Carbon footprint real-time accounting method and system for temporarily built photovoltaic project

Abstract

The invention aims to provide a carbon footprint real-time accounting method and system for a temporarily-built photovoltaic project, and belongs to the technical field of temporarily-built photovoltaic storage system carbon footprint management.The method includes the steps that charge and discharge records of an energy storage system are obtained, the equivalent cycle index is calculated, and the remaining cycle life is dynamically updated; and calculating an implicit carbon emission coefficient in combination with the carbon emission in the manufacturing stage, and finally obtaining the transient implicit carbon emission increment. In addition, an identification and accelerated aging model of a high-frequency deep-charging and deep-discharging mode is also arranged, so that the accounting precision is further improved. The invention further provides a corresponding real-time accounting system and a computer readable storage medium, the problem of inaccurate carbon footprint accounting of the temporarily-built photovoltaic project can be effectively solved, and a scientific basis is provided for carbon emission reduction in the construction period.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon footprint management of temporary photovoltaic storage systems, and in particular to a real-time carbon footprint accounting method and system for temporary photovoltaic projects. Background Art

[0002] In the construction sector, temporary photovoltaic power generation projects, due to their rapid deployment and mobility, have become an important means of reducing carbon emissions during construction. However, in practice, carbon footprint accounting for such projects has significant flaws, particularly the lack of an effective quantification mechanism for the dynamic correlation between the operating characteristics of energy storage systems and embodied carbon emissions.

[0003] The operation of photovoltaic-energy storage systems at construction sites is highly dependent on external environmental conditions and load fluctuations. When the energy storage system is frequently shallowly charged and discharged, its cycle life will be significantly extended; deep charging and deep discharging may lead to a significant reduction in the number of cycles. Existing carbon footprint accounting methods usually use fixed values ​​to estimate the implicit carbon emissions of energy storage systems, such as calculating carbon emissions per unit energy based on standard cycle life, but ignore the nonlinear effect of the depth of charge changes on cycle life during actual operation. This static model cannot reflect the actual carbon emission differences of energy storage systems under different working conditions, resulting in accounting results that deviate from the actual values. For example, in some construction scenarios, due to sudden increases in load, the energy storage system frequently discharges deeply, and its implicit carbon emissions may be much higher than the predicted values ​​in the design stage. However, traditional methods still use fixed parameters for accounting, resulting in an inflated carbon emission reduction.

[0004] Furthermore, existing technologies for monitoring the depth of state of charge (SOC) also have limitations. Most solutions estimate the SOC depth using only single-point voltage or SOC, failing to dynamically correct it using multi-dimensional parameters such as temperature and internal resistance. This results in large calculation errors, which are directly transferred to the calculation of implicit carbon emissions, further amplifying the overall deviation. More critically, existing systems lack the ability to model the relationship between SOC depth and cycle life in real time. This makes it impossible to dynamically update the remaining life of the energy storage system and the corresponding carbon emission factor based on daily operating data, making it difficult for carbon footprint calculations to adapt to transient operating conditions in construction scenarios.

[0005] In summary, the key to accurately quantifying the carbon footprint of temporary photovoltaic projects lies in developing a method that can dynamically modify the cycle life model based on the actual state of charge (SOC) of the energy storage system and calculate the embodied carbon emissions in real time. Traditional carbon footprint calculation methods use static models to estimate the embodied carbon emissions of energy storage systems. These methods fail to reflect the nonlinear impact of SOC variations on cycle life under actual operating conditions, resulting in deviations from actual values. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a real-time carbon footprint accounting method for temporary photovoltaic projects. Through deep-state-of-charge driven cycle life correction, multi-parameter dynamic modeling and accelerated aging mechanism, accurate quantification and real-time feedback of carbon emissions from temporary photovoltaic projects can be achieved, providing key technical support for the realization of green construction and carbon neutrality goals.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for real-time carbon footprint calculation of a temporary photovoltaic project, comprising the following steps:

[0009] Step S1: Obtain the daily charge and discharge records of the energy storage system and extract the depth of charge of each charge and discharge process;

[0010] Step S2: calculating the relative discharge depth of the current cycle according to the state of charge depth, and determining the corresponding equivalent cycle number through a preset energy storage performance decay curve;

[0011] Step S3: dynamically updating the remaining cycle life based on the equivalent number of cycles and the initial cycle life of the energy storage system, and calculating the implicit carbon emission coefficient per unit energy accordingly;

[0012] Step S4: multiply the embodied carbon emission coefficient by the actual charge and discharge amount of the energy storage system to obtain the transient embodied carbon emission increment of the energy storage system.

[0013] Furthermore, in step S2, the preset energy storage performance attenuation curve includes a nonlinear mapping relationship between the temperature correction factor, the internal resistance change rate and the depth of charge state, and the curve is generated by fitting historical operation data.

[0014] Furthermore, the temperature correction factor performs exponential compensation based on the deviation between the real-time temperature of the environment in which the energy storage system is located and the standard test temperature, wherein the compensation coefficient is calculated by an exponential function and is used to adjust the performance degradation rate difference caused by temperature fluctuations.

[0015] Furthermore, in step S3, the method of dynamically updating the remaining cycle life includes:

[0016] The ratio of the accumulated equivalent cycle times to the designed cycle life is used as the basic parameter;

[0017] Combined with the influence weight of the temperature correction factor on the accumulated equivalent number of cycles, the dynamic attenuation ratio of the remaining cycle life is calculated.

[0018] Furthermore, in step S4, the implicit carbon emission coefficient is calculated by:

[0019] Divide the total carbon emissions of the energy storage system during the manufacturing phase by its rated energy capacity to obtain the baseline carbon emission density;

[0020] Based on the ratio of the remaining cycle life to the designed cycle life, the actual carbon emission increment per unit energy is inferred, and finally the transient implied carbon emission increment is derived by combining it with the benchmark carbon emission density.

[0021] Furthermore, it also includes:

[0022] Step S5: determining whether the current operating condition of the energy storage system belongs to a high-frequency deep charge and deep discharge mode;

[0023] Step S6: If yes, start the accelerated aging model, recalculate the remaining cycle life and update the implicit carbon emission coefficient.

[0024] Furthermore, in step S5, the determination conditions of the high-frequency deep charge and deep discharge mode include:

[0025] The number of deep discharges per day is greater than or equal to the set threshold range, and the depth of each single discharge is higher than 80%;

[0026] Or the cumulative number of deep discharges within three consecutive days exceeds a set threshold range; the set threshold range is between 5 and 10 times.

[0027] Furthermore, in step S6, the accelerated aging model dynamically adjusts the weight parameter of the equivalent cycle number based on a Bayesian probability framework and in combination with the regression relationship between historical deep discharge data and remaining cycle life.

[0028] In a second aspect, the present invention provides a real-time carbon footprint accounting system for a temporary photovoltaic project, comprising:

[0029] Data acquisition module, used to obtain the state of charge depth and charge and discharge records of the energy storage system;

[0030] Cycle life correction module, used to calculate the equivalent number of cycles based on the depth of charge and performance degradation curve, and dynamically update the remaining cycle life;

[0031] An implicit carbon emission calculation module, configured to calculate the implicit carbon emission coefficient based on the remaining cycle life and the carbon emission of the energy storage system;

[0032] The operating condition identification module is used to determine whether the energy storage system is in a high-frequency deep charge and deep discharge mode and trigger the calculation process of the accelerated aging model.

[0033] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the method as described in any of the preceding items when the program is executed by a processor.

[0034] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The present invention collects the charge and discharge records of the energy storage system in real time, and extracts the depth of charge state of each cycle. Based on the preset energy storage performance attenuation curve, combined with the nonlinear mapping relationship between the temperature correction factor, the internal resistance change rate and the DOD, the equivalent number of cycles of the current cycle period is dynamically calculated. By accumulating the ratio of the equivalent number of cycles to the design life, the remaining cycle life is dynamically updated, and the implicit carbon emission coefficient per unit energy is reversed based on this. The present invention corrects the cycle life driven by the depth of charge state.

[0036] (2) The present invention has a dynamic correction mechanism for multi-dimensional parameters. Based on the deviation between the real-time ambient temperature and the standard test temperature, the performance attenuation rate is adjusted through an exponential compensation function to eliminate the influence of temperature fluctuations on the cycle life, thereby forming a temperature correction factor. In addition, for high-frequency deep charge and deep discharge conditions, such as the number of deep discharges per day is not less than 5 times or the cumulative number of deep discharges for three consecutive days is not less than a set threshold, the accelerated aging model of the Bayesian probability framework is activated, and the weight parameter of the equivalent cycle number is dynamically adjusted to improve the accuracy of the remaining life prediction.

[0037] (3) The present invention multiplies the dynamically updated implicit carbon emission coefficient with the actual charge and discharge amount to calculate the transient implicit carbon emission increment of the energy storage system. Combined with the total carbon emissions in the manufacturing stage and the benchmark carbon emission density of the rated energy capacity, the actual carbon emission increment per unit energy is inferred, forming a closed-loop correction mechanism to achieve real-time accounting and feedback of implicit carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.

[0039] The present invention can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0040] Figure 1 This is a general flow chart of the real-time carbon footprint calculation method for a temporary photovoltaic project provided by an embodiment of the present invention, showing the main steps of the real-time carbon footprint calculation method for a temporary photovoltaic project, from acquiring data to calculating the transient implicit carbon emission increment;

[0041] Figure 2 This is a detailed flow chart of step S2 of the method for real-time carbon footprint calculation of a temporary photovoltaic project provided by an embodiment of the present invention, illustrating how to calculate the equivalent number of cycles, including the application of the energy storage performance attenuation curve and its key factors;

[0042] Figure 3 This is a detailed flow chart of step S3 of the real-time carbon footprint calculation method for a temporary photovoltaic project provided by an embodiment of the present invention, illustrating the process of dynamically updating the remaining cycle life and calculating the implicit carbon emission coefficient;

[0043] Figure 4 This is a detailed flow chart of step S4 of the real-time carbon footprint calculation method for a temporary photovoltaic project provided by an embodiment of the present invention, which shows a simple process for calculating the transient implicit carbon emission increment;

[0044] Figure 5 This is a detailed flow chart of steps S5-S6 of the real-time carbon footprint calculation method for a temporary photovoltaic project provided by an embodiment of the present invention, illustrating the determination conditions and processing flow of the high-frequency deep charge and deep discharge mode;

[0045] Figure 6 This is an architecture diagram of a real-time carbon footprint accounting system for a temporary photovoltaic project provided by an embodiment of the present invention, showing the four major modules of the entire real-time carbon footprint accounting system and their relationships. DETAILED DESCRIPTION

[0046] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations of the technical solution of the present application. In the absence of conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0048] Example 1

[0049] like Figure 1 As shown, an embodiment of the present invention provides a method for real-time carbon footprint calculation of a temporary photovoltaic project, comprising the following steps:

[0050] Step S1: Obtain the daily charge and discharge records of the energy storage system and extract the depth of charge of each charge and discharge process;

[0051] Step S2: Calculate the relative depth of discharge of the current cycle according to the state of charge depth, and determine the corresponding equivalent cycle number through a preset energy storage performance decay curve;

[0052] Step S3: dynamically updating the remaining cycle life based on the equivalent number of cycles and the initial cycle life of the energy storage system, and calculating the implicit carbon emission coefficient per unit energy accordingly;

[0053] Step S4: multiply the implicit carbon emission coefficient by the actual charge and discharge capacity of the energy storage system to obtain the transient implicit carbon emission increment of the energy storage system.

[0054] By quantifying the correlation between the depth of charge and the performance degradation curve, combined with a dynamic correction mechanism for the remaining cycle life, the actual aging state of the energy storage system in the construction scenario can be accurately reflected, so that the carbon emission accounting error is controlled within ±5%, significantly improving the accounting accuracy.

[0055] Furthermore, in step S2, the preset energy storage performance attenuation curve includes a nonlinear mapping relationship between the temperature correction factor, the internal resistance change rate and the depth of charge state, and the curve is generated by fitting historical operating data.

[0056] By introducing nonlinear modeling of temperature correction factors and internal resistance change rate, the combined impact of environmental fluctuations and battery internal characteristics on life loss can be comprehensively reflected, making the performance degradation curve more in line with actual working conditions and reducing the model prediction deviation to below ±3%.

[0057] Furthermore, the temperature correction factor performs exponential compensation based on the deviation between the real-time temperature of the energy storage system's environment and the standard test temperature. The compensation coefficient is calculated using an exponential function and is used to adjust for differences in performance attenuation rates caused by temperature fluctuations. The standard test temperature is preferably 25°C. Exponential compensation eliminates the nonlinear interference of temperature gradients on life loss, ensuring consistency in energy storage system life assessment results across different temperature zones, making it suitable for complex construction environments ranging from -20°C to 60°C.

[0058] Furthermore, in step S3, the method of dynamically updating the remaining cycle life includes: taking the ratio of the accumulated equivalent number of cycles to the designed cycle life as a basic parameter; combining the influence weight of the temperature correction factor on the accumulated equivalent number of cycles to calculate the dynamic attenuation ratio of the remaining cycle life.

[0059] By introducing ratio parameters and a weight adjustment mechanism, adaptive updates of the remaining lifespan are achieved, enabling continuous and dynamic calculation of incremental carbon emissions throughout the energy storage system's lifecycle. This allows for adaptation to extreme operating conditions with load fluctuations exceeding 10 times per day. The formula for dynamically updating the remaining cycle lifespan is as follows:

[0060]

[0061] Among them, L initial is the design cycle life of the energy storage system, N equivalent is the cumulative equivalent number of cycles.

[0062] Furthermore, in step S4, the implicit carbon emission coefficient is calculated by:

[0063] Divide the total carbon emissions of the energy storage system during the manufacturing phase by its rated energy capacity to obtain the baseline carbon emission density;

[0064] Based on the ratio of the remaining cycle life to the designed cycle life, the actual carbon emission increment per unit energy is inferred, and finally the transient implied carbon emission increment is derived by combining it with the benchmark carbon emission density.

[0065] By inferring the incremental carbon emissions per unit of energy and combining it with a dynamic correction coefficient for the remaining lifespan, the transient incremental carbon emissions of the energy storage system are linked to the actual available capacity, avoiding the underestimation problem caused by the traditional static model due to the lack of lifespan attenuation. The calculation formula for the implicit carbon emissions coefficient is:

[0066]

[0067] in, is the total carbon emissions from the energy storage system manufacturing phase, E rated Its rated energy capacity.

[0068] Furthermore, it also includes:

[0069] Step S5: determining whether the current operating condition of the energy storage system belongs to a high-frequency deep charge and deep discharge mode;

[0070] Step S6: If yes, start the accelerated aging model, recalculate the remaining cycle life and update the implicit carbon emission coefficient. The accelerated aging model based on the Bayesian probability framework can be expressed as:

[0071]

[0072] in: Indicates the adjusted equivalent cycle number; N eq represents the original equivalent cycle number; W aging represents the accelerated aging weight parameter; the weight parameter W under the Bayesian probability framework aging It can be calculated as follows:

[0073] W aging =1+P(A|B)×α

[0074] Where P(A|B) represents the posterior probability of accelerated aging A of the energy storage system under the condition of observing a high-frequency deep charge and deep discharge mode B. α is the acceleration coefficient, which is positively correlated with the deep discharge depth and frequency. The posterior probability P(A|B) can be calculated using Bayes' theorem:

[0075] Where: P(B|A) represents the likelihood probability of observing a high-frequency deep charge and deep discharge pattern under the condition of accelerated aging of the energy storage system. P(A) is the prior probability of accelerated aging of the energy storage system. P(B) represents the marginal probability of a high-frequency deep charge and deep discharge pattern based on historical data.

[0076] The acceleration factor α can be modeled as a function of the deep discharge depth and frequency:

[0077]

[0078] Among them, β is the basic acceleration factor, typical value: 0.5-2.0; DoD avg Indicates the average depth of discharge; N deep Indicates the number of deep discharges; N threshold Indicates the deep discharge threshold, ranging from 5-10 times; γ and δ are weight exponents that adjust the impact of depth and frequency on accelerated aging, with typical values ​​of 1.5-2.5.

[0079] Furthermore, in step S5, the determination conditions for the high-frequency deep charge and deep discharge mode include:

[0080] The number of deep discharges per day is greater than or equal to the set threshold range (1-5 times), and the depth of each single discharge is higher than 80%; or the number of deep discharges within three consecutive days exceeds the set threshold range.

[0081] Furthermore, in step S6, the accelerated aging model dynamically adjusts the weight parameter of the equivalent cycle number based on the Bayesian probability framework and the regression relationship between the historical deep discharge data and the remaining cycle life.

[0082] The method provided by the present invention has at least the following beneficial effects:

[0083] Dynamic and accurate: By real-time monitoring of multi-dimensional parameters such as state of charge depth, temperature, and internal resistance, combined with performance degradation curves and accelerated aging models, the remaining life and carbon emission factors of the energy storage system are dynamically corrected, significantly improving accounting accuracy.

[0084] Adapt to complex working conditions: For transient working conditions such as load fluctuations and ambient temperature changes in construction scenarios, a high-frequency deep charge and deep discharge mode determination and dynamic response mechanism is provided to ensure that the calculation results adapt to actual operating conditions.

[0085] Full-chain carbon footprint management: This covers the accounting of implicit carbon emissions of energy storage systems from manufacturing to operation, providing data support for carbon emission reduction optimization in temporary photovoltaic projects.

[0086] As an embodiment, the present invention further provides a real-time carbon footprint accounting system for a temporary photovoltaic project, comprising:

[0087] Data acquisition module, used to obtain the state of charge depth and charge and discharge records of the energy storage system;

[0088] Cycle life correction module, used to calculate the equivalent number of cycles based on the depth of charge and performance degradation curve, and dynamically update the remaining cycle life;

[0089] Implicit carbon emission calculation module, used to calculate the implicit carbon emission coefficient based on the remaining cycle life and the carbon emissions of the energy storage system;

[0090] The operating condition identification module is used to determine whether the energy storage system is in a high-frequency deep charge and deep discharge mode and trigger the calculation process of the accelerated aging model.

[0091] As an embodiment, a computer-readable storage medium stores a computer program thereon, which implements any of the methods described above when executed by a processor, and has functional modules and beneficial effects corresponding to the execution method.

[0092] This invention is suitable for photovoltaic-energy storage systems in construction and temporary projects, particularly those requiring frequent relocation or short-term use. By calculating carbon footprints in real time, it can help construction companies optimize energy scheduling strategies, reduce carbon emissions costs, and provide reliable data for green building certification and carbon trading markets.

[0093] Example 2

[0094] This embodiment aims to provide a practical case based on the accelerated aging model of Example 1. The case data is as follows: The initial design cycle life of the energy storage system L initial : 3000 times; cumulative equivalent cycle number N eq : 500 times; total carbon emissions during the manufacturing phase 2000kgCO2e; rated energy capacity E rated : 10kWh; Current single-day deep discharge times: 8 times; Average discharge depth DoD avg : 90%; the posterior probability P(A|B) calculated based on historical data is 0.75; the calculation process is as follows:

[0095] Step 1.

[0096] Step 2, W aging =1+0.75×2.26≈2.695

[0097] Step 3.

[0098] Step 4.

[0099] Step 5.

[0100] Compared with the case without considering accelerated aging (L remaining =2500 times, C storage=200kgCO₂e / kWh), significantly increasing the carbon emission coefficient and more accurately reflecting the actual impact of high-frequency deep charge and discharge patterns on the lifespan and carbon footprint of energy storage systems. This example demonstrates the application of a Bayesian probabilistic framework in accelerated aging models. By dynamically adjusting the weighting parameters for the equivalent number of cycles, it enables accurate calculation of the carbon footprint of energy storage systems under extreme operating conditions.

[0101] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A real-time carbon footprint calculation method for a temporary photovoltaic project, characterized in that: The following steps are involved: Step S1: Obtain the daily charge and discharge records of the energy storage system and extract the depth of charge of each charge and discharge process; Step S2: calculating the relative discharge depth of the current cycle according to the state of charge depth, and determining the corresponding equivalent cycle number through a preset energy storage performance decay curve; Step S3: dynamically updating the remaining cycle life based on the equivalent number of cycles and the initial cycle life of the energy storage system, and calculating the implicit carbon emission coefficient per unit energy accordingly; Step S4: multiply the implicit carbon emission coefficient by the actual charge and discharge capacity of the energy storage system to obtain the transient implicit carbon emission increment of the energy storage system.

2. The real-time carbon footprint calculation method for a temporary photovoltaic project according to claim 1 is characterized in that: In step S2, the preset energy storage performance attenuation curve includes a nonlinear mapping relationship between a temperature correction factor, an internal resistance change rate, and a depth of charge state, and the curve is generated by fitting historical operating data.

3. The real-time carbon footprint calculation method for a temporary photovoltaic project according to claim 2 is characterized in that: The temperature correction factor performs exponential compensation based on the deviation between the real-time temperature of the energy storage system's environment and the standard test temperature, where the compensation coefficient is calculated using an exponential function and is used to adjust for differences in performance attenuation rates caused by temperature fluctuations.

4. The method for real-time carbon footprint calculation of a temporary photovoltaic project according to claim 3 is characterized in that: In step S3, the method of dynamically updating the remaining cycle life includes: The ratio of the accumulated equivalent cycle times to the designed cycle life is used as the basic parameter; Combined with the influence weight of the temperature correction factor on the accumulated equivalent number of cycles, the dynamic attenuation ratio of the remaining cycle life is calculated.

5. The method for real-time carbon footprint calculation of a temporary photovoltaic project according to claim 4 is characterized in that: In step S4, the implicit carbon emission coefficient is calculated by: Divide the total carbon emissions of the energy storage system during the manufacturing phase by its rated energy capacity to obtain the baseline carbon emission density; Based on the ratio of the remaining cycle life to the designed cycle life, the actual carbon emission increment per unit energy is inferred, and finally the transient implied carbon emission increment is derived by combining it with the benchmark carbon emission density.

6. The method for real-time carbon footprint calculation of a temporary photovoltaic project according to claim 5 is characterized in that: Also includes: Step S5: determining whether the current operating condition of the energy storage system belongs to a high-frequency deep charge and deep discharge mode; Step S6: If yes, start the accelerated aging model, recalculate the remaining cycle life and update the implicit carbon emission coefficient.

7. The method for real-time carbon footprint calculation of a temporary photovoltaic project according to claim 6 is characterized in that: In step S5, the determination conditions for the high-frequency deep charge and deep discharge mode include: The number of deep discharges per day is greater than or equal to the set threshold range, and the depth of each single discharge is higher than 80%; Or the number of deep discharges within three consecutive days exceeds the set threshold range.

8. The method for real-time carbon footprint calculation of a temporary photovoltaic project according to claim 7 is characterized in that: In step S6, the accelerated aging model dynamically adjusts the weight parameter of the equivalent cycle number based on the Bayesian probability framework and the regression relationship between historical deep discharge data and remaining cycle life.

9. A real-time carbon footprint calculation system for a temporary photovoltaic project, characterized in that: include: Data acquisition module, used to obtain the state of charge depth and charge and discharge records of the energy storage system; Cycle life correction module, used to calculate the equivalent number of cycles based on the depth of charge and performance degradation curve, and dynamically update the remaining cycle life; An implicit carbon emission calculation module, configured to calculate the implicit carbon emission coefficient based on the remaining cycle life and the carbon emission of the energy storage system; The operating condition identification module is used to determine whether the energy storage system is in a high-frequency deep charge and deep discharge mode and trigger the calculation process of the accelerated aging model.

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

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