Abstract
Technology with renewable energy have crucial Solar energy plays a crucial role in tackling the worldwide shift towards sustainable energy sources. Photovoltaic (PV) systems and fuel cells are two prominent sources of clean energy; however, they exhibit intermittent and variable power generation patterns, hindering their widespread adoption. This paper proposes a novel approach to improve performance of Hybrids (SPV-FCH) through the integration of Artificial Intelligence (AI) techniques. The synergy aims to create more reliable, continuous power generation system with joining nature renewable energy which includes consistent contribution of fuel cells. The integration of AI algorithms offers an intelligent control mechanism that optimizes the operation of the hybrid system, thereby overcoming fluctuations in irradiance of solar, the dynamic nature of energy demand. The AI-enabled control system employs predictive analytics and machine learning algorithms to forecast solar irradiance patterns, weather conditions, and energy consumption trends. By leveraging real-time data and historical patterns, the system can dynamically adjust both the components, optimizing their performance for maximum energy output, efficiency, and overall system reliability. Furthermore, the AI system enables proactive maintenance and fault detection, enhancing the overall resilience and longevity of the hybrid system. Through continuous learning and adaptation, the AI controller refines its predictions and control strategies, ensuring optimal performance under varying environmental conditions. This paper discusses the design and implementation of the AI-enabled control system for SPV-FCH hybrids, highlighting its effectiveness in achieving improved energy yield, grid stability, and cost-effectiveness. The proposed approach not only addresses the intermittent challenges associated with solar PV but also maximizes the utilization of both technologies, contributing advancement sustainable including resilient power solutions. The findings presented in this paper contribute valuable insights into the integration of AI in renewable energy systems, paving the way for smarter and more efficient hybrid power generation technologies.
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References
Merabet, A., Ahmed, K.T., Ibrahim, H., Beguenane, R., Ghias, A.M.: Energy management and control system for laboratory scale microgrid based wind-PV-battery. IEEE Trans. Sustain. Energy 8(1), 145–154 (2017)
Ghenai, C., Janajreh, I.: Design of solar-biomass hybrid microgrid system in Sharjah. Energy Procedia 103, 357–362 (2016)
Ghenai, C., Janajreh, I.: Comparison of resource intensities and operational parameters of renewable, fossil fuel, and nuclear power systems. Int. J. Therm. Environ. Eng. 5(2), 95–104 (2013)
Chen, X., Zhu, Y., Li, G., Li, S.: Energy management strategy for grid-connected solar photovoltaic-fuel cell hybrid power system. J. Power. Sources 363, 420–430 (2017)
Alkhateeb, E., Abu Hijleh, B., Rengasamy, E., Muhammed, S.: Building refurbishment strategies and their impact on saving energy in the United Arab Emirates. In: Proceedings of SBE16 Dubai, 17–19 January 2016, Dubai-UAE (2016)
Ghenai, C., Salameh, T., Merabet, A.: Technico-economic analysis of off grid solar PV/Fuel cell energy system for residential community in desert region. Int. J. Hydrog. Energy 45(20), 11460–11470 (2020). https://doi.org/10.1016/j.ijhydene.2018.05.110
Lan, H., Wen, S., Hong, Y.Y., David, C.Y., Zhang, L.: Optimal sizing of hybrid PV/diesel/battery in ship power system. Appl. Energy 158, 26–34 (2015)
Heydari, A., Askarzadeh, A.: Optimization of a biomass-based photovoltaic power plant for an off-grid application subject to loss of power supply probability concept. Appl. Energy 165, 601–611 (2016)
Sen, R., Bhattacharyya, S.C.: Off-grid electricity generation with renewable energy technologies in India: an application of HOMER. Renew. Energy 62, 388–398 (2014)
Hu, W., Shang, Q., Bian, X., Zhu, R.: Energy management strategy of hybrid energy storage system based on fuzzy control for ships. Int. J. Low-Carbon Technol. 17, 169–175 (2021). https://doi.org/10.1093/ijlct/ctab094
International Energy Agency (IEA) Reports: https://www.iea.org/reports
International Renewable Energy Agency (IRENA) Reports: https://www.irena.org/reports
IPCC Special Report on Global Warming of 1.5° C: https://www.ipcc.ch/sr15/
Gan, K., Shek, J.K.H., Mueller, M.A.: Hybrid wind–photovoltaic–diesel–battery system sizing tool development using empirical approach, life-cycle cost and performance analysis: a case study in Scotland. Energy Convers. Manag. 106, 479–494 (2015)
Lai, W., Zhang, N., Yu, J.: Artificial intelligence-based dynamic power dispatch for grid-connected solar PV-fuel cell hybrid power system. Energies 12(24), 4716 (2019)
Pang, X., Yang, H., Shen, W., Blaabjerg, F.: Techno-economic analysis and optimization of grid-connected solar photovoltaic-fuel cell hybrid systems. IEEE Trans. Sustain. Energy 10(4), 1827–1837 (2019)
Patel, N., Lu, Y., Verma, R.: Hydrogen production, storage, and management in solar photovoltaic-fuel cell hybrid systems. Int. J. Hydrog. Energy 43(29), 13209–13225 (2018)
Qu, B., Qiao, B., Zhu, Y., Liang, J., Wang, L.: Dynamic power dispatch considering electric vehicles and wind power using decomposition based multi-objective evolutionary algorithm. Energies 10(12), 1991 (2017). https://doi.org/10.3390/en10121991
Yilmaz, S., Ozcalik, H.R., Aksu, M., Karapınar, C.: Dynamic simulation of a PV-diesel-battery hybrid plant for off grid electricity supply. Energy Procedia 75, 381–387 (2015)
Lambert, T., Gilman, P., Lilienthal, P.: Micropower system modeling with HOMER, Chap. 15 in Integration of Alternative Sources of Energy, by F A. Farret and M. G. Simoes, John Wiley & Sons, Hoboken (2006)
United Nations Sustainable Development Goals (SDGs) - Goal 7: Affordable and Clean Energy: https://sdgs.un.org/goals/goal7
Wang, Y., Li, W., Liu, Z., Li, L.: An energy management strategy for hybrid energy storage system based on reinforcement learning. World Electr. Veh. J. 14(3), 57 (2023). https://doi.org/10.3390/wevj14030057
World Energy Outlook 2020 by the IEA: https://www.iea.org/reports/world-energy-outlook-2020
Zhang, T., Yang, H., Li, Z., Meng, Y.: Life cycle assessment of grid-connected solar photovoltaic-fuel cell hybrid systems. Energy 150, 393–403 (2018)
Zhang, W., Deng, Y., Qu, J.: Techno-economic optimization of solar photovoltaic-fuel cell hybrid power systems for carbon reduction and energy efficiency improvement. Energy Convers. Manag. 213, 112831 (2020). https://doi.org/10.1016/j.enconman.2020.112831
Chapala, S., Narasimham, R.L., Lakshmi, G.S.: PV and wind distributed generation system power quality improvement based on modular UPQC. In: 2023 International Conference on Advanced & Global Engineering Challenges (AGEC), Surampalem, Kakinada, India, pp. 82–87 (2023). https://doi.org/10.1109/AGEC57922.2023.00027
Rajeswaran, N., Swarupa, M.L., Maddula, R., Alhelou, H.H., Kesava Vamsi Krishna, V.: A study on cyber-physical system architecture for smart grids and its cyber vulnerability. In: Haes Alhelou, H., Hatziargyriou, N., Dong, Z.Y. (eds.) Power Systems Cybersecurity, LNCS. Power Systems, pp. 413–427. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-20360-2_17
Vostriakova, V., Swarupa, M.L., Rubanenko, O., Gundebommu, S.L.: Blockchain and climate smart agriculture technologies in agri-food security system. In: Kumar, A., Fister Jr., I., Gupta, P.K., Debayle, J., Zhang, Z.J., Usman, M. (eds.) Artificial Intelligence and Data Science. ICAIDS 2021. CCIS, vol. 1673, pp. 490–504. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21385-4_40
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Soni, P., Dave, V., Repalle, N.B. (2024). AI Based Performance Boost in Solar PV Fuel Cell Hybrids. In: Gundebommu, S.L., Sadasivuni, L., Malladi, L.S. (eds) Renewable Energy, Green Computing, and Sustainable Development. REGS 2023. Communications in Computer and Information Science, vol 2081. Springer, Cham. https://doi.org/10.1007/978-3-031-58607-1_1
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