Analysis of Wave Energy Resources Around the Saint Martin Island in Bangladesh

Abstract

Saint Martin Island is the only coral island and one of the well-known tourist spots in Bangladesh. Because of its geographic location, electricity cannot be supplied from the mainland through the electricity grid. Diesel generators and solar power are the only means of electricity generation presently available there. Surrounded by the sea, Saint Martin Island has the ideal conditions for wave energy extraction. In this research, numerical models have been developed using the Delft3D simulation software to determine the wave characteristics of different locations around Saint Martin Island. The results have been calibrated and validated against the data obtained from well-known data sources. The wave power densities have been calculated using the data obtained from the simulation models. The findings of the research show that the wave power density increases significantly from shallow water to deep water and a large amount of wave energy can be extracted during the summer and rainy monsoon seasons. The maximum hourly average value of wave power in 2016 has been determined to be 6.90 kW/m at location with a water depth of 27.80 m. Wave energy resources are also observed to be sufficiently stable with the coefficients of variation of wave power density less than 0.62, except for December, January, and May of that particular year. Moreover, the annual effective energies have been determined to be within the range of 36.57 to 57.28 MWh/m, which will be sufficient to meet the electricity requirement of the island communities.

Keywords

Renewable energy / Ocean waves / Saint Martin Island / Delft3D / Wave power density / Stability

References

[1] Aboobacker VM, Shanas PR, Alsaafani MA, Albarakati AM. Wave energy resource assessment for Red Sea. Renew Energy, 2017, 114: 46-58

[2] Akpınar A, Kömürcü Mİ. Assessment of wave energy resource of the Black Sea based on 15-year numerical hindcast data. Appl Energy, 2013, 101: 502-512

[3] Arinaga RA, Cheung KF. Atlas of global wave energy from 10 years of reanalysis and hindcast data. Renew Energy, 2012, 39(1): 49-64

[4] Besio G, Mentaschi L, Mazzino A. Wave energy resource assessment in the Mediterranean Sea on the basis of a 35-year hindcast. Energy, 2016, 94: 50-63

[5] Bouhrim H, El Marjani A. Wave energy assessment along the Moroccan Atlantic coast. J Mar Sci Appl, 2019, 18(2): 142-152

[6] Cornett AM (2008) A global wave energy resource assessment. Proceedings of the Eighteenth International Offshore and Polar Engineering Conference, Vancouver

[7] Das M, Shazeeb TI, Islam MR (2018) Numerical investigation of wave energy of Saint Martin region of Bangladesh. Proceedings of the 11th International Conference on Marine Technology (MARTEC), UTM, Kuala Lumpur, pp 39–44

[8] Deltares (2017a) Delft3D-FLOW user manual: simulation of multidimensional hydrodynamic flows and transport phenomena, including sediments. Deltares: Delft, the Netherlands, version: 3.15, SVN Revision: 52614, 173–268

[9] Deltares (2017b) Delft3D user manual: Delft3D, functional specifications. Deltares: Delft, the Netherlands, version: 3.15, SVN Revision: 52614, 1–20

[10] Deltares (2017c) Delft3D-WAVE user manual: simulation of short-crested waves with SWAN. Deltares: Delft, The Netherlands, Version: 3.15, SVN Revision: 52614, 6

[11] Espindola RL, Araújo AM. Wave energy resource of Brazil: an analysis from 35 years of ERA-interim reanalysis data. PLoS One, 2017, 12(8): e0183501

[12] Hughes MG, Heap AD. National-scale wave energy resource assessment for Australia. Renew Energy, 2010, 35(8): 1783-1791

[13] Iglesias G, Carballo R. Wave energy and nearshore hot spots: the case of the SE Bay of Biscay. Renew Energy, 2010, 35(11): 2490-2500

[14] Islam S, Khan MZ. A review of energy sector of Bangladesh. Energy Procedia, 2017, 110: 611-618

[15] Islam MT, Uddin MG, Chakma TB, Chowdhury MZ. Feasibility study of ocean wave of the bay of Bengal to generate electricity as a renewable energy with a proposed design of energy conversion system. Int J Renew Energy Res (IJRER), 2014, 4(2): 445-452

[16] Jiang B, Wu G, Ding J, Ma C, Fang Y, Wang X. Assessment of the wave energy resource in the South China Sea. Proc Instit Civil Eng-Mar Eng, 2019, 172(1): 23-33

[17] Kim G, Jeong WM, Lee KS, Jun K, Lee ME. Offshore and nearshore wave energy assessment around the Korean peninsula. Energy, 2011, 36(3): 1460-1469

[18] Kumar VS, Anoop TR. Wave energy resource assessment for the Indian shelf seas. Renew Energy, 2015, 76: 212-219

[19] Kumar VS, Dubhashi KK, Nair TB, Singh J. Wave power potential at a few shallow-water locations around Indian coast. Curr Sci, 2013, 104(9): 1219-1224

[20] Lavidas G, Venugopal V, Friedrich D. Wave energy extraction in Scotland through an improved nearshore wave atlas. Int J Mar Energy, 2017, 17: 64-83

[21] Liang B, Fan F, Liu F, Gao S, Zuo H. 22-year wave energy hindcast for the China east adjacent seas. Renew Energy, 2014, 71: 200-207

[22] Monteforte M, Re CL, Ferreri GB. Wave energy assessment in Sicily (Italy). Renew Energy, 2015, 78: 276-287

[23] Monteiro WM, Sarmento AJ, Fernandes AJ, Fernandes JM (2016) Statistical analysis of wave energy resources available for conversion at natural caves of cape-Verde Islands. Ocean Sci Discuss:1–23

[24] Morim J, Cartwright N, Etemad-Shahidi A, Strauss D, Hemer M. Wave energy resource assessment along the southeast coast of Australia on the basis of a 31-year hindcast. Appl Energy, 2016, 184: 276-297

[25] Rahman H, Mahdy AT, Islam MR, Haque N (2018) A study for assessing wave energy of the bay of Bengal. Proceedings of the international conference on innovation in engineering and technology (ICIET), 1–5. https://doi.org/10.1109/CIET.2018.8660935

[26] Rosales P, Ocampo-Torres FJ, Osuna P, Monbaliu J, Padilla-Hernández R. Wave-current interaction in coastal waters: effects on the bottom-shear stress. J Mar Syst, 2008, 71(1–2): 131-148

[27] Sarker MT, Rahman MA, Mahmud PD. Electricity demand load forecasting for a remote area of Bangladesh. Int J Sci Eng, 2017, 8(1): 265-277

[28] Silva D, Rusu E, Guedes Soares C. Evaluation of various technologies for wave energy conversion in the Portuguese nearshore. Energies, 2013, 6(3): 1344-1364

[29] Swain J, Umesh PA, Balchand AN, Bhaskaran PK. Wave hindcasting using WAM and WAVEWATCH III: a comparison study utilizing oceansat-2 (OSCAT) winds. J Oceanogr Mar Res, 2017, 5(2): 2572-3103

[30] Wan Y, Zhang J, Meng J, Wang J. A wave energy resource assessment in the China’s seas based on multi-satellite merged radar altimeter data. Acta Oceanol Sin, 2015, 34(3): 115-124

[31] Wan Y, Fan C, Zhang J, Meng J, Dai Y, Li L, Sun W, Zhou P, Wang J, Zhang X. Wave energy resource assessment off the coast of China around the Zhoushan Islands. Energies, 2017, 10(9): 1320

[32] Wan Y, Fan C, Dai Y, Li L, Sun W, Zhou P, Qu X. Assessment of the joint development potential of wave and wind energy in the South China Sea. Energies, 2018, 11(2): 398

[33] Yaakob O, Hashim FE, Omar KM, Din AH, Koh KK. Satellite-based wave data and wave energy resource assessment for South China Sea. Renew Energy, 2016, 88: 359-371

[34] Zheng C, Zhuang H, Li X, Li X. Wind energy and wave energy resources assessment in the East China Sea and South China Sea. Sci China Technol Sci, 2012, 55(1): 163-173

[35] Zheng CW, Pan J, Li JX. Assessing the China Sea wind energy and wave energy resources from 1988 to 2009. Ocean Eng, 2013, 65: 39-48

[36] Zheng C, Shao L, Shi W, Su Q, Lin G, Li X, Chen X. An assessment of global ocean wave energy resources over the last 45 a. Acta Oceanol Sin, 2014, 33(1): 92-101