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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 develops and tests a controller for residential electric water heaters (EWHs). The developed controller is operated to maximize the energy stored in EWHs during off-peak-demand times, in order to reduce their power demands during peak-demand times. Desired control actions aim to adjust the minimum temperature settings of EWHs using the unit commitment (UC). In order to eliminate the need for a direct measurement of EWH power demands, the bus-split (BS) aggregation method is employed. The BS method is employed due to its ability to extract the power demands of an EWH from household power meter readings. The UC is formulated using the energy stored in a EWH as a cost function, which is to be maximized during the off-peak-demand time. The solution of the UC problem is obtained using the Lagrange relaxation method that can offer fast convergence and reduced computations. The BS-UC controller is implemented for performance testing using power meter readings that are collected from 150 residential households during the fall, winter, spring, and summer seasons. Test results demonstrate the ability of the BS-UC controller to provide accurate and effective control of EWHs, which are complimented by a minor sensitivity to the number controlled EWHs, hot water consumption, and seasonal variations in residential load power demands. [ABSTRACT FROM AUTHOR]

The growing interest in optimizing the generation, distribution, and delivery of electric power have motivated the implementation of several smart grid functions in many power systems around the globe. Among such functions are the peak-load management, demand response, direct load control, and integration of distributed power generation. Nowadays, smart grid functions are being implemented for industrial, residential, and/or commercial loads. One of the key requirements for implementing smart grid functions is the accurate and reliable load aggregation. The bottom-up, coordinated, and bus-split aggregation methods have been found applicable for different load types that are included in smart grid functions. This paper reviews the methods and approaches for performing the load aggregation, and provides a discussion for the critical role of load aggregation in power systems operating and smart grid functions. In addition, this paper discusses the applicability of the load aggregation methods in smart grid functions for residential loads. [ABSTRACT FROM PUBLISHER]

This article presents a new approach for estimating the frequency variations ($Df$) due to applying smart grid functions on a load bus. The proposed approach is based on constructing a ZIP model for the power demands at a load bus that is controlled by smart grid functions.The constructed ZIP model provides a relationship between the active and reactive power demands and the frequency at any load bus. This relationship can be formulated as a set of nonlinear equations, which can be numerically solved for $Df$. The load-model approach is implemented for performance evaluation using load buses in the IEEE 30-bus power system under different loading levels. Performance results show that the proposed approach has a simple implementation, and can provide an accurate estimation of frequency variations due to slow and small changes load power demands. Furthermore, performance results reveal the insensitivity of the load-model approach to load power demands and/or seasonal changes in load power demands. [ABSTRACT FROM AUTHOR]