CN115173464A - Distributed photovoltaic absorption capacity evaluation method and system based on big data analysis - Google Patents
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Abstract
The invention discloses a distributed photovoltaic absorption capacity evaluation method and system based on big data analysis, and belongs to the technical field of power distribution networks. The method comprises the steps of obtaining distributed photovoltaic characteristic data and power distribution network line data, establishing a data set, and preprocessing the data; analyzing the distributed photovoltaic characteristics according to the data set, and constructing a distributed photovoltaic absorption capacity evaluation model; and introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and obtaining the maximum distributed photovoltaic installed capacity which can be absorbed by the power distribution network according to the distributed photovoltaic absorption capacity evaluation model through a particle swarm algorithm. The big data analysis and the photovoltaic absorption capacity are deeply fused, accurate assessment of the distributed photovoltaic absorption capacity level of the regional power grid is achieved, and the method has very important guiding significance for planning, construction and transformation of the future power distribution network.
Description
Technical Field
The application relates to the technical field of power distribution networks, in particular to a distributed photovoltaic absorption capacity assessment method and system based on big data analysis.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The distributed photovoltaic power generation refers in particular to a distributed power generation system which directly converts solar energy into electric energy by adopting photovoltaic modules. The novel photovoltaic power station is a novel power generation and energy comprehensive utilization mode with wide development prospect, advocates the principles of near power generation, near grid connection, near conversion and near use, can effectively improve the generated energy of the photovoltaic power station with the same scale, and effectively solves the problem of loss of electric power in boosting and long-distance transportation.
The distributed photovoltaic power generation system which is most widely applied is a photovoltaic power generation project built on the roof of an urban building. Such projects must be connected to the public power grid, together with the public power grid, to supply power to nearby users.
However, when the grid-connected distributed power supply reaches a certain proportion, negative effects are generated on the power distribution network, and obvious effects are generated on steady-state and transient-state indexes of power flow distribution, voltage distribution, power supply reliability, power quality, protection control and the like of the power distribution network. In order to replace traditional energy sources with as much new energy as possible and avoid the threat of safety operation of the distribution network caused by excessive distributed photovoltaic grid connection, it is necessary to analyze the consumption capability of the distribution network on distributed photovoltaic.
Disclosure of Invention
The factors influencing photovoltaic absorption are numerous and influence each other, the core value of big data is that the correlation among things is disclosed, and the incidence relation among the factors can be effectively quantized by using big data mining, so that the absorption capacity of the distribution network to the distributed photovoltaic is analyzed by using the big data, the big data analysis technology and the photovoltaic absorption capacity are deeply fused, and the distributed photovoltaic absorption capacity evaluation method, the distributed photovoltaic absorption capacity evaluation system, the electronic equipment and the computer readable storage medium based on the big data analysis are provided.
In a first aspect, the application provides a distributed photovoltaic absorption capacity assessment method based on big data analysis;
a distributed photovoltaic absorption capacity assessment method based on big data analysis comprises the following steps:
acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set, and preprocessing the data;
analyzing the distributed photovoltaic characteristics according to the data set, and constructing a distributed photovoltaic absorption capacity evaluation model;
and introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and obtaining the absorption maximum installed capacity of the distribution network through a particle swarm algorithm.
In a second aspect, the application provides a distributed photovoltaic absorption capacity evaluation system based on big data analysis;
a distributed photovoltaic absorption capacity assessment system based on big data analysis comprises:
the first acquisition module is used for acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set and preprocessing the data;
the second acquisition module is used for analyzing the distributed photovoltaic characteristics according to the data set and constructing a distributed photovoltaic absorption capacity evaluation model;
and the third acquisition module is used for introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and acquiring the maximum distributed photovoltaic installed capacity which can be absorbed by the power distribution network through a particle swarm algorithm.
In a third aspect, the present application provides an electronic device;
an electronic device includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the distributed photovoltaic absorption capacity evaluation method based on big data analysis.
In a fourth aspect, the present application provides a computer-readable storage medium;
a computer readable storage medium comprises instructions for storing computer instructions, which when executed by a processor, perform the steps of the above-mentioned big data analysis-based distributed photovoltaic absorption capacity evaluation method.
Compared with the prior art, the beneficial effects of this application are:
1. the big data analysis and the photovoltaic absorption capacity measurement and calculation are deeply fused, so that the accurate assessment of the distributed photovoltaic absorption capacity level of the regional power grid is realized, and the method has very important guiding significance on planning, construction and transformation of a future power distribution network;
2. the method comprehensively considers a plurality of factors influencing the distributed photovoltaic absorption capacity, adds the dynamic changes of the load and the photovoltaic output into the evaluation of the distributed photovoltaic absorption capacity, and improves the accuracy of the evaluation of the distributed photovoltaic absorption capacity level of the regional power grid;
3. the collected data volume is large, distributed photovoltaic digestion capacity evaluation is carried out based on big data analysis, and authenticity and accuracy of evaluation are improved.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a schematic flow chart of a distributed photovoltaic absorption capacity evaluation method based on big data analysis in the present application.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that 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 present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides a distributed photovoltaic absorption capacity evaluation method based on big data analysis;
as shown in fig. 1, a distributed photovoltaic absorption capacity evaluation method based on big data analysis includes:
acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set, and preprocessing the data;
analyzing the characteristics of the distributed photovoltaic system according to the data set, and constructing a distributed photovoltaic absorption capacity evaluation model;
and introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and obtaining the absorption maximum installed capacity of the distribution network through a particle swarm algorithm.
Further, the preprocessing the data specifically includes:
acquiring the relation between the voltage of the distributed photovoltaic access node and the photovoltaic output and load according to the distributed photovoltaic characteristic data;
and according to the relation between the voltage of the distributed photovoltaic access node and the photovoltaic output and load, rejecting abnormal data which is not in accordance with the relation between the voltage of the distributed photovoltaic access node and the photovoltaic output and load.
Furthermore, the time sequence characteristics of the distributed photovoltaic are obtained according to the season information, the weather information and the time information of the distributed photovoltaic characteristic data and the corresponding photovoltaic processing information.
Further, the time sequence characteristic of the distributed photovoltaic is
According to the season information, the photovoltaic output is more than spring in summer, more than winter and more than autumn;
according to weather information, the photovoltaic output is clear, cloudy and rainy;
according to the time information, the photovoltaic output time period is 05-19, wherein the photovoltaic output maximum value occurs at 11:00-15:00.
Further, acquiring the relation between the voltage of a grid-connected point and grid-connected capacity according to the distributed photovoltaic characteristic data and the line data of the power distribution network;
the relation between the grid-connected point voltage and the grid-connected capacity is that the grid-connected point voltage is in direct proportion to the grid-connected capacity.
Further, the constraint conditions comprise a power balance constraint and a power grid equipment bearing capacity constraint;
the power balance constraint is to realize the balance of electricity for electricity generation and utilization at the moment when the photovoltaic output is maximum;
the grid equipment bearing capacity constraints comprise a node voltage constraint and a branch current constraint.
Further, the node voltage is constrained to
U i min ≤U i ≤U i max
Wherein, U i min Is the minimum value, U, allowed by the voltage amplitude of the ith node i max The maximum value which can be allowed by the voltage amplitude of the ith node is obtained;
the branch current is constrained to
I i ≤I i max
Wherein, I i Is the current of the ith branch, I i max The maximum allowed current for the ith branch.
One disclosed embodiment of the present invention will be described in detail with reference to fig. 1.
A distributed photovoltaic absorption capacity evaluation method based on big data analysis is characterized in that massive data accumulated by a power supply company in a long-term development process is utilized, and the photovoltaic absorption capacity of a power distribution network is evaluated by fusing a big data analysis technology and photovoltaic absorption capacity measurement and calculation; the method comprises the following steps:
s1, acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set, and preprocessing the data; the distributed photovoltaic characteristic data comprises time information, distributed photovoltaic access node information, accessed photovoltaic capacity information, load size information, weather information, season information and the like, and the sampling interval of the distributed photovoltaic characteristic data of the substation site is 1min; the power distribution network line data comprises power distribution network architecture information, 10KV line conductor model information, specification information and allowable current information; the step S1 specifically includes:
s101, selecting distributed photovoltaic characteristic data of a plurality of transformer substations and establishing a data set;
s102, selecting power distribution network line data of a plurality of 10KV lines, and establishing a data set;
step S103, analyzing the relation between the distributed photovoltaic absorption capacity and the photovoltaic output and the load according to the data set, wherein the relation is that the node voltage is in direct proportion to the photovoltaic output and in inverse proportion to the load, and the node is a photovoltaic access node; therefore, when the photovoltaic output is small, the node voltage is large or when the load is large, the data with the large node voltage is abnormal data; and eliminating the abnormal data.
Illustratively, the number of the substations is 23, the data collected by each substation site every day is 5760 pieces of data, and the total data set size is 48355200 pieces; the 10KV lines are 185 lines and the total data set size is 555.
S2, analyzing the distributed photovoltaic characteristics according to the data set, and constructing a distributed photovoltaic absorption capacity evaluation model; the distributed photovoltaic absorption capacity evaluation model comprises the data set and distributed photovoltaic characteristics; the step S2 specifically includes:
step S201, acquiring time sequence characteristics of distributed photovoltaics according to season information, weather information and time information of the distributed photovoltaic characteristic data and corresponding photovoltaic processing information; because the photovoltaic output has randomness and volatility, the photovoltaic output cannot be considered as a power supply with constant output and is influenced by seasons, weather, moments and other aspects, and therefore the time sequence characteristics of the photovoltaic output need to be analyzed; through big data analysis, the time sequence characteristic of the distributed photovoltaic is obtained, namely according to seasonal information, the photovoltaic output is more than spring, more than winter and more than autumn; according to weather information, the photovoltaic output is clear, cloudy and rainy; according to the time information, the photovoltaic output time period is 05-19, wherein the photovoltaic output maximum value occurs at 11: 00-15;
step S202, acquiring the relation between the voltage of a grid-connected point and grid-connected capacity according to the distributed photovoltaic characteristic data and the line data of the power distribution network;
when the photovoltaic transformer substation is connected to the power distribution network, other access factors are kept unchanged, only the power output by the photovoltaic transformer substation is changed, the variable quantity of the corresponding current caused on the power transmission line due to the change of the injection power of the photovoltaic transformer substation is set to be delta I, and the voltage change value at the access point of the photovoltaic transformer substation is as follows:
ΔU＝Z 1 ΔI＝|Z 1 |(cosφ+j sinφ)|ΔI|(cosθ+j sinθ)
as a result of this, it is possible to,
ΔU＝U DG -U 0
therefore, the temperature of the molten steel is controlled,
wherein, delta S DG Is the injection power of the photovoltaic power station of the distributed power supply, delta I is the variation of the line current variation caused by the variation of the injection power of the photovoltaic power station, U 0 Voltage value, U, not incident on the photovoltaic plant DG The voltage value after the photovoltaic power station is accessed, theta is a power factor angle of the photovoltaic power station, and phi is an impedance angle of the power distribution network when the photovoltaic power station is seen from the access point; the phase shift across the line is generally not large, where the process is simplified, the vertical component of the voltage variation is ignored, resulting in,
ΔU＝Z 1 ΔI＝|Z 1 ||ΔI|cos(φ+θ)
therefore, the temperature of the molten steel is controlled,
therefore, the relation between the grid-connected point voltage and the grid-connected capacity is that the grid-connected point voltage is in direct proportion to the grid-connected capacity.
Step S203, in order to measure and calculate the maximum consumption capacity of the distribution network to the distributed photovoltaic, obtaining the load value (taking the minimum value of the three, the distributed photovoltaic output is maximum) of 12-14 noon of the minimum load day (sunny day) of the transformer substation in spring, and obtaining the measured load of each transformer substation; the distributed photovoltaic contribution is at a maximum at this time. Specifically, the distributed photovoltaic contribution is considered at 81% of the device scale.
Step 3, introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and obtaining the absorption maximum installed capacity of the distribution network through a particle swarm algorithm; the constraint conditions comprise power balance constraint and power grid equipment bearing capacity constraint; the power balance constraint is that the electricity balance is realized at the moment when the photovoltaic output is maximum; the power grid equipment bearing capacity constraint comprises a node voltage constraint and a branch current constraint;
step S301, calculating the size of a distributed photovoltaic installation which can be consumed in a saturation year under the condition of meeting the power balance constraint condition;
for example, the size of a distributed photovoltaic installation that can be consumed by a saturation year of a power grid in a certain area is calculated.
Step S3011, collecting and calculating load values (taking the minimum value of the load values at 12-14 pm of the transformer substation in each village and town region in 2021 at the minimum load day (sunny day) at noon, wherein the distributed photovoltaic output is the maximum at the moment), and obtaining the actually measured load of each transformer substation;
step S3012, collecting and calculating the capacity of distributed photovoltaics which are connected to the substation in 2021, obtaining distributed photovoltaics which are connected to the power station in 10 kilovolts or below of the power station, connecting the distributed photovoltaics to the substation in 110 kilovolts at the voltage level of 35 kilovolts, and considering the output according to 81% of the installed scale;
step S3013, calculating the actual power load of the 2021 year transformer station: the actual electric load of the transformer substation = the actual load of the transformer substation-10 kilovolts and the following distributed photovoltaic scales 0.81-35 kilovolts are connected to the photovoltaic scales 0.81 of the 110 kilovolt transformer substation;
step S3014, calculating the installed scale of the distributed photovoltaic system under 2021 year load full-absorption: the scale of the distributed photovoltaic installation under the full load absorption = actual power load/0.81;
step S3015, calculating the newly increased distributed photovoltaic installed scale in 2021: the newly-added distributed photovoltaic installation scale = the distributed photovoltaic installation scale under full load consumption-the distributed photovoltaic installation scale which is connected to the grid and is 10 kilovolts or below-is connected to the photovoltaic scale of the 110 kilovolt transformer substation at the voltage level of 35 kilovolts; at the moment, the distributed photovoltaic installation scale which can be absorbed in 2021 is obtained under the principle that the electricity balance is generated and the force is not sent to the main network in the whole year;
step S3016, calling 2016-2020 electricity utilization load data of the whole grid, combining with the prospect of regional economic development, and obtaining the average annual growth rate of the regional power grid load through comprehensive analysis, wherein the average annual growth rate of the regional power grid load is as follows: 6.41 percent and 5.35 percent;
step S3017, calculating the power load of the substation in 2025 and 2030 (considering distributed photovoltaic reduction, only power consumption): calculating the substation electrical loads in 2025 and 2030 by using the average increase rate of the load obtained in step S3016;
step S3018, calculating the scale of the newly-increased distributed photovoltaic installation machines in 2025 and 2030: the newly added distributed photovoltaic installed scale = the distributed photovoltaic installed scale under full load consumption-the distributed photovoltaic installed scale of 10kv and below grid-connected-the photovoltaic scale of 110 kv substation is accessed in the voltage class of 35 kv; the distributed photovoltaic installed scale which can be absorbed by the regional power grid in 2025 and 2030 is obtained on the basis of the balance of electricity generation and utilization and the principle that the whole power is not sent to the main grid all the year round;
step S3019, calculating the power load of the saturated annual substation (considering distributed photovoltaic reduction, only power consumption): the load increase rate of the electricity load in saturation years is less than or equal to 2% in five continuous years, so the average load increase rate is 2%;
step S3010, calculating the newly increased distributed photovoltaic installed scale in the saturation year: the newly added distributed photovoltaic installed scale = the distributed photovoltaic installed scale under full load consumption-the distributed photovoltaic installed scale of 10kv and below grid-connected-the photovoltaic installed scale of 110 kv substation is connected with the voltage level of 35 kv. At the moment, the distributed photovoltaic installed scale which can be consumed in a saturation year by the regional power grid is obtained under the principle that the electricity balance is generated and the force is not transmitted to the main grid on the whole year; at the moment, the calculated result meets the power balance constraint condition, but whether the detection of the power grid node voltage constraint and the branch current constraint condition below 35kV is met needs to be determined;
step S302, determining whether the obtained result meets node voltage constraint and branch current constraint or not by combining a particle swarm optimization, preset node voltage constraint conditions and branch current constraint conditions, distributed photovoltaic access node information and power distribution network architecture information and a distributed photovoltaic absorption capacity evaluation model, and obtaining the absorption-in-saturation-year distributed photovoltaic installed capacity.
The particle swarm algorithm is a mathematical analysis method which is used for determining the flight direction and the flight speed by simulating the behaviors of birds, fish swarms and other swarms, utilizing a biological swarms model proposed by Heppner and utilizing bird swarms. The initial state of the Particle Swarm Optimization (PSO) is a group of random particles, and then the particles are continuously searched in the solution space of the problem by taking the current optimal particles as reference until a globally optimal solution is found. In each iteration, the position and the speed of the particle are adjusted by tracking the individual optimal value and the global optimal value. Let Xi = (Xi 1, xi2, \8230;, xid) denote the ith particle, where d is the particle dimension, the best position it experiences is denoted Pbest = (pi 1, pi2, \8230;, pid), and the best position that a single particle swarm experiences is denoted Gbest = (Gi 1, gi2, \8230, gid);. The velocity of particle i is denoted Vi = (Vi 1, vi2, \8230;, vid). After the K-th search, the particle can be updated according to the following formula to obtain a new generation of particle
v id k+1 ＝v id k +c 1 ×rand 1 k ×(pbest k id -x id k )+c 2 ×rand 2 k ×(pbest k id -x id k )
x id k+1 ＝x id k +v id k+1
Wherein, rand1 and rand2 are random numbers in 0-1; c1 and c2 are learning factors; k is the number of iterations.
Example two
The embodiment provides a distributed photovoltaic absorption capacity evaluation system based on big data analysis.
A big data analysis based distributed photovoltaic absorption capacity assessment system comprises:
the first acquisition module is used for acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set and preprocessing the data;
the second acquisition module is used for analyzing the distributed photovoltaic characteristics according to the data set and constructing a distributed photovoltaic absorption capacity evaluation model;
and the third acquisition module is used for introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and acquiring the maximum distributed photovoltaic installed capacity which can be absorbed by the power distribution network according to the distributed photovoltaic absorption capacity evaluation model through a particle swarm algorithm.
It should be noted that, the above corresponds to the steps in the first embodiment, and the first obtaining module, the second obtaining module and the third obtaining module are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer executable instructions.
EXAMPLE III
The third embodiment of the invention provides electronic equipment, which comprises a memory, a processor and computer instructions stored on the memory and run on the processor, wherein when the computer instructions are run by the processor, the steps of the distributed photovoltaic absorption capacity evaluation method based on big data analysis are completed.
Example four
The fourth embodiment of the present invention provides a storage medium, configured to store a computer instruction, where the computer instruction, when executed by a processor, completes the steps of the distributed photovoltaic absorption capacity evaluation method based on big data analysis.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A distributed photovoltaic absorption capacity assessment method based on big data analysis is characterized by comprising the following steps:
acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set, and preprocessing the data;
analyzing the characteristics of the distributed photovoltaic system according to the data set, and constructing a distributed photovoltaic absorption capacity evaluation model;
and introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and obtaining the maximum distributed photovoltaic installed capacity which can be absorbed by the power distribution network according to the distributed photovoltaic absorption capacity evaluation model through a particle swarm algorithm.
2. The distributed photovoltaic absorption capacity evaluation method based on big data analysis according to claim 1, wherein the preprocessing of the data specifically comprises:
acquiring the relation between the voltage of the distributed photovoltaic access node and the photovoltaic output and load according to the distributed photovoltaic characteristic data;
and according to the relation between the voltage of the distributed photovoltaic access node and the photovoltaic output and the load, rejecting abnormal data which do not conform to the relation between the voltage of the distributed photovoltaic access node and the photovoltaic output and the load.
3. The distributed photovoltaic absorption capacity evaluation method based on big data analysis according to claim 1, wherein the time sequence characteristics of the distributed photovoltaic are obtained according to the season information, weather information and time information of the distributed photovoltaic characteristic data and the corresponding photovoltaic processing information.
4. The big data analysis-based distributed photovoltaic absorption capacity assessment method according to claim 3, wherein the time sequence characteristic of the distributed photovoltaic is
According to the season information, the photovoltaic output is more than spring in summer, more than winter and more than autumn;
according to weather information, the photovoltaic output is clear, cloudy and rainy;
according to the time information, the photovoltaic output time period is 05-19, wherein the photovoltaic output maximum value occurs at 11:00-15:00.
5. The distributed photovoltaic absorption capacity evaluation method based on big data analysis according to claim 1, wherein the relation between the voltage magnitude of a grid-connected point and the grid-connected capacity is obtained according to the distributed photovoltaic characteristic data and the power distribution network line data;
the relation between the grid-connected point voltage and the grid-connected capacity is that the grid-connected point voltage is in direct proportion to the grid-connected capacity.
6. The distributed photovoltaic absorption capacity assessment method based on big data analysis according to claim 1,
the constraint conditions comprise power balance constraint and power grid equipment bearing capacity constraint;
the power balance constraint is to realize the balance of electricity for electricity generation and utilization at the moment when the photovoltaic output is maximum;
the grid equipment bearing capacity constraints comprise a node voltage constraint and a branch current constraint.
7. The distributed photovoltaic absorption capacity assessment method based on big data analysis as claimed in claim 6, characterized by comprising: the node voltage is constrained to
U imin≤ U i≤ U imax
Wherein, U imin Is the minimum value, U, allowed by the voltage amplitude of the ith node imax The maximum value which can be allowed by the voltage amplitude of the ith node is obtained;
the branch current is constrained to
I i ≤I imax
Wherein, I i Is the current of the ith branch, I imax The maximum allowed current for the ith branch.
8. A big data analysis based distributed photovoltaic absorption capacity evaluation system is characterized by comprising:
the first acquisition module is used for acquiring distributed photovoltaic characteristic data and power distribution network line data, establishing a data set and preprocessing the data;
the second acquisition module is used for analyzing the distributed photovoltaic characteristics according to the data set and constructing a distributed photovoltaic absorption capacity evaluation model;
and the third acquisition module is used for introducing a constraint condition of the maximum absorption capacity of the distributed photovoltaic, and acquiring the maximum absorption-type photovoltaic installed capacity of the power distribution network according to the distributed photovoltaic absorption capacity evaluation model through a particle swarm algorithm.
9. An electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the big data analysis-based distributed photovoltaic absorption capacity assessment method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the big-data-analysis-based distributed photovoltaic absorption capacity assessment method according to any one of claims 1 to 7.
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| CN117674304A (en) * | 2024-02-02 | 2024-03-08 | 国网山西省电力公司经济技术研究院 | Evaluation method for distributed photovoltaic digestion capacity of power distribution network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117674304A (en) * | 2024-02-02 | 2024-03-08 | 国网山西省电力公司经济技术研究院 | Evaluation method for distributed photovoltaic digestion capacity of power distribution network |
| CN117674304B (en) * | 2024-02-02 | 2024-04-30 | 国网山西省电力公司经济技术研究院 | Evaluation method for distributed photovoltaic digestion capacity of power distribution network |
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