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.

This research studies a general modeling to evaluate different scenarios of travel patterns and their impact on the daily cost negotiated in the Real Time and Day-Ahead market, using the GAMS methodology in a MILP model, evaluating also a characterization of the PQP market (price quantity probability). The purpose of this characterization is to determine the behavior of the electric energy market, considering also the deterioration of batteries and the negotiations of it in real time in situations of shortage and overload, optimizing in this way the effects of the analysis of the cost of the application of the battery on the different travel patterns, consequently triggering the emergence of the development of the local electric transport aggregator industry.

La carga no controlada de vehículos eléctricos plantea un gran desafío para los operadores de redes de distribución y los planificadores de sistemas de potencia. En lugar de focalizarse en el control de esta carga no controlada, se propone un modelo que utiliza variables de análisis de contingencias para calcular la capacidad de potencia necesaria en el sistema de potencia. Se emplea la variable de potencia no servida para evaluar la cantidad de potencia de carga no cubierta en cada barra del sistema, seguido del cálculo de la capacidad de potencia adicional requerida, utilizando un sistema fotovoltaico y de almacenamiento y otra alternativa de generación constante en el sistema de potencia IEEE de 14 barras con información sobre algunos vehículos eléctricos y la carga diaria en el sistema de potencia de Perú. Los resultados obtenidos en el sistema de potencia con generación distribuida muestran que no hay presencia de potencia no servida, corroborando el éxito de la metodología utilizada. Este modelo brinda herramientas tanto a los operadores de redes de distribución como a los planificadores de sistemas de potencia, reduciendo el impacto en el sistema de potencia de los vehículos eléctricos y aportando una metodología aplicable a otros sistemas de distribución eléctrica con cargas no controladas.

Electric vehicle (EV) charging management can be implemented through centralized or decentralized strategies. Strategic coordination between these approaches enhances system efficiency and balances energy loads, thereby supporting the widespread adoption of EVs and fostering a sustainable, emissions-free society. In this study, distribution network operators (DNOs), acting as centralized charging managers, are responsible for mitigating the lack of coordination among electric vehicle aggregators (EVAs), which represent decentralized managers. The primary objective of the centralized management in this research is to constrain each decentralized optimization model, characterized using Monte Carlo simulations. Three EV adoption scenarios--comprising 2,000, 2,500, and 3,750 vehicles--are evaluated by comparing decentralized charging management with an unregulated charging baseline in the IEEE 14-bus power system. Improvements are required only in the highest adoption scenario, where the proposed centralized coordination model is applied. The study models energy trading constraints for each EVA, assigning one aggregator per load-bearing bus in the system. Transmission-level results are analyzed and then synthesized for application in the IEEE 13-bus distribution power system. Findings demonstrate that coordinated centralized and decentralized charging management significantly improves operational conditions in both transmission and distribution networks without necessitating changes to travel behavior.