Editorial: Data-Driven Solutions for Smart Grids

To address this complex issue, the most promising research directions are oriented toward the conceptualization of improved information processing paradigms and smart decision support systems aimed at enhancing standard operating procedures, based on pre-defined grid conditions and static operating thresholds, with a set of interactive information services, which could promptly provide the right information at the right moment to the right decision maker. To effectively support the deployment of these services in modern smart grids it will be incumbent upon the scientific community to develop advanced techniques and algorithms for reliable power system data acquisition and processing, which should support semantics and content-based data extraction and integration from heterogeneous sensor networks. This research topic contains four articles.The paper Optimal Balancing of Wind Parks with Virtual Power Plants by Vadim Omelčenko and Valery Manokhin addresses data-driven solutions in the context of optimization of virtual power plants. This work proposes the use of machine learning to process available data measurements. The goal is to balance the power production and at the same time maximize the revenue of a portfolio of power plants with different technologies (biogas, wind, batteries, etc.) considering uncertainty in both price and power production.The paper Supporting Regulatory Measures in the Context of Big Data Applications for Smart Grids by Mihai A. Mladin discusses the policy and regulatory aspects. This paper focuses in particular on big data applications to the ongoing "energy transition" process built on higher renewable energy integration and digitalization, and discusses how this can help regulatory measures through societal acceptance and involvement.The paper Data Consistency for Data-Driven Smart Energy Assessment by Gianfranco Chicco addresses the issue of data consistency and discusses data-versus model-based approaches.