In the series of posts on the battery-life saving algorithm of the University of Warwick, I made (twice) the remark that the managers of vehicle-to-grid programs would not be very keen in implementing such an algorithm. This because this algorithm, although it is hailed as a break-though, will have a negative impact on the primary purpose of these schemes, therefor tolerating (some) battery damage might be the preferred option.
That made me wonder whether I could check this. The Warwick paper was published two years ago and the Smart Solar Charging program was presented as having developed its own bidirectional charging stations, so if there is some ability to make improvements based on this supposed break-through, then this project should be the one that will show it.
There are two findings in the battery-saving algorithm paper from the University of Warwick that I want to write about in this post. Both were mentioned only in passing in the paper. Although these findings are crucial information for those who want to implement such a system in the real world, these were not mentioned in the conclusion nor in the discussion nor in the list of things they want to improve upon.
These are the two findings:
The paper on the 10% increase of lithium-ion battery life as a result of operating in a vehicle-to-grid (see previous post) is an interesting read. I was initially fascinated by the validation of their battery degradation model, but the actual result came from the integration of that model in a smart grid algorithm. This algorithm was then used in a simulation of load balancing of a building by means of electric cars and resulted in the 10% increase of battery-life figure.
That number is therefor not obtained by measuring the battery degradation in reality, it is the outcome of a mathematical model. Personally, I don’t have a problem with models and this particular model seems to have potential (the battery degradation part is validated). Models are useful for sure, but that doesn’t mean they are necessarily right. It depends for example on the data that goes in the model and the assumptions that are made. It seems that this is where it went wrong in this simulation.
The data that was fed to the algorithm came among other things from an actual building (the International Digital Laboratory). This is the description of that building:
The International Digital Laboratory (IDL) is four story office building located on the University of Warwick campus near Coventry. The University is situated in the centre of England, adjacent to the city of Coventry and on the border with Warwickshire. The building compromises of a 100-seater auditorium, two electrical laboratories, a boardroom, 3 teaching laboratories, eight meeting rooms and houses approximately 360 researchers and administration staff.
That is not a small building and it draws quite some electricity (my emphasis):
In the previous post, I wrote about a report calculating the expected electricity price in a vehicle-to-grid system and the assumptions that went into it. One of the difficulties that was detailed in the report was the aging of the battery used in a vehicle-to-grid system. In the meanwhile, I read this 2017 article from the Dutch sustainability website wattisduurzaam.nl contradicting this. The author of the article writes that it is contra-intuitive, but that research from the University of Warwick revealed that a vehicle-to-grid system can even extend the lifetime of lithium-ion batteries…
I could somehow understand “minimize”, but a vehicle-to-grid system that extends battery life is a very strong claim.
Although the article was written in a cheering mode, it also acknowledges that battery degradation is a problem in current vehicle-to-grid systems, but that this research achieved an extended battery life. Not just a tiny extension, a whopping 10 percent extension of battery life by operating in the vehicle-to-grid system.
The term “vehicle-to-grid” is mentioned twice in passing in the report detailing the impact of electric cars on our grid (see previous post). I wondered whether this vehicle-to-grid was the solution to their problem. After all, their calculation was done by averaging consumption, which is not really what will happen in the real world. But when they assume some top-down system of regulating demand, then I could somehow understand their reasoning.
I didn’t find any reference mentioning “vehicle-to-grid” in the report, but I wanted to know where the CREG got these assumptions from. I found that, to my surprise, the CREG earlier wrote a report on the impact of electric cars on a vehicle-to-grid system (pdf, Dutch ahead). The report is not new, it was published in 2010 with the data from 2007 and 2008. The subject of their research is the impact of the introduction of electric cars on the electricity spot market price.
The result of the 2010 report was similar to the 2016 report. They also researched the impact of 1 million electric cars and found that only 2.5% extra electricity needs to be produced on average (compared to 4% in the 2016 report) and that base load could easily absorb that extra electricity demand. The general conclusion of the 2010 report is that charging cars during off-peak hours will lower the spot prices. This because part of the capacity of the car battery could be used to trade on the energy market, buying electricity from the grid when it is cheap (during off-peak hours) and selling it at a high price when it is expensive (during peak hours).
It gets interesting when they explain their assumptions (on page 15 – 16):