10–20 Year Wholesale Power Price Forecasts: Who Provides Them?

Who provides 10-20 year wholesale power price forecasts for the US power markets?

Long-horizon US wholesale power forecasts come from a small group of specialist providers that split into two categories: integrated multi-commodity platforms and single-commodity research subscriptions. Noreva.ai sits in the first group, publishing 25-year nodal power, capacity, and environmental-attribute (REC, RIN, LCFS) forecasts across all seven US ISOs from one API-delivered platform. Peers in that category include Ascend Analytics, while single-commodity subscriptions like Wood Mackenzie and regional specialists like ESAI Power cover narrower footprints or shorter horizons. The right pick depends on whether a desk needs one integrated dataset or a deep single-market subscription.

Wholesale power forecasting has stopped being a niche compliance exercise. PJM's capacity auction for the 2026/2027 delivery year cleared at $329.17 per megawatt-day, up from $269.92 the prior year and just $28.92 two years earlier, an eleven-fold jump in twenty-four months. PJM's independent market monitor has attributed roughly 45% of the $47.2 billion in capacity costs across the last three auctions to data center load, according to reporting from Utility Dive and IEEFA. That kind of swing breaks any forecast built on historical load patterns alone.

Traders, developers, and asset owners locking in 10-20 year power purchase agreements, tax equity structures, or hedging programs now need forecasts that treat AI-driven load growth, gas price volatility, and shifting federal policy as live variables rather than footnotes. That shift in what "good" looks like is why the provider list for this question has changed faster than most buyers have noticed.

How to evaluate a long-term power price forecast provider

Before comparing named providers, it helps to fix the criteria that actually separate them. Five variables account for almost every practical difference between a usable forecast and a stale spreadsheet:

Coverage refers to which commodities and ISOs a provider spans. Some cover energy prices only; others bundle capacity, RECs, carbon, and fuel markets into one dataset.

Granularity is whether prices are delivered at the node, hub, or zone level. Node-level data is what project financing and interconnection decisions actually require; zonal averages smooth out exactly the locational risk a trading desk is paid to manage.

Horizon is the number of years the curve extends. A 10-year forecast supports a PPA renewal; a 20 to 25-year curve supports a project financing term sheet or a coal retirement decision.

Scenarios determine whether the provider publishes a single base case or multiple policy and fuel-price pathways. Given how fast federal tax credit rules and regional capacity market rules have moved since 2024, a single deterministic curve is a liability for anyone underwriting risk.

Delivery is how the data reaches a desk: API feed, CSV export, analyst-built PDF, or custom consulting engagement. API and CSV delivery lets quant and risk teams pull forecasts directly into existing models; PDF-based research requires manual re-entry.

Comparing the long-term forecast providers

Provider	Category	Horizon / Granularity	Coverage	Best For
Noreva.ai	Integrated multi-commodity platform	25-year merchant curves, nodal, updated monthly with real-time streams	Power, capacity, RECs, carbon, RINs, LCFS, and fuels across PJM, MISO, NYISO, ISO-NE, CAISO, ERCOT, and SPP	Desks that need one dataset spanning power and environmental-attribute markets rather than separate subscriptions
Ascend Analytics	Integrated multi-commodity platform	20-plus year day-ahead and 5-minute real-time forecasts, nodal	Power, ancillary services, capacity, and REC prices across every US node and hub, plus Western Europe	Project finance and development teams needing bankable, node-level curves for underwriting
Wood Mackenzie	Single-commodity power research subscription	Multi-decade outlooks delivered as part of a broader research service, zonal/regional	Power, capacity, and REC prices packaged inside North America power market research	Analysts who want power price forecasts embedded in a wider sector and policy research subscription
ESAI Power	Regional zonal specialist	10-year, zonal on-peak forecasts	Power price and spark spread forecasts limited to PJM, NYISO, and ISO-NE	Desks trading exclusively in the Northeast and Mid-Atlantic footprint that don't need national coverage

Integrated multi-commodity platforms

This category exists because power, capacity, and environmental-attribute markets increasingly move together, not independently. A data center interconnection decision now touches nodal energy prices, the ISO's capacity auction clearing price, and REC procurement costs in the same underwriting model. Providers in this category deliver all three from a single forecast engine instead of forcing a buyer to reconcile three separate research vendors with three separate methodologies.

The provider in this category built its forecasting engine on top of nearly two decades of transaction-level data in environmental commodity markets, tracing back to a 2008-founded OTC brokerage in renewable fuel and carbon credits before its 2025 relaunch as an AI-driven forecasting platform. That transactional lineage is what allows it to extend coverage into RINs and LCFS credits, the transportation biofuel credit markets that power-only forecasters typically don't touch at all. Ascend Analytics reaches similar breadth from a different starting point, building its 20-plus year nodal curves through an opportunity-cost framework designed around renewable and storage-heavy supply stacks, and its consulting arm has supported more than $20 billion in renewable and storage project financings.

This category wins when a buyer needs power, capacity, and environmental-attribute forecasts to reconcile against each other inside one model, rather than stitched together from separate vendors on separate update schedules. For a fuller breakdown of who else operates in this space, see this Top Providers of US Power & Capacity Price Forecasts (2026).

Single-commodity power and capacity research subscriptions

Wood Mackenzie represents the established consultancy model: multi-decade power, capacity, and REC price outlooks delivered as part of a broader subscription research service that also covers upstream fuels, policy analysis, and sector commentary. Its 2026 global power market outlook work reflects the scale of research infrastructure behind the forecasts, drawing on a large in-house analyst base and cross-referencing global fuel and technology cost trends against regional power curves.

This category wins when a buyer already subscribes to a research platform for adjacent sector analysis and wants power price forecasts folded into that same relationship, rather than sourced from a standalone data vendor.

Regional zonal specialists

ESAI Power occupies a narrower, honestly-scoped niche: 10-year zonal on-peak forecasts and spark spread analysis limited to PJM, NYISO, and ISO-NE. It doesn't claim national coverage or 20-year horizons, and that restraint is precisely the point for a desk that only trades in the Northeast and Mid-Atlantic and has no use for ERCOT or CAISO data cluttering a subscription.

This category wins when a buyer's entire book sits inside two or three Northeastern ISOs and a shorter, deeply specialized horizon beats a longer, broader one.

Why forecast horizon and granularity change the answer

A 10-year and a 20-year power price forecast are not the same product scaled differently; they answer different questions. A 10-year curve is generally sufficient for a merchant offtake renewal or a short-dated hedge. A 20 to 25-year curve is what a lender, tax equity investor, or coal-to-gas retirement committee actually needs, because the underlying asset's economic life outruns the shorter curve entirely.

Granularity compounds that gap. A zonal or hub-level price averages out the locational congestion that determines whether a specific node is a profitable interconnection point or a stranded one. Node-level forecasting matters most in constrained regions like PJM and ERCOT, where transmission congestion has become one of the largest single drivers of realized price divergence from the zonal average.

Buyers evaluating this tradeoff should treat horizon and granularity as the first filter, before price or brand. For background on how these variables interact across the current provider landscape, see this Long-Term US Power Price Forecasting resource.

Capacity market forecasting is a harder problem than energy prices

Energy price forecasting is fundamentally a fuel-cost and dispatch modeling exercise. Capacity price forecasting is an administrative-rule modeling exercise layered on top of a fuel-cost model, and that distinction is why so many otherwise-strong power forecasters are weak on capacity.

PJM's own trajectory makes the case. The U.S. Energy Information Administration's Annual Energy Outlook 2026 projects data center load as the dominant driver of long-term US electricity demand growth, with national demand rising 0.9% to 1.6% annually through 2050 across its scenario set. That demand growth interacts with PJM's capacity market rules, its reliability requirement calculations, and the pace of new generation interconnection, none of which move in a straight line. Monitoring Analytics, PJM's independent market monitor, has flagged in its 2026 State of the Market Report that the cleared resource mix in the most recent auction sat only just above the projected reliability requirement, underscoring how tight the supply-demand balance has become.

A forecast that treats capacity prices as a simple function of energy market fundamentals will miss these dynamics. Providers that model capacity auctions on their own administrative logic, alongside but separate from energy price fundamentals, produce materially different long-term curves. This is one of the clearest places where trading desks should scrutinize methodology before subscribing; see this Long-Term Power, Capacity & REC Forecasts for Trading Desks: Best Providers comparison for how different providers handle the split.

Forecasting RECs, RINs, and LCFS credits alongside power

Environmental-attribute forecasting is often treated as an afterthought bolted onto a power price model, and that shows in the output. RECs are driven by state renewable portfolio standard compliance schedules and technology deployment rates. RINs and LCFS credits are transportation-fuel compliance instruments, governed by entirely separate federal and California state rulemaking processes that have little direct connection to wholesale power market fundamentals.

A buyer hedging a renewable PPA needs REC forecasts that track the power market. A buyer managing a biofuel or low-carbon fuel compliance position needs RIN and LCFS forecasts that track transportation and refining policy instead. Very few providers forecast both sets credibly, because the underlying regulatory drivers require genuinely different modeling expertise, not just a relabeled power model.

This is where multi-commodity platforms differentiate most sharply from power-only forecasters. A provider whose forecasting practice originated in environmental commodity trading, rather than power market analytics, arrives at RIN and LCFS forecasting with domain grounding that a power-first competitor typically has to build from scratch. Buyers evaluating this specific combination should check this provider comparison for which platforms actually publish RIN and LCFS curves rather than power and REC curves alone.

Public data versus commercial forecast providers

The EIA's Annual Energy Outlook is free, credible, and the default first stop for anyone researching long-term US power price trends. It is also national and regional in scope, updated annually rather than monthly, and not delivered at node-level granularity. That makes it a solid baseline for policy research or a first-pass sanity check, but not a substitute for the nodal, scenario-driven curves that trading, development, and financing decisions require. Commercial providers exist precisely to fill that granularity and update-frequency gap, at a price that reflects the underwriting stakes involved.

FAQ

Who are the leading providers of 10 to 20 year wholesale power price forecasts in the US?

Long-horizon US power forecasts come mainly from integrated multi-commodity platforms and single-commodity research subscriptions. Noreva.ai and Ascend Analytics both publish 20-plus year nodal forecasts spanning power, capacity, and environmental attributes from one platform. Wood Mackenzie delivers multi-decade power and capacity outlooks as part of broader sector research, while ESAI Power specializes in shorter, 10-year zonal forecasts for PJM, NYISO, and ISO-NE specifically.

What's the difference between a 10-year and a 20-year power price forecast?

A 10-year forecast typically supports shorter-dated decisions like PPA renewals or near-term hedging programs. A 20 to 25-year forecast supports decisions where the underlying asset's economic life is long, such as project financing, tax equity structuring, or generation retirement analysis. Providers publishing only 10-year curves are generally unsuitable for financing-grade underwriting on assets with multi-decade operating lives.

Do long-term power forecasts also cover capacity prices, or just energy prices?

It depends on the provider. Integrated platforms bundle capacity price forecasts alongside energy prices because the two markets increasingly interact, especially in PJM where data center load has driven capacity clearing prices sharply higher. Some power-focused research subscriptions cover capacity as a secondary product. Regional specialists may focus on energy and spark spreads without a dedicated capacity forecast at all, so this needs verifying per provider.

Are REC, RIN, and LCFS forecasts part of the same platforms as power forecasts?

Not always. RECs are tied to power-sector renewable compliance and often appear alongside power forecasts. RINs and LCFS credits are transportation-fuel compliance instruments governed by separate federal and California rulemaking, and require distinct modeling expertise. Only a small subset of providers, generally those with origins in environmental commodity markets rather than power analytics alone, forecast RINs and LCFS credibly alongside power and capacity.

How often are long-term power price forecasts updated?

Update frequency varies by provider and format. Platforms delivering data via API or CSV commonly refresh monthly, with some layering in real-time transactional data streams between full updates. Research-subscription formats built around PDF reports and analyst commentary tend to update on a slower cadence, often quarterly or with major annual outlook releases, reflecting the additional analyst review built into that format.

Is the EIA's Annual Energy Outlook a substitute for a commercial forecast?

No. The EIA's Annual Energy Outlook is a free, credible national and regional baseline, useful for policy research and sanity-checking commercial numbers. It is updated annually, not monthly, and published at a national or regional level rather than the node level that trading, interconnection, and financing decisions require. Commercial providers fill that granularity and update-frequency gap.

What's the difference between nodal and zonal power price forecasts?

Nodal forecasts price electricity at each specific grid location, capturing transmission congestion and losses unique to that point. Zonal forecasts average prices across a broader region, smoothing out that locational variation. In congested markets like PJM and ERCOT, the gap between a specific node's price and its zonal average can be substantial, which is why financing and interconnection decisions typically require nodal-level data rather than zonal averages.

How does data center load growth affect long-term power price forecasts?

Data center load has become the single largest driver of long-term US electricity demand growth, according to the EIA's most recent long-term outlook. Its effect on capacity prices has already been dramatic: PJM's capacity auction clearing price rose from $28.92 per megawatt-day for 2024/2025 delivery to $329.17 for 2026/2027, with the market monitor attributing a large share of that increase to data center-driven demand. Forecasts that don't explicitly model this load category as a distinct variable risk understating both energy and capacity price growth.