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.
from the article: The demand for a wireless CO2 solution is ever increasing. One of the biggest problems with the majority of commercial available CO2 sensors is the high energy consumption which makes them unsuitable for battery operation. Possible candidates for CO2 sensing in a low power wireless application are very limited and show a problematic calibration process. This study focuses on one of those EMF candidates, which is a Ag4RbI5 based sensor. This EMF sensor is based on the potentiometric principle and consumes no energy. The EMF cell was studied in a chamber where humidity, temperature and CO2 level could be controlled. This study gives an detailed insight in the different drift properties of the potentiometric CO2 sensor and a method to amplify the sensors signal. Furthermore, a method to minimize the several types of drift is given. With this method the temperature drift can be decreased by a factor 10, making the sensor a possible candidate for a wireless CO2 sensor network.
This paper proposes a Hybrid Microgrid (HμG) model including distributed generation (DG) and a hydrogen-based storage system, controlled through a tailored control strategy. The HμG is composed of three DG units, two of them supplied by solar and wind sources, and the latter one based on the exploitation of theProton Exchange Membrane (PEM) technology. Furthermore, the system includes an alkaline electrolyser, which is used as a responsive load to balance the excess of Variable Renewable Energy Sources (VRES) production, and to produce the hydrogen that will be stored into the hydrogen tank and that will be used to supply the fuel cell in case of lack of generation. The main objectives of this work are to present a validated dynamic model for every component of the HμG and to provide a strategy to reduce as much as possible the power absorption from the grid by exploiting the VRES production. The alkaline electrolyser and PEM fuel cell models are validated through real measurements. The State of Charge (SoC) of the hydrogen tank is adjusted through an adaptive scheme. Furthermore, the designed supervisor power control allows reducing the power exchange and improving the system stability. Finally, a case, considering a summer load profile measured in an electrical substation of Politecnico di Torino, is presented. The results demonstrates the advantages of a hydrogen-based micro-grid, where the hydrogen is used as medium to store the energy produced by photovoltaic and wind systems, with the aim to improve the self-sufficiency of the system
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