What the Spark Spread Tells Us About Texas Grid Stress
Note: All prices and spark spreads shown here are derived from ERCOT HB_NORTH real-time settlement point prices (15-minute intervals), Henry Hub front-month proxy (NG=F continuous futures), and publicly available weather data, all ingested and modeled in Databricks for this project. Spark spreads are calculated using assumed heat rates (7.0 MMBtu/MWh for CCGT, 10.0 for peakers) and represent fuel-cost margins only, not total plant profitability. Values are based on my processed dataset and may differ from official ERCOT settlements, nodal prices, or plant-specific economics. HB_NORTH broadly represents the Dallas/Fort Worth region.
Over the past few months, I’ve been intrigued by energy markets, especially since I’ve been working more with the trading team at Plains on use cases like crude blending. To get a better feel for the data energy traders analyze daily, I pulled in data from ERCOT, the U.S. Energy Information Administration, and other sources into Databricks, focusing on the Texas market. This also gave me a chance to get hands-on with Lakeflow Spark Declarative Pipelines. While I wasn’t the biggest fan of its predecessor, DLT, I’ve heard along with the new branding there are significant improvements to the product as well.
The idea I had to start was to look at these various data sources to analyze how extreme weather events impact gas and power prices.
On January 26, 2026, a cold front pushed into Dallas and temperatures plummeted more than 30 degrees below the seasonal average. Over the next three days, something remarkable happened to energy markets:
| Date | Dallas Temp | Gas Price | Avg Power Price | CCGT Spark Spread |
|---|---|---|---|---|
| Jan 21 | 57°F | $4.88/MMBtu | $22.44/MWh | -$11.69 |
| Jan 26 | 21°F | $6.80/MMBtu | $168/MWh | +$119.91 |
| Jan 27 | 30°F | $6.95/MMBtu | $116/MWh | +$67.05 |
| Jan 28 | 34°F | $7.46/MMBtu | $200/MWh | +$147.67 |
| Jan 29 | 42°F | $3.92/MMBtu | $27/MWh | -$0.81 |
Gas prices jumped sharply. Power prices more than quintupled. And then, as temperatures recovered on January 29, the spread turned negative and while January 30 showed some residual elevation as the grid normalized, the event was effectively over within two days.
The spark spread captures the core economics in a single number.
The two charts above tell the same story from different angles. The top chart tracks two lines: average power price (what electricity sold for at the HB_NORTH hub) and the CCGT spark spread (the profit margin for a gas plant generating that electricity). On January 21 both lines are flat and low, a mild day with no stress on the grid. Then the cold front arrives on January 26 and both lines spike sharply upward, the power price climbing to $168/MWh and the spread jumping to $120. They stay elevated through January 28 before collapsing back to near zero on January 29 the moment temperatures recovered.
The bottom chart shows why. Dallas average temperature is plotted across the same date range. The V-shaped dip, bottoming out at an average of 21°F on January 26, is the cold snap. The visual alignment between the temperature dip and the price spike in the top chart is the entire point: weather drove demand, demand drove prices, and prices drove the spread. The mechanism isn’t abstract, you can see it happen in real time across two charts stacked on top of each other.
The fact that both lines in the top chart move together matters too. In a summer heat wave, power prices spike but gas prices stay cheap, so the spread explodes far above the power price line. In this winter event, gas prices spiked alongside power prices. Gas infrastructure tightened as heating demand and power generation competed for the same fuel. The spread was still strongly positive, but it was more compressed than a typical August afternoon. That compression is the signature of a winter stress event versus a summer one.
What Is The Spark Spread?
The spark spread is a common metric for estimating the profitability of natural gas-fired electric generators. The spark spread is the profit margin a gas-fired power plant earns for generating one megawatt-hour of electricity. It answers one question: is it worth burning gas right now? They are calculated with the following equation:
Spark spread ($/MWh) = power price ($/MWh) – [natural gas price ($/mmBtu) × heat rate (mmBtu/MWh)]
Heat rate measures how many MMBtu of fuel a power plant must burn to generate 1 MWh of electricity. Lower heat rates mean higher efficiency.
- CCGT (combined cycle gas turbine, ~49% efficient):
7.0 MMBtu/MWh: the workhorses that run most of the day. - Peaker (simple cycle, ~34% efficient):
10.0 MMBtu/MWh: expensive to run, called only when the grid is stressed.
A positive spread means the plant covers its fuel cost and makes money generating electricity. A negative spread means every megawatt-hour it produces loses money on fuel alone.
On January 28, with power at $200/MWh and gas at $7.46/MMBtu, the CCGT spread was $200 − (7.0 × $7.46) = $147.67. Many gas plants were earning extremely high margins. On January 21, with power at just $22.44 and a spread of -$11.69, few plants would choose to run purely for energy revenue.
This is the same plant, same fuel, a week apart.
Where Spark Spread Falls Short
The spark spread is widely used precisely because it is simple, but that simplicity comes with real limitations worth understanding before drawing conclusions from it.
In Energy Trading & Investing, David W. Edwards notes that spark spread can overestimate actual plant profitability by 20–30% once operational constraints are accounted for. The core criticism is that spark spread models are reactive. They assume a generator simply turns on when the spread is positive and turns off when it isn’t. Real plant dispatch doesn’t work that way.
A few of the key gaps:
- Ramp time: CCGTs take 1–4 hours to reach full output; peakers 10–30 minutes. A price spike that resolves before a cold plant can respond means the realized margin is lower than the spread implies. The optimal dispatch schedule has to be anticipated, not just observed.
- Start-up costs: Lighting off a plant burns fuel and causes mechanical wear. A brief positive spread may not justify the cost of starting, especially if the window is short.
- Fixed costs excluded: Spark spread covers fuel cost only. It does not account for pipeline costs, fuel-related finance charges, variable O&M, taxes, or fixed expenses. A plant can show a positive spark spread and still lose money overall.
- Hub vs. nodal prices: HB_NORTH is a trading hub. Actual plant economics depend on their nodal price, which can diverge significantly due to transmission congestion.
None of this makes spark spread useless. It remains one of the most widely used signals for understanding generator economics and grid stress. But as the EIA puts it, spark spread is an indicator of market conditions and not necessarily an exact measure of profitability for any one specific generator. Read it as directional, not precise.
Two Types of Grid Stress (And They Look Completely Different)
The Texas grid experiences stress in two different ways and the spark spread behaves differently in each.
Summer Heat Waves: When Gas Plants Win
August is often the best month to own a gas plant in Texas. August has the highest average CCGT spread at $19.82, making it the most profitable month to own a gas plant in Texas. The next highest months are May ($18.58) and January ($13.14), but August stands out as the clear leader. Temperatures regularly exceed 100°F, air conditioning load pushes demand to annual peaks, and gas prices stay low in summer because heating demand is absent. As a result, power prices spike while fuel costs stay cheap.
On August 20, 2024, the average power price at HB_NORTH hit $226/MWh with gas at $2.20/MMBtu. The CCGT spread that day was $210/MWh. The maximum interval price hit $4,853/MWh. In the entire month of August 2024, there was not a single day with a negative spark spread in my dataset, and according to the dataset I generated, CCGT plants were profitable 88% of all 15-minute intervals.
Shoulder Season: When Gas Plants Lose Money Every Day
Flip the calendar to late February or early March and the picture reverses completely. Temperatures are mild, demand is low, but gas prices remain elevated from winter. The spread collapses.
These are what we call the dead days, when the spread is so negative that not a single 15-minute interval is profitable:
| Date | Avg Power | Gas Price | CCGT Spread | % of Day Profitable |
|---|---|---|---|---|
| Feb 26, 2025 | $8.64 | $3.91 | -$18.70 | 0% |
| Nov 28, 2025 (Thanksgiving) | $17.53 | $4.85 | -$16.42 | 0% |
| Nov 11, 2025 | $15.17 | $4.57 | -$16.79 | 6% |
| Jan 2, 2026 | $10.92 | $3.62 | -$14.41 | 0% |
On February 26, 2025, not one 15-minute interval across the entire day covered a CCGT plant’s fuel cost.
The Seasonal Calendar
The following statistics are calculated from my dataset covering Dec 2023–Mar 2026.
Zoom out, and the pattern holds true year over year. The table below shows multi-year averages at HB_NORTH (Dec 2023–Mar 2026):
| Month | Avg CCGT Spread | % of Day Profitable | Negative Spread Days |
|---|---|---|---|
| August | $19.82 | 86% | 0 |
| May | $18.58 | 53% | 9 |
| March | $1.70 | 40% | 33 |
| November | $2.22 | 40% | 22 |
| December | $2.28 | 42% | 30 |
March has the most negative-spread days of any month, as winter gas prices linger while mild temperatures keep power demand low. December is close behind, cold enough to spike gas, but often not cold enough to push power prices high enough to compensate. The rare exception: when a proper freeze arrives, like January 2026.
The charts show the economics: August dominates with $20/MWh CCGT spreads, 86% profitable intervals. Shoulder months dip negative. Peakers struggle everywhere except summer peaks.
Peakers burn 43% more gas per MWh, so they need much higher prices to break even. They exist because August profits cover the dead months. The spark spread tells you exactly when the grid is stressed and how much plants are making (or losing).
The Demand Side Mirror: What 4CP Has to Do With It
Gas plant operators aren’t the only ones watching the spark spread. Every large industrial customer in Texas, such as refineries, petrochemical plants, data centers, and aluminum smelters, has someone whose entire job in summer is monitoring ERCOT load in real time.
The reason is something called Four Coincident Peaks (4CP). Each year, ERCOT identifies the 4 highest 15-minute demand intervals during June through September. Those four moments determine each large customer’s share of transmission charges for the following 12 months. For a major industrial facility, the bill can run into millions of dollars. Getting caught at full load during a 4CP event or curtailing prematurely on a false alarm has significant financial consequences.
In a blog I read, they highlight just how extreme 4CP costs can be. For example, a CenterPoint customer facing a $56.51/kW 4CP rate would pay roughly $2.8M annually for a 50 MW load. This is a cost determined entirely by their demand during just four 15-minute intervals across the summer. In effect, a single hour of peak demand can drive millions in yearly charges.
In one of the tables we have generated we have a column called economic_ratio_ccgt that represents the ratio of intervals where the spark spread for a CCGT (Combined Cycle Gas Turbine) plant is economically positive compared to the total intervals in a day. On the hottest summer days in our dataset, that ratio hits 1.0, meaning all 96 intervals were profitable, power prices stayed elevated all day, and the grid was running tight from open to close. Those are the days when 4CP risk is highest.
The same signal that tells a gas plant operator “run hard all day” tells an industrial energy manager “this might be the one.” Generators are sprinting. Industrials are watching every interval. Both are responding to the same underlying condition and that is scarcity on the Texas grid
Building the Data Platform on Databricks
Analyzing spark spreads across 700+ settlement nodes, 96 intervals per day, and multiple years of history requires more than a few CSVs and notebooks. To make this analysis repeatable, I built a small data platform to continuously ingest, clean, and model power, gas, and weather data.
The platform is built on Databricks using Lakeflow Spark Declarative Pipelines (SDP). At a high level, it follows a medallion-style architecture:
- Bronze: Raw ingestion of ERCOT hub prices, Henry Hub gas data, and weather feeds
- Silver: Cleaned and aligned time series across all sources
- Gold: Derived metrics like spark spreads, profitability ratios, and daily aggregates
This structure makes it easy to move from raw market data to something analytically useful without constantly rewriting transformation logic.
SDP (formerly Delta Live Tables) handles much of the pipeline orchestration and data quality enforcement. One area where it stands out is how naturally it supports things like slowly changing dimensions and built-in expectations (for example, dropping or flagging bad data at ingestion time). You can read more about the concepts here and expectations here.
That said, I still prefer the flexibility of writing custom PySpark when I need tighter control or easier local testing.
Overall, the goal wasn’t to build a perfect platform, but a fast, flexible one that makes it easy to explore questions like:
- How do spark spreads behave during extreme weather?
- When do gas prices compress generator margins?
- What does “grid stress” actually look like in the data?
The diagram below shows how the pieces fit together. From raw ERCOT prices and gas data to a daily spark spread signal:
HB_NORTH · HB_HOUSTON
HB_WEST · HB_SOUTH
Public API · No key required
Daily closing price ($/MMBtu)
via yfinance · No key required
HDD · CDD by ERCOT load zone
Free archive API · No key required
bronze_commodity_prices
bronze_weather_daily
silver_commodity_ohlcv
silver_weather_hdd_cdd
− [ Gas Price ($/MMBtu) × Heat Rate ]
CCGT heat rate: 7.0 MMBtu/MWh
Peaker heat rate: 10.0 MMBtu/MWh
temperature overlay, and seasonal patterns
on spark spread data
Note: I’m experimenting with animated diagrams here, let me know what you think!
Conclusion
It’s cool how much insight can come from combining a simple market concept with the right data infrastructure. The spark spread is a straightforward calculation, but when you view it across thousands of settlement points, weather events, fuel markets, and seasonal demand patterns, it becomes a powerful lens into how the Texas grid behaves under stress.
This project also turned into a great excuse to experiment with a modern data stack. Using Databricks SDP, I was able to ingest and model data from multiple sources including ERCOT market data, natural gas prices, weather feeds, and EIA datasets. Bringing these together in a medallion-style architecture made it easy to iterate quickly, test new ideas, and explore the relationships between weather, fuel costs, and electricity prices.
Another surprisingly helpful part of this journey has been working with Claude Code and a few MCP servers. Having an AI assistant available while exploring a new domain made it much easier to understand unfamiliar energy market concepts, locate useful datasets, and quickly prototype analysis pipelines. It has felt less like searching for information and more like collaborating with a research assistant while building the platform.
This is still very much the beginning of the exploration. I’m currently experimenting with bringing in additional datasets and expanding the platform to look at other ERCOT signals such as nodal congestion, real-time price spikes, and transmission constraints. There is a lot of interesting structure in these markets, and the combination of modern data platforms and AI-assisted development makes it easier than ever to dig into it.
Thanks for reading 😊!