share this post

on this page

The business case for a battery or a charging hub nearly always starts in a spreadsheet. Annual consumption in, an assumption about the peak, a price per kWh, and a payback period drops out at the bottom. Tidy sums. Everyone at the table nods.

Except those sums run on averages. And your energy bill runs on quarter-hours. That gap is where investments quietly disappear, because an annual total tells you nothing about when your peak lands, how often, or what happens when your chargers and your refrigeration meet each other on a Tuesday afternoon in March. An energy simulation does.

The method below holds anywhere. The cost lines and contract terms are Dutch and Belgian, where settlement runs per quarter-hour and your peak drives a large share of your grid costs.

TL;DR

An energy simulation turns your own metering data into a modelled scenario, so you know before you commit what an asset will deliver and how big it needs to be.

  • A spreadsheet works with averages, while your costs are driven by quarter-hourly peaks and fluctuating prices
  • The output is a comparison: your current situation against the scenario with the new asset, and the difference between them is your annual return
  • Buying too big locks up capital, buying too small leaves your peak problem unsolved. You only see either one upfront if you calculate at quarter-hourly resolution
  • More companies now hold contracts with different capacity limits per part of the day, which no longer fits in a single cell
  • Written for the Dutch and Belgian market, where settlement runs per quarter-hour and your peak load drives a large share of your grid costs
  • The simulation also shows whether a heavier grid connection is needed at all, or whether control alone is enough
  • If you work as an installer or energy consultant, we run the simulation together with you, so your proposal rests on figures from the client’s own site

The question is not whether you invest, but whether you know at what size it stops paying off.

What an energy simulation actually is

An energy simulation builds a virtual copy of your site: your grid connection, your consumption, your solar panels, your chargers, your refrigeration or production process. Then you run a scenario against it. What happens if I add a 300 kWh battery? And a 600 kWh one? What if I add four rapid chargers but keep my connection as it is?

The difference with a spreadsheet is that the simulation calculates at the moments things actually happen. Not “we use 1.2 GWh a year”, but 35,040 quarter-hours, each with its own consumption, generation, price and grid limit. That resolution is exactly where the money sits, because a ten-minute peak at the most expensive moment of the year sets your peak charges for the whole month.

Under the bonnet this is the same technology as a digital twin. The model is the means, not the end. What you want out of it is one answer: does this pay off, and at what size.

Generated with AI

Why a spreadsheet skews your business case

Spreadsheets are not wrong. They are simply too coarse for decisions this size. Five places where it goes astray:

  • Simultaneity. A spreadsheet adds up power ratings. In practice your refrigeration does not peak at the same moment as your chargers, and sometimes it does exactly that. That difference decides whether your battery is undersized or unnecessary.
  • Price dynamics. Use an average kWh price and you erase precisely the spread a battery earns its money on. At negative price moments, smart control can even bring in revenue without any extra hardware.
  • A static grid limit. In the Netherlands you agree a contracted capacity and pay for exceeding it; in Belgium the capacity tariff works off your monthly peak. Either way, one fixed figure in a cell does not capture it, and more companies now hold contracts with different capacity limits per part of the day.
  • Control is left out. Most business cases model the asset without the control layer. But a battery without peak shaving and load balancing delivers a fraction of its potential.
  • Outliers vanish. An average flattens out exactly the twenty quarter-hours a year that cause your overruns.

Too big and too small both cost money

Too small is painfully obvious: you have invested and your peak is still driving your grid costs, whether that shows up as exceeding your contracted capacity in the Netherlands or as a stubborn monthly peak in Belgium. The problem is unsolved and the bill keeps running.

Too big feels safe and costs just as much. You tie up capital in kilowatt-hours that never get used, you need more space and more safety provisions, and your payback period stretches. With batteries, physical size can also affect the permitting route, so over-dimensioning is more than a financial inconvenience.

Here the simulation gives you a range instead of a guess. You see which size covers your overruns and where the extra capacity stops adding anything. Translated into money: the point from which the investment per additional kilowatt-hour rises faster than the saving it buys you. That point is the heart of your business case, and it differs for every site.

Tibo EMS als oplossing voor bedrijven

Generated with AI

What comes out of a simulation, and how to read it

A simulation does not produce advice. It produces a comparison. You put at least two runs side by side: your current situation, and the situation with the new asset and its control layer. Both on the same data, the same year, the same contract.

What you then look at:

  • Peak charges. How many overruns are left, and what they still cost. For many companies this is the largest line and the quickest win.
  • Cost per kWh. What your consumption costs once it shifts to the cheap hours instead of landing whenever it happens to land.
  • Feed-in and curtailment. How much of your own generation you currently push out at a low or negative rate, and how much of that you use or store yourself in the new scenario.
  • Grid capacity. Whether you stay inside your contracted capacity, and therefore whether an upgrade request is still needed.

The difference between those two runs is your annual return. Set it against the investment and you have a payback period built from quarter-hourly data rather than from an assumption. That is what “does it pay off” means in practice: not a percentage from a rule of thumb, but the difference between two scenarios on your own metering data.

Run the same comparison a few more times at different sizes and you see the curve. The first steps bring your payback period down sharply, then it flattens, and at a certain point it climbs again. You want the knee, not the maximum capacity.

What you need in order to simulate

The input is limited and usually already available:

  • Quarter-hourly data from your grid connection covering at least a year, available from your metering provider
  • Your energy contract, including contracted capacity and tariff structure
  • A list of your existing assets and their power ratings
  • A sense of what you want to add, even if it is still rough
  • For charging infrastructure: how many vehicles, what type of charger, and when they need to be gone

That last point is routinely underestimated. With shared charging infrastructure, charging behaviour drives the business case more than charging capacity does.

Getting hold of the metering data is usually the longest step. Once it is in, running scenarios is quick, and that is precisely why it pays to do this before the proposal stage rather than alongside it.

Working as an installer or energy consultant? This changes your proposal

A simulation is not homework handed to you by your client. It is the evidence base you can put your name to.

The question that comes sooner or later is why it has to be 400 kWh and not 250. With a run on the data from that specific site, that conversation is over in two minutes, and your advice holds up when the client comes back to it a year later. That is a different conversation from defending a proposal on the strength of comparable projects, however good that experience is.

Three things it does for you in practice:

  • Your proposal rests on figures from the client’s situation, not on an assumption they cannot check.
  • You avoid over-dimensioning, and with it an investment proposal that dies on price while the engineering was sound.
  • You lock the scope before you engineer, so there is less to argue about halfway through the project.

How it works in practice: we run the simulation together with you. You know the site, the client and the installation, we know the model and the control layer. So you neither outsource it nor work it out alone. If you want to see what that collaboration looks like, our partner page is the place to start.

In practical terms it comes down to one thing: let the simulation be the starting point for the specification, not a check after the fact.

Generated with AI

From simulation to practice

The simulation is a calculation on historical data, not a guarantee. The output indicates what is achievable under the assumptions you feed it, and those assumptions deserve as much attention as the result.

What it does do is shrink the jump to execution. You do not go from a spreadsheet to a purchase order, but from a modelled scenario to an energy management system that goes on to run that same scenario. Same model, same limits, only now on live data. And when your consumption changes or you add an asset, you run it again before you buy anything.

For companies stuck on grid capacity there is one outcome that often gets skipped: sometimes the simulation shows a heavier connection is not needed at all. Control within your existing connection is then cheaper and faster than waiting in the queue, and it opens the door to congestion management.

Grid operators say as much themselves, just in different words. In the Netherlands, the national grid capacity map shows where headroom is left on the networks, but operators warn explicitly that those figures are indicative and should not be the basis for an investment that depends on extra capacity. The map tells you how the grid is doing, not what your site needs. The second part you have to work out yourself, and that holds just as well on the Belgian side of the border.

Get the figures before you break ground

An investment in a battery or a charging hub is too large to base on assumptions, and too easy to model to have to. Your data already exists. The only question is whether you use it before or after the order goes in.

Want to see how that works out for your site? In a demo we walk through a simulation on real data, so you can see exactly which choices make the difference.

Coming at this as an installer or energy consultant and want to use it with your own clients? The partner page is your starting point.

Frequently asked questions

An energy simulation is a modelled calculation of your energy situation based on your own quarter-hourly data. You build a virtual model of your site and test scenarios in it, such as adding a battery or chargers, to see what that does to your costs, your peaks and your grid usage.

Quarter-hourly data from your grid connection covering at least a year, your energy contract including contracted capacity, an overview of your existing assets and a sense of what you want to add.

The output is indicative, not a guarantee. The simulation runs on your actual historical data, which makes it more realistic than a calculation on annual averages, but its quality depends on your data and the assumptions you feed in. Treat the result as a well-founded range.

By comparing two runs on the same data: your current situation and the situation with the new asset and its control layer. The difference in peak charges, kWh costs and feed-in is your annual return, which you then set against the investment. So the payback period comes from your own metering data rather than from an assumption or a rule of thumb.

Yes, that is one of the main uses. By modelling different sizes you see which capacity covers your overruns and at what point extra capacity stops delivering.

In practice it happens together. The installer or energy consultant brings the site, the client situation and the installation knowledge, we bring the model and the control layer. That keeps the advice theirs while the calculation holds up at quarter-hourly resolution. As an end client you therefore do not have to choose between working it out yourself and outsourcing it entirely.

They sit close together. The digital twin is the virtual model of your site, the simulation is what you do with that model: running scenarios. With a live energy management system, that same model stays in use for day-to-day control.

follow us

Don't miss the next spark.

Subscribe and catch the latest in energy management.