Multi-period operational optimization of natural gas treating, blending, compressing, long-distance transmission, and supply network

Abstract Natural gas has emerged as a promising energy source due to its high quality and massive reserves. Operational optimization of natural gas networks including gas treating, blending, compressing, long-distance transmission, supplying and power generation are quite challenging since it contains highly nonlinear processes, complex thermodynamics, and a set of partial differential equations (PDE). In this article, we first introduce a multi-period optimization framework for the large scale natural gas network. Then, we develop a nonconvex mixed-integer nonlinear programming (MINLP) model aiming for better economic performance for the network. Various levels of market price and demand for electricity are considered. The MINLP framework integrates the models of CO2 removing processes, compressors, pipeline transmission and combined-cycle gas and steam turbines. The optimization framework and mathematical model are applied to a case study extracted from an industrial park at South China. Economy and energy performances are investigated and an optimum operating strategy is drawn out. The practicability of the optimization framework and mathematical model makes it more possible to apply to real world problems.