A Data-Driven Zonal Monitoring Framework Based on Renewable Variability for Power Quality Management in Smart Grids

The European energy transition, marked by the increasing share of renewable sources in the production mix, brings to the fore the issue of maintaining power quality under conditions of high variability. This study proposes an adaptive monitoring model based on a zonal classification of electrical networks according to the volatility of net renewable production (wind and photovoltaic). The approach relies on a proprietary Renewable Variability Index (RVI), developed using publicly available European datasets, to assess the mismatch between electricity consumption and renewable generation in six representative countries: Germany, Denmark, Spain, Poland, Romania, and Sweden. Based on this index, the model defines three zonal risk levels and recommends differentiated power quality monitoring strategies: continuous high-resolution observation in critical areas, adaptive monitoring in medium-risk zones, and conditional event-based activation in stable regions. The results demonstrate a significant reduction in data acquisition requirements, without compromising the capacity to detect disruptive events. By incorporating adaptability, risk sensitivity, and selective allocation of monitoring resources, the proposed framework enhances operational efficiency in smart grid environments. It aligns with current trends in smart grid digitalization, enabling scalable, context-aware control and protection mechanisms that support Europe’s sustainability and energy security objectives while contributing to the broader goals of sustainable energy transition and long-term grid resilience.