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研究生: 陳永軒
Chen, Yung-Hsuan
論文名稱: 台灣鄰近海域波浪能量評估
Assessment of Wave Energy Potential in the Waters Surrounding Taiwan
指導教授: 董東璟
Doong, Dong-Jiing
學位類別: 碩士
Master
系所名稱: 工學院 - 水利及海洋工程學系
Department of Hydraulic & Ocean Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 155
中文關鍵詞: 波浪能評估波浪能頻譜
外文關鍵詞: wave energy, wave energy assessment, spectrum
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  • 目前國際上在進行波浪能評估時,尚無一個統一的方法,因此本研究使用不同的波浪參數(能量週期T_e、尖峰週期T_p、平均週期T_z)計算波浪能,並將歐洲海洋能源中心(European Marine Energy Centre, EMEC)定義為標準評估方式-頻譜法作為基準,比較使用不同方式對波浪能計算會有多少差異。本研究使用六個浮標站(龍洞、新竹、七股、七美、花蓮、台東)的資料進行計算,經過統計2014年至2024年的浮標資料後,使用能量週期計算波能與使用頻譜計算差值約3%,展現兩者高度的相關性,使用尖峰週期與平均週期的計算結果差值分別為9%與6%,由於這兩種計算方式皆含有對海況的假設,顯示其對假設的敏感性與適用性。
    同時本研究使用WAVEWATCH III對台灣海域進行數值模擬,透過與浮標實測資料進行波高驗證,模式與實測資料的相關係數皆高於0.82,均方根誤差約在0.3公尺左右,顯示模式可以良好的模擬台灣海域的波浪條件。根據模式結果,台灣波能的波能主要集中在澎湖北部海域,在東北季風期間可以達到20 kW/m的波浪潛勢,其次為台灣東南部、綠島、蘭嶼海域,潛能也可達到9 kW/m以上,再次為東部海域,最低則是西南沿岸地區,平均不到2 kW/m。同時台灣海域的波能受到季節影響甚鉅,冬季時的平均最大波能可以達到20 kW/m以上,發生於澎湖北部海域,而夏季時則是4 kW/m,位在蘭嶼、綠島外海。
    此外本研究使用WW3的模式結果作為Surface-water Modeling System(SMS)的輸入條件,計算6個港區的防波堤前的波能密度,分別為安平港、花蓮港、八斗子漁港,永安LNG接收站、蘇澳港、成功漁港,並結合對台灣防波堤有關長度與消波塊布放情形的調查結果,粗估台灣防波堤前的波能約為0.59 GW。

    Currently, the methods are used differently in wave energy assessment internationally. Therefore, this study applied different wave parameters, including energy period(Te), peak period(Tp), mean period(Tz), to calculated wave energy, and compare the results with the spectrum method which is defined by European Marine Energy Centre (EMEC). The similarities and differences between the parameter-based methods and the spectral method were analyzed and discussed in terms of their estimated wave energy, sensitivity to sea state assumptions, with the spectrum methods defined by EMEC setting as the standard.
    This study utilized buoy data collected from six stations—Longdong, Hsinchu, Chiku, Qimei, Hualien, and Taitung—over the period 2014–2024. The results indicated that the wave energy estimates based on the energy period approximately 3% differed from those calculated via the spectrum method, while peak period and mean period differed by around 9% and 6%, respectively. Since both of these methods involve assumptions about sea states, the results reflect their sensitivity and applicability depending on the conditions.
    In addition, this study employed WAVEWATCH III (WW3) to conduct numerical simulations for the waters surrounding Taiwan. Model outputs were validated against buoy-measured significant wave heights, with correlation coefficients exceeding 0.82 and root-mean-square errors around 0.3 meters. These results show that the model can effectively simulate wave conditions in the research area.
    Results showed the wave energy in the waters surrounding Taiwan primarily concentrated in the northern Penghu, where the wave energy density can reach to 20 kW/m during northeast monsoon season, as well as Green Island and Orchid Island also exceed 10 kW/m. Moreover, the wave energy in waters surrounding Taiwan is strongly influenced by seasonal variations, wave energy in winter can exceed 20 kW/m, while in summer drop to 4 kW/m.
    Additionally, this study applied the WW3 model outputs as input conditions for the Surface-water Modeling System (SMS) to calculate the wave energy density in front of breakwaters at six ports: Anping Harbor, Hualien Harbor, Badouzi Fishing Harbor, Yong-An LNG Terminal, Suao Harbor, and Chenggong Fishing Harbor. Combined with surveyed information regarding breakwater lengths and the deployment of tetrapods in Taiwan, the wave energy in front of breakwaters was roughly estimated to be approximately 0.59 GW.

    摘要 1-I ABSTRACT 1-II 摘要 1-I ABSTRACT 1-II 誌謝 1-VI 目錄 1-I 表目錄 1-IV 圖目錄 1-V 第一章、前言 1 1-1 研究背景 1 1-2 文獻回顧 2 1-3 研究目的 4 1-4 研究架構 4 第二章、波浪能計算方法 6 2-1 頻譜計算波浪能 6 2-2 波浪參數計算波浪能 7 2-2-1 能量週期Te 8 2-2-2 尖峰週期Tp 9 2-2-3 平均週期Tz 11 2-3 波浪實測資料 13 2-3-1 研究區域 13 2-3-2 資料品管 14 2-4 浮標資料分析 15 2-4-1 波高週期聯合統計機率分布 17 2-4-2 龍洞浮標站 17 2-4-3 新竹浮標站 21 2-4-4 七股浮標站 25 2-4-5 七美浮標站 30 2-4-6 花蓮浮標站 33 2-4-7 台東浮標站 37 2-5 評估方式比較 41 第三章、全海域波能計算 47 3-1 控制方程式 47 3-2 源函數項 48 3-2-1 底床摩擦項 Sbot 49 3-2-2 碎波項 Sdb 50 3-2-3 深水四波非線性交互作用項 Snl 51 3-2-4 淺水三波非線性交互作用項 Str 53 3-2-5 源函數波譜形狀選取 54 3-2-6 ST4套組 56 3-3 模式架構 61 3-3-1 模式建置與設定 61 3-3-2 風場來源與驗證 62 3-4 研究區域 63 3-5 模式驗證 65 3-6 台灣海域波浪能量分布 70 3-6-1 台灣西北部海域波浪能量分布 74 3-6-2 台灣東部海域波浪能量分布 76 3-6-3 台灣西南部海域波浪能量分布 79 3-6-4 澎湖與中部海域波浪能量分布 82 3-6-5 台灣離岸12海浬內波浪能量 86 第四章、堤前波能計算 91 4-1 台灣防波堤盤點 91 4-2 近岸數值模式 92 4-3 模式建置 101 4-3-1 模式架構與設定 102 4-4 堤前波能計算 104 4-4-1 安平港防波堤潛能評估 104 4-4-2 全台防波堤堤前潛能評估 107 第五章、結論與建議 112 5-1 結論 112 5-2 建議 113 參考文獻 114 附錄 各浮標站使用不同參數計算波能結果 121

    [1] 財團法人工業技術研究院(2009),海洋能源發現系統評估與測試計畫,經濟部能源科技研究發展計劃97年度執行報告。
    [2] 財團法人工業技術研究院(2011),海洋能源系統及關鍵元件技術系統開發計畫,經濟部能源科技研究發展計劃100年度報告。
    [3] 張恆文、連永順、顏志偉,參數化波能推算之誤差研究,第四屆資源工程研討會,200-203(2009)
    [4] 張紘聞(2019),應用WWIII波浪模式於極端波高模擬之研究。國立成功大學碩士論文。
    [5] 張智翔(2024),台灣波能高潛勢海域波能資源後報與預測研究。國立臺灣海洋大學碩士論文。
    [6] 許家瑄(2023),近五十年台灣周遭海域與北南海波候變化分析。國立成功大學碩士論文。
    [7] 葉浩君(2020),台灣鄰近海域湧浪研究。國立成功大學碩士論文。
    [8] Ahn, S., Haas, K. A., & Neary, V. S. (2020). Wave energy resource characterization and assessment for coastal waters of the United States. Applied Energy, 267, 114922.
    [9] Ahn, S., & Neary, V. S. (2020). Non-stationary historical trends in wave energy climate for coastal waters of the United States. Ocean Engineering, 216, 108044.
    [10] Ahn, S. (2021). Modeling mean relation between peak period and energy period of ocean surface wave systems. Ocean Engineering, 228, 108937.
    [11] Alonso, R., Solari, S., & Teixeira, L. (2015). Wave energy resource assessment in Uruguay. Energy, 93, 683-696.
    [12] Arinaga, R. A., & Cheung, K. F. (2012). Atlas of global wave energy from 10 years of reanalysis and hindcast data. Renewable Energy, 39(1), 49-64.
    [13] Ardhuin, F., Chapron, B., and Collard, F.(2009a). Observation of swell dissipation across oceans. Geophysical Research Letters, 36(6)
    [14] Ardhuin, F and Jenkins. A. D.(2006). On the interaction of surface waves and upper ocean turbulence. Journal of physical oceanography, 36(3), 551-557.
    [15] Ardhuin, F. and Le Boyer, A. (2006). Numerical modelling of sea states:validation of spectral shapes. Navigation, 54(216), 55-71.
    [16] Ardhuin, F., Marie, L., Rascle, N., Forget, P., and Roland, A. (2009b). Observation and estimation of Lagrangian, Stokes, and Eulerian currents induced by wind and waves at the sea surface. Journal of Physical Oceanography, 39(11), 2820-2838.
    [17] Ardhuin, F. Rogers, E., Babanin, A. V., Filipot, F., Magne, R., Roland, A. Westhuysen. A., Queffeulou, P., Lefevre, J., Aouf. L , and Collard, F (2010). Semiempirical dissipation source functions for ocean waves. Part 1:Definition, calibration, and validation. Journal of Physical Oceanography,40(9),1917-1941.
    [18] Bi, F., Song, J., Wu, K., & Xu, Y. (2015). Evaluation of the simulation capability of the Wavewatch III model for Pacific Ocean wave. Acta Oceanologica Sinica, 34, 43-57.
    [19] Cahill, B., Oregon State University, Beaufort Research, University College Cork, & Lewis, T. (2014). WAVE PERIOD RATIOS AND THE CALCULATION OF WAVE POWER. In Proceedings of the 2nd Marine Energy Technology Symposium.
    [20] Chen, Y. L., Lin, C. C., Chen, J. H., Lee, Y. H., & Tzang, S. Y. (2023). Characteristics of wave energy resources on coastal waters of northeast Taiwan. Renewable Energy, 202, 1-16.
    [21] Chakrabarti, S. K. (1987). Hydrodynamics of offshore structures. WIT press.
    [22] Christakos, K., Lavidas, G., Gao, Z., & Björkqvist, J. V. (2024). Long-term assessment of wave conditions and wave energy resource in the Arctic Ocean. Renewable Energy,220, 119678.
    [23] Ganea, D., Amortila, V., Mereuta, E., & Rusu, E. (2017). A joint evaluation of the wind and wave energy resources close to the Greek Islands. Sustainability, 9(6), 1025.
    [24] Goda, Y. (2000) Random Seas and Design of Maritime Structures. World Scientific, Singapore. http://dx.doi.org/10.1142/3587.
    [25] Guillou, N. (2020). Estimating wave energy flux from significant wave height and peak period. Renewable Energy, 155, 1383-1393.
    [26] Gunn, K., & Stock-Williams, C. (2012). Quantifying the global wave power resource. Renewable energy,44, 296-304.
    [27] Hasselmann, K. (1962). On the non-linear energy transfer in a gravity-wave spectrum Part 1. General theory. Journal of Fluid Mechanics, 12(4), 481-500.
    [28] Hasselmann, K. (1963a). On the non-linear energy transfer in a gravity wave spectrum Part 2. Conservation theorems; wave-particle analogy; irrevesibility. Journal of Fluid Mechanics, 15(2), 273-281.
    [29] Hasselmann, K. (1963b). On the non-linear energy transfer in a gravity-wave spectrum. Part 3. Evaluation of the energy flux and swell-sea interaction for a Neumann spectrum. Journal of Fluid Mechanics, 15(3), 385-398.
    [30] Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E., Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Müller, P., Olbers, D. J., Richter, K., Sell, W., Walden., H. (1973). Measurement of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Ergänzungsheft 8-12.
    [31] Hasselmann, S., Hasselmann, K., Allender, J. H., Barnett, T. P. (1985). Computations and parameterizations of the nonlinear energy transfer in a gravity-wave specturm. Part II: Parameterizations of the nonlinear energy transfer for application in wave models. Journal of Physical Oceanography, 15(11), 1378-1391.
    [32] Hargreaves, J. C. J. D. Annan, 1998: Integration of source terms in WAM. In Proceedings of the 5th International Workshop on Wave Forecasting and Hindcasting, 128–133
    [33] Hargreaves, J. C., Annan, J. D. (2001). Comments on “Improvement of the short-fetch behavior in the wave ocean model (WAM)”. Journal of Atmospheric and Oceanic Technology, 18(4), 711-715
    [34] Hemer, M. A., Zieger, S., Durrant, T., O'Grady, J., Hoeke, R. K., McInnes, K. L., & Rosebrock, U. (2017). A revised assessment of Australia's national wave energy resource. Renewable Energy, 114, 85-107.
    [35] Holthuijsen, L. H. (2010). Waves in oceanic and coastal waters. Cambridge university press.
    [36] Hwang, P. A. (2011). A note on the ocean surface roughness spectrum. Journal of Atmospheric and Oceanic Technology, 28(3), 436-443.
    [37] Kazeminezhad, M. H., & Siadatmousavi, S. M. (2017). Performance evaluation of WAVEWATCH III model in the Persian Gulf using different wind resources. Ocean Dynamics, 67, 839-855.
    [38] Kennedy, A. B., Chen, Q., Kirby, J. T., & Dalrymple, R. A. (2000). Boussinesq modeling of wave transformation, breaking, and runup. I: 1D. Journal of waterway, port, coastal, and ocean engineering, 126(1), 39-47.
    [39] Khan, N. D., Kalair, A., Abas, N., & Haider, A. (2017). Review of ocean tidal, wave and thermal energy technologies. Renewable and Sustainable Energy Reviews,72, 590-604.
    [40] Kofoed, J., Pecher, A., Margheritini, L., Antonishen, M., Bittencourt, C., Holmes, B., Retzler, C., Berthelsen, K., Crom, I. L., Neumann, F., Johnstone, C., McCombes, T., & Myers, L. (2012). A methodology for equitable performance assessment and presentation of wave energy converters based on sea trials. Renewable Energy, 52, 99–110. https://doi.org/10.1016/j.renene.2012.10.040
    [41] Komen, G. J., Hasselmann, K., Hasselmann, K. (1984). On the existence of a fully developed wind-sea spectrum. Journal of physical oceanography, 14(8), 1271-1285.
    [42] Lin, Y., Dong, S., Wang, Z., & Soares, C. G. (2019). Wave energy assessment in the China adjacent seas on the basis of a 20-year SWAN simulation with unstructured grids. Renewable Energy, 136, 275-295.
    [43] Pitt, E. & European Marine Energy Centre Ltd. (2009). Assessment of wave energy resource. BSI.
    [44] Pastor, J., & Liu, Y. (2015). Wave energy resource analysis for use in wave energy conversion. J. Offshore Mech. Arct. Eng, 137, 011903.
    [45] Pastor, J., & Liu, Y. (2016). Wave climate resource analysis based on a revised gamma spectrum for wave energy conversion technology. Sustainability, 8(12), 1321.
    [46] Robertson, B., Bailey, H., Clancy, D., Ortiz, J., & Buckham, B. (2016). Influence of wave resource assessment methodology on wave energy production estimates. Renewable Energy, 86, 1145-1160.
    [47] Salter, S. H. (1974). Wave power. Nature, 249(5459), 720-724.
    [48] Satymov, R., Bogdanov, D., Dadashi, M., Lavidas, G., & Breyer, C. (2024). Techno-economic assessment of global and regional wave energy resource potentials and profiles in hourly resolution. Applied Energy, 364, 123119.
    [49] Seemanth, M., Bhowmick, S. A., Kumar, R., & Sharma, R. (2016). Sensitivity analysis of dissipation parameterizations in a third-generation spectral wave model, WAVEWATCH III for Indian Ocean. Ocean Engineering, 124, 252-273.
    [50] Shemdin, O., Hasselmann, K., Hsiao, S. V., & Herterich, K. (1978). Nonlinear and linear bottom interaction effects in shallow water. In Turbulent fluxes through the sea surface, wave dynamics, and prediction (pp. 347-372). Springer, Boston, MA.
    [51] Sierra, J. P., Martin, C., Mösso, C., Mestres, M., & Jebbad, R. (2016). Wave energy potential along the Atlantic coast of Morocco. Renewable Energy, 96, 20-32.
    [52] Tolman, H. L. (1991). A third-generation model for wind waves on slowly varying, unsteady, and inhomogeneous depths and currents. Journal of Physical Oceanography, 21(6), 782-797.
    [53] Tolman, H. L. (1992). Effects of numerics on the physics in a third-generation wind-wave model. Journal of physical Oceanography, 22(10), 1095-1111.
    [54] Tolman, H. L. (2003). Optimum discrete interaction approximations for wind waves. Part 1: Mapping using inverse modeling. NOAA/NWS/NCEP/MMAB, Tech. Note 227.
    [55] Tsagareli, K. N., Babanin, A. V., Walker, D. J., Young, I. R. (2010). Numerical investigation of spectral evolution of wind waves. Part I: Wind-input source function. Journal of Physical Oceanography, 40(4), 656-666.
    [56] Wan, Y., Zhang, J., Meng, J., Wang, J., & Dai, Y. (2016). Study on wave energy resource assessing method based on altimeter data—A case study in Northwest Pacific. Acta oceanologica sinica, 35, 117-129.
    [57] Wan, Y., Fan, C., Zhang, J., Meng, J., Dai, Y., Li, L., ... & Zhang, X. (2017). Wave energy resource assessment off the coast of China around the Zhoushan Islands. Energies, 10(9), 1320.
    [58] Wan, Y., Zheng, C., Li, L., Dai, Y., Esteban, M. D., López-Gutiérrez, J. S., ... & Zhang, X. (2020). Wave energy assessment related to wave energy convertors in the coastal waters of China. Energy, 202, 117741.
    [59] WAMDIG. (1988). The WAM model—A third generation ocean wave prediction model. Journal of Physical Oceanography, 18(12), 1775-1810.
    [60] Yaakob, O., Hashim, F. E., Omar, K. M., Din, A. H. M., & Koh, K. K. (2016). Satellite-based wave data and wave energy resource assessment for South China Sea. Renewable energy, 88, 359-371.
    [61] Young, I. R., Banner, M. L., Donelan, M. A., McCormick, C., Babanin, A. V., Melville, W. K., Veron, F. (2005). An integrated system for the study of wind-wave source terms in finite-depth water. Journal of Atmospheric and Oceanic Technology, 22(7), 814-831.
    [62] Zheng, C. W., Pan, J., & Li, J. X. (2013). Assessing the China Sea wind energy and wave energy resources from 1988 to 2009. Ocean engineering, 65, 39-48.
    [63] Zieger, S., Babanin, A. V., Rogers, W. E., Young, I. R. (2015). Observation-based source terms in the third-generation wave model WAVEWATCH. Ocean Modelling, 96, 2-2

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