Layout optimization for offshore wind farms in India using the genetic algorithm technique
Narender Kangari Reddy
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi,
New Delhi, 110016, India
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi,
New Delhi, 110016, India
Related authors
No articles found.
K. Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
EGUsphere, https://doi.org/10.5194/egusphere-2024-1431, https://doi.org/10.5194/egusphere-2024-1431, 2024
Short summary
Short summary
The study aimed to improve the representation of spring wheat and rice in the CLM5. The modified CLM5 model performed significantly better than the default model in simulating crop phenology, yield, carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific parameters for accurately simulating vegetation processes and land surface processes.
Kangari Narender Reddy, Shilpa Gahlot, Somnath Baidya Roy, Gudimetla Venkateswara Varma, Vinay Kumar Sehgal, and Gayatri Vangala
Earth Syst. Dynam., 14, 915–930, https://doi.org/10.5194/esd-14-915-2023, https://doi.org/10.5194/esd-14-915-2023, 2023
Short summary
Short summary
Carbon fluxes from agroecosystems change the carbon cycle and the amount of CO2 in the air. Using the Integrated Science Assessment Model (ISAM), we looked at the carbon cycle in areas where spring wheat is grown. The results showed that fluxes vary a lot between regions, mostly because planting times are different. According to our investigation into which variables have the greatest impact on the carbon cycle, nitrogen fertilizers added to crops have the greatest impact on carbon uptake.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Tanvi Gupta and Somnath Baidya Roy
Adv. Geosci., 56, 129–139, https://doi.org/10.5194/adgeo-56-129-2021, https://doi.org/10.5194/adgeo-56-129-2021, 2021
Short summary
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, https://doi.org/10.5194/adgeo-56-89-2021, https://doi.org/10.5194/adgeo-56-89-2021, 2021
Short summary
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, https://doi.org/10.5194/wes-6-1089-2021, https://doi.org/10.5194/wes-6-1089-2021, 2021
Short summary
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, https://doi.org/10.5194/esd-11-641-2020, https://doi.org/10.5194/esd-11-641-2020, 2020
Short summary
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.
Cited articles
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., Khorasanchi, M., Barbouchi, S., and Johanning, L.:
Application of an offshore wind farm layout optimization methodology at
Middelgrunden wind farm, Ocean. Eng., 139, 287–297,
https://doi.org/10.1016/j.oceaneng.2017.04.049, 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.
Short summary
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...