Energy Yield Prediction of Solar Powered E-bike Charging Station

Master thesis (2017)

Authors

A. Jamodkar Electrical Engineering, Mathematics and Computer Science

Contributors

O. Isabella (supervisor 1)
R. Santbergen (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2017 Ajay Jamodkar
More Info
expand_more

Published Date

28-07-2017

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Recent innovations in Electric Vehicles (EVs) will potentially change the future of the transportation industry. They will diversify the energy mix and reduce the dependence on fossil fuels. However, use of EVs only shifts the source of CO2 production to electricity generation plants. A smart solution to overcome this problem is the use of localized generated power and solar-powered charging stations are the best way to achieve it.

A solar powered e-bike charging station, installed on the TU Delft campus is one such example. The charging station is equipped with a meteorological station, sensors for monitoring performance, inverters and batteries. The PV system installed at the e-bike station was thoroughly modeled, considering both the location and meteorological conditions of the final installation [1]. To maximize the station’s utility, it is important to accurately predict the energy yield of the system. The modeling step comprises of several sub-models (irradiance, thermal and electrical model) which indicate the energy yield of the station as well as the power exchange with the grid. Though these models were based on (realistic) assumptions, there is a need to verify the assumptions against measured values.

In this thesis, the accuracy of existing irradiance, thermal and electrical models was evaluated by predicting the energy yield of the e-bike charging station. Further, the performance of these models, especially those related to the irradiance on the plane of the array and the instantaneous temperature of the PV modules, was improved. Also, two new decomposition models are introduced to improve the accuracy of obtaining diffuse irradiance from global horizontal irradiance specifically for the Netherlands. It was found that for accurate energy yield prediction it is necessary to optimize the models using
location specific parameters like sky view factor, albedo, INOCT etc. The energy yield predicted, using the improved models in this thesis, was only 17 kWℎ less than the measured yield for the duration Oct’16-Apr’17.