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| A Joint Scheduling and Operation Optimization Model for Multi-Batch Refined Oil Pipeline Network Based on Pressure Constraint Reconstruction |
| Liu Jingguan, Ai Xiaomeng, Fang Jiakun, Cui Shichang, Wang Shengshi |
| State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan 430074 China |
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Abstract Unlike water pipeline networks that transport a single medium, refined oil pipeline networks transport multiple media sequentially. As a result, the pipeline network parameters continuously vary over time, leading to multiple continuous time-varying variable multiplication terms in pressure constraints that exhibit strong nonlinearity. Existing research typically divides the transport of refined oil into two separate decision stages or simplifies the constraints to reduce model nonlinearity. However, most methods suffer from low safety or poor economics. To address these issues, this paper proposes a joint scheduling and operation optimization model for multi-batch refined oil pipeline network based on pressure constraint reconstruction. The model's accuracy is improved through model reconstruction and linear approximation, which accurately reflects the complex transportation conditions of refined oil pipeline networks.
First, according to different approaches to describe fluid motion in fluid mechanics, the construction of refined oil transport models switches from the traditional Eulerian-based network perspective to the Lagrangian-based batch perspective. Without compromising model accuracy, continuous time-varying variables in pressure constraints are reconstructed equivalently as discrete variables to reduce model nonlinearity. Simultaneously, some difficult-to-quantify variables are equivalently reconstructed as new variables that are easy to express and have intuitive physical meanings. Various linearization techniques, such as the big-M method, piecewise linearization and two-dimensional convex combination, are further utilized to eliminate the nonlinearity of the reconstructed model. Within the acceptable range of practical engineering errors, the two-stage joint optimization model is transformed into a tractable mixed integer linear programming model.
The proposed model is applied to a refined oil transportation pipeline network in the southwest region of China. Compared with the pump scheduling of existing models, the electricity cost of the proposed model is only 1.39% higher than the optimal solution. On the other hand, the electricity cost of the existing joint optimization model is 27.95% higher than the optimal solution. These results validate the power cost accuracy of the proposed model. Furthermore, pump station characteristics and pressure loss in the proposed model are closer to the actual values, with maximum approximation errors of only 0.41% and 1.80%, respectively. This validates the model's approximation accuracy. In contrast, the maximum approximation error of existing models reaches 19.74%, making it difficult to reflect the actual pressure conditions of refined oil pipeline networks. This can lead to incorrect pump allocation schemes, which worsens both economic efficiency and operational safety. Simulation results show that the pumping scheduling provided by the existing model could not be used in the implement field.
The simulation analysis leads to the following conclusions: (1) The proposed model has superior approximation accuracy compared to existing two-stage joint optimization models, which reflects the complex conditions of refined oil pipeline networks more accurately. (2) The proposed model can determine more accurate pump allocation schemes compared to existing models, which ensures transportation safety and improves economic efficiency. (3) A larger number of segments can improve the approximation accuracy of the constraints, which further reduces electricity costs. However, this comes at the cost of model solution speed. Therefore, the number of segments should be selected based on the actual model size and computation time.
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Received: 13 January 2023
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2023, Vol. 38 
