Algorithm for Transportation Pathways and Patterns Through Pipeline Network: A Case Study in California for the Power Generation Sector

Decarbonization and energy transition in modern energy systems require integrated tools that can analyze complex supply pathways, optimize infrastructure choices, and evaluate policy impacts. This study presents the algorithm for transportation pathways and patterns through pipeline network (ATP3), an open-source framework combining high‐resolution supply‐chain traceability, cost- and emissions‐aware network optimization, and scenario simulation. ATP3 reconstructs real-world flows by matching supply and demand nodes across an entire pipeline network, integrating the entropy weight method (EWM)-based allocation and a minimum-cost flow formulation with scenario‐driven computational simplification. In a California power generation case study, ATP3 accurately identified 154 processing-to-plant supply routes and 134 upstream field linkages. In addition, when using the EWM-based 40% baseline allocation, the model determined that 90.53% of natural gas used for electricity was imported—primarily from Arizona (48.66%), Oregon (27.43%), and Nevada (14.44%)—with Arizona supplying the largest single external volume (1034.51 MMscf·d −1 , here MMscf is million standard cubic feet, and 1 MMscf ≈ 28 317 m 3 ). In‐state production accounted for only 201.37 MMscf·d −1 (9.47%). Meanwhile, EWM allocation reduced transportation costs by 2.76% compared to uniform allocation by favoring geographically proximate sources. These results demonstrate the ability of ATP3 to bridge granular infrastructure mapping with system‐level planning, offering a robust and versatile platform for life‐cycle assessment, infrastructure planning, and policy evaluation across power, transportation, and industrial sectors. The continuously updated resource is available via the GitHub repository.