Next-Generation Energy Systems: Integrating Artificial Intelligence, Renewable Energy and Intelligent Grid Technologies

Weslynne Ashton

Next-Generation Energy Systems: Integrating Artificial Intelligence, Renewable Energy and Intelligent Grid Technologies

Author(s)	Weslynne Ashton
Country	United States
Abstract	The global energy sector is undergoing a fundamental transformation driven by the rapid deployment of renewable energy, digital technologies, energy storage, electric vehicles, and intelligent grid infrastructure. Next-generation energy systems are emerging as integrated ecosystems in which Artificial Intelligence (AI), renewable energy resources, advanced storage technologies, and intelligent grid technologies operate collectively to improve efficiency, reliability, flexibility, and sustainability. AI techniques such as machine learning, deep learning, reinforcement learning, predictive analytics, and intelligent optimisation can support electricity-demand forecasting, renewable-energy prediction, predictive maintenance, energy-storage management, demand response, power-flow optimisation, and real-time grid control. This study examines the integration of AI, renewable energy, and intelligent grid technologies within future power systems. A qualitative and comparative research methodology based on secondary academic literature, energy-policy documents, technical reports, and industry research is adopted. The study analyses the transition from conventional centralised power systems towards decentralised, flexible, data-driven, and increasingly autonomous energy networks. Particular attention is given to renewable-energy integration, intelligent energy management, distributed energy resources, battery storage, electric vehicles, demand-side flexibility, and grid resilience. The analysis indicates that AI can improve forecasting accuracy, operational efficiency, renewable-energy utilisation, asset management, and system responsiveness. Nevertheless, challenges involving cybersecurity, data quality, interoperability, algorithmic transparency, infrastructure investment, regulatory uncertainty, and workforce capabilities remain significant. The study concludes that successful next-generation energy systems will require not only advanced technologies but also coordinated policy frameworks, secure digital infrastructure, human oversight, and equitable access to clean energy.
Keywords	Artificial Intelligence, Renewable Energy, Smart Grid, Intelligent Energy Systems, Machine Learning, Energy Storage, Distributed Energy Resources, Demand Response, Digitalisation, Sustainable Energy.
Field	Engineering
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-25

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American Journal of Advanced Multidisciplinary Research and Innovation

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Next-Generation Energy Systems: Integrating Artificial Intelligence, Renewable Energy and Intelligent Grid Technologies

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