Revolve is a bespoke and flexible modelling tool designed to quantify economically the flexibility of EV charging, V2G, and storage batteries.

The webinar detailed the capabilities of the model and how it can inform your business case. Below, Greg Payne, Modelling and Simulations Lead at Cenex, has answered your questions from the webinar. You can download a copy of the webinar slides here, or email Greg.Payne@Cenex.co.uk for more information.

Is there an assumption made in the model about the energy price?

We can input independent energy prices into the model, as an import energy price and an export energy price. We don’t assume anything in a blanket way, and they can be a single fixed rate tariff or half hourly varying.

Do all connections act in the same way at any point in time?

In the portfolio mode, where you’re modelling up to 1,000 different EV chargepoints dotted around the country, what happens is it doesn’t simulate the consequences resulting from doing things at the same time or different times.
We haven’t added any constraints that say ‘we want to stagger our charging because we don’t want to blow a transformer on the distribution network’ for example; the model doesn’t know about those kinds of effects. The way this model works, is if you had a single price point where the price went from cheap to expensive at midnight it might flick all of them on at the same time.

This is a desktop modelling tool, not something you can dispatch to hardware, and that’s a valid point – we should be careful of such effects.

Currently there is a limit of 1,000 EVs, are there plans to scale up the model?

The limit of 1,000 is fairly flexible. I haven’t tried running it for larger numbers because, as an optimisation model, the more vehicles you give it, the slower it will run. We have options available to enable us to run it for larger pools of vehicles, and we can book out time on servers if we need to. While there’s no plan in place at the moment to run on a larger scale, we can if required.

Is the model publicly available? Are there any constraints on who can use this and what projects it can be used on?

The model is owned by Cenex and not publicly available. If you are interested in using the model, we can work with you on a consultancy arrangement.
We are looking at potentially licensing our models so this is something we can look at going forward. The model does require an expert user though to fully explore the data.

What were the biggest issues you experienced running the trial with 60 vehicles?

The data for those 60 vehicles came from the Electric Nation Trial from about a year ago. The only issue we experienced was that it was quite a small sample which means we don’t get the coverage or diversity of data to extrapolate perfectly – having data from a larger fleet of vehicles would’ve been better.

It was mentioned that frequency response is the most important revenue, is there any way businesses or private persons can tap into that? Is it correct that frequency response is not standard for current EV chargers?

Yes, it is correct – providing frequency response isn’t an off-the-shelf capability chargepoints have normally however this is the subject of demonstration projects.

A local energy market in Cornwall has been set up where industry, business, or domestic consumers can service the national grid, via an aggregator, and frequency response is one of the options.
It’s definitely ‘watch this space’ at the moment, because within the next year or so we can expect frequency response to be incorporated into some of the chargepoint hardware.

We are also involved in project Sciurus which is on its way to installing 1,000 vehicle-to-grid chargepoints in homes across the UK. Currently those chargepoints are earning money by charging and discharging at the right times depending on energy price but the next stage on that is to add the functionality of frequency response.

Are you optimising with knowledge of future demand so you get a theoretical maximum saving or are you optimising for quality that you can use in the real world?

We are optimising based on a perfect knowledge of the demand of the home in the future which is the upper bound of the revenue you can make.
With some enhancements we can input a forecast of the demand rather than the real demand, and depending how accurate your forecast is, it can predict your future revenue stream to see what’s the most money you can make.

Is this linked to any building loads to examine the link from EVs to the building’s energy needs?

Yes, the model works with separate nodes in it, and each node can have a chargepoint or energy generation such as PV or a wind generator. Each node is then associated with a building demand, and the resulting total demand is calculated. The charging of the EV can be optimised based on that.

How can the model provide an integrated energy and fleet management for a corporate? Are there any model monitoring systems available?

This model gives you the initial view of the business case, so it shows you what you can do in terms of installing the hardware and management system, then presents the results of what you might end up with and the financial case for it.
It isn’t a system you can dispatch in the real world so it isn’t something you can import the algorithms from on to an energy management platform, but our Jigsaw model is a little more akin to that. Jigsaw includes the algorithms that we can then tailor and potentially import on to hardware.

Do you assume each customer is individually trying to maximise profit or is it the site that is trying to maximise profits together? Is there cooperation between owners of EVs?

The way the model works is it tries to maximise the value of the entire portfolio in one go. That means that everyone is working together within that portfolio for the cooperative good.

If you have a pool of 100 owners or vehicles, and on average you’ve minimised your bill, the question is then how you share that money between the members.
The simple way would be to split the revenue between them all but you might end up with a situation where you’ve got one site that has compromised on their energy bill in order to provide more grid services so it might not always work out fairly.

We designed the scope of the model to look at the value for a portfolio and if it’s worth pursuing – we haven’t delved into the social logistics just yet.

Are there any plans to add the functionality to model time-based scope of greenhouse gas emissions from the EVs to minimise this instead of maximise savings?

Yes, there is. This is one of the enhancements we really want to put into the model, to optimise on carbon instead of price, and it’s absolutely something we’re looking into doing as one of our next development steps.

Sign up to our Jigsaw Model Webinar Here. See how modelling can inform your low emission strategic decisions. The model can be tailored to you to simulate a local network that explores the potential energy scenarios and impacts.