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Battery charging of Electric Vehicles (EVs) will increase the power demand in distribution networks. It is anticipated that this will cause voltage drops, thermal overloads and an increase in losses. These impacts will be affected by the behaviour of the owners of EVs. A typical 3-phase LV residential distribution network model is used to evaluate the effects of EV battery charging on distribution networks with Distributed Generation (DG). The uncertainties associated with the ownership of EVs, the rating of charging equipment, the occurrence and the duration of charging, together with the spatial distribution uncertainties of DG installation, were addressed with a probabilistic approach. A case study was performed for the year 2030, considering three EV and two DG penetration levels. A control function which reschedules EV battery charging was defined based on customer preferences and distribution network constraints. Thermal overloads, voltage drops, and losses associated with each case were reported. The effects of the coordinated EV battery charging on these impacts were analysed.

Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber‐physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI‐based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real‐world industrial solutions has not so far developed at the same pace. Currently, 90% of AI‐based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI‐based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non‐supervised, human‐understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real‐world case study from the automotive sector, comparing it to an extensive benchmark comprising state‐of‐the‐art unsupervised and semi‐supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI‐based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.

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