TY - JOUR
T1 - Smart Microgrid Management and Optimization
T2 - A Systematic Review Towards the Proposal of Smart Management Models
AU - Arévalo Cordero, Wilian Paul
AU - Benavides Padilla, Darío Javier
AU - Ochoa Correa, Danny Vinicio
AU - Ríos Villacorta, Alberto
AU - Torres Valverde, Leonardo David
AU - Villanueva Machado, Carlos Wyller
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/7
Y1 - 2025/7
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - energy management
KW - energy storage systems
KW - optimization
KW - predictive analytics
KW - smart microgrid
KW - Smart microgrid
KW - Energy management
KW - Energy storage sysytems
KW - Optimization
KW - Predictive analytics
KW - Artificial intelligence
UR - https://www.scopus.com/pages/publications/105011678693
UR - https://www.mdpi.com/1999-4893/18/7/429
U2 - 10.3390/a18070429
DO - 10.3390/a18070429
M3 - Artículo de revisión
AN - SCOPUS:105011678693
SN - 1999-4893
VL - 18
SP - 1
EP - 34
JO - Algorithms
JF - Algorithms
IS - 7
M1 - 429
ER -