TY - JOUR
T1 - Optimizing Microgrid Operation
T2 - Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency
AU - Arévalo Cordero, Willian Paul
AU - Ochoa Correa, Danny Vinicio
AU - Villa Ávila, Edisson Andrés
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/9
Y1 - 2024/9
N2 - Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.
AB - Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.
KW - PRISMA methodology
KW - artificial intelligence
KW - energy management
KW - microgrid operation
KW - Artificial intelligence
KW - Energy management
KW - Microgrid operation
KW - PRISMA methodology
UR - https://revistamedicahjca.iess.gob.ec/ojs/index.php/HJCA/article/view/292
U2 - 10.3390/electronics13183754
DO - 10.3390/electronics13183754
M3 - Artículo de revisión
AN - SCOPUS:85205077104
SN - 2079-9292
VL - 13
SP - 1
EP - 25
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 18
M1 - 3754
ER -
