American Journal of Advanced Multidisciplinary Research and Innovation

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Renewable Energy Integration Using Artificial Intelligence-Based Optimization Models

Author(s) Jaime Carbonell
Country United States
Abstract The global transition toward sustainable energy systems has accelerated the deployment of renewable energy sources such as solar, wind, hydropower, geothermal, biomass, and hydrogen technologies. However, the intermittent and variable nature of renewable energy presents significant challenges for grid stability, energy storage, demand forecasting, power quality, and efficient resource allocation. The integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Internet of Things (IoT), Smart Grids, Digital Twin Technology, Cloud Computing, Edge Computing, Big Data Analytics, Blockchain, Energy Internet, Optimization Algorithms, Demand Response Systems, Battery Energy Storage Systems (BESS), and Electric Vehicles (EVs) enables intelligent renewable energy integration, real-time decision-making, predictive maintenance, and sustainable power system management.
This study presents a comprehensive analysis of Renewable Energy Integration Using Artificial Intelligence-Based Optimization Models. A qualitative analytical research methodology based on secondary data is employed to examine AI-driven optimisation techniques, renewable energy integration frameworks, intelligent grid management, energy storage optimisation, implementation challenges, and future research directions. The study investigates how AI-based optimisation enhances renewable energy forecasting, economic dispatch, load balancing, energy trading, grid resilience, and carbon emission reduction across modern power systems.
The findings indicate that Artificial Intelligence significantly improves renewable energy forecasting accuracy, dynamic grid control, energy storage scheduling, predictive maintenance, demand-side management, and distributed energy resource coordination. Machine learning models analyse weather and consumption patterns to optimise solar and wind generation, while reinforcement learning enables adaptive energy management under uncertain operating conditions. Digital twins simulate grid behaviour for planning and operational optimisation, and blockchain supports secure peer-to-peer energy trading and decentralised energy markets.
Despite these opportunities, challenges remain regarding data quality, cybersecurity, infrastructure investment, interoperability, computational complexity, regulatory uncertainty, market integration, and the ethical governance of AI-based energy systems. Future research should investigate autonomous energy systems, quantum optimisation for smart grids, federated energy learning, explainable AI for power system operations, and AI-enabled carbon-neutral energy ecosystems.
The study concludes that AI-based optimisation models provide a transformative framework for renewable energy integration by combining intelligent analytics, advanced optimisation techniques, sustainable power generation, and digital innovation to support resilient, low-carbon, and economically efficient energy systems.
Keywords Renewable Energy, Artificial Intelligence, Smart Grid, Machine Learning, Energy Optimisation, Renewable Energy Integration, Digital Twin, Battery Energy Storage, Sustainable Energy, Intelligent Power Systems.
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-12-30
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