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Are AI Datacenters Increasing Electric Bills for American Households?

2026-03-03 · 14:27 UTC ·SemiAnalysis ·24 min read

Brief

SemiAnalysis compares PJM (13‑state Eastern interconnection) and ERCOT (Texas) to assess whether AI datacenter growth is driving higher household electric bills. The analysis shows the immediate driver in PJM is not wholesale energy scarcity but the structure of PJM’s forward capacity market. PJM’s 2025/26 BRA cleared at about $270/MW‑day (versus $29/MW‑day in 2024/25), with regulators capping later auctions at $329/MW‑day. Because capacity is procured via a centrally modeled Variable Resource Requirement (VRR) curve determined by PJM’s own demand and supply forecasts, modest changes in projected datacenter load (PJM/IMM estimate ~7.9 GW incremental datacenter demand in 2025/26 and ~12 GW in 2026/27) re‑shaped the VRR near the clearing point and produced a simulated price shock. The IMM’s alternate runs show removing datacenters would reduce capacity payments by $9.33 billion (64%), demonstrating the auction’s sensitivity to forecast inputs. Translating the capped BRA into retail terms yields roughly $34/MWh of capacity cost (3.4¢/kWh), adding about $25–$30/month to an average PJM household (880 kWh/month, 40% load factor).

ERCOT’s experience contrasts sharply. ERCOT runs an energy‑only market with a real‑time Operating Reserve Demand Curve (ORDC) scarcity adder (price cap ~$5,000/MWh). Although ERCOT’s April 2025 Long‑Term Load Forecast showed an eye‑popping 77.9 GW of potential datacenter load by 2030, ERCOT applied aggressive haircuts (≈49–55% reductions) and delayed in‑service dates, and forward energy markets priced only an ~11–17% increase across 2026–2030 horizons—far below PJM’s capacity shock. Operational performance reinforced the divergence: Winter Storm Fern (Jan 24–27, 2026) saw PJM lose ~21 GW of generation despite high capacity payments, while ERCOT held with no major outages; DOE issued emergency orders and identified ~35 GW of backup generation at datacenters/industrial sites. SemiAnalysis attributes PJM’s outcome to market design and forecasting errors (including methodology changes that removed ~14 GW of gas capacity and ~35 GW of offered capacity declines over four years), construction and GPU supply‑chain delays that make PJM’s datacenter build‑out forecasts optimistic, and regulatory fragmentation (13 states + FERC) that slows reform. The piece concludes the principal fault lies in policy and market structure rather than in AI demand per se, and highlights regulatory asymmetry (ERCOT’s faster reforms like SB 6 versus PJM’s multi‑jurisdictional constraints) as a durable, investable difference for hyperscalers and power sector participants.

Why it matters

PJM’s 2025/26 Base Residual Auction (BRA) capacity price jumped ~9.3x from $29/MW‑day (2024/25) to ~$270/MW‑day, with the federal regulator later imposing a $329/MW‑day cap; the auction equates to roughly $16 billion in total capacity payments for PJM customers.

Key details

  • SemiAnalysis estimates the BRA spike will add about $25–$30/month to an average PJM household bill (assuming 880 kWh/month consumption and a 40% load factor), calculated by converting $329/MW‑day to $34/MWh (3.4¢/kWh).
  • PJM’s capacity pricing is driven by a central simulated supply‑demand construct (the VRR curve) that is highly sensitive to forecast inputs; PJM’s Internal Market Monitor (IMM) found removing all datacenters from the forecast would cut capacity payments by $9.33 billion (64%), and keeping only energized datacenters would lower payments by $7.74 billion (53%).
  • Supply‑side methodology changes and retirements reduced PJM offered capacity by ~35 GW over four years; accounting changes to natural‑gas plants alone removed ~14 GW from the capacity construct.
  • ERCOT (Texas) uses an energy‑only market with a real‑time scarcity adder (ORDC) and a $5,000/MWh price cap; despite comparable hyperscaler datacenter buildout, ERCOT forward prices rose only ~11–17% and did not experience a capacity‑market shock.
  • ERCOT’s 2025 Long‑Term Load Forecast projected 77.9 GW of potential datacenter load by 2030 (up from 29.6 GW the prior year), but ERCOT applied a haircut (49.8% for generic requests, 55.4% for attested requests) and pushed in‑service dates 180 days, effectively discounting speculative claims.
Cleaned source text

Power prices misconception, PJM's poor market design, Capacity prices 9.3x growth,

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Are AI Datacenters Increasing Electric Bills for American Households?

Power prices misconception, PJM's poor market design, Capacity prices 9.3x growth, ERCOT vs PJM grid reliability and expansion

Aishwarya Mahesh, Jeremie Eliahou Ontiveros, Ajey Pandey, Dylan Patel, and Reyk Knuhtsen

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The topic of datacenter load growth and impact on power prices remains broadly misunderstood, akin to the water consumption myth that we debunked recently. It was at the forefront of the 2025 New Jersey elections, after a ~20% jump in residential electricity rates overnight in June 2025. Some even began finger-pointing at the 300MW Nebius AI Datacenter for Microsoft in the state, a laughable claim given >85% of its power is self-generated. Are AI datacenters really causing households to pay electricity 20% more expensive?

This report explores the question by analyzing the two biggest energy markets in the US, which are also the largest AI Datacenter hubs: the PJM interconnection area – the grid operating covering 13 eastern US states (including New Jersey) - and ERCOT, who oversees the electric grid in Texas. In the Lone Star State, prices have been roughly stable for the last three years. On the other hand, the 67 million residents of the PJM area are set to see their bill increase by an average of ~15% in 2026 relative to the “pre-AI-Datacenter” era? Why such a divergence? In short, empirically the fault is government policy, not AI.

Source: SemiAnalysis Energy Model, PJM, Monitoring Analytics

In PJM, we think poor market design is the main culprit. Most of the 15% increase in household electricity bills in PJM is driven by a widely misunderstood and somewhat obscure mechanism: the BRA capacity auction. The 2025/26 auction increased 9.3x over the prior year, as shown below. Worse: this increase is driven by a “simulation” and doesn’t reflect actual conditions. Is is largely a function of the demand and supply forecast made by a central planner (PJM), which as we’ll explain, has a history of huge miscalculations.

Source: PJM BRA Report

Many are finger-pointing at the surge in AI datacenters, and it is understandable. The PJM area is at the forefront of the AI boom, with Google notably training its Gemini model around Columbus Ohio, while Anthropic/Amazon’s “Project Rainier” and Meta’s “Prometheus” in Indiana and Ohio are both in our world’s top 5 largest AI Datacenters. PJM also hosts the world’s largest datacenter hub: Northern Virginia.

Now look at Texas. The state is witnessing an equivalent AI buildout, with OpenAI, Google DeepMind, Anthropic all building massive facilities. Yet power futures in Texas have moved only a few percent in the past year. No 9x spike, no crisis, very different market design.

Source: Bloomberg

Let’s dig in. This report focuses on ERCOT and PJM since they’re the two largest energy markets in the country, and are epicenters of the AI revolution. We’ll dig into their respective market design, explain how they’ve reacted to booming AI Datacenter load growth, and how that will flow to households.

Then, behind paywall, we discuss the supply chain implications. We believe that market constraints are dramatically shifting and many are missing it. This shift impacts major AI winners such as IPPs (Vistra, Constellation, Talen..), equipment suppliers, datacenter developers like cryptominers.

Let’s start with a brief explainer of what “capacity” actually is, how it flows to your monthly bill, and then dig further into market designs.

This report is a collaboration between SemiAnalysis and Archer Daniels Midland Investor Services (ADMIS), a leading futures brokerage and clearing firm.

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Capacity: paying for power plants to sit idle 95% of the time

At a high level, a monthly household electric bill is driven by the following charges:

Energy : In deregulated territories (including PJM’s 13 states, and ERCOT/Texas), this is a function of wholesale prices, i.e. real-time supply and demand for electricity.

Capacity : This charge doesn’t exist in ERCOT but matters in PJM. It is a function of supply & demand for capacity , i.e. power that will only turn on a few hours per year, at necessary peak events. In PJM, this charge is defined annually via a large auction.

Transmission & Distribution (T&D): the charges for the electrical distribution network.

Others : taxes, retail adders, ancillary services, etc. We will not dive into this, as it varies based on location.

Source: SemiAnalysis estimates, EIA

The purpose of capacity markets is to ensure households and businesses always have the electricity they need, including on peak summer or winter days. For context, on a single day, the electric load of New York City alone can vary by 2 gigawatts (GW), against a daily peak of 6-8 GW. During a heat wave, this same city can draw 10 GW of electricity as everyone runs their air conditioning at once.

In PJM, to ensure we have capacity when it’s needed, we pay power plant owners to maintain their assets on standby for >95% of the year. The price paid is determined by a forward auction, run once per year. That price is then spread across all electricity customers in the region. And that price went up by 9.3x in the 2025/26 delivery period, as detailed below.

ERCOT, on the other hand, is an “energy-only” market, with no separate capacity auction. Real-time pricing signals determine “scarcity” and incentivizes power plants to solve the issue. There are a few technicalities we detail later in the report, but the fundamental difference is the absence of a centralized annual capacity auction, instead using real-time market forces.

PJM: The $16 Billion Simulation

The core issue with the capacity market design is that it is directly impacted by the supply & demand forecast of a central planner, PJM. Any forecasting error can lead to billions of dollars of unwarranted spending. In the 2025-26 auction, that spending sums up to $16 billion, spread across every single resident and business in the PJM area.

How the Base Residual Auction Works

PJM pays for system capacity with the aforementioned forward capacity market, called the Base Residual Auction (BRA). This is a yearly auction ran two years ahead of time: for example, PJM’s 2027/28 capacity needs were auctioned at the end of 2025.

Until recently, the BRA delivered on its promise. Summer 2025 was brutally hot in PJM, with June 23 and 24 setting records as the 3rd- and 4th-highest peak days in PJM history. The lights did not go out, because there was enough generation capacity to meet the load.

But that reliability now comes at an extraordinary price. Between June 2024 and May 2025 (the 2024/25 service period), capacity cost $29/MW-day. For the current 2025/26 service period, capacity jumped 9.3x to $270/MW-day, with select locations seeing prices closer to $450/MW-day. The subsequent 2026/27 and 2027/28 auctions continued clearing at record prices. It was widely believed that the price would be even higher, but the federal regulator imposed a price cap of $329/MW-day. The most recent auction, December 17th, 2025, hit the price cap for the second straight year.

PJM blamed “runaway” power costs on both extreme weather and hyperscaler datacenter & AI power demand, and that narrative trickled into mainstream news. But that explanation obscures PJM’s own responsibility, because capacity prices are set more than a year ahead of time, based on a simulated model designed by PJM.

The Simulation Under the Hood

Capacity prices are based on an artificial supply-demand curve known internally as the Variable Resource Requirement (VRR) curve. The VRR curve is built on PJM’s internal forecast model, not on what the market thinks will happen. The projected increase in datacenter load shifted the clearing price on this curve, driving up prices independent of any public bid process.

However, the VRR curve is constructed from a web of assumptions, many of which depend on non-public models and proprietary data. Even modest changes in forecasted load can trigger large swings in clearing prices. The capacity market’s extreme sensitivity to forecast inputs means that getting the datacenter load number wrong by even a few gigawatts produces a catastrophic result: changing the curve’s shape near the clearing point, and ramping prices.

Datacenters blamed as the culprit for surging capacity prices

PJM’s Internal Market Monitor (IMM) -- an independent monitoring entity required by the Federal Energy Regulatory Commission (FERC) -- ran alternate simulations of the 2025/26 market, offering rare visibility into PJM’s otherwise-opaque methodology. According to the market monitor, datacenters should be blamed:

Removing all datacenters from the forecast reduced PJM peak load by 7,927 MW, resulting in a $9.33 billion reduction of total capacity payments -- a 64% reduction versus the actual price.

Keeping only already-energized datacenters reduced peak load by 4,654 MW, resulting in a $7.74 billion reduction \-- a 53% cut from the actual price. For the 2026/27 auction parameters with an unrestricted VRR curve, the IMM estimated ~11,993 MW of combined datacenter load.

According to the IMM’s analysis, incremental datacenter load growth alone explains roughly a doubling in capacity costs versus a hypothetical grid without that load. The IMM attributes ~7.9 GW of additional datacenter demand in 2025/26 and ~12 GW in 2026/27. No other factor came close.

But all of these simulations obscure a deeper issue: the main auction that drives electric rates is also based on a simulation. The VRR curve is an artificial supply-demand curve based on a forecast that PJM made for themselves. If that forecast is inaccurate, those inaccuracies skew the entire capacity market.

And we believe the forecast is inaccurate. Our methodology tracks precise construction timelines of every single datacenter in the PJM area and shows PJM’s forecast is likely too optimistic. This is not due to a lack of demand, rather it’s due to datacenter construction delays (as highlighted in our Industrials Model), to GPU production and assembly delays (as explained in our Accelerator Model), and other supply chain issues. New hardware platforms are often buggy at first and longer-than-usual to turn on at full capacity.

Source: SemiAnalysis Datacenter Model, PJM

We show below a great example of this. PJM’s own data shows an inability to forecast even one year out. In 2024, the datacenter load forecast was cut by 800MW versus the 2023 load forecast. In 2025, it happened again: the datacenter load forecast was cut by 1.1GW versus what had been forecasted just a year ago, in 2024 !

Source: PJM, Monitoring Analytics

Forward Energy Prices Tell a Different Story

The energy market in PJM remains closer to a real market, with supply and demand balances that return a moving price per MWh of electricity. These prices spike during heat waves, fall during mild weather, and use distributed market actors to track gas prices, transmission congestion, and renewable output -- like a market should.

PJM Western Hub forward prices -- the most liquid benchmark for energy traders’ view of the future -- have increased 12-20% in the 2028 and 2030 windows, with the 2026 window jumping somewhat higher. These are meaningful increases, but nothing resembling the 9.3x explosion in the capacity market. PJM’s simulation-heavy capacity construct is producing a price shock that the forward energy market does not validate. Traders, using real money and real risk, are not pricing in the same panic that PJM’s simulated VRR curve produced.

PJM’s supply-side forecast is also simulated

PJM’s forecast and methodology are also impacting the supply side of the forecast. A year before the AI Datacenter boom, issues had already begun to rise. As shown below, total offered capacity has been reduced by ~35GW in just four years. Where did that supply go?

Source: PJM

As shown below, while coal retirements were the biggest drivers, PJM also introduced major methodology changes that caused close to 20GW of supply to disappear. A methodology change on how PJM accounts for natural gas power plants made 14GW disappear overnight.

How Capacity Prices Hit Household Bills

The 2026/27 Base Residual Auction (BRA) clearing price of $329/MW-day represents a tangible cost increase for every load in PJM. These costs are ultimately recovered through retail rates, appearing as higher capacity charges for utilities, suppliers, and large customers. In total, the auction translates to approximately $16 billion in total capacity payments, or roughly $120,000 per MW.

To estimate the impact on retail bills, we need the following datapoints:

· Average power consumption per household: in PJM, that’s 880 kWh per month.

· “Load factor”, i.e. average usage to peak usage. Empirical data shows 40% as a common value.

· Capacity prices: at $329/MW-day, we can divide this by the number of hours per day, and apply the 0.4 load factor, to get $34/MWh (or 3.4c/kWh).

Multiplying 3.4 by the monthly consumption (880 kWh) gets us a $29.9 monthly payment. Given the auction has already been cleared, we have near-certainty that households will pay $25-30 more per month than two years ago!

Let’s now turn our attention to Texas, to see how power prices have responded to the massive AI Datacenter surge.

ERCOT: Same Load Growth, No Price Shock

The Electric Reliability Council of Texas (ERCOT) is a much simpler market to understand. It runs a unified market mechanism balancing supply and demand based on real-time prices. ERCOT does not have two separate markets, and does not make forecasts that impacts directly market needs.

Scarcity Pricing Instead of a Capacity Auction

Instead of a capacity market driven by a once-a-year auction, ERCOT uses a real-time scarcity price adder based on an Operating Reserve Demand Curve (ORDC). When the balance between electric supply and demand gets too tight -- when everyone’s air conditioner runs at the same time -- the real-time energy price spikes, from normal prices of $10-50/MWh to a cap of $5,000/MWh, with additional adders in transmission-constrained areas.

This scarcity-price structure allows capacity resources that run fewer than 100 hours per year (gas peaker plants, batteries, etc) to pay for themselves, because that handful of run-hours can still be worth millions of dollars in annual revenue to a 50 MW power plant or battery system.

Said differently: in PJM, the central operator has the responsibility to analyze the system, determine capacity needs, and guarantees payment to power plant owners to provide capacity. In ERCOT, there’s no guarantee and it falls on the asset owners to make analysis and use their judgement on whether the market has enough capacity or not. Real-time pricing signals are a proof of market constraints.

ERCOT’s Demand Forecast: Extraordinary -- and Largely Ignored

This difference is particularly interesting as ERCOT also provides demand forecasts. And the outlook is staggering. The 2025 Long-Term Load Forecast, released April 2025, identified datacenters as the single largest driver of incremental peak growth. Based partly on attestation from Texas transmission service providers, ERCOT projected 77.9 GW of potential datacenter load by 2030 -- more than double the 29.6 GW in the prior year’s outlook, an unprecedented one-year revision.

Source: ERCOT

Taken at face value, that forecast implies a structural demand shock on the order of an entire new ERCOT system layered on top of today’s load curve. In reality, no one believed in these numbers, and the market largely ignored them.

Even ERCOT recognized that their forecast was not realized and changed course. In the May 2025 Capacity, Demand and Reserves report, they applied a deliberate haircut: generic requests were discounted to 49.8%, officer-attested requests to 55.4%, and all in-service dates pushed back by 180 days. ERCOT’s internal grid analysts effectively said they would not plan for 100% of what developers claim until shovels actually move.

But the difference here is that ERCOT’s load forecasts and interconnection queue constraints do not directly drive electricity prices. PJM’s forecasts drive a simulated supply-demand curve, which in turn directly determines the bounds of what system capacity can cost. ERCOT uses its demand forecasts to guide system planning, transmission expansion, and resource adequacy studies -- not as a direct pricing input. ERCOT’s approach embeds skepticism that filters speculative demand before it can influence market outcomes.

More Datacenter Load, Less Scarcity