Articles | Volume 54
17 Oct 2020
17 Oct 2020
Layout optimization for offshore wind farms in India using the genetic algorithm technique
Narender Kangari Reddy and Somnath Baidya Roy
No articles found.
Tanvi Gupta and Somnath Baidya Roy
Adv. Geosci., 56, 129–139,Short summary
In this paper we study how the momentum extracted by wind turbines get replenished so that the wind farm can continue to function. We use a numerical model to simulate the dynamics of a hypothetical coastal wind farm in the Arabian Sea under sea breeze conditions. Results show that vertical turbulent eddies can replenish more than half of the lost momentum, but horizontal advection also plays a role near the wind farm edges especially in sparsely packed wind farms.
Aheli Das and Somnath Baidya Roy
Adv. Geosci., 56, 89–96,Short summary
In this study we evaluated subseasonal-seasonal scale forecasts of solar radiation, wind speed, temperature and relative humidity over India from 6 global models by comparing against observations. Results show that the overall quality of the forecasts are not good. However, they demonstrate enough skill suggesting that further improvement through calibration may make then useful for the renewable energy sector.
Tanvi Gupta and Somnath Baidya Roy
Wind Energ. Sci., 6, 1089–1106,Short summary
Wind turbines extract momentum from atmospheric flow and convert that to electricity. This study explores recovery processes in wind farms that replenish the momentum so that wind farms can continue to function. Experiments with a numerical model show that momentum transport by turbulent eddies from above the wind turbines is the major contributor to recovery except for strong wind conditions and low wind turbine density, where horizontal advection can also play a major role.
Shilpa Gahlot, Tzu-Shun Lin, Atul K. Jain, Somnath Baidya Roy, Vinay K. Sehgal, and Rajkumar Dhakar
Earth Syst. Dynam., 11, 641–652,Short summary
Spring wheat, a staple for millions of people in India and the world, is vulnerable to changing environmental and management factors. Using a new spring wheat model, we find that over the 1980–2016 period elevated CO2 levels, irrigation, and nitrogen fertilizers led to an increase of 30 %, 12 %, and 15 % in countrywide production, respectively. In contrast, rising temperatures have reduced production by 18 %. These effects vary across the country, thereby affecting production at regional scales.
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications, B. Am. Meteorol. Soc., 92, 157–74, https://doi.org/10.1175/2010BAMS2946.1, 2011.
Barthelmie, R. J., Pryor, S. C., Frandsen, S. T., Hansen, K. S., Schepers, J. G., Rados, K., Schlez, W., Neubert, A., Jensen, L. E., and Neckelmann, S.: Quantifying the impact of wind turbine wakes on power output at offshore wind farms, J. Atmos. Ocean. Tech., 27, 1302–1317, https://doi.org/10.1175/2010JTECHA1398.1, 2010.
CEA – Central Electricity Authority: Draft national electricity plan, Ministry of Power, Govt. of India, Vol. 1, 375 pp., available at: http://www.cea.nic.in/reports/committee/nep/nep_dec.pdf (last access: 6 June 2020), 2016.
Charhouni, N., Sallaou, M., and Mansouri, K.: Realistic Wind Farm Design Layout Optimization with Different Wind Turbines Types, Int. J. Energ. Environ. Eng., 10, 307–318, https://doi.org/10.1007/s40095-019-0303-2, 2019.
Chen, K., Song, M. X., Zhang, X., and Wang, S. F.: Wind turbine layout optimization with multiple hub height wind turbines using Greedy Algorithm, Renew. Energ., 96, 676–686, https://doi.org/10.1016/j.renene.2016.05.018, 2016.
Corten, G. P. and Brand, A. J.: Resource decrease by large scale wind farming, in: European Wind Energy Conference, 22–25 November 2004, London, ECN-RX-04-124, 2004.
Dash, P. K.: Offshore wind energy in India, Akshay Urja, MNRE, Govt. of India, Vol. 12, 23–25, available at: https://mnre.gov.in/img/documents/uploads/2e423892727a456e93a684f38d8622f7.pdf (last access: 6 June 2020), 2019.
Donovan, S.: Wind farm optimization, in: Proceedings of the 40th Annual ORSNZ Conference, Victoria University, 2–3 December 2005, Wellington, New Zealand, 196–205, 2005.
DuPont, B., Cagan, J., and Moriarty, P.: An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended Pattern Search algorithm, Energy, 106, 802–814, https://doi.org/10.1016/j.energy.2015.12.033, 2016.
Eroglu, Y. and Seçkiner, S. U.: Design of wind farm layout sing Ant Colony algorithm, Renew. Energ., 44, 53–62, https://doi.org/10.1016/j.renene.2011.12.013, 2012.
Feng, J. and Shen, W. Z.: Solving the wind farm layout optimization problem using Random Search algorithm, Renew. Energ., 78, 182–192, https://doi.org/10.1016/j.renene.2015.01.005, 2015.
FOWIND – Facilitating Offshore Wind in India Project: Feasibility study for offshore wind farm development in Tamil Nadu, MNRE, Govt. of India, 79 pp., available at: https://mnre.gov.in/img/documents/uploads/3fc822d4816d4e1093ec854144fde5d1.pdf (last access: 6 June 2020), 2018.
FOWPI – First Offshore Wind Project of India Project: Report on wind turbine layout and AEP, MNRE, Govt. of India, 45 pp., available at: https://mnre.gov.in/img/documents/uploads/3359fef1ece84cca9116de804ee255ad.pdf (last access: 6 June 2020), 2018.
Gao, X., Yang, H., Lin, L., and Koo, P.: Wind turbine layout optimization using multi-population Genetic Algorithm and a case study in Hong Kong offshore, J. Wind Eng. Ind. Aerod., 139, 89–99, https://doi.org/10.1016/j.jweia.2015.01.018, 2015.
González, J. S., Rodriguez, A. G. G., Mora, J. C., Santos, J. R., and Payan, M. B.: Optimization of wind farm turbines layout using an Evolutive Algorithm, Renew. Energ., 35, 1671–1681, https://doi.org/10.1016/j.renene.2010.01.010, 2010.
Grady, S. A., Hussaini, M. Y., and Abdullah, M. M.: Placement of wind turbines using genetic algorithm, Renew. Energy, 30, 259–270, https://doi.org/10.1016/j.renene.2004.05.007, 2005.
Greene, C. A., Thirumalai, K., Kearney, K. A., Delgado, J. M., Schwanghart, W., Wolfenbarger, N. S., Thyng, K. M., Gwyther, D. E., Gardner, A. S., and Blankenship, D.: The climate data toolbox for MATLAB, Geochem. Geophy. Geosy., 20, 3774–3781, https://doi.org/10.1029/2019GC008392, 2019.
Guirguis, D., Romero, D., and Amon, C.: Gradient-Based multidisciplinary design of wind farms with Continuous-Variable formulations, Appl. Energ., 197, 279–291, https://doi.org/10.1016/j.apenergy.2017.04.030, 2017.
Herbert-Acero, J. F., Probst, O., Réthoré, P. E., Larsen, G. C., and Castillo-Villar, K. K.: A review of methodological approaches for the design and optimization of wind farms, Energies, 7, 6930–7016, https://doi.org/10.3390/en7116930, 2014.
Højstrup, J.: Spectral coherence in wind turbine wakes, J. Wind Eng. Ind. Aerodyn., 80, 137–146, https://doi.org/10.1016/S0167-6105(98)00198-6, 1999.
Hou, P., Hu, W., Chen, C., Soltani, M., and Chen, Z.: Optimization of offshore wind farm layout in restricted zones, Energy, 113, 487–496, https://doi.org/10.1016/j.energy.2016.07.062, 2016.
ISO 2533:1975: Standard Atmosphere, International Organisation for Standardization, Geneva, Switzerland, 108 pp., 1975.
Jensen, N. O.: A note on wind turbine interaction, technical report Riso-M-2411, Risoe National Laboratory, Roskilde, Denmark, 16 pp., 1983.
Kallioras, N., Lagaros, N., Karlaftis, M., and Pachy, P.: Optimum layout design of onshore wind farms considering stochastic loading, Adv. Eng. Softw., 88, 8–20, https://doi.org/10.1016/j.advengsoft.2015.05.002, 2015.
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster efficiency, in: European wind energy association conference and exhibition, 7–9 October 1986, Rome, Italy, 407–410, 1987.
Khan, F., Gupta, T., Baidya Roy, S., and Miller, L.: Assessment of wind resource in the Palk Strait using different methods, in: 2017 AGU Fall Meeting, AGU, 11–15 December 2017, New Orleans, USA, AGU2017-270771, 2017.
Kusiak, A. and Song, Z.: Design of wind farm layout for maximum wind energy capture, Renew. Energ., 35, 685–94, https://doi.org/10.1016/j.renene.2009.08.019, 2010.
Marmidis, G., Lazarou, S., and Pyrgioti, E.: Optimal placement of wind turbines in a wind park using Monte Carlo Simulation, Renew. Energ., 33, 1455–1460, https://doi.org/10.1016/j.renene.2007.09.004, 2008.
Mayo, M. and Daoud, M.: Informed mutation of wind farm layouts to maximise energy harvest, Renew. Energ., 89, 437–448, https://doi.org/10.1016/j.renene.2015.12.006, 2016.
MirHassani, S. and Yarahmadi, A.: Wind farm layout optimization under uncertainty, Renew. Energ., 107, 288–297, https://doi.org/10.1016/j.renene.2017.01.063, 2017.
MNRE – Ministry of New and Renewable Energy: Annual Report 2019-20, Govt. of India, 171 pp., available at: https://mnre.gov.in/img/documents/uploads/file_f-1585710569965.pdf, last access: 6 June 2020.
Mosetti, G., Poloni, C., and Diviacco, D.: Optimization of wind turbine positioning in large wind farms by means of a Genetic Algorithm, J. Wind Eng. Ind. Aerodyn., 51, 105–116, https://doi.org/10.1016/0167-6105(94)90080-9, 1994.
Parada, L., Herrera, C., Flores, P., and Parada, V.: Wind farm layout optimization sing a Gaussian-Based wake model, Renew. Energ., 107, 531–541, https://doi.org/10.1016/j.renene.2017.02.017, 2017.
Pillai, A., Chick, J., Johanning, L., and Khorasanchi, M.: Offshore wind farm layout optimization using Particle Swarm Optimization, J. Ocean Eng. Mar. Energ., 4, 73–88, https://doi.org/10.1007/s40722-018-0108-z, 2018.
Samorani, M.: The wind farm layout optimization problem, in: Handbook of Wind Power Systems. Energy Systems, edited by: Pardalos, P., Rebennack, S., Pereira, M., Iliadis, N., and Pappu, V., Springer, Berlin, Heidelberg, Germany, https://doi.org/10.1007/978-3-642-41080-2_2, 2013.
Song, M., Wen, Y., Duan, B., Wang, J., and Gong, Q.: Micro-Siting optimization of a wind farm built in multiple phases, Energy, 137, 95–103, https://doi.org/10.1016/j.energy.2017.06.127, 2017.
Song, Z., Zhang, Z., and Chen, X.: The decision model of 3-dimensional wind farm layout design, Renew. Energ., 85, 248–258, https://doi.org/10.1016/j.renene.2015.06.036, 2016.
Tingey, E. and Ning, A.: Trading off sound pressure level and average power production for wind farm layout optimization, Renew. Energ., 114, 547–555, https://doi.org/10.1016/j.renene.2017.07.057, 2017.
Turner, S., Romero, D., Zhang, P., Amon, C., and Chan, T.: A new mathematical programming approach to optimize wind farm layouts, Renew. Energ., 63, 674–680, https://doi.org/10.1016/j.renene.2013.10.023, 2014.
Wagner, M., Day, J., and Neumann, F.: A fast and effective local search algorithm for optimizing the placement of wind turbines, Renew. Energ., 51, 64–70, https://doi.org/10.1016/j.renene.2012.09.008, 2013.
Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Hutter, F., Buenfil, G. L., Kheiri, A., Keedwell, E., Ocampo-Pineda, M., Özcan, E., and Peña, S. I. V.: Evolutionary computation for wind farm layout optimization, Renew. Energ., 126, 681–691, https://doi.org/10.1016/j.renene.2018.03.052, 2018.
Yamani Douzi Sorkhabi, S., Romero, D., Yan, G., Gu, M., Moran, J., Morgenroth, M., and Amon, C.: The impact of land use constraints in multi-objective energy-noise wind farm layout optimization, Renew. Energ., 85, 359–370, https://doi.org/10.1016/j.renene.2015.06.026, 2016.
Yang, K., Kwak, G., Cho, K., and Huh, J.: Wind farm layout optimization for wake effect uniformity, Energy, 183, 983–995, https://doi.org/10.1016/j.energy.2019.07.019, 2019.
Yin, P. Y., Wu, T., and Hsu, P.: Risk management of wind farm micro-siting using an enhanced Genetic Algorithm with simulation optimization, Renew. Energ., 107, 508–521, https://doi.org/10.1016/j.renene.2017.02.036, 2017.
Zhao, F., Gao, Y., Wang, T., Yuan, J., and Gao, X.: Experimental study on wake evolution of a 1.5 MW wind turbine in a complex terrain wind farm based on LiDAR measurements, Sustainability, 12, 2467, https://doi.org/10.3390/su12062467, 2020.
In this study, we apply the Genetic Algorithm technique that mimics the natural selection process observed in nature to design optimal layouts for massive wind farms off the southeastern coast of India using real wind data. Our results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) due to the reduction in wake losses.
In this study, we apply the Genetic Algorithm technique that mimics the natural selection...