A virtual power plant (VPP) balances load by coordinating many small energy assets—such as rooftop solar, batteries, electric vehicles, chargers, smart buildings and flexible commercial loads—as if they were one grid-scale resource. Software forecasts production and demand, schedules assets ahead of time, then dispatches charging, discharging or load reductions when the grid needs help.
The label “ONS NA 2019” does not resolve to a uniquely identifiable conference paper or publication. The explanation below therefore covers the 2019-era VPP load-balancing work and clearly identified later evidence without attributing claims to an unverified event.
What load balancing means in a VPP
Traditional power plants change output to follow demand. A VPP reverses that model: its control platform adjusts thousands of customer-owned devices so their combined effect looks like a predictable generator, load reduction or reserve resource.
The platform receives telemetry such as battery state of charge, solar output, charger status, building demand and customer operating limits. It combines those measurements with weather, market and feeder forecasts, then creates a target profile—for example, lower community demand from 5 p.m. to 9 p.m. During operation, the system sends commands or price signals and continuously checks whether the aggregate is meeting its target.
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How a VPP balances demand and supply
1. Forecast the next operating window
Forecasts estimate rooftop-PV production, household and commercial demand, EV charging needs, weather-sensitive heating or cooling, and the energy available in batteries. Forecast error matters: a cloud can reduce solar output, while an unexpected heat wave can increase air-conditioning demand.
2. Schedule resources day ahead
A day-ahead optimizer assigns charging, discharging and flexible-load windows. The 2019 work by Huang and colleagues modeled moving EV-charging demand and emissions while scheduling a VPP to balance peak and off-peak electricity-market load. Day-ahead plans provide a baseline, but they are not fixed promises; the control system must revise them as conditions change.
3. Dispatch in real time
When demand rises or renewable output falls, batteries can discharge, EV charging can pause or slow, and participating buildings can adjust HVAC or other equipment. When surplus solar is available, batteries and EVs can charge instead. The aggregator coordinates individual limits so the fleet responds without exceeding battery, comfort, equipment or backup-reserve constraints.
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4. Verify performance and settle the program
Meter data is compared with a baseline or dispatch instruction. Depending on the program, the VPP may be paid for energy delivered, capacity held available, ancillary services, or distribution-level relief. Customer compensation and event rules differ by utility and territory.
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| Resource | Typical VPP action | Useful contribution | Main constraint |
|---|---|---|---|
| Rooftop solar | Produce when sunlight is available; curtailment may be possible in limited programs | Reduces net demand and supplies daytime energy | Weather and daylight make output uncertain |
| Behind-the-meter batteries | Charge during surplus or low-price periods; discharge during peaks | Fast peak reduction, reserves and energy shifting | State of charge, cycle limits and the customer’s backup reserve |
| EVs and smart chargers | Delay, slow or schedule charging; vehicle-to-grid discharge requires compatible hardware and rules | Moves flexible demand away from constrained hours | Departure time, battery needs and driver override |
| HVAC and smart buildings | Pre-cool, raise or lower set points, and sequence equipment | Large short-duration demand response | Occupant comfort, building controls and weather |
| Water heating | Heat water before an event or defer heating briefly | Thermal storage with relatively predictable flexibility | Hot-water availability and equipment compatibility |
| Commercial and industrial loads | Shift processes, ventilation, refrigeration or pumping | High-capacity response from fewer sites | Production schedules, safety and contractual limits |
Solar-plus-battery aggregation was the focus of a 2019 IEEE PES General Meeting contribution by Gong, Rallabandi and Ionel. Its coordinated controls were designed to reduce variation in community net load rather than simply maximize each home’s individual output.
What the controller is optimizing
A VPP can optimize several objectives at once. Common targets include reducing the highest hourly demand, following a market dispatch signal, maintaining reserve capacity, absorbing excess renewable generation, lowering emissions, or relieving a specific distribution feeder. The optimizer must also enforce minimum battery reserves, charger deadlines, customer opt-outs, equipment ratings and comfort limits.
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Those objectives can conflict. Holding batteries in reserve improves resilience but leaves less energy for peak shaving. Maximizing solar self-consumption may reduce exports that would otherwise support the grid. A well-designed program states which objective has priority during normal operation and during an emergency event.
Response speed and service types
| Service or horizon | Typical VPP behavior | What determines performance |
|---|---|---|
| Day-ahead energy scheduling | Plans charging, discharging and flexible demand for the next day | Forecast accuracy, market prices and customer availability |
| Intraday or real-time balancing | Updates the plan as weather, load and state of charge change | Telemetry quality, communications latency and control authority |
| Capacity or peak relief | Reduces aggregate demand during forecast system or feeder peaks | Event duration, participation rate and available stored energy |
| Ancillary services | Changes output or demand quickly in response to a balancing signal | Ramp rate, measurement accuracy and market qualification |
| Distribution relief | Targets a particular feeder, transformer or local constraint | Device location, utility visibility and dispatch precision |
What the 2019 evidence established
The 2019 studies support two practical conclusions. First, a VPP can use day-ahead scheduling to balance peak and off-peak market load while considering emissions and mobile EV demand. Second, coordinated control of rooftop PV and batteries can smooth community-level load variation. These are models and control designs, not proof that every commercial program delivers the same result.
The studies also show why aggregation matters. A single home battery is small and uncertain; a large fleet can average out individual behavior, provided the operator has reliable telemetry and enough enrolled capacity.
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How large could VPPs become?
The U.S. Department of Energy’s 2023 Pathways to Commercial Liftoff work estimates that deploying 80–160 GW of VPP capacity by 2030 could address roughly 10–20% of U.S. peak load and potentially save about $10 billion per year in grid costs. Those figures are a national pathway estimate, not a guaranteed outcome or a promise for any individual utility program.
DOE’s current VPP project page describes more than 20 research, development, demonstration and deployment programs. As a concrete example, RMI reported that National Grid Massachusetts had 3,096 residential-battery customers enrolled for 21.23 MW of summer 2023 performance. That case illustrates the scale of one program, not a national average.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a household needs to enroll
Requirements depend on the aggregator and utility, but Solis documents a representative North American pathway for residential systems:
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- Compatible equipment: own a supported Solis hybrid inverter and battery.
- Connected telemetry: keep the system online and reporting through SolisCloud.
- Eligible location: live in a participating utility territory; availability is not universal across North America.
- Program enrollment: accept the current program’s dispatch, compensation and customer-protection terms.
- Backup setting: define the battery reserve that cannot be used for grid events.
During a peak-demand or emergency event, Solis says the system may discharge to the grid or reduce on-site load up to that customer-defined reserve. Solis identifies Derapi as its aggregator-connectivity platform and notes EnergyHub integration for approved-manufacturer listings. Hardware compatibility, utility participation and incentives can change by program year, so the current utility and aggregator terms control.
Questions to check before joining
- How long can an event last, and how often can events be called?
- Can you override a dispatch, and what happens to compensation if you do?
- What minimum battery reserve or EV state-of-charge requirement applies?
- Is payment based on enrollment, measured performance, energy delivered or another product?
- Who controls the device data, and what happens if internet or cellular communications fail?
- Does the program target wholesale markets, system peaks or a specific local feeder?
Limits and failure modes
Forecast error
Solar output and demand can diverge sharply from forecasts. Operators need reserves and rolling re-optimization rather than relying on one schedule.
Unavailable devices
Customers may unplug chargers, change thermostat settings, reserve a battery for an outage or lose connectivity. Aggregators therefore plan for a participation rate below 100 percent.
Conflicting customer and grid needs
A customer may value backup power more than event revenue. Programs that do not expose reserve, override and comfort settings clearly can lose participation.
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Location matters
Capacity in the wrong neighborhood may not solve a local transformer or feeder problem. Distribution-focused programs require device-location data and utility coordination, not just a large statewide fleet.
Bottom line
VPP load balancing is coordinated flexibility: forecast the fleet, schedule it, then charge, discharge or shift loads so thousands of distributed devices meet a grid objective. The 2019 literature demonstrated peak/off-peak scheduling and solar-battery load smoothing; DOE’s later analysis indicates that large-scale deployment could materially reduce peak demand and grid costs. Whether a particular customer can participate depends on compatible hardware, telemetry, utility territory, program rules and the backup or comfort limits the customer is willing to accept.
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