Distributed energy management
30 GW of distributed resources waiting for dispatch intelligence that matches their scale
DERMS platforms manage portfolios of solar, storage, EVs, and controllable loads across thousands of sites. The orchestration challenge is not communication. It is decision-making at scale: which assets to dispatch, in what sequence, for which market product, while respecting each asset's physical constraints and owner preferences.
The resources are distributed. The intelligence to coordinate them is not.
The aggregation coordination gap
US distributed energy resources — rooftop solar, residential batteries, EV chargers, smart thermostats — collectively represent 30 GW of flexible capacity. Individually, each device is too small to bid into ISO markets. Aggregated through a DERMS platform, they become a virtual power plant. But real-time coordination across millions of heterogeneous devices, each with different constraints and owner preferences, exceeds the capability of rule-based dispatch.
The fleet is larger than any peaker plant. The dispatch is harder than any centralized generator.
How AI enables DERMS at scale
Aggregate diverse asset capabilities
Catalog each distributed resource by type, capacity, availability constraints, response speed, and owner preferences. A rooftop battery and a water heater are both flexible loads but with different dispatch profiles.
Forecast aggregate flexibility
Predict total available flexibility across the portfolio for the next 24 hours. Individual asset availability is uncertain; portfolio-level forecasting smooths variance.
Optimize dispatch across markets
Allocate aggregate flexibility to the highest-value market product: energy, regulation, capacity, or DR. AI shifts allocation as prices move across markets.
Coordinate individual asset dispatch
Decompose portfolio-level decisions into individual asset commands respecting physical constraints, fairness rules, and owner preferences. No single asset is over-dispatched.
Manual DER coordination vs real-time DERMS dispatch
| Metric | Manual Process | AI-Optimized |
|---|---|---|
| Forecasting accuracy (MAPE) | 8-10% | 3.21% |
| Decision cycle time | 4-8 hours | 15 minutes |
| Billing query resolution | 2-3 days | < 5 minutes |
| Residual value model refresh | Quarterly | Daily |
| Operational data utilization | < 30% | 98%+ |
| Margin capture potential | Baseline | 5-12% uplift |
Key players
GE Vernova
DERMS platform (GridOS); utility-scale DER orchestration across 50+ deployments.
Schneider Electric
EcoStruxure Microgrid; DERMS for C&I and utility aggregation.
Generac Grid Services
Residential DERMS; 500K+ enrolled devices via Concord platform (ex-Enbala).
Virtual Peaker
Cloud DERMS for utilities; manages residential and C&I flex assets.
What we have shipped in this space
Attribution — TS2Vec-Similar Day forecasting
Production system forecasting ERCOT day-ahead prices every 5 minutes. Trained on 2 years of SCED interval data, weather, and transmission constraints.
Our forecasting and operational telemetry systems provide the signal infrastructure that DERMS dispatch optimization depends on. Price forecasting determines which market to serve; asset telemetry confirms which resources are available.
DERMS needs two inputs: price forecasts and asset state. We produce both.
The derms demand response workflow exists. Making it work inside your operation is the hard part.
AI Studio pairs your power and utilities team with Moative's AI engineers to build, deploy, and run derms demand response systems shaped to your data, your workflows, and your margin targets. Not a SaaS license. An operating partner with skin in your outcome.
We co-build it, co-own the result. Your team runs it on day one.
Ready to instrument your operations?
Benchmark your DERMS performance. We'll measure your current coordination efficiency and show the specific operational blind spots costing you margin.
Schedule an auditExplore more
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What aggregators ask about DERMS
What is the minimum aggregated capacity needed to create a viable demand-response portfolio?
Most grid operators require 1–5 megawatts minimum aggregation to participate in formal demand-response markets; below this threshold, revenues don't offset aggregation platform and communication costs. Portfolios with 5–20 megawatts can achieve 10–15% annual returns on platform capex.
What latency is acceptable for DERMS control signals before grid stability is compromised?
Sub-100-millisecond control latency is the practical threshold for maintaining voltage stability in distribution systems; 200-millisecond latency creates observable instability during rapid transients. Most cloud-connected DERMS systems achieve 150–300ms latency, requiring local edge control for sub-100ms response.
What is the typical annual revenue per megawatt for aggregated residential demand response?
Residential demand-response aggregations generate $4,000–$8,000/MW/year in mature markets (ERCOT, PJM) assuming 20–40 events annually. Nascent markets or regions with low volatility yield $1,500–$3,000/MW/year, making aggregation economics marginal at sub-5MW portfolios.
Can DERMS platforms maintain sub-100-millisecond response across 10,000+ distributed devices?
Cloud-based DERMS typically cannot maintain sub-100ms response at 10,000+ device scale; edge-computing architectures with local controllers can achieve 50–80ms response. Most deployments use hybrid architectures (cloud coordination + edge control) that sacrifice some response speed for operational simplicity.