Abstract

Abstract
Bidirectional electric vehicle charging stations (EVSE) offer new business models to users and can provide ancillary services to the electrical grid. During charging operation EMC standards and during discharging grid codes are applicable. In this study the harmonic emissions including the total harmonic distortion (THD) and total distortion demand factor (TDD) together with the grid impedance up to 2 kHz of a 10 kW bidirectional charging station are analyzed. While harmonic emission measurements of EVSE at laboratory conditions gives results for idealized conditions and field tests strongly depend on local grid conditions, the real-life operation of an EVSE at a well-known reconstructed grid gives valuable information about the harmonic behavior and impact to the grid impedance. The results show that the bidirectional EVSE can be a main pollutant of harmonic emissions at low power set-points, harmonic phase angle can change from inductive to capacitive behavior and the first resonance frequency of the grid impedance is moved to lower frequencies.

Abstract

Abstract
The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.

Abstract
Nowadays, Project Management is facing a more complex and dynamic environment, mostly because of the recent fourth industrial revolution, called Industry 4.0. The digitisation and the characterisation of all of the assumptions, methodologies, and processes of the Industry 4.0 can cause disruptive effects for the traditional project management and the role of the project manager. This new paradigm requires a more active role from the project manager, accompanied by new technical, contextual and behavioural competencies. In line with this, this work aims to identify the skills or competencies that the project manager must present to follow the fourth technological revolution. To accomplish this task, the most relevant concepts that are associated with industry 4.0 and with the project manager competencies are presented. From the literature review, it is possible to conclude the existence of more behavioural or soft skills associated with the 4.0 project manager profile, compared to the traditional project manager profile. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract