Abstract
The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions.
| Original language | English |
|---|---|
| Article number | 429 |
| Pages (from-to) | 1-34 |
| Number of pages | 34 |
| Journal | Algorithms |
| Volume | 18 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 13 Climate Action
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SDG 17 Partnerships for the Goals
Keywords
- artificial intelligence
- energy management
- energy storage systems
- optimization
- predictive analytics
- smart microgrid
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