts dependency of electrical energy production (left) and LCOE (right) on hub height. LCOE is calculated without sale of electrical energy to the utility. The Hummer 400 is analysed. Figure 6. Electrical energy production (left), and Levelized cost of energy (right) as a function of hub height (Hummer 400) Figure 6 shows that produced electrical energy is greatly enhanced with hub height. Electrical energy yield on mountain Kopaonik is approximately 2.2 times greater than in Belgrade, due to much strong wind on Kopaonik than in Belgrade, although air density on Kopaonik is decreased because of high altitude (1711 m). Figure 7 (left) depicts capacity factor in per cent as a function of hub height. It is important technical parameter because it points out what part of installed wind generator s capacity is employed in average during one year. Figure 7 (right) shows influence of altitude on electrical energy yield, as well as on capacity factor. Figure 7. Capacity factor vs. hub height (left), and Energy production, as well as capacity factor as a function of altitude (right) (Hummer 400) Figure 7 shows that wind generator Hummer 400 is characterised by satisfactorily capacity factor, as it is of low value for same wind generator in Belgrade. It is clear that this curve presents scaled curve of electrical energy produced as a function of hub height (Figure 6 left). Wind generator s power depends on air density linearly. Air density at higher height is lower than at sea level, wherefore electrical energy yield is lower. Decreasing is nearly linear, but more 7

8 exact expression regarding electrical energy yield is shown in [6]. Yet, capacity factor decreases due to reducing of electrical energy production. Figure 8 shows the capacity factor for different types of wind generators analised in this paper. All remaining analyses were done for hub height of 30 m. Figure 8. Capacity factor for different wind generators types, hub height: 30 m Figure 8 shows that capacity factors for various types of wind generators analysed in this paper differ very much. The capacity factor is biggest for Hummer 400, as it is smallest for AIR-X. That is the consequence of power output vs. wind speed curve shape. In the case of Belgrade, capacity factor is much smaller, due to small wind speed. Figure 9 shows LCOE in the case that electrical energy is not sold to power system utility (0 $/kwh), as well as in the case of sale using flat rate of $/kwh. Figure 9. Levelized cost of energy for different types of generators, hub height: 30 m Figure 9 points out that LCOE is smaller if electrical energy is sold to utility, because of the revenue. If the electrical yield is greater (Kopaonik), the decrease of LCOE is greater. Figure 9 8

9 shows also that LCOE for various wind generators differ very much, what is the consequence of electrical energy production. This way, investments could be ranked, and the best is with the smallest LCOE. Figure 10 depicts one important technical parameter energy production (yield) per one kilowatt of rated power. Figure 10. Energy production per kw of rated power, hub height: 30 m If we want to compare wind generators, it is necessarily technical parameters to be calculated based on one kilowatt of rated (installed) power. Figure 10 shows that electrical energy yield produced by one kilowatt of rated power is very different for various types of wind generators. That is direct consequence of the influence of power output vs. wind speed curve, as well as location (Kopaonik or Belgrade). 5. CONCLUSION In the paper some technical and economical parameters were analysed. Power production vs. wind speed curves of various wind generators differ among themselves very much concerning the power values and shapes. In order to compare different types of wind generators, the results were calculated based on one kilowatt of rated (installed) power. Power production curves differ among themselves much more than in the case of high-power wind generators. The basic reason for that are wind generator s design, as well as its control. Results show that technical and economical parameters for various types of small power wind generators differ very much among themselves due to various power output vs. wind speed curves. Yet, analysis points out very great electrical energy production dependence on wind speed, i.e. location. On mountain Kopaonik with average annually wind speed of 3.86 m/s, electrical energy yield is 2.2 times greater than in Belgrade for same conditions, where average annually wind speed amounts 2.16 m/s for TMY2 used. That fact is paramount in the choice of the wind generator, or wind farm planning. The output power is influenced by cut-in wind speed, as well as power output curve s shape in the operating speed range, commonly 3-10 m/s, for conditions in Republic of Serbia. Hub height also influences power output very much, due to stronger wind. This influence is quantified in this paper. The output power curves as a function of wind speed given by wind generator manufacturers relates to wind speed which is averaged in short time interval. Because of that, for electrical energy yield calculation, it is better to use hourly averaged wind values, as is done in this paper, or the values averaged in shorter time interval as 10 minutes [15]. 9

10 ACKNOWLEDGMENTS This paper is the result of the investigations carried out within the scientific Project No. TR33036 supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia. REFERENCES [1] Ackermann T., Editor, Wind Power in Power Systems, John Wiley & Sons, Ltd, England, 2005 [2] Bianchi F. D., De Battista H., Mantz R., Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design, Springer-Verlag London Limited, London, 2007 [3] Short W., Packey D. J., Holt T., A Manual for the Economic Evalution of Energy Efficiency and Renewable Energy Technologies, NREL/TP , March 1995., [4] Ćalović, M., Sarić, A., Power System Planning, Part One: Principles and Methodology of Power System Planning (in Serbian), BEOPRES, Belgrade, [5] System Advisor Model, [6] HOMER, [7] TRNSYS transient system simulation program, Solar energy laboratory University of Wisconsin, Madison; 2003, [8] Decree on the Requirements for obtaining the Status of the Privileged Power Producer and the Criteria for Assessing Fulfillments of these Requirements ( Official Gazette RS, No. 8/13), [9] Hummer 400: [10] Southwest AIR-X (SWAIR): [11] Whisper: [12] Skystream: [13] Westwind 3 kw: [14] Netinvest, [15] Van Dam J., Meadors M., Link H., Migliore P., Power Performance Test Report for the Southwest Windpower AIR-X Wind Turbine, Technical report NREL/TP , September