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USAEE 2026 Case Competition · Team Eggnomics · A report to the Commonwealth Energy Regulatory Commission

Who Pays for Virginia's Data Centres

Virginia will commit $1.32 billion a year by 2040 against data-centre load that never shows up. Getting the allocation right is worth roughly fifteen times more to households than forecasting the load perfectly ever could.

Date
Aug 2026
Role
Co-author — demand uncertainty model and the lever evaluation
Methods
Monte Carlo simulation, Forecast-error estimation, Cost allocation, Multi-criteria decision analysis
Tools
Python, pandas, NumPy, SciPy

~15×

Allocation against forecasting

$9.89/mo allocation swing against a $0.67/mo forecasting ceiling

18.42 → 4.46$/month

Residential premium at 2040

What the GS-5 minimum-take order moves off households

1.32$bn/yr

Committed against unbuilt load

Capacity and transmission, 2040 median of 50,000 draws

10,428 / 8,938MW

Surplus at 2040

Transmission and capacity a perfect-foresight planner skips

Overview

Dominion held 47 GW of executed data-centre contracts in July 2025 and filed 16.6 GW of expected demand for 2046. Neither the utility nor PJM can say which of those megawatts arrive. Virginia will build transmission and buy capacity against them regardless, because interconnection agreements and need cases will not wait for the question to settle.

That framing is the whole report. The Commonwealth cannot resolve the uncertainty, and no forecast available to it will. What it still controls is who pays for the part that does not materialise. We modelled the demand as a distribution rather than a point, priced the surplus capacity and transmission it commits, and traced the residue through to a residential bill.

At 2040 the median commitment against load that never arrives is $1.32 billion a year. The GS-5 minimum-take order recovers most of it from the customers requesting the load, leaving $319 million socialised — $4.46 a month on a typical bill. Run the same model with no such recovery and the figure is $18.42. The order is worth $13.96 a month, about $399 million a year. Perfect forecasting, by contrast, is worth $0.67.

The question

Virginia's affordability problem does not show up in its price. At 14.41 cents per kilowatt-hour the residential rate is the twenty-third lowest in the country, well under the 16.48-cent national average. A regulator reading only that number would find nothing to act on. The strain is in the bill set against an income: 373,645 households at or below 30% of area median income carry a median energy burden near 16%, against a 6% standard, and Virginia recorded 461,586 residential disconnections in 2024.

Into that sits the largest load buildout in the state's history, most of it not yet built. The commission asked how affordability should be defined and built into planning. Our answer is that it is a cost-allocation problem before it is a load-growth problem — and the section below is the part I built to prove it.

Two layers

A single-number forecast hides the difference between demand that is metered and demand that has been asked for. Splitting them is what lets the model report that about 91% of its own spread at 2040 rests on an assumption rather than on evidence — and say so in the body rather than a footnote.
Dominion zone summer peak, 2026 → 2046, split into the two layers the model treats separatelyAbove-embedded load0 → +28,236 MWLarge-load requests filed with PJM. Records demandthat has been asked for, not demand that is metered.Fails through project attritionNo published rate exists. Assumed at 0.70,bracketed by five published anchors.ASSUMED91% of the model's spread at 2040Embedded load25,193 → 28,006 MWMetered demand, growing 0.61% a year — the wholeof the zone that is not a large-load request.Fails through forecast errorMeasured across 19 vintages of PJM planning-areaforecasts: bias 1.57% at 1 year, 11.75% at 10.MEASURED9% of the spreadBoth layers land in the same forecast. Only one of them has a measured failure rate behind it.

Source: PJM 2026 load forecast, Tables B-1 and B-9b; Catalyst Cooperative (2026).

The model

Demand is a distribution, not a point. The fitted layer carries the measured over-forecast bias; beyond a ten-year horizon the curve is held flat rather than extrapolated, which understates the premium because the fitted line rises. The assumed layer gives the above-embedded slice a realisation rate centred on 0.70, bracketed by five published anchors including the California Energy Commission's milestone tiers and Berkeley Lab's queue-completion rates. It is assumed, and the report says so in the body rather than a footnote.

Surplus megawatts — those a perfect-foresight planner would not have built — are priced at PJM's Net CONE for capacity and its network rate for transmission. The two arms commit against different horizons: capacity at the three-year auction lead, because the auction re-clears against the then-current forecast, and transmission at the full planning horizon, because RTEP commits long-lived assets. The GS-5 minimum demand charges then split that cost, and the residue reaches households through the residential class share. Every figure is a median over 50,000 draws.

The estimate fell twice during the work — from $8.08 to $4.95 when a discount already applied upstream was corrected, and to $4.46 when the two commitment horizons were separated. Neither correction changed the ranking that follows.

Figure 1

The 2040 residential uncertainty premium, 50,000 draws

What a Dominion household pays each month for capacity and transmission committed against data-centre load that never arrives. The wide distribution is the counterfactual in which no minimum-take recovery applies — the basis JLARC's own $14–$37 estimate assumes. The narrow one is the same model with the GS-5 order in force. The order does not make the uncertainty smaller; it moves who carries it.

Two overlaid histograms of the 2040 residential uncertainty premium in dollars per month, each over 50,000 draws. Without cost recovery the distribution is wide and right-skewed, running from about $0 to $54 with a median of $18.42, and sits inside JLARC's $14 to $37 band. With the GS-5 minimum-take recovery in force the distribution collapses to a range of about $0 to $13 with a median of $4.46 and a 90% interval of $1.14 to $9.60. The difference between the two medians, $13.96 a month, is what the order is worth.

Source: Eggnomics (2026), Figure 3, recomputed from PJM planning-area forecasts, Va. SCC Order PUR-2025-00058 and EIA-861. Bin counts digitised from the published figure.

Data table
The 2040 residential uncertainty premium, 50,000 draws
Premium ($/month)With GS-5 (draws)No recovery (draws)
$0–528,7972,296
$5–1019,5647,177
$10–151,6409,347
$15–2009,117
$20–2507,650
$25–3006,001
$30–3504,000
$35–4002,500
$40–4501,320
$45–500505
$50–55088

Gating triggers

A distribution is not a planning rule. Each row is a trigger tied to something observable, with the action named in advance, priced by re-running the same model with that rule in force. The gates overlap by construction, so the rows do not add. The top row is a loss avoided rather than a saving, since the order is already in force.

Gating triggers
GateWho actsWorth ($/mo)
Hold the minimum-take order against ex-post reliefVa. SCC13.96
Milestone-tiered forecast inclusionPJM, Virginia advocates4.17
Capacity-market exclusion for new large loadPJM Board, FERC2.71
Level the minimum demand charge across componentsVa. SCC1.70
Stage the transmission commitmentPJM, Virginia advocates0.19

Source: Eggnomics (2026), Table 2. Priced at 2040 against the $4.46 baseline.

Figure 2

Every lever, scored against the objectives it trades off

Nine regulatory and rate-design levers scored −5 to +5 on eight criteria. Split cells carry a raised a / b / c keyed to the variants under each lever name, because for several levers the implementation choice decides the sign. Dashed edges mark a score the evidence does not settle. The two levers that clear on every count the commission controls are the large-load tariff it already holds in GS-5, and bring-your-own-capacity.

A nine-by-eight matrix scoring regulatory levers from −5 to +5. Large-load tariffs score +4 short-run and long-run affordability, +5 equity, but −1 on economic development, and are within commission control. Large-load curtailment and PJM capacity market reforms score well on some criteria but are not the commission's to adopt, sitting with PJM and FERC respectively. Class cost-allocation reform and low-income bill assistance score +4 and +5 on equity within commission control. PJM capacity market reforms are the only lever negative on long-run affordability, reliability and equity together, at −3 each.

Source: Eggnomics (2026), Table 3. Scoring criteria after Bonbright (1961); authority split per Va. SCC, PJM and FERC jurisdiction.

Data table
Every lever, scored against the objectives it trades off
LeverShort-run affordabilityLong-run affordabilityReliabilityEconomic developmentEnvironmentEquityCERC controlFeasibility
Large-load tariffs+4+40-10+5Yes+4
Large-load curtailment+1+4+3a: +3 / b: -200No, PJMn/a
Large-load BYOC+1+40a: +2 / b: 0b: 0 / c: -10Yes+5
Class cost-allocation reform+1+3000+4Yes+5
Improved small-load pricing+3+400+4a: +5 / b: 0Yes+3
Residential & C&I demand response0+2+20+1-1Yes+2
Grid-utilization monitoring0+1a: -2 / b: 00+10Yes+5
Low-income bill assistance+4+2000+5Shared, GA+3
PJM capacity market reforms+3-3-300-3No, FERCn/a

Key findings

  1. 01

    Allocation beats forecasting about fifteen to one

    Sweeping the transmission recovery share between zero and the ordered 85% moves the residential bill $9.89 a month. Removing PJM's forecast bias altogether — perfect forecasting, the ceiling on any forecasting reform — moves it $0.67. The comparison holds across the whole tested range of the least certain assumption in the model.

  2. 02

    The order already in force is the most valuable action available

    Holding GS-5 against waiver, true-up or retroactive relief is worth $13.96 a month, more than the other four gates combined. It is defensive, it needs no new authority, and it is the commission's own docket. FERC supplied the precedent when it denied AEP's request to shift surplus capacity cost onto ratepayers after forecast load failed to appear.

  3. 03

    Most of the answer rests on a number nobody publishes

    About 91% of the reported spread at 2040 comes from the realisation-rate assumption rather than from fitted data. PJM's Board created a large-load reporting obligation in January 2026 with first submissions due three weeks after this report was filed. That is a dated fact about the record, not a gap in the analysis.

  4. 04

    Three of the five gates are not Virginia's to pull

    What enters the zonal forecast is set by PJM's Manual 19 process, transmission need cases are staged through RTEP, and the capacity-market exclusion is a PJM Board decision. On those, the commission's role is advocacy and intervention at FERC. Only two are retail tariff terms it sets directly — and those are the ones it can move without anyone's agreement.

So what

The recommendation that follows is not to repel the load. It is to price its risk to the customers who cause it, and to stop treating a forecast as a fact. A commission that adopts one rule from this work should adopt the first: refuse waiver and retroactive relief against terms it has already ordered, because the instrument only works ex ante and giving it away costs households more than every other lever combined.

The method travels. The model runs on public data any state commission holds — PJM or its own RTO's forecast tables, EIA-861, LEAD, and its own docket record — so any jurisdiction facing concentrated load growth can re-run it on its own numbers rather than borrowing Virginia's.

Prepared for the 2026 USAEE Case Competition as Team Eggnomics, with Ryan Callahan, David MacDonald and Michael Smith, University of California, Davis. I built Section 2 — the demand-uncertainty model, its Monte Carlo layer and the gating triggers — and the lever evaluation matrix in Section 4. Cite as: Callahan, R., Jamhar, J., MacDonald, D. and Smith, M. (2026), Who Pays for Load That Does Not Arrive — Virginia, University of California, Davis.