Economic Analysis of PV-Wind Hybrid System at 25 Locations in Taiwan

中華民國第三十屆電力工程研討會 台灣 桃園 2009年11月28-29日 Economic Analysis of PV-Wind Hybrid System at 25 Locations in Taiwan 臺灣25個地區太陽光電-風力混合發電系統之經濟分析 凌拯民 黎方長 Jeeng-Min Ling Phuong-Truong Le 南台科技大學 電機工程系 Department of Electrical Engineering, Southern Taiwan University, Tainan, TAIWAN jmling@mail.stut.edu.tw le_truong1982@yahoo.com.vn Abstract – The aim of this study is to analyze the economic allocation problem for a photovoltaic-wind hybrid system at 25 locations in Taiwan. The HOMER software developed by the National Energy Renewable Laboratory (NERL) is applied to simulate the economic capacity problem, the comparisons of total Net Present Cost (NPC) at these 25 locations with different Hub Height and capacity shortage are conducted. Another simulation result is focus on the comparison with the combination of Photovoltaic-battery versus wind-photovoltaic-battery hybrid system in different type of wind turbine. 25 different weather station recorded data in year 2007 are used to characterize the different type of optimal capacity allocation in these areas. Keywords: Economic analysis, Hybrid PV-wind system 摘要: 本研究主要探討於台灣25個中央氣象局觀測站所在位 置架設混合式再生能源發電系統時，如何決定其最佳 容量配置之經濟分析問題。利用美國再生能源研究室 所發展之分散式電力之最佳化分析軟體(HOMER)進行測 試，分別分析比較了25個不同觀測站所在位置，於不 同風力發電機組之塔架高度及系統不同缺電率時的最 佳容量配置解，另外亦比較了太陽光電-風力- 蓄電池及太陽光電- 蓄電池兩種不同獨立式再生能源混合系統之容量配置 解。測試模擬之天候資料為中央氣象局2007年之實測 氣象數據。 關鍵字：經濟分析、混合式再生能源發電系統。 1. Introduction Nowadays, the problems caused by polluted environment have been increasing severely. That is why renewable energy has become an urgent task that is needed to be done and improved all over the world. Many hybrid renewable systems have been studied recently, such as photovoltaic (PV)-wind, PV-hydro system. Sizing of a micro-hydro-PV-hybrid system has been conducted for rural electrification in developing countries [1], the combination of PV and diesel/battery was proposed. It highlighted the use of an optimal model to size a hybrid renewable system at a village in the Cameroon. The PV- wind hybrid system in the Swedish location was presented [2]. They studied a Wind-PV hybrid system for stand-alone application and compared total net present cost (NPC) at 11 locations of the Sweden. Some interesting results about total NPC with different load primary and capacity shortage were demonstrated. Another wind-PV-battery hybrid power system at Sitakunda in the Bangladesh was presented by Nandi and Gosh [3]. They studied the optimization of a wind-photovoltaic-battery hybrid system and its performance for a typical community load. The optimal sizing comparisons for wind-photovoltaic-battery system and Photovoltaic-battery system in terms of different load primary/hub height/capacity shortage are often characterized [1-4]. The influenced factor, different types of wind turbine, will be integrated in this paper. Two different types of hybrid renewable system, PV- wind-battery and PV-battery, are compared with the optimal NPC at 25 different weather station locations of Taiwan in terms of different hub height of wind turbine and different capacity shortage of system. The Optimization Model for Distribution Power (HOMER) developed by the National Renewable Energy Laboratory (NREL) is used to simulate and compare the NPC results [5]. HOMER models a power system’s physical behavior and its life-cycle cost, which is the total cost of installing and operating the system over its life span. It allows the system planner to compare many different design options based on their technical and economic merits. The sensitivity analysis characterizes the effects of uncertainty or changes in the system. 2. The Simulation Model The simulation has been performed by the simulation software Homer. The net present cost (NPC) in the objective function during optimal simulation process can be considered as the main comparison index for optimal capacity allocation. The total NPC is defined by the following equation: ܥே௉஼ = ஼ೌ೙೙,೟೚೟஼ோி(௜,ோ௣௥௢௝) (1) Where Cann,tot is total annualized cost[$yr], CRF() is the capital recovery factor , i is the interest rate [%], Rproj is the project life time. The total annualized cost is the sum of the annualized costs of each system component, plus the other annualized cost. The capital recovery factor is a ratio used to calculate the present value of an annuity (a series of equal annual cash flows). The equation of capital recovery factor is shown by equation 2: 中華民國第三十屆電力工程研討會 台灣 桃園 2009年11月28-29日 CRF(i,n)= ௜(ଵା௜)ಿ(ଵା௜)ಿିଵ (2) Where i is the interest rate, N is the number of year. The hub height is very important factor during evaluating the wind energy. Different hub height converts different wind generation output. The conversion wind speed depends on the hub height and can be calculated by the equation below: ௏(௓೓ೠ್ ) ௏(௓ೌ೙ೠ೘ ) = ቔ ௓೓ೠ್௓ೌ೙೐೘ቕ∝ (3) Where Zhub is the hub height of wind turbine (m), Zanem is the anemometer height (m). is the law power exponent, is the wind speed at the hub height of the wind turbine (m/s), is wind speed at the hub height of the wind turbine (m/s) [6]. A capacity shortage of system is a shortfall that occurs between the required operating capacity and the actual amount of operating capacity the system can provide. HOMER keeps track of such shortages and calculates the total amount that occurs over the year [5]. With different capacity shortage, the net present cost will be changed. The NPC tend to increase with smaller capacity shortage 3. The Simulation Data for Homer 3.1. Load Profile The primary load profile recorded in the building A of the Southern Taiwan University are used to tested the two different types of renewable system. The base data of average load profile was 1004 kw/hr/day, and the scale of average of load data was decreased to 50kw/hr/day to match the capacity of testing hybrid renewable system. Figure 1 and 2 shows the summary of month-based and yearly based load profile respectively. Figure 1. Monthly load profiles Figure2. Yearly load profile 3.2 The testing data of 25 different weather stations The detailed geographical records of 25 different weather stations of the Central Weather Bureau are shown in the Table 1. The profile of average annual wind speed and global solar radiation at 25 locations are shown in the figure 3. Table1. The detailed information for 25 locations of weather station in Taiwan[5] N0 Location latitude Longitude Z1 Z2 α 1 Chenggon 1200 21’E 23005’N 33.5 12.8 0.144 2 Hengchun 1200 44’E 22000’N 21.9 14.3 0.194 3 Penghu 1190 33’E 23034’N 10.7 14.6 0.150 4 Wuci 1200 30’E 24015’N 7.2 33.2 0.130 5 Keelung 1210 43’E 25008’N 26.7 34.6 0.250 6 Alishan 1200 48’E 23030’N 2413.4 15.1 0.110 7 Anbu 1210 31’E 25011’N 837.6 7.31 0.110 8 Chiayi 1200 25’E 23029’N 26.9 14.5 0.617 9 Jhuzihhu 1210 32’E 25009’N 607.1 11.03 0.250 10 Hsinchu 1200 58’E 24048’N 26.9 15.6 0.194 11 Hualien 1210 36’E 22058’N 16.1 12 0.173 12 Ilan 1210 44’E 24045’N 7.2 26 0.150 13 Kaoshiung 1200 18’E 22034’N 2.3 14 0.105 14 Lanyu 1210 33’E 22002’N 324.0 12.50 0.110 15 Pengjiayu 1220 04’E 25037’N 101.7 12.5 0.110 16 Dongjidao 1190 39’E 23015’N 43.0 9.1 0.125 17 Suao 1210 51’E 24036’N 21.9 14.3 0.150 18 Sunmoonlake 1200 53’E 23052’N 1014.8 8 0.150 19 Taichung 1200 40’E 24008’N 84 17.2 0.250 20 Taitung 1210 08’E 22045’N 9 11.4 0.150 21 Taipei 1210 30’E 25002’N 5.3 34.90 0.150 22 Tanshui 1210 26’E 25009’N 19 12.2 0.250 23 Dawu 1200 53’E 22021’N 8.1 12.7 0.244 24 Tainan 1200 11’E 22059’N 8.1 37.6 0.218 25 Yushan 1200 57’E 23029’N 2844.8 9.20 0.150 Where Z1 is elevation from MSL, Z2 is the anemometer height Figure 3. Average wind speed and daily global solar radiation at 25 locations in Taiwan on year 2007. 3.3. The cost data of the hybrid renewable system The costs of different devices in the hybrid PV-Wind system are assumed by the Table 2. [6] 中華民國第三十屆電力工程研討會 台灣 桃園 2009年11月28-29日 Table2. Price for simulation Parameter Capacity Type Prices (usd) Wind turbine 1 10 kw Generic 28900 Wind turbine 2 7.5 kw BWC Excel-R 23030 Wind turbine 3 3 kw Generic 5875 PV 1 kw 5059 Converter 1kw 750 Battery 200A 2.4kw/h Vision 6FM200D 400 4. Simulation Results and Discussions The Table 3 and 4 show the simulation result of the optimal total NPC at 25 locations in Taiwan with different capacity shortage and hub height (set to 20m). Because of limited space, only the simulation results of testing wind turbine, type of BWC ExcelR rated 7.5kw, are shown. For PV-battery hybrid system, the optimal NPC among 25 locations occurs at Chiayi (239450USD), but the worst case appears at Keelung (622949USD) when the capacity shortage is set 3% and the hub height of wind turbine is set to 20 meter. The same place can be evaluated for the capacity shortage set to 5%. It is noted that the optimal total NPC is lower than capacity shortage is 3%. However, the place has the optimal NPC occurs at Hengchun (182302USD) and highest NPC appears at Keelung (483758USD) when the capacity shortage set to 10%. For PV-Wind-battery hybrid system, the optimal NPC occurs at different places. It is demonstrated by a yellow space when the optimal and worst results occur. The optimal total NPC occurs at Pengjiayu (127973USD) and worst case appears at Ilan (494892USD). It is noted that that places are the same when capacity shortage is 5%. If capacity shortage equal 10%, the place has the optimal NPC is Pengjiayu (96924USD), and highest cost located at Keelung (362458USD). For PV-battery hybrid system, comparisons with different capacity shortage can be shown by the Figure 4. The optimal and worst results can be concluded more obvious than the Table 3 and 4. The worst NPC occurs at Keelung, other places with low solar radiation, such as Anbu, Ilan, Suao, Jhuzihhu, Taipei, Tanshui, also have high NPC. The results are invariability in spite of different capacity shortage. Similar results but with a Wind-PV- battery hybrid system can be concluded and demonstrated by the Figure 5. Figure 4. The optimal NPC with different capacity shortage using the PV- battery hybrid system Figure 5. The optimal NPC with different capacity shortage using the Wind-PV- battery hybrid system. With the wind turbine type BWC Excel-R, the comparisons of NPC in the wind-PV-battery & PV-battery hybrid system can be shown in the Figure 6. Results show that the NPC are lower in the Win-PV-battery hybrid system than in the PV-battery system in most of the locations in Taiwan, except at the locations of Alishan, Chiayi, Kaoshiung, Taichung, Taitung and Tainan. These results sourced from different weather features, such as higher solar resources (Alishan, Chiayi, Kaoshiung and Taichung ) or lower wind resources (Taitung and Tainan). Figure 6. Comparisons with NPC in the PV-battery and wind-PV-battery system. (hub height is 20m and capacity shortage is 10%) The NPC is compared with four different hub heights of wind turbine and the results shown in Figure 7. The variations of hub height impact slightly on NPC in most of the locations. In general, wind speed tends to increase with increasing height above the ground. However, a small wind turbine installed below 50 meter, hub height is not the important factor affect the wind power. Only at Keelung the NPC changes from 311505USD to 398835USD. 中華民國第三十屆電力工程研討會 台灣 桃園 2009年11月28-29日 Figure 7. The comparisons of optimal total NPC with different hub height (capacity shortage is 10%) Finally, the impact of different wind turbines to NPC is discussed. In this study, the comparison of 3 types of wind turbine (Generic 10kw, BWC Excel-R 7,5kw, Generic 3 kw) is shown in the Figure 8. Expect Taichung, the Generic 10kw is the most expensive type of wind turbine in Taiwan. Compared to the factor of wind turbine type, the locations influence greatly on the total NPC. Figure 8. The comparisons of NPC with different types of wind turbine (hub height is 20m and capacity shortage is 10%) Table 3. Simulation result of the optimal total NPC at 25 locations in Taiwan with different capacity shortage and hub height set to 20m ( Type wind turbine BWC ExcelR 7.5kw) Capacity shortage 3% 5% No Location PV-Battery Win-PV-Battery PV-Battery Win-PV-Battery NPC C.PV N.B NPC C.PV C.W N.B NPC C.PV N.B NPC C.PV C.W N.B 1 Chenggon 351,919 50 120 248886 20 2 100 295,295 40 100 224358 20 2 80 2 Hengchun 259,969 35 80 244,080 25 2 80 214,444 25 100 207651 20 1 100 3 Penghu 345571 45 150 243199 30 1 80 295985 35 150 217894 25 1 80 4 Wuci 269686 30 150 218785 20 2 80 240381 30 100 197131 20 1 80 5 Keelung 622949 55 450 449692 30 4 250 584314 55 400 412062 30 4 200 6 Alishan 333,616 35 200 347,467 40 1 150 294,981 35 150 321,167 35 1 150 7 Anbu 483,758 50 300 360,580 25 3 200 447,132 50 250 311,619 30 3 100 8 Chiayi 239,450 30 100 239,735 25 1 100 208,975 25 80 215,525 20 1 100 9 Jhuzihhu 469710 50 280 421723 40 1 250 443410 45 280 284093 40 1 200 10 Hsinchu 294981 35 150 295872 30 1 150 269686 30 150 263543 30 1 100 11 Hualien 294981 35 150 259631 25 2 100 269686 30 150 235038 20 2 100 12 Ilan 499815 50 320 494892 50 1 280 444415 45 280 447018 45 1 250 13 Kaoshiung 260796 35 80 260769 25 1 80 214265 25 100 214265 20 1 100 14 Lanyu 343742 50 100 134951 15 1 30 292722 40 100 119431 10 2 50 15 Pengjiayu 443410 45 280 127973 8 2 40 392820 35 280 111904 10 1 40 16 Dongjidao 363275 55 80 135781 15 1 30 312106 45 80 134776 15 1 30 17 Suao 495005 55 280 446198 35 2 280 443410 45 280 411284 40 2 200 18 Sunmoonlake 294981 35 150 321167 35 1 150 265613 35 100 291256 35 1 100 19 Taichung 269686 30 150 295872 30 2 150 238927 30 100 239869 25 1 100 20 Taitung 239840 30 80 244494 25 1 100 215600 25 80 238924 25 1 80 21 Taipei 418115 40 280 419007 35 1 280 367525 30 280 371133 30 1 250 22 Tanshui 433168 40 300 420011 35 1 280 384206 45 200 384206 40 1 200 23 Dawu 294981 35 150 284539 35 1 80 243939 30 100 258795 30 1 80 24 Tainan 264987 35 100 238423 25 1 100 215389 25 100 232140 20 2 80 25 Yushan 363354 55 100 137199 15 1 40 287187 40 100 131569 10 2 30 中華民國第三十屆電力工程研討會 台灣 桃園 2009年11月28-29日 Table 4 Simulation result of the optimal total NPC at 25 locations in Taiwan with different capacity shortage and hub height set to 20m ( Type wind turbine BWC ExcelR 7.5kw) PV-battery: photovoltaic-battery hybrid system,Wind-PV-battery: Wind-photooltaic-battery hybrid system NPC : Net present cost,CPV : capacity of photovoltaic (kw), NB : number of battery,NW : number of unit wind turbine 5. Conclusion This paper discusses about the comparisons of the optimal NPC at 25 weather station locations in Taiwan. The optimal result of NPC and the relevant capacity combinations in the hybrid renewable system are helpful for any renewable system planner. The optimal capacity allocation for Wind/PV/battery and PV/Battery hybrid system at 3 different system risk (capacity shortage 3%, 5%, 10%) are compared and discussed in different 25 locations. With the conclusions of the paper, the system planner can decide which components can be installed to attain an optimal total NPC in a stand alone hybrid renewable system. Acknowledgements The authors are grateful to the financial support from The National Science Council, Taiwan, contact number is NSC- 97-2221-E-218-054. Referents [1] Joseph Kenfact et al., “Microhydro-PV-hybrid system: sizing a small hydro-PV-hybrid system for rural electrification in developing countries,” Renewable Energy, Vol. 34, No. 10, 2009, pp. 2259-2263. [2] Fiedler, Frank; Pazmino, Victor; Berruezo, Irati, ”PV-Wind Hybrid Systems for Swedish Locations,” 4th European PV-Hybrid and Mini-Grid Conference, May 29th-30th, 2008. [3] Sanjoy Kumar Nandi,Himangshu Ranjan Ghosh. “A wind-Pv- battery hybrid power system at Sitakunda in Bangladesh,” Energy Policy, Vol. 37, 2009, pp.3659-3664. [4] Felix A. Farret and M.Godoy Simoes, Integration of Alternative Sources of Energy, JOHN WILEY & SONS, INC. 2006 [5] Manual of Homer software, www.nrel.gov/homer [6] Tsang-Jung Chang , Yu-Ting Wu, Hua-Yi Hsu,Chia-Ren Chu, Chun-Min Liao, “Assessment of wind characteristics and wind Turbine characteristics in Taiwan,” Renewable Energy , Vol. 28, 2003, pp.851–871 Capacity shortage 10% No Location PV-Battery Win-PV-Battery NPC C.PV N.B NPC C.PV C.W N.B 1 Chenggon 236,749 30 80 200093 20 1 80 2 Hengchun 182,302 20 80 184200 20 1 40 3 Penghu 257858 35 80 199831 20 2 40 4 Wuci 207773 25 80 182691 15 2 50 5 Keelung 483758 50 300 362458 35 4 100 6 Alishan 235,006 30 80 261,089 30 1 80 7 Anbu 383,201 45 200 259,133 25 2 100 8 Chiayi 182,413 20 80 199,531 20 1 50 9 Jhuzihhu 370866 50 150 313447 35 2 100 10 Hsinchu 234605 30 80 230895 25 1 80 11 Hualien 243536 30 100 204178 20 1 80 12 Ilan 345571 45 150 346462 40 1 150 13 Kaoshiung 182738 20 80 199395 20 1 50 14 Lanyu 240133 30 100 101786 8 2 40 15 Pengjiayu 342230 25 280 96924 8 1 30 16 Dongjidao 233920 30 80 111904 10 1 40 17 Suao 356194 35 230 346462 40 1 150 18 Sunmoonlake 233934 30 80 239248 25 1 100 19 Taichung 182552 20 80 208539 20 1 80 20 Taitung 186306 20 80 211220 20 1 80 21 Taipei 294981 35 150 295872 30 1 150 22 Tanshui 320276 40 150 316118 35 2 100 23 Dawu 211587 25 80 211154 20 1 100 24 Tainan 187959 20 100 198314 20 1 50 25 Yushan 210232 25 100 106239 10 1 30