The energy transition is a multidisciplinary challenge that warrants solutions that are robust and sustainable. Energy flexibility, one of the key pillars of the energy transition, is an umbrella term that covers multiple innovative solutions implemented at all levels of the electric grid to ensure power quality standards, amongst other objectives. Low-tech, on the other hand, emphasizes designing, producing, and sustainably implementing solutions. Therefore, considering the multidisciplinary nature of energy transition and the existing energy flexibility solutions, the purpose of this research work is multilateral: first, it presents the concept of low-tech and its associated mechanisms; then, it addresses the misconceptions and similarities that low-tech might have with other innovation approaches; and finally, it provides an assessment of existing flexibility solutions using low-tech as a tool. The result of this assessment is presented qualitatively and indicates that indirect energy flexibility solutions rank higher on a low-tech scale relative to supply-side energy flexibility solutions and energy storage flexibility solutions.

Electric vehicles are considered by many as an emission-free or low-emission solution to meet the challenge of sustainable transportation. However, the operational input, electrical energy, has an associated cost, greenhouse gasses, which results in indirect emissions. Given this knowledge, we pose the following question: “Are zero-emission transportation targets achievable given our current energy mix?” The objective of this article is to assess the impact of a grid’s energy mix on the indirect emissions of an electric vehicle. The study considers real-world data, vehicle usage data from an electric vehicle, and carbon intensity data for India, the USA, France, the Netherlands, Brazil, Germany, and Poland. Linear programming-based optimization is used to compute the best charging scenario for each of the given grids and, consequently, the indirect emissions are compared to those of a high-efficiency 1.5 L diesel internal combustion engine for the vehicle: a 2019 Renault Clio dCi 85. The results indicate that for grids with low renewable energy penetration, such as those of Poland and India (Maharashtra), an electric vehicle, even when optimally charged, can be classified as neither a low- nor zero-emission alternative to normal thermal vehicles. Also, for grids with elevated levels of variation in their carbon intensity, there is significant potential to reduce the carbon footprint related to charging an electric vehicle. This article provides a real-world perspective of how an electric vehicle performs in the face of different energy mixes and serves as a precursor to the development of robust indicators for determining the carbon reductions related to the e-mobility transition.

Indoor Environmental Quality directly affects public health, productivity, and well-being, while also playing a vital role in developing climate-neutral, energy-efficient, and resilient buildings. This paper presents a comprehensive dataset of indoor environmental parameters that affect thermal comfort, indoor air quality, and visual comfort, which was created under the European Union’s Horizon 2020 Project Collective Intelligence for Energy Flexibility. The dataset comprises high-resolution measurements of carbon dioxide, pollutants, volatile organic compounds, air temperature, relative humidity, and illuminance on a horizontal plane, collected over a two-year period at 1-minute intervals. Data were gathered from 14 pilot buildings across four European climates: Cyprus, France, Italy, and Norway, covering diverse building types such as schools, medical centres, sports arenas, residential complexes, universities, and elder care facilities, representing about 40 % of common European building categories. Sensors were installed in specific thermal zones within each building to monitor environmental conditions. All data is organized by building and zone and supplemented with standardized Brick metadata to ensure interoperability. This comprehensive dataset, with its broad geographic coverage, variety of building types, long-term high-frequency measurements, and multimodal data, provides a valuable resource for comparative IEQ research, cross-domain modelling, and integrated assessments of comfort, ventilation, and daylighting across different climates and operational settings and is available upon request under a non-disclosure agreement provided by the consortium.

Nudging la consommation d'électricité dans les ménages : Un cas d'étude du secteur résidentiel français: La transition énergétique vers les sources d'énergie renouvelables et la production distribuée est essentielle pour atténuer le changement climatique. Cependant, les sources d'énergie renouvelables sont intermittentes, ce qui peut entraîner à l'avenir une congestion du réseau lors des pics de consommation. Par conséquent, la flexibilité d’énergie est inévitable en complément de la gestion de l'offre d’énergie et du stockage de l'énergie. Le secteur résidentiel est le plus gros consommateur d'énergie et il est donc essentiel de mettre en œuvre la flexibilité énergétique. En plus des approches existantes de la flexibilité énergétique, nous présentons dans cette thèse une flexibilité "indirecte" non monétaire par la mise en œuvre d'une expérience de science comportementale avec 175 ménages français. L'ensemble des nudges utilisés dans cette expérience n'oblige pas les ménages participants à mettre en œuvre la flexibilité énergétique pour des raisons de gain monétaire ou d'aversion aux pertes. Les alertes sont générées par des algorithmes prédictifs et envoyées par SMS à un groupe de ménages dans le but d'effectuer un décalage d’énergie ou effacement d’énergie. Les alertes sont complétées par des suggestions sur l'utilisation (ou non) d'appareils engagés pour mettre en œuvre la flexibilité énergétique. Après chaque alerte, le groupe reçoit un retour graphique dans lequel la courbe de charge mesurée par le compteur intelligent LINKY est superposée à une courbe de référence théorique propre à chaque ménage. En comparaison avec un groupe de contrôle, nous pouvons quantifier l'impact de ces signaux de nudge sur la consommation d'énergie du groupe traité. Enfin, un algorithme de désagrégation énergétique est présenté qui nous permettra de détecter les équipements utilisés lors de la flexibilité et ainsi faire une analyse de l'engagement des ménages à rechercher des appareils potentiels pour la flexibilité énergétique.