Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/7/429 |
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| author | Paul Arévalo Dario Benavides Danny Ochoa-Correa Alberto Ríos David Torres Carlos W. Villanueva-Machado |
| author_facet | Paul Arévalo Dario Benavides Danny Ochoa-Correa Alberto Ríos David Torres Carlos W. Villanueva-Machado |
| author_sort | Paul Arévalo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7a8a1d724b094b539bfd10790bf29c85 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-7a8a1d724b094b539bfd10790bf29c852025-08-20T03:13:38ZengMDPI AGAlgorithms1999-48932025-07-0118742910.3390/a18070429Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management ModelsPaul Arévalo0Dario Benavides1Danny Ochoa-Correa2Alberto Ríos3David Torres4Carlos W. Villanueva-Machado5Department of Electrical Engineering, Electronics, and Telecommunications (DEET), Universidad de Cuenca, Cuenca 010101, EcuadorDepartment of Electrical Engineering, University of Jaén, 23700 Linares, SpainDepartment of Electrical Engineering, Electronics, and Telecommunications (DEET), Universidad de Cuenca, Cuenca 010101, EcuadorFaculty of Systems, Electronics and Industrial Engineering, Universidad Técnica de Ambato, Ambato 180207, EcuadorFaculty of Systems, Electronics and Industrial Engineering, Universidad Técnica de Ambato, Ambato 180207, EcuadorFaculty of Mechanical Engineering, Universidad Nacional de Ingeniería, Lima 21036, PeruThe 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.https://www.mdpi.com/1999-4893/18/7/429smart microgridenergy managementenergy storage systemsoptimizationpredictive analyticsartificial intelligence |
| spellingShingle | Paul Arévalo Dario Benavides Danny Ochoa-Correa Alberto Ríos David Torres Carlos W. Villanueva-Machado Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models Algorithms smart microgrid energy management energy storage systems optimization predictive analytics artificial intelligence |
| title | Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models |
| title_full | Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models |
| title_fullStr | Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models |
| title_full_unstemmed | Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models |
| title_short | Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models |
| title_sort | smart microgrid management and optimization a systematic review towards the proposal of smart management models |
| topic | smart microgrid energy management energy storage systems optimization predictive analytics artificial intelligence |
| url | https://www.mdpi.com/1999-4893/18/7/429 |
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