Power Availability & Power-Cost Structure

The previous chapter answered the binary question: can you get power to this site, and when. Chapter 3.2 treated interconnection as a yes/no gate against a schedule. This chapter answers the question that decides the economics once the answer is yes: what does that power actually cost, in what structure, and who bears the cost of the grid you are about to stress? The two questions are separable on purpose. A site can clear the speed-to-power gate and still be uneconomic, because the megawatts arrive at a price, a shape, and a set of curtailment and cost-allocation terms that the GPU fleet cannot earn against.

The reason this chapter carries so much weight is a single number that recurs across every credible cost model: power is 25–60% of operating cost over the life of an AI data center, and at scale it is the largest single opex line by a wide margin (domain synthesis; Epoch AI 1 GW model, 2026). Silicon dominates capex; electricity dominates opex. That makes the power-cost structure the dominant TCO lever you can still pull after the building exists — you cannot re-spec the chips, but you can, with enough foresight at siting, change the delivered cost of energy by a factor of two or three. This chapter works through each fork in the power-cost stack and the dollars that ride on getting it wrong.

Before any tariff analysis, a site faces a physical screen that has nothing to do with price and everything to do with what steel is already in the ground. Three variables decide whether a site is powerable on a reasonable schedule at all: transmission proximity, voltage class, and substation headroom. Get these wrong and the cheapest energy market in the country is irrelevant, because you will spend three-to-five years and tens of millions building the wires before a single electron arrives.

Transmission proximity is the first cut. A gigawatt-class campus needs a high-voltage transmission tap — typically 230 kV or 345 kV, and increasingly 500/765 kV as backbone projects (ERCOT's ~2,500-mile 765 kV plan, PJM and SPP build-outs) reach the load. A site adjacent to an existing high-voltage line with spare thermal capacity is a fundamentally different proposition than one that needs miles of new line, every mile of which carries its own permitting, right-of-way acquisition, and 100+-week conductor and structure lead times. The distance to a viable tap is, in practice, a schedule variable, not a geography variable.

Voltage class sets both the equipment you must buy and the losses you will eat. The higher the delivery voltage, the more power you can move per ampere and the lower the I²R losses, but the more expensive and longer-lead the step-down transformers and the larger the customer substation footprint. A 1 GW load taken at 345 kV is an entirely different electrical interface — and a different substation cost — than 200 MW taken at 138 kV. This is an irreversible decision: the voltage class is baked into the substation, the protection scheme, and the interconnection agreement, and you cannot re-pick it mid-life without rebuilding the interface.

Substation headroom is the variable that separates a 12-month energization from a 5-year one. A utility substation with spare transformer capacity and an open bay can absorb a new large load by adding a feeder; one that is already at its firm rating requires a new transformer bank (128–160+ week lead time; up to ~60 months in constrained markets) or an entirely new substation. The highest-value pre-diligence a developer can do is to map, for every candidate site, the nameplate and firm rating of the serving substation, its current loading, and the queue of other large loads ahead of you for the same headroom. Headroom is a depletable shared resource, and you are rarely the only one shopping for it.

Operators routinely shop on a single number — a delivered ¢/kWh — and that number is almost always a trap, because it averages together components that behave very differently under an AI load shape. A defensible power-cost model decomposes the headline rate into four parts, each with its own driver, its own volatility, and its own hedge:

• Energy (the commodity). In organized markets this is the nodal price / Locational Marginal Price (LMP) — the marginal cost of energy at the specific bus where you take power. LMP is the part everyone quotes, and it is the part that varies most by location and by hour. The same MWh can cost a few dollars at a wind-rich West Texas node and a hundred-plus dollars at a congested load center in the same interval.
• Congestion / basis. LMP decomposes into a system energy price plus a congestion component plus a small loss component. Congestion is the value of transmission scarcity between generation and your node, and it is the most under-modeled line in a merchant power plan. A node downstream of a transmission constraint can settle persistently above the hub; a node co-located with stranded generation can settle below it, occasionally negative.
• Capacity. In capacity markets (PJM, ISO-NE, MISO) you pay a separate charge for the resource adequacy your load obligates the system to procure. PJM's capacity auction clearing prices spiked roughly an order of magnitude in 2025–2026 as data-center load tightened the supply-demand balance, turning capacity from a rounding error into a material line item.
• Demand charges. Regulated retail tariffs bill the largest line not on energy consumed but on peak demand (¢/kW of the monthly maximum), often with a ratchet that holds the charge near the annual peak for months. For a high-load-factor data center this is usually favorable — a flat 90%+ load factor amortizes the demand charge over enormous consumption — but for a spiky or curtailed load it can dominate.

The decomposition matters because two sites with the same delivered ¢/kWh can have completely different risk profiles. One may be 90% energy and 10% congestion (a merchant-exposed, basis-risk-heavy site); the other may be 50% capacity and demand charges (a regulated, schedule-stable site with little hourly exposure but a high floor). You cannot hedge what you have not decomposed. → PPA structures, basis risk, and the hedging toolkit (CRRs/FTRs, heat-rate hedges) live in Chapter 3.4.

The main reason an AI data center is a good power customer — and the reason it can sometimes secure rates a flickering industrial load cannot — is its load factor. A facility running training or steady inference at a flat 80–95% of contract demand around the clock is the dream customer for a utility recovering the fixed cost of a substation and a line: it amortizes those fixed charges over the maximum possible consumption, driving the effective ¢/kWh of the delivery and demand components down toward their floor. This is why a high-load-factor data center can clear an economic deal on a tariff that would crush a peaky factory. The corollary is a warning: anything that lowers your load factor — curtailment, demand-response events, training-job idle gaps, a half-full hall during ramp — raises your effective per-MWh cost on the fixed components, because the same demand and delivery charges now spread over fewer megawatt-hours. The economics of curtailment are not just about the energy you forgo; they are about the fixed charges you keep paying on power you are not drawing.

In an organized market — ERCOT, PJM, MISO, SPP, CAISO, ISO-NE — energy does not have one price; it has a price at every node, recomputed every five minutes. The Locational Marginal Price at your specific bus is what you actually pay (or, behind a PPA, what your settlement is referenced against). The temptation is to site at the cheapest-LMP node you can find. The trap is that the cheapest nodes are cheap for a reason: they sit downstream of stranded generation or behind a transmission constraint that depresses the local price — and that same constraint can reverse violently when the wind dies or the constraint binds the other way.

West Texas is the canonical illustration. Wind- and solar-rich nodes in ERCOT's West and Panhandle zones see frequent negative congestion — the local price drops below the hub because generation is bottled up behind transmission limits. A load co-located there can capture remarkably cheap energy. But the same congestion that gives you cheap power when the wind blows gives you expensive power, or curtailment, when it does not — and the basis between your node and the trading hub you likely hedged against can blow out, leaving you paying the difference. This is nodal basis risk, and it is the most expensive lesson in merchant power: a hedge struck at the hub does not protect a load settling at a node, and the gap between them is exactly where a low-LMP siting decision turns into a loss. → the full basis-risk and VPPA-settlement treatment, including negative-settlement stress tests, is in Chapter 3.4; the lender's view of that exposure in Chapter 2.5.

The defining power decision of the 2026 era is no longer just "firm grid or not" — it is how much curtailment you will accept in exchange for queue speed and a lower energy charge. Every major RTO is building a faster interconnection lane for loads that agree to be curtailed: ERCOT's mandatory curtailment / 'kill switch' for large loads interconnecting after the end of 2025, SPP's price-responsive curtailment and non-firm tracks, PJM's non-capacity-backed and interim arrangements, and the FERC flexible-load study concept. The empirical prize is large: a Duke study found roughly 98–100 GW of US grid headroom integratable at just 0.5% annual curtailment — meaning a load willing to be shed for the equivalent of about two days a year unlocks capacity that firm service simply cannot get for years.

Curtailment is therefore a priced lever, not a binary. The question is whether your workload's goodput economics survive the expected curtailment-hours. A batch-inference or checkpoint-tolerant training fleet can absorb a few hundred curtailment-hours a year by rescheduling work or riding through on storage, paying for the privilege of a faster, cheaper interconnection. An always-on inference business serving an SLA cannot — every curtailed hour is breached revenue, and the cheap non-firm energy is a false economy. The honest underwriting question is not 'what is the curtailment cap' but 'what is the cost of the expected curtailment-hours given my workload, netted against the queue-time and energy savings I am buying.' → the load-flexibility and grid-services revenue framing lives in Chapter 15.8; curtailable/non-firm interconnection mechanics in Chapter 3.2.

A gigawatt of new load does not arrive on the existing grid for free — it triggers transmission and distribution upgrades, sometimes hundreds of millions of dollars of them. The defining regulatory and political question of 2025–2026 is who pays: the data center that caused the upgrade, or the ratepayer base that did not. This was a footnote five years ago. It is now frequently the deciding variable on whether a project gets approved at all, because rising residential bills in data-center-heavy regions have made cost allocation a live political fight.

The mechanism through which the answer is set is the large-load tariff — a new customer class, now approved in 23+ states, written specifically to push the cost of the buildout onto the loads that cause it. The Oregon POWER Act template is representative and worth memorizing because variants of it are spreading: a 20 MW+ threshold defines the class; the large load pays 100% of the distribution-upgrade cost it triggers; a 90% minimum demand charge (a take-or-pay floor — you pay for 90% of contracted demand whether you draw it or not) protects the utility against stranded investment if you under-build or leave; 10–30 year contract terms match the cost recovery to the asset life; and a per-kWh surcharge (≈1¢/kWh above 100 MW in the template) layers on top. ERCOT's SB6 regime is the Texas analogue, with a 75 MW large-load definition, mandatory curtailment, and a ≥$100,000 screening study fee. Virginia, the largest US market, layered on a flat $0.011/kWh data-center consumption tax effective July 2026.

The consequence for the model is direct and large: these allocation mechanisms can add anywhere from a fraction of a cent to several cents per kWh of effective cost, and the minimum-take floor converts a variable energy bill into a fixed obligation that behaves like debt. A take-or-pay floor means the curtailment savings discussed above are partially illusory — you keep paying the demand floor on power you are not drawing. The 'who pays' regime in your jurisdiction is therefore not a compliance detail; it is a first-order input to the power-cost stack and, increasingly, to whether the project clears its social-license gate at all. → the regulatory mechanics and the federal-vs-state collision over large-load interconnection are owned by Chapter 3.2; the macro load-growth and cost-shift narrative in Chapter 16.1.

Pull the threads together and the chapter's central claim follows: after the chips are bought, power is the largest controllable variable in the lifetime cost of the asset. Silicon dominates capex and is essentially fixed at procurement — you buy the GPUs at market and depreciate them on a contested schedule (→ Chapter 1.8). The building shell is a small and slow-moving line. But energy — at 25–60% of opex, the single largest line at scale — is the cost you can still move by a factor of two or three through decisions made at siting: which node, which voltage class, which tariff, how much curtailment, behind-the-meter or grid.

The leverage is starkest when you net it against the revenue the asset earns. At roughly $10–12B of revenue per GW per year, a campus is netting an enormous topline against an energy bill that, mis-structured, can run to many hundreds of millions a year. A 2¢/kWh difference in delivered power cost — entirely achievable between a well-sited, well-hedged location and a poorly-structured one — is on the order of $175M/yr on a 1 GW facility at 100% load factor. That single siting-and-structuring decision dwarfs nearly every engineering efficiency in the rest of this guide. It is the reason power-cost structure earns its own chapter, and the reason the screen runs availability first, structure second, and only then everything else.

The final discipline is to sort the power-cost components by reversibility, the same way Chapter 1.1 sorted the scoping decisions. The irreversible components are fixed at contract: the voltage class and substation interface, the large-load tariff election and its take-or-pay floor, the contract tenor, and a fixed-price PPA's strike. The reversible components float and must be hedged or actively managed: the merchant energy and congestion exposure, the capacity-auction outcome, and the curtailment-hours you actually incur. A 15-year asset financed against a merchant energy curve is a different — and far riskier — instrument than one behind a matched-tenor fixed-price supply. Which of those two you are is a power-cost-structure decision, made here, that the lenders in Chapter 2.5 will price ruthlessly.