CN-122026303-A - Confidence level-based optical storage micro-grid admission capacity assessment method, system, equipment and medium

CN122026303ACN 122026303 ACN122026303 ACN 122026303ACN-122026303-A

Abstract

The invention discloses a confidence level-based assessment method, a system, equipment and a medium for acceptance capacity of an optical storage and filling micro-grid, which belong to the technical field of integration of power distribution networks and distributed renewable energy sources and comprise the steps of establishing a probability distribution model of random variables through historical data statistical analysis; the method comprises the steps of generating operation scenes reflecting uncertainty, each scene contains complete time sequence operation data, carrying out load flow calculation on each scene, checking the meeting condition of constraint conditions, judging whether a power distribution network can bear optical storage and charging access with specific capacity under a given confidence level by counting the proportion of the constraint conditions in all scenes, adjusting access capacity to determine admission capacity under the confidence level, outputting admission capacities corresponding to different confidence levels, and forming an admission capacity assessment curve. According to the invention, through time sequence scene analysis, the system operation risk and the weak period are comprehensively revealed, the admission capacity under the multi-confidence level is provided, and the accurate trade-off between the reliability and the economy of different application scenes is supported.

Inventors

WANG JIAN
WANG BIN
ZHANG PENGCHENG
LUO NING
WANG WEI
LI ZHEN
LI QINGSHENG
MOU XUEPENG
HU BIN
ZHANG YU
Zhao Kuanxiang
ZHU YONGQING
Yang Dongjunming
ZHANG ZHAOFENG

Assignees

贵州电网有限责任公司

Dates

Publication Date: 20260512
Application Date: 20251205

Claims (10)

1. The method for evaluating the acceptance capacity of the optical storage micro-grid based on the confidence level is characterized by comprising the following steps of, Establishing a probability distribution model of the random variable through historical data statistical analysis; Generating operation scenes reflecting uncertainty, wherein each scene contains complete time sequence operation data; carrying out load flow calculation on each scene, and checking the satisfaction condition of constraint conditions; judging whether the power distribution network can bear the optical storage and charging access of a specific capacity under a given confidence level by counting the proportion of satisfied constraint conditions in all scenes, and adjusting the access capacity to determine the admission capacity under the confidence level; and outputting the admission capacity corresponding to the different confidence levels to form an admission capacity evaluation curve.
2. The method for evaluating the acceptance of the optical storage and filling micro-grid based on the confidence level of claim 1, wherein the establishing a probability distribution model of the random variable comprises the steps of collecting historical operation data and preprocessing the collected data; carrying out statistical analysis on the cleaned photovoltaic output data, extracting probability distribution characteristics, and calculating the mean value and standard deviation of the photovoltaic output; And carrying out time interval division statistics on the charging load data, and carrying out statistics on the transition probability of the state of charge to generate probability distribution parameters.
3. The method for evaluating capacity of light storage and filling micro-grid admission based on confidence level as set forth in claim 2, wherein generating operational scenes reflecting uncertainty comprises generating a plurality of operational scenes based on probability distribution parameters, each scene containing data of complete years; For each scene, generating photovoltaic output data hour by hour according to a time sequence; And generating an energy storage state, determining a state at the next moment according to the current state and the transition probability matrix, forming a scene set, and performing load flow calculation and constraint inspection on generated scene data.
4. The method for evaluating the acceptance capacity of the optical storage and filling micro-grid based on the confidence level of claim 3, wherein the step of carrying out power flow calculation on each scene comprises the steps of carrying out power flow calculation at each moment of each scene for a given optical storage and filling access capacity, and checking whether the operation of the power distribution network meets the safety constraint; Calculating injection power of a power distribution network node in a scene, and calculating node voltage and branch current; Defining voltage constraint conditions, defining branch current constraint conditions, defining transformer capacity constraint conditions and defining constraint satisfaction rates of scenes.
5. The method for evaluating capacity of optical storage and filling micro-grid admission based on confidence level as set forth in claim 4, wherein said determining whether the distribution network can withstand the optical storage and filling access of the specific capacity under the given confidence level comprises calculating the given access capacity based on constraint satisfaction rates of all scenes A system confidence level; Expressing the opportunity constraint as that the scene proportion of the constraint satisfaction rate in the scene set is not lower than a threshold value should reach a specified confidence level; For a pair of Constraint satisfaction rate for individual scenes Sequencing to obtain a sequenced sequence Defining the constraint satisfaction rate corresponding to the p-th quantile as , wherein, For the constraint satisfaction rate corresponding to the p-th quantile, Is an upward rounding function; Defining confidence levels The following criterion of acceptance assessment is the first Constraint satisfaction rate corresponding to the fractional number is not lower than the set minimum time satisfaction rate : , wherein, For the level of confidence that the user is in the normal state, Is the minimum time satisfaction rate of the requirements; For a given access capacity By generating a scene and calculating constraint satisfaction rates, computing Checking Whether or not it is established, if so, indicating that the current capacity is at the confidence level And if not, indicating that the current capacity is reduced, and carrying out capacity optimization search on the test result.
6. The method for evaluating capacity of optical storage and micro-grid as set forth in claim 5, wherein determining the capacity of the optical storage and micro-grid based on the confidence level comprises searching for a maximum access capacity meeting the confidence level requirement by using a dichotomy method, and setting an initial lower bound of a capacity search range And an initial upper bound, wherein, Setting the current search point as a proportion value of the capacity of the power distribution network transformer, and carrying out an iterative search process to calculate the capacity of the current search point as follows: Wherein, the For the candidate capacity of the kth iteration, And Searching a lower bound and an upper bound for a kth iteration; To capacity Executing the evaluation flow to obtain a confidence level evaluation result, if And (3) establishing, namely, describing that the current capacity is feasible, and updating the search lower bound: while keeping the upper bound unchanged ; If it is And if not, the current capacity is excessively large, and the search upper bound is updated: while keeping the lower bound unchanged ; Repeating the iterative process until the search range converges, namely: , wherein, Lower bound at convergence for convergence accuracy I.e. confidence level Lower admission capacity for different confidence levels Respectively executing search process to obtain corresponding admission capacity 、、。
7. The method for evaluating the admittance capacity of the optical storage and filling micro-grid based on the confidence level of claim 6, wherein the forming of the admittance capacity evaluation curve comprises the steps of summarizing admittance capacities corresponding to different confidence levels, generating the admittance capacity evaluation curve, wherein the horizontal axis is the confidence level, and the vertical axis is the admittance capacity; the admission capacity difference at different confidence levels was calculated: Wherein, the For confidence level And (3) with Corresponding admission capacity difference; Outputting a voltage and current time sequence curve under a typical scene, displaying the operation characteristic of the power distribution network after the light storage is accessed, and selecting the constraint satisfaction rate as the first Drawing 24-hour time-varying curves of all node voltages and branch currents in a quantile scene, marking out-of-limit time periods and positions, and identifying weak links; The method comprises the steps of generating an evaluation report, wherein the evaluation report comprises recommended access capacity, corresponding confidence level, expected constraint satisfaction rate, possible out-of-limit time period and position and optimization suggestion, and giving targeted suggestions for different application scenes, wherein the capacity corresponding to 99% confidence level is adopted for important power supply areas, the capacity corresponding to 90% confidence level is adopted and an automatic power limiting control device is configured for general areas and allowable power limiting operation.
8. The confidence level-based optical storage and filling micro-grid admission capacity assessment system is applied to the confidence level-based optical storage and filling micro-grid admission capacity assessment method according to any one of claims 1-7, and is characterized by comprising a data acquisition and probability modeling module, a scene generation and timing simulation module, an opportunity constraint assessment module, a capacity optimization search module and a result output and analysis module; The data acquisition and probability modeling module establishes a probability distribution model of random variables through historical data statistical analysis; The scene generation and time sequence simulation module generates operation scenes reflecting uncertainty, and each scene contains complete time sequence operation data; The opportunity constraint evaluation module is used for carrying out load flow calculation on each scene and checking the satisfaction condition of the constraint condition; The capacity optimization searching module judges whether the power distribution network can bear the optical storage and charging access of specific capacity under a given confidence level by counting the proportion of satisfied constraint conditions in all scenes, and adjusts the access capacity to determine the admission capacity under the confidence level; And the result output and analysis module outputs the admission capacity corresponding to different confidence levels to form an admission capacity evaluation curve.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the confidence level based optical storage micro network admission capacity assessment method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the confidence level based method for assessing optical storage micro-grid admission capacity of any one of claims 1 to 7.

Description

Confidence level-based optical storage micro-grid admission capacity assessment method, system, equipment and medium Technical Field The invention relates to the technical field of integration of power distribution networks and distributed renewable energy sources, in particular to a confidence level-based assessment method, a confidence level-based assessment system, confidence level-based assessment equipment and confidence level-based assessment medium for acceptance of an optical storage micro-grid. Background Along with the rapid development of new energy automobiles and large-scale access of distributed photovoltaics, the optical storage and charging integrated micro-grid system becomes an important novel load form of the power distribution network. The photovoltaic power generation, energy storage device and charging pile are integrated in the photovoltaic power storage and charging system, and the photovoltaic power generation and energy storage system has the characteristics of large power fluctuation, strong randomness, complex interaction and the like. The capacity assessment of the power distribution network for accommodating the optical storage and filling system becomes a key problem of power grid planning and operation, but the existing assessment method has obvious defects: 1) Based on deterministic analysis, uncertainty effects cannot be quantified The traditional power distribution network acceptance assessment mainly adopts a deterministic method, namely, parameters such as photovoltaic output, energy storage state, charging load and the like are assumed to be fixed values or typical scenes, and the acceptable maximum capacity is determined based on constraint conditions such as load flow calculation analysis voltage out-of-limit, equipment overload and the like, so that the deterministic method cannot reflect the comprehensive influence of uncertain factors. 2) Timing characteristic assessment lacking consideration of continuous operation capability The existing method is based on static power flow calculation at a single moment or a few typical moments, and whether the power distribution network can bear the optical storage and charging access with specific capacity at a certain moment is estimated. However, the actual operation of the power distribution network is a continuous time sequence process, the constraint condition is not met at one moment, and the condition can not be met all the time, so that voltage out-of-limit or equipment overload can occur at other moments. 3) The evaluation index is single, and the decision requirement of different risk preferences is difficult to support The existing method generally gives a unique admission capacity value, a single value result does not reflect the confidence level or reliability level of evaluation, and a decision maker cannot know how much probability of out-of-limit occurs under the capacity, and cannot select a proper risk level according to different application scenes. Disclosure of Invention The present invention has been made in view of the above-described problems. Therefore, the invention aims to solve the technical defects of the prior art such as limited certainty analysis, missing time sequence characteristic evaluation, single evaluation index and the like. In order to solve the technical problems, the invention provides a technical scheme that the method for evaluating the acceptance capacity of the optical storage micro-grid based on the confidence level comprises the following steps, The method comprises the steps of establishing a probability distribution model of random variables through historical data statistical analysis, generating operation scenes reflecting uncertainty, wherein each scene contains complete time sequence operation data, carrying out load flow calculation on each scene, checking the meeting condition of constraint conditions, judging whether a power distribution network can bear optical storage and filling access of specific capacity under a given confidence level by counting the proportion of the constraint conditions in all scenes, adjusting access capacity to determine admission capacity under the confidence level, outputting admission capacities corresponding to different confidence levels, and forming an admission capacity assessment curve. The method for evaluating the acceptance capacity of the optical storage and filling micro-grid based on the confidence level is characterized by comprising the following steps of establishing a probability distribution model of random variables, acquiring historical operation data and preprocessing the acquired data; carrying out statistical analysis on the cleaned photovoltaic output data, extracting probability distribution characteristics, and calculating the mean value and standard deviation of the photovoltaic output; And carrying out time interval division statistics on the charging load data, and carrying out statistics on the transition probabi