Petroleum Science >2022,??lssue 1:??- DOI: https://doi.org/10.1016/j.petsci.2021.10.005
Two-stage robust power cost minimization in a natural gas compressor station Open?Access
文章信息
作者：Yize Meng, Ruoran Chen, Keren Zhang, Tianhu Deng,
作者單位：
投稿時(shí)間：
引用方式：Yize Meng, Ruoran Chen, Keren Zhang, Tianhu Deng, Two-stage robust power cost minimization in a natural gas compressor station, Petroleum Science, Volume 19, Issue 1, 2022, Pages 409-428, https://doi.org/10.1016/j.petsci.2021.10.005.
文章摘要
Abstract
Optimal operation of a compressor station is important since it accounts for 25% to 50% of a company's total operating budget. In short-term management of a compressor station, handling demand uncertainty is important yet challenging. Previous studies either require precise information about the distribution of uncertain parameters or greatly simplify the compressor model. We build a two-stage robust optimization framework of power cost minimization in a natural gas compressor station with non-identical compressors. In the first stage, decision variables are the ON/OFF state of each compressor and discharge pressure. The worst-case cost of the second stage is incorporated in the first stage. First-stage decision variables feasibility is discussed and proper feasibility cuts are also proposed for the first stage. We employ a piece-wise approximation and propose accelerate methods. Our numerical results highlight two advantages of robust approach when managing uncertainty in practical settings: (1) the feasibility of first-stage decision can be increased by up to 45%, and (2) the worst-case cost can be reduced by up to 25% compared with stochastic programming models. Furthermore, our numerical experiments show that the designed accelerate algorithm has time improvements of 1518.9% on average (3785.9% at maximum).
Optimal operation of a compressor station is important since it accounts for 25% to 50% of a company's total operating budget. In short-term management of a compressor station, handling demand uncertainty is important yet challenging. Previous studies either require precise information about the distribution of uncertain parameters or greatly simplify the compressor model. We build a two-stage robust optimization framework of power cost minimization in a natural gas compressor station with non-identical compressors. In the first stage, decision variables are the ON/OFF state of each compressor and discharge pressure. The worst-case cost of the second stage is incorporated in the first stage. First-stage decision variables feasibility is discussed and proper feasibility cuts are also proposed for the first stage. We employ a piece-wise approximation and propose accelerate methods. Our numerical results highlight two advantages of robust approach when managing uncertainty in practical settings: (1) the feasibility of first-stage decision can be increased by up to 45%, and (2) the worst-case cost can be reduced by up to 25% compared with stochastic programming models. Furthermore, our numerical experiments show that the designed accelerate algorithm has time improvements of 1518.9% on average (3785.9% at maximum).
關(guān)鍵詞
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Natural gas; Single station power minimization; Nonconvex robust optimization; C&CG algorithm