Battery storage and solar get the headlines. But the most economical, scalable flexibility inside a VPP comes from the load side — millions of HVAC systems, water heaters, and EV chargers that can respond in seconds without a single new power plant.
Battery storage and solar inverters dominate VPP conversations. But demand control — the load side — is often more economical, faster to deploy, and available in far more homes.
In a typical suburban service area, fewer than 10% of homes have a residential battery. But the vast majority own an air conditioner, water heater, pool pump, or EV charger — loads that are highly shiftable if properly orchestrated. By tapping that reservoir, a VPP can scale to tens of megawatts of dispatchable capacity without the seven-figure costs of utility-scale batteries.
A VPP aggregates geographically dispersed DERs into a centrally managed portfolio that dispatches as a single, conventional power plant — without building anything.
VPPs historically focused on generation-centric DERs: rooftop PV producing midday surpluses, batteries charging and discharging on price signals, backup generators in capacity auctions. The underexplored half — controllable loads — often delivers more economical, faster, and more broadly available flexibility than any generation asset.
Demand control encompasses three complementary approaches — each addressing a different use case, latency requirement, and market opportunity within the VPP portfolio.
Remote on/off or cycling of specific devices — AC compressors, water heater elements, pool pumps — during peak events. One of the oldest and most proven demand control mechanisms. Straightforward, reliable, and deployable at low cost across millions of legacy endpoints.
Response: 5–30 sec · Binary controlCloud-connected thermostats and IoT appliances adjust setpoints or reschedule operation autonomously. A smart thermostat can pre-cool before a peak window and raise its setpoint 1–2°F during the event, reducing HVAC draw by up to 20% without noticeable occupant discomfort.
Response: <5 sec · Granular modulationLoads toggle based on time-of-use or real-time price signals — EV charging deferred to overnight off-peak, water heaters pre-heated during midday solar surplus. Inergy's SEMS orchestrates these stacked actions automatically, maximizing total VPP revenue while ensuring comfort.
Response: 5–60 min · Stacks with DLC + AutoDREach advantage stands alone. Together they make demand control the most strategically compelling component in any VPP architecture.
Fewer than 10% of homes have batteries. 60–80%+ own controllable loads. Tapping those loads lets a VPP scale to tens of megawatts without seven-figure battery deployments — just software and small controllers.
OpenADR 2.0 and IEEE 2030.5 facilitate sub-5-second latencies. Aggregated AC compressor cycling still falls within frequency regulation's 30-second ramp requirement, enabling VPPs to earn the highest-value market revenues.
Rapid load shedding across thousands of homes achieves 10–15% local peak demand reductions within minutes of a grid stress event — preventing price spikes and avoiding emergency rolling blackouts without any new generation.
When homeowners see real-time data showing how their HVAC cycle reduced grid stress and earned them a $3.60 credit, they become active partners. Transparent feedback loops yield 90%+ retention even after multiple seasons.
During midday solar peaks, demand control nudges water heaters and pool pumps to absorb excess generation. As solar fades, controllable loads release to provide net-load support. This choreography maximizes the whole portfolio's value.
During an unanticipated heat wave, Inergy's platform enabled a utility to onboard 2,500 residences into its VPP within ten days — providing 3 MW of emergent capacity to avert transformer overloads. No procurement process, no hardware lead time.
Inergy's VPP architecture uses demand control as its linchpin, with three tightly integrated layers that span from home circuit to wholesale market settlement.
A Southwest VPP pilot, a Northeastern battery+demand hybrid, and a multifamily low-income deployment demonstrate demand control's breadth and reliability across very different contexts.
24 peak-shaving events (2hr, 3–6 PM) plus 15 frequency-regulation events where homes tracked ±0.1 Hz signals. Homes averaged 1.3 kW/home reduction. The utility deferred a $2.5M substation upgrade by two years. Regulation averaged $12/kW-yr, delivering $75–$125 seasonal bill credits per household.
During winter heating peaks, home batteries discharged into local feeders while demand control clusters simultaneously raised thermostat setpoints 1°F and cycled water heaters. This dual-action hybrid provided 6 MW for three consecutive peaks, reducing wholesale procurement costs by $150,000 and improving CVR performance at the feeder level.
Stand-alone controllers managed corridor lighting, electric baseboard heating, and a centralized hot water loop in units lacking individual thermostats. Despite atypical shared-load profiles, SEMS's adaptive baseline models accurately predicted expected consumption. Capacity payments funded LED retrofits and weatherization — creating a virtuous cycle of efficiency and resilience.
Five best practices distilled from Inergy's real-world VPP deployments — each addressing a distinct operational challenge that determines program success or failure.
Inergy's platform employs a translation layer that normalizes heterogeneous telemetry and command structures, but the industry's long-term goal should be universal interoperability. Encourage adoption of OpenADR 2.0, IEEE 2030.5, and Green Button Connect My Data in every procurement RFP.
Baseline estimation requires incorporating weather forecasts, historical trends, real-time occupancy metadata, and equipment characteristics (HVAC SEER ratings). ML models retrain continuously on fresh data. Periodic ground-truth submetering in a representative subset calibrates and validates.
Deliver event notifications well in advance, specifying type, expected duration, and comfort impact. After each event: "Your home reduced 1.2 kW during yesterday's 4–5 PM event, earning $3.60." The direct link between a 15-minute HVAC cycle and a $20 credit drives retention above 90%.
TLS 1.2+ for all SEMS-to-controller communications. AES-256 at rest. RBAC ensures no unauthorized entity can issue dispatch commands. Customer data anonymized before any third-party demonstration. Strict opt-in agreements delineating what data is collected, how, and by whom. CCPA and GDPR compliant.
A home might pre-heat its water heater to absorb midday energy (arbitrage), modulate HVAC for frequency regulation at 3 PM (ancillary), and defer EV charging to 10 PM (TOU savings). SEMS orchestrates these stacked actions automatically — maximizing total VPP revenue within comfort constraints.
The strategic case for demand control goes far beyond immediate peak-shaving. These are durable, compounding advantages that strengthen over time.
Even as battery costs decline, they remain 3–4× more expensive per kW than leveraging a controllable load. Demand control unlocks latent flexibility in existing infrastructure — no new hardware needed at the feeder or substation level.
Most homes already own one or more controllable endpoints. Software patches or small controllers unlock that flexibility without months-long hardware rollouts. Proven in 2024: 2,500 homes onboarded in 10 days, 3 MW delivered during a heat wave emergency.
A VPP relying solely on batteries may struggle when storage depletes during multi-day weather extremes. Controllable loads provide a renewable, inexhaustible backup buffer — homes can shed demand day after day without degradation.
Transparent communication of event impacts and a no-penalty opt-out policy yields 90%+ retention even after two consecutive years of multiple DR events. High retention preserves the VPP's aggregated capacity — preventing the gradual drop-off that undermines market bids.
Once homes are onboarded with Inergy's controllers, the same infrastructure supports personalized home-energy insights, smart thermostat scheduling, EV charger custodial management, weatherization programs, and community solar subscriptions. Demand control becomes the "thin layer" — a platform for every future grid service, not just a point solution for today's peak events.
Demand control's performance is proven. The remaining barriers are market design and policy — and each has a clear fix.
Market operators have begun to recognize demand control's capabilities. PJM Interconnection reduced minimum regulation bid sizes from 1 MW to 100 kW for aggregated DER portfolios. CAISO now accepts demand response bids in wholesale markets via Proxy Demand Resource rules. Performance-based ratemaking is aligning utility incentives with peak reduction goals. But regulatory pathways still differ by jurisdiction — and where markets remain inaccessible, alternative value streams (DSO contracts, retail DR programs) provide meaningful revenue while the case for reform is built.
Lower minimums from 1 MW to 100 kW — or 50 kW — so smaller residential aggregations can participate directly in capacity and ancillary service auctions, expanding competition and diversifying resource types.
Bundle demand control with weatherization and LED upgrades through targeted rebates or low-interest financing — ensuring low-income and underserved communities participate at no net cost while broadening VPP resource pools.
Create an expedited interconnection track for demand control modules so utilities can register them as grid resources quickly — eliminating the administrative friction that currently delays program rollouts by months.
Clarify that anonymized, aggregated load data (randomized IDs, census-tract-level location) can be shared with authorized market entities for VPP settlement. Mandate OpenADR 2.0, IEEE 2030.5, and Green Button in all utility demand control procurement RFPs.
Access the full white paper including three detailed case study methodologies, the complete SEMS VPP architecture specification, regulatory landscape analysis, five technical best practices, and the full policy recommendation framework.