How Well Do Emission Factors Approximate Emission Changes from Electricity System Models?

Area of Science:

• Environmental Science
• Energy Systems Engineering
• Computational Science

Background:

• Estimating carbon emissions from new grid technologies (e.g., electric vehicles, solar panels) is crucial for climate policy.
• Existing marginal and average emission factors yield varied results, complicating method selection.
• Accurate assessment of these factors' performance in real-world grids is challenging due to measurement difficulties.

Purpose of the Study:

• To evaluate the accuracy of different emission factor calculation methods in approximating actual power grid emission changes.
• To compare the performance of marginal and average emission factors under various conditions.

Main Methods:

• Developed and tested multiple forms of marginal and average emission factors.
• Utilized an electricity system dispatch model as a controlled environment for simulating grid operations.
• Compared emission factor estimates against the model's simulated emission changes.

Main Results:

• Average emission factors showed lower accuracy for demand shift emissions.
• Demand-based marginal emission factors also exhibited lower accuracy at hourly resolutions.
• Incremental and thermal marginal emission factors demonstrated high accuracy in reproducing grid emission changes across scenarios.
• Annual time averages provided similar accuracy to finer resolutions for most factors, except demand-based.

Conclusions:

• Incremental and thermal marginal emission factors are more reliable for estimating grid emission changes.
• Average and demand-based marginal factors require careful application, especially for demand-driven changes.
• Annual averages can be a practical alternative for certain emission factor types, simplifying analysis.