Utilization of Renewable energy for Industrial Applications using Quantum Computing
DOI:
https://doi.org/10.58260/j.nras.2202.0102Keywords:
Quantum Computing, Industrial Applications, Renewable energyAbstract
Even though the energy and utilities industry have trouble integrating new technologies for a long time, the benefits of quantum computing make it worth the trouble. In the past, it has been hard for the utilities industry to use new technology to drive strategic initiatives. But those who do see big improvements in how well they run their businesses. For example, utilities that put a lot of money into digital transformation have the chance to cut their operating costs by about 25%. Quantum computing is a new field of technology that is not as well developed as digital projects, but it is still something that modern renewable energy and utility companies should look into. Quantum computing will give utilities a lot more computing power, which will let them solve business problems that were too hard to try before.
References
Anand Singh Rajawat, kanishk Barhanpurkar , Rabindra Nath Shaw Ankush Ghosh, Chapter five - IoT in renewable energy generation for conservation of energy using artificial intelligence, Applications of AI and IOT in Renewable Energy 2022, Pages 89-105
Upadhyay, S.N., Satrughna, J.A.K. & Pakhira, S. Recent advancements of two-dimensional transition metal dichalcogenides and their applications in electrocatalysis and energy storage. emergent mater. 4, 951–970 (2021). https://doi.org/10.1007/s42247-021-00241-2.
G, Ramya, Suresh, Arthi, Murugesanet al. “Renewable Energy Enhanced Smart Energy Management System Using Internet of Things.” ECS Transactions, vol. 107, no. 1, 24 Apr. 2022, pp. 5335–5342, 10.1149/10701.5335ecst.
Giani, A. Quantum computing opportunities in renewable energy. Nat Comput Sci 1, 90–91 (2021). https://doi.org/10.1038/s43588-021-00032-z.
A., Becquin, G. et al ,Quantum Technology and Application Consortium – QUTAC., Bayerstadler,. Industry quantum computing applications. EPJ Quantum Technol. 8, 25 (2021). https://doi.org/10.1140/epjqt/s40507-021-00114-x
Koronen, C., Åhman, M. & Nilsson, L.J. Data centres in future European energy systems—energy efficiency, integration and policy. Energy Efficiency 13, 129–144 (2020). https://doi.org/10.1007/s12053-019-09833-8.
Emad, D., El-Hameed, M.A., Yousef, M.T. et al. Computational Methods for Optimal Planning of Hybrid Renewable Microgrids: A Comprehensive Review and Challenges. Arch Computat Methods Eng 27, 1297–1319 (2020). https://doi.org/10.1007/s11831-019-09353-9
Maheshwari, S., Shetty, S., Ratnakar, R. et al. Role of Computational Science in Materials and Systems Design for Sustainable Energy Applications: An Industry Perspective. J Indian Inst Sci 102, 11–37 (2022). https://doi.org/10.1007/s41745-021-00275-9
Aaryashree, Sahoo, S., Walke, P. et al. Recent developments in self-powered smart chemical sensors for wearable electronics. Nano Res. 14, 3669–3689 (2021). https://doi.org/10.1007/s12274-021-3330-8
Chinde, V., Hirsch, A., Livingood, W. et al. Simulating dispatchable grid services provided by flexible building loads: State of the art and needed building energy modeling improvements. Build. Simul. 14, 441–462 (2021). https://doi.org/10.1007/s12273-020-0687-1.
Ahmed, F., Naeem, M., Ejaz, W. et al. Renewable Energy Assisted Sustainable and Environment Friendly Energy Cooperation in Cellular Networks. Wireless Pers Commun 108, 2585–2607 (2019). https://doi.org/10.1007/s11277-019-06539-z
Srinivasan, S. Power Relationships: Marginal Cost Pricing of Electricity and Social Sustainability of Renewable Energy Projects. Technol Econ Smart Grids Sustain Energy 4, 13 (2019). https://doi.org/10.1007/s40866-019-0070-4
Subramanian, S., Mohan, R., Shanmugam, S.K. et al. Speed control and quantum vibration reduction of Brushless DC Motor using FPGA based Dynamic Power Containment Technique. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02969-5
Ning, H., Liu, H. Cyber-physical-social-thinking space based science and technology framework for the Internet of Things. Sci. China Inf. Sci. 58, 1–19 (2015). https://doi.org/10.1007/s11432-014-5209-2
Das, R.N., Roy, K. Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future. Mol Divers 17, 151–196 (2013). https://doi.org/10.1007/s11030-012-9413-y
Bibri, S.E., Krogstie, J. The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis. J Big Data 4, 38 (2017). https://doi.org/10.1186/s40537-017-0091-6
Singla, P., Duhan, M. & Saroha, S. A comprehensive review and analysis of solar forecasting techniques. Front. Energy 16, 187–223 (2022). https://doi.org/10.1007/s11708-021-0722-7.
Sharifi, A., Ahmadi, M. & Ala, A. The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic. Environ Sci Pollut Res 28, 46964–46984 (2021). https://doi.org/10.1007/s11356-021-15292-5.
Bedi, P., Goyal, S.B., Rajawat, A.S., Shaw, R.N., Ghosh, A. (2022). Application of AI/IoT for Smart Renewable Energy Management in Smart Cities. In: Piuri, V., Shaw, R.N., Ghosh, A., Islam, R. (eds) AI and IoT for Smart City Applications. Studies in Computational Intelligence, vol 1002. Springer, Singapore. https://doi.org/10.1007/978-981-16-7498-3_8.
Rajawat, A.S., Bedi, P., Goyal, S.B., Shaw, R.N., Ghosh, A., Aggarwal, S. (2022). AI and Blockchain for Healthcare Data Security in Smart Cities. In: Piuri, V., Shaw, R.N., Ghosh, A., Islam, R. (eds) AI and IoT for Smart City Applications. Studies in Computational Intelligence, vol 1002. Springer, Singapore. https://doi.org/10.1007/978-981-16-7498-3_12
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
