Two-stage robust optimization of power cost minimization problem in gunbarrel natural gas networks by approximate dynamic programming
Yize Meng, Ruoran Chen, Tianhu Deng
Abstract
In short-term operation of natural gas network, the impact of demand uncertainty is not negligible. To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas networks. The demands between pipelines and compressor stations are uncertain with a budget parameter, since it is unlikely that all the uncertain demands reach the maximal deviation simultaneously. During solving the two-stage robust model we encounter a bilevel problem which is challenging to solve. We formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the calculation. Numerical results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming algorithm. Numerical results also verify the advantage of robust model compared with deterministic model when facing uncertainties. These findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.