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Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols. [ABSTRACT FROM AUTHOR]

This article presents the employment of battery storage systems (BSSs) to improve power system survivability. This application of BSSs is based on setting BSS charging/discharging as responses to integrating distributed generation units and/or adjusting load demands (load-side activities). The improvement of power system survivability is quantified by the survivability index $\Gamma$ , which can exceed its boundary values due to load-side activities. The improvement of power system survivability is achieved by maintaining $\Gamma$ within its boundary values post a load-side activity. The proposed approach to improve power system survivability is implemented and tested for the Barbados power system. Tests are conducted for integrating solar power generation units, and implementing demand response. Test results show that accurate BSS charging/discharging can maintain the index $\Gamma$ within its boundary values post-load-side activities. [ABSTRACT FROM AUTHOR]