CN115514001A - Method, device, equipment and medium for calculating photovoltaic receiving capacity of power distribution network - Google Patents
Method, device, equipment and medium for calculating photovoltaic receiving capacity of power distribution network Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a medium for calculating photovoltaic receiving capacity of a power distribution network, wherein the method comprises the following steps: acquiring an equipment operation limit parameter and a preset power distribution network element model; building a photovoltaic receiving capacity evaluation index considering load and photovoltaic output randomness; constructing a photovoltaic receiving capacity optimization model considering the flexibility of a topological structure; and solving the photovoltaic receiving capacity optimization model by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network. Firstly, providing a power distribution network photovoltaic receiving capacity evaluation model considering load and randomness of photovoltaic output; secondly, the maximum photovoltaic receiving capacity of the power distribution network is calculated by using the network topology structure and the photovoltaic access capacity of the power distribution network as optimization variables and using a genetic algorithm and a neighborhood search algorithm, and the probability load flow calculation is performed by using the linear load flow, so that the calculation speed is increased.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a method, a device, equipment and a medium for calculating photovoltaic receiving capacity of a power distribution network.
Background
Distributed photovoltaic plants refer to power generation systems with small installed scale, arranged in the vicinity of the users, using distributed resources, typically connected to a grid with a voltage level of less than 35 kv or less. The distributed photovoltaic power station refers in particular to a distributed photovoltaic power station system which adopts photovoltaic components and directly converts solar energy into electric energy. The distributed photovoltaic power station system which is most widely applied is a photovoltaic power generation project built on the roof of an urban building.
The distributed photovoltaic has the advantages of convenience in installation, no need of long-distance transmission, high utilization rate and the like, and is low in pollution and outstanding in environmental protection benefit. The distributed photovoltaic power station project has no noise and can not pollute air and water in the power generation process. And local power utilization tension can be relieved to a certain extent. Is an important way of utilizing renewable energy. The distribution network is a main carrier for receiving distributed photovoltaic, and the evaluation of the photovoltaic receiving capacity of the distribution network has important significance for improving the utilization efficiency of renewable energy sources and ensuring the safe and reliable operation of the distribution network. Some scholars propose a power distribution network dynamic reconstruction method aiming at improving photovoltaic receiving capacity, but influence of photovoltaic and load randomness on the photovoltaic receiving capacity of a power distribution network is not considered, and the photovoltaic receiving capacity cannot be effectively improved.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a medium for calculating photovoltaic receiving capacity of a power distribution network, and aims to solve the problem that the photovoltaic receiving capacity cannot be effectively improved because the influence of photovoltaic and load randomness on the photovoltaic receiving capacity of the power distribution network is not considered in the dynamic reconstruction method of the power distribution network aiming at improving the photovoltaic receiving capacity in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a method for calculating photovoltaic receiving capacity of a power distribution network is provided, and includes the following steps:
acquiring an equipment operation limit parameter and a preset power distribution network element model;
building a photovoltaic acceptance capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model;
building a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness;
and solving the photovoltaic receiving capacity optimization model by using a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network.
Further, the step of constructing a photovoltaic acceptance capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model specifically includes the following steps:
based on the equipment operation limit parameters and a preset power distribution network element model, describing the load and the random prediction error of the photovoltaic output by adopting normal distribution to obtain a probability density function of the photovoltaic active output and a probability density function of a load value;
generating a preset number of random scenes by using a Monte Carlo method according to the probability density function of the photovoltaic active power output and the probability density function of the load value, and carrying out load flow calculation on the random scenes to obtain the degree and the probability of voltage out-of-limit, line and transformer transmission power out-of-limit;
and constructing a photovoltaic receiving capacity evaluation index based on the out-of-limit voltage, the out-of-limit transmission power degree and probability of the line and the transformer.
Further, the photovoltaic receptivity evaluation index includes: the system comprises a voltage out-of-limit evaluation index, a line power out-of-limit evaluation index and a transformer power out-of-limit evaluation index.
Further, the step of constructing a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness includes the following steps:
selecting a typical day according to the diurnal variation characteristics of photovoltaic output and load, taking the opening and closing state of a power distribution network line switch and the photovoltaic access capacity as optimization variables, taking the maximum photovoltaic access capacity as an optimization objective function, and constructing a photovoltaic receiving capacity optimization model in a network reconstruction optimization mode.
Furthermore, the photovoltaic receiving capacity optimization model meets the power distribution network topological structure constraint, the voltage constraint, the line capacity constraint and the transformer capacity constraint by taking the maximum sum of photovoltaic access capacities as an objective function.
Further, the step of solving the photovoltaic receptivity optimization model by using a genetic algorithm and a neighborhood search algorithm specifically includes:
chromosome coding; decimal coding is adopted for photovoltaic access capacity; coding the opening and closing states of the power distribution network line switch by adopting a decimal coding mode based on a random spanning tree; the photovoltaic access capacity and the codes of the on-off states of the power distribution network line switches are independent of each other;
calculating the fitness; generating a random scene for each chromosome through a Monte Carlo method, obtaining load flow data corresponding to each moment through load flow calculation, and calculating the photovoltaic acceptance capacity evaluation index; when the chromosome meets the constraint, the photovoltaic access capacity is the fitness of the chromosome; when the chromosome does not satisfy the constraint, setting the fitness of the chromosome to 0;
selecting two individuals at random each time from the population by adopting a championship selection method, and selecting the individual with a larger adaptive value to enter the next generation;
randomly selecting two truncation points from genes representing the line switch state of chromosomes needing to be subjected to cross operation, inserting a gene string sandwiched by the two points into the corresponding position of a first truncation point of another chromosome, and removing genes identical to the gene string to obtain filial generations of the two chromosomes;
randomly selecting a section of gene string for the gene representing the photovoltaic access capacity, and exchanging the gene string with the gene string at the same position of another chromosome;
for a chromosome needing mutation operation, two truncation points in a gene string are randomly selected from genes representing the opening and closing states of a power distribution network line switch, and then the gene string between the two points is subjected to reverse order to obtain a new chromosome.
Furthermore, a mode of combining mutation operation and neighborhood search is adopted for the genes representing the photovoltaic access capacity, and after one gene is randomly selected, a new gene is randomly generated in a certain neighborhood of the photovoltaic access capacity corresponding to the gene.
In a second aspect, a distribution network photovoltaic receiving capacity computing device is provided, including:
the acquisition module is used for acquiring the equipment operation limit parameters and a preset power distribution network element model;
the first construction module is used for constructing a photovoltaic receiving capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model;
the second construction module is used for constructing a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness;
and the calculation solving module is used for solving the photovoltaic receiving capacity optimization model by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network.
In a third aspect, an electronic device is provided, which includes a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the above-mentioned power distribution grid photovoltaic capacity calculation method.
In a fourth aspect, a computer-readable storage medium is provided, where at least one instruction is stored, and when executed by a processor, the at least one instruction implements the above-mentioned power distribution network photovoltaic admission capacity calculation method.
Compared with the prior art, the invention has the beneficial effects that:
according to the method for calculating the photovoltaic receiving capacity of the power distribution network, the flexibility of the topological structure is considered, firstly, a power distribution network photovoltaic receiving capacity evaluation model considering the randomness of the load and the photovoltaic output is provided, and the photovoltaic receiving capacity of the power distribution network can be effectively improved under the condition of considering the randomness of the load and the photovoltaic output.
Secondly, the network topology structure and the photovoltaic access capacity of the power distribution network are used as optimization variables, the maximum photovoltaic receiving capacity of the power distribution network is calculated by using a genetic algorithm and a neighborhood search algorithm, and the probability power flow calculation is performed by using the linear power flow, so that the calculation speed is increased.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram illustrating a principle of a method for calculating photovoltaic receiving capacity of a power distribution network according to the present invention;
FIG. 2 is a flow chart of a calculation method of photovoltaic receiving capacity of a power distribution network according to the invention;
fig. 3 is a block diagram of a distribution network photovoltaic receiving capacity calculating device according to the present invention;
fig. 4 is a block diagram of an electronic device according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further explanation of the invention as claimed. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Example 1
As shown in fig. 1 and fig. 2, a method for calculating photovoltaic receiving capacity of a distribution network includes the following steps:
s1, obtaining equipment operation limit parameters and a preset power distribution network element model.
In the scheme, the equipment operation limit parameters and the preset power distribution network element model are provided by a power distribution network digital simulation model, and the power distribution network digital simulation model is constructed to comprise the power distribution network element model and the equipment operation limit parameters.
Wherein:
the power distribution network element model comprises line parameters (resistance, reactance, conductance and susceptance), transformer parameters (leakage reactance, transformation ratio and tap position), load node parameters (active and reactive values), photovoltaic power generation node parameters (active values) and thermal power unit parameters (active and reactive values);
the equipment operation limit parameters include: the system comprises a line rated current, a transformer rated capacity, a photovoltaic node maximum output and a thermal power generating unit maximum output.
The data in the digital simulation model of the power distribution network is used for the following step process.
S2, building a photovoltaic acceptance capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model; the method specifically comprises the following steps:
s21, due to the fact that the actual values of the load and the photovoltaic output usually have certain errors with the predicted values, node voltage and equipment power are changed. Therefore, according to the scheme, based on the equipment operation limit parameters and the preset power distribution network element model, the load and the photovoltaic output random prediction error are described by adopting normal distribution, and the probability density function of the photovoltaic active output and the probability density function of the load value are obtained.
Wherein, the probability density function f (P) of the photovoltaic active power output PV ) Comprises the following steps:
in the formula: p is PV The actual value of the photovoltaic active output is obtained; mu.s PV Taking a predicted value of photovoltaic power generation active output for mathematical expectation; sigma PV The square difference of the photovoltaic power generation active power output normal distribution is shown.
Similarly, the probability density function f (S) of the load value LD ) Comprises the following steps:
in the formula: s LD Is the actual value of the load; mu.s LD Taking a predicted value of the load for mathematical expectation; sigma LD Is the squared difference of the normal distribution of the load.
S22, generating a preset number of random scenes by using a Monte Carlo method according to the probability density function of the photovoltaic active output and the probability density function of the load value, namely the mathematical expression of the random distribution characteristics of the load and the photovoltaic output, and carrying out load flow calculation on the random scenes to obtain the degree and the probability of voltage out-of-limit and line and transformer transmission power out-of-limit.
And S23, comprehensively considering the out-of-limit voltage, the out-of-limit transmission power degree and probability of the line and the transformer, and constructing a photovoltaic receiving capacity evaluation index.
Specifically, the photovoltaic receptivity evaluation index constructed in the scheme includes: the system comprises a voltage out-of-limit evaluation index, a line power out-of-limit evaluation index and a transformer power out-of-limit evaluation index.
(1) Voltage out-of-limit evaluation index
In the obtained random scene, the frequency of the voltage exceeding the upper limit is more, the degree of the voltage exceeding the upper limit is more serious, the unreasonable configured photovoltaic access capacity is shown, the probability and the degree of the voltage exceeding the upper limit are comprehensively considered, and the evaluation index epsilon of the voltage exceeding the upper limit is defined u Comprises the following steps:
wherein N is all Is the total number of samples (i.e., random scenes); u shape c,de The maximum value of the out-of-limit degree of the node voltage in the scene c.
For node i, its node voltage out-of-limit degree U i,c,de Can be expressed as:
U i is the voltage at node i; u shape i,min And U i,max The minimum and maximum voltage values allowed for node i.
(2) Line power out-of-limit evaluation index
Comprehensively considering line transmission power out-of-limit probability and out-of-limit degree, defining line power out-of-limit evaluation index epsilon l Comprises the following steps:
wherein S is c,L,de The maximum value of the out-of-limit degree of the line transmission power in the scene c.
For line l, its transmission power out-of-limit degree S in scene c c,l,de Can be expressed as:
wherein S is l Is the transmission power of line l; s l,max The maximum transmission power allowed for line l.
(3) Power out-of-limit evaluation index of transformer
Comprehensively considering the out-of-limit probability and the out-of-limit degree of the transmission power of the transformer, defining the out-of-limit evaluation index epsilon of the power of the transformer tr Comprises the following steps:
wherein S is c,T,de The degree of out-of-limit of the transmission power of the transformer in scene c.
S c,T,de Can be expressed as:
wherein S is T Is the transmission power of the transformer; s T,max The maximum transmission power allowed by the transformer.
And S3, constructing a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness.
The method comprises the following specific steps:
selecting a typical day according to the diurnal variation characteristics of photovoltaic output and load, reflecting and applying evaluation indexes in a constraint function of an optimization model, namely formulas (10) - (12), taking the opening and closing state of a line switch of the power distribution network and the photovoltaic access capacity as optimization variables, taking the maximum photovoltaic access capacity as an optimized objective function, and improving the photovoltaic access capacity through network reconstruction optimization.
The photovoltaic receiving capacity optimization model meets the power distribution network topological structure constraint, the voltage constraint, the line capacity constraint and the transformer capacity constraint at the same time by taking the maximum sum of photovoltaic access capacities as an objective function.
The objective function of photovoltaic maximum access capacity optimization is as follows:
wherein f is the sum of the photovoltaic access capacity; m PV A node set accessed by photovoltaic; p i,PVC Accessing the photovoltaic capacity for the node i;
the optimization model needs to satisfy the following constraints:
(1) Power distribution network topological structure constraint
The distribution network runs radially, and no ring network or isolated node exists in the system.
(2) Voltage confinement
For any time t, the voltage out-of-limit evaluation index epsilon t,u Less than a set value epsilon t,u,set ：
ε t,u <ε t,u,set (10)
(3) Line capacity constraint
For any time t, the line transmission power out-of-limit evaluation index epsilon t,l Less than a set value epsilon t,l,set ：
ε t,l <ε t,l,set (11)
(4) Transformer capacity constraint
For any time t, the out-of-limit evaluation index epsilon of the transmission power of the transformer t,tr Less than a set value epsilon t,tr,set ：
ε t,tr <ε t,tr,set (12)
Wherein, the voltage out-of-limit index set value epsilon t,u,set Line power out-of-limit index set value epsilon t,l,set And the power out-of-limit index set value epsilon of the transformer t,tr,set The voltage out-of-limit probability index is determined through the out-of-limit probability and the out-of-limit degree, wherein the voltage out-of-limit degree is calculated according to a formula (6), and the voltage out-of-limit probability index is calculated according to the following method:
wherein N is all Is the total number of samples, N ov Is the number of samples for which the voltage is out of limit.
For example, the following steps are carried out: if the sample with the out-of-limit allowable voltage accounts for 4.56% of all samples and the out-of-limit allowable voltage in the sample with the out-of-limit allowable voltage is 0.1, the set value epsilon of the out-of-limit voltage index t,u,set Set to 0.00456.
S4, solving the photovoltaic receiving capacity optimization model (formula 9) by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network; the method specifically comprises the following steps:
(1) Chromosome coding
The photovoltaic access capacity and the coding of the on-off state of the power distribution network line switch are mutually independent, the photovoltaic access capacity is coded in a decimal mode, and the photovoltaic access capacity is optimized by taking 10kW as the minimum unit for calculation. And a decimal coding mode based on a random spanning tree is adopted for the opening and closing states of the power distribution network line switch.
(2) Fitness calculation
Generating a random scene for each chromosome through a Monte Carlo method, obtaining load flow data such as node voltage and line power corresponding to each moment through load flow calculation, and calculating the photovoltaic acceptance capacity evaluation index; when the chromosome meets the constraint condition, the photovoltaic access capacity is the fitness of the chromosome; wherein, the constraint condition refers to four constraint conditions of the optimization model, including: power distribution network topological structure constraint, voltage constraint, line capacity constraint and transformer capacity constraint; when the chromosome does not satisfy the constraint, the fitness of the chromosome is set to 0.
(3) Selecting
And (4) selecting two individuals at random each time from the population by adopting a championship selection method, and selecting the individual with a larger adaptive value to enter the next generation.
(4) Crossing
Randomly selecting two truncation points from genes representing the opening and closing states of a power distribution network line switch for chromosomes needing cross operation, inserting a gene string sandwiched by the two points into the corresponding position of a first truncation point of another chromosome, and removing genes identical to the gene string to obtain filial generations of the two chromosomes;
and randomly selecting a section of gene string of the genes representing the photovoltaic access capacity, and exchanging the gene string with the gene string at the same position of another chromosome.
And calculating the fitness of the chromosomes obtained after crossing again, wherein the photovoltaic capacity is the fitness of the chromosomes when the constraints are met. Wherein, the constraint conditions refer to four constraint conditions of the optimization model, including: the method comprises the following steps of power distribution network topological structure constraint, voltage constraint, line capacity constraint and transformer capacity constraint.
(5) Variation of
For chromosomes needing mutation operation, two truncation points in a gene string are randomly selected from genes representing the opening and closing states of a power distribution network line switch, and then the gene strings between the two points are reversely sequenced to obtain a new chromosome.
And (4) re-entering the mutated chromosome into the step (2) for fitness calculation.
In a preferred scheme, in order to improve the evolution speed, a mode of combining mutation operation and neighborhood search is adopted for the genes representing the photovoltaic access capacity, and after one gene is randomly selected, a new gene is randomly generated in a certain neighborhood of the photovoltaic access capacity corresponding to the gene.
Example 2
As shown in fig. 3, based on the unified inventive concept of the foregoing embodiment, the present invention further provides a distribution network photovoltaic receiving capacity calculating device, including:
and the acquisition module is used for acquiring the equipment operation limit parameters and the preset power distribution network element model.
The first construction module is used for constructing a photovoltaic acceptance capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model.
In the first building module, the photovoltaic receptivity evaluation index is built in the following manner:
based on the equipment operation limit parameters and a preset power distribution network element model, describing the load and the random prediction error of the photovoltaic output by adopting normal distribution to obtain a probability density function of the photovoltaic active output and a probability density function of a load value;
generating a preset number of random scenes by using a Monte Carlo method according to the probability density function of the photovoltaic active power output and the probability density function of the load value, and carrying out load flow calculation on the random scenes to obtain the degree and the probability of voltage out-of-limit, line and transformer transmission power out-of-limit;
and constructing a photovoltaic receiving capacity evaluation index based on the out-of-limit voltage, the out-of-limit transmission power degree and probability of the line and the transformer.
In the first building module, the photovoltaic acceptance capacity evaluation index comprises: the system comprises a voltage out-of-limit evaluation index, a line power out-of-limit evaluation index and a transformer power out-of-limit evaluation index.
And the second construction module is used for constructing a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness.
In the second construction module, the photovoltaic receptivity optimization model is constructed in the following manner:
selecting a typical day according to the diurnal variation characteristics of photovoltaic output and load, taking the opening and closing state of a power distribution network line switch and the photovoltaic access capacity as optimization variables, taking the maximum photovoltaic access capacity as an optimization objective function, and constructing a photovoltaic receiving capacity optimization model in a network reconstruction optimization mode. The photovoltaic receiving capacity optimization model meets the power distribution network topological structure constraint, the voltage constraint, the line capacity constraint and the transformer capacity constraint at the same time by taking the maximum sum of photovoltaic access capacities as an objective function.
And the calculation solving module is used for solving the photovoltaic receiving capacity optimization model by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network.
In the calculation solving module, the method for solving the photovoltaic receptivity optimization model comprises the following steps:
chromosome coding; decimal coding is adopted for photovoltaic access capacity; coding the opening and closing states of the power distribution network line switch by adopting a decimal coding mode based on a random spanning tree; the photovoltaic access capacity and the codes of the on-off states of the power distribution network line switches are independent of each other;
calculating the fitness; generating a random scene for each chromosome through a Monte Carlo method, obtaining load flow data corresponding to each moment through load flow calculation, and calculating the photovoltaic acceptance capacity evaluation index; when the chromosome meets the constraint, the photovoltaic access capacity is the fitness of the chromosome; when the chromosome does not satisfy the constraint, setting the fitness of the chromosome to 0;
selecting two individuals at random each time from the population by adopting a championship selection method, and selecting the individual with a larger adaptive value to enter the next generation;
randomly selecting two truncation points from genes representing the line switch state of chromosomes needing to be subjected to cross operation, inserting a gene string sandwiched by the two points into the corresponding position of a first truncation point of another chromosome, and removing genes identical to the gene string to obtain filial generations of the two chromosomes;
randomly selecting a section of gene string for the gene representing the photovoltaic access capacity, and exchanging the gene string with the gene string at the same position of another chromosome;
for a chromosome needing mutation operation, two truncation points in a gene string are randomly selected from genes representing the opening and closing states of a power distribution network line switch, and then the gene string between the two points is subjected to reverse order to obtain a new chromosome.
Example 3
The invention also provides an electronic device 100 for implementing the method for calculating the photovoltaic receiving capacity of the power distribution network; the electronic device 100 comprises 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 may be used for storing a computer program 103, and the processor 102 implements the steps of the power distribution network photovoltaic receiving capacity calculation method according to 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 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data) created according to the use of the electronic apparatus 100, and the like. 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 memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one Processor 102 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, and the processor 102 is the control center of the electronic device 100 and connects the various parts of the electronic device 100 with various interfaces and lines.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement a power distribution grid photovoltaic capacity calculation method, and the processor 102 can execute the plurality of instructions to implement:
acquiring an equipment operation limit parameter and a preset power distribution network element model;
building a photovoltaic acceptance capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model;
building a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness;
and solving the photovoltaic receiving capacity optimization model by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network.
Example 4
The integrated modules/units of the electronic device 100 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, and Read-Only Memory (ROM).
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for calculating photovoltaic receiving capacity of a power distribution network is characterized by comprising the following steps:
acquiring an equipment operation limit parameter and a preset power distribution network element model;
building a photovoltaic acceptance capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model;
building a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness;
and solving the photovoltaic receiving capacity optimization model by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network.
2. The method for calculating the photovoltaic receptivity of the power distribution network according to claim 1, wherein the step of constructing the photovoltaic receptivity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model specifically comprises the following steps:
based on the equipment operation limit parameters and a preset power distribution network element model, describing the load and the random prediction error of the photovoltaic output by adopting normal distribution to obtain a probability density function of the photovoltaic active output and a probability density function of a load value;
generating a preset number of random scenes by using a Monte Carlo method according to the probability density function of the photovoltaic active power output and the probability density function of the load value, and carrying out load flow calculation on the random scenes to obtain the degree and the probability of voltage out-of-limit, line and transformer transmission power out-of-limit;
and constructing a photovoltaic receiving capacity evaluation index based on the out-of-limit voltage, the out-of-limit transmission power degree and probability of the line and the transformer.
3. The method for calculating the photovoltaic admission capacity of the power distribution network according to claim 1, wherein the photovoltaic admission capacity evaluation index comprises: the system comprises a voltage out-of-limit evaluation index, a line power out-of-limit evaluation index and a transformer power out-of-limit evaluation index.
4. The method for calculating the photovoltaic receiving capacity of the power distribution network according to claim 1, wherein the step of constructing the photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness comprises the following steps:
selecting a typical day according to the diurnal variation characteristics of photovoltaic output and load, taking the opening and closing state of a power distribution network line switch and the photovoltaic access capacity as optimization variables, taking the maximum photovoltaic access capacity as an optimization objective function, and constructing a photovoltaic receiving capacity optimization model in a network reconstruction optimization mode.
5. The method for calculating the photovoltaic admission capacity of the power distribution network according to claim 4, wherein the photovoltaic admission capacity optimization model simultaneously satisfies topological structure constraints, voltage constraints, line capacity constraints and transformer capacity constraints of the power distribution network by taking the maximum sum of photovoltaic access capacities as an objective function.
6. The method for calculating the photovoltaic receptivity of the power distribution network according to claim 1, wherein the step of solving the photovoltaic receptivity optimization model by using a genetic algorithm and a neighborhood search algorithm specifically comprises:
chromosome coding; decimal coding is adopted for photovoltaic access capacity; coding the opening and closing states of the power distribution network line switch by adopting a decimal coding mode based on a random spanning tree; the photovoltaic access capacity and the codes of the on-off states of the power distribution network line switches are independent of each other;
calculating the fitness; generating a random scene for each chromosome through a Monte Carlo method, obtaining load flow data corresponding to each moment through load flow calculation, and calculating the photovoltaic acceptance capacity evaluation index; when the chromosome meets the constraint, the photovoltaic access capacity is the fitness of the chromosome; when the chromosome does not satisfy the constraint, setting the fitness of the chromosome to 0;
selecting two individuals at random each time from the population by adopting a championship selection method, and selecting the individual with a larger adaptive value to enter the next generation;
randomly selecting two truncation points from genes representing the line switch state of chromosomes needing to be subjected to cross operation, inserting a gene string sandwiched by the two points into the corresponding position of a first truncation point of another chromosome, and removing genes identical to the gene string to obtain filial generations of the two chromosomes;
randomly selecting a section of gene string for the gene representing the photovoltaic access capacity, and exchanging the gene string with the gene string at the same position of another chromosome;
for a chromosome needing mutation operation, two truncation points in a gene string are randomly selected from genes representing the opening and closing states of a power distribution network line switch, and then the gene string between the two points is subjected to reverse order to obtain a new chromosome.
7. The method for calculating the photovoltaic receiving capacity of the power distribution network according to claim 6, wherein the method of combining mutation operation and neighborhood search is adopted for the genes representing the photovoltaic access capacity, and after one gene is randomly selected, a new gene is randomly generated in a certain neighborhood of the photovoltaic access capacity corresponding to the gene.
8. A distribution network photovoltaic receiving capacity computing device, comprising:
the acquisition module is used for acquiring the equipment operation limit parameters and a preset power distribution network element model;
the first construction module is used for constructing a photovoltaic receiving capacity evaluation index considering load and photovoltaic output randomness based on the equipment operation limit parameters and a preset power distribution network element model;
the second construction module is used for constructing a photovoltaic receiving capacity optimization model considering the flexibility of the topological structure based on the photovoltaic receiving capacity evaluation index considering the load and the photovoltaic output randomness;
and the calculation solving module is used for solving the photovoltaic receiving capacity optimization model by utilizing a genetic algorithm and a neighborhood search algorithm to obtain the maximum photovoltaic receiving capacity of the power distribution network.
9. An electronic device, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the power distribution grid photovoltaic receptivity calculation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores at least one instruction which, when executed by a processor, implements a method for calculating the photovoltaic admission capacity of a distribution network according to any one of claims 1 to 7.
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| CN116613823A (en) * | 2023-07-18 | 2023-08-18 | 华北电力科学研究院有限责任公司 | Power quality assessment method, device and system for power distribution network |
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| CN116613823A (en) * | 2023-07-18 | 2023-08-18 | 华北电力科学研究院有限责任公司 | Power quality assessment method, device and system for power distribution network |
| CN116613823B (en) * | 2023-07-18 | 2023-12-08 | 华北电力科学研究院有限责任公司 | Power quality assessment method, device and system for power distribution network |
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