Abstract
As a promising energy resource, offshore natural gas is primarily used for power generation. The comprehensive offshore gas-to-power system, which includes extraction, treatment, compression, pipeline transmission, and power generation, is extensive and operates within various regulatory, operational, and financial constraints. This complexity offers opportunities to optimize one or more system operations to enhance profitability while fulfilling user demands and environmental considerations. In this research, we present a model-based, computer-aided framework that intuitively connects upstream natural gas operations with downstream power generation and distribution. We develop a multi-period Mixed-Integer Nonlinear Programming (MINLP) model that integrates gas treatment, compression, and long-distance transmission with power generation. The model combines first-principle mechanistic process models with a linepack model that calculates the gas volume storable in long-distance pipelines for transmission. The linepack model facilitates gas storage and withdrawal across different periods to accommodate demand scheduling. We apply this framework using the MINLP model in three scenarios: profit maximization, cost minimization, and supply-demand balancing using linepack. The results demonstrate improved economic performance for offshore natural gas-based power generation in China under varying periodic power demands.