Carbon Emission Flow Tracing: Fast Algorithm and California Grid Study

TL;DR

This paper introduces a fast, graph-based algorithm for precisely tracing how carbon emissions from individual power generators are distributed across the grid, demonstrated through a detailed California grid case study.

Contribution

It presents a novel, efficient method for exact quantification of nodal emission rates applicable to AC and DC power flows, validated on a large-scale realistic grid model.

Findings

Accurately estimates power flow, generation mixes, and emissions in California.

Reveals spatial and temporal emission patterns across the grid.

Provides an open-source tool for future grid emission analysis.

Abstract

Power systems decarbonization are at the focal point of the clean energy transition. While system operators and utility companies increasingly publicize system-level carbon emission information, it remains unclear how emissions from individual generators are transported through the grid and how they impact electricity users at specific locations. This paper presents a novel and computationally efficient approach for exact quantification of nodal average and marginal carbon emission rates, applicable to both AC and DC optimal power flow problems. The approach leverages graph-based topological sorting and directed cycle removal techniques, applied to directed graphs formed by generation dispatch and optimal power flow solutions. Our proposed algorithm efficiently identifies each generator's contribution to each node, capturing how emissions are spatially distributed under varying system conditions. To validate its effectiveness and reveal locational and temporal emission patterns in the real world, we simulate the 8,870-bus realistic California grid using actual CAISO data and the CATS model. Based on year long hourly data on nodal loads and renewable generation, obtained or estimated from CAISO public data, our method accurately estimates power flow conditions, generation mixes, and systemwide emissions, and delivers fine grained spatiotemporal emission analysis for every California county. Both our algorithm and the California study are open-sourced, providing a foundation for future research on grid emissions, planning, operations, and energy policy.