CN113222230A - Flow distribution method and device of natural gas pipe network under accident condition - Google Patents
Flow distribution method and device of natural gas pipe network under accident condition Download PDFInfo
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Abstract
The invention provides a flow distribution method and a device of a natural gas pipe network under accident conditions, wherein the method comprises the following steps: acquiring flow demand information of a natural gas pipe network in a preset time period, and calculating and obtaining gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information; calculating to obtain priority information of each user through a preset weight according to the flow demand information; constructing a gas supply quantity calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms; and calculating according to the air supply quantity calculation model and the parameters of the natural gas pipe network to obtain the maximum air supply quantity of the natural gas pipe network under the accident condition, and calculating according to the maximum air supply quantity, the priority information of each user and the air consumption prediction information of each demand user to obtain a flow distribution scheme.
Description
Technical Field
The invention relates to the field of energy management and control, in particular to a method and a device for distributing the flow of a natural gas pipe network under an accident condition.
Background
With the rapid development of low-carbon economy, the demand of natural gas as a clean and efficient fossil energy source tends to increase at a high rate, and meanwhile, the proportion of natural gas in a primary energy consumption structure also increases year by year. The natural gas pipe network system is used as a link for connecting natural gas resources and markets. The gas supply reliability of the natural gas pipe network system is improved, the gas demand of natural gas users is guaranteed, and the method is significant.
The failure event of the current natural gas pipe network system seriously affects the supply safety of natural gas, reduces the gas supply reliability of the natural gas pipe network system, causes insufficient natural gas supply for users, causes serious public safety problems, and brings serious adverse effects to energy safety and public safety. Therefore, there is a need and an urgent need to research a flow distribution scheme under an accident condition of a natural gas pipeline network system, and minimize the influence of a failure event on the reliability of gas supply of the pipeline network system, so as to guarantee the gas demand of a natural gas user to the maximum extent.
In the prior art, a flow distribution scheme under the accident condition of a natural gas pipeline network is mainly determined by adopting a graph theory algorithm. The technology converts the gas supply flow calculation of the natural gas pipe network system in different states into the maximum flow problem in the graph theory. The method comprises the following specific steps: a complex gas transmission pipe network gas supply calculation model is established by comprehensively considering the upper and lower limits of gas transmission capacity of each pipe section, the upper limit of gas source air input, the upper and lower limits of user gas consumption and node flow balance constraint conditions in the pipe network, and the influence of the failure of a coupling unit on the gas transmission capacity of a pipeline is further calculated, so that the gas supply of the pipe network under the accident condition is calculated.
The existing natural gas pipe network flow distribution method under the accident condition has the following two defects: 1) the influence of uncertainty of market demand and user importance on the calculation of the air supply quantity is ignored; 2) hydraulic constraints of natural gas pipe networks are not considered.
Disclosure of Invention
The invention aims to provide a flow distribution method and a flow distribution device for a natural gas pipe network under accident conditions, so as to fill the blank in the field and overcome the defects of the prior related technology.
In order to achieve the above object, the method for distributing the flow of the natural gas pipe network under the accident condition provided by the invention specifically comprises the following steps: acquiring flow demand information of a natural gas pipe network in a preset time period, and calculating and obtaining gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information; calculating to obtain priority information of each user through a preset weight according to the flow demand information; constructing a gas supply quantity calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms; and calculating according to the air supply quantity calculation model and the parameters of the natural gas pipe network to obtain the maximum air supply quantity of the natural gas pipe network under the accident condition, and calculating according to the maximum air supply quantity, the priority information of each user and the air consumption prediction information of each demand user to obtain a flow distribution scheme.
In the flow distribution method for the natural gas pipe network under the accident condition, preferably, the step of calculating and obtaining the gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information comprises the following steps: acquiring the gas utilization characteristics and fluctuation characteristics of each gas utilization user according to the flow demand information; and matching a preset prediction model according to the gas utilization characteristics and the fluctuation characteristics, and calculating and obtaining gas utilization prediction information of each demand user according to the prediction model and the flow demand information.
In the method for distributing the flow rate of the natural gas pipe network under the accident condition, preferably, the prediction model comprises a time series model, a BP neural network model, a support vector machine model and a logistic regression model; calculating gas utilization prediction information by a user with a gas utilization fluctuation amplitude smaller than a first preset threshold through a time series model; calculating gas utilization prediction information by a user with the fluctuation range larger than a first preset threshold and smaller than a second preset threshold, wherein the fluctuation range of the gas utilization is not in accordance with the requirements of a preset rule condition through a BP neural network model; the fluctuation amplitude of the gas utilization is larger than a first preset threshold and smaller than a second preset threshold, and the gas utilization prediction information is calculated by a user with the fluctuation characteristics meeting the requirements of a preset rule condition through a support vector machine model; and calculating the gas utilization prediction information by the user with the gas utilization fluctuation amplitude larger than a second preset threshold through the logistic regression model.
In the flow distribution method for the natural gas pipe network under the accident condition, preferably, the obtaining of the priority information of each user through calculation of a preset weight according to the flow demand information includes: obtaining the gas usage type of each demand user according to the flow demand information; obtaining a corresponding weight according to the gas use type; and calculating according to the weight to obtain the priority information of each user.
In the above method for distributing the flow rate of the natural gas pipe network under the accident condition, preferably, the obtaining of the priority information of each user according to the weight calculation includes: when the user contains a plurality of gas usage types, the user priority information is obtained by the following formula:
in the above-mentioned formula, the compound of formula,
the importance of the ith demand point on the t day is shown, and i and t are positive integers;
the demand amounts of different gas use application types on the t day are respectively;
weights for different types of gas usage.
In the flow distribution method for the natural gas pipe network under the accident condition, preferably, the constraint conditions include pipe network flow constraint, pipe network pressure constraint and pipe network hydraulic constraint.
In the flow distribution method for the natural gas pipe network under the accident condition, preferably, the pipe network flow constraint includes:
in the above formula, Cij(t) the pipe transport capacity of the pipeline (i, j) on day t; ebRepresenting a collection of bidirectional pipes; y isij(t) is a binary decision variable for controlling the flow direction of the bidirectional pipeline at the moment t; x is the number ofij(t) represents the flow from the ith node to the jth node at time t; e is the set of all the pipes of the natural gas pipeline network.
In the flow distribution method of the natural gas pipe network under the accident condition, preferably, the pipe network pressure constraint comprises pressure constraint of each node of the pipe network, upstream and downstream pressure constraint of a compressor station and upstream and downstream pressure constraint of a regulating valve;
the pressure constraint of each node of the pipe network comprises the following steps:
Pi,min≤Pi≤Pi,max；
the upstream and downstream pressure constraints of the compressor station comprise:
the upstream and downstream pressure constraints of the regulator valve include:
in the above formula, Pi is the pressure of the ith node; pi, min and Pi, max are the upper and lower limits of the pressure of the ith node respectively;
respectively an ith compressor station upstream pressure, downstream pressure, upstream pressure limit, and downstream pressure limit;
the ith regulator valve upstream pressure, downstream pressure, upstream pressure limit, and downstream pressure limit, respectively.
In the flow distribution method for the natural gas pipe network under the accident condition, preferably, the hydraulic constraint of the pipe network comprises:
in the above formula, pi (t) and pj (t) are the pressures at the i-th and j-th nodes at the t-th time, respectively; lambda is the hydraulic friction coefficient, and Z is the compression factor of natural gas under the pipe transmission condition; delta*The relative density of natural gas; t is the gas transmission temperature; k, calculating the length of the segment by the L gas transmission pipeline; d is the inner diameter of the gas transmission pipe; c0Is a constant.
The invention also provides a flow distribution device of the natural gas pipe network under the accident condition, which comprises: the device comprises a prediction module, a priority calculation module, a model construction module and an analysis module; the prediction module is used for acquiring flow demand information of the natural gas pipe network in a preset time period and calculating and acquiring gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information; the priority calculating module is used for calculating and obtaining priority information of each user through a preset weight according to the flow demand information; the model construction module is used for constructing a gas supply calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms; the analysis module is used for calculating and obtaining the maximum air supply quantity of the natural gas pipe network under the accident condition according to the air supply quantity calculation model and the natural gas pipe network parameters, and calculating and obtaining a flow distribution scheme according to the maximum air supply quantity, the priority information of each user and the air consumption prediction information of each demand user.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
The invention has the beneficial technical effects that: comprehensively considering market demand volatility, user importance, pipe network pipe transmission capacity, air source air input upper limit, user air consumption upper and lower limits, node flow balance, node pressure constraint and pipe network hydraulic calculation; the method makes up the defects of the existing natural gas pipe network flow distribution model and method under the accident condition. The method comprises the steps of obtaining historical demand data of each demand point based on basic data provided on site, and predicting and calculating market demand and user importance of each demand point within the duration time of accident conditions; and the optimal distribution of the pipe network flow under the accident condition is realized by combining a pipe network air supply quantity calculation model considering the side influence required by the market and hydraulic calculation, so that the influence of a failure event on the reliability of the pipe network system air supply is minimized, and the air consumption requirement of a natural gas user is ensured to the maximum extent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic flow chart of a flow distribution method of a natural gas pipe network under an accident condition according to an embodiment of the present invention;
FIG. 2 is a schematic view illustrating a calculation process of the gas usage prediction information according to an embodiment of the present invention;
FIG. 3 is a method for calculating user priority information according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a piecewise linearization provided by an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a flow distribution device of a natural gas pipeline network under an accident condition according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, unless otherwise specified, the embodiments and features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
Referring to fig. 1, the method for distributing the flow of the natural gas pipeline network under the accident condition provided by the present invention specifically includes:
s101, flow demand information of a natural gas pipe network in a preset time period is obtained, and gas consumption prediction information of each demand user is obtained through calculation of a preset prediction model according to the flow demand information;
s102, calculating according to the flow demand information through a preset weight to obtain priority information of each user; constructing a gas supply quantity calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms;
s103, calculating according to the air supply calculation model and the natural gas pipe network parameters to obtain the maximum air supply of the natural gas pipe network under the accident condition, and calculating according to the maximum air supply, the priority information of each user and the air consumption prediction information of each demand user to obtain a flow distribution scheme.
Referring to fig. 2, in an embodiment of the present invention, the calculating, according to the flow demand information and through a preset prediction model, the gas usage prediction information of each demand user includes:
s201, acquiring gas utilization characteristics and fluctuation characteristics of each gas utilization user according to the flow demand information;
s202, matching a preset prediction model according to the gas utilization characteristics and the fluctuation characteristics, and calculating and obtaining gas utilization prediction information of each demand user according to the prediction model and the flow demand information.
Specifically, the prediction model comprises a time series model, a BP neural network model, a support vector machine model and a logistic regression model; calculating gas utilization prediction information by a user with a gas utilization fluctuation amplitude smaller than a first preset threshold through a time series model; calculating gas utilization prediction information by a user with the fluctuation range larger than a first preset threshold and smaller than a second preset threshold, wherein the fluctuation range of the gas utilization is not in accordance with the requirements of a preset rule condition through a BP neural network model; the fluctuation amplitude of the gas utilization is larger than a first preset threshold and smaller than a second preset threshold, and the gas utilization prediction information is calculated by a user with the fluctuation characteristics meeting the requirements of a preset rule condition through a support vector machine model; and calculating the gas utilization prediction information by the user with the gas utilization fluctuation amplitude larger than a second preset threshold through the logistic regression model.
In actual work, the embodiment can predict the market demand in the accident condition duration and determine the importance of each demand point (branch point) through demand side analysis. Natural gas users are classified according to gas usage, and mainly include four types, i.e., gas users, CNG users, power plant users, and industrial users. Generally, each distribution point of a natural gas pipeline network comprises a plurality of natural gas users. The gas use characteristics and fluctuation characteristics of each user are different, so that different evaluation methods are required to predict the market demand of each user. The invention adopts a time sequence method, a BP neural network, a support vector machine and a logistic regression model as four methods for user demand prediction to carry out modeling, and the application range of each method is shown in the following table 1.
TABLE 1
Referring to fig. 3, in an embodiment of the present invention, obtaining priority information of each user through a preset weight calculation according to the traffic demand information includes:
s301, acquiring the gas usage type of each demand user according to the flow demand information;
s302, obtaining a corresponding weight according to the gas use type;
s303, calculating according to the weight to obtain the priority information of each user.
Wherein, the calculation of the priority information of each user according to the weight value comprises: when the user contains a plurality of gas usage types, the user priority information is obtained by the following formula:
in the above-mentioned formula, the compound of formula,
the importance of the ith demand point on the t day is shown, and i and t are positive integers;
the demand amounts of different gas use application types on the t day are respectively;
weights for different types of gas usage.
In actual work, users can be classified in advance according to the gas use purpose of the users, and the grades are urban gas users, CNG users, power plant users and industrial users from high to low, and correspond to four grades of fully guaranteed users, users capable of reducing pressure in small quantity, users capable of reducing pressure and users capable of interrupting; for the distribution points containing a plurality of users and a plurality of types of users, the importance degree of the distribution points can be calculated by the following formula:
in the formula (I), the compound is shown in the specification,
the importance of the ith demand point and the tth day,
the demands of city gas users, CNG users, power plant users and industrial users on the t day respectivelyThe amount of the compound (A) is,
the weights of users at all levels are obviously the higher the user level is, the larger the weight is.
In an embodiment of the present invention, the constraint condition includes a pipe network flow constraint, a pipe network pressure constraint, and a pipe network hydraulic constraint.
Wherein, the pipe network flow constraint comprises:
in the above formula, Cij(t) the pipe transport capacity of the pipeline (i, j) on day t; ebRepresenting a collection of bidirectional pipes; y isij(t) is a binary decision variable for controlling the flow direction of the bidirectional pipeline at the moment t; x is the number ofij(t) represents the flow from the ith node to the jth node at time t; e is the set of all the pipes of the natural gas pipeline network.
The pipe network pressure constraints comprise pressure constraints of all nodes of the pipe network, upstream and downstream pressure constraints of the compressor station and upstream and downstream pressure constraints of the regulating valve;
the pressure constraint of each node of the pipe network comprises the following steps:
Pi,min≤Pi≤Pi,max；
the upstream and downstream pressure constraints of the compressor station comprise:
the upstream and downstream pressure constraints of the regulator valve include:
in the above formula, Pi is the pressure of the ith node; pi, min and Pi, max are the upper and lower limits of the pressure of the ith node respectively;
respectively an ith compressor station upstream pressure, downstream pressure, upstream pressure limit, and downstream pressure limit;
the ith regulator valve upstream pressure, downstream pressure, upstream pressure limit, and downstream pressure limit, respectively.
The hydraulic restraint of the pipe network comprises:
in the above formula, pi (t) and pj (t) are the pressures at the i-th and j-th nodes at the t-th time, respectively; lambda is the hydraulic friction coefficient, and Z is the compression factor of natural gas under the pipe transmission condition; delta*The relative density of natural gas; t is the gas transmission temperature; k, calculating the length of the segment by the L gas transmission pipeline; d is the inner diameter of the gas transmission pipe; c0Is a constant.
Specifically, in actual work, for a large and complex natural gas pipeline network, the invention adopts a directed weighted graph, G is (V, E), and the representation is carried out on the pipeline network, wherein V represents a node set in the graph and comprises a gas source point, a station yard and a demand point, and E represents an arc in the graph, namely a set of natural gas pipelines. Gas Source, gas storage and LNG receiving station in natural gas pipe network system are regarded as Source nodes (Source)) Each demand point is regarded as a Sink node (Sink), the compressor station is regarded as a general node, and the natural gas pipeline is regarded as a network arc containing a pipeline transmission capacity constraint. In general, a natural gas pipeline network system includes a plurality of source nodes S ═ S1,s2,...,smD ═ D } and sink nodes1,d2,...,dnAnd the flow from the source node can be theoretically delivered to each sink, i.e. the so-called multi-source and multi-sink problem. The super source point s connected with all the source nodes and the super sink point d connected with each sink node are set to be converted into a single source point and single sink point directed weighted graph.
In addition, in order to consider the influence of the importance of users on the flow distribution of the pipe network, the flow distribution problem of the natural gas pipe network under the accident condition is converted into a two-stage optimization problem. The first stage is the maximum flow problem under given constraints, and aims to calculate the maximum gas quantity which can be supplied to each user by a natural gas pipe network under given constraints. The second stage is the optimal flow distribution problem under given constraint and maximum flow, and aims to determine the flow distribution scheme of the natural gas pipe network under the given constraint and the maximum flow and consider the importance of each distribution point. The concrete model is as follows:
first stage objective function: in the duration of the accident condition, the total gas amount supplied to each demand point by the natural gas pipeline network is the largest, as shown in the following.
In the formula, T is the duration of the accident condition; day, t is time, day; d is a set of demand points; d is a virtual sink; x is the number ofid(t) represents the flow from the demand point to the virtual sink at the time t, namely the air quantity supplied by the pipe network to the ith demand point, 104Nm3/day。
Constraint conditions are as follows:
1) pipe network flow restraint:
the sum of the flows entering the node is equal to the sum of the flows leaving.
In the formula, xij(t) represents the flow from the ith node to the jth node at time t, 104Nm3A/day; (i, j) and (j, l) denote a pipe between the node i to the node j and a pipe between the node j to the node l, respectively.
The flow direction constraint of the bidirectional pipeline indicates that at most one pipeline in the pipeline flows.
In the formula, EbRepresenting a set of bidirectional pipes, yijAnd (t) is a binary decision variable for controlling the flow direction of the bidirectional pipeline at the moment t.
The sum of the air supply amounts at the air source points is equal to the sum of the air amounts at the supply demand points.
In the formula, xsj(t) represents the amount of gas supplied at the jth gas supply point at time t, 104Nm3/day。
The supply flow of the air source point is less than the upper limit of the air source capacity, the flow of the supply demand point is less than the demand of the demand point, the flow of any arc in the network does not exceed the pipe transmission capacity of the arc, and the flow cannot be negative.
In the formula Cij(t) the pipe transport capacity of the pipeline (i, j) on day t, 104Nm3And/day, which is an element of the capability matrix C.
2) Pipe network pressure restraint:
and (5) pressure constraint of each node of the pipe network.
Pi,min≤Pi≤Pi,max；
In the formula, PiPressure at the i-th node, MPa, Pi,minAnd Pi,maxThe upper and lower limits of the pressure of the ith node are respectively MPa.
It should be noted that the compressor station and the upstream and downstream nodes of the regulator valve need to satisfy their upstream and downstream pressure constraints in addition to the node pressure constraints shown in equation 16.
Upstream and downstream pressure constraints of the compressor station.
In the formula
Respectively the ith compressor station upstream pressure, downstream pressure, upstream pressure limit and downstream pressure limit, MPa.
And (4) regulating the upstream and downstream pressure constraints of the valve (interface pressure regulation for pipelines with different pressure grades).
In the formula
The upstream pressure and the downstream pressure of the ith regulating valve are respectively. Upstream and downstream pressure limits, MPa.
3) Hydraulic restraint of a pipe network:
in the formula, pi(t) and pj(t) the pressure of the ith and the j node at the t moment, MPa, lambda is the hydraulic friction coefficient, Z is the compression factor of natural gas under the condition of pipe transportation, and delta*The relative density of natural gas, T is gas transmission temperature, K, L are calculated length of gas transmission pipeline, km, D is inner diameter of gas transmission pipeline, m, C0Is a constant whose value depends on the unit of each parameter, which is the parameter of the pipe between node i and node j.
Since the hydraulic constraint of the pipe network is a nonlinear constraint, and the contained unknowns are many. In order to simplify the constraint and facilitate the model solution, the hydraulic constraint is tried to be processed in a piecewise linearization way, and the specific flow is as follows:
A) aiming at a specific common pipeline, the natural gas pipeline is uniformly divided into 10 sections according to the set natural gas output range,
respectively taking 7 starting point pressures according to the range of the starting point pressures
B) In each flow range, the flow is evenly divided into 5 sections, and 6 flow points are taken
C) Solving the 10 multiplied by 6 multiplied by 7-420 group of end pressure values according to the calculation formula of the pipe conveying process
w corresponds to a group kl，m。
D) In each flow range, the starting point pressure, the end point pressure and the natural gas flow in the pipeline are divided into 6 multiplied by 7 groups
A pipeline coefficient (a in the formula) and a constant b are fitted according to the linear regression formula f (x) ═ Ax + b. Wherein
Finally, a linear function of each flow range is obtained, as shown in fig. 4. Since the formula also contains a square term, the formula is defined in addition when the model is established
Representing the square of the pressure.
For the optimization model, the piecewise linear constraint is also required to be converted into a continuous linear constraint, and the piecewise linear function is converted into the linear constraint by using a variable of 0 to 1. For an n-segment linear function f (x), the division point is b1≤b2≤...≤bn≤bn+1Introduction of a continuous variable wkAnd the variable z is 0 to 1kX and f (x) are expressed as
Continuous variable wkAnd the variable z is 0 to 1kThe following constraints are satisfied:
therefore, by the method, the nonlinear pipe network can be hydraulically restrained
All can be translated into linear constraints as follows:
where the parameters have the meaning indicated above, the subscript ij denotes the pipeline from node i to node j,
notably, the constraint condition is non-linear due to the presence of the bidirectional pipeline hydraulic constraint; in the first-stage optimization model, the flow direction of the bidirectional pipeline is determined by solving the problem of maximum air supply only considering the flow constraint of the pipe network and the pressure constraint of the pipe network; then solving the maximum air supply quantity problem considering pipe network flow constraint, pipe network pressure constraint and pipe network hydraulic constraint; by solving the first stage optimization problem, the maximum total gas quantity Q which can be supplied to each user at each moment in the accident condition duration time of the natural gas pipe network under the given constraint condition can be calculatedmax(t)，104Nm3。
Decision variables: flow x of pipelineijFlow direction y of a bidirectional pipeijAnd the pressure P of the joint at the two ends of the pipelinei。
The optimization objective function of the second stage: in the duration of the accident condition, on the premise of supplying the maximum total gas quantity of each demand point, the equivalent total cost of the pipe network is the lowest:
in the formula (f)i(t) is the unit equivalent transportation cost of the ith demand point, 1/(10)4Nm3) It is clear that the higher the importance of the demand point, the lower the cost per unit flow. Therefore, the importance of each demand point can be determined
Calculating unit flow cost:
constraint conditions are as follows: the difference from the first stage optimization is that the total flow constraint is increased:
in the formula, Qmax(t) is the maximum air supply amount calculated in the first stage. The other constraints are the same as in the first stage.
When the pipe network is in an accident condition, the pipe transmission capacity of each arc in the pipe network system is changed due to the failure of a unit in the system, so that the capacity matrix C of the pipe network system is changed, and the air supply quantity of the system is changed; the influence of the failure of different types of units in the pipe network system on the capacity matrix C is shown in Table 2, wherein the influence degree of the failure of the pipeline and the compressor on the capacity matrix C can be accurately determined by performing water-thermal simulation on the pipe network system.
TABLE 2
Therefore, the air supply quantity calculation under the accident condition of the natural gas pipe network can be converted into a two-stage mixed integer linear programming problem. By solving the optimization problem, the flow distribution scheme of the natural gas pipe network under the accident condition can be obtained.
Referring to fig. 5, the present invention further provides a flow distribution device for a natural gas pipeline network under an accident condition, the device includes: the device comprises a prediction module, a priority calculation module, a model construction module and an analysis module; the prediction module is used for acquiring flow demand information of the natural gas pipe network in a preset time period and calculating and acquiring gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information; the priority calculating module is used for calculating and obtaining priority information of each user through a preset weight according to the flow demand information; the model construction module is used for constructing a gas supply calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms; the analysis module is used for calculating and obtaining the maximum air supply quantity of the natural gas pipe network under the accident condition according to the air supply quantity calculation model and the natural gas pipe network parameters, and calculating and obtaining a flow distribution scheme according to the maximum air supply quantity, the priority information of each user and the air consumption prediction information of each demand user.
The invention has the beneficial technical effects that: comprehensively considering market demand volatility, user importance, pipe network pipe transmission capacity, air source air input upper limit, user air consumption upper and lower limits, node flow balance, node pressure constraint and pipe network hydraulic calculation; the method makes up the defects of the existing natural gas pipe network flow distribution model and method under the accident condition. The method comprises the steps of obtaining historical demand data of each demand point based on basic data provided on site, and predicting and calculating market demand and user importance of each demand point within the duration time of accident conditions; and the optimal distribution of the pipe network flow under the accident condition is realized by combining a pipe network air supply quantity calculation model considering the side influence required by the market and hydraulic calculation, so that the influence of a failure event on the reliability of the pipe network system air supply is minimized, and the air consumption requirement of a natural gas user is ensured to the maximum extent.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
As shown in fig. 6, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 6; furthermore, the electronic device 600 may also comprise components not shown in fig. 6, which may be referred to in the prior art.
As shown in fig. 6, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A flow distribution method of a natural gas pipe network under accident conditions is characterized by comprising the following steps:
acquiring flow demand information of a natural gas pipe network in a preset time period, and calculating and obtaining gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information;
calculating to obtain priority information of each user through a preset weight according to the flow demand information;
constructing a gas supply quantity calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms;
and calculating according to the air supply quantity calculation model and the parameters of the natural gas pipe network to obtain the maximum air supply quantity of the natural gas pipe network under the accident condition, and calculating according to the maximum air supply quantity, the priority information of each user and the air consumption prediction information of each demand user to obtain a flow distribution scheme.
2. The flow distribution method for the natural gas pipe network under the accident condition according to claim 1, wherein the step of obtaining the gas consumption prediction information of each demand user through calculation of a preset prediction model according to the flow demand information comprises the following steps:
acquiring the gas utilization characteristics and fluctuation characteristics of each gas utilization user according to the flow demand information;
and matching a preset prediction model according to the gas utilization characteristics and the fluctuation characteristics, and calculating and obtaining gas utilization prediction information of each demand user according to the prediction model and the flow demand information.
3. The flow distribution method for the natural gas pipe network under the accident condition according to claim 1, wherein the prediction model comprises a time series model, a BP neural network model, a support vector machine model and a logistic regression model;
calculating gas utilization prediction information by a user with a gas utilization fluctuation amplitude smaller than a first preset threshold through a time series model;
calculating gas utilization prediction information by a user with the fluctuation range larger than a first preset threshold and smaller than a second preset threshold, wherein the fluctuation range of the gas utilization is not in accordance with the requirements of a preset rule condition through a BP neural network model;
the fluctuation amplitude of the gas utilization is larger than a first preset threshold and smaller than a second preset threshold, and the gas utilization prediction information is calculated by a user with the fluctuation characteristics meeting the requirements of a preset rule condition through a support vector machine model;
and calculating the gas utilization prediction information by the user with the gas utilization fluctuation amplitude larger than a second preset threshold through the logistic regression model.
4. The flow distribution method for the natural gas pipe network under the accident condition according to claim 1, wherein the obtaining of the priority information of each user through calculation of the preset weight according to the flow demand information comprises:
obtaining the gas usage type of each demand user according to the flow demand information;
obtaining a corresponding weight according to the gas use type;
and calculating according to the weight to obtain the priority information of each user.
5. The flow distribution method for the natural gas pipe network under the accident condition according to claim 1, wherein the obtaining of the priority information of each user according to the weight calculation comprises:
when the user contains a plurality of gas usage types, the user priority information is obtained by the following formula:
in the above-mentioned formula, the compound of formula,
the importance of the ith demand point on the t day is shown, and i and t are positive integers;
the demand amounts of different gas use application types on the t day are respectively;
weights for different types of gas usage.
6. The method for distributing the flow of the natural gas pipe network under the accident condition according to claim 1, wherein the constraint conditions comprise pipe network flow constraint, pipe network pressure constraint and pipe network hydraulic constraint.
7. The method for distributing the flow of the natural gas pipe network under the accident condition according to claim 6, wherein the pipe network flow constraint comprises:
in the above formula, Cij(t) the pipe transport capacity of the pipeline (i, j) on day t; ebRepresenting a collection of bidirectional pipes; y isij(t) is a binary decision variable for controlling the flow direction of the bidirectional pipeline at the moment t; x is the number ofij(t) represents the flow from the ith node to the jth node at time t; e is the set of all the pipes of the natural gas pipeline network.
8. The flow distribution method for the natural gas pipe network under the accident condition of claim 6, wherein the pipe network pressure constraints comprise pressure constraints of all nodes of the pipe network, upstream and downstream pressure constraints of a compressor station and upstream and downstream pressure constraints of a regulating valve;
the pressure constraint of each node of the pipe network comprises the following steps:
Pi,min≤Pi≤Pi,max；
the upstream and downstream pressure constraints of the compressor station comprise:
the upstream and downstream pressure constraints of the regulator valve include:
in the above formula, PiIs the pressure at the ith node; pi,minAnd Pi,maxThe upper limit and the lower limit of the pressure of the ith node are respectively;
respectively an ith compressor station upstream pressure, downstream pressure, upstream pressure limit, and downstream pressure limit;
the ith regulator valve upstream pressure, downstream pressure, upstream pressure limit, and downstream pressure limit, respectively.
9. The method for distributing the flow of the natural gas pipe network under the accident condition according to claim 6, wherein the hydraulic constraint of the pipe network comprises:
in the above formula, pi (t) and pj (t) are the pressures at the i-th and j-th nodes at the t-th time, respectively; lambda is the hydraulic friction coefficient, and Z is the compression factor of natural gas under the pipe transmission condition; delta*The relative density of natural gas; t is the gas transmission temperature; k, calculating the length of the segment by the L gas transmission pipeline; d is the inner diameter of the gas transmission pipe; c0Is a constant.
10. The utility model provides a flow distribution device of natural gas pipe network under accident condition which characterized in that, the device contains: the device comprises a prediction module, a priority calculation module, a model construction module and an analysis module;
the prediction module is used for acquiring flow demand information of the natural gas pipe network in a preset time period and calculating and acquiring gas consumption prediction information of each demand user through a preset prediction model according to the flow demand information;
the priority calculating module is used for calculating and obtaining priority information of each user through a preset weight according to the flow demand information;
the model construction module is used for constructing a gas supply calculation model of the natural gas pipe network under the accident condition by taking given constraint parameters as constraint conditions of the weighted graph with terms;
the analysis module is used for calculating and obtaining the maximum air supply quantity of the natural gas pipe network under the accident condition according to the air supply quantity calculation model and the natural gas pipe network parameters, and calculating and obtaining a flow distribution scheme according to the maximum air supply quantity, the priority information of each user and the air consumption prediction information of each demand user.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 9 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 9 by a computer.
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