Energy storage participation standby market reward function establishment method and related device

Abstract

The embodiment of the invention provides an energy storage participation standby market reward function establishment method and a related device, and relates to the field of energy storage management and control, and the method comprises the steps: obtaining the historical charging and discharging data of each battery cell, constructing a battery dQ / dV curve for each battery cell based on the historical charging and discharging data of the battery cell, obtaining each wave crest meeting a preset condition based on the battery dQ / dV curve, determining wave crest features corresponding to each wave crest and constructing a feature vector, calculating a mahalanobis distance between the feature vector and a preset feature vector, calculating standby reward coefficients of the battery cell based on the mahalanobis distance, and determining a charging and discharging strategy based on each standby reward coefficient, so that the accuracy of energy storage control is improved, and the attenuation of the service life of the battery cell is delayed.

Description

Technical Field

[0001] This invention relates to the field of energy storage management, and more specifically, to a method and related apparatus for establishing a reward function for energy storage participation in the standby market. Background Technology

[0002] With the advancement of new power system construction and the continuous increase in renewable energy penetration, energy storage systems are playing an increasingly crucial role in grid peak shaving, frequency regulation, and backup support. To maximize the economic efficiency of energy storage assets, multi-market joint operation strategies have become a key focus of current research and engineering applications. Among these, the electricity market and the backup market are the two typical scenarios with the highest participation from energy storage.

[0003] In the electric energy market, energy storage engages in arbitrage through methods such as "low charging and high discharging," requiring actual charge and discharge operations. This significantly increases the battery's cycle life, accelerating capacity decay and internal resistance growth. In the standby market, however, energy storage only needs to commit to a certain power regulation capability and maintain a corresponding capacity margin for a future period to obtain compensatory profits. Whether it is actually utilized is uncertain; if not triggered, no real energy exchange occurs, resulting in minimal impact on battery life.

[0004] Since the total lifecycle cost of an energy storage system is closely related to its state of health (SOH), and the capacity decay process exhibits non-linear characteristics—especially after reaching the "knee point," where capacity drops drastically—a reasonable scheduling strategy should consider battery aging dynamics: when the battery is in good health, it should prioritize participation in the energy market to obtain high returns; as aging deepens, it should gradually shift to participating in the standby market to reduce cycle losses, delay the arrival of the degradation inflection point, thereby extending the overall service life and improving total lifecycle returns.

[0005] Therefore, in optimization-type scheduling models, battery health degradation or incentive mechanisms need to be quantified into a computable objective function term. Existing technologies have utilized parameters such as battery capacity, internal resistance, and cycle count to assess health status and incorporated them into energy management systems. However, these indicators often fail to sensitively reflect early-stage material degradation behavior at the microscopic spectral level and lack clear physical mechanism support.

[0006] Therefore, it is necessary to propose a method for constructing a reward function for energy storage participation in the standby market based on the capacity increment curve, so as to promote the evolution of energy storage systems from passive response to proactive health management. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related apparatus for establishing a reward function for energy storage participation in the standby market, which can improve the accuracy of energy storage control.

[0008] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for establishing a reward function for energy storage participation in the standby market, the method comprising: Obtain historical charge and discharge data for each battery cell; For each cell, a battery dQ / dV curve is constructed based on the cell's historical charge and discharge data; Based on the battery dQ / dV curve, obtain each peak that meets the preset conditions; Determine the peak features corresponding to each peak and construct a feature vector; Calculate the Mahalanobis distance between the feature vector and the preset feature vector; The reserve bonus coefficient of the battery cell is calculated based on the Mahalanobis distance; The charging and discharging strategy is determined based on each reserve reward coefficient.

[0009] In an optional implementation, the step of constructing the battery dQ / dV curve for each cell based on the cell's historical charge-discharge data includes: For each battery cell, the first historical charge-discharge data with a charge-discharge rate less than a preset charge-discharge rate is taken from the historical charge-discharge data of the battery cell; The first historical charge-discharge data is smoothed using a filter to obtain the second historical charge-discharge data. dQ / dV values ​​are calculated based on the second historical charge / discharge data; The capacity and voltage of adjacent data points in the second historical charge and discharge data are differentially calculated, and the average voltage of the adjacent data points is assigned to obtain the battery dQ / dV curve.

[0010] In an optional implementation, the step of determining the peak features corresponding to each peak and constructing a feature vector includes: For each of the wave peaks, determine the peak height, peak voltage, full width at half maximum (FWHM), and peak area integral. The peak height, peak voltage, full width at half maximum (FWHM), and peak area integral are used as the peak features corresponding to the peak. Feature vectors are constructed based on the peak features corresponding to each peak.

[0011] In an optional implementation, the step of determining the peak height, peak voltage, full width at half maximum (FWHM), and peak area integral of the peak includes: The dQ / dV value corresponding to the peak is determined from the battery dQ / dV curve and used as the peak height; The voltage value corresponding to the wave peak is determined as the peak voltage; Determine half of the peak height as the reference height; Calculate the width of the wave crest at the reference height, as the half-width at half-maximum (WHM); Numerical integration is performed on the peak interval corresponding to the half-width at half-maximum (WHM) to obtain the peak area integral.

[0012] In an optional implementation, the Mahalanobis distance between the feature vector and the preset feature vector is calculated using the following formula: ; Where x is an eigenvector, y is a preset eigenvector, and A is the covariance matrix formed by the eigenvector and the preset eigenvector.

[0013] In an optional implementation, the step of calculating the reserve bonus coefficient of the battery cell based on the Mahalanobis distance includes: Determine the upper and lower limits of the reserve reward coefficient; Determine the Mahalanobis distance threshold and growth factor; The reserve reward coefficient of the battery cell is calculated based on the upper limit of the reserve reward coefficient, the lower limit of the reserve reward coefficient, the Mahalanobis distance threshold, the growth factor, and the Mahalanobis distance.

[0014] In an optional implementation, the backup reward coefficient satisfies the following formula: ; Where K is the reserve reward coefficient. This serves as the lower limit for the reserve reward coefficient. This is the upper limit of the backup reward coefficient. As a growth factor, The Mahalanobis distance, This is the Mahalanobis distance threshold.

[0015] In an optional implementation, the step of determining the charging and discharging strategy based on each of the backup reward coefficients includes: The aforementioned reserve reward coefficients are sorted in descending order; Obtain the target reserve reward coefficients of the top-ranked items from the sorted list; The battery cells corresponding to the target reserve reward coefficient will be included in the reserve market.

[0016] Secondly, embodiments of this application provide an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for establishing the energy storage participation reserve market reward function.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for establishing the reward function for energy storage participation in the standby market.

[0018] This application has the following beneficial effects: This application acquires historical charge and discharge data for each battery cell, constructs a battery dQ / dV curve based on the historical charge and discharge data for each cell, obtains peaks that meet preset conditions based on the battery dQ / dV curve, determines the peak characteristics corresponding to each peak and constructs a feature vector, calculates the Mahalanobis distance between the feature vector and the preset feature vector, calculates the reserve reward coefficient of the battery cell based on the Mahalanobis distance, and determines the charge and discharge strategy based on each reserve reward coefficient, thereby improving the accuracy of energy storage control and delaying the degradation of battery cell life. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A block diagram of an electronic device provided in an embodiment of the present invention; Figure 2 This is one of the flowcharts illustrating a method for establishing a reward function for energy storage participation in the standby market, provided by an embodiment of the present invention. Figure 3 A second flowchart illustrating a method for establishing a reward function for energy storage participation in the standby market, provided as an embodiment of the present invention; Figure 4 The third flowchart illustrates a method for establishing a reward function for energy storage participation in the standby market, as provided in an embodiment of the present invention. Figure 5 The fourth flowchart illustrates a method for establishing a reward function for energy storage participation in the standby market, as provided in an embodiment of the present invention. Figure 6 The fifth flowchart illustrates a method for establishing a reward function for energy storage participation in the standby market, as provided in an embodiment of the present invention. Figure 7 This is a structural block diagram of an energy storage participation reward function establishment device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] In the description of this invention, it should be noted that if terms such as "upper," "lower," "inner," or "outer" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed, they are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0025] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0026] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0027] Extensive research by the inventors revealed that existing technologies utilize parameters such as battery capacity, internal resistance, and cycle count to assess battery health and incorporate them into energy management systems. However, these indicators often fail to sensitively reflect early-stage material degradation at the microscopic spectral level and lack clear physical mechanisms to support their findings.

[0028] In view of the above-mentioned problems, this embodiment provides a method and related apparatus for establishing a reward function for energy storage participation in the standby market. This method can acquire historical charge / discharge data of each battery cell, construct a battery dQ / dV curve for each cell based on the historical charge / discharge data, obtain peaks that meet preset conditions based on the battery dQ / dV curve, determine the peak characteristics corresponding to each peak and construct a feature vector, calculate the Mahalanobis distance between the feature vector and the preset feature vector, calculate the standby reward coefficient of the battery cell based on the Mahalanobis distance, and determine the charge / discharge strategy based on each standby reward coefficient. This improves the accuracy of energy storage control and delays the degradation of battery cell lifespan. The solution provided in this embodiment will be described in detail below.

[0029] This embodiment provides an electronic device capable of controlling energy storage. In one possible implementation, the electronic device can be a user terminal, such as, but not limited to, a server, smartphone, personal computer (PC), tablet computer, personal digital assistant (PDA), mobile internet device (MID), etc.

[0030] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of the electronic device 100 provided in the embodiments of this application. The electronic device 100 may further include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0031] The electronic device 100 includes an energy storage participation reserve market reward function establishment device 110, a memory 120, and a processor 130.

[0032] The components of the memory 120 and processor 130 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The energy storage participation in the standby market reward function establishment device 110 includes at least one software function module that can be stored in the memory 120 in the form of software or firmware or embedded in the operating system (OS) of the electronic device 100. The processor 130 is used to execute the executable modules stored in the memory 120, such as the software function modules and computer programs included in the energy storage participation in the standby market reward function establishment device 110.

[0033] The memory 120 may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 120 is used to store programs, and the processor 130 executes the programs after receiving execution instructions.

[0034] Please refer to Figure 2 , Figure 2 For application Figure 1 The flowchart below describes a method for establishing a reward function for energy storage participation in the standby market for an electronic device 100. The method includes detailed explanations of each step.

[0035] S201: Obtain historical charge and discharge data for each battery cell.

[0036] S202: For each cell, construct the battery dQ / dV curve based on the cell's historical charge and discharge data.

[0037] S203: Based on the battery dQ / dV curve, obtain each peak that meets the preset conditions.

[0038] S204: Determine the peak features corresponding to each peak and construct the feature vector.

[0039] S205: Calculate the Mahalanobis distance between the feature vector and the preset feature vector.

[0040] S206: Calculate the reserve bonus coefficient of the battery cell based on Mahalanobis distance.

[0041] S207: Determine the charging and discharging strategy based on each reserve reward coefficient.

[0042] The dQ / dV curve of each cell is constructed by using historical charge and discharge data of the cells. Multiple peak features that meet preset conditions, such as peak height, peak position, full width at half maximum (FWHM), and peak area, are extracted from the dQ / dV curve. These features reflect the state of different electrochemical reaction channels.

[0043] The preset conditions can be set to peak height > 0.1 and minimum distance between peaks 50 data points.

[0044] The peak features of these multiple peaks are integrated into a feature vector to characterize the overall aging degree of the current battery cell.

[0045] The current feature vector of the battery cell, which contains multiple peaks, is compared with the preset feature vector when the battery cell is newly manufactured. Mahalanobis distance is used to measure the overall deviation between the two.

[0046] The reserve bonus coefficient of the current battery cell is calculated based on the Mahalanobis distance. Finally, the Mahalanobis distance is substituted into a Sigmoid function to calculate the reserve bonus coefficient.

[0047] In subsequent scheduling strategies, the charging and discharging strategies are scheduled based on the reserve reward coefficient.

[0048] There are multiple ways to construct the battery dQ / dV curve based on the historical charge and discharge data of each cell. In one implementation method, such as... Figure 3 As shown, it includes the following steps: S301: For each cell, take the first historical charge and discharge data whose charge and discharge rate is less than the preset charge and discharge rate from the historical charge and discharge data of the cell.

[0049] The historical charge and discharge data of the battery cell includes current, voltage, and rated capacity.

[0050] S302: For each cell, take the first historical charge / discharge data whose charge / discharge rate is less than the preset charge / discharge rate from the cell's historical charge / discharge data.

[0051] The historical charge and discharge data of the battery cell includes current, voltage, and rated capacity.

[0052] S303: Calculate the dQ / dV value based on the second historical charge and discharge data.

[0053] S304: Differentiate the capacity and voltage of adjacent data points in the second historical charge and discharge data, and assign the average voltage of adjacent data points to obtain the battery dQ / dV curve.

[0054] From the historical charge and discharge data of the battery cell, obtain the first historical charge and discharge data with a charge and discharge rate less than the preset charge and discharge rate. The preset charge and discharge rate can be set to 0.3C.

[0055] At a 0.3C rate, charging and discharging takes about 3.3 hours. The current is gentle, the internal chemical reaction of the cell is slow, and the heat generation is low. This can more realistically reflect the intrinsic electrochemical behavior of the electrode material. High-rate charging and discharging will produce significant polarization voltage, which will mask the true electrode reaction characteristics. At low rates, the voltage plateau is more obvious, which is beneficial for subsequent differential calculations and characteristic peak identification.

[0056] The Savitzky-Golay filter is used to smooth the current and voltage in the first historical charging data. The Savitzky-Golay filter is a data smoothing method based on local polynomial least squares fitting, which better preserves the local characteristics of the signal compared to the traditional moving average method. The first historical charge-discharge data often contains measurement noise; direct differentiation amplifies this noise interference. The SG filter can suppress noise while preserving the characteristics of the electrochemical reaction peaks, improving the signal-to-noise ratio of the dQ / dV curve.

[0057] Perform differential calculations on adjacent data points of the second historical charge / discharge data: Capacity difference: ΔQ = Q_i - Q_{i-1}; Voltage differential: ΔV = V_i - V_{i-1}; dQ / dV_i ≈ ΔQ / ΔV; Voltage assignment: The calculated dQ / dV value is assigned to the representative voltage, usually the average voltage of two adjacent points: (V_i + V_{i-1}) / 2. This method can more accurately reflect the rate of change of capacity within the voltage range.

[0058] Repeat the above steps for all data points in the second historical charge-discharge data to obtain the curve of dQ / dV changing with voltage. Finally, plot the dQ / dV-V curve, with voltage on the horizontal axis and dQ / dV on the vertical axis. The characteristic peaks on the curve correspond to the phase transition reactions of the electrode material.

[0059] There are multiple ways to determine the peak features corresponding to each peak and construct feature vectors. In one implementation, such as... Figure 4 As shown, it includes the following steps: S401: For each peak, determine the peak height, peak voltage, half width at half maximum (FWHM), and peak area integral.

[0060] S402: Peak height, peak voltage, full width at half maximum (FWHM), and peak area integral are used as the peak characteristics corresponding to the peak.

[0061] S403: Construct feature vectors based on the peak features corresponding to each peak.

[0062] The specific implementation methods for determining the peak height, peak voltage, full width at half maximum (FWHM), and peak area integral of a wave peak can be as follows: Determine the dQ / dV value corresponding to the peak from the battery dQ / dV curve as the peak height, determine the voltage value corresponding to the peak as the peak voltage, determine half of the peak height as the reference height, calculate the width of the peak at the reference height as the half-width at half-maximum (HWHM), and perform numerical integration on the peak interval corresponding to the HWHM to obtain the peak area integral.

[0063] For each peak, the dQ / dV value corresponding to the peak position is read. This value is defined as the peak height, which is used to characterize the intensity of the electrochemical reaction corresponding to that peak.

[0064] Peak voltage determination: Read the voltage value corresponding to the peak index position. This voltage value is defined as the peak voltage, which is used to identify the equilibrium potential of the electrochemical phase transition corresponding to the peak.

[0065] Half-width at half-maximum (HWHM) calculation: Using 50% of the peak height as a baseline height, the width of the peak at that height is calculated, i.e., the HWHM. This parameter is used to quantify the width and shape of the peak, reflecting the polarization degree and kinetic characteristics of the electrochemical reaction.

[0066] Peak area integral: Within the peak interval defined by the full width at half maximum (FWHM), the dQ / dV curve is numerically integrated, and the result is defined as the peak area. This parameter is used to quantify the capacity contributed by the electrochemical phase transition process represented by the peak.

[0067] If no peak is detected within the preset voltage window, a specific invalid flag value, such as 0 or NaN, is assigned to all the above peak features to indicate that peak features were not effectively extracted from the data of this cycle.

[0068] The method for establishing the reward function for energy storage participation in the standby market proposed in this application gives the dQ / dV curve a clear physical meaning. For example: a decrease in peak height indicates a loss of active material: the peak height is directly related to the amount of active material participating in the phase transition.

[0069] A sustained decrease in a peak indicates that the corresponding positive or negative electrode material is deactivating. When material loss accumulates to a critical point, the entire electrode structure may collapse, leading to a sharp drop in capacity. Peak area reduction → loss of cyclic lithium or material: Peak area directly corresponds to the capacity stored / released during the phase transition. Its reduction is the most direct manifestation of performance degradation. Peak position shift → electrode chemical potential shift: Changes in the voltage position of the peak reflect changes in electrode thermodynamics. Overall shifts often originate from the loss of active lithium, altering the stoichiometric balance between electrodes. Shifts in specific peaks may indicate increased impedance in the phase transition reaction or decreased material structural stability. Peak broadening → deterioration of reaction kinetics: Peak broadening signifies increased polarization in the electrochemical reaction, possibly due to thickening of the SEI film, increased internal resistance, or cracks in the material particles leading to a poorer lithium-ion diffusion path. This is a crucial early signal of an inflection point, as it is directly related to the accelerated increase in internal resistance.

[0070] By constructing feature engineering based on the above characteristics, it is possible to quantify the degradation of energy storage systems, thereby constructing a reward function for participating in the standby market, which rewards older energy storage systems for participating more in the standby market.

[0071] There are several ways to calculate the reserve bonus coefficient of a battery cell based on Mahalanobis distance. In one implementation method, such as... Figure 5 As shown, it includes the following steps: S501: Determine the upper and lower limits of the reserve reward coefficient.

[0072] S502: Determine the Mahalanobis distance threshold and growth factor.

[0073] S503: Calculate the reserve reward coefficient of the battery cell based on the upper limit of the reserve reward coefficient, the lower limit of the reserve reward coefficient, the Mahalanobis distance threshold, and the growth factor using Mahalanobis distance.

[0074] The Mahalanobis distance between the feature vector and the preset feature vector is calculated using the following formula: ; Where x is an eigenvector, y is a preset eigenvector, and A is the covariance matrix composed of the eigenvector and the preset eigenvector.

[0075] The reserve bonus coefficient satisfies the following formula: ; Where K is the reserve reward coefficient. This serves as the lower limit for the reserve reward coefficient. This is the upper limit of the backup reward coefficient. As a growth factor, The Mahalanobis distance, This is the Mahalanobis distance threshold.

[0076] < At that time, the indicator cell was in a mild degradation period, and the reserve bonus coefficient increased slowly. = When the indicator cell enters a significant decline period, the reserve bonus coefficient grows the fastest, providing the strongest marginal incentive signal. > At that time, the indicator cells had severely degraded, and the growth of the reserve bonus coefficient slowed down again, gradually approaching the upper limit K_max. , which is the growth factor, characterizing the growth rate of the backup reward function.

[0077] The reserve reward coefficient function has the following beneficial properties: Monotonically increasing: The more severe the recession, the higher the reward.

[0078] Bounded: The reward coefficient has an upper limit to avoid infinite rewards.

[0079] Smoothness: Changes are smooth, avoiding abrupt changes. It can be embedded in neural networks for gradient descent operations.

[0080] Adjustable: The system operator can adjust parameters to control the total reward budget.

[0081] There are several ways to determine the Mahalanobis distance threshold: In one example: under laboratory conditions, a true incremental capacity curve can be obtained. First, complete aging data of a batch of cells of the same model from brand new to scrap is collected, recording the capacity and incremental capacity analysis characteristics during the cycling process. For each cell sample, the inflection point of accelerated capacity decay, i.e., the "knee point," is identified through its capacity-cycle curve. The Mahalanobis distance value corresponding to each cell at the knee point is extracted, and the median of these Mahalanobis distance values ​​is used as the Mahalanobis distance threshold. This method ensures It can objectively reflect the critical state of the battery cell as it transitions from normal aging to accelerated degradation.

[0082] In another example: during production operation, a true incremental capacity curve cannot be obtained. However, early cycle data from a small amount of brand-new cells, such as the first 50-100 cycles, can be used to construct an initial health status benchmark cluster, and the mean of its Mahalanobis distance can be calculated. and standard deviation Throughout the entire lifespan of the battery cell, its Mahalanobis distance is continuously monitored. Evolution trend relative to the initial baseline. Mahalanobis distance threshold. It is not a fixed value, but is determined through one of two dynamic methods: one is the statistical outlier threshold method, which... Set as The initial mean plus several times the standard deviation, i.e. The first method triggers an alert when the distance consistently exceeds this threshold; the second method is the trend inflection point identification method, which analyzes... - The first derivative (rate of change) of the cycle number curve is used to identify the inflection point where the derivative first shows a sustained sharp increase. Finally, as more cells in the system reach the end of their lifespan and complete knee-point data is obtained, this inference method based on early trends will be gradually updated and replaced with a data-driven approach.

[0083] There are multiple ways to determine the charging and discharging strategy based on each reserve reward coefficient. In one implementation, such as... Figure 6 As shown, it includes the following steps: S601: Sort the reserve reward coefficients in descending order.

[0084] S602: Obtain the target backup reward coefficient of the first-ranked preset quantity from the sorting.

[0085] S603: The cells corresponding to the target reserve bonus coefficient will be used in the reserve market.

[0086] The reserve reward coefficients are sorted in descending order, and the cells corresponding to the top 10 reserve reward coefficients are selected to participate in the reserve market.

[0087] In another example, the reserve reward factor is used as a parameter of the simulation system. Since the reserve reward factor increases with the severity of the degradation, it can be factored into the system losses during the startup of the energy storage PCS.

[0088] For example, operation and maintenance costs can be calculated based on the reserve bonus coefficient, where the operation and maintenance cost = number of energy storage start-ups * reserve bonus coefficient. The formula shows that the more severe the energy storage degradation of a battery cell, the higher the operation and maintenance costs resulting from start-ups and shutdowns. Therefore, cells with higher reserve bonus coefficients will participate in the reserve market, rather than participating in the actual charging and discharging of the energy market.

[0089] Please refer to Figure 7 This application embodiment also provides an application for Figure 1 The energy storage participation in the standby market reward function establishment device 110 of the electronic device 100 includes: Module 111 is used to acquire historical charge and discharge data of each battery cell; Module 112 is used to construct the battery dQ / dV curve for each cell based on the cell's historical charge and discharge data; The acquisition module 111 is also used to acquire each peak that meets the preset conditions based on the battery dQ / dV curve; The determining module 113 is used to determine the peak features corresponding to each peak and construct a feature vector; Calculation module 114 is used to calculate the Mahalanobis distance between the feature vector and a preset feature vector; and to calculate the reserve bonus coefficient of the battery cell based on the Mahalanobis distance. The determining module 113 is also used to determine the charging and discharging strategy based on each backup reward coefficient.

[0090] This application also provides an electronic device 100, which includes a processor 130 and a memory 120. The memory 120 stores computer-executable instructions, which, when executed by the processor 130, implement a method for establishing a reward function for energy storage participation in the standby market.

[0091] This application embodiment also provides a computer-readable storage medium storing a computer program. When the computer program is executed by the processor 130, it implements the method for establishing the reward function for energy storage participation in the standby market.

[0092] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0093] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for establishing a reward function for energy storage participation in the standby market, characterized in that, The method includes: Obtain historical charge and discharge data for each battery cell; For each cell, a battery dQ / dV curve is constructed based on the cell's historical charge and discharge data; Based on the battery dQ / dV curve, obtain each peak that meets the preset conditions; Determine the peak features corresponding to each peak and construct a feature vector; Calculate the Mahalanobis distance between the feature vector and the preset feature vector; The reserve bonus coefficient of the battery cell is calculated based on the Mahalanobis distance; The charging and discharging strategy is determined based on each reserve reward coefficient.

2. The method according to claim 1, characterized in that, The step of constructing the battery dQ / dV curve for each cell based on the cell's historical charge and discharge data includes: For each battery cell, the first historical charge-discharge data with a charge-discharge rate less than a preset charge-discharge rate is taken from the historical charge-discharge data of the battery cell; The first historical charge-discharge data is smoothed using a filter to obtain the second historical charge-discharge data. dQ / dV values ​​are calculated based on the second historical charge / discharge data; The capacity and voltage of adjacent data points in the second historical charge and discharge data are differentially calculated, and the average voltage of the adjacent data points is assigned to obtain the battery dQ / dV curve.

3. The method according to claim 1, characterized in that, The step of determining the peak features corresponding to each peak and constructing feature vectors includes: For each of the wave peaks, determine the peak height, peak voltage, full width at half maximum (FWHM), and peak area integral. The peak height, peak voltage, full width at half maximum (FWHM), and peak area integral are used as the peak features corresponding to the peak. Feature vectors are constructed based on the peak features corresponding to each peak.

4. The method according to claim 3, characterized in that, The steps for determining the peak height, peak voltage, full width at half maximum (FWHM), and peak area integral of the wave peak include: The dQ / dV value corresponding to the peak is determined from the battery dQ / dV curve and used as the peak height; The voltage value corresponding to the wave peak is determined as the peak voltage; Determine half of the peak height as the reference height; Calculate the width of the wave crest at the reference height, as the half-width at half-maximum (WHM); Numerical integration is performed on the peak interval corresponding to the half-width at half-maximum (WHM) to obtain the peak area integral.

5. The method according to claim 1, characterized in that, The Mahalanobis distance between the eigenvector and the preset eigenvector is calculated using the following formula: ； Where x is an eigenvector, y is a preset eigenvector, and A is the covariance matrix formed by the eigenvector and the preset eigenvector.

6. The method according to claim 1, characterized in that, The step of calculating the reserve bonus coefficient of the battery cell based on the Mahalanobis distance includes: Determine the upper and lower limits of the reserve reward coefficient; Determine the Mahalanobis distance threshold and growth factor; The reserve reward coefficient of the battery cell is calculated based on the upper limit of the reserve reward coefficient, the lower limit of the reserve reward coefficient, the Mahalanobis distance threshold, the growth factor, and the Mahalanobis distance.

7. The method according to claim 6, characterized in that, The reserve reward coefficient satisfies the following formula: ； Where K is the reserve reward coefficient. This serves as the lower limit for the reserve reward coefficient. This is the upper limit of the backup reward coefficient. As a growth factor, The Mahalanobis distance, This is the Mahalanobis distance threshold.

8. The method according to claim 1, characterized in that, The step of determining the charging and discharging strategy based on each of the aforementioned reserve reward coefficients includes: The aforementioned reserve reward coefficients are sorted in descending order; Obtain the target reserve reward coefficients of the top-ranked items from the sorted list; The battery cells corresponding to the target reserve reward coefficient will be included in the reserve market.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method according to any one of claims 1-8.

10. A storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-8.

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