Ing. Ondřej Štogl

As the EU Green Deal accelerates the transition to a sustainable economy, access to reliable sustainability data from small and medium-sized enterprises (SMEs) becomes increasingly crucial for meeting regulatory and market demands. However, SMEs face significant challenges in complying with EU Taxonomy reporting due to complex criteria, high costs, and limited resources. The EU LIFE-CET-21 funded CONFESS project aims to bridge this gap by developing the Clean Leap Tool - a practical and accessible solution tailored to SME’s unique needs. This paper presents the methodology and framework behind the tool, with a particular focus on adapting EU Taxonomy criteria to reduce reporting complexity for clean energy SMEs. The Design Science Research Methodology (DSRM) as proposed by Peffers et al. (2007) was used to systematically develop and evaluate an innovative tool addressing the identified problem of EU Taxonomy reporting complexity for SMEs. This study rigorously follows all six DSRM activities to develop and validate a comprehensive solution for voluntary and simplified EU Taxonomy reporting, ensuring both methodological consistency and practical applicability of the resulting Clean Leap Tool. The methods for criteria simplification follow a structured, four-step process ((1) improving user-friendliness by eliminating ambiguities and enhancing data accessibility, (2) strengthening SME capacities through guidelines and tools, (3) modifying assessment criteria to improve proportionality, and (4) substituting or exempting criteria where simplification is not feasible), suggested by (Giannotti et al. 2024), where the steps are used in combination to enhance accessibility and reduce reporting burdens. The resulting Clean Leap Tool ensures a more manageable compliance process while maintaining By providing a user-friendly web application that simplifies criteria and streamlines the reporting process, it significantly lowers the barriers SMEs encounter in complying with EU Taxonomy requirements. The tool’s modular structure enables corporates to familiarize themselves with necessary standards without incurring substantial upfront costs. Beyond SMEs, its applicability could be extended to larger companies, particularly as voluntary sustainability reporting gains prominence due to regulatory developments such as the Omnibus Directive.

As cities worldwide accelerate their transition to electrified transport, robust insights into public EV charging demand, remain pivotal, for infrastructure development and grid reliability. This paper evaluates a high-resolution dataset from Prague, integrating geospatial and demographic indicators to uncover key spatiotemporal charging behaviors. Our findings demonstrate pronounced morning, afternoon, and evening peaks in different urban zones, highlighting the interplay between land-use patterns and grid constraints. We further discuss how incorporating predictive modeling can facilitate proactive planning, reducing bottlenecks and ensuring equitable access to charging infrastructure. Ultimately, this approach can be extended to diverse urban settings, fostering more sustainable and resilient transport systems.

The observed transition towards electric vehicles (EVs) globally calls for a well-planned rollout of public charging stations to accommodate the anticipated increasing EV charging demand. Grid operators face challenges with infrastructure expansion while also seeing new opportunities open, such as utilizing charging load patterns to balance grid power flows. Currently, there is limited availability of insights covering the locally heterogeneous behavior observed at public charging points distributed across population centers. This research fills this research gap by examining real world charging data. By combining charging session data with geospatial information, we can extract characteristics of local EV charging demand load curves. The study provides insights through the analysis of temporal patterns in demand, distribution of charging events across various urban areas, and characteristics of electrical load profiles, which are instrumental in planning further infrastructure expansion. The analysis clearly shows distinct charging demand during the weekdays and weekends, different charging demand splits across various location types, and also the difference in the charging behavior depending on the type of the area surrounding the chargers.

Optimal Power Flow (OPF) is central to the efficient and secure operation of electric power systems. The Alternating Current OPF (ACOPF) variant accounts for nonlinear Kirchhoff laws and is known to be both non-convex and, in many instances, NP-hard. Despite longstanding research, achieving fast, accurate, and robust ACOPF solutions at scale remains challenging-especially under evolving grid conditions such as high renewables. Recent progress in machine learning (ML) offers a promising paradigm for approximating solutions with near-real-time speed after offline training. In this paper, we propose a detailed methodological framework spanning data extraction, neural network training, and model comparisons (Gaussian Mixture Models and Linear Regression). We integrate these ML pipelines with classical OPF theory, referencing comprehensive overviews as well as novel ML-based OPF strategies. Experimental results on five standard IEEE power system networks demonstrate that neural networks are generally competitive or superior to simpler statistical methods in complex cases, while Linear Regression can be surprisingly strong for smaller networks. We further analyze feasibility issues, constraint enforcement, and out-of-distribution conditions. Our findings highlight both the promise of data-driven approaches and the nuances of ensuring reliable, real-time dispatch solutions.

The global shift to electric vehicles (EVs) necessitates strategic deployment of public charging infrastructure to meet growing demand. Our study focuses on Prague, Czechia, analyzing real-world data to understand EV charging demand. Utilizing charging session and geospatial data, we categorize charging points by urban context and assess factors influencing demand. Geospatial analysis helps identify potential charging sites based on accessibility, location, and population density. Insights from temporal demand variations, share of charging instances per area type, and load profile characteristics guide infrastructure planning. Despite limitations in data scope and geographical specificity, this study offers valuable insights into public charging behavior, laying groundwork for future enhancements and predictive modeling to inform efficient charger placement in urban EV infrastructure.

Electric vehicles (EVs), as facilitators of grid stability and flexibility, provide a critical solution to the energy infrastructure's evolving demands, underscored by the growing integration of renewable energy sources (RES) and the rapid increase in EV adoption worldwide. This trend is particularly evident in Europe which is experiencing dramatic increases in both the adoption of RES and EVs. Vehicle-to-grid (V2G) technology allows EVs to operate as a two-way power flow to both draw and feed electricity into the grid. This multidisciplinary overview examines the role of V2G systems in enhancing grid performance, identifying corporate vehicle fleets as key flexibility providers, and integration with Smart Grid technologies as a key element for successful V2G implementation. In a scoping analysis of recent literature (2005–2024), we identify challenges such as privacy, security, and regulatory compliance as well as a critical gap in establishing economically sustainable models for aggregators, distribution system operators (DSOs), generation companies (GENCOs), and end-users. Drawing from these insights, we then discuss the necessity for future research to develop models that ensure equitable benefits across stakeholders and the importance of models that can adapt to country-specific mechanisms. The findings from our overview argue that the integration of EVs, V2G, and RES are essential components for developing future energy systems that are resilient, adaptable, decarbonized, and sustainable.

This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our results indicate significant impacts from the type of Basic Administrative Units on predicted load curves, which contributes to the understanding and optimization of EV charging infrastructure in urban settings and allows Distribution System Operators (DSO) to more efficiently plan EV charging infrastructure expansion.

The transition to a sustainable energy system is critically dependent on the integration of renewable energy sources (RES) and the enhancement of grid stability. Electric vehicles (EVs) stand at the confluence of this transformation, not only revolutionizing transportation but also serving as a dynamic component in stabilizing and flexing the electricity grid. This contribution offers a brief overview of Vehicle-to-Grid (V2G) technology, outlining its significance in the evolving energy landscape. By summarizing the current state of the art, methodology, and the basic principles underlying V2G, the synopsis highlights how EVs can support grid stability and flexibility. It touches upon the historical context of V2G development to underscore the technology's potential and challenges. In essence, this overview aims to elucidate V2G's role in fostering a more resilient and sustainable energy future, emphasizing its importance in grid development.

The impact of the European Union’s carbon footprint reduction targets and their effects on the transport sector can be seen in the gradual trend towards replacing the combustion engine vehicles fleet with plug-in hybrid or fully electric vehicles. With the rapid growth of electric vehicles, many questions and issues arise associated with EV charging. The key to sustaining this rapid fleet renewal is constructing a dense public charging station network. This research aims to assist stakeholders and EV charging infrastructure developers in planning the strategic deployment of public charging stations in terms of number, charging power, and their placement in strategic locations in a city.