Energy Basics – Ambient Temperature Impact on Gas Turbine Performance

This post is one of many in a series explaining some energy insights for a technical and non-technical audience.  Previous posts include topics such as the First & Second Law of Thermodynamics or the difference between kW and kWh.  

Gas turbine power output increases when it is cold and decreases when it is hot.

Understanding why will impress both your technical and commercial colleagues!  The energy engineer must also take account of this relationship in any modeling work of gas turbine plants.

To explain the reason for the variance you need to link together a few insights:

  1. A gas turbine is a fixed volume machine.  You can only squeeze a fixed volume of air through the compressor and turbine.
  2. The density of air increases when it is cold.  Colder air means more mass of air in the same amount of volume.
  3. The amount of power generated in the turbine increases with a higher mass of air flowing through the turbine.

So colder air means we get a higher mass flow rate of air in the gas turbine leading to more power generated.

When it gets hot the opposite effect occurs.  Power output decreases due to less mass flowing through the turbine.

Ambient temperature also has an affect on the compressor.  Colder air improves compressor efficiency.  This means the compressor consumes less power, leading to a more efficiency gas turbine.

Effect of ambient temperature on gas turbine performance. Source – Rahman et. al (2011) – Thermodynamic performance analysis of gas-turbine power-plant

De Sa & Zubaidy (2011) proposed an empirical relationship for a 265 MW gas turbine:

For every K rise in ambient temperature above ISO conditions the Gas Turbine loses 0.1% in terms of thermal efficiency and 1.47 MW of its Gross (useful) Power Output.

This is a problem in hot climates where peak demand for electricity (for space cooling) will occur at the same time as poor gas turbine performance.

When an energy engineer models a gas turbine system she needs to be careful to account for this variation.  Most ideal is using a years worth of ambient temperature data on an hourly basis.

A simple linear regression between the variable (such as gas turbine output or efficiency) and ambient temperature will account for the variation for each hour.  Multiple linear regression can be used if both ambient temperature and gas turbine load are varying.

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