Interoperability is a challenge for the realisation of smart grids. In this work, we apply the methodology for interoperability testing and the design of experiments developed at the Smart Grids Interoperability Laboratory of the Joint Research Centre of the European Commission on a simple use case. The methodology is based on the Smart Grid Architecture Model (SGAM) of CEN/CENELEC/ETSI and includes the concept of Basic Application Profiles (BAP) and Basic Application Interoperability Profiles (BAIOP). The relevant elements of the methodology are the design of experiments and the sensitivity/uncertainty analysis, which can reveal the limits of a system under test and give valuable feedback about the critical conditions which do not guarantee interoperability. The design and analysis of experiments employed in the Joint Research Centre (JRC) methodology supply information about the crucial parameters that either lead to an acceptable system performance or to a failure of interoperability. The use case on which the methodology is applied describes the interaction between a data concentrator and one or more smart meters. Experimental results are presented that show the applicability of the methodology and the design of experiments in practice. The system is tested under different conditions by varying two parameters: the rate at which meter data are requested by the data concentrator and the number of smart meters connected to the data concentrator. With this use case example the JRC methodology is illustrated at work, and its effectiveness for testing interoperability of a system under stress conditions is highlighted.

Electric vehicle (EV) charging is widely considered as a key enabling technology that can support system stability and provide ancillary services to the grid. The present work aims to advance the state of the art in dynamic charging of individual EVs within existing AC-charging facilities. The paper proposes two alternative control solutions for tracking of power setpoints in EVs, based on adaptive feedforward and feedback linear controllers, respectively. The control design, which does not require any ad-hoc hardware adjustment of the standard EV charging infrastructure, nor higher-level communication, is supported by extensive real-world tests that have been performed on the workplace charging facility operated in the JRC Ispra campus. The experimental results validate the effectiveness of the proposed control methods in tracking two different current setpoints for flexibility scheme qualification.

Sensitivity analysis is the process of attributing the variability of model outputs to uncertainties in input parameters and assumptions. Why is this technique important? Which methods and concepts should be prioritized in teaching it? And what instructional strategies are best suited to different audiences? This work seeks to address these questions by drawing on the experience gained from 12 summer schools conducted by the authors since 1999, along with numerous specialized training courses delivered to academia, research organizations, and international institutions. We aim to distill these experiences into practical guidance for teaching sensitivity analysis, offering both methodological foundations and pedagogical strategies to support educators and future practitioners.