A transmission network line planning method, device, electronic equipment and storage medium

Abstract

The present invention discloses a transmission network line planning method, device, electronic device and storage medium, which are used to solve the technical problems that the uncertainty of new energy output leads to poor power supply reliability of the transmission system, and line overload and load shedding under line N-1 faults are prone to occur. The present invention generates a transmission network opportunity constraint planning model that takes into account source-load uncertainty and N-1 safety constraints, and solves it through a particle swarm optimization algorithm. It can effectively optimize the transmission network planning scheme under the dual uncertainty of new energy output and load demand, ensure the power supply reliability under line N-1 faults, and maximize the consumption of wind power new energy output, reduce the output of traditional generator sets, and achieve the optimal cost operation of the transmission system under the planning scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of line planning, and in particular to a method, device, electronic equipment and storage medium for planning a line in a power transmission network. Background Art

[0002] In actual scenarios, the penetration rate of new energy represented by wind power and photovoltaic power is increasing. New energy power generation has inherent volatility, intermittency and randomness, which makes the traditional deterministic transmission network planning technology method ineffective. On the other hand, with the rapid development of new power loads such as large-scale electric vehicles, the uncertainty of power consumption on the load side has increased significantly, which is coupled with the uncertainty of new energy power generation output, posing a huge challenge to the planning of the transmission network system in the context of energy transformation.

[0003] At present, the research on transmission network planning considering uncertainty factors mainly focuses on fuzzy optimization technology and stochastic programming technology. The existing methods have the following main shortcomings: 1) Fuzzification technology relies on the selection of fuzzy membership function, and the planning scheme is highly subjective; 2) Stochastic programming technology needs to consider a large number of scenarios and has a large amount of calculation; 3) There is little research on transmission network planning considering the N-1 safety constraint of the line under the dual uncertainty of source and load.

[0004] However, the uncertainty of renewable energy output will seriously affect the power supply reliability of the transmission system, especially when combined with the uncertainty of load demand, which will easily lead to problems such as line overload and load shedding under line N-1 fault. Summary of the invention

[0005] The present invention provides a transmission network line planning method, device, electronic device and storage medium, which are used to solve the technical problem that the uncertainty of renewable energy output will seriously affect the power supply reliability of the transmission system, especially the superposition effect with the uncertainty of load demand, which leads to line overload and load shedding under line N-1 fault.

[0006] The present invention provides a transmission network line planning method, comprising:

[0007] Obtain new energy unit output, load demand forecast data, candidate line parameters and preset electrical parameters;

[0008] Taking the minimization of the annualized new line investment cost and the annualized transmission system operation cost as the optimization goal, taking the system flow constraint, line capacity constraint, generator capacity constraint, line N-1 safety constraint, new line number constraint and source-load uncertainty constraint as the constraint conditions, the output of the new energy unit, the load demand forecast data, the parameters of the selected line and the preset electrical parameters are used to generate a transmission network opportunity constraint planning model;

[0009] The particle swarm optimization algorithm is used to solve the transmission network opportunity constraint programming model and generate the initial line planning scheme;

[0010] Eliminate the initial route planning scheme containing isolated islands and isolated nodes to obtain an updated route planning scheme;

[0011] Performing optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result;

[0012] Determine whether the current number of iterations reaches the preset number of iterations;

[0013] If not, the initial route planning scheme is updated using the optimization result, and the initial route planning scheme containing isolated islands and isolated nodes is removed to obtain an updated route planning scheme;

[0014] If yes, the optimization result is used to plan the transmission network line.

[0015] Optionally, it also includes:

[0016] Generate the adjacency matrix of each initial route planning scheme respectively;

[0017] It is determined whether there are isolated islands and isolated nodes in the initial line planning scheme according to the adjacency matrix.

[0018] Optionally, the step of performing optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result includes:

[0019] Generating a net prediction error distribution function of a transmission network system using the output of the new energy unit and the load demand prediction data;

[0020] The net prediction error distribution function of the transmission network system is used to transform the source-load uncertainty constraint into a definite form to obtain an optimized transmission network opportunity constraint programming model;

[0021] Based on the optimized transmission network opportunity constrained programming model, the transmission system power flow optimization is performed with minimization of the annualized transmission system operating cost as the optimization goal to obtain an updated annualized transmission system operating cost;

[0022] The initial line planning scheme is subjected to a system power flow analysis under a line N-1 fault scenario to obtain an updated annualized new line investment cost.

[0023] Optionally, the source-load uncertainty constraint includes a wind power output uncertainty constraint, a photovoltaic output uncertainty constraint and a load demand uncertainty constraint.

[0024] The present invention also provides a transmission network line planning device, comprising:

[0025] The acquisition module is used to obtain the output of new energy units, load demand forecast data, parameters of the selected lines and preset electrical parameters;

[0026] A model building module is used to take the annualized new line investment cost and the annualized transmission system operation cost as the minimum optimization target, take the system flow constraint, line capacity constraint, generator capacity constraint, line N-1 safety constraint, new line number constraint and source-load uncertainty constraint as constraint conditions, and use the output of the new energy unit, the load demand forecast data, the parameters of the selected line and the preset electrical parameters to generate a transmission network opportunity constraint planning model;

[0027] An initial line planning scheme generation module is used to solve the transmission network opportunity constraint planning model using a particle swarm optimization algorithm to generate an initial line planning scheme;

[0028] A elimination module is used to eliminate the initial route planning scheme containing isolated islands and isolated nodes to obtain an updated route planning scheme;

[0029] An optimization module is used to perform optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result;

[0030] A judgment module is used to judge whether the current number of iterations reaches a preset number of iterations;

[0031] A return module is used to update the initial route planning scheme by using the optimization result if no, and return the initial route planning scheme with islands and isolated nodes removed to obtain an updated route planning scheme;

[0032] The transmission network line planning module is used to use the optimization result to carry out transmission network line planning.

[0033] Optionally, it also includes:

[0034] An adjacency matrix generation module is used to generate the adjacency matrix of each initial route planning scheme respectively;

[0035] The isolated island and isolated node judgment module is used to judge whether there are isolated islands and isolated nodes in the initial line planning scheme according to the adjacency matrix.

[0036] Optionally, the optimization module includes:

[0037] A transmission network system net prediction error distribution function generation submodule, used to generate a transmission network system net prediction error distribution function using the new energy unit output and the load demand prediction data;

[0038] A form conversion submodule, used for converting the source-load uncertainty constraint into a definite form by using the net prediction error distribution function of the transmission network system to obtain an optimized transmission network opportunity constraint programming model;

[0039] An updated annualized transmission system operating cost generation submodule is used to optimize the transmission system power flow based on the optimized transmission network opportunity constraint programming model and take minimization of the annualized transmission system operating cost as the optimization goal to obtain an updated annualized transmission system operating cost;

[0040] The updated annualized new line investment cost generation submodule is used to perform system power flow analysis on the initial line planning scheme under the line N-1 fault scenario to obtain an updated annualized new line investment cost.

[0041] Optionally, the source-load uncertainty constraint includes a wind power output uncertainty constraint, a photovoltaic output uncertainty constraint and a load demand uncertainty constraint.

[0042] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0043] The memory is used to store program code and transmit the program code to the processor;

[0044] The processor is used to execute the transmission network line planning method as described in any one of the above items according to the instructions in the program code.

[0045] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the transmission network line planning method as described in any one of the above items.

[0046] It can be seen from the above technical scheme that the present invention has the following advantages: by generating a transmission network opportunity constraint planning model that takes into account source-load uncertainty and N-1 safety constraints, and solving it through a particle swarm optimization algorithm, the present invention can effectively optimize the transmission network planning scheme under the dual uncertainty of new energy output and load demand, ensure the power supply reliability under line N-1 faults, and maximize the consumption of wind power new energy output, reduce the output of traditional generator sets, and achieve the optimal cost operation of the transmission system under the planning scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A flowchart of a method for planning a transmission network line provided by an embodiment of the present invention;

[0049] Figure 2 A diagram showing the structure of a Garver-6 node calculation system provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of a typical daily output curve of a new energy unit provided in an embodiment of the present invention;

[0051] Figure 4 Iteration graph for solving the particle swarm algorithm provided by the embodiment of the present invention;

[0052] Figure 5 A network diagram of the Garver-6 node system optimization planning solution provided in an embodiment of the present invention;

[0053] Figure 6 A structural block diagram of a transmission network line planning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The embodiments of the present invention provide a transmission network line planning method, device, electronic device and storage medium, which are used to solve the technical problem that the uncertainty of renewable energy output will seriously affect the power supply reliability of the transmission system, especially the superposition effect with the uncertainty of load demand, which leads to line overload and load shedding under line N-1 fault.

[0055] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 , Figure 1 A flowchart of the steps of a transmission network line planning method provided by an embodiment of the present invention.

[0057] The present invention provides a method for planning a transmission network line, which may specifically include the following steps:

[0058] Step 101, obtaining the output of new energy units, load demand forecast data, parameters of the selected lines and preset electrical parameters;

[0059] Step 102, taking the minimization of the annualized new line investment cost and the annualized transmission system operation cost as the optimization goal, taking the system flow constraint, line capacity constraint, generator capacity constraint, line N-1 safety constraint, new line number constraint and source-load uncertainty constraint as the constraint conditions, using the output of new energy units, load demand forecast data, parameters of the selected lines and preset electrical parameters to generate a transmission network opportunity constraint planning model;

[0060] In an embodiment of the present invention, the annualized new line investment cost and the annualized transmission system operating cost can be minimized as the optimization goal, and the system flow constraint, line capacity constraint, generator capacity constraint, line N-1 safety constraint, new line number constraint and source-load uncertainty constraint are used as constraint conditions. The output of new energy units, load demand forecast data, parameters of the selected lines and preset electrical parameters are used to generate a transmission network opportunity constraint planning model.

[0061] In the specific implementation, the objective function of the transmission network opportunity constrained programming model considering source-load uncertainty and N-1 security constraints mainly includes the annualized new line investment cost C inv and the annualized transmission system operating cost C ope , the specific formula is as follows:

[0062] minC inv +C ope

[0063] Among them, C inv is the annualized investment cost of new lines; C ope is the annualized transmission system operating cost.

[0064] Annualized investment cost of new lines C inv It is mainly composed of the set of newly built lines, the number of newly built lines, the length of newly built lines and the line capital recovery coefficient, which can be expressed as:

[0065]

[0066]

[0067] Among them, L C is the set of candidate routes; γ is the route capital recovery coefficient; x l is the number of new lines of line l; C L,l is the unit investment cost of line l; L Dis,l is the length of line l; Y is the operating life of the new line in years; r is the capital discount rate.

[0068] Annualized transmission system operating cost C ope It is mainly composed of the power generation cost of traditional generators and the penalty cost of wind and solar power abandonment, which can be expressed as:

[0069]

[0070] Where T is the annual operating hours of the transmission line; N node is the total number of transmission network nodes; G i is the set of traditional generators connected to node i; P G,j,t is the output power of traditional generator set j at time t; a j 、b j and c j is the cost coefficient of traditional generator set j; G RE,i is the set of new energy generators connected to node i; C RE-cut,g is the unit power abandonment cost of the g-type new energy unit; P RE-cut,g is the abandoned power of the g-th type of new energy unit at time t.

[0071] The constraints of the transmission network opportunity constraint programming model considering source-load uncertainty and N-1 safety constraints mainly include: system flow constraints, line capacity constraints, generator capacity constraints, line N-1 safety constraints, new line number constraints and source-load uncertainty constraints, which can be expressed as:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] 0≤x l ≤x max,l

[0079]

[0080]

[0081] Among them, P G,j,t is the output power of traditional generator set j at time t; P L,l,t is the power flow of line l at time t; P RE,g,t is the output power forecast value of the g-type new energy unit at time t; P RE-cut,g is the abandoned power of the g-type new energy unit at time t; P D,i,t is the load demand forecast value of node i at time t; b l is the susceptance of line l; θ S,l,tis the phase angle of the node at the beginning of line l; θ E,l,t is the phase angle of the end node of line l; P Lmax,l is the rated transmission power of line l; P Gmax,j and P Gmin,j are the upper and lower limits of the output power of the traditional generator set j; P Dcut,l,t is the load shedding power of node i at time t; x l is the number of new lines of line l; x max,l P is the upper limit of the number of new lines for line l; r {·} is the probability of event {·} being true; ξ RE,g,t is the output prediction error of the g-th type of new energy unit at time t; D,i,t is the prediction error of the load at node i at time t; α1 and α2 are the confidence levels that the positive and negative spinning reserve constraints need to meet, respectively.

[0082] Step 103, using a particle swarm optimization algorithm to solve the transmission network opportunity constraint programming model and generate an initial line planning solution;

[0083] After constructing the transmission network opportunity constraint programming model, the particle swarm optimization algorithm can be used to solve the transmission network opportunity constraint programming model and generate an initial line planning scheme.

[0084] In the specific implementation, the number of iterations N can be set according to the algorithm solution requirements. iter , particle size N pop , the confidence levels α1 and α2 that the positive and negative spinning reserve constraints need to meet. Then, we need to constrain 0≤x according to the number of new circuits in the line. l ≤x max,l , generate the initial route planning scheme where x l is the number of new lines of line l, N C is the total number of transmission lines to be built.

[0085] Step 104, eliminating the initial route planning scheme containing isolated islands and isolated nodes to obtain an updated route planning scheme;

[0086] In a specific implementation, the following steps may be included before step 104:

[0087] S41, generating adjacency matrices of the initial line planning solutions respectively;

[0088] S42, judging whether there are isolated islands and isolated nodes in the initial line planning scheme according to the adjacency matrix.

[0089] In a specific implementation, a corresponding adjacency matrix can be generated according to the initial route planning scheme in the generated individual particles, and the connectivity of the initial route planning scheme can be judged; if there are islands and isolated nodes in the generated initial planning scheme, the particle is eliminated, and the connectivity of the initial route planning scheme in the next particle is judged; if there are no islands and isolated nodes in the generated initial route planning scheme, the next step is performed and it is used as the updated route planning scheme.

[0090] Step 105, performing optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result;

[0091] Then, a cyclic iterative calculation is performed to solve the transmission network opportunity constrained programming model to obtain the optimal planning solution.

[0092] In one example, the steps of performing optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result include:

[0093] S51, using the output and load demand forecast data of new energy units to generate a net forecast error distribution function of the transmission network system;

[0094] S52, using the net prediction error distribution function of the transmission network system to transform the source-load uncertainty constraint into a definite form, and obtaining an opportunity-constrained programming model for optimizing the transmission network;

[0095] S53, based on the opportunity constrained programming model for optimizing the transmission network, the transmission system power flow is optimized with the minimization of the annualized transmission system operation cost as the optimization goal, and the updated annualized transmission system operation cost is obtained;

[0096] S54, performing a system power flow analysis on the initial line planning scheme under the line N-1 fault scenario to obtain an updated annualized new line investment cost.

[0097] In the specific implementation, by converting the uncertainty constraint form of source and load into a definite form, the problem of large computational complexity caused by the need to consider a large number of scenarios in traditional stochastic planning technology can be effectively avoided. In addition, by reasonably setting the confidence levels α1 and α2 that the positive and negative rotating reserve constraints need to meet, the conservatism of the planning scheme can meet the needs of different planning scenarios.

[0098] Specifically, the output and load demand forecast data of new energy units can be expressed as the sum of their actual values ​​and the corresponding forecast errors, expressed as:

[0099]

[0100]

[0101] Among them, P RE,g,tis the predicted output power value of the g-th type of new energy unit at time t; is the actual value of the output power of the g-th type of new energy unit at time t; is the actual load demand value of node i at time t; P D,i,t is the load demand forecast value of node i at time t; RE,g,t is the output prediction error of the g-th type of new energy unit at time t; D,i,t is the prediction error of the node i load at time t.

[0102] New energy unit output prediction errorξ RE,g,t and load demand forecast errorξ D,i,t It satisfies the normal distribution with a mean of 0, and its standard can be expressed as:

[0103]

[0104]

[0105] Among them, σ RE,g,t is the standard deviation of the output prediction error of the g-type new energy unit; σ D,i,t is the standard deviation of the load forecast error at node i; RE,g and ρ Ins,g is the correlation coefficient of the output prediction error of the g-th type of new energy unit; ρ D is the correlation coefficient of load forecast error; P Ins,g It is the rated installed capacity of the G-type new energy units.

[0106] The net prediction error of the transmission grid system at time t can be expressed as:

[0107]

[0108] Among them, ξ pre,t is the net prediction error of the transmission system at time t.

[0109] Due to the output prediction error of new energy units ξ RE,g,t and load demand forecast errorξ D,i,t Satisfying the normal distribution with a mean of 0, the net prediction error ξ of the transmission system at time t can be obtained pre,t The standard deviation of is:

[0110]

[0111] Among them, σ pre,t is the net prediction error ξ of the transmission system at time t pre,t The standard deviation of .

[0112] Assume that the distribution function of the net forecast error of the transmission system at time t is φ pre,t, the source-load uncertainty constraint form can be transformed into a definite form as follows:

[0113]

[0114]

[0115] in, is the distribution function of the net forecast error of the transmission system at time t pre,t The inverse function of .

[0116] Next, the optimal power flow calculation is performed to obtain the updated annualized transmission system operating cost. The power flow optimization of the transmission system with the minimization of the annualized transmission system operating cost as the optimization goal can maximize the consumption of new energy output and reduce the power generation cost of traditional units.

[0117] Next, perform N-1 safety check and analyze the system flow under the line N-1 fault scenario for the generated planning scheme. If there is no line overload and load shedding in all time periods under the line N-1 fault scenario, the annualized new line investment cost C of the scheme is calculated. inv Otherwise, the solution is discarded and the process returns to step 104.

[0118] Step 106, determining whether the current number of iterations reaches a preset number of iterations;

[0119] Step 107, if not, update the initial route planning scheme using the optimization result, and return to the initial route planning scheme without the islands and isolated nodes to obtain the updated route planning scheme;

[0120] Step 108: If yes, use the optimization result to plan the transmission network line.

[0121] In the embodiment of the present invention, it is necessary to perform cyclic iterative calculation based on the particle swarm algorithm to solve the optimal planning scheme of the transmission network and determine whether the set maximum number of iterations N has been reached. iter , if the maximum number of iterations N is reached iter , then the loop calculation ends and the optimal planning solution is output in is the optimal number of new lines for line l, N C is the total number of transmission lines to be built in the transmission network; otherwise, continue the iterative calculation.

[0122] The present invention generates a transmission network opportunity constraint planning model that takes into account source-load uncertainty and N-1 safety constraints, and solves it through a particle swarm optimization algorithm. It can effectively optimize the transmission network planning scheme under the dual uncertainty of renewable energy output and load demand, ensure the power supply reliability under line N-1 faults, and maximize the consumption of wind power renewable energy output, reduce the output of traditional generator sets, and achieve the optimal cost operation of the transmission system under the planning scheme.

[0123] For ease of understanding, the embodiments of the present invention are described below by using specific examples:

[0124] Take the improved Garver 6 system as an example for analysis. The system topology is as follows: Figure 2 The system installed capacity and load demand information are shown in Table 1, and the line parameters are shown in Table 2. The typical daily output curves of wind power and photovoltaic power are shown in Figure 3 As shown in the figure, the benchmark capacity is set to 100MW, the line cost is 300,000 yuan / km, the capital discount rate is 10%, the line payback period is 25 years, the abandoned energy cost is 0.5 yuan / kWh, and the confidence levels α1 and α2 are set to 0.95.

[0125] Table 1 Installed capacity and load parameters of each node

[0126]

[0127]

[0128] Table 2 Transmission network line parameters

[0129]

[0130] The transmission network line planning method proposed in the present invention is used to model and solve the problem, and the optimal transmission network line planning scheme and cost are shown in Table 3. The algorithm solution iterative process is as follows Figure 4 As shown in Figure 2, the optimized transmission network topology is as follows: Figure 5 As shown. It can be seen from Table 3 that, under the set confidence level, the annualized total cost of line planning obtained by the present invention is 16.54 million yuan, of which the annual investment cost is 13.8812 million yuan and the annual operating cost is 2.6588 million yuan. At the same time, under the transmission network optimization planning scheme obtained by the present invention, when any line N-1 fails, there is no overload on the transmission system line, and the load shedding amount is 0. Therefore, the method proposed in the embodiment of the present invention can be effectively applied to the transmission network planning considering N-1 safety constraints under the dual uncertainty of source and load.

[0131] Table 3 Transmission network planning results

[0132]

[0133] Among them, 2-3(3) in the line planning scheme means that three new transmission lines are built between nodes 2 and 3.

[0134] See also Figure 6 , Figure 6 A structural block diagram of a transmission network line planning device provided in an embodiment of the present invention.

[0135] An embodiment of the present invention provides a transmission network line planning device, including:

[0136] The acquisition module 601 is used to obtain the output of the new energy unit, load demand forecast data, parameters of the line to be selected and preset electrical parameters;

[0137] Model building module 602, for minimizing the annualized new line investment cost and the annualized transmission system operation cost as optimization objectives, taking system flow constraints, line capacity constraints, generator capacity constraints, line N-1 safety constraints, new line number constraints and source-load uncertainty constraints as constraint conditions, using new energy unit output, load demand forecast data, candidate line parameters and preset electrical parameters to generate a transmission network opportunity constraint planning model;

[0138] An initial line planning scheme generating module 603 is used to solve the transmission network opportunity constraint programming model using a particle swarm optimization algorithm to generate an initial line planning scheme;

[0139] A removal module 604 is used to remove the initial route planning scheme containing isolated islands and isolated nodes to obtain an updated route planning scheme;

[0140] The optimization module 605 is used to perform optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result;

[0141] A determination module 606 is used to determine whether the current number of iterations reaches a preset number of iterations;

[0142] Return module 607, for if no, using the optimization result to update the initial route planning scheme, and returning to the initial route planning scheme with islands and isolated nodes removed to obtain an updated route planning scheme;

[0143] The transmission network line planning module 608 is used to use the optimization result to perform transmission network line planning.

[0144] In an embodiment of the present invention, it also includes:

[0145] An adjacency matrix generation module is used to generate the adjacency matrix of each initial route planning scheme respectively;

[0146] The isolated island and isolated node judgment module is used to judge whether there are isolated islands and isolated nodes in the initial line planning scheme according to the adjacency matrix.

[0147] In the embodiment of the present invention, the optimization module 605 includes:

[0148] A transmission network system net prediction error distribution function generation submodule is used to generate a transmission network system net prediction error distribution function using new energy unit output and load demand prediction data;

[0149] A form conversion submodule is used to convert the source-load uncertainty constraint into a definite form by using the net prediction error distribution function of the transmission network system to obtain an optimized transmission network opportunity constraint programming model;

[0150] The updated annualized transmission system operation cost generation submodule is used to optimize the transmission system power flow based on the opportunity-constrained programming model for optimizing the transmission network, taking the minimization of the annualized transmission system operation cost as the optimization goal, and obtain the updated annualized transmission system operation cost;

[0151] The updated annualized new line investment cost generation submodule is used to perform system power flow analysis on the initial line planning scheme under the line N-1 fault scenario to obtain the updated annualized new line investment cost.

[0152] In the embodiment of the present invention, the source-load uncertainty constraint includes a wind power output uncertainty constraint, a photovoltaic output uncertainty constraint and a load demand uncertainty constraint.

[0153] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0154] The memory is used to store the program code and transmit the program code to the processor;

[0155] The processor is used to execute the transmission network line planning method of the embodiment of the present invention according to the instructions in the program code.

[0156] The embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the transmission network line planning method of the embodiment of the present invention.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0159] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0160] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks 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 terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. 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.

[0161] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0164] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0165] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning a transmission network line, characterized in that: include: Obtain new energy unit output, load demand forecast data, candidate line parameters and preset electrical parameters; Taking the minimization of the annualized new line investment cost and the annualized transmission system operation cost as the optimization goal, taking the system flow constraint, line capacity constraint, generator capacity constraint, line N-1 safety constraint, new line number constraint and source-load uncertainty constraint as the constraint conditions, the output of the new energy unit, the load demand forecast data, the parameters of the selected line and the preset electrical parameters are used to generate a transmission network opportunity constraint planning model; The particle swarm optimization algorithm is used to solve the transmission network opportunity constraint programming model and generate the initial line planning scheme; Eliminate the initial route planning scheme containing isolated islands and isolated nodes to obtain an updated route planning scheme; Performing optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result; Determine whether the current number of iterations reaches the preset number of iterations; If not, the initial route planning scheme is updated using the optimization result, and the initial route planning scheme containing isolated islands and isolated nodes is removed to obtain an updated route planning scheme; If so, adopting the optimization result to plan the transmission network line; Among them, the objective function of the transmission network opportunity constrained programming model considering source-load uncertainty and N-1 security constraints includes the annualized new line investment cost C inv and the annualized transmission system operating cost C ope , the specific formula is as follows: minC inv +C ope Among them, C inv is the annualized investment cost of new lines; C ope is the annualized transmission system operating cost; Annualized investment cost of new lines C inv It is composed of the set of newly built lines, the number of newly built lines, the length of newly built lines and the line capital recovery coefficient, which can be expressed as: Among them, L C is the set of candidate routes; γ is the route capital recovery coefficient; x l is the number of new lines of line l; C L,l is the unit investment cost of line l; L Dis,l is the length of line l; Y is the operating life of the new line in years; r is the capital discount rate; Annualized transmission system operating cost C ope It is composed of the power generation cost of traditional generators and the penalty cost of wind and solar power abandonment, expressed as: Where T is the annual operating hours of the transmission line; N node is the total number of transmission network nodes; G i is the set of traditional generators connected to node i; P G,j,t is the output power of traditional generator set j at time t; a j 、b j and c j is the cost coefficient of traditional generator set j; G RE,i is the set of new energy generators connected to node i; C RE-cut,g is the unit power abandonment cost of the g-type new energy unit; P RE-cut,g,t is the abandoned power of the g-th type of new energy unit at time t; The constraints of the transmission network opportunity constraint programming model considering source-load uncertainty and N-1 security constraints include: system flow constraints, line capacity constraints, generator capacity constraints, line N-1 security constraints, new line number constraints and source-load uncertainty constraints, which can be expressed as: 0≤x l ≤x max,l Among them, P G,j,t is the output power of traditional generator set j at time t; P L,l,t is the power flow of line l at time t; P RE,g,t is the output power forecast value of the g-type new energy unit at time t; P RE-cut,g,t is the abandoned power of the g-type new energy unit at time t; P D,i,t is the load demand forecast value of node i at time t; b l is the susceptance of line l; θ S,l,t is the phase angle of the node at the beginning of line l; θ E,l,t is the phase angle of the end node of line l; P Lmax,l , P Lmin,l is the upper and lower limits of the rated transmission power of line l; P Gmax,j and P Gmin,j are the upper and lower limits of the output power of the traditional generator set j; P Dcut,l,t is the load shedding power of node i at time t; x l is the number of new lines of line l; x max,l is the upper limit of the number of new lines for line l; P r {·} is the probability of event {·} being true; ξ RE,g,t is the output prediction error of the g-th type of new energy unit at time t; D,i,t is the prediction error of the load of node i at time t; α1 and α2 are the confidence levels that the positive and negative spinning reserve constraints need to meet, respectively; B is the node set.

2. The method according to claim 1, characterized in that Also includes: Generate the adjacency matrix of each initial route planning scheme respectively; It is determined whether there are isolated islands and isolated nodes in the initial line planning scheme according to the adjacency matrix.

3. The method according to claim 1, characterized in that The step of performing optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result includes: Generating a net prediction error distribution function of a transmission network system using the output of the new energy unit and the load demand prediction data; The net prediction error distribution function of the transmission network system is used to transform the source-load uncertainty constraint into a definite form to obtain an optimized transmission network opportunity constraint programming model; Based on the optimized transmission network opportunity constrained programming model, the transmission system power flow optimization is performed with minimization of the annualized transmission system operating cost as the optimization goal to obtain an updated annualized transmission system operating cost; The initial line planning scheme is subjected to a system power flow analysis under a line N-1 fault scenario to obtain an updated annualized new line investment cost.

4. The method according to claim 1, characterized in that The source-load uncertainty constraints include wind power output uncertainty constraints, photovoltaic output uncertainty constraints and load demand uncertainty constraints.

5. A transmission network line planning device, characterized in that: include: The acquisition module is used to obtain the output of new energy units, load demand forecast data, parameters of the selected lines and preset electrical parameters; A model building module is used to take the annualized new line investment cost and the annualized transmission system operation cost as the minimum optimization target, take the system flow constraint, line capacity constraint, generator capacity constraint, line N-1 safety constraint, new line number constraint and source-load uncertainty constraint as constraint conditions, and use the output of the new energy unit, the load demand forecast data, the parameters of the selected line and the preset electrical parameters to generate a transmission network opportunity constraint planning model; An initial line planning scheme generation module is used to solve the transmission network opportunity constraint planning model using a particle swarm optimization algorithm to generate an initial line planning scheme; A elimination module is used to eliminate the initial route planning scheme containing isolated islands and isolated nodes to obtain an updated route planning scheme; An optimization module is used to perform optimal power flow calculation and N-1 safety verification on the updated line planning scheme to obtain an optimization result; A judgment module is used to judge whether the current number of iterations reaches a preset number of iterations; A return module is used to update the initial route planning scheme by using the optimization result if no, and return the initial route planning scheme with islands and isolated nodes removed to obtain an updated route planning scheme; A transmission network line planning module, used for, if yes, using the optimization result to perform transmission network line planning; Among them, the objective function of the transmission network opportunity constrained programming model considering source-load uncertainty and N-1 security constraints includes the annualized new line investment cost C inv and the annualized transmission system operating cost C ope , the specific formula is as follows: minC inv +C ope Among them, C inv is the annualized investment cost of new lines; C ope is the annualized transmission system operating cost; Annualized investment cost of new lines C inv It is composed of the set of newly built lines, the number of newly built lines, the length of newly built lines and the line capital recovery coefficient, which can be expressed as: Among them, L C is the set of candidate routes; γ is the route capital recovery coefficient; x l is the number of new lines of line l; C L,l is the unit investment cost of line l; L Dis,l is the length of line l; Y is the operating life of the new line in years; r is the capital discount rate; Annualized transmission system operating cost C ope It is composed of the power generation cost of traditional generators and the penalty cost of wind and solar power abandonment, expressed as: Where T is the annual operating hours of the transmission line; N node is the total number of transmission network nodes; G i is the set of traditional generators connected to node i; P G,j,t is the output power of traditional generator set j at time t; a j 、b j and c j is the cost coefficient of traditional generator set j; G RE,i is the set of new energy generators connected to node i; C RE-cut,g is the unit power abandonment cost of the g-type new energy unit; P RE-cut,g,t is the abandoned power of the g-th type of new energy unit at time t; The constraints of the transmission network opportunity constraint programming model considering source-load uncertainty and N-1 security constraints include: system flow constraints, line capacity constraints, generator capacity constraints, line N-1 security constraints, new line number constraints and source-load uncertainty constraints, which can be expressed as: 0≤x l ≤x max,l Among them, P G,j,t is the output power of traditional generator set j at time t; P L,l,t is the power flow of line l at time t; P RE,g,t is the output power forecast value of the g-type new energy unit at time t; P RE-cut,g,t is the abandoned power of the g-type new energy unit at time t; P D,i,t is the load demand forecast value of node i at time t; b l is the susceptance of line l; θ S,l,t is the phase angle of the node at the beginning of line l; θ E,l,t is the phase angle of the end node of line l; P Lmax,l , P Lmin,l is the upper and lower limits of the rated transmission power of line l; P Gmax,j and P Gmin,j are the upper and lower limits of the output power of the traditional generator set j; P Dcut,l,t is the load shedding power of node i at time t; x l is the number of new lines of line l; x max,l is the upper limit of the number of new lines for line l; P r {·} is the probability of event {·} being true; ξ RE,g,t is the output prediction error of the g-th type of new energy unit at time t; D,i,t is the prediction error of the load of node i at time t; α1 and α2 are the confidence levels that the positive and negative spinning reserve constraints need to meet, respectively; B is the node set.

6. The device according to claim 5, characterized in that Also includes: An adjacency matrix generation module is used to generate the adjacency matrix of each initial route planning scheme respectively; The isolated island and isolated node judgment module is used to judge whether there are isolated islands and isolated nodes in the initial line planning scheme according to the adjacency matrix.

7. The device according to claim 5, characterized in that The optimization module comprises: A transmission network system net prediction error distribution function generation submodule, used to generate a transmission network system net prediction error distribution function using the new energy unit output and the load demand prediction data; A form conversion submodule, used for converting the source-load uncertainty constraint into a definite form by using the net prediction error distribution function of the transmission network system to obtain an optimized transmission network opportunity constraint programming model; An updated annualized transmission system operating cost generation submodule is used to optimize the transmission system power flow based on the optimized transmission network opportunity constraint programming model and take minimization of the annualized transmission system operating cost as the optimization goal to obtain an updated annualized transmission system operating cost; The updated annualized new line investment cost generation submodule is used to perform system power flow analysis on the initial line planning scheme under the line N-1 fault scenario to obtain an updated annualized new line investment cost.

8. The device according to claim 5, characterized in that The source-load uncertainty constraints include wind power output uncertainty constraints, photovoltaic output uncertainty constraints and load demand uncertainty constraints.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the transmission network line planning method according to any one of claims 1-4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the transmission network line planning method according to any one of claims 1-4.

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