This paper presents data-driven insights from a case study that was carried out in an University EV charging plaza where EV charging demand is met with the combination of the University campus grid and installed solar capacity. First, we assessed the plaza dependency on the grid for meeting EV charging demand and intake of excess solar energy using the available dataset. By modifying the plaza network to accommodate a small approx. 50 kWh battery storage can significantly reduce the grid dependency of the plaza by approx. 30% compared to the present situation and can also increase the green energy utility for EV charging by 10-20%. Having an battery storage could also help overcome the limitations due to the campus grid capacity during EV charging peak demand by means of scheduling algorithms. Second, we assessed the utility rate of the plaza which indicated that the average utility of charging infrastructure is about 30% which has an increasing trend over the analysed period. The low utility and EV charging peak demand may be the result of current EV user behavior where the average idle time during charging sessions is found to be approx. 90 minutes. Reduction in idle time by one third may increase the capacity and utility of plaza by two to two and half times the forecasted daily demand. By having the campus grid capacity and user information may further help with effect EV demand forecasting and scheduling.
This is a review of the literature on community energy. We analyze more than 250 studies that appeared in the academic literature in the period 1997-2018. We investigate the timing regarding the appearance of these studies, the geographical orientation of the research, and the journals in which the articles appeared. We also analyse the keywords used to identify the research. Further, we relate the articles to the theoretical perspectives employed. We also analyse keywords used by the authors in relation to the particular approaches employed and reflect on the country specifics of the case studies. We find that the majority of studies on community energy did appear in the last couple of years. Especially the UK, US, Germany and the Netherlands are being investigated. Energy Policy published most of the studies. Different theoretical perspectives study community energy, especially Governance, Sociology, Economics, Planning, Technology, and Transition. We conclude that the study of community energy is still in its infancy as there is little commonality in the terminology and key concepts used. Studying community energy requires further improvement in order to better integrate the different theoretical perspectives and to ground policy decisions.
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Gepubliceerd in Mikroniek, nr. 6 2018 In manufacturing environments where collaborative robots are employed, conventional computer vision algorithms have trouble in the robust localisation and detection of products due to changing illumination conditions and shadows caused by a human sharing the workspace with the robotic system. In order to enhance the robustness of vision applications, machine learning with neural networks is explored. The performance of machine-learning algorithms versus conventional computer vision algorithms is studied by observing a generic user scenario for the manufacturing process: the assembly of a product by localisation, identification and manipulation of building blocks.
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