A simplified operation optimization method for pipeline network compressor station merger based on response surface methodology

Abstract

The present invention discloses a simplified operation optimization method for merging compressor stations in a pipeline network based on a response surface method, comprising the following steps: constructing a global pipeline network topology structure and identifying local pipe sections that can be merged with multiple compressor stations; collecting basic pipeline network operation data and determining the operating condition range of the local pipe sections; performing preliminary simplified merging processing on the local pipe sections that can be merged with multiple compressor stations, and adopting a response surface experimental design and analysis method to construct a set of optimal operating solutions for multiple operating conditions and a response model for the local pipe sections; performing merging and simplifying processing on the global pipeline network topology structure and the optimization model to solve the optimal operating solution for the global pipeline network; adopting an approximate operating condition selection method to select the optimal operating solution for the local pipe section under the current operating condition; and quickly solving the optimal operating solution based on the latest operating condition. The present invention can effectively improve the efficiency of pipeline network operation optimization solution, and by combining the response surface experimental design and analysis method to fully cover the operating condition range, it provides fast and reliable decision support for the real-time operation scheduling of the pipeline network.

Description

Technical Field

[0001] The present invention relates to the field of natural gas pipeline network operation optimization, and in particular to a pipeline network compressor station merging and simplified operation optimization method based on response surface methodology. Background Art

[0002] Natural gas pipeline networks are crucial infrastructure for energy transmission. Their operation optimization plays a key role in ensuring energy supply security and improving energy efficiency. It is also crucial for reducing pipeline network operating costs and achieving low-carbon environmental goals. With the continued growth in natural gas demand and the expansion of pipeline networks, the coordinated operation and control of the numerous compressor stations along these lines has become a core research issue. Compressor stations are not only critical for ensuring stable natural gas transmission but also a major source of energy consumption and carbon emissions during pipeline network operation. The rationality of their operation plans directly impacts the economic efficiency and safety of the pipeline system. However, with the expansion of pipeline networks and their increasing complexity, the dimensionality and complexity of pipeline network operation optimization problems have significantly increased. Furthermore, due to multiple factors, such as fluctuating gas source production and shifting user demand, the operating conditions of natural gas pipeline networks are dynamic and volatile, requiring rapid, real-time adjustments to operational plans to adapt to these changing conditions. This places higher demands on the efficiency and timeliness of pipeline network operation optimization methods. There is an urgent need to develop efficient and reliable optimization methods to address the complex and volatile operating environment of natural gas pipeline networks.

[0003] Currently, a large number of scholars have conducted relevant research in the field of natural gas pipeline network operation optimization technology. By establishing optimization models and combining traditional optimization algorithms such as dynamic programming with heuristic random optimization algorithms such as genetic algorithms for solution, a variety of pipeline network operation optimization methods have been developed. Related applications have shown that these methods can optimize pipeline network operation plans to a certain extent. They are mainly suitable for small-scale natural gas pipeline networks with simple structures and low solution dimensions. They can quickly solve and effectively reduce pipeline network operation costs. However, when applied to large-scale natural gas pipeline networks with increasing complexity and scale, existing optimization methods have difficulty finding feasible and optimal operation plans in the high-dimensional optimization space. The solution time is too long, making existing methods unable to meet the application requirements of actual pipeline network operating conditions and the timeliness requirements of operation scheduling. As a result, they have not yet been effectively applied in the actual operation scheduling of pipeline networks.

[0004] To this end, a pipeline network compressor station merging and simplified operation optimization method based on the response surface methodology is established. By effectively identifying the local pipe sections that can be merged with multiple compressor stations in the natural gas pipeline network, combined with the response surface experimental design and analysis method, a set of optimal operation schemes for multiple operating conditions of the local pipe sections is constructed, and a response model between the operating parameters of the local pipe sections and the operating cost is fitted. Then, the global pipeline network topology structure and optimization model are merged and simplified respectively, thereby achieving dimensionality reduction and simplification of the large-scale natural gas pipeline network operation optimization problem. It can significantly reduce the optimization space and computational burden of the large-scale pipeline network operation optimization problem, greatly improve the solution speed, effectively support the rapid acquisition of the optimal operation plan for real-time operating conditions, and meet the timeliness requirements of pipeline network operation scheduling. Summary of the Invention

[0005] In response to the above problems, the purpose of the present invention is to provide a simplified operation optimization method for pipeline compressor station mergers based on the response surface methodology, which can quickly and reliably solve the optimal operation plan with the lowest real-time operating cost by simplifying the dimensionality reduction of the global pipeline operation optimization problem.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing the operation of pipe network compressor stations by merging and simplifying them based on response surface methodology, which comprises the following steps:

[0007] Step 1: Collect basic data of the pipe network structure, construct the global pipe network topology, and use the simplified and merged compressor station identification method to identify the global pipe network topology and obtain the mergeable local pipe sections of multiple compressor stations;

[0008] Step 2: Collect basic operating data of the pipeline network and determine the operating range of the local pipeline sections that can be merged at each multi-compressor station;

[0009] Step 3: Traverse and analyze the mergeable local pipeline sections of each multi-compressor station. If there are branch pipelines or more than two sub-transmission stations or upload stations along the local pipeline section, a preliminary simplified merging method is used based on the station location distribution and operating condition range to simplify the branch pipelines and approximately merge multiple sub-transmission stations or upload stations to obtain the mergeable local pipeline sections of the multi-compressor stations after preliminary simplification and merging.

[0010] Step 4: For each multi-compressor station with a locally merged pipe section, a corresponding factor level table is established based on the operating parameters and ranges. The Box-Behnken method is then used to design a response surface experiment plan that includes multiple groups of different operating parameter combinations.

[0011] Step 5: Using the pipeline network operation cost minimization optimization model, for each combined local pipe section at multiple compressor stations, the optimal operation plan and minimum operation cost for each combination of operating condition parameters in the response surface experiment scheme are solved. A set of optimal operation plans for multiple operating conditions of the local pipe section is constructed, and a response model between the operating condition parameters of the local pipe section and the operation cost is fitted.

[0012] Step 6: Use the pipeline network operation cost minimization optimization model to solve the global pipeline network operation optimization problem. Using the response model established in Step 5, merge and simplify the local pipe sections that can be merged in each multi-compressor station in the global pipeline network topology and optimization model. Combined with the optimization algorithm, the optimal operation plan for the global pipeline network is obtained.

[0013] Step 7: Based on the optimal global network operation plan in step 6, determine the current operating condition parameters of the local pipe section that can be merged at each multi-compressor station, and use the approximate operating condition selection method to select the most approximate plan from the set of optimal operating plans for multiple operating conditions of the local pipe section as the optimal operating plan for the current local pipe section;

[0014] Step 8: After completing steps 1 to 5, when the operating conditions of the pipeline network change, repeat steps 6 and 7 based on the latest operating condition parameters to quickly obtain the optimal operating plan with the lowest operating cost under the real-time conditions.

[0015] Furthermore, in step 1, the compressor station identification method may include the following steps:

[0016] S11: Identify pipeline intersection nodes based on the global pipeline network topology, pipeline flow direction, and compressor station distribution. If there is a compressor station along one or more pipelines connected to the intersection node, split the global pipeline network topology at the pipeline intersection node, traverse all pipeline intersection nodes, and obtain a local pipeline topology.

[0017] S12: Traverse and analyze the local pipeline topology structure obtained in S11. If the local pipeline topology structure contains two or more compressor stations, the node of the first compressor station upstream of the local pipeline is used as the starting point and the node of the last compressor station downstream of the local pipeline is used as the end point to further delineate the local pipeline section that can be merged with multiple compressor stations.

[0018] Furthermore, in step 2, the operating condition ranges of the local pipeline sections that can be merged by multiple compressor stations include: the inlet pressure operating range of the starting compressor station, the outlet flow operating range and the outlet pressure operating range of the terminal compressor station, as well as the download volume operating range of the distribution stations along the local pipeline section and the upload volume operating range of the upload stations.

[0019] Furthermore, in step 3, the preliminary simplified merging processing method includes the following steps:

[0020] S31: If there is a branch pipeline along the local pipe section, the download capacity operating ranges of the branch transmission stations and the upload capacity operating ranges of the upload stations along the branch pipeline are accumulated respectively. If the total download capacity is greater, all stations along the branch pipeline are merged into one branch transmission station in the local pipe section topology. Conversely, if the total upload capacity is greater, they are merged into one upload station. The merged station is set at the connection between the branch pipeline and the local pipe section. The absolute value of the difference between the total download capacity and the total upload capacity is used as the download capacity operating range or the upload capacity operating range of the merged station.

[0021] S32: If there are two or more sub-transmission stations between any two compressor stations in the local pipeline section, then, based on the load capacity operating range, the remaining sub-transmission stations between the two compressor stations are merged with the sub-transmission station with the largest load capacity operating range in the local pipeline section topology, and the load capacity operating ranges are accumulated;

[0022] S33: If there are two or more loading stations between any two compressor stations in the local pipe section, then, based on the loading capacity operating range, merge the remaining loading stations between the two compressor stations with the loading station with the largest loading capacity operating range in the local pipe section topology, and accumulate the loading capacity operating ranges;

[0023] S34: If there are still three or more sub-stations along the local pipeline section after the S31-S33 merging process, the two sub-stations with the largest download capacity operating ranges are retained in the local pipeline section topology. The remaining sub-stations are merged with the nearest retained sub-station, and the download capacity operating ranges are accumulated.

[0024] S35: If there are still three or more upload stations along the local pipe section after the S31-S33 merging process, the two upload stations with the largest upload volume operating range will be retained in the local pipe section topology structure, and the remaining upload stations will be merged with the nearest retained upload station, and the upload volume operating range will be accumulated.

[0025] Furthermore, in step 4, the target response value of the response surface experiment scheme is the pipeline segment operating cost. The experimental factors include the inlet pressure and inlet flow rate of the starting compressor station of the mergeable local pipeline segment, and the outlet pressure of the terminal compressor station. Depending on the specific conditions of the mergeable local pipeline segment, the experimental factors may also include the download volume of 0 to 2 distribution stations and the upload volume of 0 to 2 upload stations. The level range of the experimental factors is determined by the operating condition range.

[0026] Furthermore, in step 6, the merging and simplification process is specifically to replace the mergeable local pipe sections of multiple compressor stations with one merged compressor station in the global pipe network topology structure, and further process the optimization model, including the following steps:

[0027] S61: In the objective function of the optimization model, the operating cost calculation formula of the multiple compressor stations involved in the local pipeline section is replaced with the response model between the operating parameters and the operating cost established in step 5;

[0028] S62: In the constraints of the optimization model, all constraints involved in the local pipe section are replaced with the operating constraints of a compressor station, including: compressor station flow constraint, compressor station inlet and outlet pressure constraints, and node flow balance constraint;

[0029] S63: Among the decision variables of the optimization model, the outlet pressure decision variables of the multiple compressor stations involved in the local pipeline section are merged and replaced with the outlet pressure decision variable of one compressor station.

[0030] Furthermore, in step 7, the method for selecting the approximate working condition includes the following steps:

[0031] S71: Use the operating condition parameters X of each scheme label in the optimal operating scheme of the local pipe section multi-condition set label and the current operating condition parameter X current , calculate the Euclidean distance Dist considering the normalization of data magnitude as the distance evaluation index, as shown in formula (1):

[0032]

[0033] Where: Dist is the distance evaluation index, N local is the number of operating parameters of the local pipe section, is the i-th parameter in the operating condition parameters of the scheme label, is the i-th parameter of the current operating condition parameter;

[0034] S72: According to the distance evaluation index of each scheme in the multi-operating condition optimal operation scheme set calculated in S71, the scheme with the smallest distance evaluation index is selected as the most approximate scheme for the current operating condition.

[0035] Due to the adoption of the above technical scheme, the present invention has the following advantages: 1. The present invention identifies the local pipe sections that can be merged by multiple compressor stations, constructs a response model between the operating parameters of the local pipe sections and the operating costs, and then merges and simplifies the global pipeline network topology structure and the optimization model respectively, thereby achieving effective dimensionality reduction and simplification of the large-scale natural gas pipeline network operation optimization problem, which can significantly reduce the optimization space and computational burden of the large-scale pipeline network operation optimization problem, greatly improve the solution speed, effectively support the rapid acquisition of the best operation plan for real-time operating conditions, and meet the timeliness requirements of pipeline network operation scheduling; 2. The present invention summarizes the historical operation data of the pipeline network, clarifies the operating condition range of the local pipe sections that can be merged by multiple compressor stations, combines the response surface experimental design and analysis method, and establishes a local pipe that can effectively cover the operating condition range. The present invention proposes a set of optimal operating solutions for multiple operating conditions of a local pipe section and proposes an approximate operating condition evaluation method, so as to quickly and reliably obtain the optimal operating solution for a local pipe section under different operating conditions; 3. The present invention establishes a method for identifying compressor stations that can be simplified and merged, which can effectively identify and reasonably split the complex topological structure of the pipeline network, and further delineates the local pipe sections that can be merged with multiple compressor stations based on the location distribution of the compressor stations, providing a reliable basis for subsequent merging and simplifying processing, and ensuring the feasibility of ultimately solving the optimal operating solution; 4. The present invention establishes a preliminary simplified merging processing method, which can reasonably simplify and merge the branch pipelines, sub-transmission stations and uploading stations of the local pipe section according to the station location distribution and the operating condition range, effectively reducing the number of experimental factors in the response surface design experiment and improving the operability of the response surface experimental scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0037] Figure 1 A flow chart of a method for optimizing the combined and simplified operation of compressor stations in a pipeline network based on the response surface methodology provided by an embodiment of the present invention;

[0038] Figure 2 Schematic diagram of the global pipe network topology structure of a pipe network provided by an embodiment of the present invention

[0039] Figure 3 Schematic diagram of identification results of mergeable local pipe sections of multiple compressor stations in a pipe network according to an embodiment of the present invention

[0040] Figure 4This is a schematic diagram of the results of merging and simplifying local pipe sections in a pipeline network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The natural gas pipeline network is composed of various types of stations and pipelines such as loading stations, distribution stations and compressor stations, and has a complex topological connection structure. This characteristic causes the large-scale natural gas pipeline network operation optimization problem to have not only a high solution dimension, but also non-convex and nonlinear characteristics, which makes it difficult to quickly and reliably obtain feasible and optimal operation plans, unable to adapt to the dynamic changes in pipeline network operating conditions, and difficult to meet the timeliness requirements of pipeline network operation scheduling. To this end, the present invention proposes a pipeline compressor station merged and simplified operation optimization method based on the response surface method. By combining the response surface experimental design and analysis technology, the large-scale pipeline network operation optimization problem is reduced in dimension and simplified, significantly reducing the optimization space and computational burden of the large-scale pipeline network operation optimization problem, greatly improving the solution speed, effectively supporting the rapid acquisition of the optimal operation plan for real-time operating conditions, and meeting the timeliness requirements of pipeline network operation scheduling.

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] The present invention provides a simplified operation optimization method for merging compressor stations in a pipeline network based on response surface methodology, such as Figure 1 As shown, the following steps are included:

[0044] Step 1: Collect basic data of the pipe network structure, construct the global pipe network topology, and use the simplified and mergeable compressor station identification method to identify the global pipe network topology and obtain the mergeable local pipe sections of multiple compressor stations;

[0045] Specifically, the basic data of the pipeline network structure include: pipeline number, pipeline flow direction, pipeline design transmission capacity, pipeline starting node and ending node, node number, and node type.

[0046] In specific implementation, considering that the operating conditions of some compressor stations in a local section of the natural gas pipeline network are only related to the operating conditions of the upstream and downstream compressor stations in this local section, the optimal operating plan for some compressor stations in the local section can be solved in advance under multiple operating conditions, thereby reducing the optimization space dimension of the global pipeline network optimization problem. Therefore, through identification and splitting, the simplified merging compressor station identification method can select local sections that can be merged and simplified from the complex topological structure of a large-scale pipeline network, laying the foundation for achieving dimensionality reduction and simplification of the global pipeline network operation optimization problem, including the following steps:

[0047] S11: Identify pipeline intersection nodes based on the global pipeline network topology, pipeline flow direction, and compressor station distribution. If there is a compressor station along one or more pipelines connected to the intersection node, split the global pipeline network topology at the pipeline intersection node, traverse all pipeline intersection nodes, and obtain a local pipeline topology.

[0048] S12: Traverse and analyze the local pipeline topology structure obtained in S11. If the local pipeline topology structure contains two or more compressor stations, the node of the first compressor station upstream of the local pipeline is used as the starting point and the node of the last compressor station downstream of the local pipeline is used as the end point to further delineate the local pipeline section that can be merged with multiple compressor stations.

[0049] Step 2: Collect basic operating data of the pipeline network and determine the operating range of the local pipeline sections that can be merged at each multi-compressor station;

[0050] Specifically, the basic pipeline operation data includes: pipeline length, pipeline diameter, pipeline roughness, pipeline design maximum operating pressure, allowable pressure range for distribution station downloads, allowable flow range for compressor stations, allowable pressure range for inlet and outlet of compressor stations, as well as historical operating data of pressure and flow at each node and pipeline in the pipeline network.

[0051] Specifically, the operating condition ranges of the local pipeline sections that can be merged by multiple compressor stations include: the inlet pressure range of the starting compressor station, the inlet flow range of the starting compressor station and the outlet pressure range of the terminal compressor station, as well as the download volume range of the distribution stations along the pipeline section and the upload volume range of the upload stations.

[0052] Step 3: Traverse and analyze the mergeable local pipeline sections of each multi-compressor station. If there are branch pipelines or more than two sub-transmission stations or upload stations along the local pipeline section, a preliminary simplified merging method is used based on the station location distribution and operating condition range to simplify the branch pipelines and approximately merge multiple sub-transmission stations or upload stations to obtain the mergeable local pipeline sections of the multi-compressor stations after preliminary simplification and merging.

[0053] In practice, since the optimization of natural gas pipeline network operations primarily targets compressor stations, a preliminary simplified merging method can rationally simplify and merge branch pipelines, distribution stations, and loading stations within a local section of the pipeline based on their location distribution and operating conditions. This effectively reduces the number of experimental factors in the response surface design experiment, and includes the following steps:

[0054] S31: If there is a branch pipeline along the local pipe section, the download capacity operating ranges of the branch transmission stations and the upload capacity operating ranges of the upload stations along the branch pipeline are accumulated respectively. If the total download capacity is greater, all stations along the branch pipeline are merged into one branch transmission station in the local pipe section topology. Conversely, if the total upload capacity is greater, they are merged into one upload station. The merged station is set at the connection between the branch pipeline and the local pipe section. The absolute value of the difference between the total download capacity and the total upload capacity is used as the download capacity operating range or the upload capacity operating range of the merged station.

[0055] S32: If there are two or more sub-transmission stations between any two compressor stations in the local pipeline section, then, based on the load capacity operating range, the remaining sub-transmission stations between the two compressor stations are merged with the sub-transmission station with the largest load capacity operating range in the local pipeline section topology, and the load capacity operating ranges are accumulated;

[0056] S33: If there are two or more loading stations between any two compressor stations in the local pipe section, then, based on the loading capacity operating range, merge the remaining loading stations between the two compressor stations with the loading station with the largest loading capacity operating range in the local pipe section topology, and accumulate the loading capacity operating ranges;

[0057] S34: If there are still three or more sub-stations along the local pipeline section after the S31-S33 merging process, the two sub-stations with the largest download capacity operating ranges are retained in the local pipeline section topology. The remaining sub-stations are merged with the nearest retained sub-station, and the download capacity operating ranges are accumulated.

[0058] S35: If there are still three or more upload stations along the local pipe section after the S31-S33 merging process, the two upload stations with the largest upload volume operating range will be retained in the local pipe section topology structure, and the remaining upload stations will be merged with the nearest retained upload station, and the upload volume operating range will be accumulated.

[0059] Step 4: For each multi-compressor station with a locally merged pipe section, a corresponding factor level table is established based on the operating parameters and ranges. The Box-Behnken method is then used to design a response surface experiment plan that includes multiple groups of different operating parameter combinations.

[0060] In specific implementation, the target response value of the response surface experiment scheme is the operating cost of the local pipeline segment. Experimental factors include the inlet pressure of the starting compressor station and the outlet pressure and outlet flow rate of the terminal compressor station in the local pipeline segment where multiple compressor stations can be combined. Depending on the specific situation of the local pipeline segment where multiple compressor stations can be combined, experimental factors may also include the download volume of 0 to 2 distribution stations and the upload volume of 0 to 2 upload stations. Furthermore, the range of experimental factor levels is determined by the operating conditions.

[0061] Specifically, the parameters in the operating condition combination include experimental factors involved in the mergeable local pipe section, specifically, a value combination of the experimental factors within the operating condition range of the mergeable local pipe section.

[0062] Step 5: Using the pipeline network operation cost minimization optimization model, for each combined local pipe section at multiple compressor stations, the optimal operation plan and minimum operation cost for each combination of operating condition parameters in the response surface experiment scheme are solved. A set of optimal operation plans for multiple operating conditions of the local pipe section is constructed, and a response model between the operating condition parameters of the local pipe section and the operation cost is fitted.

[0063] In specific implementation, the pipeline network operation cost minimization optimization model takes minimizing the sum of the operating costs of the compressor stations along the pipeline as the optimization goal, comprehensively considering the pressure and flow constraints of the stations along the pipeline, as well as the operation constraints of the pipeline itself, to solve the compressor station outlet pressure combination scheme with the lowest operating cost. The pipeline network operation cost minimization optimization model includes the pipeline network operation cost minimization objective function, constraints and decision variables:

[0064] S51: The objective function for minimizing the operating cost of the pipeline network is to minimize the sum of the operating costs of the compressor stations along the pipeline, as shown in formula (2):

[0065]

[0066] Where: F is the total operating cost of the pipeline, i is the i-th compressor station along the pipeline, N c is the number of compressor stations along the pipeline, and is the operating characteristic parameter of the i-th compressor station, is the inlet pressure of the i-th compressor station, is the inlet flow of the i-th compressor station, is the outlet pressure of the i-th compressor station, η i is the operating efficiency of the i-th compressor station, pr i energy is the unit energy consumption economic cost of the i-th compressor station;

[0067] S52: The constraints include: pipeline pressure constraint, pipeline gas flow rate constraint, pipeline hydraulic pressure drop constraint, upload station upload pressure constraint, distribution station download pressure constraint, compressor station flow constraint, compressor station inlet and outlet pressure constraints, and node flow balance constraint;

[0068] Specifically, the pipeline pressure constraint represents the starting point of the pipeline and endpoint pressure Less than the maximum design pressure of the pipeline As shown in formula (3):

[0069]

[0070] Specifically, the pipeline gas flow rate constraint represents the gas flow rate in the pipeline Less than the maximum erosion velocity As shown in formula (4):

[0071]

[0072] Specifically, the pipeline hydraulic pressure drop constraint indicates that the pressure at both ends of the pipeline node should satisfy the hydraulic pressure drop equation, as shown in formula (5):

[0073]

[0074] Where: is the mass flow rate of pipe (i, j), kg / s, and are the starting and ending pressures of pipeline (i, j), Pa, D (i,j) is the inner diameter of the pipe (i, j), m, λ (i,j) is the hydraulic friction coefficient of pipeline (i, j), Z is the natural gas compression factor; R is the natural gas constant, T (i,j) is the average temperature of natural gas in pipeline (i, j), K, L (i,j) is the length of pipe (i, j), m.

[0075] The present invention uses the Colebrook-White formula to calculate the friction coefficient, which has the advantage of high accuracy, as shown in formula (6):

[0076]

[0077] Where: ε (i,j) is the absolute roughness of the pipe (i, j), m, and Re is the Reynolds number of the fluid in the pipe (i, j).

[0078] Specifically, the upload pressure constraint of the upload station characterizes the upload pressure of the upload station. Meet the maximum loading pressure and minimum load pressure The restriction is shown in formula (7):

[0079]

[0080] Specifically, the pressure constraint of the sub-transmission station represents the pressure of the sub-transmission station. Meet the maximum loading pressure and minimum load pressure The restriction is shown in formula (8):

[0081]

[0082] Specifically, the compressor station flow constraint characterizes the flow through the compressor station Satisfy the processing capacity constraints of the equipment within the station, as shown in formula (9):

[0083]

[0084] Specifically, the compressor station inlet and outlet pressure constraints characterize the inlet Exit The pressure meets the operating pressure limit of the compressor, as shown in formula (10):

[0085]

[0086] Specifically, the node flow balance constraint indicates that according to the law of conservation of mass, the inflow of any node should be equal to the outflow, as shown in Equation (11):

[0087]

[0088] Where: is the absolute volume flow rate between the i-th node and the j-th element, m 3 / s,α (i,j) To represent the flow direction between the i-th node and the j-th element, if the flow is from the j-th element to the i-th node, it is -1, if the flow is from the i-th node to the j-th element, it is 1, U i is the set of components connected to the i-th node.

[0089] S53: The decision variables include: the outlet pressure of the i-th compressor station along the pipeline As shown in formula (12):

[0090]

[0091] In specific implementation, the above-mentioned pipeline operation cost minimization optimization model is adopted, combined with heuristic optimization algorithms such as genetic algorithm or particle swarm algorithm. It is possible to establish multiple optimization models for each multi-compressor station that can merge local pipe sections, and solve the optimal operation plan under different operating conditions.

[0092] Specifically, the optimal operation plan set for multiple operating conditions of a local pipe section is a set of optimal operation plans obtained by using the operating condition parameters as labels and adopting the pipeline network operation cost minimization optimization model under the operating condition parameters.

[0093] Step 6: Use the pipeline operation cost minimization optimization model to solve the global pipeline network operation optimization problem. Combined with the response model established in step 5, the local pipe sections that can be combined in each multi-compressor station are merged and simplified in the global pipeline network topology and optimization model. The optimization algorithm is used to solve and obtain the optimal operation plan for the global pipeline network.

[0094] In specific implementation, the merging and simplification process is to replace the mergeable local pipe sections of multiple compressor stations with a merged compressor station in the global pipe network topology structure, and further simplify the optimization model to improve the solution efficiency, including the following steps:

[0095] S61: In the objective function of the optimization model, the operating cost calculation formula of the multiple compressor stations involved in the local pipeline section is replaced with the response model between the operating parameters and the operating cost established in step 5;

[0096] S62: In the constraints of the optimization model, all constraints involved in the local pipe section are replaced with the operating constraints of a compressor station, including: compressor station flow constraint, compressor station inlet and outlet pressure constraints, and node flow balance constraint;

[0097] S63: Among the decision variables of the optimization model, the outlet pressure decision variables of the multiple compressor stations involved in the local pipeline section are merged and replaced with the outlet pressure decision variable of one compressor station.

[0098] Step 7: Based on the optimal global network operation plan in step 6, determine the current operating condition parameters of the local pipe section that can be merged at each multi-compressor station, and use the approximate operating condition selection method to select the most approximate plan from the set of optimal operating plans for multiple operating conditions of the local pipe section as the optimal operating plan for the current local pipe section;

[0099] In specific implementation, the approximate operating condition selection method can compare the operating condition parameters of the current local pipe section with the labels of each scheme in the set of multi-operating condition optimal operating schemes for the local pipe section, and select the scheme with the most similar operating condition as the optimal operating scheme for the current local pipe section, including the following steps:

[0100] S71: Adopt the optimal operation scheme of multiple operating conditions in the local pipe section and the operating condition parameters X of each scheme label label and the current operating condition parameter X current , calculate the Euclidean distance Dist considering the normalization of data magnitude as the distance evaluation index, as shown in formula (1).

[0101] S72: According to the distance evaluation index of each scheme in the multi-operating condition optimal operation scheme set calculated in S71, the scheme with the smallest distance evaluation index is selected as the most approximate scheme for the current operating condition.

[0102] Step 8: After completing steps 1 to 5, when the operating conditions of the pipeline network change, repeat steps 6 and 7 based on the latest operating condition parameters to quickly obtain the optimal operating plan with the lowest operating cost under the real-time conditions.

[0103] In the specific implementation, steps 1 to 5 have completed the establishment of a set of optimal operating solutions for multiple operating conditions of local pipe sections that can be merged in multiple compressor stations, as well as the merging and simplification of the global pipeline network topology structure and optimization model. When the operating conditions change, the optimal operating solution for the real-time conditions can be directly and quickly sought, which can effectively meet the timeliness requirements of pipeline network operation scheduling.

[0104] Example:

[0105] A response surface methodology-based optimization method for merging and simplifying the operation of pipeline compressor stations proposed in the present invention is adopted. Taking a long-distance natural gas pipeline network as an example, combined with the relevant data required for the implementation of the method, a multi-faceted evaluation is conducted focusing on the solution time and optimization effect to further illustrate the present invention and verify its reliability and effectiveness.

[0106] A long-distance natural gas pipeline network has a 4048 km pipeline, 6 loading stations, 27 distribution stations and 12 compressor stations along the line, and has a complex pipeline network topology. The global pipeline network topology of the pipeline network is as follows: Figure 2 The mergeable compressor station identification method is used to identify the global pipe network topology and obtain the mergeable local pipe sections of multiple compressor stations as shown in Figure 3 As shown, four pipeline intersection nodes are identified in the pipeline network, which are circled with solid lines. Figure 3 After splitting, 10 local pipeline topological structures are obtained, which are marked with solid line boxes. Figure 3 It is further delineated that multiple compressor stations can merge three local pipe sections, with dotted boxes in Figure 3 Mark in.

[0107] By collecting basic pipeline network operating data, we determined the operating ranges for each consolidable segment of multiple compressor stations. Consolidable segment 1 has compressor station 1 as its starting point and compressor station 4 as its terminal. Along the segment are one loading station and eight distribution stations. The operating ranges are shown in Table 1.

[0108] Table 1 Operating range of the local pipe section 1 that can be combined in multiple compressor stations

[0109] project Working range project Working range Compressor station 1 inlet pressure (MPa) (6,8) <![CDATA[Download volume of the distribution station 5 (m 3 / s)]]> (82,123) Compressor station 4 outlet pressure (MPa) (9,10.5) <![CDATA[Download volume of the distribution station 6 (m 3 / s)]]> (85,127) <![CDATA[Outlet flow rate of compressor station 4 (m 3 / s)]]> (152,228) <![CDATA[Download volume of the distribution station 7 (m 3 / s)]]> (48,72) <![CDATA[Upload amount of Upload Station 2 (m 3 / s)]]> (350,530) <![CDATA[Download volume of the branch transfer station 8 (m 3 / s)]]> (121,182) <![CDATA[Download volume of Sub - feeder Station 3 (m 3 / s)]]> (74,112) <![CDATA[Download volume of the distribution station 9 (m 3 / s)]]> (129,193) <![CDATA[Download volume of the distribution station 4 (m 3 / s)]]> (63,95)

[0110] Multiple compressor stations can be combined. The starting compressor station of local pipeline section 2 is compressor station 6, and the terminal compressor station is compressor station 7. There are three sub-stations along the line. The operating conditions are shown in Table 2.

[0111] Table 2 Operating range of multiple compressor stations that can merge local pipe section 2

[0112] project Working range project Working range Compressor station 6 inlet pressure (MPa) (4.5,6) <![CDATA[Download volume of the distribution station 12 (m 3 / s)]]> (96,144) Compressor station 7 outlet pressure (MPa) (6,7) <![CDATA[Download volume of the distribution station 13 (m 3 / s)]]> (44,66) <![CDATA[Outlet flow rate of compressor station 7 (m 3 / s)]]> (102,154) <![CDATA[Download volume of the distribution station 14 (m 3 / s)]]> (20,30)

[0113] Multiple compressor stations can be combined. The starting compressor station of local pipeline section 3 is compressor station 8, and the terminal compressor station is compressor station 10. There are two loading stations and three distribution stations along the line. The operating condition range is shown in Table 3.

[0114] Table 3 Operating range of multiple compressor stations where local pipe section 3 can be combined

[0115] project Working range project Working range Compressor station 8 inlet pressure (MPa) (4.5,6) <![CDATA[Upload amount of upload station 5 (m 3 / s)]]> (110,156) Compressor station 10 outlet pressure (MPa) (6,7) <![CDATA[Download volume of the distribution station 17 (m 3 / s)]]> (51,77) <![CDATA[Outlet flow rate of compressor station 10 (m 3 / s)]]> (119,178) <![CDATA[Download volume of the distribution station 18 (m 3 / s)]]> (63,95) <![CDATA[Upload amount of upload station 4 (m 3 / s)]]> (88,132) <![CDATA[Download volume of the distribution station 19 (m 3 / s)]]> (46,69)

[0116] For situations where branch pipelines or multiple loading and distribution stations exist along a locally mergeable pipeline section with multiple compressor stations, a preliminary approximate merging method is used based on the station location distribution and operating range to approximate the branch pipelines and multiple distribution stations or loading stations. This yields a preliminary merged locally merged pipeline section with multiple compressor stations. In locally mergeable pipeline section 1 with multiple compressor stations, after approximate merging, there is one local pipeline section loading station and two local pipeline section distribution stations. In locally mergeable pipeline section 2 with multiple compressor stations, after approximate merging, there is one local pipeline section distribution station. In locally mergeable pipeline section 3 with multiple compressor stations, after approximate merging, there are two local pipeline section loading stations and two local pipeline section distribution stations.

[0117] Based on the preliminarily merged local pipe sections of the multi-compressor stations, factor level tables of the local pipe sections that can be merged for each multi-compressor station are further established, as shown in Tables 4, 5, and 6.

[0118] Table 4 Factor levels for merging local pipe sections 1 in multiple compressor stations

[0119]

[0120] Table 5 Factor levels for merging local pipe sections 2 in multiple compressor stations

[0121]

[0122]

[0123] Table 6 Factor levels of the merging of local pipe sections 3 in multiple compressor stations

[0124]

[0125] Based on the factor level table of the local pipe sections that can be merged by multiple compressor stations, the Box-Behnken method is used to combine them within the operating range of the operating parameters to form a variety of operating parameter value combinations. Accordingly, three response surface experimental schemes are established. The pipeline operation cost minimization optimization model is used to establish corresponding optimization models for each local pipe section that can be merged by multiple compressor stations. The optimal operating scheme is solved for each operating condition in the response surface experimental scheme, and three local pipe section multi-condition optimal operating scheme sets are formed accordingly. Among them, the local pipe section multi-condition optimal operating scheme set for the local pipe section 1 that can be merged by multiple compressor stations contains the optimal operating schemes for 54 different operating conditions, the local pipe section multi-condition optimal operating scheme set for the local pipe section 2 that can be merged by multiple compressor stations contains the optimal operating schemes for 29 different operating conditions, and the local pipe section multi-condition optimal operating scheme set for the local pipe section 3 that can be merged by multiple compressor stations contains the optimal operating schemes for 62 different operating conditions.

[0126] At the same time, a response model between the operating parameters (X) of the local pipe section and the operating cost (Y) is constructed based on the response surface experimental scheme, as shown in Equations (13), (14), and (15), respectively.

[0127]

[0128]

[0129] The response model has high calculation accuracy. The P values of the above model equations obtained by variance analysis are all less than 0.0001, and the R 2 They are 0.9982, 0.9999 and 0.9951 respectively, indicating that the response surface experimental scheme designed based on the operating condition range of the local pipe sections that can be merged in multiple compressor stations can effectively cover the operating condition range, and the constructed response model can accurately calculate the operating cost of the local pipe sections that can be merged in multiple compressor stations according to different operating condition parameters.

[0130] Furthermore, the network operation cost minimization optimization model is used to solve the global network operation optimization problem. Combined with the established response model, each local pipe section that can be merged with multiple compressor stations is merged and simplified in the optimization model. In the global network topology, the local pipe section that can be merged with multiple compressor stations is replaced with a merged compressor station. Figure 4 As shown in the figure, corresponding processing is performed on the objective function, constraints, and decision variables of the global pipeline network optimization model. After the merging and simplification process, the decision variables of the global pipeline network optimization model are reduced from 12 to 6, a 50% decrease in the number of decision variables. The number of constraints in the optimization model is reduced from 237 to 113, a 52.32% decrease in the number of constraints. It can be seen that the merging and simplification method proposed in this invention can effectively reduce the scale and optimization dimension of the optimization model, and reduce the complexity of solving the optimization model.

[0131] The global pipeline network optimization model after merging and simplification can quickly solve and obtain the optimal operating plan for the simplified global pipeline network. Furthermore, by determining the current operating condition parameters of the local pipeline section that can be merged at each multi-compressor station, an approximate operating condition selection method is adopted to select the most approximate plan from the set of multi-condition optimal operating plans for the local pipeline section as the optimal operating plan for the current local pipeline section, thereby realizing the rapid establishment of the optimal operating plan for the global pipeline network.

[0132] Furthermore, based on the aforementioned long-distance natural gas pipeline network and using actual operating conditions as a case study, we employed both an unsimplified global pipeline network optimization model and the proposed simplified operation optimization method for compressor stations. Combining multiple optimization algorithms, we determined the optimal global network operation plan. The proposed method was validated and analyzed in terms of solution time and optimization effectiveness. The specific parameters of the actual operating condition case are shown in Table 7.

[0133] Table 7 Specific parameters of actual operating condition cases

[0134]

[0135]

[0136] Based on the above operating condition cases, the unsimplified global pipeline network optimization model and the simplified operation optimization method for the pipeline network compressor station merge proposed in this invention were solved using the particle swarm algorithm and the genetic algorithm, respectively. Under the same computer hardware conditions, the optimization results are shown in Table 8.

[0137] Table 8 Comparison of optimization results

[0138]

[0139] Compared to the unsimplified global pipeline network optimization model, the proposed simplified operation optimization method for merging and simplifying pipeline compressor stations reduces the time required for solution by 34.27% and 64.97% using genetic algorithms and particle swarm optimization algorithms, respectively, while increasing operating costs by only 0.95% and 0.23%, respectively. This demonstrates that the proposed simplified operation optimization method for merging and simplifying pipeline compressor stations based on the response surface methodology can effectively reduce the time required to solve the optimal operation plan for a natural gas pipeline network while achieving good optimization results. Furthermore, the proposed method has great potential for promotion and is highly reliable and practical for rapidly solving the operation optimization of large-scale natural gas pipeline networks. By pre-establishing a set of optimal operation plans for multiple operating conditions in a local segment and pre-simplifying the optimization model, it effectively reduces the time required to solve the optimal operation plan for the pipeline network. Furthermore, combined with the response surface experimental design method, it effectively covers the operating condition range of the local segment, ensuring the feasibility of the solution operation plan, and providing fast and reliable decision support for the real-time operation scheduling of the pipeline network.

Claims

1. A simplified operation optimization method for pipeline network compressor stations based on response surface methodology, characterized in that The following steps are involved: Step 1: Collect basic data of the pipe network structure, construct the global pipe network topology, and use the simplified and mergeable compressor station identification method to identify the global pipe network topology and obtain the mergeable local pipe sections of multiple compressor stations; Step 2: Collect basic operating data of the pipeline network and determine the operating range of the local pipeline sections that can be merged at each multi-compressor station; Step 3: Traverse and analyze the mergeable local pipeline sections of each multi-compressor station. If there are branch pipelines or more than two sub-transmission stations or upload stations along the local pipeline section, a preliminary simplified merging method is used based on the station location distribution and operating condition range to simplify the branch pipelines and approximately merge multiple sub-transmission stations or upload stations to obtain the mergeable local pipeline sections of the multi-compressor stations after preliminary simplification and merging. Step 4: For each multi-compressor station with a locally merged pipe section, a corresponding factor level table is established based on the operating parameters and ranges. The Box-Behnken method is then used to design a response surface experiment plan that includes multiple groups of different operating parameter combinations. Step 5: Using the pipeline network operation cost minimization optimization model, for each combined local pipe section at multiple compressor stations, the optimal operation plan and minimum operation cost for each combination of operating condition parameters in the response surface experiment scheme are solved. A set of optimal operation plans for multiple operating conditions of the local pipe section is constructed, and a response model between the operating condition parameters of the local pipe section and the operation cost is fitted. Step 6: Use the pipeline network operation cost minimization optimization model to solve the global pipeline network operation optimization problem. Using the response model established in Step 5, merge and simplify the local pipe sections that can be merged in each multi-compressor station in the global pipeline network topology and optimization model. Combined with the optimization algorithm, the optimal operation plan for the global pipeline network is obtained. Step 7: Based on the optimal global network operation plan in step 6, determine the current operating condition parameters of the local pipe section that can be merged at each multi-compressor station, and use the approximate operating condition selection method to select the most approximate plan from the set of optimal operating plans for multiple operating conditions of the local pipe section as the optimal operating plan for the current local pipe section; Step 8: After completing steps 1 to 5, when the operating conditions of the pipeline network change, repeat steps 6 and 7 based on the latest operating condition parameters to quickly obtain the optimal operating plan with the lowest operating cost under the real-time conditions.

2. The method for optimizing the operation of pipe network compressor stations based on response surface methodology according to claim 1, characterized in that: In step 1 of claim 1, the method for identifying a compressor station that can be combined includes the following steps: S11: Identify pipeline intersection nodes based on the global pipeline network topology, pipeline flow direction, and compressor station distribution. If there is a compressor station along one or more pipelines connected to the intersection node, split the global pipeline network topology at the pipeline intersection node, traverse all pipeline intersection nodes, and obtain a local pipeline topology. S12: traverse and analyze the local pipeline topology structure obtained in S11. If the local pipeline topology structure includes two or more compressor stations, the node of the first compressor station upstream of the local pipeline is used as the starting point, and the node of the last compressor station downstream of the local pipeline is used as the end point, and the local pipeline section that can be merged with multiple compressor stations is further delineated. In step 3 of claim 1, the preliminary simplified merging processing method includes the following steps: S31: If there is a branch pipeline along the local pipe section, the download capacity operating ranges of the branch transmission stations and the upload capacity operating ranges of the upload stations along the branch pipeline are accumulated respectively. If the total download capacity is greater, all stations along the branch pipeline are merged into one branch transmission station in the local pipe section topology. Conversely, if the total upload capacity is greater, they are merged into one upload station. The merged station is set at the connection between the branch pipeline and the local pipe section. The absolute value of the difference between the total download capacity and the total upload capacity is used as the download capacity operating range or the upload capacity operating range of the merged station. S32: If there are two or more sub-transmission stations between any two compressor stations in the local pipeline section, then, based on the load capacity operating range, the remaining sub-transmission stations between the two compressor stations are merged with the sub-transmission station with the largest load capacity operating range in the local pipeline section topology, and the load capacity operating ranges are accumulated; S33: If there are two or more loading stations between any two compressor stations in the local pipe section, then, based on the loading capacity operating range, merge the remaining loading stations between the two compressor stations with the loading station with the largest loading capacity operating range in the local pipe section topology, and accumulate the loading capacity operating ranges; S34: If there are still three or more sub-stations along the local pipeline section after the S31-S33 merging process, the two sub-stations with the largest download capacity operating ranges are retained in the local pipeline section topology. The remaining sub-stations are merged with the nearest retained sub-station, and the download capacity operating ranges are accumulated. S35: If there are still three or more upload stations along the local pipe section after the merging process of S31-S33, the two upload stations with the largest upload capacity operating ranges are retained in the local pipe section topology structure, and the remaining upload stations are merged with the nearest retained upload station, and the upload capacity operating ranges are accumulated; In step 4 of claim 1, the target response value of the response surface experiment scheme is the pipeline operation cost, and the experimental factors include the inlet pressure and inlet flow of the starting compressor station of the mergeable local pipeline section and the outlet pressure of the terminal compressor station. In addition, depending on the specific conditions of the mergeable local pipeline section, the experimental factors also include the download volume of 0 to 2 branch stations and the upload volume of 0 to 2 upload stations, and the level range of the experimental factors is determined by the operating condition range; In step 6 of claim 1, the merging and simplifying process is specifically to replace the mergeable local pipe sections of multiple compressor stations with one merged compressor station in the global pipe network topology, and further process the optimization model, including the following steps: S61: In the objective function of the optimization model, the operating cost calculation formula of the multiple compressor stations involved in the local pipeline section is replaced with the response model between the operating parameters and the operating cost established in step 5 of claim 1; S62: In the constraints of the optimization model, all constraints involved in the local pipe section are replaced with the operating constraints of a compressor station, including: compressor station flow constraint, compressor station inlet and outlet pressure constraints, and node flow balance constraint; S63: Among the decision variables of the optimization model, the outlet pressure decision variables of the multiple compressor stations involved in the local pipeline section are merged and replaced with the outlet pressure decision variable of one compressor station.

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