PGLib-CO2: A Power Grid Library for Real-Time Computation and Optimization of Carbon Emissions

TL;DR

PGLib-CO2 is an open-source power grid library that incorporates generator-level carbon emissions data and introduces advanced computational methods for real-time carbon emission metrics, facilitating sustainable power system research.

Contribution

This work introduces PGLib-CO2, enriching power system test cases with detailed carbon profiling and developing novel differentiable and MPP-based methods for efficient real-time emission computation.

Findings

Differentiable LMCE provides accurate sensitivity analysis.

MPP-based approach accelerates real-time LMCE computation.

Numerical tests show improved speed and accuracy over traditional methods.

Abstract

Achieving a sustainable electricity infrastructure requires the explicit integration of carbon emissions into power system modeling and optimization. However, existing open-source test cases for power system research lack generator-level carbon profiling, preventing the benchmark of carbon-aware operational strategies. To address this gap, this work introduces PGLib-CO2, an open-source extension to the PGLib-OPF test case library. The proposed PGLib-CO2 enriches standard grid test cases with CO2 and CO2-equivalent emission intensity factors to achieve realistic, generator-level carbon profiling with an expanded list of fuel types. Using the standardized data, PGLib-CO2 allows us to enhance the algorithms for computing key carbon emission metrics. We first utilize the differentiable programming paradigm for computing LMCE by treating the OPF-based grid dispatch as a differentiable layer. This method provides a rigorous marginal sensitivity for general convex cost functions, eliminating the need of using a small incremental change in numerical perturbation. Moreover, to accelerate the real-time LMCE computation, we develop an MPP-based approach that shifts the optimization burden to offline phase of identifying the OPF critical regions. Since each critical region is characterized by a pre-computed affine dispatch function, the online phase reduces to identifying the region followed by efficiently evaluating the region-specific LMCE values. Numerical evaluations on IEEE test systems demonstrate that the differentiable LMCE computation attains the precise sensitivity information, and the MPP-based approach retrieves the LMCE signals faster than the direct optimization approach. By bridging high-fidelity data with advanced parametric computation, PGLib-CO2 provides a reproducible and computationally efficient foundation for future research in sustainable power system operations.