Energy abundance isn't about generation — it's about transmission. ERCOT's broken cost allocation socializes expenses that should follow impact.
“Energy abundance” is language used to represent a world where power is so widely available that it becomes an afterthought in our lives, and therefore sold at an ever-diminishing cost to end consumers. While that future is still the hopes of many, the real barrier isn’t energy production, but transmission.
Texas is at the center of this issue. Recently, the Public Utility Commission of Texas (PUCT) approved a 765kV transmission line project as part of the Permian Basin Reliability Plan. This $10+ billion project will take around six years to complete and is widely viewed as essential to grid reliability. But what is contested among ERCOT market participants and regulators is this: how should these costs be allocated? Who should pay for it?
Currently, the Electric Reliability Council of Texas (ERCOT) uses a cost allocation methodology called Four-Coincident Peak (4CP), which assigns transmission costs based solely on how much electricity a load consumed during four 15-minute peak intervals in the summer (June–September). When the 4CP methodology was introduced in 1996, the intent was that every load on the grid pays their fair share of transmission costs based on their proportion of total system load during peak demand intervals.
For a while, this methodology worked as intended. Grids must be built to meet peak demand, and most large consumers didn’t have the flexibility or tools to suppress their load during those specific times. However, today, 4CP creates major distortions.
Large loads have learned how to avoid 4CP charges. By forecasting when those four 15-minute intervals will hit and ramping down their consumption in advance, they can dramatically reduce their allocated transmission costs. This load behavior isn’t subtle -it’s now strategic, coordinated, and expected. And because all large loads are attempting to outmaneuver each other, this creates a compounding game-theory feedback loop that leads to plateauing of the demand curve and widening forecasting errors for grid operators.
Summer peak demand periods are no longer an accurate reflection of what’s actually stressing the grid. Loads are artificially disappearing during these windows, and the true operational stressors on the transmission system —loads, generators, batteries, and other market participants— go unaccounted for. Transmission cost allocation becomes a game of avoidance, not a reflection of impact.
This mismatch grows even more problematic when we consider the root causes of transmission upgrades. The $10+ billion Permian Basin Reliability Plan project is being driven in large part by the rise of hyperscale data centers —massive, power-hungry facilities located remotely to access cheap land and generation. And yet, under 4CP, transmission costs for that infrastructure will be socialized across loads regardless of who caused them. Consumers who had no role in driving these transmission upgrades, and may never benefit from these lines, still end up footing the bill, while those who did drive the need for new transmission lines develop strategies to avoid their share of the costs.
The central failure of 4CP is that cost causation does not line up with cost recovery.
The lines being built to relieve congestion or deliver power to remote data centers are driven by specific injections and withdrawals that stress the grid. Whether a generator interconnection request triggered the need for a new line, or a hyperscale data center created massive localized demand, the associated costs are rolled into a general transmission charge that most loads cannot avoid by gaming the 4CP windows.
This creates two major inequities:
Grids can do better. Specifically, we can begin designing a congestion-based transmission cost allocator—one that ties transmission cost recovery to measurable physical impacts on the grid.
The underlying data is already available. Independent System Operators calculate shift factors and shadow prices as part of their standard market operations. Shift factors (Power Transfer Distribution Factors, or PTDFs) tell us how much flow on a transmission line changes in response to a power injection or withdrawal at a specific location. Shadow prices show the cost of relieving a transmission constraint by 1MW. When a transmission line hits its limit and a binding constraint occurs, the system becomes sensitive to who is pushing (generation) or pulling (load) on that line. With shift factors and shadow prices, we can quantify both who is causing the congestion and how expensive that congestion is.
A congestion-based methodology would use this information to allocate transmission costs in proportion to impact. This would require at least two complementary ledgers to work:
1. Operational Congestion Ledger for Real-Time Behavior
This ledger would track real-time grid activity and distribute charges —or credits— to resources based on their contribution to congestion. If a generator, battery, or load increases stress on a congested line, it would pay. If it relieves congestion (through counterflow), it would be credited.
This system would finally introduce a real-time price signal for transmission cost allocation. Flexible loads—like bitcoin mining, battery charging, or data centers with dispatchable backup generation— would be incentivized to shift consumption and support grid stability, not just avoid 4CP windows.
But the operational congestion is volatile and short-lived. Constraints can arise and disappear within minutes based on load patterns, outages, intermittent renewables, and more. As such, this real-time operational ledger alone cannot support multi-billion-dollar infrastructure planning and cost recovery. A second ledger is needed.
2. Planning Ledger for Long-Term Cost Recovery
This second ledger would support the Transmission Cost of Service (TCOS) recovery over the long term. When the Public Utility Commission of Texas approves a transmission project, ERCOT already has a power flow case showing the injections and withdrawals that caused the need for the line. The planning ledger would freeze that sensitivity snapshot and assign cost recovery proportionally to the busbars/substations responsible.
This captures both generation and load interconnection queues, ensuring that developers whose demand created the need for new infrastructure also bear part of the cost. This is not unlike Pennsylvania, New Jersey, and Maryland's solution-based distribution factor( DFAX) methodology, but expanded to include economic projects and real-time operational behavior.
Generators, batteries, and loads all play a role in creating (or relieving) grid congestion. A congestion-based allocation would finally recognize that fact, and shift us away from socialized costs and toward incentive-aligned grid development.
This doesn’t mean radical upheaval. A congestion-based system could be piloted in stages, built on the existing market infrastructure, and adjusted based on real-time performance.
ERCOT is preparing to invest more than $10 billion in transmission over the next six years. Under today’s 4CP rules, more large loads are avoiding charges and pushing costs onto retail consumers least equipped to manage them. “Grandma” shouldn’t have to track 15-minute peak intervals each summer and flip her breaker to save money on transmission upgrades she neither caused nor benefits from.
The ideas outlined here aren’t offered as a perfect solution. They’re meant to spark imagination and provide a higher-level starting point for debate.. Most of today’s conversation around 4CP reform remains mired in the weeds: 12CP, net load variants, minimum charges, and similar tweaks. What’s missing is a broader view: taking stock of the quantifiable data we already have, and exploring how that data could be used to build a smarter, more equitable transmission cost allocation framework. Transmission upgrades and congestion aren’t equally shared, so transmission costs shouldn’t be either.
This is not an argument for immediate overhaul. It’s a call for thoughtful experimentation —small, deliberate steps toward a grid that reflects the physical realities of congestion, rather than arbitrary 15-minute snapshots once a month. The tools exist. The data exists. What’s needed now is the courage to imagine a better system.
