Yıl 2023,
Cilt: 7 Sayı: 1, 8 - 12, 15.04.2023
Prasun Bhattacharjee
,
Somenath Bhattacharya
Kaynakça
- 1. Obama, B., The irreversible momentum of clean energy. Science, 2017. 355(6321): pp. 126-129.
- 2. Chaurasiya, P. K., Warudka, V., and S. Ahmed, Wind energy development and policy in India: A review. Energy Strategy Reviews, 2019. 24: pp. 342-357.
- 3. BP, BP Statistical Review of World Energy. 2022. [Online]. Available: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html. [Accessed 19 April 2022].
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- 5. Ministry of Power of Government of India, Renewable Generation Report. 2021. [Online]. Available: https://cea.nic.in/renewable-generation-report/?lang=en. [Accessed 23 July 2021].
- 6. Global Wind Energy Council, India Wind Outlook Towards 2022: Looking beyond headwinds. 2020. [Online]. Available: https://gwec.net/india-wind-outlook-towards-2022-looking-beyond-headwinds/. [Accessed 23 July 2021].
- 7. Hasager, C. B., Bingöl, F., Badger, M., Karagali, I. and E. Sreevalsan, Offshore Wind Potential in South India from Synthetic Aperture Radar. Information Service Department Risø National Laboratory for Sustainable Energy Technical University of Denmark, 2011.
- 8. Nagababu, G., Simha R, R., Naidu, N. K., Kachhwaha, S. S., and V. Savsani, Application of OSCAT satellite data for offshore wind power. In 5th International Conference on Advances in Energy Research, 2015, Mumbai, India.
- 9. Singh, R., and A. Kumar S.M., Estimation of Off Shore Wind Power Potential and Cost Optimization of Wind Farm in Indian Coastal Region by Using GAMS, In International Conference on Current Trends Towards Converging Technologies (ICCTCT), 2018.
- 10. Kumar, M. B. H., Balasubramaniyan, S., Padmanaban, S., and J. B. Holm-Nielsen, Wind energy potential assessment by weibull parameter estimation using multiverse optimization method: A case study of Tirumala region in India. Energies, 2019. 12(11): pp. 2158.
- 11. Nagababu, G., Kachhwaha, S.S., Naidu, N. K., and V. Savsani, Application of reanalysis data to estimate offshore wind potential in EEZ of India based on marine ecosystem considerations. Energy, 2017. 118: pp. 622–631.
- 12. Kumar, R., Stallard, T., and P. K. Stansby, Large‐scale offshore wind energy installation in northwest India: Assessment of wind resource using Weather Research and Forecasting and levelized cost of energy. Wind Energy, 2020. 24(2): pp. 174–192.
- 13. Moreno, S. R., Pierezan, J., Coelho, L. dos S., and V. C. Mariani, Multi-objective lightning search algorithm applied to wind farm layout optimization. Energy, 2021. 216: p. 119214.
- 14. Pérez-Aracil, J., Casillas-Pérez, D., Jiménez-Fernández, S., Prieto-Godino, L., and S. Salcedo-Sanz, A versatile multi-method ensemble for wind farm layout optimization. Journal of Wind Engineering and Industrial Aerodynamics, 2022. 225: p. 104991.
- 15. Thomas, J. J., Bay, C. J., Stanley, A. P., and A. Ning, Gradient-Based Wind Farm Layout Optimization Results Compared with Large-Eddy Simulations. Wind Energy Science Discussions, 2022. pp. 1-28.
- 16. Jana, R. K., and P. Bhattacharjee, A multi-objective genetic algorithm for design optimisation of simple and double harmonic motion cams. International Journal of Design Engineering, 2017. 7(2): pp. 77-91.
- 17. Turkoglu, B., and E. Kaya, Training multi-layer perceptron with artificial algae algorithm. Engineering Science and Technology, an International Journal, 2020. 23(6): pp. 1342-1350.
- 18. Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Hutter, F., Buenfil, G. L., Kheiri, A., Keedwell, E., Ocampo-Pineda, M., Özcan, E., Peña, S. I. V., Goldman, B., Rionda, S. B., Hernández-Aguirre, A., Veeramachaneni, K., and S. Cussat-Blanc, Evolutionary computation for wind farm layout optimization. Renewable Energy, 2018. 126: pp. 681-691.
- 19. Turing, A., Computing Machinery and Intelligence (1950). In The Essential Turing, Oxford University Press, 2004.
- 20. Akarsu, C. H., and Küçükdeniz, T., Job shop scheduling with genetic algorithm-based hyperheuristic approach. International Advanced Researches and Engineering Journal, 2022. 6(1): pp. 16-25.
- 21. Bhattacharjee, P., Jana, R. K., and S. Bhattacharya, S. (2021). A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India. In ITM Web of Conferences, 2021. 40: p. 03016.
Optimizing the wind power generation cost in the Tirumala Region of India
Yıl 2023,
Cilt: 7 Sayı: 1, 8 - 12, 15.04.2023
Prasun Bhattacharjee
,
Somenath Bhattacharya
Öz
Global warming is impacting almost every nation of the world and causing excessive socioeconomic damage to human civilization. India is presently the second most inhabited country on the planet and possesses the noteworthy potential to curb global greenhouse gas emissions. Most of the stakeholders of the global communities have signed the Paris treaty of 2015 to curtail the surface temperature rise. As the central government of India has announced its target to attain net zero-emission by the end of 2070, the electricity generation sector of the country needs to utilize renewable resources like wind energy rapidly. This paper focuses to optimize the wind energy generation cost in the Tirumala region of the country using the Genetic Algorithm and Particle Swarm intelligence concurrently. Tirumala is located in the area of Tirupati in the southern state of Andhra Pradesh. A relative analysis of the optimization outcomes validates the superiority of the Genetic Algorithm over the Binary Particle Swarm Optimization Algorithm for minimizing the wind energy generation cost. The application of the Genetic Algorithm has been proven to cut down the generation cost by up to 7.56% as compared to the usage of Binary Particle Swarm Optimization for similar terrain conditions and wind flow conditions in the Tirumala Area.
Kaynakça
- 1. Obama, B., The irreversible momentum of clean energy. Science, 2017. 355(6321): pp. 126-129.
- 2. Chaurasiya, P. K., Warudka, V., and S. Ahmed, Wind energy development and policy in India: A review. Energy Strategy Reviews, 2019. 24: pp. 342-357.
- 3. BP, BP Statistical Review of World Energy. 2022. [Online]. Available: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html. [Accessed 19 April 2022].
- 4. Global Wind Energy Council, Global Wind Energy Outlook. 2014. [Online]. Available: http://www.gwec.net/wp-content/uploads/2014/10/GWEO2014_WEB.pdf. [Accessed 06 September 2020].
- 5. Ministry of Power of Government of India, Renewable Generation Report. 2021. [Online]. Available: https://cea.nic.in/renewable-generation-report/?lang=en. [Accessed 23 July 2021].
- 6. Global Wind Energy Council, India Wind Outlook Towards 2022: Looking beyond headwinds. 2020. [Online]. Available: https://gwec.net/india-wind-outlook-towards-2022-looking-beyond-headwinds/. [Accessed 23 July 2021].
- 7. Hasager, C. B., Bingöl, F., Badger, M., Karagali, I. and E. Sreevalsan, Offshore Wind Potential in South India from Synthetic Aperture Radar. Information Service Department Risø National Laboratory for Sustainable Energy Technical University of Denmark, 2011.
- 8. Nagababu, G., Simha R, R., Naidu, N. K., Kachhwaha, S. S., and V. Savsani, Application of OSCAT satellite data for offshore wind power. In 5th International Conference on Advances in Energy Research, 2015, Mumbai, India.
- 9. Singh, R., and A. Kumar S.M., Estimation of Off Shore Wind Power Potential and Cost Optimization of Wind Farm in Indian Coastal Region by Using GAMS, In International Conference on Current Trends Towards Converging Technologies (ICCTCT), 2018.
- 10. Kumar, M. B. H., Balasubramaniyan, S., Padmanaban, S., and J. B. Holm-Nielsen, Wind energy potential assessment by weibull parameter estimation using multiverse optimization method: A case study of Tirumala region in India. Energies, 2019. 12(11): pp. 2158.
- 11. Nagababu, G., Kachhwaha, S.S., Naidu, N. K., and V. Savsani, Application of reanalysis data to estimate offshore wind potential in EEZ of India based on marine ecosystem considerations. Energy, 2017. 118: pp. 622–631.
- 12. Kumar, R., Stallard, T., and P. K. Stansby, Large‐scale offshore wind energy installation in northwest India: Assessment of wind resource using Weather Research and Forecasting and levelized cost of energy. Wind Energy, 2020. 24(2): pp. 174–192.
- 13. Moreno, S. R., Pierezan, J., Coelho, L. dos S., and V. C. Mariani, Multi-objective lightning search algorithm applied to wind farm layout optimization. Energy, 2021. 216: p. 119214.
- 14. Pérez-Aracil, J., Casillas-Pérez, D., Jiménez-Fernández, S., Prieto-Godino, L., and S. Salcedo-Sanz, A versatile multi-method ensemble for wind farm layout optimization. Journal of Wind Engineering and Industrial Aerodynamics, 2022. 225: p. 104991.
- 15. Thomas, J. J., Bay, C. J., Stanley, A. P., and A. Ning, Gradient-Based Wind Farm Layout Optimization Results Compared with Large-Eddy Simulations. Wind Energy Science Discussions, 2022. pp. 1-28.
- 16. Jana, R. K., and P. Bhattacharjee, A multi-objective genetic algorithm for design optimisation of simple and double harmonic motion cams. International Journal of Design Engineering, 2017. 7(2): pp. 77-91.
- 17. Turkoglu, B., and E. Kaya, Training multi-layer perceptron with artificial algae algorithm. Engineering Science and Technology, an International Journal, 2020. 23(6): pp. 1342-1350.
- 18. Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Hutter, F., Buenfil, G. L., Kheiri, A., Keedwell, E., Ocampo-Pineda, M., Özcan, E., Peña, S. I. V., Goldman, B., Rionda, S. B., Hernández-Aguirre, A., Veeramachaneni, K., and S. Cussat-Blanc, Evolutionary computation for wind farm layout optimization. Renewable Energy, 2018. 126: pp. 681-691.
- 19. Turing, A., Computing Machinery and Intelligence (1950). In The Essential Turing, Oxford University Press, 2004.
- 20. Akarsu, C. H., and Küçükdeniz, T., Job shop scheduling with genetic algorithm-based hyperheuristic approach. International Advanced Researches and Engineering Journal, 2022. 6(1): pp. 16-25.
- 21. Bhattacharjee, P., Jana, R. K., and S. Bhattacharya, S. (2021). A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India. In ITM Web of Conferences, 2021. 40: p. 03016.