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Reaching carbon neutrality by 2050 will require transmission system operators to accommodate unprecedented levels of variable solar and wind generation while preserving system reliability. To address this systemic constraint, Réseau de Transport d’Électricité (RTE), together with the research community, introduced Learn To Run A Power Network (L2RPN), a crowd-sourced competition designed to accelerate the integration of intermittent renewable sources into grid operations. L2RPN relies on 16 years of weekly scenarios (832 in total) modeled on a 118-node network under realistic operational constraints, framing real-time grid management as a Markov Decision Process. The six competing teams approached the problem by designing autonomous agents that combined heuristics, optimization, data scaling, supervised learning, and reinforcement learning techniques. We deliver a comprehensive assessment of all six teams’ performances under the competition’s stringent framework. Furthermore, we conduct an extensive examination of the publicly released winning solution, which ensures consistent decision-making across scenarios, performs real-time multimodal actions in less than 5 seconds, and achieves efficient topology control through action-space reduction and a neural policy that predicts beneficial grid actions with over 80% accuracy. Concurrently, we trained a neural alert module using 315,000 samples generated from leading agents, obtaining 93.9% recall in identifying hazardous states and enabling agents to anticipate potential failures. Ultimately, this study highlights both the potential and current limitations of AI in real-time grid operation, while establishing a transparent foundation for developing more robust and trustworthy systems to support the energy transition.

The emergence of 5G technology is revolutionizing the development of smart cities by providing unparalleled speed, ultra-low latency, and massive device connectivity. These advancements enable seamless integration of IoT devices, real-time monitoring systems, and innovative urban solutions. This paper investigates the role of 5G in addressing key urban challenges—such as network congestion, energy efficiency, and data security—while emphasizing its transformative potential for advancing smart city architectures. Particular attention is given to integrating 5G with real-time digital twins, which bridge the physical and digital worlds to optimize urban operations, improve resource allocation, and enhance public safety. Despite its promise, 5G deployment faces obstacles including high infrastructure costs, substantial energy consumption, and limited signal coverage. This study reviews current trends, existing implementations, and emerging issues surrounding 5G in smart cities, offering perspectives on its limitations and potential mitigation strategies. It underscores the importance of collaboration among stakeholders to fully unlock 5G’s capabilities and develop scalable, secure, and sustainable smart city solutions. 

This paper introduces a novel hybrid artificial intelligence technique designed to enhance energy management efficiency within smart grids, with a particular focus on Renewable Energy Communities (RECs). The proposed approach integrates a Time Delay Neural Network (TDNN) to predict upcoming values of key energy variables in the community. These forecasts then feed into a stochastic Model Predictive Control (MPC) framework, which optimizes community operations through effective Battery Energy Storage System (BESS) control strategies. Evaluation on a public dataset for a 24-hour prediction horizon yielded Mean Absolute Errors (MAE) of 1.60 kW for photovoltaic generation, 2.15 kW for total energy consumption, and 0.30 kW for common services. When integrated with stochastic MPC, these predictions yielded higher financial returns than those from alternative methods. Overall, the system increased total income by 18.72% compared to the same management approach without any forecasting component.