A method for the multi-objective optimization of the operation of natural gas pipeline networks considering supply reliability and operation efficiency. (5th December 2019) - Su, Huai; Zio, Enrico; Zhang, Jinjun; Li, Xueyi; Chi, Lixun; Fan, Lin; Zhang, Zongjie
Highlights: A method is developed for operation optimization in gas pipeline networks. A novel method of supply reliability analysis in pipeline networks is developed. A multi-objective optimization model is developed for gas pipeline optimization. The uncertainties of both pipeline systems and demand sides are considered. The model is solved by NSGAⅡ and verified in gas grids with different topologies. Abstract: Reliable gas supply for minimum risk of supply shortage and minimum power demand for low energy cost are two fundamental objectives of natural gas pipeline networks. In this paper, a multi-objective optimization method is developed to trade-off reliability and power demand in the decision process. In the optimization, the steady state behavior of the natural gas pipeline networks is considered, but the uncertainties of the supply conditions and customer consumptions are accounted for. The multi-objective optimization regards finding operational strategies that minimize power demand and risk of gas supply shortage. To quantify the probability of supply interruption in pipeline networks, a novel limit function is introduced based on the mass conservation equation. Then, the risk of interruption is calculated by combining the probability of interruption and its consequences, measured in utility terms. The multi-objective optimization problem is solved by the NSGA-II algorithm and its effectiveness is tested on two typical pipeline networks, i.e., a tree-topology network and a loop-topology network. The results show that the developed optimization model is able to find solutions which effectively compromise the need of minimizing gas supply shortage risk and reducing power demand. Finally, a sensitivity analysis is conducted to analyze the impact of demand uncertainties on the optimization results.