Capacity planning and configuration optimization method for grid-connection type energy storage power station based on multi-source data

Abstract

The application discloses a network type energy storage power station capacity planning and configuration optimization method based on multi-source data, relates to the technical field of configuration optimization, and comprises the following steps: calculating the cavity length and cavity height of a subsurface cavity based on the sound wave response signal of a corridor ground surface structure; calculating the cavity risk coefficient of a target corridor based on the cavity length and cavity height; calculating the updated discharge depth based on the cavity risk coefficient and the planning discharge depth in an electric station design parameter table; generating a shadow band section of the target corridor, marking the energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow band section; 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 application can improve the reliability of the operation safety of the power station.

Description

Technical Field

[0001] This invention relates to the field of configuration optimization technology, and in particular to a method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data. Background Technology

[0002] With the large-scale grid connection of new energy sources and the increasing demand for power system dispatch, energy storage power stations play a crucial role in the stable operation and energy balance of the power grid. As a core component of the power station, the operating status of the energy storage unit directly affects the overall safety and power supply reliability of the station. In practical applications, energy storage units are typically centrally located within fixed sites and electrically connected and uniformly managed through a corridor-style structure. During long-term operation, factors such as micro-leakage of the liquid cooling system, infiltration of rainwater and cleaning water, fine cracks caused by thermal cycling of the concrete slab, and continuous erosion of the base fill gradually evolve into a narrow underground cavity beneath the concrete surface of the corridor. These cavities are often hidden beneath the concrete surface. In the event of a fire or coolant leak, the cavity area can easily become a conduit for low-level smoke and combustible gases, accelerating the lateral spread of fire and toxic gases within the corridor. It is precisely because of this concealed underground environment that energy storage units face additional operational risks.

[0003] When an energy storage unit is located above a subsurface cavity, the decreased bearing capacity of the foundation and the increased rate of fire spread will directly increase the probability of the unit's failure. At the power plant level, this potential hazard may lead to concentrated failures of local units, resulting in insufficient capacity redundancy and reducing the power plant's dispatch flexibility and safety margin under conditions of large load fluctuations or sudden accidents.

[0004] Existing design methods largely rely on rated parameters for capacity planning, failing to adequately consider the impact of subsurface cavities on the operating environment of energy storage units. This leads to discrepancies between planned capacity and actual available capacity. If this problem persists, power plants will frequently experience insufficient output capacity, inadequate reserve margins, and delays in emergency response, ultimately posing a serious threat to the safe operation and energy allocation of the power system. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies that rely heavily on rated parameters when planning capacity, failing to fully consider the impact of subsurface cavities on the operating environment of energy storage units, resulting in discrepancies between capacity planning results and actual available capacity. The invention proposes a capacity planning and configuration optimization method for grid-type energy storage power stations based on multi-source data.

[0006] To address the problems existing in the prior art, the present invention adopts the following technical solution:

[0007] A capacity planning and configuration optimization method for grid-type energy storage power stations based on multi-source data includes:

[0008] S1. Calculate the cavity length and cavity height of the subsurface cavity based on the acoustic response signal of the corridor surface structure;

[0009] S2. Calculate the cavity risk coefficient of the target corridor based on the cavity length and cavity height;

[0010] S3. Calculate and update the discharge depth based on the cavity risk coefficient and the planned discharge depth in the power plant design parameter table;

[0011] S4. Generate the shadow zone of the target corridor, and mark the energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow zone.

[0012] S5. Calculate the sum of high-risk power of all high-risk cells, and calculate the sum of high-risk capacity of all high-risk cells based on the updated discharge depth.

[0013] S6. Based on the cavity center coordinates of the subsurface cavity and the sum of low-risk units and high-risk power, the configuration optimization of the energy storage power station is determined, and the effective total capacity of the energy storage power station is planned according to the sum of high-risk capacity.

[0014] Preferably, the calculation of the cavity length and cavity height of the subsurface cavity based on the acoustic response signal of the corridor surface structure includes:

[0015] Locate the target corridor for the energy storage power station cabin;

[0016] Inject a low-amplitude square wave excitation current into the grounded conductor electrical circuit of the target corridor;

[0017] Under the action of a low-amplitude square wave excitation current, acoustic wave sensors are used to collect acoustic wave response signals of the corridor surface structure.

[0018] If the acoustic sensor collects the secondary acoustic echo reflected by the subsurface cavity from the acoustic response signal, then the time delay between the secondary acoustic echo and the low-amplitude square wave excitation current is recorded.

[0019] Calculate the cavity length of the subsurface cavity based on sound speed and time delay;

[0020] The acoustic response signal is converted to obtain the original voltage waveform of the corridor surface structure.

[0021] Perform a Fourier transform on the original voltage waveform to obtain the amplitude spectrum;

[0022] Search for the first and second main peaks of the amplitude spectrum within a preset fixed interval, and extract the first frequency corresponding to the first main peak and the second frequency corresponding to the second main peak in the amplitude spectrum, respectively.

[0023] The bandwidth is obtained by calculating the difference between the first frequency and the second frequency.

[0024] The cavity height of the subsurface cavity is calculated based on the speed of sound and bandwidth.

[0025] Preferably, the cavity risk coefficient of the target corridor is calculated based on the cavity length and cavity height, including:

[0026] The ratio of the cavity length to the total length of the target corridor is calculated to obtain the cavity length percentage.

[0027] Obtain the safe threshold height for the cavity height;

[0028] The ratio of cavity height to safety threshold height is calculated to obtain the cavity height percentage;

[0029] Multiply the cavity length percentage by the cavity height percentage to obtain the cavity risk coefficient of the target corridor.

[0030] Preferably, based on the cavity risk coefficient and the planned discharge depth in the power plant design parameter table, the updated discharge depth is calculated, including:

[0031] Obtain the planned discharge depth from the power plant design parameter table;

[0032] The updated discharge depth is obtained by linearly reducing the planned discharge depth based on the cavity risk coefficient.

[0033] Preferably, generating the shadow band segment of the target corridor includes:

[0034] Acoustic sensors were deployed at both ends of the target corridor, and low-amplitude square wave excitation currents were injected synchronously. The end time delays were recorded, where the end time delay represents the... The time delay of the secondary acoustic echo received by the acoustic wave sensor at each endpoint;

[0035] Based on the speed of sound and end time delay, calculate the horizontal distance from the endpoint of the target corridor to the nearest interface of the subsurface cavity;

[0036] Set the mileage of the first endpoint of the target corridor to 0;

[0037] The horizontal distance from the first endpoint to the nearest interface of the subsurface cavity is taken as the cavity starting point mileage of the subsurface cavity, where the cavity starting point mileage represents the distance from the first endpoint to the cavity starting point.

[0038] The cavity endpoint mileage of the subsurface cavity is obtained by subtracting the total length of the corridor from the horizontal distance from the second endpoint to the nearest interface of the subsurface cavity. The cavity endpoint mileage represents the distance from the first endpoint to the cavity endpoint.

[0039] The shadow zone of the target corridor is generated based on the cavity start mileage and cavity end mileage.

[0040] Preferably, marking energy storage units of the energy storage power station as high-risk or low-risk units based on the shaded zone section includes:

[0041] Read the longitudinal center coordinates of each energy storage unit;

[0042] If the longitudinal center coordinates belong to the shaded area, the energy storage unit is marked as a high-risk unit; otherwise, the energy storage unit is marked as a low-risk unit.

[0043] Preferably, the sum of high-risk power of all high-risk cells is calculated, and the sum of high-risk capacity of all high-risk cells is calculated based on the updated depth of discharge, including:

[0044] The rated power of all high-risk units is summed to obtain the total high-risk power.

[0045] The available capacity of the high-risk cell is obtained by multiplying the updated depth of discharge by the rated energy of the high-risk cell.

[0046] Sum all available capacity to obtain the total high-risk capacity.

[0047] Preferably, the configuration optimization of the energy storage power station is determined based on the cavity center coordinates of the subsurface cavity and the sum of low-risk units and high-risk power, including:

[0048] The cavity center coordinates of the subsurface cavity are determined based on the cavity start mileage and cavity end mileage.

[0049] The absolute difference between the longitudinal center coordinates of the low-risk unit and the cavity center coordinates is calculated to obtain the longitudinal distance between the longitudinal center coordinates of the low-risk unit and the cavity center coordinates.

[0050] All low-risk units are sorted according to their vertical distance to obtain a preliminary replacement list;

[0051] The target substitute list is obtained by extracting the preliminary substitute list based on the sum of high-risk power.

[0052] High-risk units are switched to hot standby mode by exiting the main circuit through the power conversion system;

[0053] Power is supplemented to the main power supply unit of the energy storage power station based on the target replacement list.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] 1. In this invention, the solution calculates the cavity length and cavity height of the subsurface cavity through acoustic response signals, and further generates shadow zone segments, thereby accurately locating high-risk energy storage units affected by underground cavities. This method makes the hidden underground structural problems explicit and transforms them into quantifiable risk indicators, so that capacity planning no longer relies solely on rated parameters, but is modified in combination with the actual operating environment, thus improving the reliability of power plant operation safety.

[0056] 2. In this invention, the planned discharge depth is reduced by using the cavity risk coefficient, and then the available capacity of high-risk units is calculated. The power station can reserve sufficient safety margin in risky environments to avoid battery aging acceleration or thermal runaway due to excessive discharge depth. The corrected capacity planning can truly reflect the operational capability of the power station when affected by underground cavities, eliminating the hidden dangers of insufficient capacity redundancy and delayed accident response.

[0057] 3. In this invention, by dispatching the low-risk unit with the optimal location to supplement power while the high-risk unit exits the main power supply circuit, the overall output capacity of the power station is ensured to be no less than the original level. This substitution strategy based on spatial location and capacity matching not only realizes the real-time reconfiguration of the power supply array of the power station, but also further optimizes the power configuration, discharge depth allocation and reserve margin under the corrected effective total capacity, so that the power station can still maintain stable power supply in the event of sudden accidents or load fluctuations, and enhances the flexibility and emergency response capability of the power grid dispatch. Attached Figure Description

[0058] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0059] Figure 1 This is a flowchart illustrating a method for capacity planning and configuration optimization of a grid-type energy storage power station based on multi-source data, provided in an embodiment of the present invention. Detailed Implementation

[0060] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0061] Example: This example provides a method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data. See [link to example]. Figure 1 Specifically, including:

[0062] S1. Calculate the cavity length and cavity height of the subsurface cavity based on the acoustic response signal of the corridor surface structure;

[0063] In an embodiment of the present invention, calculating the cavity length and cavity height of the subsurface cavity based on the acoustic response signal of the corridor surface structure includes:

[0064] Locate the target corridor for the energy storage power station cabin;

[0065] Inject a low-amplitude square wave excitation current into the grounded conductor electrical circuit of the target corridor;

[0066] Under the action of a low-amplitude square wave excitation current, acoustic wave sensors are used to collect acoustic wave response signals of the corridor surface structure.

[0067] If the acoustic sensor collects the secondary acoustic echo reflected by the subsurface cavity from the acoustic response signal, then the time delay between the secondary acoustic echo and the low-amplitude square wave excitation current is recorded.

[0068] Specifically, based on the corridor numbers marked in the site design drawings, the corridor area formed by the arrangement of the corridor-type energy storage containers to be tested is determined, and this area is defined as the target corridor. When injecting a low-amplitude square wave excitation current into the electrical circuit of the grounding conductor of the target corridor, a closed loop is first constructed between the grounding conductor of the target corridor and the signal generation module of the test equipment. The signal generation module is set and output with a peak value of 1A and a pulse width of 50ms, and the low-amplitude square wave excitation current is injected into the electrical circuit formed by the grounding conductor system of the target corridor. When using acoustic wave sensors to collect the acoustic wave response signal of the corridor surface structure, piezoelectric acoustic wave sensors are deployed along the length of the target corridor surface at intervals not exceeding 0.5 meters. Upon entry, all acoustic sensors are simultaneously activated to continuously collect signals covering at least the maximum theoretical length of the target corridor corresponding to the sound wave propagation time. For example, if the target corridor is 100 meters long, the continuous collection time should be no less than 0.59 seconds to obtain the acoustic vibration signal generated by the current excitation of the target corridor's surface structure. If the signals collected by the acoustic sensors contain a secondary pulse signal with a similar shape to the low-amplitude square wave excitation current pulse, an amplitude that decays, and a fixed time difference from the excitation current pulse, i.e., a secondary acoustic echo formed by reflection through the subsurface cavity, the difference between the triggering time of the secondary acoustic echo and the triggering time of the low-amplitude square wave excitation current is recorded by timestamp, and this difference is defined as the time delay between the secondary acoustic echo and the low-amplitude square wave excitation current.

[0069] Specifically, the target corridor refers to a specific area within an energy storage power station used for the installation and operation of battery units. It is a longitudinally arranged passageway for multiple energy storage units, facilitating electrical connections, ventilation, and thermal management between the compartments. The grounding conductor electrical circuit refers to the grounding system connecting each compartment of the energy storage power station, ensuring the electrical safety and stable operation of the battery compartments. Through this circuit, the grounding system of the energy storage unit is connected to the main electrical circuit, forming a closed electrical system to prevent the risk of electric shock caused by electrical faults. The secondary acoustic echo reflected by the subsurface cavity refers to the acoustic wave signal that is reflected again by the interface of the underground cavity during propagation and then detected by the receiving sensor. This reflection is caused by the subsurface cavity. After the sound wave is emitted from the sensor, reflected by the cavity, and returns after a time delay, it forms a secondary echo signal. By measuring the time delay of this echo, the size and location of the cavity can be calculated.

[0070] Specifically, subsurface cavities refer to naturally evolved underground voids beneath the concrete surface of the energy storage power station corridor, formed by the combined effects of micro-leakage of the liquid cooling system, rainfall infiltration, and gradual soil erosion. They are typically 10-30 cm wide and 5-15 cm high, extending longitudinally along the corridor. Due to the integrity of the concrete slab, these cavities are often difficult to detect through routine inspections. Subsurface cavities can form low-level flue gas diversion channels in the event of thermal runaway or coolant leakage, accelerating the lateral spread of fire and toxic gases.

[0071] The cavity length of the subsurface cavity is calculated based on the speed of sound and time delay. The formula for calculating the cavity length is as follows:

[0072]

[0073] In the formula, It is the cavity length. It's the speed of sound. It's a time delay;

[0074] Specifically, according to the basic principles of sound wave propagation, sound waves are reflected when propagating in a medium. When measuring the length of a subsurface cavity, the emitted sound wave propagates from the sensor to the surface of the cavity and is reflected back to the sensor. By calculating the time delay of the sound wave from emission to its return, the distance the sound wave travels can be estimated based on the known speed of sound. To obtain the cavity length, the propagation time needs to be divided by two. Because sound wave propagation is round-trip, the time delay represents the time it takes for the sound wave to travel to and from the destination. Therefore, only by dividing the total time by two can the one-way propagation distance, i.e., the cavity length, be obtained.

[0075] The acoustic response signal is converted to obtain the original voltage waveform of the corridor surface structure.

[0076] Perform a Fourier transform on the original voltage waveform to obtain the amplitude spectrum;

[0077] Search for the first and second main peaks of the amplitude spectrum within a preset fixed interval, and extract the first frequency corresponding to the first main peak and the second frequency corresponding to the second main peak in the amplitude spectrum, respectively.

[0078] The bandwidth is obtained by calculating the difference between the first frequency and the second frequency.

[0079] Specifically, when converting the acoustic response signal, the analog electrical signal output from the acoustic sensor is input to the analog-to-digital converter (ADC). The sampling frequency of the ADC is set to be at least five times the highest frequency of the acoustic response signal. The ADC converts the analog acoustic response signal into a digital sequence of original voltage waveforms. These original voltage waveforms are plotted with timestamps on the x-axis and voltage amplitude on the y-axis. When performing a Fourier transform on the original voltage waveform, a Fast Fourier Transform (FFT) algorithm is used. A time window of 1024 sampling points is selected to slide across the original voltage waveform, and the frequency distribution of the signal within each time window is calculated to obtain the result. The amplitude spectrum is represented by frequency on the x-axis and signal amplitude on the y-axis. When searching for the first and second main peaks of the amplitude spectrum within a preset fixed interval, the frequency search interval (e.g., 1kHz-5kHz) is pre-calibrated based on the physical characteristics of the subsurface cavity. The amplitude spectrum is traversed within the fixed interval, and the peak with the largest amplitude is extracted as the first main peak. The first frequency corresponding to the first main peak is recorded. The peak with the second largest amplitude and a frequency interval from the first main peak is then extracted as the second main peak, and the second frequency corresponding to the second main peak is recorded. When calculating the difference between the first and second frequencies, the difference between the second frequency and the first frequency is calculated, and the result is the bandwidth.

[0080] The cavity height of the subsurface cavity is calculated based on the sound velocity and bandwidth. The formula for calculating the cavity height is as follows:

[0081]

[0082] In the formula, It is the cavity height of the subsurface cavity. It's the speed of sound. It refers to bandwidth.

[0083] Specifically, based on the propagation characteristics of sound waves, the frequency and propagation speed of sound waves are related. When a sound wave encounters a subsurface cavity and resonates, standing waves of different frequencies are generated within the cavity, forming a certain bandwidth. The bandwidth reflects the size and vibration characteristics of the cavity. By measuring the bandwidth and the known speed of sound, the height of the cavity can be calculated. The relationship between bandwidth and sound wave propagation is inversely proportional; therefore, the height of the cavity can be calculated using the formula mentioned above. This formula is based on the speed of sound and bandwidth, using their physical relationship to estimate the vertical dimensions of the cavity.

[0084] S2. Calculate the cavity risk coefficient of the target corridor based on the cavity length and cavity height;

[0085] In an embodiment of the present invention, the cavity risk coefficient of the target corridor is calculated based on the cavity length and cavity height, including:

[0086] The ratio of the cavity length to the total length of the target corridor is calculated to obtain the cavity length percentage.

[0087] Obtain the safe threshold height for the cavity height;

[0088] The ratio of cavity height to safety threshold height is calculated to obtain the cavity height percentage;

[0089] Multiply the cavity length percentage by the cavity height percentage to obtain the cavity risk coefficient of the target corridor.

[0090] Specifically, the risk impact of subsurface cavities on target corridors is determined by the extent of their spread in the length dimension and the intensity of their effect in the height dimension. The cavity length ratio is calculated by the ratio of the cavity length to the total corridor length, and is used to quantify the extent of the cavity's extension along the corridor axis. A higher cavity length ratio indicates a wider coverage area of ​​the energy storage unit's bottom region, a longer path for heavy gas to propagate laterally along the corridor, and a greater threat range to surrounding energy storage units. The cavity height ratio is calculated by the ratio of the cavity height to the safety threshold height. The safety threshold height is set based on engineering standards such as soil bearing capacity and critical gas flow dimensions. A higher height ratio indicates that the cavity's development in the vertical direction is closer to the critical risk state, and its flow-guiding efficiency as a low-level flue gas channel and its hollowing-out weakening effect on the soil under the concrete slab are stronger. Multiplying the cavity length ratio and height ratio yields the risk coefficient because the actual harm of the risk is shaped by both the scope of influence and the intensity of the effect: the cavity length ratio characterizes the spatial scale of the risk coverage, while the cavity height ratio characterizes the destructive capacity of the risk itself. The product of the two can accurately reflect the comprehensive threat level of subsurface cavities to energy storage sites in terms of fire propagation speed and foundation stability.

[0091] It should be noted that the safe threshold height for the cavity is set based on the structural safety standards of the energy storage power station and the risk conditions that may lead to failure. During the design phase, a maximum permissible cavity height is determined by considering the load-bearing capacity of the target corridor, the flow characteristics of thermal runaway gases, and the required safety clearances for equipment. If the actual height of the cavity exceeds this safe threshold, it may lead to structural instability, affect the electrical connections and heat dissipation function of the compartment, and even accelerate the spread of fire and toxic gases. Therefore, the safe threshold height for the cavity is usually determined based on the power station's design standards, industry regulations, and the pressure conditions of the actual operating environment to ensure that the cavity will not damage or interfere with the equipment within the corridor, thereby maintaining the normal operation and safety of the power station.

[0092] In general, calculating the cavity risk coefficient of the target corridor can effectively assess the potential threat of subsurface cavities to the safety of energy storage power stations. Since subsurface cavities are often located at the bottom of the corridor, and their shape and size are affected by factors such as leakage from the liquid cooling system and rainwater infiltration, the size of the cavity directly affects gas diffusion, fire spread, and equipment stability. By combining the length and height of the cavity, it is possible not only to reflect the proportion of the cavity to the corridor, but also to determine whether there is a risk of excessive expansion of the cavity, and to provide timely warnings of potential structural instability or safety hazards.

[0093] S3. Calculate and update the discharge depth based on the cavity risk coefficient and the planned discharge depth in the power plant design parameter table;

[0094] In an embodiment of the present invention, the updated discharge depth is calculated based on the cavity risk coefficient and the planned discharge depth in the power plant design parameter table, including:

[0095] Obtain the planned discharge depth from the power plant design parameter table;

[0096] Based on the cavity risk coefficient, a linear derating calculation is performed on the planned discharge depth to obtain the updated discharge depth. The formula for calculating the updated discharge depth is as follows:

[0097]

[0098] In the formula, It is to update the depth of discharge. It is the planned discharge depth. It is the cavity risk factor. It is the sensitivity coefficient for depreciation.

[0099] Specifically, risks such as accelerated fire spread caused by subsurface cavities, electrical stress superposition due to foundation settlement, and temperature control deviations induced by changes in thermal boundaries increase the probability of energy storage unit failure. The planned discharge depth is set based on ideal operating conditions and does not consider the hidden threats posed by cavities. When the cavity risk factor increases, the energy storage unit needs to reserve more power to cope with emergency situations (such as active derating after a fire warning) while reducing the risk of battery aging and thermal runaway caused by deep discharge. The derating sensitivity coefficient, calibrated experimentally or through simulation, represents the degree of influence of a unit risk factor on the discharge depth and is strongly correlated with the battery type, cabin layout, and other operating conditions of the energy storage unit. (Formula) The constructed linear derating relationship quantifies the comprehensive risk of the cavity into a reduction in the depth of discharge: the higher the cavity risk coefficient, the greater the reduction, which forces the energy storage unit to reduce the power output of a single charge-discharge cycle, thereby reserving sufficient safety margin. This avoids failures caused by deep discharge superimposed on structural stress and adapts to the emergency response needs under accelerated fire spread, so that the updated depth of discharge accurately matches the actual risk environment, ensuring the safe operation and life extension of the energy storage unit.

[0100] Specifically, the power station design parameter table refers to a technical document compiled by the designer during the planning and design phase of the energy storage power station, based on multiple dimensions such as battery technical specifications, grid access standards, and application scenario requirements. This document integrates core operating parameters such as the rated capacity, power configuration, and charging and discharging strategies of the energy storage unit, serving as a theoretical benchmark for the performance boundaries and operating modes of the energy storage system in the initial design of the power station. The planned discharge depth refers to the maximum preset discharge ratio for the energy storage unit extracted from this document. This ratio is set based on ideal operating conditions, considering only conventional factors such as battery chemical characteristics and normal charge and discharge cycle life loss. It does not take into account the implicit risks to the safe discharge of the energy storage unit, such as the accelerated spread of fire caused by subsurface cavities and the changes in electrical stress caused by foundation settlement. Therefore, it is necessary to dynamically correct this preset value by combining it with the cavity risk coefficient to make the discharge depth adapt to the risk environment in actual operation.

[0101] S4. Generate the shadow zone of the target corridor, and mark the energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow zone.

[0102] In an embodiment of the present invention, generating the shadow band segment of the target corridor includes:

[0103] Acoustic sensors were deployed at both ends of the target corridor, and low-amplitude square wave excitation currents were injected synchronously. The end time delays were recorded, where the end time delay represents the... The time delay of the secondary acoustic echo received by the acoustic wave sensor at each endpoint;

[0104] Specifically, acoustic wave sensors are placed at both ends of the target corridor. Based on the corridor's elongated spatial shape, low-amplitude square wave excitation currents are injected simultaneously at both ends, causing the current-excited structural vibration sound waves to propagate uniformly within the corridor's concrete and the underlying soil. When the sound waves encounter the interface of the subsurface cavity, reflection occurs due to the abrupt change in medium, forming a secondary acoustic echo. The data is then recorded. The end time delay of each endpoint is the time difference between when the acoustic sensor at that endpoint receives the secondary acoustic echo and when the excitation current triggers.

[0105] Based on the speed of sound and end-point time delay, the horizontal distance from the endpoint of the target corridor to the nearest interface of the subsurface cavity is calculated. The formula for calculating the horizontal distance is as follows:

[0106]

[0107] In the formula, It is the first The horizontal distance from each endpoint to the nearest interface of the subsurface cavity It's the speed of sound. It is the first End time delay at each endpoint, It is an endpoint identifier;

[0108] Specifically, analyzing the physical process of sound wave propagation, the structural vibration sound wave excited by the low-amplitude square wave excitation current originates from the sound wave sensor at the endpoint and propagates uniformly in the corridor concrete and the soil below. When the sound wave contacts the nearest interface of the subsurface cavity, reflection occurs due to the abrupt change in medium properties, and the resulting secondary sound echo is received by the sensor at the same endpoint. The propagation trajectory of the sound wave is a round trip path from the endpoint to the nearest interface of the cavity and back to the endpoint, and its total displacement is equal to the product of the sound velocity and the end time delay. Since the outward and return distances of the round trip path are equal, both being the horizontal distance from the endpoint to the nearest interface of the subsurface cavity, the total displacement corresponds to... Based on the distance formula derived from uniform motion, the horizontal distance of a one-way journey can be obtained by dividing the total displacement by 2.

[0109] Set the mileage of the first endpoint of the target corridor to 0;

[0110] The horizontal distance from the first endpoint to the nearest interface of the subsurface cavity is taken as the cavity starting point mileage of the subsurface cavity, where the cavity starting point mileage represents the distance from the first endpoint to the cavity starting point.

[0111] The cavity endpoint mileage of the subsurface cavity is obtained by subtracting the total length of the corridor from the horizontal distance from the second endpoint to the nearest interface of the subsurface cavity. The cavity endpoint mileage represents the distance from the first endpoint to the cavity endpoint.

[0112] The shaded zone segment of the target corridor is generated based on the cavity start mileage and cavity end mileage, where the expression of the shaded zone segment is as follows:

[0113]

[0114] In the formula, It is the starting mileage of the cavity. It is the final mileage of the cavity. This is the safety buffer width.

[0115] Specifically, in the target corridor, the mileage of the northern endpoint is first set to 0, so that the corridor forms a unidirectionally increasing mileage coordinate system. Then, the horizontal distance from the first endpoint to the nearest interface of the subsurface cavity is taken as the cavity's starting mileage, which represents the cavity's starting position in the corridor coordinate system. The total length of the corridor is then subtracted from the horizontal distance from the second endpoint to the nearest interface of the cavity to obtain the cavity's ending mileage, thus determining the actual range of the cavity extending along the corridor. Considering that the surrounding soil may continue to erode or have additional effects in the event of fire or gas leakage, a safety buffer width is extended in both directions of the corridor at both ends of the cavity. The starting coordinate is subtracted from the safety buffer width, and the ending coordinate is added to the safety buffer width to form a shadow zone. The shadow zone covers the subsurface cavity itself and the buffer area with potential diffusion or collapse, thus accurately marking the shadow zone section of the target corridor threatened by the cavity.

[0116] Specifically, the subsurface cavity nearest interface refers to the cavity boundary location where sound waves first encounter and are reflected when propagating below the corridor. This location represents the interface between the cavity entity and the surrounding soil closest to the corridor. The cavity start mileage refers to the horizontal distance from the first endpoint to the subsurface cavity nearest interface in the longitudinal coordinate system of the target corridor. This distance is used to mark the starting position of the cavity in the corridor. The cavity end mileage refers to the coordinates calculated with the first endpoint as zero, combined with the total length of the corridor and the horizontal distance from the second endpoint to the cavity nearest interface. This coordinate is used to mark the ending position of the cavity in the corridor. The target corridor's shaded zone refers to the section obtained by taking the cavity start mileage and cavity end mileage as the core interval and adding safety buffer widths at both ends. This section covers the cavity body and its potential impact range, used to represent the corridor area affected by the cavity, providing a precise spatial boundary for risk analysis and capacity planning.

[0117] It should be noted that the safety buffer width is a compensation distance determined comprehensively based on factors such as the uncertainty of sound wave propagation, the error of the sampling accuracy of the measuring equipment, the heterogeneity of the underground soil, and the randomness of the cavity boundary morphology. This width is obtained by conducting multiple statistical analyses on the end time delay data measured in experiments, combined with the range of changes in the elastic wave velocity of the soil and the expansion and fluctuation of the cavity boundary in typical engineering examples, and adopting the principle of safety factor amplification. Thus, an additional distance is added at both ends of the cavity starting point and cavity ending point, so that the shaded zone not only covers the physical range of the real cavity, but also includes the boundary expansion range caused by potential errors.

[0118] In embodiments of the present invention, marking energy storage units of an energy storage power station as high-risk or low-risk units based on shaded zone sections includes:

[0119] Read the longitudinal center coordinates of each energy storage unit;

[0120] Specifically, each energy storage unit has a clear geometric boundary in the corridor direction. Its longitudinal center coordinates can be obtained by averaging the geometric coordinates of the front and rear ends of the unit in the corridor direction. The value of the longitudinal center coordinates is calibrated by establishing a longitudinal mileage reference system with the starting point of the corridor as the zero point, so that the center position of all energy storage units can be represented on a unified coordinate axis.

[0121] If the longitudinal center coordinates belong to the shaded area, the energy storage unit is marked as a high-risk unit; otherwise, the energy storage unit is marked as a low-risk unit.

[0122] Specifically, comparing the longitudinal center coordinates of energy storage units with the shaded zone and determining their risk level is based on the physical impact of subsurface cavities on load transfer and accident propagation paths above the corridor. Since the completion of a subsurface cavity reduces the bearing capacity of the foundation and facilitates rapid flow of low-level flue gas or fire plumes, energy storage units located directly above this zone are more susceptible to structural instability, thermal runaway, or gas accumulation. Therefore, their spatial location directly determines the intensity of potential risks. By using the shaded zone as the boundary, it is possible to accurately identify which energy storage units are within the high-risk exposure range and mark them as high-risk units. Energy storage units outside the zone are considered low-risk units. This allows for the correspondence between spatial distribution and risk levels in capacity planning and safety redundancy configuration, ensuring the scientific rigor and relevance of the assessment results.

[0123] S5. Calculate the sum of high-risk power of all high-risk cells, and calculate the sum of high-risk capacity of all high-risk cells based on the updated discharge depth.

[0124] In embodiments of the present invention, the sum of high-risk power of all high-risk cells is calculated, and the sum of high-risk capacity of all high-risk cells is calculated based on the updated depth of discharge, including:

[0125] The rated power of all high-risk units is summed to obtain the total high-risk power.

[0126] The available capacity of the high-risk cell is obtained by multiplying the updated depth of discharge by the rated energy of the high-risk cell.

[0127] Sum all available capacity to obtain the total high-risk capacity.

[0128] Specifically, the total high-risk power refers to the sum of the rated power of all units identified as high-risk, reflecting the overall power output capacity of high-risk units under rated operating conditions; the rated energy of a high-risk unit refers to the total theoretical electrical energy that a high-risk unit can release at the standard discharge depth specified in the design parameter table, characterizing the energy storage capacity of the unit under ideal operating conditions; the usable capacity of a high-risk unit refers to the actual usable electrical energy obtained by introducing an updated discharge depth correction factor on the basis of the rated energy, reflecting the true operable capacity of the high-risk unit when affected by risk factors such as subsurface cavities; the total high-risk capacity refers to the result of summing the usable capacity of all high-risk units, characterizing the overall usable electrical energy boundary of the entire energy storage power station under risk correction conditions.

[0129] Specifically, calculating the sum of high-risk power and the sum of high-risk capacity aims to combine the electrical performance of high-risk units with cavity risk factors, thereby obtaining capacity assessment results that better reflect actual operating conditions. By summing the rated power of all high-risk units, the overall power capacity of these units under normal operating conditions can be reflected. Multiplying the updated depth of discharge by the rated capacity of the high-risk units yields the available capacity of each high-risk unit after considering risk corrections. Summing the available capacity of all high-risk units, the resulting sum of high-risk capacity comprehensively characterizes the capacity safety boundary and available upper limit of the entire power plant under the influence of subsurface cavity risks. This method not only explicitly introduces risk factors into capacity assessment but also provides quantitative basis for subsequent configuration optimization and safe scheduling.

[0130] S6. Based on the cavity center coordinates of the subsurface cavity and the sum of low-risk units and high-risk power, the configuration optimization of the energy storage power station is determined, and the effective total capacity of the energy storage power station is planned according to the sum of high-risk capacity.

[0131] In embodiments of the present invention, the configuration optimization of the energy storage power station is determined based on the cavity center coordinates of the subsurface cavity and the sum of low-risk units and high-risk power, including:

[0132] The cavity center coordinates of the subsurface cavity are determined based on the cavity start mileage and cavity end mileage.

[0133] Specifically, the cavity's starting and ending mileages are used as inputs. The cavity's center coordinates are calculated on the longitudinal mileage coordinates of the corridor. The cavity's center coordinates are equal to the cavity's starting mileage plus the cavity's ending mileage, divided by 2. The data is recorded in meters as the benchmark for subsequent distance measurements.

[0134] The absolute difference between the longitudinal center coordinates of the low-risk unit and the cavity center coordinates is calculated to obtain the longitudinal distance between the longitudinal center coordinates of the low-risk unit and the cavity center coordinates.

[0135] All low-risk units are sorted according to their vertical distance to obtain a preliminary replacement list;

[0136] The target substitute list is obtained by extracting the preliminary substitute list based on the sum of high-risk power.

[0137] Specifically, firstly, based on the longitudinal distance of all low-risk energy storage units to the center of the subsurface cavity, the low-risk energy storage units are arranged in ascending order of longitudinal distance to form a preliminary replacement list. The energy storage units at the front represent those whose locations are closer to the cavity center and are more suitable as replacement targets. The total high-risk power is used as the demand benchmark for replacement capacity. The rated power of the low-risk units is added to the preliminary replacement list until the accumulated value is greater than or equal to the total high-risk power. The low-risk units within this range are then selected to form the final target replacement list. This ensures that the power scale of the replacement units can cover the total high-risk power and that they are located as close as possible to the risk cavity area in terms of spatial location, thereby improving the reliability and efficiency of the replacement configuration.

[0138] High-risk units are switched to hot standby mode by exiting the main circuit through the power conversion system;

[0139] Power is supplemented to the main power supply unit of the energy storage power station based on the target replacement list.

[0140] Specifically, upon receiving the shadow zone determination and target replacement list, the power conversion system first issues a switching command to disconnect high-risk units located within the shadow zone from the main power supply circuit and switch them to hot standby mode. The actions performed include opening AC or DC side switches, stopping power output to the bus, maintaining the DC bus pre-charge voltage for easy reactivation, and marking the operating mode as hot standby in the energy management system, recording the switching time and allowable discharge depth. This achieves rapid isolation and reversible standby for units affected by risk. Subsequently, according to the target replacement list, low-risk units are sequentially connected to the main power supply unit. The actions performed include completing voltage, frequency, and phase synchronization before grid connection, closing and closing parallel switches, increasing output power according to a preset ramp rate, and sharing load with grid-connected units. Simultaneously, the energy management system updates the parallel system member list and total online power, ensuring that the total online power is not lower than the level before the high-risk units were removed. These two steps, by rearranging the online and standby configuration of the parallel system without changing the power plant's hardware scale, achieve real-time reconfiguration of the power supply array and minimum set replenishment of power gaps, balancing safety isolation and power supply capacity maintenance, reflecting configuration optimization based on risk perception.

[0141] Capacity planning is performed on the effective total capacity of the energy storage power station based on the sum of high-risk capacities: the sum of high-risk capacities represents the portion of the capacity with potential failure risks during the operation of the grid-type energy storage power station; then, when calculating the effective total capacity of the grid-type energy storage power station, the rated total capacity of all energy storage units in the grid-type energy storage power station is subtracted from the sum of high-risk capacities to obtain the corrected effective total capacity; after obtaining the corrected effective total capacity, the total power is first redistributed according to the overall rated power demand of the power station, adjusting the power ratio originally configured according to the total rated capacity to the power ratio based on the corrected capacity, to ensure that the actual output power matches the safe and available capacity. The power supply is then configured; subsequently, the discharge depth of each energy storage unit in the power station is reset and distributed according to the corrected capacity, so that each energy storage unit can maintain a reasonable charge and discharge cycle during long-term operation, avoiding over-discharge due to overestimation of the capacity of the energy storage unit; finally, based on the corrected total capacity and the uncertainties in the operating environment, an appropriate reserve margin is set, reserving a portion of the corrected capacity as a safety redundancy, so that power can be supplemented in time during sudden accidents or load fluctuations. The resulting power configuration, discharge depth allocation and reserve margin adjustment can ensure that the power station can still maintain stable, safe and efficient operation under the condition of capacity reduction.

[0142] It should be noted that the multi-source data of this invention includes structural detection data and power plant operating parameter data. The structural detection data includes acoustic response signals, echo time difference, reflection data under the concrete slab, and detection results from ground-penetrating radar and foundation settlement sensors. The power plant operating parameter data includes the planned discharge depth, rated power, and rated energy in the power plant design parameter table. These data come from different monitoring channels and operational stages, forming the multi-source data foundation upon which this invention relies for capacity planning and configuration optimization.

[0143] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data, characterized in that, Includes the following steps: S1. Calculate the cavity length and cavity height of the subsurface cavity based on the acoustic response signal of the corridor surface structure; The specific steps for calculating the cavity length and cavity height of the subsurface cavity are as follows: Locate the target corridor for the energy storage power station cabin; Inject a low-amplitude square wave excitation current into the grounded conductor electrical circuit of the target corridor; Under the action of a low-amplitude square wave excitation current, acoustic wave sensors are used to collect acoustic wave response signals of the corridor surface structure. If the acoustic sensor collects the secondary acoustic echo reflected by the subsurface cavity from the acoustic response signal, then the time delay between the secondary acoustic echo and the low-amplitude square wave excitation current is recorded. Calculate the cavity length of the subsurface cavity based on sound speed and time delay; The acoustic response signal is converted to obtain the original voltage waveform of the corridor surface structure. Perform a Fourier transform on the original voltage waveform to obtain the amplitude spectrum; Search for the first and second main peaks of the amplitude spectrum within a preset fixed interval, and extract the first frequency corresponding to the first main peak and the second frequency corresponding to the second main peak in the amplitude spectrum, respectively. The bandwidth is obtained by calculating the difference between the first frequency and the second frequency. Calculate the cavity height of the subsurface cavity based on sound velocity and bandwidth; S2. Calculate the cavity risk coefficient of the target corridor based on the cavity length and cavity height; S3. Calculate and update the discharge depth based on the cavity risk coefficient and the planned discharge depth in the power plant design parameter table; S4. Generate the shadow zone of the target corridor, and mark the energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow zone. S5. Calculate the sum of high-risk power of all high-risk cells, and calculate the sum of high-risk capacity of all high-risk cells based on the updated discharge depth. The specific steps for calculating the total high-risk power and the total high-risk capacity are as follows: The rated power of all high-risk units is summed to obtain the total high-risk power. The available capacity of the high-risk cell is obtained by multiplying the updated depth of discharge by the rated energy of the high-risk cell. Sum all available capacity to obtain the total high-risk capacity; S6. Based on the cavity center coordinates of the subsurface cavity and the sum of low-risk units and high-risk power, the configuration optimization of the energy storage power station is determined, and the effective total capacity of the energy storage power station is planned according to the sum of high-risk capacity.

2. The method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data according to claim 1, characterized in that, The cavity risk coefficient of the target corridor is calculated based on the cavity length and cavity height, including: The ratio of the cavity length to the total length of the target corridor is calculated to obtain the cavity length percentage. Obtain the safe threshold height for the cavity height; The ratio of cavity height to safety threshold height is calculated to obtain the cavity height percentage; Multiply the cavity length percentage by the cavity height percentage to obtain the cavity risk coefficient of the target corridor.

3. The method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data according to claim 1, characterized in that, Based on the cavity risk coefficient and the planned discharge depth in the power plant design parameter table, the updated discharge depth is calculated, including: Obtain the planned discharge depth from the power plant design parameter table; The updated discharge depth is obtained by linearly reducing the planned discharge depth based on the cavity risk coefficient.

4. The method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data according to claim 2, characterized in that, Generate the shadow band segment of the target corridor, including: Acoustic sensors were deployed at both ends of the target corridor, and low-amplitude square wave excitation currents were injected synchronously. The end time delays were recorded, where the end time delay represents the... The time delay of the secondary acoustic echo received by the acoustic wave sensor at each endpoint; Based on the speed of sound and end time delay, calculate the horizontal distance from the endpoint of the target corridor to the nearest interface of the subsurface cavity; Set the mileage of the first endpoint of the target corridor to 0; The horizontal distance from the first endpoint to the nearest interface of the subsurface cavity is taken as the cavity starting point mileage of the subsurface cavity, where the cavity starting point mileage represents the distance from the first endpoint to the cavity starting point. The cavity endpoint mileage of the subsurface cavity is obtained by subtracting the total length of the corridor from the horizontal distance from the second endpoint to the nearest interface of the subsurface cavity. The cavity endpoint mileage represents the distance from the first endpoint to the cavity endpoint. The shadow zone of the target corridor is generated based on the cavity start mileage and cavity end mileage.

5. The method for capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data according to claim 1, characterized in that, Based on the shaded zone, energy storage units in the energy storage power station are marked as high-risk or low-risk units, including: Read the longitudinal center coordinates of each energy storage unit; If the longitudinal center coordinates belong to the shaded area, 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 capacity planning and configuration optimization of grid-type energy storage power stations based on multi-source data according to claim 4, characterized in that, Based on the cavity center coordinates of the subsurface cavity and the sum of low-risk and high-risk power, the configuration optimization of the energy storage power station is determined, including: The cavity center coordinates of the subsurface cavity are determined based on the cavity start mileage and cavity end mileage. The absolute difference between the longitudinal center coordinates of the low-risk unit and the cavity center coordinates is calculated to obtain the longitudinal distance between the longitudinal center coordinates of the low-risk unit and the cavity center coordinates. All low-risk units are sorted according to their vertical distance to obtain a preliminary replacement list; The target replacement list is obtained by truncating the preliminary replacement list based on the sum of high-risk power. High-risk units are switched to hot standby mode by exiting the main circuit through a power conversion system; Power is supplemented to the main power supply unit of the energy storage power station based on the target replacement list.

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