本研究主要利用離岸測風塔數據，搭配短期固定式光達的量測資料以及長期的再分析資料(MERRA)和中央氣象局的線上資料庫，對彰濱地區作離岸風電的評估。
固定式光達架設於彰化沿岸的車輛研究測試中心(車測中心)，與海上測風塔相距約13公里進行為期約70天的量測。短期時間的研究將針對車測中心以及海上測風塔之間的交互模擬。此研究將利用十分鐘數據，將離岸的風資源數據導入WindSim模擬軟體，推測近岸的風速風向資料；反之也利用近岸風資源去推測離岸的十分鐘風速風向資料。
再分析數據以及氣象站資料將以測量相關堆測法(Measure-Correlate-Predict method, MCP)，將一年離岸測風塔的觀測資料，嵌合成中長期的風資源資料，藉此去進行風場發電量的評估。利用中長期的風資源，WindSim軟體將針對不同數量的風機做發電量預估，之後搭配淨現值以及成本去進行經濟效益的評估。發電量的評估以及或然率將配合風機、風場資料、儀器設備對於風場評估的不確定性以達到彰濱區域的離岸風電評估目的。

In this research, the data from offshore meteorological mast are mainly used. With the match up of short-term data measured by fixed LiDAR and the long-term online datasets (MERRA and CWB station), the offshore wind power generation will be evaluated.
Fixed LiDAR was been setup at Automotive Research & Testing Center (ARTC), with the distance 13 km to meteorological mast, for about 70 days measuring. The study for short-term data will be focus on the interactive simulation between ARTC and meteorological mast. The 10-min data from offshore meteorological mast will be imported into WindSim software, and predict the wind speed and wind direction on nearshore; whereas the nearshore wind resource will be used to simulate the 10-min wind speed and wind direction on offshore.
The data from MERRA and CWB station were used for Measure-Correlate-Predict method. A year measured data from meteorological mast will be synthesized into medium and long-term data for the assessment of wind power generation in wind farm. As the importation of medium and long-term data into WindSim, the program will evaluate the power generation for different amount of turbines. The economic benefits will be considered by the net present value and cost. For the aim of the evaluation on offshore wind power in this study, the assessment and probability of annual energy production were discussed with the uncertainty of wind turbines, wind data and instruments.

[1] J. L. Sawin, K. Seyboth, and F. Sverrison, “Renewables 2017 Global Status Report”, REN21 Secretariat, Paris, France, 2017.
[2] J. L. Sawin, K. Seyboth, and F. Sverrison, “Renewables 2016 Global Status Report”, REN21 Secretariat, Paris, France, 2016.
[3] T. J. Price, James Blyth - Britain's First Modern Wind Power Engineer, Wind Engineering, Vol. 29: pp. 191–200, 2005.
[4] 能源統計年報, Available at “https://www.moeaboe.gov.tw/ecw/populace /content/ContentLink.aspx?menu_id=378”.
[5] Bureau of Energy, Ministry of Economic Affairs, Four-year Wind Power Promotion Plan, Available at “https://www.moeaboe.gov.tw/ecw/ populace/content/ContentDesc.aspx?menu_id=5493”.
[6] 4C Offshore, Global Offshore Wind Speeds Rankings, Available at “http://www.4coffshore.com/windfarms/windspeeds.aspx”.
[7] T. H. Tai, Assessment of Wind Turbine Installation and Evaluation on Wind Power Generation Using On-site Wind Data, National Cheng Kung University Department of Mechanical Engineering, Master’s Thesis, June 2016.
[8] S. Emeis, M. Harris, and R. M. Banta, Boundary-layer anemometry by optical remote sensing for wind energy applications, Meteorol Zeitschrift, Vol. 16, pp. 337-347, 2007.
[9] J. Li and X. B. Yu, LiDAR technology for wind energy potential assessment: demonstration and validation at a site around Lake Erie, Energy Convers Manage, Vol. 144, pp. 252-261, 2017.
[10] T. F. Pedersen, On wind turbine power performance measurements at inclined airflow, Wind Energy, Vol. 7, pp. 163-176, 2004.
[11] S. Vogt and P. Thomas, SODAR - A useful remote sounder to measure wind and turbulence, J. Wind Eng. Ind. Aerodynamics, Vol. 54-55,pp. 163-172, 1995.
[12] D. A. Smith, M. Harris, A. S. Coffey, T. Mikkelsen, H. E. Jorgensen, J. Mann, and R. Danielian, Wind lidar evaluation at the Danish wind test site in Hovsore, Wind Energy, Vol. 9, pp. 87-93, 2006.
[13] S. Lang and E. McKeogh, LIDAR and SODAR measurements of wind speed and direction in upland terrain for wind energy purposes, Remote Sens., Vol. 3, pp. 1871-1901, 2011.
[14] O. Edenhofer, R. Pichs-Madruga, and Y. Sokona, Renewable energy sources and climate change mitigation: special report of the intergovernmental panel on climate change, Cambridge University Press, 2011.
[15] P. D. Alferdo, C. B. Hasager, J. Lange, J. Anger, M. Badger, and F. Bingöl, Remote Sensing for Wind Energy, DTU Wind Energy-EReport-0029(EN), ISBN: 978-87-92896-41-4, 2013.
[16] X. F. Wang, X. W. Zeng, J. L. Li, X. Yang, and H. J. Wang, A review on recent advancements of substructures for offshore wind turbines, Energy Conversion and Management, Vol. 158, pp. 103-119, 2018.
[17] R. B. Stull, An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, Dordrecht, pp. 666, 1988.
[18] J. Wieringa, How far can agrometeorological station observations be considered representative? , 23rd Conference on Agricultural and Forest Meteorology (ed. N. M. Albuquerque), Am. Meteorol. Soc., 1998.
[19] International Electrotechnical Committee IEC. 61400-1, Wind Turbines Part1: Design Requirements, third ed., IEC, Geneva, Switzerland, 2005.
[20] International Electrotechnical Committee IEC. 61400-3, Wind Turbines Part3: Design Requirements for Offshore Wind Turbines, first ed., IEC, Geneva, Switzerland, 2009.
[21] E. Hau, Wind Turbines, Fundamentals, Technologies, Application, Economics, second ed., Springer, Berlin, 2006.
[22] S. Mathew, Wind energy, Fundamentals, Resource Analysis and Economics, Springer, Berlin, 2006.
[23] International Electrotechnical Committee IEC 61400-12, Wind Turbine Generator Systems-Part 12: Wind Turbine Power, 1998.
[24] C. Ozay and M. S. Celiktas, Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region, Energy Conversion and Management, Vol. 121, pp. 49-54, 2016.
[25] S. H. Pishgar-Komleh, A. Keyhani, and P. Sefeedpari, Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran), Renewable and Sustainable Energy Reviews, Vol. 42, pp. 313-322, 2015.
[26] S. Ali, S. M. Lee, and C. M. Jang, Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island - Incheon, South Korea, Renewable Energy, Vol. 123, pp. 652-663, 2018.
[27] Piotr Wais, Two and three-parameter Weibull distribution in available wind power analysis, Renewable Energy, Vo. 103, pp. 15-29, 2017.
[28] H. Saleh, A. Abou El-Azm Aly, and S. Abdel-Hady, Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt, Energy, Vol. 44, pp. 710-719, 2012.
[29] W. Werapun, Y. Tirawanichakul, and J. Waewsak, Comparative Study of Five Methods to Estimate Weibull Parameters for Wind Speed on Phangan Island, Thailand, Energy Procedia, Vol. 79, pp. 976-981, 2015.
[30] T. P. Chang, Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application, Applied Energy, Vol. 88, pp. 272-282, 2011.
[31] J. V. Ramsdell, S. Houston, and H. L. Wegley, Measurement strategies for estimating long-term average wind speed, Solar Energy, Vol. 25, pp. 495-503, 1980.
[32] L. Landberg, L. Myllerup, O. Rathmann, E. L. Petersen, B. H. Jørgensen, J. Badger, and N. G. Mortensen, Wind resource estimation-an overview, Wind Energy, Vol. 6, pp. 261-271, 2003.
[33] C. G. Justus, K. Mani, and A. S. Mikhail, Interannual and month-to-month variations of wind speed, Journal of Applied Meteorology, Vol. 18, pp. 913-920, 1998.
[34] L. Landberg and N. G. Mortensen, A comparison of physical and statistical methods for estimating the wind resource at a site. In: Pitcher KF. Editor. Proceedings of the 15th British wind energy association conference. Mechanical Engineering Publications Ltd.; pp. 119-25, 1993.
[35] A. J. Bowen and N. G. Mortensen, Wasp prediction errors due to site orography. Riso National Laboratory, 2004.
[36] (http:/help.emd.dk/knowledgebase/content/WindPRO2.8_MCP.pdf) [accessed on 22 March 2013].
[37] M. Taylor, P. Mackiewic, M. C. Brower, and M. Markus, An analysis of wind resource uncertainty in energy production estimates, In: Proceedings of the European wind energy conference, London, UK, November 22-25, 2004.
[38] A. Oliver and K. Zarling, The effect of seasonality on wind speed prediction bias in the plains, In: Proceedings of the AWEA 2010 wind power conference and exhibition, Dallas Texas, USA, May 23-25, 2010.
[39] WindPower program, Wind turbine power output variation with steady wind speed, Available at “http://www.wind-power-program.com”.
[40] M. Ayuso and C. Kjaer, World energy resources Wind 2016, World energy council, 2017.
[41] Energy Numbers, Capacity factors at Danish offshore wind farms, Available at “http://energynumbers.info/capacity-factors-at-danish-offshore-wind-farms”.
[42] N. O. Jenson, A note on wind generator interaction, Technical report Riso-M2411, 1983.
[43] T. Han, The assessment of dynamic wake effects on loading, The Netherland: Department of Aerospace Engineering Delft University of Technology, Master’s Thesis, 2011.
[44] J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind energy explained—Theory, design and application, John Wiley & Sons, Chichester, England, ISBN 0-471-49972-2, 2002.
[45] M. Abbes and M. Allagui, Centralized control strategy for energy maximization of large array wind turbines, Sustainable Cities and Society, Vol. 25, pp. 82-89, 2016.
[46] G. Giebel, G. Kariniotakis, and R. Brownsword, The state of the art in short-term prediction of wind power – from a Danish perspective, In Proceedings of the 4th international workshop on large-scale integration of wind power and transmission networks for offshore windfarms, Billund, Denmark, October 20–21, 2003.
[47] R. V. Coquilla and J. Obermeier, Calibration Uncertainty Comparisons Between Various Anemometers, In American Wind Energy Association AWEA, May 2008.
[48] M. Brower, Wind Resource Assessment: A Practical Guide to Developing a Wind Project, John Wiley & Sons, Inc., Canada, 1st ed., 2012.
[49] GL Garrad Hassan, Uncertainty Analysis, WindFarmer theory manual, England, 2011.
[50] S. D. Kwon, Uncertainty analysis of wind energy potential assessment, Applied Energy, Vol. 87, pp. 856-865, 2010.
[51] A. Lira, P. Rosas, A. Araujo, and N. Castro, Uncertainties in the estimate of wind energy production, Tech. rep. Grupo de Estudos do Setor Eletrico do Instituto de Economia da Universidade Federal do Rio de Janeiro, 2016.
[52] C. Y. Hsuan, T. H. Lin, Y. C. Li, Y. T. Wu, W. K. Hsu, T. F. Tsai, and C. C. Tu, The Construction of Taipower Offshore Meteorological Mast, 27th National Conference on Combustion and Energy, 2017.
[53] P. Gómez, “Test of a ground-based lidar instrument: WLS200S-11”, DTU-Wind Energy, Roskilde, Denmark, 2013.
[54] J. Xia, P. Wang, and M. Min, “Observation and Validation of Wind Parameters Measured by Doppler Wind Lidar Windcube”, OALib Journal, 2011.
[55] A. Albers and A. W. Janssen, Evaluation of Windcube, Deutsche WindGuard Consulting GmbH, Germany, 2008.
[56] S. E. Lane, J. F. Barlow, and C. R. Wood, “An assessment of a three-beam Doppler Lidar wind profiling method for use in urban areas”, Journal of wind Engineering and Industrial Aerodynamics, Vol. 119, pp.53-59, 2013.
[57] Leosphere, WINDCUBE v2 Lidar remote sensor, User Manual, pp. 68, 2012.
[58] R. D. Koster, MERRA-2: Initial Evaluation of the Climate, NASA/TM-2015-104606, Vol. 43, 2015.
[59] N. Simisiroglou, S.-P. Breton, G. Crasto, K.S. Hansen, and S. Ivanell, “Numerical CFD comparison of Lillgrund employing RANS”, Energy Procedia, Vol.53, pp.342-351, 2014.
[60] F. Castellani, D. Astolfi, M. Burlando, and L. Terzi, Numerical modelling for wind farm operation assessment in complex terrain, J. Wind Eng. Ind. Aerodyn., Vol. 147, pp. 320-329, 2015.
[61] A. Z. Dhunny, M. R. Lollchund, and S. D. D. V. Rughooputh, Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD), Renewable Energy, Vol. 101, pp. 1-9, 2017.
[62] EWEA, Comparative Resource and Energy Yield Assessment Procedures Exercise Part II, 2013, Website at: “http://www.ewea.org/events/ workshops/past-workshops/resource-assessment-2013/”.
[63] G. Crasto, A. R. Gravdahl, F. Castellani, and E. Piccioni, Wake modeling with the Actuator Disc concept, Energy Procedia, Vol. 24, pp. 385-392, 2012.
[64] F. Castellani, A. Gravdahl, G. Crasto, E. Piccioni, and A. Vignaroli, A practical approach in the CFD simulation of off-shore wind farms through the actuator disc technique, Energy Procedia, Vol. 35, pp. 274-284, 2013.
[65] C. Y. Lu, P. H. Lin, Y. C. Lee, and Z. C. You, The measurement of mixing height by Lidar ceilometer at differential landscapes in Taiwan, Atmospheric Sciences, Vol. 44, pp.149-171, 2016. (呂佳穎, 林博雄, 李育棋, 游志淇, 雷射雲幕儀應用於台灣各種地貌之混和層高度量測)
[66] N. J. Cook, The Deaves and Harris ABL model applied to heterogeneous terrain, Journal of Wind Engineering and Industrial Aerodynamics, Vol. 66, pp. 197–214, 1997.
[67] D. R. Drew, J. F. Barlow, and S. E. Lane, Observations of wind speed profiles over Greater London, UK, using a Doppler lidar, J. Wind Eng. Ind. Aerodyn., Vol. 121, pp. 98-105, 2013.
[68] C. Hwang, J. H. Jeon, G. H. Kim, E. Kim, M. Park, and I. K. Yu, Modelling and simulation of the wake effect in a wind farm, Journal of International Council on Electrical Engineering, Vol. 5, pp. 74-77, 2015.
[69] C. Mone, M. Hand, M. Bolinger, J. Rand, D. Heimiller, and J. Ho, “2015 Cost of Wind Energy Review”, National Renewable Energy Laboratory, Colorado, United States, 2017.
[70] B. Valpy, K. Freeman, and A. Roberts, “Future renewable energy costs: offshore wind”, KIC InnoEnergy Renewable Energies, Netherland, 2016.
[71] J. S. Touma, Dependence of the Wind Profile Power Law on Stability for Various Locations, J. Air Pollution Control Association, Vol. 27, pp. 863-866, 1977.
[72] A. S. Bachhal, Optimization of Wind Farm Layout taking Load Constraints into Account, University of Agder Deparment of Engineering and Science, Master's thesis, 2017.
[73] M. Lackner, Challenges in Offshore Wind Energy Aerodynamics: Floating Wind Turbines and Wind Farms, The UMass Wind Energy Center, Available at “https://windenergyigert.umass.edu/sites/windenergyigert /files/Lackner%20IGERT%20seminar%20-%20Aerodynamics%20-%203-1-12.pdf”.