Energy management of renewable based power grids using artificial intelligence: Digital twin of renewables

Fan, Xuezhou; Li, Yajuan

doi:10.1016/j.solener.2023.111867

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…Optimization via artificial intelligence can help to reduce the environmental impact of the combined energy systems using genetic algorithm to lessen the effect of carbon dioxide emission on the environment (Hai et al 2022). However, integrating microgrids with renewable energy technologies and sharing with external grid networks are very challenging due to maintaining optimum power flows in the industry (Fan and Li 2023).…”

Section: Renewable Energymentioning

confidence: 99%

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Ganthavee,

Trzcinski

2024

Environ Chem Lett

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The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.

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Section: Renewable Energymentioning

confidence: 99%

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Ganthavee,

Trzcinski

2024

Environ Chem Lett

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“…Research is needed to incorporate AI engineering processes into EDT processes in addition to utilizing XAI [144]. Through simulation of behavioral characteristics of VRE sources using EDT, system operators can better optimize grid performance, resulting in increased energy efficiency and cost savings [146].…”

Section: Digital Twins Coupled With Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Yousef,

Rocha-Meneses

2023

Energies

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This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

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“…Renewable energy sources still face inherent issues like weather dependency, intermittency, and high upfront costs even with enormous technological advances (Bandh et al, 2024;Demirbas, 2009;Medina et al, 2022). Still, the long-term advantages of renewable energy-including the protection of the environment, lower greenhouse gas emissions, and the financial challenges often exceed the disadvantages (Fan and Li, 2023;Franco and Salza, 2011). Integration of renewables with energy storage possibilities, smarter grid approaches, and preserving energy security become increasingly crucial as the world's energy landscape shifts toward sustainability Tan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Energy management of renewable based power grids using artificial intelligence: Digital twin of renewables

Cited by 11 publications

References 14 publications

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

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