A method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering

Abstract

This invention provides a method for optimizing the operation and scheduling of multi-source pipeline networks based on energy metering. The method includes: dividing the pipeline network into multiple gas source networks with different nodes according to the network's structural parameters; constructing constraints for an energy metering-based multi-source pipeline network operation and scheduling optimization model based on the network's structural parameters, gas source quality parameters, natural gas purchase and sales parameters, and user demand parameters; constructing the energy metering-based multi-source pipeline network operation and scheduling optimization model based on the constraints and the multi-objective functions of maximizing operating profit and maximizing user satisfaction; and solving the energy metering-based multi-source pipeline network operation and scheduling optimization model to output the optimization results. This method provides strong support for solving key issues in the integrated operation of multi-source pipeline networks after implementing an energy metering system, and promotes the integration and upgrading of the natural gas industry chain.

Description

Technical Field

[0001] This invention relates to the field of multi-source pipeline network operation optimization, and specifically to a multi-source pipeline network operation scheduling optimization method based on energy metering. Background Technology

[0002] With the expansion of natural gas pipeline networks and more frequent natural gas trade, upstream gas sources and downstream users are becoming increasingly diversified. Traditional volumetric metering is insufficient to address the numerous complex issues encountered in centralized pipeline network control, nor can it reflect users' quality requirements. Energy metering, which uses natural gas energy as the settlement unit, measures the calorific value of natural gas in addition to volumetric metering. It calculates the total energy of the natural gas flowing through the pipeline by combining the calorific value per unit volume with the volumetric volume. Natural gas, as a clean energy source, derives its greatest value from the heat generated during combustion. However, natural gas is a mixture of multiple components, and its calorific value varies between regions and even among different gas sources for the same user. Therefore, to realize the commercial value of natural gas, it is necessary to establish an energy metering system. Currently, international natural gas trade, including pipeline natural gas and liquefied natural gas (LNG) trade, as well as spot, futures, and options trading, uses energy as the unit of measurement for natural gas. This is also the future development trend of natural gas trading metering in my country.

[0003] With the nationwide trend towards a unified gas supply network, pipeline gas supply schemes are becoming increasingly diversified. Energy metering can better represent user satisfaction with natural gas quality, but the challenge remains: how to optimize pipeline operation under energy metering, i.e., ensuring that user gas demand is met while minimizing pipeline operating costs and maximizing operating profits. Therefore, rationally selecting gas sources and their supply volumes, developing operational plans, and improving operating profits under an energy metering system are both urgent and crucial.

[0004] In summary, this invention provides an optimization method for the operation and scheduling of multi-source pipeline networks based on energy metering. It considers the differences in gas quality, calorific value, gas price, and supply capacity among different gas sources, as well as the varying gas quality and volume demands of different users. This method conducts research on the optimization of multi-source pipeline network operation under an energy metering system, playing a positive role in promoting the orderly, rapid, efficient, and controllable establishment of a natural gas energy metering system. It provides strong support for solving key issues in the integrated operation of multi-source pipeline networks after the implementation of the energy metering system, and is of great significance for ensuring energy security for national economic and social development, thus promoting the integration and upgrading of the entire natural gas industry chain. Summary of the Invention

[0005] To address the issue of optimizing the operation scheme of multi-gas source pipeline networks under energy metering, this invention proposes an optimization method for multi-gas source pipeline network operation scheduling based on energy metering. This method can obtain a multi-gas source pipeline network operation scheduling scheme that meets users' gas demand and is economical, and has certain guiding significance for multi-gas source pipeline network operation scheduling.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering, comprising:

[0008] S1: Based on the multi-source pipeline network structure parameters, the pipeline network is divided into multi-source pipeline networks with different nodes;

[0009] S2: Based on the structural parameters of the multi-source pipeline network, the gas quality parameters, the natural gas purchase and sale parameters, and the user demand parameters, construct the constraints of the multi-source pipeline network operation scheduling optimization model based on energy metering.

[0010] S3: Construct a multi-gas-source pipeline network operation scheduling optimization model based on energy metering, according to the constraints and the multi-objective functions of maximizing operating profit and maximizing user satisfaction.

[0011] S4: Solve the multi-gas source pipeline network operation scheduling optimization model based on energy metering and output the multi-gas source pipeline network operation scheduling optimization results.

[0012] Furthermore, the energy pricing method adopted in this invention is mainly based on the benchmark calorific value, and the unit of measurement is converted through the current volume price. At the same time, a price fluctuation coefficient will be introduced during the conversion process to characterize the price fluctuation caused by the difference between the actual calorific value of natural gas in various regions and by various users and the calorific value benchmark published by the state.

[0013] The fluctuation coefficient w is:

[0014]

[0015] In the formula, w is the natural gas price volatility coefficient; H act This represents the actual calorific value of natural gas; H0 represents the benchmark calorific value for converting natural gas prices from volume-based pricing to energy-based pricing.

[0016] The energy metering of the natural gas sales price F sell Purchase price F purchase and transportation price F pipe The conversion formula is:

[0017]

[0018]

[0019]

[0020] In the formula, F sell The selling price of natural gas under energy metering; F v，sell The selling price of natural gas based on volumetric measurement; F purchase The purchase price of natural gas under energy metering; F v，purchase The purchase price of natural gas by volume; F pipe The purchase price of natural gas under energy metering; F v，pipe This represents the purchase price of natural gas based on volumetric measurement.

[0021] Furthermore, before step S1, it is also necessary to obtain the structural parameters of the target pipeline network, gas source supply parameters, natural gas purchase and sale parameters, and user demand parameters.

[0022] The pipeline network structure parameters include gas source location, user location, compressor station location, and pipeline length;

[0023] The gas supply parameters include: gas supply pressure, gas supply capacity boundary, and gas quality parameters.

[0024] The natural gas purchase and sale parameters include: natural gas purchase price, natural gas pipeline transportation price, and natural gas sales price;

[0025] The user requirement parameters include: user air volume requirement and user temperament requirement.

[0026] Furthermore, the nodes mentioned in step S1 include: gas source node, user node, pipeline node, and compressor station node.

[0027] The gas source node is used to represent a gas source with gas supply capability;

[0028] The user nodes are used to represent downstream users;

[0029] The aforementioned pipe nodes are used to represent pipes that connect various types of nodes;

[0030] The compressor station node is used to represent a compressor station with a pressurization function.

[0031] Furthermore, the constraints mentioned in step S2 include gas pressure constraints, gas flow constraints, compressor constraints, and natural gas quality constraints.

[0032] The gas pressure constraints include user node pressure drop constraints, pressure constraints, and pipeline pressure constraints.

[0033] The aforementioned user node pressure drop constraint refers to the basic pressure drop formula expressed in terms of volumetric flow rate, and the constraint relationship is as follows:

[0034]

[0035] In the formula, Q i,e d represents the volumetric flow rate of natural gas within the pipe segment of element e at node i; i,e P is the inner diameter of the pipe segment of node i-e element; i|(i,e) P represents the starting pressure of the pipe segment from node i to element e; e|(i,e) The endpoint pressure of node i-e element segment e; z is the natural gas compressibility factor; Δ is the relative density of natural gas; T i,e L represents the average thermodynamic temperature of shale gas within the pipe segment of node i-e element. i,e Let be the pipe length of node i-e element segment e;

[0036] The user node pressure constraint refers to the pressure on user node u being required to meet the user's minimum demand pressure. The constraint relationship is as follows:

[0037]

[0038] In the formula, P represents the minimum demand pressure for user node u. u The pressure on user node u;

[0039] The aforementioned pipeline pressure constraint refers to the pressure at pipeline node l being between the minimum allowable operating pressure and the maximum allowable operating pressure. The constraint relationship is as follows:

[0040] P l min ≤P l ≤P l max

[0041] In the formula, P l The operating pressure of pipeline node l; The minimum operating pressure of pipeline node l; The maximum allowable operating pressure for pipeline node l;

[0042] The gas flow constraints include node flow balance constraints, gas source node flow constraints, and demand node flow constraints.

[0043] The node flow balance constraint refers to the requirement that the sum of the flows flowing into node i should be equal to the sum of the flows flowing out of node i. The constraint relationship is as follows:

[0044]

[0045] In the formula, Q i Let β be the flow rate of node i; ie Q is the connection coefficient between node i and element e; e The flow rate of component e;

[0046] The aforementioned gas source node flow constraint refers to the constraint that the flow rate of gas source node g is subject to the gas supply capacity constraint, and the constraint relationship is as follows:

[0047]

[0048] In the formula, Q is the lower bound of the gas supply capacity of gas source node g; g The gas supply to gas source node g; This is the upper bound of the gas supply capacity of gas source node g;

[0049] The user node flow balancing constraint refers to the requirement that the flow of user node u should meet the minimum gas demand of the node's users. The constraint relationship is as follows:

[0050]

[0051] In the formula, The required traffic for user node u; Q u Inject traffic into user node u;

[0052] The compressor constraints include intake pressure constraints, exhaust pressure constraints, pressure ratio constraints, speed constraints, intake flow constraints, operating power constraints, and number of units in operation constraints.

[0053] The aforementioned intake pressure constraint refers to the requirement that the intake pressure of compressor j in compressor station node c should be greater than the minimum intake pressure. The constraint relationship is as follows:

[0054]

[0055] In the formula, The minimum allowable intake pressure for compressor j in compressor station node c; The intake pressure of compressor j in compressor station node c;

[0056] The aforementioned exhaust pressure constraint refers to the requirement that the exhaust pressure of compressor j in compressor station node c should be less than the maximum exhaust pressure. The constraint relationship is as follows:

[0057]

[0058] In the formula, The discharge pressure of compressor j in compressor station node c; The maximum allowable discharge pressure of compressor j in compressor station node c;

[0059] The pressure ratio constraint relationship is as follows:

[0060]

[0061] In the formula, εc,j Let J be the compression ratio of compressor j in compressor station node c;

[0062] If the pressure ratio ε c,j If the value is greater than 1, then compressor j needs to be run to increase the pressure, and the compressor operating variable b... c,j =1, compressor j operating power N c,j Assign a positive value; if the pressure ratio ε c,j Less than or equal to 1, compressor j operating variable b c,j =0, the compressor stops running, and the operating power of compressor j is N. c,j The value is 0, and the formula for calculating the compressor power is:

[0063]

[0064] In the formula, N c,j K represents the operating power of compressor j at node c of the compressor station; k represents the specific heat of the gas. The gas compressibility factor under the suction condition of compressor j; η is the gas compressibility factor under compressor discharge conditions j; c,j Let J be the efficiency of compressor j at node c of compressor station;

[0065] The compressor operating power constraint refers to the requirement that the operating power of compressor j should be between the minimum and maximum allowable operating power. The constraint relationship is as follows:

[0066]

[0067] In the formula, N represents the minimum allowable operating power of compressor j in compressor station node c; c,j Let J be the operating power of compressor j in compressor station node c; The maximum allowable operating power of compressor j in compressor station node c;

[0068] The compressor speed constraint refers to the requirement that the speed of compressor j should be between the minimum and maximum allowable speeds. The constraint relationship is as follows:

[0069]

[0070] In the formula, r is the minimum permissible speed of compressor j in compressor station node c; c,j Let J be the rotational speed of compressor j in compressor station node c; The maximum permissible speed of compressor j in compressor station node c;

[0071] The compressor intake flow constraint refers to the requirement that the intake flow of compressor j should be between the minimum and maximum allowable intake flow rates. The constraint relationship is as follows:

[0072]

[0073]

[0074]

[0075] In the formula, q represents the minimum allowable intake flow rate of compressor j in compressor station node c; c,j Let J be the intake air flow rate of compressor j in compressor station node c; A represents the maximum allowable intake air flow rate of compressor j in compressor station node c; su B su C su A st B st C st These are compressor characteristic parameters;

[0076] The aforementioned constraint on the number of compressors in operation refers to the fact that the number of compressors in operation within node c of the compressor station is less than the total number of compressors equipped in the compressor station. The constraint relationship is as follows:

[0077]

[0078] In the formula, δ c,j χ is the startup variable for compressor j at compressor station node c; c The number of compressors within compressor station node c;

[0079] The aforementioned natural gas quality constraints include interchangeability constraints, user node calorific value constraints, and user node component content constraints.

[0080] The aforementioned interchangeability constraint refers to the requirement that the mixed natural gas supplied from multiple gas sources must meet the interchangeability requirements in the standard. This invention addresses this by setting a gas supply ratio coefficient x for gas source node g. g The constraint relationship is as follows:

[0081]

[0082] In the formula, x is the lower bound of the gas supply ratio for gas source node g; g The gas supply ratio for gas source node g; This is the upper limit of the gas supply ratio for gas source node g;

[0083] The aforementioned user node calorific value constraint refers to the requirement that the calorific value of natural gas at user node u must be greater than the minimum required calorific value of natural gas. The constraint relationship is as follows:

[0084]

[0085] In the formula, H represents the minimum calorific value of natural gas required by user node u. u The calorific value of the natural gas received at user node u;

[0086] The aforementioned methane content constraint at user node u refers to the requirement that the methane content of the natural gas at user node u must be greater than the minimum required methane content. The constraint relationship is as follows:

[0087]

[0088] In the formula, The minimum methane content required for user node u; The methane content of the natural gas received at user node u.

[0089] Furthermore, the objective functions described in step S3 are maximizing operating profit and maximizing user satisfaction, respectively.

[0090] The objective function, maximizing operating profit, consists of gas sales revenue, gas purchase cost, pipeline transportation cost, and compressor station operating expenses. The objective function relationship is as follows:

[0091] maxf1 = S sell -S purchase -S pipe -S compreesor

[0092] In the formula, f1 represents the pipeline network operating profit under energy metering; S sell S represents the total revenue from natural gas sales under energy metering. purchase S represents the total cost of purchasing natural gas under energy metering; pipe S represents the total cost of pipeline transportation. compressor For the operating costs of the compressor station;

[0093] The total revenue from natural gas sales under the aforementioned energy metering is defined as the revenue earned by marketers from selling natural gas purchased upstream to downstream users, as shown in the following formula:

[0094]

[0095] In the formula, H represents the natural gas sales price at user node u under energy metering; u Q represents the calorific value of the natural gas at user node u; u The amount of natural gas delivered at user node u;

[0096] The gas purchase cost under energy metering is defined as the cost for marketers to purchase gas from upstream suppliers, as shown in the following formula:

[0097]

[0098] In the formula, H represents the natural gas purchase price at gas source node g under energy metering; g Q represents the calorific value of the natural gas at gas source node g; g This represents the amount of natural gas injected at gas source node g.

[0099] The total pipeline transportation cost is defined as the expense incurred in transporting natural gas to the station via pipeline, as shown in the following formula:

[0100]

[0101] In the formula, Q represents the pipeline transportation rate for pipeline node l under energy metering; l L represents the pipeline flow rate at pipeline node l. i,e H represents the pipe length of node i to element e; i H represents the calorific value of the natural gas at upstream node i of the pipeline; e The calorific value of the natural gas in downstream component e of the pipeline;

[0102] The operating cost of the compressor station is defined as the operating cost of the compressors within the compressor station node, and the relationship is as follows:

[0103]

[0104] In the formula, W c,j F represents the power consumption of compressor j in compressor station node c; c,j Let $\frac{1}{2}$ be the unit energy consumption price of compressor $j in compressor station node c.

[0105] This invention uses the calorific value content index α u and methane content index γ u The satisfaction level of user node u with the temperament is measured, where the heat value index α is used. u This indicates the difference between the actual calorific value of the incoming gas and the calorific value required by the contract; methane content index γ u The relationship between the actual methane content in the incoming gas and the contractually required methane content is expressed by the following formula:

[0106]

[0107]

[0108] In the formula, The contract requires a heat value for user node u; The actual heat value of the incoming air for user node u; (CH4) represents the methane content required by the user node u contract; (CH4) represents the methane content of the actual incoming gas at user node u;

[0109] The second objective function is to maximize user satisfaction, which means minimizing the sum of the calorific value content and methane content indicators. The objective function relationship is as follows:

[0110]

[0111] In the formula, Y u This represents the total number of user nodes u that did not meet the standards.

[0112] Furthermore, in step S4, the ε-constraint method is used to solve the multi-objective optimization model. That is, the primary optimization objective is selected according to the priority of different objectives and the preferences of users and marketers, and the secondary optimization objectives are transformed into constraints. Based on this, the multi-objective optimization model is transformed into a single-objective optimization model, as shown in the following equation:

[0113]

[0114] In the formula, x is the model optimization variable; f1(x) is the primary objective function; f2(x) is the secondary objective function; ζ is the lower bound of the constraint in the single objective model; h(x) is the equality constraint; g(x) is the inequality constraint;

[0115] In the single-objective model, the lower bound ζ of the constraint can be repeatedly adjusted and tested during the solution process. Each time the single-objective model is solved, the Pareto optimal solution of the dual-objective model can be obtained. Finally, by solving a certain number of single-objective models, the Pareto solution set of the dual-objective model is obtained.

[0116] Furthermore, the optimization results of the multi-source pipeline network operation scheduling mentioned in step S4 include: pipeline network operating costs, operating profits, gas sales revenue, user satisfaction, gas supply volume, and compressor station pressurization scheme.

[0117] In summary, this invention provides a method for optimizing the operation and scheduling of multi-source pipeline networks based on energy metering: The pipeline network is divided into multiple gas source gathering and transmission networks with gas source nodes, user nodes, pipeline nodes, and compressor station nodes, based on the network's structural parameters; constraints are constructed for the energy metering-based multi-source pipeline network operation and scheduling optimization model based on the network's structural parameters, gas source quality parameters, and user demand parameters; the model is then constructed based on these constraints and the multi-objective functions of maximizing operating profit and maximizing user satisfaction; the primary optimization objective is selected based on the priority of different objectives and the preferences of users and marketers; secondary optimization objectives are transformed into constraints using the ε-constraint method; the multi-objective optimization model is then transformed into a single-objective optimization model, and the model is solved to output the optimization results for the multi-source pipeline network operation and scheduling. Existing research largely focuses on the optimization of multi-source pipeline network operation under volumetric metering systems. However, implementing an energy metering system significantly impacts operating profits. Unlike traditional volumetric metering optimization problems, energy metering involves a close correlation between the purchase, transportation, and sales prices of natural gas and its calorific value, while also considering the different user demands for natural gas quality. This invention focuses on optimizing multi-source pipeline network operation schemes from an energy metering perspective. It considers the differences in quality, calorific value, gas price, and supply capacity among different gas sources, as well as the varying quality and volume demands of different users. A multi-source pipeline network operation optimization model is established with maximizing operating profits and user satisfaction as multiple objective functions. This model can select the primary optimization objective based on the priority of different objectives and the preferences of users and marketers. Using the ε-constraint method, secondary optimization objectives are transformed into constraints, enhancing the practicality of the optimization scheme. Attached Figure Description

[0118] Figure 1 This is a flowchart of the present invention;

[0119] Figure 2 This is a specific application embodiment of a long-distance pipeline structure in China.

[0120] Figure 3 The Pareto solution front obtained in a specific application embodiment of the present invention;

[0121] Figure 4 This is a comparison of the compressor speeds of various compressor stations under different conditions in a specific application embodiment of the present invention;

[0122] Figure 5 The pipeline pressure drop under different conditions is shown in specific application embodiments of the present invention. Detailed Implementation

[0123] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0124] like Figure 1 As shown, the present invention provides a multi-gas source pipeline network operation scheduling optimization method based on energy metering, comprising the following steps:

[0125] S1: Based on the structural parameters of the multi-source pipeline network, the pipeline network is divided into multi-source gathering and transmission pipeline networks with different nodes;

[0126] S2: Based on the structural parameters of the multi-source pipeline network, the gas quality parameters, the natural gas purchase and sale parameters, and the user demand parameters, construct the constraints of the multi-source pipeline network operation scheduling optimization model based on energy metering.

[0127] S3: Construct a multi-gas-source pipeline network operation scheduling optimization model based on energy metering, according to the constraints and the multi-objective functions of maximizing operating profit and maximizing user satisfaction.

[0128] S4: Solve the multi-gas source pipeline network operation scheduling optimization model based on energy metering and output the multi-gas source pipeline network operation scheduling optimization results.

[0129] In one embodiment, the energy pricing method adopted by the present invention is mainly based on the benchmark calorific value, and the unit of measurement is converted through the current volume price. At the same time, a price fluctuation coefficient will be introduced during the conversion process to characterize the price fluctuation caused by the difference between the actual calorific value of natural gas in various regions and by various users and the calorific value benchmark published by the state.

[0130] The fluctuation coefficient w is:

[0131]

[0132] In the formula, w is the natural gas price volatility coefficient; H act This represents the actual calorific value of natural gas; H0 represents the benchmark calorific value for converting natural gas prices from volume-based pricing to energy-based pricing.

[0133] The energy metering of the natural gas sales price F sell Purchase price F purchase and transportation price F pipe The conversion formula is:

[0134]

[0135]

[0136]

[0137] In the formula, F sell The selling price of natural gas under energy metering; F v，sell The selling price of natural gas based on volumetric measurement; F purchase The purchase price of natural gas under energy metering; F v，purchase The purchase price of natural gas by volume; F pipe The purchase price of natural gas under energy metering; F v，pipe This represents the purchase price of natural gas based on volumetric measurement.

[0138] In one embodiment, before step S1, it is also necessary to obtain the structural parameters of the target pipeline network, gas supply parameters, natural gas purchase and sale parameters, and user demand parameters.

[0139] The pipeline network structure parameters include gas source location, user location, compressor station location, and pipeline length;

[0140] The gas supply parameters include: gas supply pressure, gas supply capacity boundary, and gas quality parameters.

[0141] The natural gas purchase and sale parameters include: natural gas purchase price, natural gas pipeline transportation price, and natural gas sales price;

[0142] The user requirement parameters include: user air volume requirement and user temperament requirement.

[0143] In one embodiment, the nodes mentioned in step S1 include: gas source node, user node, pipeline node, and compressor station node;

[0144] The gas source node is used to represent a gas source with gas supply capability;

[0145] The user nodes are used to represent downstream users;

[0146] The aforementioned pipe nodes are used to represent pipes that connect various types of nodes;

[0147] The compressor station node is used to represent a compressor station with a pressurization function.

[0148] In one embodiment, the constraints mentioned in step S2 include gas pressure constraints, gas flow constraints, compressor constraints, and natural gas quality constraints.

[0149] The gas pressure constraints include user node pressure drop constraints, pressure constraints, and pipeline pressure constraints.

[0150] The aforementioned user node pressure drop constraint refers to the basic pressure drop formula expressed in terms of volumetric flow rate, and the constraint relationship is as follows:

[0151]

[0152] In the formula, Q i,e d represents the volumetric flow rate of natural gas within the pipe segment of element e at node i; i,e P is the inner diameter of the pipe segment of node i-e element; i|(i,e) P represents the starting pressure of the pipe segment from node i to element e; e|(i,e) The endpoint pressure of node i-e element segment e; z is the natural gas compressibility factor; Δ is the relative density of natural gas; T i,e L represents the average thermodynamic temperature of shale gas within the pipe segment of node i-e element. i,e Let be the pipe length of node i-e element segment e;

[0153] The user node pressure constraint refers to the pressure on user node u being required to meet the user's minimum demand pressure. The constraint relationship is as follows:

[0154]

[0155] In the formula, P represents the minimum demand pressure for user node u. u The pressure on user node u;

[0156] The aforementioned pipeline pressure constraint refers to the pressure at pipeline node l being between the minimum allowable operating pressure and the maximum allowable operating pressure. The constraint relationship is as follows:

[0157] P l min ≤P l ≤P l max (3)

[0158] In the formula, P l The operating pressure of pipeline node l; The minimum operating pressure of pipeline node l; The maximum allowable operating pressure for pipeline node l;

[0159] The gas flow constraints include node flow balance constraints, gas source node flow constraints, and demand node flow constraints.

[0160] The node flow balance constraint refers to the requirement that the sum of the flows flowing into node i should be equal to the sum of the flows flowing out of node i. The constraint relationship is as follows:

[0161]

[0162] In the formula, Q iLet β be the flow rate of node i; ie Q is the connection coefficient between node i and element e; e The flow rate of component e;

[0163] The aforementioned gas source node flow balance constraint refers to the constraint that the flow rate of gas source node g is subject to the gas supply capacity constraint, and the constraint relationship is as follows:

[0164]

[0165] In the formula, Q is the lower bound of the gas supply capacity of gas source node g; g The gas supply to gas source node g; This is the upper bound of the gas supply capacity of gas source node g;

[0166] The user node flow balancing constraint refers to the requirement that the flow of user node u should meet the minimum gas demand of the node's users. The constraint relationship is as follows:

[0167]

[0168] In the formula, The required traffic for user node u; Q u Inject traffic into user node u;

[0169] The compressor constraints include intake pressure constraints, exhaust pressure constraints, compression ratio constraints, speed constraints, intake flow constraints, operating power constraints, and number of units in operation constraints.

[0170] The aforementioned intake pressure constraint refers to the requirement that the intake pressure of compressor j in compressor station node c should be greater than the minimum intake pressure. The constraint relationship is as follows:

[0171]

[0172] In the formula, The minimum allowable intake pressure for compressor j in compressor station node c; The intake pressure of compressor j in compressor station node c;

[0173] The aforementioned exhaust pressure constraint refers to the requirement that the exhaust pressure of compressor j in compressor station node c should be less than the maximum exhaust pressure. The constraint relationship is as follows:

[0174]

[0175] In the formula, The intake pressure of compressor j in compressor station node c; The maximum allowable discharge pressure of compressor j in compressor station node c;

[0176] The pressure ratio constraint relationship is as follows:

[0177]

[0178] In the formula, ε c,j Let J be the pressure ratio of compressor j in compressor station node c;

[0179] If the pressure ratio ε c,j If the value is greater than 1, then compressor j needs to be run to increase the pressure, and the compressor operating variable b... c,j =1, compressor j operating power N c,j Assign a positive value; if the pressure ratio ε c,j Less than or equal to 1, compressor j operating variable b c,j =0, the compressor stops running, and the operating power of compressor j is N. c,j The value is 0, and the formula for calculating the compressor power is:

[0180]

[0181] In the formula, N c,j K represents the operating power of compressor j at node c of the compressor station; k represents the specific heat of the gas. The gas compressibility factor under the suction condition of compressor j; η is the gas compressibility factor under compressor discharge conditions j; c,j Let J be the efficiency of compressor j at node c of compressor station;

[0182] The compressor operating power constraint refers to the requirement that the operating power of compressor j should be between the minimum and maximum allowable operating power. The constraint relationship is as follows:

[0183]

[0184] In the formula, N represents the minimum allowable operating power of compressor j in compressor station node c; c,j Let J be the operating power of compressor j in compressor station node c; The maximum allowable operating power of compressor j in compressor station node c;

[0185] The compressor speed constraint refers to the requirement that the speed of compressor j should be between the minimum and maximum allowable speeds. The constraint relationship is as follows:

[0186]

[0187] In the formula, r is the minimum permissible speed of compressor j in compressor station node c; c,j Let J be the rotational speed of compressor j in compressor station node c; The maximum permissible speed of compressor j in compressor station node c;

[0188] The compressor intake flow constraint refers to the requirement that the intake flow of compressor j should be between the minimum and maximum allowable intake flow rates. The constraint relationship is as follows:

[0189]

[0190]

[0191]

[0192] In the formula, q represents the minimum allowable intake flow rate of compressor j in compressor station node c; c,j Let J be the intake air flow rate of compressor j in compressor station node c; A represents the maximum allowable intake air flow rate of compressor j in compressor station node c; su B su C su A st B st C st These are compressor characteristic parameters;

[0193] The aforementioned constraint on the number of compressors in operation refers to the fact that the number of compressors in operation within node c of the compressor station is less than the total number of compressors equipped in the compressor station. The constraint relationship is as follows:

[0194]

[0195] In the formula, δ c,j χ is the startup variable for compressor j at compressor station node c; c This refers to the number of compressors within the compressor station node.

[0196] The aforementioned natural gas quality constraints include interchangeability constraints, calorific value constraints at demand nodes, and component content constraints at demand nodes.

[0197] The aforementioned interchangeability constraint refers to the requirement that the mixed natural gas supplied from multiple gas sources must meet the interchangeability requirements in the standard. This invention addresses this by setting a gas supply ratio coefficient x for gas source node g. g The constraint relationship is as follows:

[0198]

[0199] In the formula, x is the lower bound of the gas supply ratio for gas source node g; g The gas supply ratio for gas source node g; This is the upper limit of the gas supply ratio for gas source node g;

[0200] The aforementioned user node calorific value constraint refers to the requirement that the calorific value of natural gas at user node u must be greater than the minimum required calorific value of natural gas. The constraint relationship is as follows:

[0201]

[0202] In the formula, H represents the minimum calorific value of natural gas required by user node u. u The calorific value of the natural gas received at user node u;

[0203] The aforementioned methane content constraint at user node u refers to the requirement that the methane content of the natural gas at user node u must be greater than the minimum required methane content. The constraint relationship is as follows:

[0204]

[0205] In the formula, The minimum methane content required for user node u; The methane content of the natural gas received at user node u.

[0206] In one embodiment, the objective functions in step S3 are maximizing operating profit and maximizing user satisfaction, respectively.

[0207] The objective function, maximizing operating profit, consists of gas sales revenue, gas purchase cost, pipeline transportation cost, and compressor station operating expenses. The objective function relationship is as follows:

[0208] maxf1 = S sell -S purchase -S pipe -S compreesor (19)

[0209] In the formula, f1 represents the pipeline network operating profit under energy metering; S sell S represents the total revenue from natural gas sales under energy metering. purchase S represents the total cost of purchasing natural gas under energy metering; pipe S represents the total cost of pipeline transportation. compressor For the operating costs of the compressor station;

[0210] The total revenue from natural gas sales under the aforementioned energy metering is defined as the revenue earned by marketers from selling natural gas purchased upstream to downstream users, as shown in the following formula:

[0211]

[0212] In the formula, H represents the natural gas sales price at user node u under energy metering; u Q represents the calorific value of the natural gas at user node u; u The amount of natural gas delivered at user node u;

[0213] The gas purchase cost under energy metering is defined as the cost for marketers to purchase gas from upstream suppliers, as shown in the following formula:

[0214]

[0215] In the formula, H represents the natural gas purchase price at gas source node g under energy metering; g Q represents the calorific value of the natural gas at gas source node g; g The natural gas supply at gas source node g;

[0216] The total pipeline transportation cost is defined as the expense incurred in transporting natural gas to the station via pipeline, as shown in the following formula:

[0217]

[0218] In the formula, Q represents the pipeline transportation rate for pipeline node l under energy metering; l L represents the pipeline flow rate at pipeline node l. i,e H represents the pipe length of node i to element e; i H represents the calorific value of the natural gas at upstream node i of the pipeline; e The calorific value of the natural gas in downstream component e of the pipeline;

[0219] The operating cost of the compressor station is defined as the operating cost of the compressors within the compressor station node, and the relationship is as follows:

[0220]

[0221] In the formula, W c,j F represents the power consumption of compressor j in compressor station node c; c,j Let $\frac{1}{2}$ be the unit energy consumption price of compressor $j in compressor station node c.

[0222] This invention uses the calorific value content index α u and methane content index γ u The satisfaction level of user node u with the temperament is measured, where the heat value index α is used. u This indicates the difference between the actual calorific value of the incoming gas and the calorific value required by the contract; methane content index γ u The relationship between the actual methane content in the incoming gas and the contractually required methane content is expressed by the following formula:

[0223]

[0224]

[0225] In the formula, The contract requires a heat value for user node u; The actual heat value of the incoming air for user node u; (CH4) represents the methane content required by the user node u contract; (CH4) represents the methane content of the actual incoming gas at user node u;

[0226] The second objective function is to maximize user satisfaction, which means minimizing the sum of the calorific value content and methane content indicators. The objective function relationship is as follows:

[0227]

[0228] In the formula, Y u This represents the total number of user nodes u that did not meet the standards.

[0229] The symbols in formulas (1) to (25) are explained in Tables 1, 2 and 3.

[0230] Table 1. Index and Set of Multi-Source Pipeline Network Operation Scheduling Optimization Models Based on Energy Metering

[0231] i∈I=W∪L∪J∪C∪G Node set u∈U User node set l∈L Pipeline node set j∈J Compressor assembly c∈C Compressor station node set g∈G Gas source node set e∈E Component set A = {(i,e)|i∈I,e∈E} Pipe segment assembly

[0232] Table 2. Known parameters of the multi-gas source pipeline network operation scheduling optimization model based on energy metering.

[0233]

[0234]

[0235] Table 3 Decision variables for the multi-gas source pipeline network operation scheduling optimization model based on energy metering

[0236] <![CDATA[Q g ]]> Gas supply volume of gas source node g <![CDATA[δ c,j ]]> The start-up variable of compressor j in compressor station node c <![CDATA[r c,j ]]> The rotational speed of compressor j in compressor station node c

[0237] In one embodiment, step S4 uses the ε-constraint method to solve the multi-objective optimization model. This involves selecting the primary optimization objective based on the priority of different objectives and the preferences of users and marketers, transforming the secondary optimization objectives into constraints, and then converting the multi-objective optimization model into a single-objective optimization model. The relationship is as follows:

[0238]

[0239] In the formula, x is the model optimization variable; f1(x) is the primary objective function; f2(x) is the secondary objective function; ζ is the lower bound of the constraint in the single objective model; h(x) is the equality constraint; g(x) is the inequality constraint;

[0240] In the single-objective model, the lower bound ζ of the constraint can be repeatedly adjusted and tested during the solution process. Each time the single-objective model is solved, the Pareto optimal solution of the dual-objective model can be obtained. Finally, by solving a certain number of single-objective models, the Pareto solution set of the dual-objective model is obtained.

[0241] In one embodiment, the multi-source pipeline network operation scheduling optimization results mentioned in step S4 include: pipeline network operating costs, operating profits, gas sales revenue, user satisfaction, gas supply volume, and compressor station pressurization scheme.

[0242] To further illustrate this scheme, this invention uses the parameters of a multi-source gas pipeline network in China as a specific application example to conduct research on the optimization of operation scheduling of a multi-source gas pipeline network under energy metering. This long-distance pipeline consists of 4 gas source nodes (G3 and G4 are the scheduling gas sources), 30 user nodes, and 7 compressor station nodes; the pipeline is 2229 km long, with a trunk diameter of 1016 mm, and a designed annual transmission capacity of 150 × 10⁻⁶ mm. 8 m 3 The design pressure is 10.0 MPa, and the minimum operating pressure of the pipeline is... The minimum operating pressure of the pipeline is 3.0 MPa. The pressure is 9.85 MPa, and the pipe structure is as follows: Figure 2 As shown in Table 4, the structural parameters of the multi-gas source pipeline network are as follows.

[0243] Table 4 Structural parameters of a multi-gas-source pipeline network in China

[0244] Pipeline start point Pipeline terminus Pipeline length (km) Pipeline start point Pipeline terminus Pipeline length (km) C1 U1 118.76 U13 U14 9.94 C1 U2 95.64 C7 U15 81.57 U2 U3 111.87 U15 U16 87.63 U2 C2 105.71 U16 U17 49.19 U2 C2 43.53 U16 U18 113.49 C2 C3 138.41 U18 U19 79.42 C2 C4 109.65 U19 U20 109.07 C4 U4 91.53 U20 U21 52.64 U4 U5 89.18 U21 U22 102.88 U5 U6 138.55 U22 U23 29.08 C5 U7 24.31 U23 U24 43.06 U7 U8 48.24 U24 U25 21.77 U8 U9 69.79 U24 U26 7.32 C6 U10 57.71 U26 U27 28.33 U10 U11 98.39 U21 U28 109.57 U11 U12 33.27 U28 U29 67.25 U12 U13 59.45 U29 U30 47.99

[0245] Table 5 shows the node configuration parameters of a compressor station in a multi-source gas pipeline network in China.

[0246] Table 5 Configuration parameters of a compressor station node in a multi-source gas pipeline network in China

[0247]

[0248] The gas quality parameters of a gas source node in a multi-source gas pipeline network in China are shown in Table 6.

[0249] Table 6 Gas quality parameters of a gas source node in a multi-source pipeline network in China

[0250]

[0251] Table 7 shows the gas supply capacity boundary of a multi-source gas pipeline network in China.

[0252] Table 7. Gas Supply Capacity Boundary of a Multi-Source Gas Pipeline Network in China

[0253]

[0254] Table 8 shows the gas volume and quality requirements of a user node in a multi-source gas pipeline network in China.

[0255] Table 8. Gas volume and quality demand of a user node in a multi-source gas pipeline network in China.

[0256]

[0257]

[0258] This specific application example uses maximizing operating profit as the primary optimization objective, transforming maximizing user satisfaction into a constraint. Based on this, the bi-objective optimization model is converted into a set of single-objective optimization models. In the actual solution process, based on the difference in gas quality between the scheduled gas source and the original gas source, and their supply capacity, the initial range of user satisfaction f2 is determined to be 99.6989%–100%, with a step size of 0.0009%. When f2 exceeds 99.7735%, the model cannot find a feasible solution. This is because when the gas supply from gas source G3 is less than 1413.2 × 10⁻⁶... 4 m 3 When the gas supply pressure is less than the pipeline pressure, the supplied natural gas cannot be successfully integrated into the pipeline. Therefore, the value range of f2 is adjusted to 99.6989% to 99.7735%, and it is expected to solve 82 single-objective problems. However, since some problems cannot be solved feasiblely, 72 single-objective problems are actually solved.

[0259] The Pareto solution set obtained from this specific application example is as follows: Figure 3 As shown. The objective function user satisfaction f2 varies from 99.70% to 99.77%, and the operating profit varies from RMB 14.0307 million to RMB 17.5244 million. The operating profit decreases as user satisfaction increases. Each point on the Pareto front represents a Pareto optimal solution. Three representative points A, B, and C are selected on the Pareto front for analysis. Points A, B, and C are Pareto optimal solutions under the conditions of minimum, medium, and maximum operating profit, respectively. (1) Under point A, user satisfaction is the highest and operating profit is the lowest. (2) Under point C, operating profit is the highest and user satisfaction is the lowest. (3) Under point B, the values ​​of the two objective functions are between points A and B.

[0260] The objective function values ​​for each point are shown in Table 9. Table 9 shows that the user satisfaction rate at point A differs from that at point C by 0.075%, while the profit difference is 3.4936 million yuan. This difference is primarily due to variations in compressor station operating costs (302,700 yuan), gas purchase costs (1.0573 million yuan), and pipeline transportation costs (2.1737 million yuan), while the gas sales revenue differs by only 40,100 yuan. The profit difference is mainly caused by changes in pipeline transportation costs.

[0261] Table 9 shows the optimization results of the objective functions for points A to C.

[0262]

[0263] Table 10 shows the gas supply volume of each gas source at points A, B, and C. As can be seen from Table 10, gas source G4 at each point supplies gas at its maximum capacity of 850 × 10⁻⁶. 4 m 3 Gas is supplied by / d. The purchase price of G4 gas is 47.98 yuan / GJ, the highest among the five gas sources, but its calorific value is 38.08 MJ / m³. 3 Among the five gas sources, IN5 has the highest calorific value, so when IN5 supplies gas at its maximum capacity, the supplied natural gas can be delivered to the demand node DN29, effectively reducing operating costs. G3 has the lowest calorific value, at 36.32 MJ / m³. 3 The large-scale introduction of natural gas supplied by G3 may lead to substandard natural gas quality received by downstream users, resulting in decreased user satisfaction. However, because G3 is located in the middle of this multi-source gas pipeline network and has the lowest purchase price (32.76 yuan / GJ), the larger the supply volume of G3, the higher the operating profit. G2 has a lower calorific value (36.44 MJ / m³). 3 The gas supply of G2 decreases as the gas supply of G3 increases.

[0264] Table 10 Gas supply from points A to C

[0265]

[0266] The pressurization schemes for points A, B, and C are shown in Table 10. As can be seen from Table 10, as the gas supply from gas source G3 increases, the number of compressors operating across the entire line gradually decreases, and compressor stations C5 and C6 can meet the pressurization requirements of the entire line even when not in operation.

[0267] Table 10 shows the boosting solutions for scenarios A, B, and C.

[0268]

[0269]

[0270] The compressor speeds of each compressor station under conditions A through C are as follows: Figure 4 As shown. By Figure 4 It can be seen that under different conditions, the rotation speeds of compressor stations C1, C2, C7 and C8 are the same, which are 4781.25 rpm, 5873 rpm, 5633 rpm and 4412 rpm respectively. This is because C1, C2 and C8 process the same amount of gas, while C7 operates at the lowest speed of 5633 rpm to reduce energy consumption costs. The start-up mode of compressor stations CS3 to CS6 varies depending on the actual situation.

[0271] The pressure drop along the pipeline trunk line under the conditions of points A to C is as follows: Figure 5 Show. Depend on Figure 5 It can be seen that the pressure drop in the trunk line exhibits the same trend before 300km and after 1100km (i.e., after C7). However, between 300km and 1100km, the pressure drop varies due to differences in the gas volume transported and the pressurization methods used. At points A through C, the lowest pressure at each trunk line node is 3.70MPa, while the highest pressure occurs at point B, reaching 7.45MPa. Therefore, the pressure at each node of the trunk line falls between 3.0MPa and 9.85MPa, meeting the pipeline transportation requirements.

[0272] The above description is merely an embodiment of the present specification and is not intended to limit the embodiments of the present specification. For those skilled in the art, various modifications and variations can be made to the embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present specification should be included within the scope of the claims of the embodiments of the present specification.

Claims

1. A method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering, characterized in that, Includes the following steps: S1: Based on the multi-source pipeline network structure parameters, the pipeline network is divided into multi-source pipeline networks with different nodes; S2: Based on the structural parameters of the multi-source pipeline network, the gas quality parameters, the natural gas purchase and sale parameters, and the user demand parameters, construct the constraints of the multi-source pipeline network operation scheduling optimization model based on energy metering. S3: Construct a multi-gas-source pipeline network operation scheduling optimization model based on energy metering, according to the constraints and the multi-objective functions of maximizing operating profit and maximizing user satisfaction. S4: Solve the multi-gas source pipeline network operation scheduling optimization model based on energy metering and output the multi-gas source pipeline network operation scheduling optimization results.

2. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, The energy metering method adopted is based on the benchmark calorific value and converts the unit of measurement through the current volume price. At the same time, a price fluctuation coefficient will be introduced during the conversion process to represent the price fluctuation caused by the difference between the actual calorific value of natural gas in various regions and by various users and the calorific value benchmark published by the state. The fluctuation coefficient w is: In the formula, w is the natural gas price volatility coefficient; H act This represents the actual calorific value of natural gas; H0 represents the benchmark calorific value for converting natural gas prices from volume-based pricing to energy-based pricing. The energy metering of the natural gas sales price F sell Purchase price F purchase and transportation price F pipe The conversion formula is: In the formula, F sell The selling price of natural gas under energy metering; F v，sell The selling price of natural gas based on volumetric measurement; F purchase The purchase price of natural gas under energy metering; F v，purchase The purchase price of natural gas by volume; F pipe The purchase price of natural gas under energy metering; F v，pipe This represents the purchase price of natural gas based on volumetric measurement.

3. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, Before step S1, it is also necessary to obtain the structural parameters of the target pipeline network, gas source supply parameters, natural gas purchase and sale parameters, and user demand parameters. The pipeline network structure parameters include gas source location, user location, compressor station configuration, and pipeline length; The gas supply parameters include: gas supply pressure, gas supply capacity boundary, and gas quality parameters. The natural gas purchase and sale parameters include: natural gas purchase price, natural gas pipeline transportation price, and natural gas sales price; The user requirement parameters include: user air volume requirement and user temperament requirement.

4. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, The nodes mentioned in step S1 include: gas source node, user node, pipeline node, and compressor station node; The gas source node is used to represent a gas source with gas supply capability; The user nodes are used to represent downstream users; The aforementioned pipe nodes are used to represent pipes that connect various types of nodes; The compressor station node is used to represent a compressor station with a pressurization function.

5. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, The constraints mentioned in step S2 include gas pressure constraints, gas flow constraints, compressor constraints, and natural gas quality constraints; The gas pressure constraints include user node pressure drop constraints, pressure constraints, and pipeline pressure constraints. The aforementioned user node pressure drop constraint refers to the basic pressure drop formula expressed in terms of volumetric flow rate, and the constraint relationship is as follows: In the formula, Q i,e d represents the volumetric flow rate of natural gas within the pipe segment of element e at node i; i,e P is the inner diameter of the pipe segment of node i-e element; i|(i,e) P represents the starting pressure of the pipe segment from node i to element e; e|(i,e) Z represents the end pressure of node i-e element segment e; Z is the natural gas compressibility factor. Δ represents the relative density of natural gas; T i,e L represents the average thermodynamic temperature of shale gas within the pipe segment of node i-e element. i,j Let be the pipe length of node i-e element segment e; The user node pressure constraint refers to the pressure on user node u being required to meet the user's minimum demand pressure. The constraint relationship is as follows: In the formula, P represents the minimum demand pressure for user node u. u The pressure on user node u; The aforementioned pipeline pressure constraint refers to the pressure at pipeline node l being between the minimum allowable operating pressure and the maximum allowable operating pressure. The constraint relationship is as follows: P l min ≤P l ≤P l max In the formula, P l The operating pressure of pipeline node l; The minimum operating pressure of pipeline node l; The maximum allowable operating pressure for pipeline node l; The gas flow constraints include node flow balance constraints, gas source node flow constraints, and demand node flow constraints. The node flow balance constraint refers to the requirement that the sum of the flows flowing into node i should be equal to the sum of the flows flowing out of node i. The constraint relationship is as follows: In the formula, Q i Let β be the flow rate of node i; ie Q is the connection coefficient between node i and element e; e The flow rate of component e; The aforementioned gas source node flow balance constraint refers to the constraint that the flow rate of gas source node g is subject to the gas supply capacity constraint, and the constraint relationship is as follows: In the formula, Q is the lower bound of the gas supply capacity of gas source node g; g The gas supply to gas source node g; This is the upper bound of the gas supply capacity of gas source node g; The user node flow balancing constraint refers to the requirement that the flow of user node u should meet the minimum gas demand of the node's users. The constraint relationship is as follows: In the formula, The required traffic for user node u; Q u Inject traffic into user node u; The compressor constraints include intake pressure constraints, exhaust pressure constraints, pressure ratio constraints, speed constraints, intake flow constraints, operating power constraints, and number of units in operation constraints. The aforementioned intake pressure constraint refers to the requirement that the intake pressure of compressor j in compressor station node c should be greater than the minimum intake pressure. The constraint relationship is as follows: In the formula, The minimum allowable intake pressure for compressor j in compressor station node c; Let J be the intake pressure of compressor j in compressor station node c; The aforementioned exhaust pressure constraint refers to the requirement that the exhaust pressure of compressor j in compressor station node c should be less than the maximum exhaust pressure. The constraint relationship is as follows: In the formula, The discharge pressure of compressor j in compressor station node c; The maximum allowable discharge pressure of compressor j in compressor station node c; The pressure ratio constraint relationship is as follows: In the formula, ε c,j Let J be the pressure ratio of compressor j in compressor station node c; If the pressure ratio ε c,j If the value is greater than 1, then compressor j needs to be run to increase the pressure, and the compressor operating variable b... c,j =1, compressor j operating power N c,j Assign a positive value; if the pressure ratio ε c,j Less than or equal to 1, compressor j operating variable b c,j =0, the compressor stops running, and the operating power of compressor j is N. c,j The value is 0, and the formula for calculating the compressor power is: In the formula, N c,j K represents the operating power of compressor j at node c of the compressor station; k represents the specific heat of the gas. The gas compressibility factor under the suction condition of compressor j; The gas compressibility factor under compressor discharge conditions; η c,j Let J be the efficiency of compressor j at node c of compressor station; The compressor operating power constraint refers to the requirement that the operating power of compressor j should be between the minimum and maximum allowable operating power. The constraint relationship is as follows: In the formula, N represents the minimum allowable operating power of compressor j in compressor station node c; c,j Let J be the operating power of compressor j in compressor station node c; The maximum allowable operating power of compressor j in compressor station node c; The compressor speed constraint refers to the requirement that the speed of compressor j should be between the minimum and maximum allowable speeds. The constraint relationship is as follows: In the formula, r is the minimum permissible speed of compressor j in compressor station node c; c,j Let J be the rotational speed of compressor j in compressor station node c; The maximum permissible speed of compressor j in compressor station node c; The compressor intake flow constraint refers to the requirement that the intake flow of compressor j should be between the minimum and maximum allowable intake flow rates. The constraint relationship is as follows: In the formula, q represents the minimum allowable intake flow rate of compressor j in compressor station node c; c,j Let be the intake air flow rate of compressor j in compressor station node c; A represents the maximum allowable intake air flow rate of compressor j in compressor station node c; su B su C su A st B st C st These are compressor characteristic parameters; The aforementioned constraint on the number of compressors in operation refers to the fact that the number of compressors in operation within node c of the compressor station is less than the total number of compressors equipped in the compressor station. The constraint relationship is as follows: In the formula, δ c,j χ is the startup variable for compressor j at compressor station node c; c The number of compressors within compressor station node c; The aforementioned natural gas quality constraints include interchangeability constraints, user node calorific value constraints, and user node component content constraints. The aforementioned interchangeability constraint refers to the requirement that the mixed natural gas supply from multiple gas sources must meet the interchangeability requirements in the standard, which is achieved by setting the gas supply ratio coefficient x of gas source node g. g The constraint relationship is as follows: In the formula, x is the lower bound of the gas supply ratio for gas source node g; g The gas supply ratio for gas source node g; This is the upper limit of the gas supply ratio for gas source node g; The aforementioned user node calorific value constraint refers to the requirement that the calorific value of natural gas at user node u must be greater than the minimum required calorific value of natural gas. The constraint relationship is as follows: In the formula, H represents the minimum calorific value of natural gas required by user node u. u The calorific value of the natural gas received at user node u; The aforementioned methane content constraint at user node u refers to the requirement that the methane content of the natural gas at user node u must be greater than the minimum required methane content. The constraint relationship is as follows: In the formula, The minimum methane content required for user node u; The methane content of the natural gas received at user node u.

6. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, The objective functions mentioned in step S3 are maximizing operating profit and maximizing user satisfaction, respectively. The objective function, maximizing operating profit, consists of gas sales revenue, gas purchase cost, pipeline transportation cost, and compressor station operating expenses. The objective function relationship is as follows: maxf1=S sell -S purchase -S pipe -S compreesor In the formula, f1 represents the pipeline network operating profit under energy metering; S sell S represents the total revenue from natural gas sales under energy metering. purchase S represents the total cost of purchasing natural gas under energy metering; pipe S represents the total cost of pipeline transportation. compressor For the operating costs of the compressor station; The total revenue from natural gas sales under the aforementioned energy metering is defined as the revenue earned by marketers from selling natural gas purchased upstream to downstream users, as shown in the following formula: In the formula, The price of natural gas sold at user node u under energy metering; H u Q represents the calorific value of the natural gas at user node u; u The amount of natural gas delivered at user node u; The gas purchase cost under energy metering is defined as the cost for marketers to purchase gas from upstream suppliers, as shown in the following formula: In the formula, H represents the natural gas purchase price at gas source node g under energy metering; g Q represents the calorific value of the natural gas at gas source node g; g The natural gas supply at gas source node g; The total pipeline transportation cost is defined as the expense incurred in transporting natural gas to the station via pipeline, as shown in the following formula: In the formula, Q represents the pipeline transport rate for pipeline node l under energy metering; l L represents the pipeline flow rate at pipeline node l. i,e H represents the pipe length of node i to element e; i H represents the calorific value of the natural gas at upstream node i of the pipeline; e The calorific value of the natural gas in downstream component e of the pipeline; The operating cost of the compressor station is defined as the operating cost of the compressors within the compressor station node, and the relationship is as follows: In the formula, W c,j F represents the power consumption of compressor j in compressor station node c; c,j Let $\frac{ ... Using the calorific value index α u and methane content index γ u The satisfaction level of user node u with the temperament is measured, where the heat value index α is used. u This indicates the difference between the actual calorific value of the incoming gas and the calorific value required by the contract; methane content index γ u The relationship between the actual methane content in the incoming gas and the contractually required methane content is expressed by the following formula: In the formula, The contract requires a heat value for user node u; The actual heat value of the incoming air for user node u; The methane content required by the user node u contract; The actual methane content of the incoming gas at user node u; The second objective function is to maximize user satisfaction, which means minimizing the sum of the calorific value content and methane content indicators. The objective function relationship is as follows: In the formula, Y u This represents the total number of user nodes u that did not meet the standards.

7. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, In step S4, the ε-constraint method is used to solve the multi-objective optimization model. This involves selecting the primary optimization objective based on the priority of different objectives and the preferences of users and marketers, transforming the secondary optimization objectives into constraints, and finally converting the multi-objective optimization model into a single-objective optimization model. The relationship is as follows: In the formula, x is the model optimization variable; f1(x) is the primary objective function; f2(x) is the secondary objective function; ζ is the lower bound of the constraint in the single objective model; h(x) is the equality constraint; g(x) is the inequality constraint; In the single-objective model, the lower bound ζ of the constraint can be repeatedly adjusted and tested during the solution process. Each time the single-objective model is solved, the Pareto optimal solution of the dual-objective model can be obtained. Finally, by solving a certain number of single-objective models, the Pareto solution set of the dual-objective model is obtained.

8. The method for optimizing the operation and scheduling of multi-gas source pipeline networks based on energy metering as described in claim 1, characterized in that, The optimization results of multi-source pipeline operation scheduling mentioned in step S4 include: pipeline operation cost, operation profit, gas sales revenue, user satisfaction, gas supply volume, and compressor station pressurization scheme.

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