A Dynamic Programming Approach to Power Consumption Minimization in Gunbarrel Natural Gas Networks with Nonidentical Compressor Units

Tianhu Deng (), Yong Liang (), Shixuan Zhang (), Jingze Ren () and Shuyi Zheng ()
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Tianhu Deng: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;
Yong Liang: Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing 100084, China;
Shixuan Zhang: Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing 100084, China;
Jingze Ren: PBC School of Finance, Tsinghua University, Beijing 100083, China;
Shuyi Zheng: Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

INFORMS Journal on Computing, 2019, vol. 31, issue 3, 593–611

Abstract: Inspired by the widespread and increasing usage of natural gas, we study the power consumption minimization problem associated with natural gas pipeline transmission in gunbarrel networks with nonidentical compressors. To accurately and flexibly model both gas flow dynamics and compressor working domains, we formulate the problem as a dynamic programming problem. Then we propose an approximate solution approach based on state dimension reduction. We analyze the problem properties and characterize conditions under which optimality is not compromised by the proposed solution approach. Next, we conduct numerical experiments using two data sets based on real networks in China and a data set from the public library GasLib. Numerical results demonstrate that the proposed solution approach significantly reduces computation time without compromising optimality in most cases. Specifically, the proposed solution approach obtains optimal solutions more than a 100 times faster than the exhaustive search when gas pressures are discretized at 0.01 MPa. Further, the optimality gaps do not exceed 0.4%.

Keywords: natural gas; gunbarrel natural gas networks; dynamic programming; dimension reduction (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:31:y:2019:i:3:p:593-611

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