High-Resolution mapping for wind energy potential assessment in Chaiyaphum Province
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Abstract
Assessing wind power capacity Evaluate the wind energy potential in Chaiyaphum Province and identify suitable locations for wind turbine installation. involves generating microscale wind maps. This study utilized collaborative atmospheric modeling combining Mesoscale Compressible Community Model (MC2) and Microscale Model (Ms-micro) with a spatial resolution of 200 m, using the atmospheric database from NCEP/FNL for 10 years. At an altitude of 150 m, the wind map data indicate that Chaiyaphum province has an average wind speed of 3.34 m/s. Locations experiencing wind speeds over 3.51 m/s are Kut Lo Subdistrict, Tha Ma Fai Wan Subdistrict, Sap Si Thong Subdistrict, Sa Phon Thong Subdistrict, and Tha Hin Non Ngam Subdistrict. Energy research determined that a 2.5 MW generates the highest power output compared to other turbines. The Kut Lo District region generated the maximum electrical energy among all places, producing 20,994 MWh/year in this investigation. The 24% capacity factor indicates the potential of wind energy for assessing the feasibility of investing in new wind power facilities in the northeastern portion of Thailand.
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กรมพัฒนาพลังงานทดแทนและอนุรักษ์พลังงาน กระทรวงพลังงาน. (2566a). รายงานสถานการณ์พลังงานของประเทศไทย เดือนมกราคม–กรกฎาคม 2566. https://kc.dede.go.th/knowledge-view.aspx?p=505
กรมพัฒนาพลังงานทดแทนและอนุรักษ์พลังงาน กระทรวงพลังงาน. (2566b). สัดส่วนการใช้พลังงานทดแทน. https://www.dede.go.th/articles?id=449&menu_id=1
กองนโยบายและแผนการใช้ที่ดิน กรมพัฒนาที่ดิน กระทรวงเกษตรและสหกรณ์. (2022). ข้อมูลสภาพการใช้ที่ดิน. https://webapp.ldd.go.th/lpd/LandUseInfor.php
กลุ่มงานยุทธศาสตร์และข้อมูลเพื่อการพัฒนาจังหวัด สำนักงานจังหวัดชัยภูมิ. (2567, มกราคม 14). ข้อมูลจังหวัดชัยภูมิ. https://www.chaiyaphum.go.th/page_about/about1.php
Bank of Thailand. (2023). Thailand’s macroeconomic indicators. https://www.bot.or.th/th/home.html
Deepo, W., Kongjeen, Y., & Plangklang, B. (2021). Performance analysis of an 18 MW wind farm in Nakhon Ratchasima Province. Proceeding of the 2021 9th International Electrical Engineering Congress, IEECON 2021, 121–124. https://doi.org/10.1109/iEECON51072.2021.9440265
Dominic, C. (2021, November 12). Global net zero commitments. https://commonslibrary.parliament.uk/global-net-zero-commitments/
EPPO. (2020). Power development plan: PDP 2018 revision 1. In Energy Policy Plan. https://www.egat.co.th/home/egat-development-plan/
Gasset, N., Landry, M., & Gagnon, Y. (2012). A comparison of wind flow models for wind resource assessment in wind energy applications. Energies, 5(11), 4288–4322. https://doi.org/10.3390/en5114288
Gibb, D., Ledanois, N., Ranalder, L., & Yaqoob, H. (2022). Renewables 2022 global status report. https://www.ren21.net/gsr-2022/
IRENA. (2023). Renewable power generation costs in 2020. https://www.irena.org
Janjai, S., Masiri, I., Promsen, W., Pattarapanitchai, S., Pankaew, P., Laksanaboonsong, J., Bischoff-Gauss, I., & Kalthoff, N. (2014). Evaluation of wind energy potential over Thailand by using an atmospheric mesoscale model and a GIS approach. Journal of Wind Engineering and Industrial Aerodynamics, 129, 1–10. https://doi.org/10.1016/j.jweia.2014.03.010
Kim, B., Lee, K., Ko, K., & Choi, J. (2022). Offshore wind resource assessment off the coast of Daejeong, Jeju Island using 30-year wind estimates. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-18447-7
Martinez, A., & Iglesias, G. (2022). Climate change impacts on wind energy resources in North America based on the CMIP6 projections. Science of the Total Environment, 806. https://doi.org/10.1016/j.scitotenv.2021.150580
Mundu, M. M., Nnamchi, S. N., Ukagwu, K. J., Peter, B. A., Nnamchi, O. A., & Ssempewo, J. I. (2022). Numerical modelling of wind flow for solar power generation in a case study of the tropical zones. Modeling Earth Systems and Environment, 8(3), 4123–4134. https://doi.org/10.1007/s40808-021-01343-w
Pierrot, M. (2016). Gamesa G126/2500. In Wind Power. https://www.thewindpower.net/turbine_en_1088_gamesa_g126-2500.php
Pierrot, M. (2018a). Gamesa G128/4500. In Wind Power. https://www.thewindpower.net/turbine_en_81_gamesa_g128-4500.php
Pierrot, M. (2018b). Gamesa G132/3300. In Wind Power. https://www.thewindpower.net/turbine_en_1105_gamesa_g132-3300.php
Pierrot, M. (2018c). Gamesa G132/5000. In Wind Power. https://www.thewindpower.net/turbine_en_774_gamesa_g132-5000.php
Polnumtiang, S., & Tangchaichit, K. (2022a). Potential wind power generation at Khon Kaen, Thailand. Wind and Structures. https://doi.org/10.12989/was.2022.35.6.000
Polnumtiang, S., & Tangchaichit, K. (2022b). Wind energy resource assessment for Mukdahan, Thailand. International Journal of Green Energy, 19(2), 137–148. https://doi.org/10.1080/15435075.2021.1941039
Research Data Archive. (2023). NCEP FNL operational model global tropospheric analyses, continuing from July 1999. https://rda.ucar.edu/datasets/ds083.2/
Tawinprai, S., Polnumtiang, S., Suksomprom, P., Waewsak, J., & Tangchaichit, K. (2022). Modeling of wind energy potential using a high-resolution grid over Mekong riverside region in the northeastern part of Thailand. Theoretical and Applied Climatology, 150(3–4), 1587–1604. https://doi.org/10.1007/s00704-022-04235-w
Tawinprai, S., Polnumtiang, S., Suksomprom, P., Waewsak, J., & Tangchaichit, K. (2023a). A modelling approach for evaluating the wind resource and power generation using a high-resolution grid at selected regions in the northeast of Thailand. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-022-01669-z
Tawinprai, S., Polnumtiang, S., Suksomprom, P., Waewsak, J., & Tangchaichit, K. (2023b). A modelling approach for evaluating the wind resource and power generation using a high-resolution grid at selected regions in the northeast of Thailand. Modeling Earth Systems and Environment, 9(3), 3229–3241. https://doi.org/10.1007/s40808-022-01669-z
Waewsak, J., Landry, M., & Gagnon, Y. (2015). Offshore wind power potential of the Gulf of Thailand. Renewable Energy, 81, 609–626. https://doi.org/10.1016/j.renene.2015.03.069