Guest Editorial Enabling Technologies and Methodologies for Knowledge Discovery and Data Mining in Smart Grids

The Special Section on Enabling Technologies and Methodologies for Knowledge Discovery and Data Mining in Smart Grids of the IEEE Transactions on Industrial InformaticS addresses a number of timely and relevant issues related to modern smart grids operation and control. This Special Section is opened by the paper 'Optimal Battery Sizing in Microgrids Using Probabilistic Unit Commitment' by Khorramdel and colleagues proposing advanced tools for properly coordinating the operation of distributed renewable power generators in order to mitigate their negative impacts on power system operation and control. Paper two explores the potential role of microgrids and energy storage systems, which have been considered as the most promising enabling technologies for dealing with an increased penetration of intermittent and nonprogrammable generators in electrical distribution systems. Another paper entitled 'Feature Extraction and Power Quality Disturbances Classification using Smart Meters' by Borges and colleagues addresses the strategic issues of data heterogeneity and knowledge discovery from large datasets, which represent two major problems in modern smart grids, since the deployment of the metering infrastructures is unlikely to grow over time with the same hardware and software architectures, and the number of grid sensors is expected to increase over several orders of magnitude.