The Spark Spread & Power Plant Economics

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Power trading isn't just buying and selling electricity - it's a game of conversion. A gas-fired power plant is a machine that "buys" gas and carbon and "sells" electricity. The spark spread is the calculation that tells traders whether that conversion is profitable, and it's the single most important number in UK power markets.

This guide explains the economics that determine when gas plants run and when they sit idle. You'll learn why natural gas is the marginal fuel in the UK (it's the last plant needed to meet demand most hours), and how the spark spread represents the gross margin of a CCGT plant. When the spread is positive, you dispatch the plant. When it's negative, you're burning money and should stay off.

You'll work through the formula step by step, starting with efficiency (the market benchmark of 49.13%, which means you need 2.035 MWh of gas to produce 1 MWh of electricity). Then you'll add carbon costs to calculate the Clean Spark Spread - the margin after accounting for pollution. You'll see where the 0.394 tCO₂/MWh emissions factor comes from (combustion chemistry combined with plant efficiency), and why UK traders must include carbon in every generation decision.

The guide covers practical implementation: converting UK gas prices from pence per therm to £/MWh, working through real calculations with winter 2025 prices, and building a Python function to automate the formula.

You'll understand why the spark spread varies hourly - following a twin-peak pattern (morning and evening spikes when demand surges) while gas prices stay relatively stable throughout the day. This creates shape risk: your plant might be profitable for only 6 hours per day, but if you've hedged a baseload contract, you're committed to 24 hours.

You'll learn how renewables have changed the game. When wind generation floods the market, power prices can drop below the cost of gas, creating deeply negative spreads. You'll see why plants sometimes run at a loss anyway - providing ancillary services to National Grid, avoiding startup costs that can hit £20k-100k, or fulfilling take-or-pay gas contracts. Modern CCGT operators make 48 separate decisions per day (one every 30 minutes), treating the plant as a portfolio of options rather than a simple on/off switch.

The guide also covers how traders use spark spreads in practice: reverse-engineering power price forecasts from gas moves (price discovery), deciding whether to bid plants into the day-ahead auction (asset dispatch), and locking in margins months ahead by selling power futures and buying gas futures simultaneously (hedging).

You'll see the other spread types - Dark Spread for coal, Quark Spread for nuclear, Bark Spread for biomass - and understand why spark spreads dominate UK trading.

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How This Fits the Curriculum

Physical Foundations taught you merit order dispatch and why gas plants are marginal price-setters. Ships and Storage showed you where gas prices come from - the global infrastructure connecting Henry Hub to NBP. This guide combines those inputs: you now understand both sides of the spark spread formula (power prices from merit order, gas prices from global markets) and can calculate the margin that determines which plants run.

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Prerequisites

Complete Physical Foundations and The Global Gas Market guides first. You need to understand merit order dispatch and global gas pricing dynamics.

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Mastery Tip

Build the Python calculator from Section 3, then manually track power and gas prices for one week using free sources (Elexon BMRS for day-ahead power, National Grid for gas). Calculate the spread for morning peak (7-9am) and evening peak (5-7pm) each day. Track which hours show positive spreads versus which hours CCGTs actually dispatched (visible on BMRS). The gap between theory (positive spread = should run) and reality (plants running at losses, or idle during positive spreads) will reveal the physical constraints and commercial strategies that pure formulas miss.

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