Energy markets are built on a simple idea: if prices change, behaviour will follow. In theory it works. In practice, it often doesn't.
Day-ahead pricing, time-of-use tariffs and dynamic signals all assume that changing the price changes behaviour. But most systems can't respond freely — they run to schedules, make decisions in advance, and optimise for more than cost. Price signals exist; behaviour only partially follows. The fix is systems that sense, decide and act continuously.
Day-ahead pricing, time-of-use tariffs, dynamic signals — all designed to nudge demand in the right direction. In theory, it works. In practice, it often doesn’t. There’s a gap between what the market signals and what systems actually do. As systems become more volatile, more distributed, and more reliant on flexibility, that gap becomes harder to ignore.
Most market design rests on a simple premise: change the price, and the system will respond. It underpins demand response, EV charging strategies, C&I optimisation, and procurement. At a high level, it holds together. But once you get closer to operations, things start to break down.
Price signals can work — but only when everything lines up. And most of the time, it doesn’t.
But only when systems can actually respond, decisions aren’t manual, and incentives are clear. There are pockets where this works well — usually where assets, software, and incentives are tightly integrated. That’s not the norm. Most of the time, price is there — but nothing meaningful happens.
Assets don’t respond freely to price. Industrial processes run to schedule. EV fleets charge when vehicles are available. Buildings follow how people use them. Price is just one input — often not the most important one.
Markets move hour by hour. Most operations don’t. Decisions are set in advance, or updated occasionally. So even if prices change, the system often doesn’t.
A price signal doesn’t tell you what to do. To act on it, you need some view of what’s coming, a way to evaluate options, and a way to actually execute. Without that, it’s just information.
In practice, no one is optimising purely for price. There’s always something else: reliability, operational risk, contractual constraints. So even when the signal is clear, it doesn’t always drive the decision.
Price signals exist, but behaviour only partially follows.
It’s not that markets are wrong. It’s that they assume a level of responsiveness most systems simply don’t have yet.
This gap has always been there. What’s changed is how much it matters. Markets are more volatile, systems are more distributed, and flexibility is no longer optional. Renewables and storage only amplify this. More variability means stronger signals — and higher expectations that something will respond. But responding requires coordination, not just awareness. You can see this starting to surface in things like 24/7 energy and hourly matching, where alignment between supply and demand has to happen in real time, not on average.
If price signals aren’t enough on their own, the real question is: what actually makes systems respond?
The idea that someone sees a price and reacts to it doesn’t scale. What’s emerging instead is systems that make decisions continuously, and respond without waiting for manual intervention.
The hard part isn’t predicting price. It’s deciding what to do in systems with constraints, uncertainty, and competing objectives. That’s where things get difficult — and interesting. You see it in EV charging, C&I optimisation, and portfolio decisions. These aren’t one-off choices. They’re ongoing trade-offs.
For price signals to matter, they have to be built into how systems operate — control systems, software platforms, operational workflows. It’s less observe → decide → act and more sense → decide → act, continuously.
The biggest issue isn’t capability. It’s that things don’t join up. Systems don’t connect, data doesn’t flow cleanly, and decisions don’t translate into action. That’s where most of the friction sits.
The next phase of the energy transition isn’t about better signals. It’s about whether systems can actually respond to them. A more realistic picture looks like: markets generate signals, systems interpret them, infrastructure executes them. When those line up, behaviour follows. When they don’t, the gap stays.
This gap — between signals and behaviour — is where we tend to work, usually when something doesn’t behave the way it’s supposed to. That might be testing how a new capability will work in practice, making sense of a system that’s becoming more complex, or building something that doesn’t exist yet. Often it sits around EVs and flexible demand, new ways of participating in markets, and situations where the requirements aren’t fully defined. In those cases, the challenge isn’t theoretical — it’s making something actually work.
That's usually where things get interesting — and where we get involved: structuring the problem, testing the options, and building something that works in practice.