Recent advancements in information and communication technologies have greatly helped strengthen the capability of modern smart grids in online situational awareness and decision making against potentially risky/insecure conditions. In particular, the extensive configuration of various advanced sensing devices has enabled the collection of massive operational information to improve the observability of the grids. With huge volumes of operational information available, emerging computational intelligence and machine learning technologies can be leveraged to augment the intelligence level of system-wide situational awareness and decision making, thereby making the grids smarter than ever before.Considering the need for addressing challenges related to the above new trend, we thus launched this Research Topic to provide a worldwide platform that collects innovative works reporting recent advances in data-driven intelligent situational awareness and decision making for smart grid operation. After an extensive call-for-paper and peer-review process of more than one year, we have accepted 16 papers in total for publication. All the accepted papers have been categorized into two groups according to their subjects: 1) system dynamics awareness, control, and protection; and 2) system optimized dispatch, operation, and restoration. Specific highlights of each paper are summarized in the following.
Part I: System Dynamics Awareness, Control, and ProtectionIn the article "Online correction of transient voltage security region boundary based on load parameter variations in power systems", Zhang et al. proposed a data-driven transient voltage security region (TVSR) estimation approach with the impact of composite load model and distributed photovoltaic (PV) power parameters. The relationship between TVSR boundary and load-leading parameters combination scenarios is further mined through the CatBoost learning framework. A more accurate TVSR boundary can be provided by this approach.In the article "Stator single-line-to-ground fault protection for powerformers based on HSGC and CNN", Liu et al. proposed In the article "Transactive energy system: Concept,configuration,and mechanism",Zhou et al. 105 introduced the basic concept of a transactive energy system (TES). The TES configuration is described 106 from the perspectives of its physics, information, transaction, and regulation. The TES mechanism 107 allows participants to conduct various transactions with any other party to the extent that regulatory 108 policy permits. Finally, some challenges to the development of TESs are discussed. 109In the article "Accommodation capacity evaluation of renewable energy in power systems 110 considering peak and frequency regulation", Yu et al. proposed an evaluation method of renewable 111 energy accommodation capacity (REAC) based on a source-network-storage interaction model. The 112 deep peak regulation and frequency response of thermal power units are explicitly modeled and 113 incorporated in the REAC evaluation. 114In the article "A data-driven hybrid interval reactive power optimization based on the security limits 115 method and improved particle swarm optimization", Chen et al. proposed a data-driven hybrid 116 approach based on the security limits method (SLM) and the improved particle swarm optimization 117 (IPSO) to solve the reactive power optimization with interval uncertainty. The historical data is 118
