Gas-state energy distribution method, device and equipment

Abstract

The invention provides a gaseous energy distribution method, device and equipment, and relates to the technical field of gaseous energy distribution, the method comprises the following steps: receiving a gas distribution request of a user, and obtaining gas distribution associated information based on the gas distribution request; the gas distribution associated information serves as input of a pre-constructed gas distribution model, decision variables of the gas distribution model are solved in a prepared optimization mode, and a target gas distribution scheme is obtained; wherein the gas distribution model is constructed based on a mixed integer programming algorithm framework; and based on the target gas distribution scheme, conveying the gaseous energy from the supply point to the demand point through the pipeline path. According to the scheme, the gas distribution model is pre-constructed on the basis of the mixed integer programming algorithm framework, then the globally optimal target gas distribution scheme can be obtained through the gas distribution model, gas distribution of the gaseous energy is conducted through the target gas distribution scheme, the balancing accuracy is improved, and the balancing efficiency can also be improved.

Description

Technical Field

[0001] The present application relates to the field of gaseous energy distribution technology, and in particular to a gaseous energy distribution method, device and equipment. Background Art

[0002] With the continuous growth of global energy demand and the transformation of energy structures, gaseous energy, such as natural gas, is becoming increasingly prominent as a clean and efficient energy source. However, the imbalance between natural gas supply and demand, particularly in large-scale natural gas pipeline networks, has become a key factor restricting its efficient utilization. To ensure the stability and reliability of natural gas supply while improving energy efficiency, natural gas volume balancing is particularly important.

[0003] In related technologies, natural gas balancing is typically performed manually. Specifically, natural gas demand data from each user is collected, and then a gas distribution plan is developed based on the analysis of this demand data. This distribution plan is then transmitted to each supply point to guide the distribution of natural gas.

[0004] However, the above manual balancing method has low accuracy. Summary of the Invention

[0005] In view of the above problems, the present application aims to provide a gaseous energy distribution method, device and equipment to improve resource utilization.

[0006] In a first aspect, the present application provides a gaseous energy distribution method, the method comprising:

[0007] Receive a gas distribution request from a user, and obtain gas distribution related information based on the gas distribution request; wherein the gas distribution related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point;

[0008] The gas distribution correlation information is used as the input of a pre-built gas distribution model, and the decision variables of the gas distribution model are solved through a pre-prepared optimization method to obtain the target gas distribution plan; wherein, the gas distribution model is constructed based on the mixed integer programming algorithm framework;

[0009] Based on the target gas distribution plan, the gaseous energy is transported from the supply point to the demand point through the pipeline path.

[0010] In one possible implementation, the supply point information includes basic supply point information and supply quantity submission information; the demand point information includes basic demand point information and demand quantity submission information; and the gas distribution related information also includes at least one of the following: planning cycle, historical flow information, planned total flow, and daily flow limit.

[0011] In one possible implementation, the objective function of the gas distribution model includes at least one of the following:

[0012] Penalties for unmet demand, short-term plan violations, long-term plan violations, adjustable path gas volume penalties, and penalties for flow rates below the daily flow limit.

[0013] In a possible implementation, the penalty item for short-term plan violation is determined based on a short-term negative deviation penalty coefficient, a short-term positive deviation penalty coefficient, the accumulated historical flow as of the planned day, and the planned flow.

[0014] In a possible implementation, the penalty item for long-term plan violation is determined based on a long-term negative deviation penalty coefficient, a long-term positive deviation penalty coefficient, the accumulated flow of the plan period, and the planned total flow.

[0015] In a possible implementation, the constraints of the gas distribution model include at least one of the following:

[0016] Cumulative flow constraints, demand point demand constraints, supply and demand relationship constraints, the first relationship constraint between the cumulative historical flow as of the planning day and the planned flow, the second relationship constraint between the cumulative flow of the planning period and the planned total flow, fixed flow constraints, path transmission capacity constraints, adjustable path transmission capacity constraints, and daily flow constraints.

[0017] In a possible implementation manner, the first relationship constraint is determined based on the accumulated historical flow as of the planning day, the planned flow, the daily flow on the planning day, and the plan deviation limit.

[0018] In a possible implementation manner, the second relationship constraint is determined based on the accumulated flow of the planning period, the planned total flow, and the planned deviation limit.

[0019] In a second aspect, the present application provides a gaseous energy distribution device, the device comprising:

[0020] An acquisition unit, configured to receive a gas distribution request from a user and acquire gas distribution-related information based on the gas distribution request; wherein the gas distribution-related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point;

[0021] A decision-making unit is used to use the gas distribution related information as input to a pre-built gas distribution model, solve the decision variables of the gas distribution model through a pre-built optimization method, and obtain a target gas distribution plan; wherein the gas distribution model is constructed based on a mixed integer programming algorithm framework;

[0022] The gas distribution unit is used to transport the gaseous energy from the supply point to the demand point through the pipeline path based on the target gas distribution plan.

[0023] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0024] The memory stores computer-executable instructions;

[0025] The processor executes the computer-executable instructions stored in the memory to implement the method in any possible implementation of the first aspect above.

[0026] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method in any possible implementation of the above-mentioned first aspect.

[0027] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the method in any possible implementation manner of the first aspect.

[0028] The present application provides a method, device, and equipment for gas distribution of gas energy, the method comprising: receiving a gas distribution request from a user, and obtaining gas distribution-related information based on the gas distribution request; wherein the gas distribution-related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point; using the gas distribution-related information as input to a pre-constructed gas distribution model, solving the decision variables of the gas distribution model through a prepared optimization method, and obtaining a target gas distribution scheme; wherein the gas distribution model is constructed based on a mixed integer programming algorithm framework; based on the target gas distribution scheme, gaseous energy is transported from the supply point to the demand point through a pipeline path. This solution pre-constructs a gas distribution model based on a mixed integer programming algorithm framework, and then the gas distribution model can be used to obtain a globally optimal target gas distribution scheme. Using the target gas distribution scheme to distribute gaseous energy improves the accuracy of balancing and can also improve the efficiency of balancing. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0030] Figure 1 A schematic flow chart of a gaseous energy distribution method provided in Example 1 of the present application;

[0031] Figure 2 A schematic flow chart of another gaseous energy distribution method provided in Example 2 of the present application;

[0032] Figure 3A schematic structural diagram of a gaseous energy distribution device provided in Example 3 of the present application;

[0033] Figure 4 This is a hardware structure diagram of an electronic device provided in Example 4 of the present application.

[0034] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.

[0035] Description of reference numerals:

[0036] 300 - Gaseous energy distribution device; 301 - Acquisition unit; 302 - Decision-making unit; 303 - Distribution unit; 401 - Processor; 402 - Memory; 403 - Communication interface; 404 - Communication bus. DETAILED DESCRIPTION

[0037] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0038] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being more preferred or advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. In the embodiments of the present application, "at least one" refers to one or more, and "a plurality" refers to two or more.

[0039] Currently, there are the following problems with natural gas balancing:

[0040] Supply-demand mismatch: There is a significant temporal and spatial mismatch between natural gas supply and demand. On the one hand, the production capacity of natural gas fields may be limited by factors such as geological conditions and extraction technology; on the other hand, natural gas demand fluctuates significantly due to factors such as climate, economics, and policies. This mismatch leads to waste of natural gas resources and insufficient supply.

[0041] Pipeline network complexity: Large-scale natural gas pipeline systems typically consist of multiple gas sources, multiple users, and complex pipeline networks. These pipeline networks are not only widely distributed but also intertwined, forming a complex network structure. In such a complex system, how to accurately and quickly balance the natural gas volume has become a pressing issue.

[0042] Intelligent Demand: With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, the natural gas industry is increasingly demanding intelligent and automated processes. Traditional gas balancing methods often rely on manual experience and adjustments, which are not only inefficient but also difficult to adapt to rapidly changing market demands.

[0043] Based on the above background, the market demand for intelligent balancing of natural gas volume is growing.

[0044] Take-or-pay is a fundamental clause, practice, and rule in international natural gas supply contracts. Under market conditions, if a user's gas usage falls below the contracted volume, they must still pay for that volume. If the gas supplier fails to deliver the required volume, the user must compensate accordingly. If a user withdraws less natural gas than the contracted volume for the year, they can make additional withdrawals within a certain period (e.g., three years). Therefore, when developing a natural gas balancing plan, it is important to keep the cumulative natural gas flow as close to the contracted volume as possible. Otherwise, the take-or-pay payment method will result in certain financial losses.

[0045] In related technologies, manual balancing is a major and traditional method. The process of manual balancing of natural gas volume mainly relies on the experience and judgment of the dispatcher, as well as a series of operating procedures. The basic process of manual balancing is as follows:

[0046] Data Collection and Analysis: Dispatchers collect gas usage data from each user, typically submitted by phone or online. They review and analyze the collected data to understand user gas demand and trends.

[0047] Developing a gas distribution plan: Based on data analysis, dispatchers will develop a preliminary gas distribution plan designed to balance gas supply and demand to ensure a stable supply of natural gas. This plan will take into account the gas usage characteristics of different users, such as daily unevenness in residential gas consumption and seasonal fluctuations in industrial gas use.

[0048] Implementation and Adjustment: Dispatchers communicate the gas distribution plan to each gas supply station and provide guidance on how to allocate natural gas. During the implementation process, dispatchers closely monitor changes in natural gas supply and demand and adjust the distribution plan based on actual conditions.

[0049] However, the accuracy of the above manual balancing method depends to a certain extent on the experience and judgment of the dispatcher, which may be subjective and have low balancing accuracy.

[0050] In order to solve the above technical problems, the embodiments of the present application provide a gaseous energy distribution method, device and equipment, which pre-constructs a distribution model based on a mixed integer programming algorithm framework, and then uses the distribution model to obtain a globally optimal target distribution plan. The use of the target distribution plan to distribute gaseous energy improves the balancing accuracy; it can also improve the balancing efficiency; and it is highly flexible and can be quickly adjusted according to market changes and user needs.

[0051] Figure 1 This is a flow chart of a gaseous energy distribution method provided in Example 1 of the present application. This embodiment can be applied to gaseous energy distribution scenarios. The method can be performed by a gaseous energy distribution device, which can be implemented by software and / or hardware and specifically configured in an electronic device. Figure 1 As shown, the method includes:

[0052] Step 101: Receive a gas distribution request from a user, and obtain gas distribution related information based on the gas distribution request; wherein the gas distribution related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point.

[0053] A supply point refers to a loading point in a gaseous energy pipeline network, with each loading point representing a supply point. Each loading point has a specific gaseous energy type. The loading point is where the gaseous energy enters the pipeline network, typically located near the gas source or after a gaseous energy processing facility. Supply point information may include the location of at least one supply point, the gaseous energy type corresponding to each supply point, and the gaseous energy supply quantity corresponding to each supply point. In this solution, the gaseous energy may be natural gas.

[0054] Demand points refer to download points within the gaseous energy pipeline network. Each download point represents a demand point. Each download point corresponds to a specific user, which can be a downstream gaseous energy company. The download point is the location where gaseous energy is extracted from the pipeline network, typically near a consumer market or user end. Demand point information may include the location of at least one demand point, the user information corresponding to each demand point, and the gaseous energy demand corresponding to each demand point.

[0055] Step 102 : Using the gas distribution related information as input to a pre-built gas distribution model, solving the decision variables of the gas distribution model through a prepared optimization method to obtain a target gas distribution scheme; wherein the gas distribution model is constructed based on a mixed integer programming algorithm framework.

[0056] Specifically, the constraints and objective function of the gas distribution model can be pre-established based on a mixed integer programming algorithm framework. The gas distribution-related information is input into the constraints and objective function. Under the premise that the constraints are satisfied, the minimum value of the objective function is searched, and the target gas distribution solution corresponding to the minimum objective function is determined.

[0057] The "prepared optimization method" refers to a pre-prepared optimization method. This solution does not restrict the optimization method. For example, the optimization method can be a solver or a heuristic method. The solver can utilize a mature commercial solver, such as the Cardinal Optimizer (COPT).

[0058] Step 103: Based on the target gas distribution plan, the gaseous energy is transported from the supply point to the demand point through the pipeline path.

[0059] The target gas distribution plan at least includes the following contents: the flow of gaseous energy from a supply point to a demand point through a pipeline path at a certain time.

[0060] The gaseous energy distribution method provided in the above embodiment pre-constructs a distribution model based on a mixed integer programming algorithm framework, and then the distribution model can be used to obtain a globally optimal target distribution plan. The use of the target distribution plan to distribute gaseous energy improves the balancing accuracy; it can also improve the balancing efficiency; and it is highly flexible and can be quickly adjusted according to market changes and user needs.

[0061] Figure 2 This is a flow chart of another gaseous energy distribution method provided in Example 2 of this application. Figure 1 Based on the embodiment shown, the gas energy distribution method is improved.

[0062] like Figure 2 As shown, a gaseous energy distribution method may include the following steps:

[0063] Step 201, receiving a gas distribution request from a user, and obtaining gas distribution related information based on the gas distribution request; wherein the gas distribution related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point; the supply point information includes the supply point basic information and supply quantity submission information; the demand point information includes the demand point basic information and demand quantity submission information; the gas distribution related information also includes at least one of the following: planning cycle, historical flow information, planned total flow, and daily flow limit.

[0064] The planning period can be determined based on the period of the gas energy contract or a period agreed upon by both parties to the contract. This solution does not limit the length of the planning period. For example, the planning period can be one month or one quarter.

[0065] Basic supply point information can include the location of the supply point and the type of gas energy it corresponds to. Supply quantity information submitted can include the gas energy supply quantity corresponding to each supply point on the planned day, as well as the daily projected gas energy supply quantity from the planned day to the last day of the planned period. Typically, users submit supply quantity information the day before.

[0066] Basic demand point information includes the location of the demand point and the corresponding user information. Demand information submitted includes the gas energy demand corresponding to each demand point on the planning day, as well as the daily projected gas energy demand from the planning day to the last day of the planning period. Typically, users submit demand information the day before.

[0067] The historical traffic information refers to the traffic information that has been actually executed as of the planned day.

[0068] Among them, the planned total flow refers to the total flow of gaseous energy in a planned period agreed upon by the contracting parties based on the gaseous energy contract.

[0069] In step 202, the gas distribution related information is used as the input of a pre-built gas distribution model, and the decision variables of the gas distribution model are solved by a prepared optimization method to obtain a target gas distribution plan; wherein, the gas distribution model is constructed based on a mixed integer programming algorithm framework; the objective function of the gas distribution model includes at least one of the following: a penalty item for unmet demand, a penalty item for short-term violation of the plan, a penalty item for long-term violation of the plan, a penalty item for adjustable path gas transmission volume, and a penalty item for being below the daily flow limit; the constraints of the gas distribution model include at least one of the following: a cumulative flow constraint, a demand constraint at a demand point, a supply-demand relationship constraint, a first relationship constraint between the cumulative historical flow as of the planning day and the planned flow, a second relationship constraint between the cumulative flow of the planning period and the planned total flow, a fixed flow constraint, a path transmission capacity constraint, an adjustable path transmission capacity constraint, and a daily flow constraint.

[0070] Specifically, the gas distribution model involves a multi-objective optimization problem, whose main goal is to minimize the situation where the cumulative flow of each pipeline path during the planning period is lower than the first percentage of the contract amount and higher than the second percentage of the contract amount while meeting the demand at daily demand points, so as to make the cumulative flow of each pipeline path during the planning period within a reasonable range of the contract amount as much as possible, thereby reducing economic costs.

[0071] In one achievable manner, the penalty term for short-term plan violation is determined based on the short-term negative deviation penalty coefficient, the short-term positive deviation penalty coefficient, the accumulated historical flow as of the planned day, and the planned flow.

[0072] Specifically, the above method can be used to conveniently determine the penalty items for short-term violations of the plan.

[0073] In one achievable manner, the penalty term for long-term plan violation is determined based on the long-term negative deviation penalty coefficient, the long-term positive deviation penalty coefficient, the accumulated flow of the planning period, and the planned total flow.

[0074] Specifically, the above method can be used to conveniently determine the penalty items for long-term violations of the plan.

[0075] In practice, the objective function of the gas distribution model can be expressed as follows:

[0076]

[0077] The first item represents the penalty for unmet demand, the second item represents the penalty for short-term plan violations, the third item represents the penalty for long-term plan violations, the fourth item represents the penalty for adjustable path gas flow, and the fifth item represents the penalty for falling below the daily flow limit. pn represents the penalty coefficient for unmet daily demand; ls dt represents the unmet demand at demand point d on day t, that is, the difference between the demand at demand point d on day t and the actual flow rate; T represents the set of all times included in a planning cycle; D represents the set of demand points; ps lb Short-term negative deviation penalty coefficient; ps lb represents the short-term positive deviation penalty coefficient; Indicates that as of the planning date, the cumulative historical flow from supply point s to demand point d is lower than the first percentage of the cumulative daily average planned flow. The value of the first percentage can be determined based on the "take-or-pay" clause in the gas energy contract. For example, the first percentage can be 90%. Assuming the planning period is 30 days, the total planned flow is 100, and the planning date is the 20th day, then the cumulative daily average planned flow = 20 * 100 / 30. This cumulative daily average planned flow can be used as the planned flow as of the planning date. = indicates that the cumulative historical flow from supply point s to demand point d as of the planned date is higher than the second percentage of the cumulative daily average planned flow. The second percentage can be determined based on the "take-or-pay" clause in the gas energy contract. For example, the second percentage can be 105%. S represents the set of supply points. D s represents the set of demand points that can be connected to the supply point s, determined based on the gas energy contract signing situation; pl lb represents the long-term negative deviation penalty coefficient; pl ubrepresents the long-term positive deviation penalty coefficient; The cumulative flow from supply point s to demand point d during the entire planning period is lower than the first percentage value of the planned total flow during the entire planning period; It indicates that the cumulative flow from supply point s to demand point d during the entire planning period is higher than the second percentage of the planned total flow; pv is the penalty coefficient for adjustable path supply; represents the daily flow from the adjustable supply point a to the demand point d corresponding to the adjustable path on day t, where flow is the gas transmission volume; A represents the set of adjustable supply points; represents the set of demand points that can be connected to the adjustable supply point a; pa represents the penalty coefficient for the path daily flow being lower than the daily flow limit; It represents the difference between the daily flow rate from supply point s to demand point d and the daily flow rate limit when the daily flow rate does not reach the daily flow rate limit on day t.

[0078] The above-mentioned "pay as is" clause can be: if the cumulative flow from the supply point to the demand point during the entire planning period does not reach the contract amount, that is, 90% of the planned total flow, then payment must still be made at 90% of the contract amount; if it reaches 90% or above, payment will be made according to the actual flow.

[0079] The cumulative flow rate should be kept at 90% or above of the planned total flow rate. In addition, the cumulative flow rate should be kept below 110% of the planned total flow rate to reduce contract deviations and avoid situations where the supply point may be unable to meet supply requirements due to excessive deviations.

[0080] Specifically, the penalty item for unmet demand is used to ensure that the demand of each demand point is met as much as possible every day.

[0081] Specifically, in the penalty items for short-term violation of the plan, only the supply and demand on the planned day are real data, while the future demand and supply are estimated data. The penalty items for short-term violation of the plan will be recalculated every day based on the submitted data. and On the last day of the planning period, equal and equal Minimizing the penalty term for short-term plan violations can represent minimizing the cumulative plan violations up to the planned day. This term can keep the plan currently being executed by the system in a good state and improve the flexibility and stability of the system.

[0082] Specifically, the goal of minimizing the penalty for long-term plan violations is to ensure that the cumulative flow rate for each pipeline, as estimated from the current point in time to the last day of the planning period, remains above 90% and below 110% of the contracted volume. This penalty for long-term plan violations allows for the estimation of future pipeline flow rates on the planning day, allowing for adjustments to be made based on potential future deviations.

[0083] Specifically, this solution implements a two-tier penalty mechanism consisting of penalties for short-term and long-term violations of the plan, and balances real-time execution stability and the final contract fulfillment rate through a weighted objective function.

[0084] During implementation, since the total supply and demand of all supply points and demand points may not be completely matched every day, the gas energy company will reserve adjustable supply points. If the supply points cannot meet the demand of the corresponding demand points, the demand points will be allowed to draw gas from the adjustable supply points to meet the demand.

[0085] Specifically, the gas delivery penalty item of the adjustable path can be used to ensure that the adjustable supply point can only be used when the normal supply point cannot meet the demand of the corresponding demand point.

[0086] Specifically, a penalty for falling below the daily flow limit can be used to ensure that the flow rate of each pipeline route reaches the daily flow limit. Since each gas transmission along a pipeline route has a cost, if the single gas transmission volume (i.e., flow rate) is too small, the cost-effectiveness of gas transmission will be too low. Therefore, this solution sets a penalty for falling below the daily flow limit.

[0087] In practice, the cumulative flow constraint can be expressed as follows:

[0088]

[0089] Among them, Y sd represents the cumulative flow from supply point s to demand point d during the entire planning period; F sd It represents the cumulative historical flow from supply point s to demand point d that has been actually executed as of the planning date; represents the daily flow from supply point s to demand point d on day t; S represents the set of supply points; D s It represents the set of demand points that can be connected to the supply point s, determined based on the gas energy contract signing situation.

[0090] In practice, the demand constraint of the demand point can be used to ensure that the demand of each demand point is met as much as possible. The demand constraint of the demand point can be expressed as follows:

[0091]

[0092] in, represents the daily flow from the supply point s to the demand point d on day t; represents the daily flow from the adjustable supply point a to the demand point d corresponding to the adjustable path on day t; ls dt represents the unmet demand at demand point d on day t; S d A represents the set of supply points that can be connected to the demand point d, determined based on the gas energy contract signing situation; d It represents the set of adjustable supply points that can be connected to the demand point d based on the adjustable path. The gas energy contract can provide a part of the adjustable supply points that can be connected to the demand point d, and can also increase some adjustable supply points that can be connected to the demand point d based on actual conditions; dm dt It represents the demand of demand point d on day t, where the demand on the planned day is the actual demand and the demand after the planned day is the estimated demand. dm can be obtained from the demand submission information. dt ; T represents the set of all times included in a planning cycle; D represents the set of demand points.

[0093] In practice, the supply-demand relationship constraint can be used to ensure that the usage of each supply point does not exceed its supply. The supply-demand relationship constraint can be expressed as follows:

[0094]

[0095] in, represents the daily flow from supply point s to demand point d on day t; D s It represents the set of demand points that can be connected to the supply point s, determined based on the gas energy contract signing situation; represents the daily flow from the supply point s to the demand point d on day t, represents the set of demand points that can be connected to the adjustable supply point a, A represents the set of adjustable supply points; sp st It represents the supply quantity of supply point s on day t, where the supply quantity on the planned day is the actual supply quantity, and the supply quantity after the planned day is the estimated supply quantity. sp can be obtained from the supply quantity submission information. st ; S represents the set of supply points; T represents the set of all times included in a planning cycle.

[0096] In one achievable manner, the first relationship constraint is determined based on the accumulated historical flow as of the planning day, the planned flow, the daily flow on the planning day, and the plan deviation limit.

[0097] In practice, the first relation constraint between the cumulative historical flow and the planned flow as of the planning date can be expressed as follows:

[0098]

[0099] Among them, F sd It represents the cumulative historical flow from supply point s to demand point d that has been actually executed as of the planning date; It represents the daily flow from the supply point s to the demand point d on the planned day; Indicates that as of the planning date, the cumulative historical flow from supply point s to demand point d is lower than the first percentage of the cumulative daily average planned flow; In the gas energy contract, the cumulative average planned quantity from the supply point s to the demand point d as of the planning date is: the cumulative average planned quantity = the cumulative number of days as of the planning date * the total planned flow corresponding to one planning cycle / the total number of days corresponding to one planning cycle. As the planned flow rate as of the planned date, the planned total flow rate can be determined based on the signed gas energy contract; lb represents the lower limit of the planned deviation, and lb can be set to 0.9; Indicates that as of the planning date, the cumulative historical flow from supply point s to demand point d is higher than the second percentage of the cumulative daily average planned volume; ub represents the upper limit of the plan deviation, which can be set to 1.1; S represents the supply point set; D s It represents the set of demand points that can be connected to the supply point s, determined based on the gas energy contract signing situation.

[0100] Specifically, the first relationship constraint can be determined conveniently through the above method.

[0101] In one implementable manner, the second relationship constraint is determined based on the accumulated flow of the planning period, the planned total flow, and the plan deviation limit.

[0102] In practice, the second relation constraint between the cumulative flow of the planning period and the planned total flow can be expressed as follows:

[0103]

[0104] Among them, Y sd It represents the cumulative flow from supply point s to demand point d during the entire planning period; The value indicating that the cumulative flow from supply point s to demand point d during the entire planning period is lower than the first percentage of the planned total flow during the entire planning period; Indicates that the cumulative flow from supply point s to demand point d during the entire planning period is higher than the second percentage of the planned total flow; P sd represents the planned total flow from supply point s to demand point d during the entire planning period; lb represents the lower limit of the planned deviation, which can be set to 0.9; ub represents the upper limit of the planned deviation, which can be set to 1.1; S represents the set of supply points; D s It represents the set of demand points that can be connected to the supply point s, determined based on the gas energy contract signing situation.

[0105] Specifically, the second relationship constraint can be determined conveniently through the above method.

[0106] In practice, the fixed flow constraint can be expressed as follows:

[0107]

[0108] in, represents the daily flow from the supply point s to the demand point d on day t; represents the preset flow from supply point s to demand point d on day t, which is a fixed value set manually; S represents the set of supply points; D s It represents the set of demand points that can be connected to the supply point s according to the signing of the gas energy contract; T represents the set of all times included in a planning cycle.

[0109] Specifically, fixed flow constraints can be used to implement manual intervention in flow settings, making it easier to deal with emergencies in actual production processes.

[0110] In practice, path capacity constraints can be used to ensure that the flow rate of each pipeline path is within the pipeline path's capacity. Since each gas transmission in a pipeline path has a cost, if the single gas transmission volume (i.e., flow rate) is too small, the cost-effectiveness of gas transmission will be too low. Therefore, a daily flow rate lower limit can be set equal to the daily flow rate limit to ensure cost-effectiveness of gas transmission.

[0111] The path transport capacity constraint can be expressed as follows:

[0112]

[0113] Among them, α min represents the lower limit of daily flow rate of the pipeline path; α max Indicates the upper limit of the daily flow rate of the pipeline path; if there is gas transmission from the supply point s to the demand point d on the tth day, then = 1, if there is no gas transmission from the supply point s to the demand point d on day t, then is 0, is a Boolean variable; represents the daily flow from supply point s to demand point d on day t; S represents the set of supply points; D s It represents the set of demand points that can be connected to the supply point s according to the signing of the gas energy contract; T represents the set of all times included in a planning cycle.

[0114] In practice, since there is a cost for each gas transmission along the pipeline path, if the single gas transmission volume, i.e., the flow rate, is too small, the gas transmission cost-effectiveness will be too low. Therefore, the daily flow rate lower limit can be set equal to the daily flow rate limit, and the daily flow rate lower limit can be used to ensure the gas transmission cost-effectiveness. The adjustable path transmission capacity constraint can be expressed as follows:

[0115]

[0116] Among them, α min Indicates the lower limit of the daily flow rate of the pipeline path; if there is gas transmission from the adjustable supply point a to the demand point d corresponding to the adjustable path on day t, then =1, if there is no gas transmission from the adjustable supply point a to the demand point d corresponding to the adjustable path on day t, then is 0, is a Boolean variable; represents the daily flow from the adjustable supply point a to the demand point d corresponding to the adjustable path on day t; dm dt represents the demand at demand point d on day t, T represents the set of all times included in a planning cycle; D represents the set of demand points; A represents the set of adjustable supply points; Represents the set of demand points that can be connected to the adjustable supply point a.

[0117] In practice, since each gas transmission in a pipeline path has a cost, if the single gas transmission volume, i.e., the flow rate, is too small, the gas transmission cost-effectiveness will be too low. Therefore, a daily flow constraint can be used to ensure that once the pipeline path is activated, its daily flow rate reaches the daily flow limit as much as possible. The daily flow constraint can be expressed as follows:

[0118]

[0119] in, represents the daily flow from the supply point s to the demand point d on day t; It represents the difference between the daily flow rate from the supply point s to the demand point d on the tth day and the daily flow rate limit when the daily flow rate does not reach the daily flow rate limit; β target represents the daily flow limit; if there is gas transmission from the supply point s to the demand point d on day t, then = 1, if there is no gas transmission from the supply point s to the demand point d on day t, then is 0, is a Boolean variable; S represents the supply point set; D s It represents the set of demand points that can be connected to the supply point s according to the signing of the gas energy contract; T represents the set of all times included in a planning cycle.

[0120] Specifically, in response to sudden fluctuations in supply and demand, this solution introduces adjustable path connections with adjustable supply points, and through the design of soft constraints and penalty coefficients, it can flexibly supplement the gap demand while ensuring the priority of the contract path. For example, Implement soft constraints on daily traffic. For example, penalty coefficients can include long-term negative deviation penalty coefficients, long-term positive deviation penalty coefficients, short-term negative deviation penalty coefficients, and short-term positive deviation penalty coefficients.

[0121] Specifically, this solution can achieve the following effects through the above method:

[0122] Full automation and high precision: Algorithmic models replace manual experience, eliminating subjective errors and achieving millimeter-level accuracy in gas volume balancing, with the average daily deviation rate reduced to below 0.5%.

[0123] Dynamic responsiveness: Rolling updates based on real-time data allow adjustments to balancing plans in supply and demand fluctuation scenarios, improving response speed by 90%.

[0124] Optimize contract fulfillment rates: Through long-term and short-term deviation penalty mechanisms, the probability of cumulative traffic deviating from the contract amount during the planning cycle is reduced, significantly reducing economic losses under "take-or-pay" clauses.

[0125] Improved resource utilization efficiency: The use of adjustable paths for gas supply is allowed, but the use of adjustable paths should be minimized to improve pipeline network utilization.

[0126] Step 203: Based on the target gas distribution plan, the gaseous energy is transported from the supply point to the demand point through the pipeline path.

[0127] The target gas distribution plan includes at least the following: the flow rate from each supply point to each demand point transported through the pipeline path on the planned day.

[0128] During implementation, this solution calculates the daily gas distribution plan from the start of the planning day to the end of the planning cycle. After the calculation is complete, only the target gas distribution plan for the day is issued, and future gas distribution plans are used as a reference only. Assuming a 30-day planning cycle, this solution can roll out a new gas distribution plan every day for 30 days, revising the plan daily based on newly submitted data.

[0129] Figure 3 This is a schematic diagram of the structure of a gaseous energy distribution device provided in Example 3 of this application. The device can be in the form of software and / or hardware. Figure 3 As shown, a gaseous energy distribution device 300 includes: an acquisition unit 301, a decision unit 302 and a gas distribution unit 303.

[0130] The acquisition unit 301 is configured to receive a gas distribution request from a user and acquire gas distribution related information based on the gas distribution request; wherein the gas distribution related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point;

[0131] The decision unit 302 is configured to use the gas distribution related information as input to a pre-built gas distribution model, solve the decision variables of the gas distribution model through a pre-built optimization method, and obtain a target gas distribution solution; wherein the gas distribution model is constructed based on a mixed integer programming algorithm framework;

[0132] The gas distribution unit 303 is used to transport the gaseous energy from the supply point to the demand point through the pipeline path based on the target gas distribution plan.

[0133] In one achievable method, the supply point information includes basic supply point information and supply quantity submission information; the demand point information includes basic demand point information and demand quantity submission information; and the gas distribution related information also includes at least one of the following: planning cycle, historical flow information, planned total flow, and daily flow limit.

[0134] In one implementation, the objective function of the gas distribution model includes at least one of the following:

[0135] Penalties for unmet demand, short-term plan violations, long-term plan violations, adjustable path gas volume penalties, and penalties for flow rates below the daily flow limit.

[0136] In one achievable manner, the penalty term for short-term plan violation is determined based on the short-term negative deviation penalty coefficient, the short-term positive deviation penalty coefficient, the accumulated historical flow as of the planned day, and the planned flow.

[0137] In one achievable manner, the penalty term for long-term plan violation is determined based on the long-term negative deviation penalty coefficient, the long-term positive deviation penalty coefficient, the accumulated flow of the planning period, and the planned total flow.

[0138] In one implementation, the constraints of the gas distribution model include at least one of the following:

[0139] Cumulative flow constraints, demand point demand constraints, supply and demand relationship constraints, the first relationship constraint between the cumulative historical flow as of the planning day and the planned flow, the second relationship constraint between the cumulative flow of the planning period and the planned total flow, fixed flow constraints, path transmission capacity constraints, adjustable path transmission capacity constraints, and daily flow constraints.

[0140] In one achievable manner, the first relationship constraint is determined based on the accumulated historical flow as of the planning day, the planned flow, the daily flow on the planning day, and the plan deviation limit.

[0141] In one implementable manner, the second relationship constraint is determined based on the accumulated flow of the planning period, the planned total flow, and the plan deviation limit.

[0142] The gaseous energy distribution device provided in the embodiment of the present application has the same implementation principle and technical effects as those in the aforementioned gaseous energy distribution method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding contents in the aforementioned gaseous energy distribution method embodiment.

[0143] Figure 4 This is a hardware structure diagram of an electronic device provided in Example 4 of the present application. This embodiment provides an electronic device comprising: at least one processor 401, and a memory 402 communicatively coupled to the at least one processor 401; the memory 402 stores computer-executable instructions; and the processor 401 executes the computer-executable instructions stored in the memory 402 to implement the gaseous energy distribution method described in any of the preceding embodiments.

[0144] Figure 4 The electronic device shown also includes a communication interface 403 and a communication bus 404, wherein the processor 401, the memory 402 and the communication interface 403 are connected to each other via the communication bus 404. The communication bus 404 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 In the figure, only one thick line is used to represent the communication bus 404, but this does not mean that there is only one communication bus 404 or only one type of communication bus 404. The processor 401 may also be referred to as a controller, without limitation to the name.

[0145] In the embodiment of the present application, the memory 402 stores instructions that can be executed by at least one processor 401. The at least one processor 401 can execute the gaseous energy distribution method discussed above by executing the instructions stored in the memory 402. The processor 401 can implement Figure 4 The functions of each module in the device shown.

[0146] Among them, the processor 401 is the control center of the device, which can use various interfaces and lines to connect the various parts of the entire control device, and monitor the device as a whole by running or executing instructions stored in the memory 402 and calling data stored in the memory 402, the various functions of the device and processing data.

[0147] In one possible design, processor 401 may include one or more processing units. Processor 401 may integrate an application processor and a modem processor. The application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 401. In some embodiments, processor 401 and memory 402 may be implemented on the same chip or on separate chips.

[0148] The processor 401 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the gaseous energy distribution method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0149] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 402 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 402 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0150] By designing and programming the processor 401, the code corresponding to the gaseous energy distribution method described in the above embodiment can be fixed into the chip, so that the chip can execute the code when it is running. Figure 1 or Figure 2The steps of the gaseous energy distribution method of the embodiment shown are as follows: How to design and program the processor 401 is a technique well known to those skilled in the art and will not be described in detail here.

[0151] The present application also provides a computer-readable storage medium having computer-executable instructions stored therein. When executed by a processor, the computer-executable instructions are used to implement the gaseous energy distribution method described in any of the above embodiments. Therefore, these instructions will not be described in detail here. In addition, the description of the beneficial effects of using the same method will not be repeated. For technical details not disclosed in the computer storage medium embodiments involved in the present invention, please refer to the description of the method embodiments of the present invention.

[0152] In some possible embodiments, various aspects of the gaseous energy distribution method provided in the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on the device, the program code is used to enable the control device to execute the steps of the gaseous energy distribution method according to various exemplary embodiments of the present application described above in this specification.

[0153] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0157] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A gaseous energy distribution method, characterized in that: The method comprises: Receive a gas distribution request from a user, and obtain gas distribution related information based on the gas distribution request; wherein the gas distribution related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point; The gas distribution correlation information is used as the input of a pre-built gas distribution model, and the decision variables of the gas distribution model are solved through a pre-prepared optimization method to obtain the target gas distribution plan; wherein, the gas distribution model is constructed based on the mixed integer programming algorithm framework; Based on the target gas distribution plan, the gaseous energy is transported from the supply point to the demand point through the pipeline path.

2. The method according to claim 1, characterized in that The supply point information includes basic information of the supply point and supply quantity submission information; the demand point information includes basic information of the demand point and demand quantity submission information; the gas distribution related information also includes at least one of the following: planning cycle, historical flow information, planned total flow, and daily flow limit.

3. The method according to claim 2, characterized in that The objective function of the valve distribution model includes at least one of the following: Penalties for unmet demand, short-term plan violations, long-term plan violations, adjustable path gas volume penalties, and penalties for flow rates below the daily flow limit.

4. The method according to claim 3, characterized in that The penalty item for short-term violation of the plan is determined based on the short-term negative deviation penalty coefficient, the short-term positive deviation penalty coefficient, the accumulated historical flow as of the planned day, and the planned flow.

5. The method according to claim 3, characterized in that The penalty item for long-term violation of the plan is determined based on the long-term negative deviation penalty coefficient, the long-term positive deviation penalty coefficient, the accumulated flow of the plan period and the planned total flow.

6. The method according to claim 2, characterized in that The constraints of the gas distribution model include at least one of the following: Cumulative flow constraints, demand point demand constraints, supply and demand relationship constraints, the first relationship constraint between the cumulative historical flow as of the planning day and the planned flow, the second relationship constraint between the cumulative flow of the planning period and the planned total flow, fixed flow constraints, path transmission capacity constraints, adjustable path transmission capacity constraints, and daily flow constraints.

7. The method according to claim 6, characterized in that The first relationship constraint is determined based on the accumulated historical flow as of the planning day, the planned flow, the daily flow on the planning day, and the plan deviation limit.

8. The method according to claim 6, characterized in that The second relationship constraint is determined based on the accumulated flow of the planning period, the planned total flow and the plan deviation limit.

9. A gas energy distribution device, characterized in that: The device comprises: An acquisition unit, configured to receive a gas distribution request from a user and acquire gas distribution-related information based on the gas distribution request; wherein the gas distribution-related information includes at least one of the following: supply point information, demand point information, and pipeline path information between the supply point and the demand point; A decision-making unit is used to use the gas distribution related information as input to a pre-built gas distribution model, solve the decision variables of the gas distribution model through a pre-built optimization method, and obtain a target gas distribution plan; wherein the gas distribution model is constructed based on a mixed integer programming algorithm framework; The gas distribution unit is used to transport the gaseous energy from the supply point to the demand point through the pipeline path based on the target gas distribution plan.

10. An electronic device, characterized in that: comprising a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the gaseous energy distribution method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the gaseous energy distribution method according to any one of claims 1 to 8 is implemented.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the gaseous energy distribution method described in any one of claims 1 to 8 is implemented.

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