Optimal Operation of Gas Transport Pipelines with Detailed MILP and NLP Models

This study proposes a comprehensive strategy to optimize the operation of real-world gas pipeline networks and support decision-making. The goal is to improve environmental sustainability by minimizing the CO2 emissions associated with the energy required for gas compression, accounting for both gas-turbine-driven and electrocompressors. The model incorporates dynamic gas transport equations, detailed compressor performance maps, and control valve operations into a unified framework. To solve the problem, we use a graph-reduction procedure as well as mixed-integer linear and nonlinear programs in an integrated framework. Assuming a cyclic steady state, the model allows for finding the optimal pressure spatial distribution as well as the commitment and loads of the compressors. Applications of the algorithm to the Italian network demonstrate its potential to improve operational decisions, particularly in scenarios where operators have limited expertise or in sector-coupling contexts with the electric grid. Case studies highlight the importance of simultaneously optimizing the compressor loads and the distribution of pressure across the network. When electric compressors are included, the optimizer can further reduce the environmental impact by leveraging the time-varying carbon intensity of the electricity mix.