In build · CEDeris

By Compounding Energy

Year 3 of platform roadmap Targeted for the CEAtlas Expert tier — see pricing for inclusion.

Distributed energy resources, aggregated and dispatched.

EV chargers, heat pumps, residential batteries, rooftop solar, smart hot-water cylinders — the load behind every meter is now a controllable asset. CEDeris is the platform layer that forecasts, aggregates, and dispatches them at the scale of a portfolio, on the same 27-zone European dispatch model that powers CEGridSight and the same battery optimiser that powers CE BESS Arbitrage.

Get on the early-access list CEAtlas Expert pricing

What it does

Three pillars.

Bottom-up forecasting

Per-asset hourly forecasts for EV charging sessions, heat-pump COP-weighted demand, residential battery state-of-charge, and behind-the-meter PV — driven by weather, household type, tariff signal, and observed historical behaviour. Aggregates honestly: tail risk doesn't disappear when 50,000 households are summed.

VPP dispatch

Co-optimised participation in wholesale, balancing, and distribution-flexibility markets across a heterogeneous fleet — with per-asset opt-in, comfort constraints, and customer-fairness rules built into the objective rather than tacked on afterwards.

Network-aware

DSO constraints — feeder thermal limits, voltage envelopes, transformer ratings — enter the dispatch as hard constraints, not advisory caps. Every aggregator instruction is feasible against the local network, not just the wholesale price.

Methodology

For the quants in the room.

Plain-English first; this section for anyone vetting the maths. Skip if you're not running the aggregator yourself.

Forecast model

Per-asset, conditioned on weather and tariff.

EV charging arrival/departure and energy-per-session modelled as marked point processes per household. Heat-pump load conditioned on outdoor temperature and a building thermal model with two RC time constants. Residential PV uses the same Perez transposition + Monin–Obukhov stack as CompoundVision. All trained on metered data and re-fit weekly.

Dispatch

Hierarchical decomposition.

Aggregate-level MILP sets price-quantity bids into wholesale and balancing markets; per-asset LP allocates the cleared volume back across the fleet under each asset's comfort, state-of-charge, and customer-opt-in constraints. Solved by CEMeridian (in build) once it ships; HiGHS today.

Network model

DC-OPF on the LV/MV feeder.

Feeder topology imported from DSO licence area data; thermal and voltage limits enforced as linear constraints in the per-asset LP. Where feeder data is unavailable, we fall back to a transformer-bus aggregation with conservative defaults rather than ignoring the network.

Customer model

Comfort first, revenue second.

Heat-pump dispatch respects an indoor-temperature comfort band per household. EV dispatch respects a per-session energy and ready-by time. Residential battery dispatch respects a per-customer minimum reserve. Comfort violations are forbidden, not penalised — the optimiser cannot trade them away for revenue.

Who it's for

Aggregators, suppliers, DSOs.

VPP operators & aggregators

A platform layer underneath your customer app — forecasting, optimisation, and bid generation for assets you've already onboarded. You keep the customer relationship; we handle the dispatch.

Energy retailers

Time-of-use and dynamic-tariff design that knows what your customers actually do — a heat-pump customer's tariff is co-designed against the dispatch model that will run their pump.

DSOs & flexibility platforms

Procure flexibility you can trust to deliver. Every bid is backed by an asset-level dispatch plan that respects your feeder limits, not just the wholesale signal.

Status

In active build · year three.

CEDeris is on the year-three slot of the platform roadmap. We're prototyping the per-asset forecast layer against an EV-charging dataset now; production launch follows once CE BESS Arbitrage and CEMeridian are stable. Customers on early access shape the spec — particularly on tariff structures, opt-in semantics, and DSO data formats.

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