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

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, in particular, the locational marginal carbon emissions (LMCE). We first utilize the differentiable programming paradigm for computing LMCE by treating the optimal power flow (OPF)-based grid dispatch as a differentiable layer. This method provides a rigorous marginal sensitivity for 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 a multiparametric programming (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 in sub-millisecond timescales—orders of magnitude 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.