A method, device, equipment and medium for calculating photovoltaic acceptance capacity of distribution network

Abstract

The present invention discloses a method, device, equipment and medium for calculating the photovoltaic acceptance capacity of a distribution network. The method includes: obtaining equipment operation limit parameters and a preset distribution network component model; constructing a photovoltaic acceptance capacity evaluation index that considers the randomness of load and photovoltaic output; constructing a photovoltaic acceptance capacity optimization model that considers the flexibility of the topological structure; solving the photovoltaic acceptance capacity optimization model using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network. The method of the present invention first proposes a distribution network photovoltaic acceptance capacity evaluation model that considers the randomness of load and photovoltaic output; secondly, taking the network topology structure and photovoltaic access capacity of the distribution network as optimization variables, a genetic algorithm and a neighborhood search algorithm are used to calculate the maximum photovoltaic acceptance capacity of the distribution network, and a linear power flow is used to perform probabilistic power flow calculation, thereby improving the calculation speed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system automation, and specifically relates to a method, device, equipment and medium for calculating photovoltaic acceptance capacity of a distribution network. Background Art

[0002] Distributed photovoltaic power stations refer to power generation systems that use decentralized resources, have a small installed capacity, and are located near users. They are generally connected to a power grid with a voltage level of less than 35 kV or lower. Distributed photovoltaic power stations specifically refer to distributed photovoltaic power station systems that use photovoltaic modules to directly convert solar energy into electrical energy. The most widely used distributed photovoltaic power station system is a photovoltaic power generation project built on the roofs of urban buildings.

[0003] Distributed photovoltaics have the advantages of easy installation, no need for long-distance transmission, high utilization rate, low pollution, and outstanding environmental benefits. Distributed photovoltaic power station projects are noiseless and will not pollute the air and water during the power generation process. And it can alleviate the local power shortage to a certain extent. It is an important way to utilize renewable energy. The distribution network is the main carrier for accepting distributed photovoltaics. Evaluating the photovoltaic acceptance capacity of the distribution network is of great significance to improving the utilization efficiency of renewable energy and ensuring the safe and reliable operation of the distribution network. Some scholars have proposed a dynamic reconstruction method for distribution networks with the goal of improving photovoltaic acceptance capacity, but they have not considered the impact of photovoltaic and load randomness on the photovoltaic acceptance capacity of the distribution network, and cannot effectively improve the photovoltaic acceptance capacity. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, equipment and medium for calculating the photovoltaic acceptance capacity of a distribution network, so as to solve the problem that the dynamic reconstruction method of the distribution network in the above-mentioned background technology aims to improve the photovoltaic acceptance capacity, but does not consider the impact of photovoltaic and load randomness on the photovoltaic acceptance capacity of the distribution network, and cannot effectively improve the photovoltaic acceptance capacity.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] In a first aspect, a method for calculating the photovoltaic acceptance capacity of a distribution network is provided, comprising the following steps:

[0007] Obtain equipment operating limit parameters and preset distribution network component models;

[0008] Based on the equipment operating limit parameters and the preset distribution network component model, a photovoltaic acceptance capacity evaluation index is constructed that takes into account the randomness of load and photovoltaic output;

[0009] Based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output, a photovoltaic acceptance capacity optimization model considering the flexibility of topological structure is constructed;

[0010] The photovoltaic acceptance capacity optimization model is solved by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network.

[0011] Furthermore, the step of constructing a photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output based on the equipment operation limit parameters and the preset distribution network component model specifically includes the following:

[0012] Based on the equipment operating limit parameters and the preset distribution network component model, the normal distribution is used to describe the random prediction errors of load and photovoltaic output, and the probability density function of photovoltaic active output and the probability density function of load value are obtained;

[0013] According to the probability density function of the photovoltaic active output and the probability density function of the load value, a preset number of random scenarios are generated by using the Monte Carlo method, and power flow calculations are performed on the random scenarios to obtain the degree and probability of voltage over-limit and line and transformer transmission power over-limit;

[0014] Based on the degree and probability of voltage exceeding the limit, line and transformer transmission power exceeding the limit, a photovoltaic acceptance capacity evaluation index is constructed.

[0015] Furthermore, the photovoltaic acceptance capacity evaluation index includes: a voltage over-limit evaluation index, a line power over-limit evaluation index and a transformer power over-limit evaluation index.

[0016] Furthermore, the step of constructing a photovoltaic acceptance capacity optimization model considering the flexibility of topological structure based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output includes the following:

[0017] According to the daily variation characteristics of photovoltaic output and load, a typical day is selected, the opening and closing status of the distribution network line switches and the photovoltaic access capacity are used as optimization variables, the maximum photovoltaic access capacity is used as the optimization objective function, and a photovoltaic acceptance capacity optimization model is constructed through network reconstruction optimization.

[0018] Furthermore, the photovoltaic acceptance capacity optimization model takes the maximum sum of photovoltaic access capacity as the objective function, while satisfying the distribution network topology constraints, voltage constraints, line capacity constraints, and transformer capacity constraints.

[0019] Furthermore, the step of solving the photovoltaic acceptance capacity optimization model by using a genetic algorithm and a neighborhood search algorithm specifically includes:

[0020] Chromosome coding; decimal coding is used for photovoltaic access capacity; the on-off state of the distribution network line switch is coded using a decimal coding method based on a random spanning tree; the coding of photovoltaic access capacity and the on-off state of the distribution network line switch are independent of each other;

[0021] Fitness calculation: for each chromosome, a random scene is generated by the Monte Carlo method, the corresponding power flow data at each moment is obtained by power flow calculation, and the photovoltaic acceptance capacity evaluation index is calculated; when the chromosome meets the constraints, the photovoltaic access capacity is the fitness of the chromosome; when the chromosome does not meet the constraints, the fitness of the chromosome is set to 0;

[0022] The tournament selection method is used to randomly select two individuals from the population for comparison, and the individual with the larger fitness value is selected to enter the next generation;

[0023] For the chromosome that needs to be crossover, two truncation points are randomly selected from the genes representing the circuit switch state, and the gene string sandwiched between the two points is inserted into the corresponding position of the first truncation point of the other chromosome, and the genes identical to the gene string are removed to obtain the hybrid offspring of the two chromosomes;

[0024] For the gene representing the photovoltaic access capacity, a gene string is randomly selected and exchanged with the gene string at the same position on another chromosome;

[0025] For the chromosomes that need to be mutated, two truncation points in the gene string are randomly selected from the genes representing the on and off states of the distribution network line switches, and then the gene string between the two points is reversed to obtain a new chromosome.

[0026] Furthermore, for the genes representing the photovoltaic access capacity, a combination of mutation operation and neighborhood search is used. After a gene is randomly selected, new genes are randomly generated within a certain neighborhood of the photovoltaic access capacity corresponding to the gene.

[0027] In a second aspect, a device for calculating photovoltaic acceptance capacity of a distribution network is provided, characterized in that it includes:

[0028] An acquisition module is used to obtain equipment operating limit parameters and a preset distribution network component model;

[0029] The first construction module is used to construct a photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output based on the equipment operation limit parameters and the preset distribution network component model;

[0030] A second construction module is used to construct a photovoltaic acceptance capacity optimization model considering the flexibility of the topological structure based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output;

[0031] The calculation and solution module is used to solve the photovoltaic acceptance capacity optimization model by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network.

[0032] In a third aspect, an electronic device is provided, characterized in that it includes a processor and a memory, and the processor is used to execute a computer program stored in the memory to implement the above-mentioned method for calculating the photovoltaic acceptance capacity of the distribution network.

[0033] In a fourth aspect, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the above-mentioned method for calculating the photovoltaic acceptance capacity of the distribution network is implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The method for calculating the photovoltaic acceptance capacity of the distribution network proposed in the present invention takes into account the flexibility of the topological structure and first proposes a distribution network photovoltaic acceptance capacity evaluation model that takes into account the randomness of load and photovoltaic output. Under the condition of taking into account the randomness of load and photovoltaic output, the photovoltaic acceptance capacity of the distribution network can be effectively improved.

[0036] Secondly, taking the network topology structure and photovoltaic access capacity of the distribution network as optimization variables, the genetic algorithm and neighborhood search algorithm are used to calculate the maximum photovoltaic acceptance capacity of the distribution network, and the linear power flow is used for probabilistic power flow calculation to improve the calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0038] Figure 1 A schematic diagram of the principle of a method for calculating the photovoltaic acceptance capacity of a distribution network according to the present invention;

[0039] Figure 2 A flow chart of a method for calculating photovoltaic acceptance capacity of a distribution network according to the present invention;

[0040] Figure 3 This is a structural block diagram of a device for calculating photovoltaic acceptance capacity of a distribution network according to the present invention;

[0041] Figure 4 The present invention is a structural block diagram of an electronic device. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0043] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0044] Example 1

[0045] like Figure 1 and Figure 2 As shown, a method for calculating the photovoltaic acceptance capacity of a distribution network includes the following steps:

[0046] S1. Obtain equipment operating limit parameters and preset distribution network component models.

[0047] In this solution, the equipment operation limit parameters and the preset distribution network component model are provided by the distribution network digital simulation model. The construction of the distribution network digital simulation model includes the distribution network component model and the equipment operation limit parameters.

[0048] in:

[0049] The distribution network component model includes line parameters (resistance, reactance, conductance, susceptance), transformer parameters (leakage reactance, ratio, tap position), load node parameters (active power, reactive power values), photovoltaic power generation node parameters (active power values), and thermal power unit parameters (active power, reactive power values);

[0050] The equipment operating limit parameters include: line rated current, transformer rated capacity, photovoltaic node maximum output, and thermal power unit maximum output.

[0051] The data in the above distribution network digital simulation model is used in the following steps.

[0052] S2. Based on the equipment operating limit parameters and the preset distribution network component model, a photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output is constructed; specifically, the following steps are included:

[0053] S21. Since the actual values ​​of load and photovoltaic output usually have certain errors from the predicted values, which causes changes in node voltage and equipment power, this solution uses normal distribution to describe the random prediction errors of load and photovoltaic output based on the equipment operating limit parameters and the preset distribution network component model, and obtains the probability density function of photovoltaic active output and the probability density function of load value.

[0054] Among them, the probability density function of photovoltaic active output f(P PV )for:

[0055]

[0056] Where: P PV is the actual value of photovoltaic active power output; μ PV is the mathematical expectation, taking the predicted value of the active output of photovoltaic power generation; σ PV It is the square difference of the normal distribution of the active output of photovoltaic power generation.

[0057] Similarly, the probability density function of the load value f(S LD )for:

[0058]

[0059] Where: S LD is the actual value of the load; μ LD is the mathematical expectation, taking the predicted value of the load; σ LD is the squared deviation of the normal distribution of load.

[0060] S22. Based on the probability density function of the photovoltaic active output and the probability density function of the load value, i.e., the mathematical representation of the random distribution characteristics of the load and the photovoltaic output, a preset number of random scenarios are generated using the Monte Carlo method, and power flow calculations are performed on the random scenarios to obtain the degree and probability of voltage exceeding the limit and line and transformer transmission power exceeding the limit.

[0061] S23. Comprehensively consider the degree and probability of voltage over-limit, line and transformer transmission power over-limit, and construct a photovoltaic acceptance capacity evaluation index.

[0062] Specifically, the photovoltaic acceptance capacity evaluation indicators constructed in this scheme include: voltage over-limit evaluation index, line power over-limit evaluation index and transformer power over-limit evaluation index.

[0063] (1) Voltage over-limit evaluation index

[0064] In the obtained random scenarios, the more often the voltage exceeds the upper limit, the more serious the voltage exceeds the upper limit, indicating that the configured photovoltaic access capacity is more unreasonable. Considering the probability and degree of voltage exceeding the limit, the voltage exceeding the upper limit evaluation index ε is defined u for:

[0065]

[0066] Among them, N all is the total number of samples (i.e., random scenes); U c,de is the maximum value of the node voltage exceeding the limit in scenario c.

[0067] For node i, the node voltage exceeds the limit level U i,c,de It can be expressed as:

[0068]

[0069] U i is the voltage at node i; U i,min and U i,max are the minimum and maximum voltage allowed for node i.

[0070] (2) Line power over-limit evaluation index

[0071] Considering the probability and degree of line transmission power exceeding the limit, the line power exceeding the limit evaluation index ε is defined l for:

[0072]

[0073] Among them, S c,L,de is the maximum value of the line transmission power exceeding the limit in scenario c.

[0074] For line l, its transmission power exceeding the limit in scenario c is S c,l,de It can be expressed as:

[0075]

[0076] Among them, S l is the transmission power of line l; S l,max is the maximum transmission power allowed by line l.

[0077] (3) Transformer power over-limit evaluation index

[0078] Considering the probability and degree of transformer power over-limit, the transformer power over-limit evaluation index ε is defined. tr for:

[0079]

[0080] Among them, S c,T,de The transformer power transmission limit in scenario c.

[0081] S c,T,de It can be expressed as:

[0082]

[0083] Among them, S T is the transmission power of the transformer; S T,max is the maximum transfer power allowed by the transformer.

[0084] S3. Based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output, a photovoltaic acceptance capacity optimization model considering the flexibility of topological structure is constructed.

[0085] The specific steps include the following:

[0086] Typical days are selected based on the intraday variation characteristics of PV output and load. The evaluation indicators are reflected and applied in the constraint functions of the optimization model, namely, formulas (10)-(12). The on / off status of the distribution network line switches and the PV access capacity are used as optimization variables, and the maximum PV access capacity is used as the optimization objective function to improve the PV access capacity through network reconstruction optimization.

[0087] The photovoltaic acceptance capacity optimization model takes the maximum sum of photovoltaic access capacity as the objective function, while satisfying the distribution network topology constraints, voltage constraints, line capacity constraints, and transformer capacity constraints.

[0088] The objective function of optimizing the maximum photovoltaic access capacity is:

[0089]

[0090] Where f is the sum of photovoltaic access capacity; M PV is the set of nodes connected to photovoltaic power generation; P i,PVC The capacity of photovoltaic connected to node i;

[0091] The optimization model needs to meet the following constraints:

[0092] (1) Distribution network topology constraints

[0093] The distribution network operates radially, and there are no ring networks or isolated nodes in the system.

[0094] (2) Voltage Constraint

[0095] At any time t, the voltage over-limit evaluation index ε t,u Less than a certain set value ε t,u,set :

[0096] ε t,u <ε t,u,set (10)

[0097] (3) Line capacity constraints

[0098] At any time t, the line transmission power over-limit evaluation index ε t,l Less than a certain set value ε t,l,set :

[0099] ε t,l <ε t,l,set (11)

[0100] (4) Transformer capacity constraints

[0101] At any time t, the transformer transmission power over-limit evaluation index ε t,tr Less than a certain set value ε t,tr,set :

[0102] ε t,tr <ε t,tr,set (12)

[0103] Among them, the voltage over-limit index setting value ε t,u,set 、Line power over-limit index setting value ε t,l,set and transformer power over-limit index setting value ε t,tr,set It is determined by the over-limit probability and over-limit degree. The calculation method of voltage over-limit degree is shown in formula (6), and the calculation method of voltage over-limit probability index is as follows:

[0104]

[0105] Among them, N all is the total number of samples, N ov is the number of samples where the voltage exceeds the limit.

[0106] For example, if the samples with voltage over-limit are allowed to account for 4.56% of all samples and the voltage over-limit degree is allowed to be 0.1, the voltage over-limit indicator setting value ε t,u,set Set to 0.00456.

[0107] S4. Solve the photovoltaic acceptance capacity optimization model (Formula 9) by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network. The steps specifically include:

[0108] (1) Chromosome encoding

[0109] The coding of photovoltaic access capacity and the on / off status of the distribution network line switch are independent of each other. The photovoltaic access capacity is coded in decimal. For the convenience of calculation, the photovoltaic access capacity is optimized with 10kW as the minimum unit. The on / off status of the distribution network line switch is coded in decimal based on a random spanning tree.

[0110] (2) Fitness calculation

[0111] For each chromosome, a random scenario is generated by the Monte Carlo method, and the power flow data such as the node voltage and line power corresponding to each moment are obtained by power flow calculation, and the photovoltaic acceptance capacity evaluation index is calculated; when the chromosome meets the constraint conditions, the photovoltaic access capacity is the fitness of the chromosome; wherein the constraint conditions refer to the four constraint conditions of the above-mentioned optimization model, including: distribution network topology constraint, voltage constraint, line capacity constraint, and transformer capacity constraint; when the chromosome does not meet the constraint, the fitness of the chromosome is set to 0.

[0112] (3)Select

[0113] The tournament selection method is used to randomly select two individuals from the population each time for comparison, and the individual with the larger fitness value is selected to enter the next generation.

[0114] (4) Cross

[0115] For the chromosomes that need to be crossovered, two truncation points are randomly selected from the genes representing the on / off status of the power distribution network line switches, and the gene string sandwiched between the two points is inserted into the corresponding position of the first truncation point of the other chromosome, and the genes identical to the gene string are removed to obtain the hybrid offspring of the two chromosomes;

[0116] For the gene representing the photovoltaic access capacity, a gene string is randomly selected and exchanged with the gene string at the same position on another chromosome.

[0117] The fitness of the chromosome obtained after the crossover is recalculated. When the constraints are met, the photovoltaic capacity is the fitness of the chromosome. The constraints refer to the four constraints of the above optimization model, including: distribution network topology constraints, voltage constraints, line capacity constraints, and transformer capacity constraints.

[0118] (5) Variation

[0119] For the chromosomes that need to be mutated, two truncation points in the gene string are randomly selected from the genes representing the on and off states of the distribution network line switches, and then the gene string between the two points is reversed to obtain a new chromosome.

[0120] The mutated chromosome is re-entered into step (2) for fitness calculation.

[0121] In the preferred solution, in order to increase the evolution speed, a combination of mutation operation and neighborhood search is used for the gene representing the photovoltaic access capacity. After a gene is randomly selected, a new gene is randomly generated within a certain neighborhood of the photovoltaic access capacity corresponding to the gene.

[0122] Example 2

[0123] like Figure 3 As shown, based on the unified inventive concept of the above embodiments, the present solution also provides a distribution network photovoltaic acceptance capacity calculation device, including:

[0124] The acquisition module is used to obtain the equipment operating limit parameters and the preset distribution network component model.

[0125] The first construction module is used to construct a photovoltaic acceptance capacity evaluation index that takes into account the randomness of load and photovoltaic output based on equipment operating limit parameters and a preset distribution network component model.

[0126] In the first building block, the photovoltaic acceptance capacity evaluation index is constructed as follows:

[0127] Based on the equipment operating limit parameters and the preset distribution network component model, the normal distribution is used to describe the random prediction errors of load and photovoltaic output, and the probability density function of photovoltaic active output and the probability density function of load value are obtained;

[0128] According to the probability density function of the photovoltaic active output and the probability density function of the load value, a preset number of random scenarios are generated by using the Monte Carlo method, and power flow calculations are performed on the random scenarios to obtain the degree and probability of voltage over-limit and line and transformer transmission power over-limit;

[0129] Based on the degree and probability of voltage exceeding the limit, line and transformer transmission power exceeding the limit, a photovoltaic acceptance capacity evaluation index is constructed.

[0130] In the first building block, the photovoltaic acceptance capacity evaluation indicators include: voltage over-limit evaluation indicators, line power over-limit evaluation indicators and transformer power over-limit evaluation indicators.

[0131] The second construction module is used to construct a photovoltaic acceptance capacity optimization model considering the flexibility of the topological structure based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output.

[0132] In the second building block, the photovoltaic acceptance capacity optimization model is constructed as follows:

[0133] According to the characteristics of the daily variation of photovoltaic output and load, a typical day is selected, the on-off state of the distribution network line switch and the photovoltaic access capacity are used as optimization variables, the maximum photovoltaic access capacity is used as the optimization objective function, and the photovoltaic acceptance capacity optimization model is constructed through network reconstruction optimization. Among them, the photovoltaic acceptance capacity optimization model takes the maximum sum of photovoltaic access capacity as the objective function, and satisfies the distribution network topology constraints, voltage constraints, line capacity constraints, and transformer capacity constraints.

[0134] The calculation and solution module is used to solve the photovoltaic acceptance capacity optimization model by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network.

[0135] In the calculation and solution module, the method for solving the photovoltaic acceptance capacity optimization model is as follows:

[0136] Chromosome coding; decimal coding is used for photovoltaic access capacity; the on-off state of the distribution network line switch is coded using a decimal coding method based on a random spanning tree; the coding of photovoltaic access capacity and the on-off state of the distribution network line switch are independent of each other;

[0137] Fitness calculation: for each chromosome, a random scene is generated by the Monte Carlo method, the corresponding power flow data at each moment is obtained by power flow calculation, and the photovoltaic acceptance capacity evaluation index is calculated; when the chromosome meets the constraints, the photovoltaic access capacity is the fitness of the chromosome; when the chromosome does not meet the constraints, the fitness of the chromosome is set to 0;

[0138] The tournament selection method is used to randomly select two individuals from the population for comparison, and the individual with the larger fitness value is selected to enter the next generation;

[0139] For the chromosome that needs to be crossover, two truncation points are randomly selected from the genes representing the circuit switch state, and the gene string sandwiched between the two points is inserted into the corresponding position of the first truncation point of the other chromosome, and the genes identical to the gene string are removed to obtain the hybrid offspring of the two chromosomes;

[0140] For the gene representing the photovoltaic access capacity, a gene string is randomly selected and exchanged with the gene string at the same position on another chromosome;

[0141] For the chromosomes that need to be mutated, two truncation points in the gene string are randomly selected from the genes representing the on and off states of the distribution network line switches, and then the gene string between the two points is reversed to obtain a new chromosome.

[0142] Example 3

[0143] The present invention also provides an electronic device 100 for implementing the above-mentioned method for calculating the photovoltaic acceptance capacity of a distribution network; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of a method for calculating the photovoltaic acceptance capacity of a distribution network in embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0144] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0145] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for calculating the photovoltaic acceptance capacity of a distribution network, and the processor 102 can execute the plurality of instructions to implement:

[0146] Obtain equipment operating limit parameters and preset distribution network component models;

[0147] Based on the equipment operating limit parameters and the preset distribution network component model, a photovoltaic acceptance capacity evaluation index is constructed that takes into account the randomness of load and photovoltaic output;

[0148] Based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output, a photovoltaic acceptance capacity optimization model considering the flexibility of topological structure is constructed;

[0149] The photovoltaic acceptance capacity optimization model is solved by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network.

[0150] Example 4

[0151] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0152] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, 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 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 code.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to 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 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the 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.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the photovoltaic acceptance capacity of a distribution network, characterized in that: The following steps are involved: Obtain equipment operation limit parameters and preset distribution network component models; distribution network component models include line parameters, transformer parameters, load node parameters, photovoltaic power generation node parameters, and thermal power unit parameters; line parameters include resistance, reactance, conductance, and susceptance; transformer parameters include leakage reactance, ratio, and tap position; load node parameters include active and reactive values; photovoltaic power generation node parameters include active values; and thermal power unit parameters include active and reactive values; equipment operation limit parameters include: line rated current, transformer rated capacity, photovoltaic node maximum output, and thermal power unit maximum output; Based on the equipment operation limit parameters and the preset distribution network component model, a photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output is constructed; specifically, the following steps are used: based on the equipment operation limit parameters and the preset distribution network component model, the normal distribution is used to describe the random prediction error of load and photovoltaic output, and the probability density function of photovoltaic active output and the probability density function of load value are obtained; according to the probability density function of photovoltaic active output and the probability density function of load value, a preset number of random scenarios are generated by using the Monte Carlo method, and the power flow calculation is performed on the random scenarios to obtain the degree and probability of voltage over-limit, line and transformer transmission power over-limit; based on the degree and probability of voltage over-limit, line and transformer transmission power over-limit, a photovoltaic acceptance capacity evaluation index is constructed; The photovoltaic acceptance capacity evaluation indicators include: voltage over-limit evaluation indicators, line power over-limit evaluation indicators and transformer power over-limit evaluation indicators; Define the voltage over-limit evaluation index ε u for: Among them, N all is the total number of samples (i.e., random scenes); U c,de is the maximum value of the node voltage over-limit degree in scenario c; For node i, the node voltage exceeds the limit level U i,c,de It is expressed as: U i is the voltage at node i; U i,min and U i,max is the minimum and maximum voltage allowed for node i; Define the line power over-limit evaluation index ε l for: Among them, S c,L,de is the maximum value of the line transmission power exceeding the limit in scenario c; For line l, its transmission power exceeding the limit in scenario c is S c,l,de It is expressed as: Among them, S l is the transmission power of line l; S l,max is the maximum transmission power allowed for line l; Define the transformer power over-limit evaluation index ε tr for: Among them, S c,T,de is the degree of transformer power transmission exceeding the limit in scenario c; S c,T,de It is expressed as: Among them, S T is the transmission power of the transformer; S T,max is the maximum transmission power allowed by the transformer; Based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output, a photovoltaic acceptance capacity optimization model considering the flexibility of topological structure is constructed; including: selecting a typical day according to the daily variation characteristics of photovoltaic output and load, taking the opening and closing state of the distribution network line switch and the photovoltaic access capacity as optimization variables, taking the maximum photovoltaic access capacity as the optimization objective function, and constructing a photovoltaic acceptance capacity optimization model through network reconstruction optimization; wherein, the photovoltaic acceptance capacity optimization model takes the maximum sum of photovoltaic access capacity as the objective function, and satisfies the distribution network topological structure constraints, voltage constraints, line capacity constraints, and transformer capacity constraints at the same time; Distribution network topology constraints: The distribution network operates in a radial manner, and there are no ring networks or isolated nodes in the system; Voltage constraint: For any time t, the voltage limit evaluation index ε t,u Less than a certain set value ε t,u,set : e t,u <e t,u,set Line capacity constraint: At any time t, the line transmission power limit evaluation index ε t,l Less than a certain set value ε t,l,set : e t,l <e t,l,set Transformer capacity constraint: For any time t, the transformer transmission power over-limit evaluation index ε t,tr Less than a certain set value ε t,tr,set : e t,tr <e t,tr,set Among them, the voltage over-limit index setting value ε t,u,set 、Line power over-limit index setting value ε t,l,set and transformer power over-limit index setting value ε t,tr,set The voltage over-limit probability index is calculated by the over-limit probability and over-limit degree as follows: Among them, N all is the total number of samples, N ov is the number of samples where the voltage exceeds the limit; The photovoltaic acceptance capacity optimization model is solved by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network.

2. The method for calculating the photovoltaic acceptance capacity of the distribution network according to claim 1, characterized in that: The step of solving the photovoltaic acceptance capacity optimization model by using a genetic algorithm and a neighborhood search algorithm specifically includes: Chromosome coding; decimal coding is used for photovoltaic access capacity; the on-off state of the distribution network line switch is coded using a decimal coding method based on a random spanning tree; the coding of photovoltaic access capacity and the on-off state of the distribution network line switch are independent of each other; Fitness calculation: for each chromosome, a random scene is generated by the Monte Carlo method, the corresponding power flow data at each moment is obtained by power flow calculation, and the photovoltaic acceptance capacity evaluation index is calculated; when the chromosome meets the constraints, the photovoltaic access capacity is the fitness of the chromosome; when the chromosome does not meet the constraints, the fitness of the chromosome is set to 0; The tournament selection method is used to randomly select two individuals from the population for comparison, and the individual with the larger fitness value is selected to enter the next generation; For the chromosome that needs to be crossover, two truncation points are randomly selected from the genes representing the circuit switch state, and the gene string sandwiched between the two points is inserted into the corresponding position of the first truncation point of the other chromosome, and the genes identical to the gene string are removed to obtain the hybrid offspring of the two chromosomes; For the gene representing the photovoltaic access capacity, a gene string is randomly selected and exchanged with the gene string at the same position on another chromosome; For the chromosomes that need to be mutated, two truncation points in the gene string are randomly selected from the genes representing the on and off states of the distribution network line switches, and then the gene string between the two points is reversed to obtain a new chromosome.

3. The method for calculating the photovoltaic acceptance capacity of the distribution network according to claim 2, characterized in that: For the genes representing the photovoltaic access capacity, a combination of mutation operation and neighborhood search is used. After a gene is randomly selected, a new gene is randomly generated within a certain neighborhood of the photovoltaic access capacity corresponding to the gene.

4. A device for calculating photovoltaic acceptance capacity of a distribution network, used to execute the method of claim 1, characterized in that: include: An acquisition module is used to obtain equipment operating limit parameters and a preset distribution network component model; The first construction module is used to construct a photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output based on the equipment operation limit parameters and the preset distribution network component model; A second construction module is used to construct a photovoltaic acceptance capacity optimization model considering the flexibility of the topological structure based on the photovoltaic acceptance capacity evaluation index considering the randomness of load and photovoltaic output; The calculation and solution module is used to solve the photovoltaic acceptance capacity optimization model by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic acceptance capacity of the distribution network.

5. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the method for calculating the photovoltaic acceptance capacity of a distribution network as claimed in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the method for calculating the photovoltaic acceptance capacity of the distribution network according to any one of claims 1 to 3 is implemented.

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