
Planning tomorrow’s server load, cooling, and battery use against a volatile electricity price curve is normally a manual, conservative exercise. P2X replaces the spreadsheet with a few clicks, returning a cost-optimal 24-hour schedule, and a cleared market price for every connected asset, in under five seconds.
A data centre operator or PtX facility manager has to plan the next day of operations: when to run servers at full capacity, when to throttle, when to charge or discharge a UPS battery, and how to coordinate cooling, all while minimising the electricity bill against a price curve that moves hour to hour. Today that planning is typically done manually, in spreadsheets, using engineering intuition. The operator checks price forecasts, estimates cooling requirements, and lands on conservative decisions that leave real savings on the table.
It gets harder still when multiple assets need to participate in a local energy market. Coordinating bids across several sites is time-consuming, and each asset is usually planned in isolation, with no visibility into how the market will actually clear.
The engineer places the facility on a map, along with any generators (such as wind or PV) and consumers involved, then opens the facility’s configuration page to set its key parameters: server power range, chiller efficiency, and cooling ratio, along with the price it is willing to buy or sell at. A renewable partner can optionally be linked so that on-site generation covers part of the demand instead of relying entirely on grid import.
One click on Apply and Calculate returns a power profile and cooling load chart for the next 24 hours, along with the total operational cost, with compute shifted toward off-peak hours to bring the bill down. From there, the Market Clearing page runs an economic dispatch algorithm across every generator and consumer on the map, computing accepted dispatch volumes and hourly shadow prices. The Electricity Profiles page then lets the engineer compare the raw, unoptimised schedule against the optimal one, confirming the savings side by side.
Configure the facility once and let the optimiser handle a decision that used to take manual estimation and conservative guesswork.
The optimisation weighs server power limits, cooling capacity, and electricity prices together, producing a schedule a human planner could not compute by hand.
Market clearing shows dispatch volumes and shadow prices for every participant on the map, replacing bids planned in isolation with a shared, visible outcome.
Both the facility schedule and the market clearing result come back in under five seconds, fast enough to test several configurations in one sitting.
Linking a wind or PV partner lets on-site generation cover part of demand directly, reducing dependence on grid import where it makes sense.
A direct comparison between the raw and optimal schedules shows the cost reduction plainly, rather than asking anyone to take the improvement on faith.