A Comprehensive Review of Artificial Intelligence for Shaping Renewable Energy Power Systems

ahmed ezzat; alaa mahmoud; ahmed abd el hafez

doi:10.51646/jsesd.v14i1.369

Authors

ahmed ezzat Elect. Dept., industrial technical institute, Mid Valley Technological College, Sohag, Egypt. https://orcid.org/0009-0004-6973-4157
alaa mahmoud Elect. Dep., Faculty of Technology and Education, Sohag University, Sohag, Egypt.
ahmed abd el hafez Elect. Eng. Dept., Fac. of Eng., Assiut University, Assiut, Egypt.

DOI:

https://doi.org/10.51646/jsesd.v14i1.369

Keywords:

Renewable Energy Sources, Artificial Intelligence, Optimization, Energy, Power System, Sustainability.

Abstract

Renewable Energy Sources (RESs) are widely penetrating power systems, due to their environmental compatibility and shortage reserve of the fossil fuels. This mandates the application of intelligent, innovative and smart techniques for forecasting, controlling and managing of RESs. However, RESs suffer from uncertainty, weather and operating condition dependence, which considers as a major challenge of the conventional controlling strategy. Artificial Intelligence (AI) enjoys the advantage of adapting the control and operating routines according to the system status, which is attributed to the numerous training scenarios. AI in the areas of RESs could improve their reliability, security and sustainability. Moreover, AI could boost the operation of different energy storage systems, which are considered integral part for different RESs system. This article comprehensively analyzes several literatures regarding AI for RESs. Moreover, comprehensive comparisons between conventional controlling and driving systems of AI in fields of RESs are given in the article. The article moreover addresses the storage system for RESs and the impact of application of AI in improving the energy management of such systems. The article acts as simple and reliable tools for researchers and engineers in the area of AI for RES.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

M. A. Russo, D. Carvalho, N. Martins, and A. Monteiro, “Future perspectives for wind and solar electricity production under high-resolution climate change scenarios,” J. Clean. Prod., vol. 404, p. 136997, Jun. 2023, doi: 10.1016/J.JCLEPRO.2023.136997. DOI: https://doi.org/10.1016/j.jclepro.2023.136997

Y. F. Nassar, H. J. El-Khozondar, M. Elnaggar, F. F. El-batta, R. J. El-Khozondar, and S. Y. Alsadi, “Renewable energy potential in the State of Palestine: Proposals for sustainability,” Renew. Energy Focus, vol. 49, p. 100576, Jun. 2024, doi: 10.1016/J.REF.2024.100576. DOI: https://doi.org/10.1016/j.ref.2024.100576

A. Demirbas, “Global Renewable Energy Projections,” Energy Sources, Part B, vol. 4, no. 2, pp. 212–224, Oct. 2009, doi: 10.1080/15567240701620499. DOI: https://doi.org/10.1080/15567240701620499

X. Han, he Hongwen, J. Wu, J. Peng, and Y. Li, “Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle,” Appl. Energy, vol. 254, p. 113708, Nov. 2019, doi: 10.1016/j.apenergy.2019.113708. DOI: https://doi.org/10.1016/j.apenergy.2019.113708

M. A. Mossa, N. El Ouanjli, O. Gam, and T. D. Do, “Enhancing the Performance of a Renewable Energy System Using a Novel Predictive Control Method,” Electron., vol. 12, no. 16, Aug. 2023, doi: 10.3390/ELECTRONICS12163408. DOI: https://doi.org/10.3390/electronics12163408

M. Alilou, H. Azami, A. Oshnoei, B. Mohammadi-Ivatloo, and R. Teodorescu, “Fractional-Order Control Techniques for Renewable Energy and Energy-Storage-Integrated Power Systems: A Review,” Fractal Fract., vol. 7, no. 5, May 2023, doi: 10.3390/FRACTALFRACT7050391. DOI: https://doi.org/10.3390/fractalfract7050391

M. B. Abdelghany and A. Al-Durra, “A coordinated model predictive control of grid-connected energy storage systems,” Proc. Am. Control Conf., vol. 2023-May, pp. 1862–1867, 2023, doi: 10.23919/ACC55779.2023.10155903. DOI: https://doi.org/10.23919/ACC55779.2023.10155903

R. Dhanasekar, L. Vijayaraja, and S. G. Kumar, “Control techniques in sustainable applications,” Power Convert. Drives Control. Sustain. Oper., pp. 631–658, Aug. 2024, doi: 10.1002/9781119792918.CH21. DOI: https://doi.org/10.1002/9781119792918.ch21

M. Shoaei, Y. Noorollahi, A. Hajinezhad, and S. F. Moosavian, “A review of the applications of artificial intelligence in renewable energy systems: An approach-based study,” Energy Convers. Manag., vol. 306, Apr. 2024, doi: 10.1016/J.ENCONMAN.2024.118207. DOI: https://doi.org/10.1016/j.enconman.2024.118207

L. A. Yousef, H. Yousef, and L. Rocha-Meneses, “Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions,” Energies, vol. 16, no. 24, Dec. 2023, doi: 10.3390/EN16248057. DOI: https://doi.org/10.3390/en16248057

H. Szczepaniuk and E. K. Szczepaniuk, “Applications of Artificial Intelligence Algorithms in the Energy Sector,” Energies, vol. 16, no. 1, Jan. 2023, doi: 10.3390/EN16010347. DOI: https://doi.org/10.3390/en16010347

A. Razmjoo et al., “Moving Toward the Expansion of Energy Storage Systems in Renewable Energy Systems—A Techno-Institutional Investigation with Artificial Intelligence Consideration,” Sustain., vol. 16, no. 22, Nov. 2024, doi: 10.3390/SU16229926. DOI: https://doi.org/10.3390/su16229926

A. A. Mahmoud, O. A. Albadry, M. I. Mohamed, H. El-Khozondar, Y. Nassar, and A. A. Hafez, “Charging Systems/Techniques of Electric Vehicle:,” Sol. Energy Sustain. Dev. J., vol. 13, no. 2, pp. 18–44, Jun. 2024, doi: 10.51646/JSESD.V13I2.203. DOI: https://doi.org/10.51646/jsesd.v13i2.203

J. D. Velásquez, L. Cadavid, and C. J. Franco, “Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances,” Energies, vol. 16, no. 19, Oct. 2023, doi: 10.3390/EN16196974. DOI: https://doi.org/10.3390/en16196974

A. Abdalla, W. El-Osta, Y. F. Nassar, W. Husien, E. I. Dekam, and G. M. Miskeen, “Estimation of Dynamic Wind Shear Coefficient to Characterize Best Fit of Wind Speed Profiles under Different Conditions of Atmospheric Stability and Terrains for the Assessment of Height-Dependent Wind Energy in Libya,” Appl. Sol. Energy (English Transl. Geliotekhnika), vol. 59, no. 3, pp. 343–359, Jun. 2023, doi: 10.3103/S0003701X23600212/METRICS. DOI: https://doi.org/10.3103/S0003701X23600212

Y. F. Nassar and A. A. Salem, “The reliability of the photovoltaic utilization in southern cities of Libya,” Desalination, vol. 209, no. 1–3, pp. 86–90, Apr. 2007, doi: 10.1016/J.DESAL.2007.04.013. DOI: https://doi.org/10.1016/j.desal.2007.04.013

Onyinyechukwu Chidolue, Cosmas Dominic Daudu, Valentine Ikenna Illojianya, Adetomilola Victoria Fafure, Kenneth Ifeanyi Ibekwe, and Bright Ngozichukwu, “Control systems in renewable energy: A review of applications in Canada, USA, and Africa,” World J. Adv. Eng. Technol. Sci., vol. 11, no. 1, pp. 029–036, Jan. 2024, doi: 10.30574/WJAETS.2024.11.1.0011. DOI: https://doi.org/10.30574/wjaets.2024.11.1.0011

A. Mahmoud, M. Ahmed, and A. Hafez, “Multi-Port Converters for Interfacing Renewable Energy Sources:,” Sol. Energy Sustain. Dev. J., vol. 13, no. 2, pp. 230–253, Sep. 2024, doi: 10.51646/JSESD.V13I2.246. DOI: https://doi.org/10.51646/jsesd.v13i2.246

J. O. Gidiagba, N. N. -Ehiobu, O. A. Ojunjobi, K. A. Ofonagoro, and C. Daraojimba, “ENSURING THE FUTURE OF RENEWABLE ENERGY: A CRITICAL REVIEW OF RELIABILITY ENGINEERING APPLICATIONS IN RENEWABLE ENERGY SYSTEMS,” Mater. Corros. Eng. Manag., vol. 4, no. 2, pp. 60–69, Sep. 2023, doi: 10.26480/MACEM.02.2023.60.69. DOI: https://doi.org/10.26480/macem.02.2023.60.69

D. Yousri, M. Abd Elaziz, D. Oliva, L. Abualigah, M. A. A. Al-qaness, and A. A. Ewees, “Reliable applied objective for identifying simple and detailed photovoltaic models using modern metaheuristics: Comparative study,” Energy Convers. Manag., vol. 223, p. 113279, Nov. 2020, doi: 10.1016/J.ENCONMAN.2020.113279. DOI: https://doi.org/10.1016/j.enconman.2020.113279

Q. Li, T. Lin, Q. Yu, H. Du, J. Li, and X. Fu, “Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control,” Energies, vol. 16, no. 10, May 2023, doi: 10.3390/EN16104143. DOI: https://doi.org/10.3390/en16104143

T. M. Babu, K. Chenchireddy, K. K. Kumar, V. Nehal, S. Srihitha, and M. R. Vikas, “Intelligent control strategies for grid-connected photovoltaic wind hybrid energy systems using ANFIS,” Int. J. Adv. Appl. Sci., vol. 13, no. 3, p. 497, Sep. 2024, doi: 10.11591/IJAAS.V13.I3.PP497-506. DOI: https://doi.org/10.11591/ijaas.v13.i3.pp497-506

A. Elkodama, A. Ismaiel, A. Abdellatif, S. Shaaban, S. Yoshida, and M. A. Rushdi, “Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review,” Energies, vol. 16, no. 17, Sep. 2023, doi: 10.3390/EN16176394. DOI: https://doi.org/10.3390/en16176394

S. A. Juma, S. P. Ayeng’o, and C. Z. M. Kimambo, “A review of control strategies for optimized microgrid operations,” IET Renew. Power Gener., 2024, doi: 10.1049/RPG2.13056. DOI: https://doi.org/10.1049/rpg2.13056

M. Davoudi and H. Zarei Zohdi, “An overview on optimal control of renewable resources, methods and challenges,” J. Renew. New Energy, vol. 10, no. 1, pp. 153–165, Mar. 2023, doi: 10.52547/JRENEW.10.1.153. DOI: https://doi.org/10.52547/jrenew.10.1.153

G. Venkatesan, M. Marimuthu, V. Gomathy, N. Saranya, H. Anandaram, and U. Arun Kumar, “Integrating Machine Learning and IoT Technologies for Advancements in Solar Energy Systems,” Proc. 3rd Int. Conf. Appl. Artif. Intell. Comput. ICAAIC 2024, pp. 1699–1705, 2024, doi: 10.1109/ICAAIC60222.2024.10575346. DOI: https://doi.org/10.1109/ICAAIC60222.2024.10575346

M. Ferrara et al., “Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives,” Energies, vol. 17, no. 14, p. 3371, Jul. 2024, doi: 10.3390/EN17143371. DOI: https://doi.org/10.3390/en17143371

G. S. Rupa, R. S. S. Nuvvula, P. P. Kumar, A. Ali, and B. Khan, “Machine Learning-Based Optimization Techniques for Renewable Energy Systems,” pp. 389–394, Jul. 2024, doi: 10.1109/ICSMARTGRID61824.2024.10578295. DOI: https://doi.org/10.1109/icSmartGrid61824.2024.10578295

M. Cauz, A. Bolland, N. Wyrsch, and C. Ballif, “Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems,” Jun. 2024, Accessed: Aug. 29, 2024. [Online]. Available: https://arxiv.org/abs/2406.19825v1

Q. Yuan, F. Yang, A. Li, and T. Ma, “A Novel Hybrid Control Strategy for the Energy Storage Modular Multilevel Converters,” IEEE Access, vol. 9, pp. 59466–59474, 2021, doi: 10.1109/ACCESS.2021.3073108. DOI: https://doi.org/10.1109/ACCESS.2021.3073108

Z. Wang, Z. Tan, and Y. Yu, “On the Sample Complexity of Storage Control,” IEEE Trans. Smart Grid, vol. 14, no. 6, pp. 4398–4408, Nov. 2023, doi: 10.1109/TSG.2023.3263862. DOI: https://doi.org/10.1109/TSG.2023.3263862

D. Sobczynski and P. Pawlowski, “Energy storage systems for renewable energy sources,” 2021 Sel. Issues Electr. Eng. Electron. WZEE 2021, Sep. 2021, doi: 10.1109/WZEE54157.2021.9576964. DOI: https://doi.org/10.1109/WZEE54157.2021.9576964

F. Salvadori et al., “Energy Storage Applications in Renewable Energy Systems,” pp. 73–118, 2024, doi: 10.1007/978-3-031-37909-3_3. DOI: https://doi.org/10.1007/978-3-031-37909-3_3

X. Tang and Z. Qi, “Energy storage control in renewable energy based microgrid,” IEEE Power Energy Soc. Gen. Meet., 2012, doi: 10.1109/PESGM.2012.6345000. DOI: https://doi.org/10.1109/PESGM.2012.6345000

M. Y. Worku, “Recent Advances in Energy Storage Systems for Renewable Source Grid Integration: A Comprehensive Review,” Sustain., vol. 14, no. 10, May 2022, doi: 10.3390/SU14105985. DOI: https://doi.org/10.3390/su14105985

M. K. Kar, S. Kanungo, S. Dash, and R. N. Ramakant Parida, “Grid connected solar panel with battery energy storage system,” Int. J. Appl. Power Eng., vol. 13, no. 1, pp. 223–233, Mar. 2024, doi: 10.11591/IJAPE.V13.I1.PP223-233. DOI: https://doi.org/10.11591/ijape.v13.i1.pp223-233

K. A. Khamkar, S. Nethagani, R. Priya, S. N. Bolleddu, A. Verma, and M. K. Chakravarthi, “Development of a Highly Efficient Energy Storage System for Renewable Energy Applications,” 7th Int. Conf. I-SMAC (IoT Soc. Mobile, Anal. Cloud), I-SMAC 2023 - Proc., pp. 904–909, 2023, doi: 10.1109/I-SMAC58438.2023.10290461. DOI: https://doi.org/10.1109/I-SMAC58438.2023.10290461

S. Salehizadeh, S. Zandi Lak, and M. R. Rahimpour, “Energy Storage Technologies for Renewable Energy Sources,” Ref. Modul. Earth Syst. Environ. Sci., 2024, doi: 10.1016/B978-0-323-93940-9.00253-X. DOI: https://doi.org/10.1016/B978-0-323-93940-9.00253-X

S. Hashemi and J. Østergaard, “Efficient Control of Energy Storage for Increasing the PV Hosting Capacity of LV Grids,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 2295–2303, May 2018, doi: 10.1109/TSG.2016.2609892. DOI: https://doi.org/10.1109/TSG.2016.2609892

E. Banguero, A. Correcher, Á. Pérez-Navarro, F. Morant, and A. Aristizabal, “A review on battery charging and discharging control strategies: Application to renewable energy systems,” Energies, vol. 11, no. 4, Apr. 2018, doi: 10.3390/EN11041021. DOI: https://doi.org/10.3390/en11041021

A. Cano, P. Arévalo, and F. Jurado, “Neural network predictive control in renewable systems (HKT-PV) for delivered power smoothing,” J. Energy Storage, vol. 87, May 2024, doi: 10.1016/J.EST.2024.111332. DOI: https://doi.org/10.1016/j.est.2024.111332

M. Şimşir and A. Ghayth, “Global Trends in Electric Vehicle Battery Efficiency and Impact on Sustainable Grid,” Sol. Energy Sustain. Dev. J., vol. 13, no. 2, pp. 1–17, Jun. 2024, doi: 10.51646/jsesd.v13i2.202. DOI: https://doi.org/10.51646/jsesd.v13i2.202

F. H. Jufri, J. Jung, B. Sudiarto, and I. Garniwa, “Development of Virtual Inertia Control with State-of-Charge Recovery Strategy Using Coordinated Secondary Frequency Control for Optimized Battery Capacity in Isolated Low Inertia Grid,” Energies, vol. 16, no. 14, Jul. 2023, doi: 10.3390/EN16145463. DOI: https://doi.org/10.3390/en16145463

A. G. Olabi et al., “Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems,” Therm. Sci. Eng. Prog., vol. 39, Mar. 2023, doi: 10.1016/J.TSEP.2023.101730. DOI: https://doi.org/10.1016/j.tsep.2023.101730

Y. Zeng et al., “Active Disturbance Rejection Control Using Artificial Neural Network for Dual-Active-Bridge-Based Energy Storage System,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 11, no. 1, pp. 301–311, Feb. 2023, doi: 10.1109/JESTPE.2021.3138341. DOI: https://doi.org/10.1109/JESTPE.2021.3138341

P. Saikia, H. Bastida, and C. E. Ugalde-Loo, “An effective predictor of the dynamic operation of latent heat thermal energy storage units based on a non-linear autoregressive network with exogenous inputs,” Appl. Energy, vol. 360, Apr. 2024, doi: 10.1016/J.APENERGY.2024.122697. DOI: https://doi.org/10.1016/j.apenergy.2024.122697

D. Biagioni, X. Zhang, C. Adcock, M. Sinner, P. Graf, and J. King, “Comparative analysis of grid-interactive building control algorithms: From model-based to learning-based approaches,” Eng. Appl. Artif. Intell., vol. 133, Jul. 2024, doi: 10.1016/J.ENGAPPAI.2024.108498. DOI: https://doi.org/10.1016/j.engappai.2024.108498

G. H. Alves, G. C. Guimarães, and F. A. M. Moura, “Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing,” Energies, vol. 16, no. 14, Jul. 2023, doi: 10.3390/EN16145262. DOI: https://doi.org/10.3390/en16145262

K. Valarmathi, J. Seetha, N. V. Krishnamoorthy, M. Hema, and G. Ramkumar, “An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application,” Sustain. Comput. Informatics Syst., vol. 42, Apr. 2024, doi: 10.1016/J.SUSCOM.2024.100982. DOI: https://doi.org/10.1016/j.suscom.2024.100982