The financial transmission rights (FTRs) market continues to have a highly concentrated profitability landscape, with PJM and MISO accounting for more than 91% of total FTR profits over the study period from June 2025 to May 2026. The following analysis was prepared using Yes Energy's FTR Positions Dataset, which provides complete visibility into FTR positions and results across all seven US ISOs.
Key Findings
Total reported FTR profit exceeded $4.27 billion.
PJM generated approximately 70% of all observed profits.
MISO contributed another 21%.
Profitability accelerated significantly during the winter months, particularly in January 2026.
Market performance was heavily concentrated among a small number of participants, with several traders and utilities capturing outsized returns.
ERCOT showed positive aggregate profitability but significantly lower average profit per position than PJM and MISO.
→ Deeper Analysis of the Top FTR Participants
→ Strategic Takeaways for FTR Participants
| ISO | Total Profit ($) | Share of Total |
| PJM | ~$3B | 70.3% |
| MISO | ~$898M | 21.0% |
| NYISO | ~$114M | 2.7% |
| ERCOT | ~$110M | 2.6% |
| CAISO | ~$96M | 2.2% |
| ISO-NE | ~$47M | 1.1% |
| SPP | ~$5M | 0.1% |
Source: FERC.gov
The results reinforce a trend that many market participants have observed for several years.
PJM and MISO continue to provide the deepest congestion opportunities and the largest FTR monetization potential.
Both markets exhibited substantially higher aggregate profits than ERCOT, CAISO, NYISO, ISO-NE, and SPP combined.
The dataset reveals strong seasonality.
| Highest Profit Months | |
| Month | Total |
| January 2026 | ~$1.21B |
| February 2026 | ~$468M |
| July 2025 | ~$390M |
| May 2026 | ~$370M |
| October 2025 | ~$349M |
| Weakest Month | |
| Month | Total |
| August 2025 | ~$2.9M |
January 2026 produced nearly three times the profitability of most other months.
Key drivers include:
Extreme winter weather
Elevated congestion volatility
Generator outages
Significant basis separation across constrained regions
This suggests that weather-driven congestion remains one of the strongest contributors to FTR value creation.
| Highest Aggregate Profits | |
| Participant | Profit |
| DC Energy | ~$553M |
| Dominion Energy (LSE) | ~$437M |
| Exelon | ~$347M |
| Appian Way Energy | ~$200M |
| Peakstone Energies | ~$176M |
| FirstEnergy | ~$173M |
| Saracen | ~$149M |
| Elliott Bay | ~$138M |
| Appalachian Power | ~$128M |
| Hamilton Liberty | ~$103M |
Several themes emerge:
1. Traditional utilities remain major FTR holders.
2. Specialized congestion traders continue to dominate.
3. Market expertise and sophisticated congestion forecasting remain key differentiators.
A closer look at the leading participants reveals that the market is not dominated by a single trading strategy. Instead, there appear to be three distinct groups.
1. Utility/LSE hedgers (Dominion Energy, Exelon, FirstEnergy, Appalachian Power)
2. Specialized congestion traders (DC Energy, Appian Way Energy Partners, Saracen, Elliott Bay, Hamilton Liberty)
3. Hybrid participants combining proprietary trading and physical market exposure
DC Energy generated approximately $553M in profit.
| Profit Distribution by Market | |
| ISO | Profit |
| MISO | $225M |
| PJM | $137M |
| ERCOT | $73M |
| SPP | $65M |
| CAISO | $28M |
| NYISO | $17M |
| ISO-NE | $8M |
Key Insight
Unlike most top performers, DC Energy earned profits across every major ISO.
This suggests:
A highly scalable congestion forecasting strategy
Extensive transmission modeling capability
Strong risk diversification
Ability to monetize both long-term and short-term congestion events
Seasonal Trend
DC Energy’s strongest month was January 2026, when it generated more than $93M, but profits remained positive throughout the entire study period.
This consistency is uncommon and indicative of a congestion trading team with many years of experience.
Dominion Energy generated approximately $437M, entirely within PJM.
What Stands Out
Unlike DC Energy, Dominion's profitability appears highly concentrated.
The participant captured:
$170M in May 2026
$115M in July 2025
$69M in October 2025
Interpretation
This profile is more consistent with:
Large hedge portfolios
Transmission rights tied to generation and load obligations
Structural congestion exposure rather than purely speculative congestion trading
Dominion's performance demonstrates how valuable FTRs can be when aligned with physical generation and load positions.
Exelon generated approximately $347M.
Key Observation
Nearly 60% of Exelon's annual profits occurred during:
January 2026 ($131M)
February 2026 ($39M)
March 2026 ($36M)
Interpretation
This concentration suggests exposure to major winter congestion events.
Potential drivers include:
PJM generator outages
Fuel switching dynamics
Winter peak load patterns
Regional transmission constraints
Exelon appears to have benefited substantially from weather-related congestion.
Appian Way Energy Partners generated approximately $200M.
| Market Breakdown | |
| ISO | Profit |
| PJM | $155M |
| NYISO | $28M |
| MISO | $16M |
What Makes This Interesting
Unlike the utility participants, Appian Way's profits were spread across multiple eastern markets.
This profile is consistent with:
Relative-value opportunities
Cross-market transmission analytics
The company appears less dependent on a single transmission system than many competitors.
Peakstone Energies generated approximately $176M.
Notable Characteristics
Peakstone had remarkably few losing months.
Even its weakest months remained profitable.
Implication
This suggests:
Tight risk controls
Portfolio diversification
Reduced dependence on extreme congestion events
Many participants earned large profits during congestion spikes. Peakstone appears to have generated returns more consistently throughout the year.
Another interesting way to evaluate FTR performance is not by absolute profit, but by capital efficiency.
1. Profit per MW of contract exposure (how much profit was generated for each MW of FTR held)
2. Profit relative to acquisition cost (a proxy for return on invested capital)
Using this dataset, several participants stand out very differently from how they do when ranked solely by total profit.
| Participant | Total Profit | Profit per MW |
| Hamilton Liberty | $103M | $433/MW |
| FirstEnergy | $173M | $224/MW |
| Dominion Energy (LSE) | $437M | $108/MW |
| PPL Energy Plus | $92M | $61/MW |
| Appalachian Power | $128M | $36/MW |
| Appian Way Energy Partners | $200M | $12/MW |
| Elliott Bay | $138M | $10/MW |
| Exelon | $347M | $10/MW |
| Fuelwinds Energy | $103M | $8/MW |
| Duke Energy | $89M | $7/MW |
Key Observation
The most efficient participants are not necessarily the largest traders.
For example:
DC Energy generated the highest total profit ($553M), but only about $2.61 per MW of contract exposure.
Hamilton Liberty generated less total profit, but extracted more than 160 times as much profit per MW.
This suggests Hamilton Liberty and similar participants may be focusing on:
Highly targeted congestion positions
Concentrated high-conviction trades
Another useful metric is the profit generated relative to the cost of acquiring the FTRs.
Among the larger profitable participants:
| Participant | Approx. Profit / Cost |
| DC Energy | 4.18x |
| FirstEnergy | 4.15x |
| Fuelwinds Energy | 3.94x |
| Saracen | 1.99x |
| Appian Way Energy Partners | 1.60x |
| Duke Energy | 1.34x |
| Solios Power | 1.08x |
| Hamilton Liberty | 1.02x |
| Appalachian Power | 0.91x |
| Dominion Energy (LSE) | 0.91x |
Key Observation
This tells a very different story from profit per MW.
Most Efficient Capital Deployment
The leaders are:
DC Energy
FirstEnergy
Fuelwinds Energy
These firms generated roughly $4 of profit for every $1 spent on FTR positions.
This is generally indicative of:
Strong congestion forecasting
Effective risk-adjusted bidding
Consistent secondary market optimization
The data suggests two different paths to success.
Examples
DC Energy
Saracen
Fuelwinds
Characteristics
Large MW exposure
Multi-market participation
Lower profit per MW
Extremely strong return on capital
Goal: Generate steady returns through scale and diversification.
Examples
Dominion Energy
FirstEnergy
Hamilton Liberty
Appalachian Power
Characteristics
Very high profit per MW
More concentrated exposure
Fewer but more impactful positions
Goal: Capture structural congestion where participants have unique knowledge or physical market insight.
The highest-performing firms are not necessarily those with the largest FTR books — they are often those that:
Identify the right constraints.
Focus on the highest-value paths.
Deploy capital selectively.
Avoid tying up capital in low-conviction positions.
Yes Energy tools such as congestion analytics, outage intelligence, shift-factor analysis, and short-term transmission modeling can help participants move from a "more MWs" strategy to a "better MWs" strategy, improving:
Profit per MW
Return on capital
Auction bid efficiency
Portfolio concentration on the most attractive congestion opportunities
One of the most interesting findings is the difference in return profiles between utility participants and proprietary traders.
Examples
Dominion Energy
Exelon
FirstEnergy
Appalachian Power
Characteristics
Larger individual profit spikes
Heavy PJM concentration
Strong winter event exposure
Likely tied to physical hedging needs
Examples
DC Energy
Appian Way Energy Partners
Saracen
Elliott Bay
Hamilton Liberty
Characteristics
More geographically diversified
More consistent monthly profitability
Greater participation across multiple ISOs
A common thread among nearly all top performers was January 2026.
| Participant | January 2026 Profit |
| Exelon | $131M |
| FirstEnergy | $106M |
| DC Energy | $93M |
| Appalachian Power | $102M |
| Appian Way Energy Partners | $51M |
| Peakstone Energies | $47M |
| Elliott Bay | $33M |
When nearly every successful participant experiences a major profit spike during the same month, it typically indicates:
A system-wide congestion event
Extreme weather conditions
Significant transmission outages
Broad market dislocations
This suggests that a large share of annual FTR profitability was driven by a relatively small number of high-impact congestion events.
The top performers demonstrate two successful approaches to FTR markets.
Represented by DC Energy and Saracen.
Success factors:
Multi-ISO coverage
Broad congestion forecasting
Large position portfolios
Represented by Dominion Energy, Exelon, FirstEnergy, and Appalachian Power.
Success factors:
Deep knowledge of local transmission systems
Physical asset alignment
Long-term congestion exposure
The data suggests that the most durable FTR profits come from either:
1. Owning structural congestion associated with generation/load portfolios, or
2. Building sophisticated multi-market congestion analytics that can identify opportunities across several ISOs simultaneously.
Key Insight
Losses were concentrated among both:
Physical market participants using FTRs as hedges.
Financial traders being exposed to forecast errors in congestion.
This highlights the continuing challenge of accurately predicting transmission congestion over monthly and annual horizons.
Although ERCOT generated approximately $110M in total profits, this accounted for only 2.6% of the overall profitability across markets.
Potential explanations include:
Absence of a traditional capacity market
Different congestion management framework
More dynamic transmission expansion
Increasing competition among congestion revenue right (CRR) participants
The data suggest that while ERCOT remains attractive for congestion trading, profit opportunities appear materially smaller than those observed in PJM and MISO during the study period.
The concentration of profits during winter months indicates that weather-linked congestion events continue to create the largest opportunities.
Participants who integrate load forecasting, renewable generation forecasting, and weather analytics appear best positioned to capture congestion value.
PowerSignals integrates weather data, load forecasts, and wind and solar production into a single environment, giving FTR participants the inputs to anticipate weather-driven congestion before it shows up in prices.
Combined, these markets generated more than 90% of observed profits.
For organizations allocating analytical resources, PJM and MISO remain the highest-priority markets.
Yes Energy's PJM and MISO coverage – constraint histories, congestion data, and outage intelligence – is built around exactly the markets where the largest FTR opportunities continue to concentrate.
The gap between top and bottom performers exceeds hundreds of millions of dollars.
This reinforces the importance of:
Transmission topology modeling
Outage forecasting
Generator commitment analysis
Short-term congestion forecasting
PowerSignals supports each of these workflows through constraint-level congestion analysis, shift-factor tooling, and visibility into the topology and outage conditions that drive binding constraints.
Several participants experienced substantial losses despite broad market profitability.
Successful firms appear to combine long-term auction positions, monthly rebalancing, secondary market transactions, and congestion event trading to manage risk across varying market conditions.
The Yes Energy FTR Positions Dataset – the source of the participant-level analysis in this report – gives traders direct visibility into how the market's most successful firms are sizing and rotating positions across auctions and ISOs.
The 2025–2026 FTR market period was characterized by:
Strong overall profitability ($4.27B total profit)
Dominance of PJM and MISO congestion opportunities
Significant winter-driven congestion events
High concentration of returns among specialized trading organizations
The results suggest that the competitive edge in today's FTR markets increasingly comes from sophisticated congestion forecasting, weather analytics, and short-term transmission modeling capabilities rather than simple directional congestion bets.
If your goal is to improve FTR performance — especially the types of results seen from participants like DC Energy, Appian Way Energy Partners, and Exelon — it is less about "predicting congestion" directly and more about identifying, quantifying, and monitoring the drivers of congestion before they show up in prices.
Based on the FTR performance analysis, most gains and losses come from forecasting errors in:
Transmission outages: addressed through Yes Energy's planned, forced, and real-time outage intelligence.
Generation outages: captured through the same outage feeds, with alerting on material changes.
Renewable output: supported by wind and solar production forecasts integrated into PowerSignals.
Load patterns: informed by Yes Energy's load forecasts and historical load overlays.
Constraint binding frequency: visible through constraint-level congestion analysis in PowerSignals.
Shift factor impacts: quantified through Yes Energy's shift-factor tooling.
Transmission expansion and topology changes: tracked through transmission topology modeling.
The challenge is not finding data — it's connecting thousands of market variables into actionable congestion views fast enough to make portfolio decisions.
One of the strongest capabilities is visibility into historical and real-time transmission constraints.
Using Yes Energy, traders can:
Identify frequently binding constraints.
Analyze historical congestion drivers.
Compare day-ahead versus real-time constraint behavior.
Quantify impacts on nodes and paths using shift-factor analysis.
Evaluate which constraints most affect specific FTR positions.
FTR Impact
Instead of buying a path because it has historically been profitable, traders can determine:
Which constraint actually creates the value, and whether that constraint is likely to recur.
This is often the difference between a structural congestion trade and a speculative congestion trade.
Explore the FTR Workflow in Yes Energy.
Many of the largest profit months in Yes Energy’s dataset — including January 2026 — resulted from transmission and generation outages.
Yes Energy aggregates:
Planned outages
Forced outages
Historical outages
Real-time outage changes and allows users to create alerts around these events
FTR Impact
A trader can:
Identify congestion risks before monthly auctions.
Evaluate outage persistence.
Determine whether a profitable historical path depended on temporary conditions.
For short-term FTR modeling, outage awareness is often one of the largest sources of alpha.
The best-performing traders typically look for recurring congestion patterns.
PowerSignals provides:
Historical locational marginal price (LMP) data
Historical congestion data
Constraint histories
Weather and load overlays
Market maps and visualization tools
FTR Impact
This enables analysis such as:
Summer versus winter congestion regimes
Renewable-driven congestion
Peak-load constraints
Seasonal path profitability
For example, if your analysis shows January profitability dominates annual returns, you can investigate:
Which constraints bound during those periods
How frequently they occurred
Whether the underlying drivers still exist
A notable characteristic of DC Energy's performance was diversification across ISOs.
Yes Energy covers multiple North American markets and standardizes data across them.
FTR Impact
This allows traders to:
Compare congestion opportunities across PJM, MISO, ERCOT, NYISO, and others.
Allocate risk where expected returns are highest.
Avoid concentrating exposure in a single ISO.
Many mid-sized FTR shops struggle because they are overly focused on one market.
Increasing renewable penetration has made congestion more weather-sensitive.
Yes Energy integrates:
Weather data
Wind production
Solar production
Load forecasts
Gas market information
FTR Impact
Traders can better model:
Wind-driven congestion in MISO and SPP
Solar-driven congestion in ERCOT and CAISO
Weather-driven load shifts
Seasonal transmission stress
This is especially important because renewable variability increasingly influences congestion outcomes and FTR valuation.
For sophisticated FTR participants, the biggest opportunity is combining Yes Energy data with modeling.
A common workflow is:
In this framework, Yes Energy becomes the data and intelligence layer feeding:
Power flow models
Auction valuation models
Monte Carlo congestion simulations
Examples
Dominion Energy
Exelon
These firms likely benefit from deep knowledge of local transmission systems and structural congestion.
Examples
DC Energy
Appian Way Energy Partners
These firms appear to rely on systematic congestion analytics across multiple markets.
The areas where Yes Energy can most directly improve performance are:
1. Faster identification of emerging congestion opportunities
2. Better outage intelligence
3. Historical constraint analysis
4. Improved short-term congestion forecasting
5. More effective risk management and portfolio monitoring
The results suggest that the competitive edge in today's FTR markets increasingly comes from sophisticated congestion forecasting, weather analytics, and short-term transmission modeling capabilities rather than simple directional congestion bets.
Yes Energy supports many of the analytical workflows that distinguish the top performers in this report. PowerSignals provides the constraint, outage, weather, and renewable forecasting layer underlying short-term congestion analysis, and the FTR Positions Dataset offers the participant-level visibility used in this report. To see how either fits into an FTR program, request a demo.