Grid Edge – an energy tech start-up span out of Aston University – is aiming to bridge the gap between the energy industry and its consumers.
Focused on enabling commercial energy users to participate in demand response and manage their energy consumption, Grid Edge uses machine learning and data to enable flexibility services from commercial energy users.
Jim Scott, co-founder and chief product officer at Grid Edge, spoke with Current± about how to allow commercial energy users to provide flexibility services, the need for collaboration between commercial buildings and DNOs and the different terminology surrounding AI and data.
Why is it important to facilitate demand response from commercial energy users?
It’s our view that the fundamental issue is that the grid support required for technology like EVs or heat pumps has always been done on the grid side’s terms; it’s never really been done on a customer’s terms.
All of our customers have really ambitious carbon targets and have their neck on the line about it... And I think their view is that they’re being let down a little bit by the industry.
There's been very little active communication between consumers and their supply side partners and those relationships have been built around procurement and billing. I do think both DNOs and suppliers have been trying to change this recently as suppliers look more towards service focused offerings and DNOs are trying to become more consumer centric.
What we’re doing at Grid Edge is building the tools to help building operators engage with the future energy system. We think that system will be dominated not by how many kWh you use or generate but by when you consume or generate. We want our customers to be ready to proactively address that opportunity.
What can the DNOs and suppliers do to help support this form of flexibility?
We would like to see a much more collaborative approach from the networks. We think that active network management is well within the capabilities of most commercial energy users but we don't see those schemes coming to market properly before the next price review at the least. It’s a shame as the current practice seems to be a less efficient deployment of the money coming into the DNOs.
In terms of suppliers, I would like to see them really start to push the boundaries of what their customers can engage with around flexibility services. We see some of our upstream partners starting to do just that at the moment with some really innovative tariff products being developed. But there seems to be a lack of confidence when it comes to integrating those offers into the core business sales process. We think the consumers are ready to engage on this topic if it can just be packaged in the right way.
How much can commercial energy users contribute to alleviating grid constraints?
I think the answer to constraints should be a blend of technologies. I can see a case for supply side storage in certain conditions but I think the lowest cost flexibility options will always be on the demand side if the incentives and business models can be aligned correctly. We think it's more of a continuum between high-availability, high-cost supply side storage and lower-availability, lower-cost demand side dynamic storage and demand side management.
On the other side of things, all of our customers want to put in new electrical capacity and some of them are talking about doubling or tripling their load in three years due to changing out gas boilers, installations of EV charging kit or building expansions.
Capacity constraints are definitely on the mind of those customers who are thinking longer term about large electrification projects and they’ve learned to open a dialogue with the DNOs about large projects early in development. We don't see many sites where the customer is being offered any lower cost alternative to upgrade, however, and we expect to see more of our customers being offered active network management style connection agreements in the near future.
What we’re trying to do is give the customer the power to have that conversation on their terms instead of having to be told by the DNO what the deal is. At the moment, the terms are all written out as what suits the grid and the customer has to jump. The result of that is that you’ll probably end up with lots of batteries and diesel generators and you won’t use the inherent flexibility we already have.
Grid Edge's website uses the term AI but you've said you prefer the term data science. Why the difference?
When we talk to investors, we often use the phrase AI when the reality is we, like most analytics companies, use a wide range of methods and algorithms to solve the problem at hand. We tend to say AI as I don’t think the investor community is necessarily quite ready to know the difference between a statistical method and an analytical method.
I do think it's just about finding the right fit and it's pretty clear when it comes to talking about the technology that some investors do really get it and can have quite a detailed conversation and understand the nuance, whilst for others it’s maybe a bit of a trend.
I'd like to think as these technologies become more common place the terminology will move away from the broad catch-all lexicon and back to descriptions of what type of methods and technologies are at work.