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Start of funding 01.01.2015
Bidirectional integration of e-mobility storage capacity into smart micro-grids
Dr. Georg Jung
University of applied sciences Hof
Institut für Informationssysteme (issys)
Prof. Dr. Rajit Gadh
University of California, Los Angeles (UCLA)
Smart Grid Energy Research Center (SMERC)
The main challenge in increasing the fraction of renewable energies (e. g., wind, sun) within the
energy mix lies in the impossibility to control the highly fluctuating amount of primary input.
Even in large, continental-scale, energy grids (macro-grid), where some of these fluctuations
cancel out due to high number and distribution of participants, the residual fluctuation still
causes substantial problems that demand intelligent solutions (smart grid).
In small scale networks (single municipal units, energy-autarkic housing, industrial plants,
etc.), fewer participants face disproportionally higher fluctuations in renewable power. Thus, a
micro-grid setting requires an even tighter orchestration of all components.
E-mobility necessarily entails storage of electrical energy (batteries). In most micro-grid
settings that include e-mobility, the integration of this already available storage capacity o ers
a substantial increase in flexibility. In cooperation between UCLA and Hof University we will
leverage the potential of e-mobility integration into micro-grids.
Final report:
Grid integration of electric vehicles is a significant challenge, especially if charging is dependent on parameters from the grid such as voltage and power supplied by renewables. During our cooperation we developed a simplified model reflecting the situation on campus in Westwood as an example. True to the original it contains a photovoltaics system and a number of electric vehicles with a shortage of charging stations. We were able to reproduce the low level of eigen-consumption (i.e.: consuming self generated power) which is the motivation for the project and a problem known to exist in reality. In response to this problem we implemented a mathematical optimization algorithm. The graphical evaluation shows a significant improvement; however, a set of evaluation parameters is desirable for increasingly complex problems. The next step would be to design an implementation plan that accounts for human factors (inaccuracy in break, arrival and departure times etc.) as well as hardware/circumstances (non-automated charging, manual un-/plugging).