Special Session Overview

The intensifying challenges of climate change combined with a surging energy demand underscore the crucial need to refine existing smart grid observability methodologies. Smart grid has revolutionized the interaction between renewable energy sources and consumers. At the same time, the Advanced Metering Infrastructure offers a wealth of data on energy usage, paving the way for detailed profiling of consumer demand, efficient load and renewable energy forecasting, as well as improved monitoring and data handling. With the escalating complexity of the grid, there is a growing need for advanced computational methods to oversee grid ecosystems, considering both utility and end-user perspectives. This necessitates more observable, accessible, and controllable network infrastructures. This special session offers an opportunity for researchers from various research fields, such as machine learning, artificial neural networks, fuzzy logic, evolutionary algorithms, power and energy systems, communications, optimization, and control engineering, to share their insights on how computational intelligence techniques can enhance observability and electrical signal processing for Smart Grids and Sustainable Energy Systems. Electrical signal processing is a key component of such systems, as it enables the extraction of useful information, such as load profiles, power quality, energy consumption, and generation patterns. Computational intelligence techniques, such as artificial neural networks, fuzzy logic, and evolutionary algorithms, can improve the performance and accuracy of electrical signal processing methods, by providing adaptive, robust, and scalable solutions to the challenges posed by the smart grid. This special session aims to bridge the gap between computational intelligence techniques and the pressing issues related to electrical power and energy systems. The session is supported by the IEEE CIS Task Force on Computational Intelligence in the Energy Domain (CI4Energy).