PV systems are used more and more. Not always is it possible to install them in the optimal direction for maximum energy output over the year. At the Johan Cruijff ArenA the PV panels are placed all around the roof in all possible directions. Panels oriented to the north will have a lower energy gain than those oriented to the south. The 42 panel groups are connected to 8 electricity meters. Of these 8 energy meters monthly kWh produced are available. The first assignment is to calculate the energy gains of the 42 panel groups, and connect these in the correct way with the 8 energy meter readings, so simulated data is in accordance with measured data.Of the year 2017 there are also main electricity meter readings available for every quarter of an hour. A problem with these readings is that only absolute values are given. When electricity is taken of the grid this is a positive reading, but when there is a surplus of solar energy and electricity is delivered to the grid, this is also a positive reading. To see the effect on the electricity demand of future energy measures, and to use the Seev4-City detailed CO2 savings calculation with the electricity mix of the grid, it is necessary to know the real electricity demand of the building.The second assignment is to use the calculations of the first assignment to separate the 15 minute electricity meter readings in that for real building demand and for PV production.This document first gives information for teachers (learning goals, possible activities, time needed, further reading), followed by the assignment for students.
In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model with socio-demographic statistics of neighborhoods and charging history of vehicles. Models are obtained for three user-types: regular users, electric car-share participants and taxis. Regular users are later split into three subgroups based on their charging behaviour throughout the day: Visitors, Commuters and Residents
Plasma treatment is a commonly used technology to modify the wetting behavior of polymer films in the production process for, e.g., printed electronics. As the effect of the plasma treatment decreases in time, the so-called "aging effect", it is important to gain knowledge on how this effect impacts the wetting behavior of commonly used polymers in order to be able to optimize production processing times. In this article the authors study the wetting behavior of polyethylene naphthalate (PEN), polyethylene terephthalate (PET), polycarbonate (PC), fluorinated ethylene propylene (FEP) and polyimide (PI) polymer films after plasma treatment in time. The plasma treatment was performed using a novel maskless DBD plasma patterning technology, i.e., Plasma Printing, at atmospheric pressure under nitrogen atmosphere. After treatment, the samples were stored at room temperature at 30%-40% relative humidity for up to one month. An increase in wettability is measured for all polymers directly after Plasma Printing. The major increase in wettability occurs after a small number of treatments, e.g., low energy density. More treatments show no further beneficial gain in wettability. The increase in wettability is mainly due to an increase in the polar part of the surface energy, which can probably be attributed to chemical modification of the surface of the investigated polymers. With the exception of FEP, during storage of the plasma treated polymers, the wettability partially declines in the first five days, after which it stabilizes to approximately 50% of its original state. The wettability of FEP shows little decline during storage. As the storage time between production steps is mostly under two days, Plasma Printing shows good promise as a pre-treatment step in the production of printed electronics. d c 2013 Society for Imaging Science and Technology.
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