Most batteries are installed and left on their factory settings. They store solar surplus when the roof makes more than the site uses and discharge whenever the site draws from the grid. That is default mode. An operating platform runs the same battery to a plan built from forecasts. This article shows what changes, using three modelled days, and what stays exactly the same: the hardware, its protections and the limits the owner sets.
Key points
- Default mode reacts to the present. A plan uses forecasts of load, solar and price to decide when to charge, when to discharge and how much to hold back.
- In Aivy’s three modelled days, the same battery run to a plan lowered the half-hour that sets the demand charge by 19% at an office, 44% at a cold store and 57% at a two-shift factory.
- Nothing about the battery changes: same hardware, same protections, same manufacturer limits, same backup reserve. The plan works inside all of them.
- The order of priority is fixed: the battery’s own protections, then the grid, then the manufacturer’s limits, then the owner’s settings including the reserve, then any VPP event, and only then Aivy’s plan.
What is a battery’s default mode?
Default mode is the behaviour a battery ships with: charge when there is more solar than the site is using, discharge when the site draws from the grid, and stop at a floor the installer sets. It is simple and it is not wrong. On a flat tariff with a small battery and a midday peak, it is close to the best you can do.
Its weakness is that it only sees the present moment. It does not know that the tariff charges for the worst half-hour of the month, that the evening peak is three hours away, or that tomorrow will be cloudy. So it spends charge in the afternoon that it will need at 6 pm, and it recharges whenever it can, including in the half-hour that sets the demand charge.
What does an operating platform do differently?
An operating platform runs the same battery to a plan. The plan is rebuilt every day and adjusted through the day as the forecasts change.
- Forecast. Each day Aivy forecasts the site’s load, its solar and the price for the hours ahead, and keeps updating through the day.
- Plan. A trading strategy, built over five years at South Australian power stations, turns those forecasts into a plan: when to charge, when to discharge and how much to hold back.
- Cap. For a site on a demand tariff, the plan sets a grid cap and keeps the battery ready for the half-hours that would break it.
- Recharge inside the limits. The battery refills from solar surplus or cheap hours at times that do not create a new peak.
- People oversee. Limits are set by the owner; the plan runs inside them, and big changes wait for the owner’s OK.
Under the plan, the battery often does things default mode never would: it holds charge through a sunny afternoon, tops up from the grid at a cheap hour before a cloudy day, or keeps part of its capacity back for a known start-up at 6 am. Each of those looks odd in isolation. Each of them is there because of a half-hour later in the day.
How much difference does it make? Three modelled days
Aivy modelled three business types over 28 days, with the same rooftop solar and the same battery at each site and each site’s own load, on an Australian network demand tariff. Each battery was run twice: in default mode, and to Aivy’s plan. The charts show one day from each site. The dashed grey line is default mode. The black line is the grid draw under the plan, held beneath the cap.
Office: peak half-hour 19% lower than default mode
Cold store: peak half-hour 44% lower than default mode
Factory, two shifts: peak half-hour 57% lower than default mode
The half-hour that sets the demand charge came in 19% lower at the office, 44% lower at the cold store and 57% lower at the factory. The spread is the point: the gain depends on how far the site’s peak sits from its average and on when that peak falls. A daytime office gains least because its peak is already close to the solar hours. A factory with a 6 am start-up and an evening shift gains most because default mode is empty at both.
These are modelled days, not measured customer sites, and the scale is relative rather than a savings estimate. What they show reliably is the mechanism: the saving comes from timing, not from a bigger battery.
What stays the same
The battery does not change. Same cells, same inverter, same installer, same protections. The plan has no access to any of them beyond what the manufacturer’s own platform allows, and it never overrides a protection or a manufacturer limit. Cycling stays inside the manufacturer’s terms and is tracked month by month.
The owner’s settings do not change either. The backup reserve is set by the owner and the plan works above it; a VPP event, where the battery earns by helping the grid, never dips into it. Production limits at a business site are written into the plan before anything else is. In short: you set the limits, Aivy runs the battery inside them, and big changes wait for your OK.
The order of priority is fixed and worth stating in full. First the battery’s own protections. Then the grid’s requirements. Then the manufacturer’s limits. Then the owner’s settings, including the reserve. Then any VPP event the owner has joined. And only then Aivy’s plan, which takes whatever room is left and makes the most of it.
Does this apply to homes as well as businesses?
Yes, with one difference. A business battery is usually planned around a demand charge and a production schedule. A home battery is planned around the household’s tariff, its routine and the reserve the household sets. The method is the same: forecast the house’s use and the roof’s output, read the tariff, and decide when to charge, discharge and hold.
At home the plan runs through the Aivy app, which connects cloud to cloud to supported models from multiple leading brands, with no extra hardware. Nothing about the battery or its warranty changes. If the household says it will be away, or a storm is forecast, the plan adjusts, and anything that would change the reserve waits for a tap in the app.
How to tell which mode your battery is in
Most owners do not know. Four things to look at in the battery’s own app or portal:
- Look at a weekday afternoon. If the battery is discharging at 2 pm on a sunny day while the roof is still producing, it is probably reacting, not planning.
- Look at 6 pm. If the battery is near empty when the evening peak starts, it spent its charge too early.
- Look at the day before a cloudy day. A battery on a plan tops up ahead of poor solar. A battery in default mode cannot see tomorrow.
- Ask who set the rules. If nobody can say what the battery is trying to achieve this month, it is on factory settings.
If three of the four apply, the battery is working for its factory settings rather than for you. That is normal, and it is also the gap an operating platform closes. Send us a month of battery data or a bill and we will tell you what a plan would have done with it.
Questions people ask
Does running a battery to a plan make it cycle more?
It depends on the plan. Often more than in default mode, because the battery works harder for the owner; sometimes less. The cycling limit comes from the manufacturer’s terms, is built into every plan and is tracked monthly.
Do I need new hardware?
For a home battery, no: the Aivy app connects cloud to cloud to supported models. For a business site or a power station, what the connection needs is confirmed for each project before anything is switched on.
Who sets the limits?
You do. The reserve, production limits and anything else that matters to you are set first, and the plan runs inside them. Big changes wait for your OK.
Which states does Aivy operate in?
Across the National Electricity Market: Queensland, New South Wales, the Australian Capital Territory, Victoria, South Australia and Tasmania.
Written by the Aivy Energy team in Adelaide. Aivy runs batteries for businesses and operates solar and battery power stations across the National Electricity Market: Queensland, New South Wales, the ACT, Victoria, South Australia and Tasmania. Figures are from Aivy’s own modelled scenarios unless a source is named.
