CN-121172860-B - Network-structured energy storage power station capacity planning and configuration optimization method based on multi-source data

CN121172860BCN 121172860 BCN121172860 BCN 121172860BCN-121172860-B

Abstract

The invention discloses a networking type energy storage power station capacity planning and configuration optimization method based on multi-source data, which relates to the technical field of configuration optimization and comprises the steps of calculating the cavity length and the cavity height of a subsurface cavity based on acoustic wave response signals of a corridor ground surface structure; the method comprises the steps of calculating a cavity risk coefficient of a target corridor based on the cavity length and the cavity height, calculating an updated depth of discharge based on the cavity risk coefficient and a planned depth of discharge in a power station design parameter table, generating a shadow zone section of the target corridor, marking energy storage units of an energy storage power station as high risk units or low risk units based on the shadow zone section, calculating a high risk power sum of all high risk units, and calculating a high risk capacity sum of all high risk units based on the updated depth of discharge. The invention can improve the reliability of the operation safety of the power station.

Inventors

WANG QIUHUI
LI BAOJIAN
ZHANG ZHANLONG
LIU ZHEMING
DENG HONGSHENG
JIANG HONGBO
WU TING
LIU WENLONG

Assignees

中国电建集团江西省电力设计院有限公司

Dates

Publication Date: 20260508
Application Date: 20250826

Claims (6)

1. The networking type energy storage power station capacity planning and configuration optimization method based on the multi-source data is characterized by comprising the following steps of: s1, calculating the cavity length and the cavity height of a subsurface cavity based on acoustic response signals of a corridor ground surface structure; the concrete steps for calculating the cavity length and the cavity height of the subsurface cavity are as follows: Positioning a target corridor of the energy storage power station cabin; Injecting low-amplitude square wave excitation current into the grounding conductor electric loop of the target corridor; Under the action of low-amplitude square wave excitation current, acquiring an acoustic wave response signal of the corridor ground surface structure by using an acoustic wave sensor; If the acoustic wave sensor collects a secondary acoustic echo reflected by the acoustic wave response signal through the subsurface cavity, recording time delay between the secondary acoustic echo and the low-amplitude square wave excitation current; Calculating the cavity length of the subsurface cavity based on the sound velocity and the time delay; Performing signal conversion on the acoustic wave response signals to obtain an original voltage waveform of the corridor ground surface structure; performing Fourier transform on the original voltage waveform to obtain an amplitude spectrum; searching a first main peak and a second main peak of the amplitude spectrum in a preset fixed interval, and respectively extracting a first frequency corresponding to the first main peak and a second frequency corresponding to the second main peak in the amplitude spectrum; Performing difference calculation on the first frequency and the second frequency to obtain a frequency bandwidth; Calculating a cavity height of the subsurface cavity based on the sound velocity and the frequency bandwidth; s2, calculating a cavity risk coefficient of the target corridor based on the cavity length and the cavity height; s3, calculating updated discharge depth based on the cavity risk coefficient and the planned discharge depth in the power station design parameter table; S4, generating a shadow zone section of the target corridor, and marking energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow zone section; S5, calculating the high risk power sum of all the high risk units, and calculating the high risk capacity sum of all the high risk units based on the updated discharge depth; The specific steps for calculating the sum of the high risk power and the sum of the high risk capacity are as follows: summing rated powers of all the high risk units to obtain a high risk power sum; multiplying the updated depth of discharge by the rated energy of the high risk unit to obtain the available capacity of the high risk unit; summing all available capacities to obtain a sum of high-risk capacities; and S6, determining to configure and optimize the energy storage power station based on the cavity center coordinates of the subsurface cavity, the low-risk units and the sum of high-risk power, and planning the capacity of the effective total capacity of the energy storage power station according to the sum of high-risk capacity.
2. The method for optimizing capacity planning and configuration of a network-structured energy storage power station based on multi-source data according to claim 1, wherein calculating a cavity risk coefficient of a target corridor based on a cavity length and a cavity height comprises: calculating the ratio of the length of the cavity to the total length of the corridor of the target corridor to obtain the length ratio of the cavity; Acquiring the safety threshold height of the cavity height; Calculating the ratio of the cavity height to the safety threshold height to obtain the cavity height ratio; Multiplying the cavity length ratio by the cavity height ratio to obtain a cavity risk coefficient of the target corridor.
3. The method of optimizing capacity planning and configuration of a multi-source data based grid-structured energy storage power station of claim 1, wherein calculating an updated depth of discharge based on a cavity risk factor and a planned depth of discharge in a table of power station design parameters comprises: acquiring the planned depth of discharge of a power station design parameter table; and performing linear derating calculation on the planned depth of discharge based on the cavity risk coefficient to obtain updated depth of discharge.
4. The method of optimizing capacity planning and configuration of a networked energy storage power station based on multi-source data according to claim 2, wherein generating a shadow zone section of a target corridor comprises: acoustic wave sensors are distributed at two end points of a target corridor, low-amplitude square wave excitation current is synchronously injected, and end time delay of the end points is recorded, wherein the end time delay represents the first time A time delay of the secondary acoustic echo received by the acoustic wave sensor of each endpoint; calculating the horizontal distance from the end point of the target corridor to the nearest interface of the subsurface cavity based on the sound velocity and the end time delay; Setting the mileage of the 1 st endpoint of the target corridor to 0; Taking the horizontal distance from the 1 st end point to the nearest interface of the subsurface cavity as a cavity starting point mileage of the subsurface cavity, wherein the cavity starting point mileage represents the distance from the 1 st end point to the cavity starting point; Subtracting the total length of the corridor and the horizontal distance from the 2 nd endpoint to the nearest interface of the subsurface cavity to obtain the cavity endpoint mileage of the subsurface cavity, wherein the cavity endpoint mileage represents the distance from the 1 st endpoint to the cavity endpoint; a shadow band zone of the target corridor is generated based on the cavity starting mileage and the cavity ending mileage.
5. The method of optimizing capacity planning and configuration of a multi-source data based grid-structured energy storage power station of claim 1, wherein marking energy storage units of the energy storage power station as high risk units or low risk units based on shadow belt segments comprises: reading the longitudinal center coordinates of each energy storage unit; If the longitudinal center coordinates belong to the shaded band segment, the energy storage unit is marked as a high risk unit, otherwise, the energy storage unit is marked as a low risk unit.
6. The method for optimizing capacity planning and configuration of a grid-structured energy storage power station based on multi-source data according to claim 4, wherein the determining of configuration optimization of the energy storage power station based on cavity center coordinates of subsurface cavities, low risk units and high risk power sum comprises: determining a cavity center coordinate of the subsurface cavity based on the cavity starting mileage and the cavity ending mileage; absolute difference value calculation is carried out on the longitudinal center coordinates of the low-risk units and the cavity center coordinates, so that the longitudinal distance from the longitudinal center coordinates of the low-risk units to the cavity center coordinates is obtained; Sequencing all the low-risk units according to the longitudinal distance to obtain a preliminary replacement list; intercepting the preliminary replacement list based on the sum of the high risk power to obtain a target replacement list; Switching the high risk unit to a hot standby state exiting the primary loop by the power conversion system; and carrying out power supplementation on the main power supply unit of the energy storage power station according to the target replacement list.

Description

Network-structured energy storage power station capacity planning and configuration optimization method based on multi-source data Technical Field The invention relates to the technical field of configuration optimization, in particular to a network-structured energy storage power station capacity planning and configuration optimization method based on multi-source data. Background Along with the continuous improvement of new energy large-scale grid connection and power system scheduling demands, the energy storage power station plays a vital role in the stable operation and energy balance of the power grid. The energy storage unit is used as a core component of the power station, and the running state of the energy storage unit directly influences the overall safety and the power supply reliability of the power station. In practical application scenarios, the energy storage units are generally arranged in a centralized manner in a fixed station, and are electrically connected and managed in a unified manner through a corridor structure. In the long-term operation process, factors such as micro leakage of a liquid cooling system, rainfall and downward leakage of cleaning water, fine cracks caused by concrete slab thermal circulation, continuous flushing of base soil and the like gradually slowly evolve a long and narrow underground cavity below a corridor concrete surface layer, the cavities are often hidden below the concrete surface layer, once fire or cooling liquid leakage occurs, a diversion channel of low-level smoke and combustible gas is easily formed in a cavity area, and transverse diffusion of fire and toxic gas in the corridor is accelerated. It is this hidden underground environmental change that causes the energy storage unit to face additional operational risks. When the energy storage unit is positioned above the subsurface cavity range, the reduction of the bearing capacity of the foundation and the increase of the fire spreading speed directly increase the probability of the unit to fail. At the power station level, the hidden trouble may cause concentrated failure of local units, thereby forming the problem of insufficient capacity redundancy configuration, and reducing the scheduling flexibility and safety margin of the power station under large load fluctuation or sudden accidents. Most of the existing design methods depend on rated parameters when capacity planning is carried out, and the influence of the subsurface cavity on the running environment of the energy storage unit cannot be fully considered, so that deviation is generated between a capacity planning result and actual available capacity. If the problem exists for a long time, the conditions of insufficient output capacity, insufficient standby margin, delayed accident disposal and the like of the power station frequently occur in operation, and serious threat is finally caused to the safe operation and energy allocation of the power system. Disclosure of Invention The invention aims to solve the defects that in the prior art, most of capacity planning depends on rated parameters, influences of subsurface cavities on the running environment of an energy storage unit cannot be fully considered, and deviation is generated between a capacity planning result and actual available capacity, and provides a network-structured energy storage power station capacity planning and configuration optimization method based on multi-source data. In order to solve the problems existing in the prior art, the invention adopts the following technical scheme: the networking type energy storage power station capacity planning and configuration optimization method based on the multi-source data comprises the following steps: s1, calculating the cavity length and the cavity height of a subsurface cavity based on acoustic response signals of a corridor ground surface structure; s2, calculating a cavity risk coefficient of the target corridor based on the cavity length and the cavity height; s3, calculating updated discharge depth based on the cavity risk coefficient and the planned discharge depth in the power station design parameter table; S4, generating a shadow zone section of the target corridor, and marking energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow zone section; S5, calculating the high risk power sum of all the high risk units, and calculating the high risk capacity sum of all the high risk units based on the updated discharge depth; and S6, determining to configure and optimize the energy storage power station based on the cavity center coordinates of the subsurface cavity, the low-risk units and the sum of high-risk power, and planning the capacity of the effective total capacity of the energy storage power station according to the sum of high-risk capacity. Preferably, calculating the cavity length and the cavity height of the subsurface cavity based on the acoustic resp