Do EVs Really Help to Store Locally Generated PV Energy? A Model Based on Real Mobility Data

Research Highlights

- Locally generated photovoltaic energy can reduce electricity demand peaks from electric vehicle charging at noon

- Electric vehicle charging at the home location can increase the self-consumption rate by 20% for average households with typical PV systems



Rooftop photovoltaic (PV) systems are becoming widespread in many countries. They offer high return on investments in many countries, deliver electricity with no direct CO2emissions, but can lead to stress for the distribution grid whenever the local supply is not sufficiently matched by local demand. Conversely, electric vehicles (EVs) can cause demand peaks, also with adverse effects for the grid, if many EVs are charged at the same time. High hopes are placed on the combination of EVs and PV to mitigate demand and supply peaks and to increase the self-consumption rate of households. Yet, it is unclear if PV and EV peak times sufficiently overlap to realize the hoped-for effects. 



We provide a simulation approach that merges GPS driving data from conventional combustion-based vehicles, the expected demand profile of private households, and solar irradiance time series. This allows us to estimate and compare the energy demand and peak times of residential electric vehicle charging with the households’ electricity demand profile and to assess the impact of photovoltaic self-consumption on the household’s demand profile. 



The use of residential PV systems for electric vehicle charging can increase the self-consumption rate of households by about 20 % and help alleviate stress on the electric grid. In particular, peak charging power demand at noon can be decreased by about 40 % (with a large variably depending on the system parameters).

Selected publications

Wenig, J., Sodenkamp, M., Staake, T. (2015) Data-based Assessment of Plug-in Electric Vehicle Driving. Lecture Notes in Computer Science (9424), pp. 115-126.


The research has been funded in part by the Technology Alliance of Upper Franconia (TAO), Germany.

Date: 2014 - 2018


Jürgen Wenig, Thorsten Staake


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