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 optimization, 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.

• Demand response and EV charging programmes can reduce peak load
• Tariffs can shift consumption
• Costs can be optimised

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.

Operational Constraints

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.

Decisions aren’t continuous

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.

Price isn’t usable on its own

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.

Cost isn’t the only objective

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 behavior 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
• 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?

Moving beyond passive response

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

Making decisions under complexity

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 optimization
• portfolio decisions

These aren’t one-off choices. They’re ongoing trade-offs.

Embedding decisions into systems

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

Integration is the real problem

The biggest issue isn’t capability. It’s that things don’t join up.

• systems don’t connect
• data doesn’t flow cleanly
• 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, behavior follows. When they don’t, the gap stays.

This gap — between signals and behavior — 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
• situations where the requirements aren’t fully defined

In those cases, the challenge isn’t theoretical. It’s making something actually work.

If you're dealing with situations where systems aren’t behaving the way the market suggests they should, that’s usually where things get interesting.

That’s typically where we get involved — helping structure the problem, test the options, and build something that works in practice.

If that sounds familiar, feel free to get in touch.