An approach to assess offshore wind power potential using bathymetry and near-hub-height reanalysis data

被引:10
作者
Tahir, Zia ul Rehman [1 ]
Abdullah, Muhammad [1 ,2 ]
Ahmad, Sajeer [1 ,3 ]
Kanwal, Ammara [1 ]
Farhan, Muhammad [1 ]
Bin Saeed, Usama [1 ]
Ali, Tariq [1 ]
Amin, Imran [1 ]
机构
[1] Univ Engn & Technol Lahore, Fac Mech Engn, Lahore, Pakistan
[2] Univ Coll Cork, Dept Mech Engn, Cork, Ireland
[3] Khalifa Univ, Dept Mech Engn, Abu Dhabi, U Arab Emirates
关键词
Wind resource assessment; Bathymetry; ERA-5 reanalysis data; Commercial wind turbine; Offshore wind farm; RESOURCE ASSESSMENT; ENERGY RESOURCE; SOLAR-RADIATION; RENEWABLE ENERGY; FARMS; CHINA; OPTIMIZATION; VARIABILITY; SIMULATION; TURBINES;
D O I
10.1016/j.oceaneng.2023.114458
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Offshore wind power is emerging option in wind industry due to relatively consistent wind behavior over ocean. This study aims to access offshore wind resource employing a novel approach using bathymetry data, in-situ measured and 5th generation European Reanalysis (ERA-5) wind data. The measured wind data for four nearcoast locations was used in Exclusive Economic Zone (EEZ) of Pakistan. The percentage bias for wind speed and wind direction of ERA-5 data compared to measured data for coastline location is -0.2% and -0.9% respectively, which shows suitability of ERA-5 data for offshore resource assessment with acceptable accuracy. Annual mean wind speed and wind power density of the EEZ is 6.1 m/s and 263 W/m2 respectively. The northeastern part of the EEZ near coastline of Sindh province is most suitable for offshore wind farms. An offshore wind farm site suitable for monopile foundation having wind power class as Fair was selected. The performance of more than 400 commercial wind turbines was evaluated and a 4.5 MW turbine having highest net capacity factor (NCF) was selected. The capacity factor of suggested wind farm having installed capacity of 540 MW is computed as 55% which can avoid 1.45 million tons in CO2eq emissions annually.
引用
收藏
页数:17
相关论文
共 31 条
[21]   An economic evaluation of potential offshore wind farm sites in South Korea using a real options approach [J].
Sim, Jaehun .
ENERGY REPORTS, 2023, 10 :29-37
[22]   High Accuracy Forecasting with Limited Input Data: Using FFNNs to Predict Offshore Wind Power Generation [J].
Zaunseder, Elaine ;
Mueller, Larissa ;
Blankenburg, Sven .
PROCEEDINGS OF THE NINTH INTERNATIONAL SYMPOSIUM ON INFORMATION AND COMMUNICATION TECHNOLOGY (SOICT 2018), 2018, :61-68
[23]   Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain [J].
Cannon, D. J. ;
Brayshaw, D. J. ;
Methven, J. ;
Coker, P. J. ;
Lenaghan, D. .
RENEWABLE ENERGY, 2015, 75 :767-778
[24]   Long-Term Assessment of Morocco's Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data [J].
Zekeik, Younes ;
OrtizBevia, Maria J. ;
Alvarez-Garcia, Francisco J. ;
Haddi, Ali ;
El Mourabit, Youness ;
RuizdeElvira, Antonio .
JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (03)
[25]   Economic assessment and ranking of wind power potential using fuzzy-TOPSIS approach [J].
Muhammad Mohsin ;
Jijian Zhang ;
Rahman Saidur ;
Huaping Sun ;
Sadiq Mohammed Sait .
Environmental Science and Pollution Research, 2019, 26 :22494-22511
[26]   Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data [J].
Hadjipetrou, Stylianos ;
Mariethoz, Gregoire ;
Kyriakidis, Phaedon .
REMOTE SENSING, 2023, 15 (02)
[27]   Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean [J].
Ulazia, Alain ;
Saenz, Jon ;
Ibarra-Berastegui, Gabriel ;
Gonzalez-Roji, Santos J. ;
Carreno-Madinabeitia, Sheila .
APPLIED ENERGY, 2017, 208 :1232-1245
[28]   A Hierarchical Data-Driven Wind Farm Power Optimization Approach Using Stochastic Projected Simplex Method [J].
Xu, Zhiwei ;
Geng, Hua ;
Chu, Bing .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (04) :3560-3569
[29]   Correlation analysis of offshore wind and wave power potential at Indian exclusive economic zone during 2014-23 using deep learning model [J].
Vasavi, S. ;
Sobhana, M. ;
Singha, Bittu .
CURRENT SCIENCE, 2025, 128 (03) :269-282
[30]   Optimal location selection for offshore wind-PV-seawater pumped storage power plant using a hybrid MCDM approach: A two-stage framework [J].
Wu, Yunna ;
Zhang, Ting ;
Xu, Chuanbo ;
Zhang, Buyuan ;
Li, Lingwenying ;
Ke, Yiming ;
Yan, Yudong ;
Xu, Ruhang .
ENERGY CONVERSION AND MANAGEMENT, 2019, 199