CN-120675197-B - Collaborative control method based on optical storage and charging and micro-grid

CN120675197BCN 120675197 BCN120675197 BCN 120675197BCN-120675197-B

Abstract

The invention relates to the technical field of micro-grid control, in particular to a micro-grid collaborative control method based on optical storage and charging, which comprises the steps of collecting multi-source data in real time; the method comprises the steps of predicting a photovoltaic output predicted value, screening a temporary unit, determining an energy storage unit, determining a cooperative abnormal unit, dynamically correcting a power threshold value, and executing power adjustment control. The self-adaptive closed loop is constructed by organically coupling the output power, the state of charge value, the load demand and the multidimensional real-time parameters of the user behavior, wherein the power fluctuation threshold value and the charging request growth rate are combined to trigger the temporary unit screening, the rapid response to sudden load change is ensured, the state of charge value and queuing time are used for jointly determining the energy storage unit scheduling, the power generation side and the demand side are balanced, the cooperative index is generated by the repeated frequency and the power-load normalization weighting, the cooperative abnormality is accurately identified, and the problem of low cooperative control response speed caused by model staticization and single index judgment is effectively solved.

Inventors

MA WEI
LIU YUANGANG
YANG CHUNHUA
LI JING
YANG YI
TANG HAIFU
YAO SHUN
TANG PENG
WANG YONGHUA
TENG ZHIJUN
TAN JIANGUO

Assignees

怀化建南机器厂有限公司

Dates

Publication Date: 20260505
Application Date: 20250625

Claims (9)

1. The method for controlling the micro-grid based on the optical storage and the charging is characterized by comprising the following steps of: Collecting output power of each photovoltaic unit on each feeder line in a photovoltaic array running based on preset photovoltaic power generation power in a micro-grid, a high-current load switch state value in each feeder line, a state of charge value of an energy storage system, load demands of a building transformer end, a charging request growth rate of a target side, average queuing time and interrupted reconnection frequency in real time; Predicting a photovoltaic output predicted value according to all the output power, the state of charge value, the load demand, the charging request growth rate, the average queuing time length, the interrupt reconnection frequency and a preset prediction model in a preset history time length; Screening a plurality of temporary units according to the output power, a preset total power fluctuation threshold value and the charging request growth rate; determining a plurality of energy storage units according to the average queuing time length, the output power of each temporary unit and the state of charge value; determining a plurality of cooperative abnormal units according to the interrupt reconnection frequency, the output power of each energy storage unit and the load demand; adjusting the preset total power fluctuation threshold according to the layout positions of the cooperative abnormal units in the feeder lines, the number of the units and the state value of the heavy current load switch to obtain an adjusted power fluctuation threshold; Correcting the photovoltaic output predicted value according to the unit number of the cooperative abnormal units redetermined by adopting the power fluctuation adjusting threshold in a preset correction period to obtain a corrected predicted value; Executing a control instruction for adjusting the preset photovoltaic power generation power on the cooperative abnormal unit according to the corrected predicted value and the preset predicted value range; the photovoltaic output predicted value is corrected according to the unit number of the cooperative abnormal units redetermined by adopting the power fluctuation adjusting threshold value in a preset correction period, and the process for obtaining the corrected predicted value comprises the following steps: Calculating standard deviation of the unit quantity in the preset correction period to obtain a correction quantity fluctuation value; And when the fluctuation value of the correction quantity is larger than a preset fluctuation threshold of the correction quantity, increasing the photovoltaic output predicted value according to the relative deviation between the fluctuation value of the correction quantity and the fluctuation threshold of the preset correction quantity and a preset correction coefficient to obtain the correction predicted value, wherein Y '=Y× [1+i× (L-L0)/L0 ], Y' is the correction predicted value, Y is the photovoltaic output predicted value, i is the preset correction coefficient, L is the fluctuation value of the correction quantity, and L0 is the fluctuation threshold of the preset correction quantity.
2. The collaborative control method based on optical storage and micro-grid according to claim 1, wherein the process of screening out temporary units according to the output power, a preset total power fluctuation threshold and the charge request growth rate comprises: Calculating the sum of all the output powers to obtain the total output power; Calculating standard deviation of all the output total power in a preset temporary time length to obtain a total power fluctuation value; and when the total power fluctuation value is larger than the preset total power fluctuation threshold value and the charging request growth rate is larger than a preset standard growth rate, screening a plurality of temporary units according to the output power and the total power fluctuation value.
3. The collaborative control method based on optical storage and micro-grid according to claim 2, wherein screening out a plurality of temporary units according to the output power and the total power fluctuation value comprises: Calculating standard deviation of all output power of each photovoltaic unit in the next preset temporary time period to obtain an output power fluctuation value; Calculating the relative deviation of the output power fluctuation value and the total power fluctuation value to obtain a temporary judgment value; And when the temporary judgment value is larger than a preset temporary judgment threshold value, judging the photovoltaic unit as the temporary unit so as to screen out a plurality of temporary units.
4. The optical storage and micro grid cooperative control method according to claim 3, wherein the process of determining a plurality of energy storage units according to the average queuing time period, the output power of each temporary unit, and the state of charge value comprises: When the state of charge value is smaller than a preset state of charge threshold value, calculating a difference value between the preset state of charge threshold value and the state of charge value to obtain a state of charge difference value; when the charge difference value is larger than a preset charge difference value threshold, the average queuing time length is smaller than a preset time length threshold, and the output power is larger than a preset standard power, judging the temporary unit as the energy storage unit so as to determine a plurality of energy storage units; And when the charge difference value is larger than a preset charge difference value threshold value, and the average queuing time length is larger than or equal to the preset time length threshold value, determining a plurality of energy storage units according to the output power in the preset determined time length.
5. The method of claim 4, wherein determining the plurality of energy storage units according to the output power within a predetermined period of time comprises: calculating the standard deviation of the output power in a preset determined time length to obtain a determined power fluctuation value; and when the determined power fluctuation value is larger than a preset determined fluctuation threshold value, judging the temporary unit as the energy storage unit so as to determine a plurality of energy storage units.
6. The method of claim 5, wherein determining a plurality of collaborative anomaly units based on the outage reconnection frequency, the output power of each energy storage unit, and the load demand comprises: Obtaining the interrupted reconnection frequency within a preset cooperative time length to obtain a reconnection frequency set; Obtaining the output power in the preset cooperative time length to obtain an output power set; acquiring the load demands in the preset cooperative time length to obtain a load demand set; Performing maximum-minimum value normalization processing on the output power at each moment in the preset cooperative time period according to the output power set to obtain a power normalization value; carrying out maximum-minimum value normalization processing on the load demands at each moment in the preset cooperative time period according to the load demand set to obtain a demand normalization value; calculating the product of the power normalization value and the preset power weight to obtain a first product, and calculating the product of the demand normalization value and the preset demand weight to obtain a second product; Calculating the sum of the first product and the second product to obtain a synergy index; Acquiring all the collaborative indexes to obtain a collaborative index set; calculating the correlation coefficient of the reconnection frequency set and the synergy index set to obtain the synergy anomaly degree; and when the collaborative anomaly degree is greater than a preset standard anomaly degree, judging the energy storage unit as the collaborative anomaly unit so as to determine a plurality of collaborative anomaly units.
7. The collaborative control method based on optical storage and micro-grid according to claim 6, wherein the process of adjusting the preset total power fluctuation threshold according to the layout position, the number of units and the state value of the heavy current load switch of the collaborative abnormal units in each feeder line to obtain the adjusted power fluctuation threshold comprises: obtaining the layout positions of the cooperative abnormal units in the feeder lines to obtain the relative distances between the cooperative abnormal units and the starting points of the feeder lines; acquiring feeder numbers of the collaborative and abnormal units, and counting the number of the collaborative and abnormal units in each feeder according to the feeder numbers to obtain the number of the units; performing maximum-minimum value normalization processing on the relative distance at the current moment according to all the relative distances to obtain a plurality of normalized distances; calculating standard deviations of all the normalization distances to obtain distribution concentration; calculating a concentration factor according to the distribution concentration and the number of units; normalizing the current distribution concentration according to all the distribution concentrations in the preset adjustment time length to obtain a normalized concentration; Normalizing the current unit number according to all the unit numbers in the preset adjustment time length to obtain a normalized number; carrying out weighted summation on the preset concentrated weight, the normalization concentrated degree, the preset quantity weight, the normalization quantity, the preset state value weight and the state value of the heavy current load switch to obtain a risk index; And when the risk index is larger than a preset risk index threshold, increasing the preset total power fluctuation threshold according to the relative deviation between the risk index and the preset risk index threshold and a preset adjustment coefficient to obtain the adjustment power fluctuation threshold.
8. The method according to claim 7, wherein the process of executing the control instruction for adjusting the preset photovoltaic power generation power to the cooperative abnormal unit according to the corrected predicted value and the preset predicted value range includes: executing a control instruction for reducing the preset photovoltaic power generation power according to the relative deviation between the corrected predicted value and the maximum value of the preset predicted value range and a preset control coefficient when the corrected predicted value is larger than the maximum value of the preset predicted value range; and when the correction predicted value is smaller than the minimum value of the preset predicted value range, executing a control instruction for increasing the photovoltaic power generation power according to the relative deviation between the minimum value of the preset predicted value range and the correction predicted value and the preset control coefficient.
9. The method of claim 8, wherein predicting the predicted photovoltaic output value according to all of the output power, the state of charge value, the load demand, the charge request growth rate, the average queuing time period, the interrupt reconnection frequency, and a predetermined prediction model within a predetermined history period comprises: Performing time sequence alignment, missing value filling and normalization processing on all the output power, the state of charge value, the charging pile load demand, the target side charging request growth rate, the average queuing time and the interrupt reconnection frequency to construct a multidimensional input feature matrix; And inputting the multidimensional input feature matrix into the preset prediction model to predict the photovoltaic output predicted value.

Description

Collaborative control method based on optical storage and charging and micro-grid Technical Field The invention relates to the technical field of micro-grid control, in particular to a micro-grid collaborative control method based on optical storage and charging. Background With the large-scale deployment of distributed photovoltaic and electric automobile charging facilities, residential building-level micro-grids are rapidly evolving towards the 'optical storage and charging' integrated cooperative control direction. However, inherent volatility of photovoltaic power generation, charge-discharge limitations of energy storage systems, and sudden increases in charging pile loads all present serious challenges for grid stability. Therefore, a cooperative control strategy of optical storage and micro-grid is urgently needed to improve the rapid response capability and the overall dispatching efficiency of the micro-grid to fluctuation and sudden demand. The patent document with the publication number of CN117254526A discloses an energy collaborative optimization control method of an integrated station of an optical storage, filling and detection micro-grid, which comprises the steps of S1, collecting load data in the integrated station of the optical storage, filling and detection micro-grid and power generation data of a photovoltaic system, S2, establishing load models in different time periods based on historical load data of a transformer, S3, establishing power generation models of the photovoltaic system in different time periods based on the historical power generation data, S4, comparing the load models of the transformer with power generation models in corresponding time periods and preset power output power of an energy storage system respectively, S5, switching different working modes based on comparison results, integrated electricity price time and real-time power generation power of the photovoltaic system, and adjusting working states of the photovoltaic system, the energy storage system and the power grid so as to meet load value requirements. The method for controlling the energy collaborative optimization of the integrated station of the optical storage charging detection micro-grid has the following problems that a transformer load model is simply compared with a photovoltaic power generation model, real-time response capability for dynamic coupling of the transformer load model and the photovoltaic power generation model and sudden change of a load side is lacked, the load model and the power generation model are cracked, the model is built only based on a fixed time period, sudden fluctuation of load and power generation cannot be adapted, a model window is difficult to adjust in time, a preset energy storage output is directly adopted, a dynamic adjustment strategy is not available, energy storage charging and discharging cannot be flexibly distributed according to photovoltaic fluctuation or sudden increase of load, different working modes are switched with electricity price time periods only based on a plurality of groups of comparison results, support for unit-level or feeder-level fine granularity strategies is lacked, and local optimization is difficult to realize. Disclosure of Invention Therefore, the invention provides a collaborative control method based on optical storage and micro-grid, which is used for solving the problem of low collaborative control response speed caused by model staticization and single index judgment in the prior art through dynamic threshold adjustment driven by real-time multidimensional data. In order to achieve the above object, the present invention provides a method for controlling a micro-grid based on optical storage and charging, comprising: Collecting output power of each photovoltaic unit on each feeder line in a photovoltaic array running based on preset photovoltaic power generation power in a micro-grid, a high-current load switch state value in each feeder line, a state of charge value of an energy storage system, load demands of a building transformer end, a charging request growth rate of a target side, average queuing time and interrupted reconnection frequency in real time; Predicting a photovoltaic output predicted value according to all the output power, the state of charge value, the load demand, the charging request growth rate, the average queuing time length, the interrupt reconnection frequency and a preset prediction model in a preset history time length; Screening a plurality of temporary units according to the output power, a preset total power fluctuation threshold value and the charging request growth rate; determining a plurality of energy storage units according to the average queuing time length, the output power of each temporary unit and the state of charge value; determining a plurality of cooperative abnormal units according to the interrupt reconnection frequency, the output power of each energy storage unit and the