CN-121980750-A - Distributed photovoltaic platform area simulation method, system, equipment and medium based on energy consumption evaluation

CN121980750ACN 121980750 ACN121980750 ACN 121980750ACN-121980750-A

Abstract

The invention belongs to the technical field of distributed photovoltaic platform area detection, and particularly relates to a distributed photovoltaic platform area simulation method, system, equipment and medium based on energy consumption evaluation. According to the method, a target area is selected, parameters are initialized, toughness recovery targets and economic optimization parameters are set, characteristic extraction is carried out through a fixed extreme disturbance scene library or historical extreme event data collected in recent years to obtain three extreme disturbance historical scenes, simulation of access capacity is carried out on the scenes according to the set toughness recovery targets, average recovery time of each capacity scene is obtained according to simulation data to obtain a capacity-recovery time relation, maximum access capacity is obtained according to the set recovery time, data fitting is carried out according to the capacity-recovery time relation, a final fitting coefficient is obtained by a fitting function through a least square method, maximum access capacity is obtained, simulation exercise is carried out by combining a cost function, and optimal access capacity is obtained.

Inventors

QIAO FUQUAN
NIE YINGKUN
Qu Lebin
DAI YONG
JIANG PENG
ZHAO JIANWEN
LI TENGCHANG
KONG YAFEI
ZHAI LU
CUI CHENGLIANG

Assignees

国网山东省电力公司泰安供电公司

Dates

Publication Date: 20260505
Application Date: 20251217

Claims (10)

1. A distributed photovoltaic platform area simulation method based on energy consumption evaluation is characterized by comprising the following steps: Step S1, determining a target area and initializing parameters, setting a toughness recovery target or economic optimization parameter of the target area, wherein the toughness recovery target comprises recovery time, and the economic optimization parameter comprises cost values required by unit capacity, and the recovery time and the toughness loss cost; s2, constructing an extreme disturbance scene library of a target area, and accessing a plurality of extreme disturbance scene libraries into a plurality of photovoltaic capacity scenes to perform Monte Carlo simulation exercise of a plurality of toughness recovery targets; and S3, analyzing the simulation exercise data to obtain a capacity-recovery time relation, and determining the maximum access amount or the optimal access amount of the distributed photovoltaic in the target area by combining the set toughness recovery target.
2. The distributed photovoltaic platform area simulation method based on energy consumption evaluation according to claim 1, wherein the specific steps of S2 are as follows: Step SP21, constructing a power grid fault scene library, a meteorological disaster scene library and a power fluctuation scene library; And step SP22, the three extreme disturbance scene libraries are connected into a plurality of photovoltaic capacity scenes planned according to the toughness recovery target, repeated simulation exercise of the toughness recovery target is carried out, and recovery time required from the occurrence of disturbance to the toughness recovery target is recorded in each simulation.
3. The distributed photovoltaic platform area simulation method based on energy consumption evaluation according to claim 1, wherein the specific steps of S2 are as follows: step ST21, collecting all historical extreme event data of the region where the target region is located, recording each historical extreme event as an extreme disturbance scene, and constructing an extreme disturbance scene library, wherein key fields of each scene comprise the position of the historical event, the fault type, the hazard degree, the actual recovery time of the corresponding fault and an affected equipment list; Step ST22, extracting geographic features, climate features and power grid features of a target area to form an area feature vector; step ST23, performing similarity calculation on the regional feature vector of the target region and the extreme disturbance scene libraries by using a weighted K nearest neighbor algorithm, and matching a plurality of extreme disturbance scene libraries most relevant to the target region; and S24, accessing the most relevant extreme disturbance scenes into the photovoltaic capacity scenes planned according to the toughness recovery target, performing multiple times of simulation exercises of the toughness recovery target, and recording recovery time required from the occurrence of disturbance to the toughness recovery target in each simulation.
4. The distributed photovoltaic platform area simulation method based on energy consumption evaluation according to claim 2 or 3, wherein the specific steps of S3 are as follows: Step SA31, calculating the average recovery time of simulation events for the data of each access capacity scene to obtain a capacity-recovery time curve; step SA32, calculating the maximum access capacity according to the toughness recovery target set in step S1 and combining the capacity-recovery time curve 。
5. The distributed photovoltaic platform area simulation method based on energy consumption evaluation according to claim 2 or 3, wherein the specific steps of S3 are as follows: Step SE31, calculating the average recovery time of the simulation event for the data of each access capacity scene to form a data set; Step SE32, fitting data to the data set by using a polynomial curve data model, and adjusting a fitting coefficient by using a least square method in the data fitting process to obtain an optimal capacity-recovery time characteristic function; step SE33, calculating the statistical index according to the capacity-recovery time relationship determined by the feature function Evaluating the fitting goodness, wherein the index numerical range is (0, 1), and counting the index The calculation formula of (2) is as follows: Wherein SST is the total sum of squares, SSR is the regression sum of squares, SSE is the residual sum of squares, if the statistical index is greater than or equal to the preset threshold, executing step S34, otherwise executing step S32; Step S34, substituting the recovery time in step S1 as input into the characteristic function, and reversely obtaining the maximum access capacity 。
6. The distributed photovoltaic district simulation method based on energy consumption evaluation according to claim 4, wherein the method is characterized in that according to the maximum access capacity The method for obtaining the optimal access capacity with the lowest life cycle cost comprises the following steps: step SA33, constructing a cost function model comprising an investment cost function cost, a toughness loss cost function loss and an investment cost function The formula is as follows: Wherein a is the cost value required for unit capacity, p is access capacity, toughness loss cost function The expression is as follows: Wherein Ta (p) is the recovery time in a capacity-recovery time curve corresponding to the access capacity p, and b is the recovery time toughness loss cost; step SA34, according to maximum access capacity A simulated capacity interval (0, Performing simulation exercise by combining a cost function model in the interval; And step SA35, obtaining the access optimal capacity with the lowest total cost of the whole life cycle according to the simulation data.
7. The distributed photovoltaic district simulation method based on energy consumption evaluation according to claim 5, wherein the method is characterized in that according to the maximum access capacity The method for obtaining the optimal access capacity with the lowest life cycle cost comprises the following steps: step SE35, constructing a cost function model comprising an investment cost function cost, a toughness loss cost function loss and an investment cost function The formula is as follows: Wherein a is the cost value required for unit capacity, p is access capacity, toughness loss cost function The expression is as follows: Wherein Ta (p) is the recovery time in a capacity-recovery time curve corresponding to the access capacity p, and b is the recovery time toughness loss cost; step SE36, according to maximum access capacity A simulated capacity interval (0, Performing simulation exercise by combining a cost function model in the interval; And step SE37, obtaining the access optimal capacity with the lowest total cost of the whole life cycle according to the simulation data.
8. The distributed photovoltaic platform area simulation system based on the energy consumption evaluation is characterized in that the system is used for realizing the distributed photovoltaic platform area simulation method based on the energy consumption evaluation according to any one of claims 1 to 7; the system comprises a configuration module, an extreme disturbance scene library module, a simulation module and a data analysis module; The configuration module is used for selecting a target area, initializing parameters and setting toughness recovery targets or economic optimization parameters according to the target area; The system comprises an extreme disturbance scene library module, a matching scene sub-module, a local feature vector matching module and a local feature vector matching module, wherein the extreme disturbance scene library module comprises a fixed extreme disturbance scene library sub-module and a matching scene sub-module, and the fixed extreme disturbance scene library comprises three extreme disturbance scene libraries including a power grid fault scene library, a weather disaster library scene library and a power fluctuation scene library; the simulation module comprises a scene simulation sub-module and a cost simulation sub-module; the data analysis module comprises a scene data analysis sub-module and a cost data analysis sub-module; The system comprises a scene simulation sub-module, a cost data analysis sub-module and a cost data analysis sub-module, wherein the scene simulation sub-module is used for performing simulation exercise of access capacity data according to scenes to obtain scene simulation data of each access capacity, the scene analysis sub-module is used for establishing a capacity-recovery time relation through the scene simulation data to determine the maximum capacity which can be accessed by a target area, the cost simulation sub-module is used for determining a capacity interval of the exercise according to the maximum access capacity obtained by the data analysis module and then performing cost simulation exercise by combining a cost function to obtain cost exercise data, and the cost data analysis sub-module is used for obtaining the optimal access capacity through analysis of the cost simulation data.
9. An electronic device comprising a memory (102), a processor (101), a display module (103) and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the energy consumption evaluation based distributed photovoltaic district simulation method according to any of claims 1 to 7 when the program is executed.
10. A readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the distributed photovoltaic cell simulation method based on energy consumption evaluation according to any one of claims 1 to 7.

Description

Distributed photovoltaic platform area simulation method, system, equipment and medium based on energy consumption evaluation Technical Field The invention belongs to the technical field of distributed photovoltaic platform area detection, and particularly relates to a distributed photovoltaic platform area simulation method, system, equipment and medium based on energy consumption evaluation. Background As dual carbon targets advance, the permeability of distributed photovoltaics in the distribution network continues to increase. However, the intermittence and randomness of photovoltaic power generation present a great challenge to the safe and stable operation of power distribution networks, particularly base station areas. Currently, distributed photovoltaic access capacity planning relies mainly on static energy consumption capability assessment, i.e. calculating the maximum accessible capacity based on steady state operating constraints, such as voltage deviation, line and transformer thermal stability limits. Such conventional methods have significant limitations in that, first, they focus on the load carrying capacity of the system under normal conditions, and completely ignore the dynamic process and recovery capacity after extreme disturbances occur. An access scheme which looks safe in steady state, such as equipment failure, extreme weather, sudden changes in photovoltaic power, can recover slowly after encountering disturbance, and even lead to long-time and large-range power interruption, and the system has insufficient toughness. Secondly, the existing planning method generally lacks regional pertinence, generally adopts a general and preset typical fault set to carry out safety verification, and fails to fully consider specific risk differences faced by different geographic positions, climate conditions and power grid structures, so that planning results can be excessively impounded or conserved. Therefore, the method and the system have the advantages that the maximum accessible capacity is calculated only by means of static steady-state constraint, the dynamic recovery process of the system after extreme disturbance is completely ignored, toughness indexes such as recovery time, capacity recovery duty ratio and the like are not included, steady-state safety but dynamic fragile planning schemes are easily caused, long-time power supply interruption can be possibly caused under extreme events, scene setting is generalized, a preset typical fault set is adopted, geographical, climate and power grid characteristics of a target area are not combined, planning results are easy to imposter or conservative, capacity calculation depends on experience, nonlinear rule depiction and reliability verification are lacked, full life cycle cost is not considered, only single safety capacity is output, and practical feasibility requirements of a process are difficult to adapt. Disclosure of Invention In order to solve the problems, the invention provides a distributed photovoltaic platform area simulation method, a system, equipment and a medium based on energy consumption evaluation, wherein the method comprises the steps of selecting a target area, initializing parameters, setting toughness recovery targets and economic optimization parameters, and carrying out feature extraction and matching to obtain three extreme disturbance historical scenes through a fixed extreme disturbance scene library or collecting historical extreme event data in recent years; according to the set toughness recovery targets, carrying out simulation on access capacity on the scenes, obtaining average recovery time of each capacity scene according to simulation data to obtain a capacity-recovery time relation, obtaining maximum access capacity according to the set recovery time, carrying out data fitting on the capacity-recovery time relation through a polynomial curve data model, obtaining a final fitting coefficient by a fitting function through a least square method, obtaining maximum access capacity, and carrying out simulation exercise by combining a cost function to obtain the optimal access capacity. In order to achieve the above purpose, the present invention adopts the following technical scheme: the invention provides a distributed photovoltaic platform area simulation method based on energy consumption evaluation, which comprises the following steps of: Step S1, determining a target area and initializing parameters, setting a toughness recovery target or economic optimization parameter of the target area, wherein the toughness recovery target comprises recovery time, and the economic optimization parameter comprises cost values required by unit capacity, and the recovery time and the toughness loss cost; s2, constructing an extreme disturbance scene library of a target area, and accessing a plurality of extreme disturbance scene libraries into a plurality of photovoltaic capacity scenes to perform Monte Carlo simulation