Nikos D. Hatziargyriou ^*

• National Technical University of Athens, Athens, Greece

The digitalization of energy is a sine qua non for the operation and control of modern power systems. Next to the challenging complexities system operators face an explosive increase of data available through smart meters, sensors and other measurement units installed in the network. Thus, they need to handle efficiently not only energy, but also data by managing millions of daily real-time signals through satellite, powerline communication systems, radio, fiber-optic lines, and other communication technologies. Electricity distribution companies turn to "data centric" companies. The management and exploitation of these data is the key feature of the transition of traditional power grids to smart grids. This information together with the admirable achievements in information and telecommunication technologies offer tremendous opportunities for cost-effective planning of power systems, better management and predictive maintenance of assets, wide monitoring and operation control, fast fault recognition and restoration, direct information and tele-service of customers, etc. Next to the analytical, model-based methods used for power system analysis and control, a wide range of data driven methods have been developed, some of the most effective ones based on Machine Learning (ML). The paper " A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions" by Athula Rajapakse et al, focuses on the of ML application for power system protection and control to overcome the inadequacies of conventional protection methods. This review paper examines the challenges introduced by the integration of ML into power system protection, provides suggestions to overcome them and identifies potential future research directions.A successful example of the application of data driven methods in power systems is described in the paper "Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers" by Taha Selim Ustun et al. The authors use a new feature extraction method named the Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network to extract three nonactive power quantities that serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method allows to determine the precise location of the dominant harmonic generating source, helping to improve the power quality of the system performance.Data-driven techniques have been considered as an effective enabling technology for reducing the computational burden of both static and dynamic power system security analysis, however the availability of proper data for training the necessary ML structures remains a basic challenge. The paper "The role of data-driven methods in power system security assessment from aggregated grid data" by Alfredo Vaccaro et al. faces this problem. It classifies established using ML and feature selection algorithms for power system security assessment and forecasting by processing aggregated grid data that can be reliably predicted from several hours to one day ahead by the TSOs. The paper provides a valuable case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios identifying the most promising computing paradigms.Next to the numerous benefits brought by the digitalization of energy, critical security challenges emerge. The wide variety of Information and Communication technologies (ICT) exposes many vulnerable spots which pave the way for different types of cyber attacks. The use of heterogeneous communication technologies, such as ZigBee, wireless mesh networks, cellular network communication and powerline communication with their complex interconnections along with the possible protocol incompatibilities can result in serious security gaps. In addition, the operation of power systems is still heavily dependent on proprietary and legacy technologies whose design did not originally account for security measures. The paper "Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions" by Andrew Syrmakesis analyzes and classifies the state-of-the-art research methodologies for strengthening the cyber resilience of power systems. It categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground for cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods and provides suggestions for future research directions.The Field Chief Officer of Frontiers in Smart Grids extends his sincere thanks to the authors and reviewers of this special issue who have provided this series of papers highlighting state of the art technological developments in the on-going power grid transformation to smart grids.