Energy storage market income measuring and calculating method based on full life cycle financial model

Abstract

The invention relates to the field of data processing, in particular to an energy storage market income measuring and calculating method based on a full-life-cycle financial model, and the method comprises the steps: constructing a basic parameter model; generating a market operation scene; determining a charging and discharging power track and an energy conversion path; calculating a scene-level income decomposition result; and calculating financial evaluation indexes, checking and selecting output or further optimization. By constructing a technology-market-finance three-layer coupled closed-loop measurement and calculation model, the physical realizability of income measurement and calculation is ensured, the price uncertainty is quantified into statistical distribution of finance indexes, whether the strategy is steady or not is evaluated, finally checking is performed, when checking is not passed, automatic tracing is performed for parameter adjustment, iterative calculation is performed again, and the accuracy of income measurement and calculation is improved. The problems of static distortion and low accuracy of the measurement result caused by dependence on single power market data and incapability of adapting to the battery attenuation condition are effectively solved.

Description

Energy Storage Market Revenue Calculation Method Based on Full Life Cycle Financial Model Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method for calculating the revenue of the energy storage market based on a full life-cycle financial model. Background Technology

[0002] With the high proportion of renewable energy connected to the grid, electrochemical energy storage has become a key asset supporting the new power system, and its role is shifting from backup power to a diversified value creator. However, current energy storage investment decisions face three core contradictions: First, market returns are diversified and highly volatile, and traditional static calculations cannot capture their randomness and multi-scenario characteristics; second, battery life is strongly coupled with charging and discharging strategies, and traditional isolated models cannot quantify the economic trade-off of "sacrificing lifespan for returns"; third, the full life-cycle cash flow is complex, and traditional accounting cannot accurately map dynamic lifespan loss to the corresponding financial cycle, resulting in insufficient reliability of key investment indicators. Therefore, there is an urgent need for a refined full life-cycle calculation method that can dynamically couple technology, market, and operation.

[0003] Chinese Patent Application Publication No. CN119962982A discloses a method, apparatus, equipment, and medium for calculating the economic viability of an energy storage power station. The method includes: acquiring revenue model data, cost data, and operating parameters of the energy storage power station; calculating the average peak-valley electricity price of the energy storage power station in the second time period based on the electricity price clearing data of the energy storage power station in the first time period from the operating parameters and revenue model data; wherein the operating cycle of the energy storage power station comprises K time periods, including the first and second time periods, where the first and second time periods are two of the K time periods, and K is an integer greater than 1; calculating the cash inflow and cash outflow of the energy storage power station in each of the K time periods based on the average peak-valley electricity price, cost data, and operating parameters; and calculating the financial internal rate of return, financial net present value, and investment payback period of the energy storage power station based on the cash inflow and cash outflow in each time period to determine the economic level of the energy storage power station.

[0004] Therefore, the economic calculation method for energy storage power stations has the following problems: the traditional financial indicators used in the calculation rely on static economic levels; the reliance on a single average peak-valley electricity price fails to accurately reflect the volatility risk of the real electricity market; the changes in profitability caused by battery degradation are not considered; and there is a lack of a verification mechanism for the rationality of the calculation results themselves. Summary of the Invention

[0005] To address this, the present invention provides a method for calculating the market revenue of energy storage based on a full life-cycle financial model. This method overcomes the problems of static distortion and low accuracy of calculation results caused by reliance on a single electricity market data and inability to adapt to battery degradation in existing technologies through multi-market scenario simulation and consistency verification result adjustment mechanisms.

[0006] To achieve the above objectives, this invention provides a method for calculating the market revenue of energy storage based on a full life-cycle financial model, comprising: Step S1, constructing a basic parameter model including cost parameters, technical parameters, capacity decay model, and grid connection constraint parameters according to the technical roadmap of the target energy storage system; Step S2, generating multiple market operation scenarios based on the basic parameter model and the price fluctuation characteristics of historical price sequences in the electricity market; Step S3, determining the charging and discharging power trajectory and energy conversion path under each market operation scenario according to each market operation scenario and a preset charging and discharging rule base; Step S4, further analyzing the charging and discharging power trajectory and energy conversion path based on the charging and discharging power trajectory and the energy conversion path. Step S5: Calculate energy arbitrage revenue, ancillary service revenue, and capacity compensation revenue, and map the lifespan loss cost caused by the number of charge / discharge cycles and the depth of discharge to the corresponding calculation period to obtain scenario-level revenue decomposition results; Step S6: Input the scenario-level revenue decomposition results into the full life-cycle cash flow model to form a cross-year cash flow distribution sequence, and calculate several financial evaluation indicators based on the cash flow distribution sequence; Step S7: Based on the distribution characteristics of each financial evaluation indicator in each market operation scenario, perform life-cycle consistency verification, and based on the verification results of each financial evaluation indicator, select to output a revenue calculation report or adjust the charging / discharging strategy mapping rules.

[0007] Further, step S1 includes: Step S11, determining the cost parameters, including total project investment, capital ratio, fixed operation and maintenance costs, and charge / discharge operation and maintenance costs, based on the project investment estimate and operation and maintenance plan; Step S12, obtaining the technical parameters, including installed capacity, rated energy storage capacity, charge / discharge efficiency, maximum charge / discharge rate, initial state of charge range, and cycle life, based on the technical specifications and performance test reports of the energy storage equipment; Step S13, fitting an empirical attenuation curve based on the attenuation characteristics of the actual technical route to establish a capacity attenuation model; Step S14, analyzing and determining the grid connection constraint parameters, including maximum allowable charge / discharge power, maximum charge / discharge rate, grid access point capacity limit, minimum duration required to participate in different markets, and response speed, based on grid connection technology regulations and target market rules; Step S15, integrating the parameters and models determined in steps S11 to S14 to construct the basic parameter model.

[0008] Further, step S2 includes: step S21, extracting key statistical features and typical price curve patterns based on the fluctuation characteristics, peak-valley characteristics, and seasonality of the historical price series of the electricity market; step S22, performing time clustering based on the key statistical features and the typical price curve patterns to obtain several market operation scenarios; step S23, mapping and verifying the compatibility of each market operation scenario with the grid connection constraint parameters and technical parameters in the basic parameter model to obtain a set of operation scenarios.

[0009] Furthermore, the market operation scenarios in step S22 include at least high-frequency fluctuation scenarios, typical peak and trough scenarios, extreme price event scenarios, multiple market coupling scenarios, and long-term capacity signal scenarios.

[0010] Further, step S3 includes: step S31, matching scenario types according to the time-series price signals of each market operation scenario and the preset charging and discharging rule library to obtain a charging and discharging strategy combination; step S32, obtaining the charging and discharging power command at each moment in each market operation scenario according to the time-series simulation results of each market operation scenario and the charging and discharging strategy combination, forming a charging and discharging power trajectory; step S33, based on the charging and discharging power trajectory, charging and discharging efficiency and initial state of charge, obtaining the state of charge change sequence according to energy conservation, forming an energy conversion path.

[0011] Further, step S31 includes: S311, extracting market feature vectors based on the time-series price signals of each market operation scenario; S312, obtaining the strategy type identifier of the market operation scenario based on the similarity between the market feature vectors and all preset standard scenario vectors in the preset charging and discharging rule base; S313, obtaining a charging and discharging strategy combination that includes at least a charging price trigger threshold and a discharging price trigger threshold based on the strategy type identifier and the corresponding basic threshold parameters in the preset charging and discharging rule base.

[0012] Further, step S4 includes: step S41, obtaining energy arbitrage revenue, ancillary service revenue, and capacity compensation revenue by performing dot product and integral operations and market rule settlement based on the charging and discharging power trajectory and the time-series price signal corresponding to the market operation scenario; step S42, calculating the direct lifetime loss share based on the discharge depth data determined by the charging and discharging power trajectory and the state of charge sequence, and thereby calculating the lifetime loss cost; step S43, performing positional superposition of the energy arbitrage revenue, ancillary service revenue, capacity compensation revenue, and lifetime loss cost mapped to the corresponding calculation period to obtain the scenario-level revenue decomposition result.

[0013] Further, step S5 includes: step S51, performing scenario probability weighted calculation based on the scenario-level revenue decomposition results of each market operation scenario to obtain the expected net revenue cash flow for each year of the entire life cycle; step S52, collecting and allocating the initial investment, operation and maintenance costs, replacement costs of the energy storage system, and the expected net revenue cash flow for each year to form a cross-year net cash flow distribution sequence; step S53, using the net present value obtained by period discounting calculation based on the net cash flow distribution sequence and the preset discount rate, the internal rate of return obtained by iterative calculation of the net present value, and the investment payback period obtained by summing the net cash flow of each period and performing linear interpolation calculation as the financial evaluation indicators.

[0014] Further, step S6 includes: step S61, calculating the mean, standard deviation, and P90 quantile of each financial evaluation indicator based on the net present value sequence, internal rate of return sequence, and investment payback period sequence under each market operation scenario; step S62, performing life cycle consistency verification based on the distribution characteristics of the financial evaluation indicators to obtain verification results; step S63, if any two verification results pass, taking each financial evaluation indicator as the final calculation result and outputting a revenue calculation report; and if any two verification results fail, generating an adjustment instruction to adjust the charging and discharging strategy mapping rules.

[0015] Further, step S62 includes: step S621, net present value consistency verification, calculating the coefficient of variation based on the mean and standard deviation of the net present value, and determining that the verification passes when the coefficient of variation is less than a preset first risk threshold; step S622, internal rate of return consistency verification, calculating the probability that the internal rate of return is less than a preset benchmark rate of return based on the internal rate of return, and determining that the verification passes when the probability is less than or equal to a preset second risk threshold; step S623, investment payback period consistency verification, determining that the verification passes when the P90 quantile of the investment payback period is less than a preset maximum payback period.

[0016] Compared with existing technologies, the beneficial effects of this invention are that by constructing a closed-loop calculation model that couples technology, market, and finance, it solves the core problem of the disconnect between technology degradation, market fluctuations, and financial results in traditional methods. The capacity degradation model and lifespan loss cost mapping ensure the physical feasibility of revenue calculation, dynamically linking how long the battery can last with how much money the project can make. Multi-scenario simulation and charge / discharge strategy response quantify price uncertainty into a statistical distribution of financial indicators, thereby assessing whether the strategy is robust under different markets. Finally, lifecycle consistency verification is performed, and the aforementioned distribution is diagnosed by setting a risk threshold. When the verification fails, the system can automatically trace back to the charge / discharge strategy mapping rules to adjust parameters and re-iterate the calculation, enabling the system to have self-verification, risk warning, and strategy optimization capabilities. This greatly improves the accuracy, reliability, and scientific nature of investment return assessment for energy storage projects and investment decisions, effectively solving the problems of static distortion and low accuracy of calculation results caused by relying on a single electricity market data and being unable to adapt to battery degradation.

[0017] Furthermore, by systematically integrating four major categories of parameters—cost, technology, degradation, and grid connection—a precise and traceable digital twin foundation is established for full lifecycle revenue calculation. This allows for the quantitative modeling of all dimensions of energy storage systems, from capital structure and physical performance to performance degradation patterns and external rules. Subsequent revenue simulations thus possess a solid physical foundation and commercial authenticity. Technical parameters serve as the physical basis for calculating instantaneous power and energy. The capacity degradation model dynamically adjusts technical parameters based on the number of cycles and discharge depth during operation, accurately reflecting performance degradation. Cost parameters quantify this technological degradation into financial costs. Meanwhile, grid connection constraint parameters set insurmountable rigid boundaries for all charging and discharging behaviors, ensuring that the entire chain from charging and discharging one kilowatt-hour of battery power to generating one cent of revenue for the project is within a calculable and verifiable closed-loop logic. This fundamentally overcomes the calculation distortion problems caused by isolated parameters and the disconnect between technology and economics in traditional methods.

[0018] Furthermore, by extracting key statistical features such as volatility and peaks and troughs from historical data, the basis of market uncertainty is quantified. Energy storage revenue is highly dependent on and limited by the external market and its own technology. Therefore, effective scenarios must simultaneously possess market representativeness and operational feasibility. Through time clustering, these features are combined into discrete scenario patterns with typical significance to cover the spectrum of possible future market states. Finally, through mapping and compatibility verification, the power, duration, and other requirements of each market scenario are compared and screened with the grid connection constraints and technical parameters in the basic parameter model to ensure that each generated scenario is technically feasible and to avoid invalid calculations that are divorced from technological reality.

[0019] Furthermore, by constructing a structured, multi-dimensional scenario system, the potential returns and robustness of energy storage under different time scales, market dimensions, and risk levels are quantified. This overcomes the limitations of single or random scenarios in traditional calculations. The commercial value of energy storage stems from its combined capabilities in capturing short-term price differences, providing ancillary services, mitigating extreme risks, coordinating multiple markets, and securing long-term guarantees in complex market environments. Therefore, test scenarios must be proactively designed to cover these core value dimensions. High-frequency fluctuation scenarios test intraday arbitrage and frequency regulation response capabilities; typical peak-valley scenarios assess basic returns based on regular price differences; extreme price event scenarios simulate the impact of tail risks on returns and system resilience; multi-market coupling scenarios verify the potential for joint optimization of energy and various ancillary service markets; and long-term capacity signal scenarios are used to capture long-term returns such as capacity compensation or leasing. These five types of scenarios together constitute a complete test environment from short-term to long-term, from normal to extreme, and from single to coordinated, enabling the final distribution of financial indicators to truly and comprehensively reflect the expected return spectrum and risk profile of the project, providing a solid basis for investment decisions.

[0020] Furthermore, realizing the value of energy storage relies on transforming high-dimensional, continuous market price signals into technically feasible and economically optimal charging and discharging actions in real time, and accurately tracking the resulting changes in the system's internal energy state. A pre-set charging and discharging rule base serves as the core knowledge, mapping different market scenarios into specific combinations of strategy thresholds. These threshold combinations act as decision rules in time-series simulations, driving the model to generate charging and discharging power trajectories that dynamically interact with the market price curve. Finally, combining charging and discharging efficiency and initial state of charge, and based on the law of conservation of energy, the power trajectory is integrated hourly to accurately derive the state of charge change sequence reflecting the actual energy storage state of the battery. This rigorously correlates abstract market prices and strategy rules with specific equipment power and battery energy state, laying the foundation for subsequent accurate calculation of revenue and cost losses.

[0021] Furthermore, by transforming the complex market environment matching problem into a computable and quantifiable feature space similarity comparison problem, the system first extracts market feature vectors representing the core patterns from the original time-series price signals and maps them from the time domain to the feature domain. Then, by calculating the similarity between this vector and the preset standard scenario vectors in the rule base, the system retrieves and matches the preset strategy type that best fits the current market characteristics from the knowledge base. Finally, based on this strategy type identifier, the system calls the optimized basic threshold parameters bound to it in the rule base to quickly generate accurate strategy combinations that can directly drive simulation. This ensures that the strategy decision-making is supported by expert experience and can respond in real time to the subtle characteristics of the specific market, thus laying an adaptive strategy foundation for subsequent accurate profit calculation.

[0022] Furthermore, by precisely aligning and coupling market transactions, technical losses, and financial costs on a unified time scale, and based on the charging and discharging power trajectory, through point-by-point multiplication of power and price and dot product and integral operations of summation over time, while strictly adhering to the settlement rules of different markets, the three types of external market revenue—energy arbitrage, ancillary services, and capacity compensation—are accurately calculated. Simultaneously, based on the same power trajectory and its resulting state of charge sequence, the discharge depth data for each cycle is reverse-analyzed, and the accurate direct lifetime loss share is calculated using a decay model, which is then quantified as the current financial cost. Finally, within the same time period, the three types of revenue are aligned, superimposed, and decomposed with the dynamically generated costs, ensuring that every penny of revenue is deducted from the real battery life cost incurred to earn it. This makes the final net income a solid and reliable financial benchmark reflecting the true profitability of the energy storage system in this scenario.

[0023] Furthermore, by refining granular revenue and cost data from multiple scenarios into ultimate evaluation indicators with clear economic significance and direct support for investment decisions, investment decisions require clear and robust single value judgments. Therefore, it is necessary to converge discrete scenario-based results into deterministic full-lifecycle cash flows and apply financial axioms for value assessment. First, the revenue decomposition results of multiple scenarios are probability-weighted and integrated to obtain a unique expected net revenue cash flow for each year. Then, this expected revenue and rigid costs occurring at different points in time are aggregated and allocated on the time axis to form a complete and realistic cross-year net cash flow sequence. Finally, the absolute value of this cash flow sequence is assessed by calculating the net present value using discounting, the profitability efficiency is assessed by iteratively solving the internal rate of return, and the investment payback period is assessed by cumulative interpolation. This results in a set of universally accepted financial evaluation indicators that combine technical insights with financial rigor, providing a solid and reliable quantitative foundation for investment decisions.

[0024] Furthermore, by assessing the uncertainty of financial evaluation indicators and verifying their inherent logical rationality, a qualitative leap from passive calculation to proactive risk management and self-optimization is achieved. First, the discrete financial indicator results under multiple scenarios are transformed into statistical distribution characteristics that characterize their central tendency, dispersion, and risk boundaries, thereby quantifying abstract market uncertainty into specific and comparable risk parameters. Based on these distribution characteristics, preset verification rules are executed to logically diagnose the robustness and technical feasibility of the calculation results and output the verification results. Based on these results, a judgment logic is executed. If it passes, the output is confirmed; if it fails, an adjustment instruction is automatically generated and fed back to the strategy mapping rule base. This ensures that the final calculation scheme is both able to withstand risk stress testing and is technically and economically self-consistent, thus upgrading the entire method from a calculation tool to an auxiliary decision-making system with continuous optimization capabilities.

[0025] Furthermore, different financial indicators reveal the risks of the project in different dimensions. Net present value measures absolute returns, and the coefficient of variation is used to measure the volatility of its unit returns. If it is less than a threshold, it indicates that the expected returns are relatively stable. For internal rate of return, which measures profitability efficiency, the probability of it being lower than the benchmark rate of return is directly calculated. If it is less than a threshold, it indicates that the profitability target is likely to be achieved and the project is feasible. For investment payback period, which measures liquidity risk, the most pessimistic scenario is considered, namely the high-risk quantile P90, to ensure that the cost can be recovered within the maximum acceptable period even in adverse scenarios. The three rules together verify the calculation results from three dimensions: return stability, reliability of profitability target achievement, and capital security in extreme situations. The output results are direct and objective data basis for strategy iteration or result confirmation in subsequent steps. Attached Figure Description

[0026] Figure 1 is a flowchart of the energy storage market revenue calculation method based on the full life cycle financial model in this embodiment; Figure 2 is a flowchart of step S2 in this embodiment; Figure 3 is a flowchart of step S3 in this embodiment; Figure 4 is a flowchart of step S31 in this embodiment. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please refer to Figure 1, which is a flowchart of the energy storage market revenue calculation method based on the full life cycle financial model in this embodiment. This embodiment provides an energy storage market revenue calculation method based on the full life cycle financial model, including: Step S1, constructing a basic parameter model containing cost parameters, technical parameters, capacity decay model, and grid connection constraint parameters based on the technical route of the target energy storage system; Step S2, generating multiple market operation scenarios based on the basic parameter model and the price fluctuation characteristics of the historical price series of the electricity market; Step S3, determining the charging and discharging power trajectory and energy conversion path under each market operation scenario based on each market operation scenario and a preset charging and discharging rule base; Step S4, based on the charging and discharging power trajectory and energy conversion path under each market operation scenario and the preset charging and discharging rule base, determining the charging and discharging power trajectory and energy conversion path under each market operation scenario; Step S4, based on the charging and discharging power trajectory and energy conversion path, determining the charging and discharging power trajectory and energy conversion path under each market operation scenario and the preset charging and discharging rule base, determining the charging and discharging power trajectory and energy conversion path under each market operation scenario. The discharge power trajectory and the energy conversion path are used to calculate energy arbitrage revenue, ancillary service revenue, and capacity compensation revenue, respectively. The lifetime loss cost caused by the number of charge-discharge cycles and the depth of discharge is mapped to the corresponding calculation period to obtain the scenario-level revenue decomposition results. Step S5: Input the scenario-level revenue decomposition results into the full life cycle cash flow model to form a cross-year cash flow distribution sequence, and calculate several financial evaluation indicators based on the cash flow distribution sequence. Step S6: Based on the distribution characteristics of each financial evaluation indicator in each market operation scenario, perform life cycle consistency verification, and based on the verification results of each financial evaluation indicator, select to output a revenue calculation report or adjust the charge-discharge strategy mapping rules.

[0030] The pre-built charging and discharging rule base is a set of pre-established and validated strategy mappings that automatically transforms abstract market scenario characteristics into executable charging and discharging control parameters. It includes at least a standard scenario feature vector table, a strategy type identifier table, and a strategy parameter lookup table. The standard scenario feature vector table stores several predefined typical market scenarios, such as high-frequency fluctuations, typical peaks and troughs, and extreme high prices, with each vector representing a statistical characteristic of the corresponding scenario's price series, such as volatility, peak-to-trough ratio, and price quantile. The strategy type identifier table is bound to each standard scenario, defining the recommended strategy logic name or code for that scenario, such as high-frequency arbitrage, two-charge-two-discharge, and conservative backup. The strategy parameter lookup table is the execution output of the rule base, indexed by strategy type, storing a set of specific parameters that can directly drive the model's operation. These typically include core thresholds, operating boundaries, and advanced rules, such as charging price trigger thresholds, discharging price trigger thresholds, upper and lower limits of state of charge (SOC), maximum charging and discharging power coefficients, minimum charging and discharging time intervals, and cross-market priorities.

[0031] The revenue calculation report is an integrated document of structured data and conclusions output by this method to guide investment decisions and operational strategies. It is not only a simple list of financial indicators, but also a systematic presentation of all key links and risk quantification results in the whole life cycle calculation process. The report includes the following core contents: (1) Project and model basic information: Clarify the basic parameter model on which this calculation is based, including the technical specifications of the energy storage system, cost composition, key parameters of the selected capacity decay model, and grid connection constraints; (2) Market environment setting and scenario analysis: Explain the generated set of market operation scenarios, including the definition of various scenarios (such as high-frequency fluctuations, typical peaks and valleys), representative price curves, and their set prior probabilities; (3) Core financial evaluation indicators and distribution characteristics: Net present value: Report its mean, standard deviation, coefficient of variation, and key quantiles to intuitively show the expected level of project value and volatility risk; Internal rate of return: Report its mean, distribution range, and probability of being lower than the preset benchmark rate of return to clearly reveal the risk of achieving profitability; Investment recovery period: Report its mean and P90 quantile to clarify the return of funds even under adverse scenarios. (4) Profit decomposition and cost composition analysis: Based on the scenario-level profit decomposition results, the report will show the expected contribution ratio of various profits such as energy arbitrage, auxiliary services, and capacity compensation, and clearly list the periodic operation and maintenance costs generated by the dynamic mapping of battery life loss, explaining the source of profit and cost structure; (5) Life cycle consistency verification conclusion: Clearly give the judgment on whether each verification result (net present value volatility, internal rate of return compliance rate, investment payback period risk) is passed; (6) Final conclusion and strategy recommendation: Based on the verification results, give a clear investment feasibility conclusion. If the verification is passed, the final financial indicators after risk adjustment will be output; if it is not passed, the report will clearly point out the problem (such as excessive profit volatility or excessive payback period) and output the specific adjustment instructions for the "charging and discharging strategy mapping rules" to provide direct input for the next round of optimization iteration.

[0032] A pre-defined charging and discharging rule base collects long-term, multi-dimensional historical electricity market data, including energy price series and various ancillary service price series. It generates a large number of historical market scenario samples and, by inviting domain experts or using a rule-based cost-benefit simulator, labels each historical scenario sample with an optimal or near-optimal charging and discharging strategy type (e.g., 'high-frequency arbitrage', 'two-charge, two-discharge'). For each historical scenario sample, its market feature vector is extracted. This vector may include, but is not limited to, the mean, standard deviation, peak-valley price difference, the percentage of time the price is above a certain threshold, and the price change trend over the previous N hours. Using the market feature vector as input and the labeled strategy type identifier as output, a classification algorithm (e.g., support vector machine, random forest, or neural network) is used for training to form a 'scenario feature-strategy type' mapping model. This model constitutes the core logic of strategy matching in the rule base. For each strategy type, all historical best operation cases are collected, and a set of average parameters that maximize long-term returns in this type of scenario are solved through statistical induction or optimization algorithms. These parameters are then stored in a strategy parameter lookup table, thereby constructing a preset charging and discharging rule library. In subsequent use, the library is updated online or iterated offline based on new market operation data and return feedback.

[0033] In this embodiment, the full life-cycle cash flow model is used to conduct cross-year analysis of the revenue and costs of energy storage systems or investment projects under different market environments, in order to calculate the project's net present value (NPV), internal rate of return (IRR), and payback period (PP), thereby providing support for investment decisions. This model generates an annual cash flow series that can be used for financial evaluation by deeply coupling the physical operating status of the energy storage system with market interaction results.

[0034] The model input includes two types of data. The first type is basic financial and project parameters, including the total investment amount of the energy storage project, equity ratio, loan interest rate and term (if financing is involved), income tax rate, benchmark discount rate, annual fixed operation and maintenance costs, and variable operation and maintenance rates related to charge and discharge volumes—static or preset parameters at the project level. The second type is technical simulation output data, which directly incorporates the calculation results from steps S4 and S51. This mainly includes the expected net operating cash flow for each year after scenario probability weighting. This cash flow includes market revenue and deducts lifetime depreciation costs and variable operation and maintenance costs calculated based on battery cycle count and depth of discharge. Simultaneously, the model receives battery health status data for each year, such as the percentage of capacity decay, to determine whether battery replacement is triggered.

[0035] During the construction of the annual cash flow sequence, the cash flow during the construction period (year 0) is mainly capital expenditure. During the operation period, the cash flow for each year is calculated according to the following rules: cash inflow is the market revenue portion of the expected net operating cash flow for that year; cash outflow includes several items: (1) annual fixed operation and maintenance costs; (2) financial expenses paid according to the loan plan; (3) battery replacement investment triggered when the cumulative capacity decay reaches a preset threshold (e.g., 70% of the rated capacity), after which the model will update the battery capacity status and adjust the subsequent depreciation base accordingly; and (4) income tax, whose taxable income is based on the operating profit, minus financial expenses, and then minus the annual depreciation of the energy storage system assets, wherein the depreciation adopts the straight-line method, and the depreciation period can be set according to the project evaluation period. The net cash flow for each year is obtained by algebraically summing the cash inflow and cash outflow.

[0036] Based on the above annual net cash flow sequence, the model further calculates the project's core financial evaluation indicators. Net present value (NPV) is obtained by discounting the annual net cash flows to the beginning of the construction period using a benchmark discount rate and summing the results. Internal rate of return (IRR) is calculated using Newton's iteration method or other numerical methods; the discount rate that makes the NPV exactly zero over the entire lifecycle is the IRR. The payback period is obtained by calculating the point in time when the cumulative net cash flow turns from negative to positive. Specifically, this involves determining the years before and after the turnaround and using linear interpolation to calculate the precise number of years.

[0037] By constructing a closed-loop calculation model that couples technology, market, and finance, the core problem of the disconnect between technology degradation, market fluctuations, and financial results in traditional methods is solved. The capacity degradation model and lifespan loss cost mapping ensure the physical feasibility of revenue calculation, dynamically linking battery lifespan with project profitability. Multi-scenario simulation and charge / discharge strategy response quantify price uncertainty into a statistical distribution of financial indicators, thereby assessing the robustness of the strategy under different markets. Finally, lifecycle consistency verification is performed, diagnosing the aforementioned distribution through preset risk thresholds. When the verification fails, the system can automatically trace back to the charge / discharge strategy mapping rules for parameter adjustment and re-iterate the calculation, enabling the system to have self-verification, risk warning, and strategy optimization capabilities. This greatly improves the accuracy, reliability, and scientific nature of investment return assessment for energy storage projects, effectively solving the problems of static distortion and low accuracy of calculation results caused by reliance on single electricity market data and inability to adapt to battery degradation.

[0038] Specifically, step S1 includes: Step S11, determining the cost parameters, including total project investment, capital ratio, fixed operation and maintenance costs, and charge / discharge operation and maintenance costs, based on the project investment estimate and operation and maintenance plan; Step S12, obtaining the technical parameters, including installed capacity, rated energy storage capacity, charge / discharge efficiency, maximum charge / discharge rate, initial state of charge range, and cycle life, based on the technical specifications and performance test reports of the energy storage equipment; Step S13, collecting accelerated aging test data or publicly available empirical degradation data based on the actual technical route of the battery used in the target energy storage system, and establishing an empirical degradation curve or semi-empirical model by fitting the relationship between the number of cycles, average depth of discharge, and capacity retention rate to obtain a capacity degradation model; Step S14, analyzing and determining the grid connection constraint parameters, including maximum allowable charge / discharge power, maximum charge / discharge rate, grid access point capacity limit, minimum duration required to participate in different markets, and response speed, based on grid connection technology regulations and target market rules; Step S15, integrating the parameters and models determined in steps S11 to S14 to construct the basic parameter model.

[0039] By systematically integrating four major categories of parameters—cost, technology, degradation, and grid connection—a precise and traceable digital twin foundation is established for full lifecycle revenue calculation. This allows for the quantitative modeling of all dimensions of energy storage systems, from capital structure and physical performance to performance degradation patterns and external rules. Subsequent revenue simulations thus possess a solid physical foundation and commercial authenticity. Technical parameters provide the physical basis for calculating instantaneous power and energy; the capacity degradation model dynamically adjusts these parameters based on the number of cycles and depth of discharge during operation, accurately reflecting performance degradation; cost parameters quantify this technological degradation into financial costs; and grid connection constraint parameters set insurmountable rigid boundaries for all charging and discharging behaviors, ensuring that the entire chain from charging / discharging one kilowatt-hour of battery power to generating one cent of revenue for the project is within a calculable and verifiable closed-loop logic. This fundamentally overcomes the calculation distortion problems caused by isolated parameters and the disconnect between technology and economics in traditional methods.

[0040] Please refer to Figure 2, which is a flowchart of step S2 in this embodiment. In this embodiment, step S2 includes: Step S21: Based on the historical price series of the electricity market, extract daily statistical features such as standard deviation and peak-valley price difference from the dimensions of volatility, peak-valley characteristics and seasonality, and mark time attributes to form a feature vector; at the same time, use a clustering algorithm based on dynamic time warping to perform pattern recognition on the standardized daily price curve, determine the number of clusters by elbow method, and use the center trajectory of each cluster as the typical price curve pattern; Step S22: Based on the fused feature vector composed of standardized statistical features and curve pattern similarity of all historical trading days, use K-means++ clustering algorithm and elbow method to determine the optimal number of categories N; each cluster result defines a market operation scenario, and uses the median of the original price curve in the class as the representative curve, the sample frequency in the class as the prior probability, and assigns a clear commercial identifier based on its statistical feature distribution, and finally outputs several market operation scenarios; Step S23: Map and verify the compatibility of each market operation scenario with the grid connection constraint parameters and technical parameters in the basic parameter model to obtain a set of operation scenarios.

[0041] By extracting key statistical features such as volatility and peaks and troughs from historical data, the basis of market uncertainty is quantified. Energy storage revenue is highly dependent on and limited by external markets and its own technology. Therefore, effective scenarios must simultaneously possess market representativeness and operational feasibility. Through time clustering, these features are combined into typical discrete scenario patterns to cover the spectrum of possible future market states. Finally, through mapping and compatibility verification, the power, duration, and other requirements of each market scenario are compared and screened with grid connection constraints and technical parameters in the basic parameter model to ensure that each generated scenario is technically feasible and avoids invalid calculations that are divorced from technological reality.

[0042] Specifically, the market operation scenarios in step S22 include at least high-frequency fluctuation scenarios, typical peak and trough scenarios, extreme price event scenarios, multiple market coupling scenarios, and long-term capacity signal scenarios.

[0043] By constructing a structured, multi-dimensional scenario system, this study quantifies the revenue potential and robustness of energy storage under different time scales, market dimensions, and risk levels. This overcomes the limitations of traditional calculations that rely on single or random scenarios. The commercial value of energy storage stems from its combined capabilities in complex market environments, including capturing short-term price differences, providing ancillary services, mitigating extreme risks, coordinating multiple markets, and securing long-term guarantees. Therefore, test scenarios must be proactively designed to cover these core value dimensions. High-frequency fluctuation scenarios test intraday arbitrage and frequency regulation response capabilities; typical peak-valley scenarios assess basic returns based on regular price differences; extreme price event scenarios simulate the impact of tail risks on returns and system resilience; multi-market coupling scenarios verify the potential for joint optimization of energy and various ancillary service markets; and long-term capacity signal scenarios are used to capture long-term returns such as capacity compensation or leasing. These five types of scenarios together constitute a complete test environment from short-term to long-term, from normal to extreme, and from single to coordinated, ensuring that the final financial indicator distribution can truly and comprehensively reflect the expected return spectrum and risk profile of the project, providing a solid basis for investment decisions.

[0044] Please refer to Figure 3, which is a flowchart of step S3 in this embodiment. In this embodiment, step S3 includes: step S31, matching scenario types according to the time-series price signals of each market operation scenario and the preset charging and discharging rule library to obtain a charging and discharging strategy combination; step S32, obtaining the charging and discharging power command at each moment in each market operation scenario according to the time-series simulation results of each market operation scenario and the charging and discharging strategy combination, forming a charging and discharging power trajectory; step S33, based on the charging and discharging power trajectory, charging and discharging efficiency and initial state of charge, obtaining the state of charge change sequence according to energy conservation, forming an energy conversion path.

[0045] Realizing the value of energy storage relies on transforming high-dimensional, continuous market price signals into technically feasible and economically optimal charging and discharging actions in real time, and accurately tracking the resulting changes in the system's internal energy state. A pre-set charging and discharging rule base serves as the core knowledge, mapping different market scenarios into specific combinations of strategy thresholds. These threshold combinations act as decision rules in time-series simulations, driving the model to generate charging and discharging power trajectories that dynamically interact with the market price curve. Finally, combining charging and discharging efficiency and initial state of charge, and based on the law of conservation of energy, the power trajectory is integrated hourly to accurately derive the sequence of state of charge changes reflecting the actual energy storage state of the battery. This rigorously correlates abstract market prices and strategy rules with specific device power and battery energy state, laying the foundation for subsequent accurate calculation of revenue and cost losses.

[0046] Please refer to Figure 4, which is a flowchart of step S31 in this embodiment. In this embodiment, step S31 includes: step S311, extracting market feature vectors based on the time-series price signals of each market operation scenario; step S312, obtaining the strategy type identifier of the market operation scenario based on the similarity between the market feature vectors and all preset standard scenario vectors in the preset charging and discharging rule base; step S313, obtaining a charging and discharging strategy combination that includes at least a charging price trigger threshold and a discharging price trigger threshold based on the strategy type identifier and the corresponding basic threshold parameters in the preset charging and discharging rule base.

[0047] By transforming the complex market environment matching problem into a computable and quantifiable feature space similarity comparison problem, the system first extracts market feature vectors representing the core patterns from the original time-series price signals and maps them from the time domain to the feature domain. Then, by calculating the similarity between this vector and preset standard scenario vectors in the rule base, the system retrieves and matches the preset strategy type that best fits the current market characteristics from the knowledge base. Finally, based on this strategy type identifier, the system calls the optimized basic threshold parameters bound to it in the rule base to quickly generate accurate strategy combinations that can directly drive simulation. This ensures that strategy decisions are supported by expert experience and can respond in real time to the subtle characteristics of specific markets, thus laying an adaptive strategy foundation for subsequent accurate profit calculation.

[0048] Specifically, step S4 includes: Step S41, based on the charging / discharging power trajectory, calculating the discharge revenue and charging cost according to the clearly defined charging / discharging power sequence used in the energy market and the corresponding time-series price signal of the energy market, and calculating the sum and difference of the discharge revenue and charging cost within the calculation period to obtain the net energy arbitrage profit; and, based on the power or capacity sequence used for ancillary services and the corresponding time-series price signal of the ancillary service market, calculating the ancillary service capacity provided for each time step multiplied by the capacity price, adding the actual regulation performance contribution multiplied by the performance price, and summing the above items within the calculation period to obtain the ancillary service revenue; and, based on the available capacity committed by the energy storage system in the capacity market or capacity compensation mechanism and the contractually agreed capacity price, calculating the committed capacity... Multiply the quantity by the capacity price and then by the compensation period to obtain the capacity compensation revenue; Step S42: Based on the charging and discharging power trajectory and the state of charge sequence, identify and obtain the complete charging and discharging cycle sequence within the calculation period through the charging and discharging state switching detection and cycle partitioning algorithm. Each cycle contains its discharge depth data. Calculate the direct lifetime loss share based on the discharge depth data and the discharge depth-equivalent lifetime loss relationship curve of the energy storage system technology route. Calculate the unit loss cost through the direct lifetime loss share and the lifetime cost of the energy storage system to obtain the lifetime loss cost corresponding to that cycle; Step S43: Based on the energy arbitrage revenue, ancillary service revenue, capacity compensation revenue, and the lifetime loss cost mapped to the corresponding calculation period, perform alignment and superposition to obtain the scenario-level revenue decomposition result.

[0049] By precisely aligning and coupling market transactions, technical losses, and financial costs on a unified time scale, and based on the charging and discharging power trajectory, the system performs point-by-point multiplication of power and price, dot product and integral operations of time dimension summation, and strictly follows the settlement rules of different markets to accurately calculate the three types of external market revenue: energy arbitrage, ancillary services, and capacity compensation. Simultaneously, based on the same power trajectory and its resulting state of charge sequence, the system reverse-analyzes the discharge depth data for each cycle and calculates the accurate direct lifetime loss share through a decay model, which is then quantified as the current financial cost. Finally, within the same time period, the three types of revenue are aligned, superimposed, and decomposed with the dynamically generated costs, ensuring that every penny of revenue is deducted from the real battery life cost incurred to earn it. This makes the final net profit a solid and reliable financial benchmark reflecting the true profitability of the energy storage system in this scenario.

[0050] Specifically, step S5 includes: step S51, performing scenario probability weighted calculation based on the scenario-level revenue decomposition results of each market operation scenario to obtain the expected net revenue cash flow for each year of the entire life cycle; step S52, collecting and allocating the initial investment, operation and maintenance costs, replacement costs of the energy storage system, and the expected net revenue cash flow for each year to form a cross-year net cash flow distribution sequence; step S53, using the net present value obtained by period discounting calculation based on the net cash flow distribution sequence and the preset discount rate, the internal rate of return obtained by iterative calculation of the net present value, and the investment payback period obtained by summing the net cash flow of each period and performing linear interpolation as the financial evaluation indicators.

[0051] By extracting granular revenue and cost data from multiple scenarios into ultimate evaluation indicators with clear economic significance that can directly support investment decisions, investment decisions require clear and robust single value judgments. Therefore, it is necessary to converge discrete scenario-based results into deterministic full-lifecycle cash flows and apply financial axioms for value assessment. First, the revenue decomposition results of multiple scenarios are probability-weighted and merged to obtain a unique expected net revenue cash flow for each year. Then, this expected revenue and rigid costs occurring at different points in time are aggregated and allocated on the time axis to form a complete and realistic cross-year net cash flow sequence. Finally, the absolute value of this cash flow sequence is assessed by calculating the net present value using discounting, the profitability efficiency is assessed by iteratively solving the internal rate of return, and the investment payback period is assessed by cumulative interpolation. This results in a set of universally accepted financial evaluation indicators that combine technical insights with financial rigor, providing a solid and reliable quantitative foundation for investment decisions.

[0052] Specifically, step S6 includes: step S61, calculating the mean, standard deviation, and P90 quantile of each financial evaluation indicator based on the net present value sequence, internal rate of return sequence, and investment payback period sequence under each market operation scenario; step S62, performing life cycle consistency verification based on the distribution characteristics of the financial evaluation indicators to obtain verification results; step S63, if any two verification results pass, taking each financial evaluation indicator as the final calculation result and outputting a revenue calculation report; and if any two verification results fail, generating an adjustment instruction to adjust the charging and discharging strategy mapping rules.

[0053] By assessing the uncertainty of financial evaluation indicators and verifying their inherent logical rationality, a qualitative leap has been achieved from passive calculation to proactive risk management and self-optimization. First, the discrete financial indicator results under multiple scenarios are transformed into statistical distribution characteristics representing their central tendency, dispersion, and risk boundaries, thereby quantifying abstract market uncertainty into concrete and comparable risk parameters. Based on these distribution characteristics, pre-set verification rules are executed to logically diagnose the robustness and technical feasibility of the calculation results and output verification results. Based on these results, a judgment logic is executed; if it passes, the output is confirmed; if it fails, an adjustment instruction is automatically generated and fed back to the strategy mapping rule base. This ensures that the final calculation scheme is both able to withstand risk stress testing and is technically and economically self-consistent, elevating the entire method from a calculation tool to an auxiliary decision-making system with continuous optimization capabilities.

[0054] Specifically, step S62 includes: step S621, net present value consistency verification, calculating the coefficient of variation based on the mean and standard deviation of the net present value, and determining that the verification passes when the coefficient of variation is less than a preset first risk threshold; step S622, internal rate of return consistency verification, calculating the probability that the internal rate of return is less than a preset benchmark rate of return based on the internal rate of return, and determining that the verification passes when the probability is less than or equal to a preset second risk threshold; step S623, investment payback period consistency verification, determining that the verification passes when the P90 quantile of the investment payback period is less than a preset maximum payback period.

[0055] The preset first risk threshold is the maximum allowable value of the net present value variation coefficient, which depends on the investor's tolerance for return volatility. It is usually set between 0.3 and 0.5. In this embodiment, it is set to 0.4, which can effectively filter out project plans with excessive return volatility due to overly aggressive or ineffective market strategies.

[0056] The preset benchmark rate of return is the minimum rate of return that the project must achieve. It depends on the industry average rate of return, the cost of capital, and investor expectations. It is usually set between 8% and 12%. In this embodiment, it is set to 10%, which provides an objective and reasonable financial benchmark for assessing whether the internal rate of return has been met, and ensures that the calculation results meet the basic commercial feasibility.

[0057] The preset second risk threshold is the maximum permissible probability that the internal rate of return is lower than the benchmark rate of return. It depends on the investor's tolerance for the risk of failing to meet the profit target and is usually set between 10% and 20%. In this embodiment, it is set to 15%, which transforms the vague concept of the probability of achieving the target into a probabilistic indicator that can be precisely verified. It allows for a certain risk of the project not achieving the target, but controls it within an acceptable range.

[0058] The preset maximum payback period is the longest allowable period for investment payback under adverse scenarios. It depends on the investor's liquidity requirements and the characteristics of the project type. It is usually set between 7 and 10 years. In this embodiment, it is set to 8 years, focusing on the worst-case scenario. This ensures that even in extremely unfavorable market scenarios, the project can recover costs within an acceptable time, thereby managing the risk of long-term capital occupation.

[0059] Different financial indicators reveal the risks of the project in different dimensions. Net present value measures absolute return, and the coefficient of variation is used to measure the volatility of its unit return. If it is less than a threshold, it indicates that the expected return is relatively stable. For internal rate of return, which measures profitability efficiency, the probability of it being lower than the benchmark return is directly calculated. If it is less than a threshold, it indicates that the probability of achieving profitability is high and the project is feasible. For investment payback period, which measures liquidity risk, we focus on the most pessimistic scenario, namely the high-risk quantile P90, to ensure that the cost can be recovered within the maximum acceptable period even in adverse scenarios. The three rules together verify the calculation results from three dimensions: return stability, reliability of profitability achievement, and capital security in extreme situations. The output results are direct and objective data basis for strategy iteration or result confirmation in subsequent steps.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the revenue of the energy storage market based on a full life-cycle financial model, characterized in that, include: Step S1: Based on the technical roadmap of the target energy storage system, construct a basic parameter model including cost parameters, technical parameters, capacity decay model, and grid connection constraint parameters. Step S2: Based on the basic parameter model, generate multiple market operation scenarios according to the price fluctuation characteristics of historical electricity market price sequences. Step S3: Determine the charging and discharging power trajectory and energy conversion path under each market operation scenario based on each scenario and a preset charging and discharging rule base. Step S4: Based on the charging and discharging power trajectory and the energy conversion path, calculate energy arbitrage revenue, ancillary service revenue, and capacity compensation revenue respectively, and map the lifetime loss cost caused by the number of charging and discharging cycles and the depth of discharge to the corresponding calculation period to obtain scenario-level revenue decomposition results. Step S5: Input the scenario-level revenue decomposition results into the full life-cycle cash flow model to form a cross-year cash flow distribution sequence, and calculate several financial evaluation indicators based on the cash flow distribution sequence. Step S6: Based on the distribution characteristics of each financial evaluation indicator under each market operation scenario, perform a life-cycle consistency check, and based on the check results of each financial evaluation indicator, choose to output a revenue calculation report or adjust the charging and discharging strategy mapping rules.

2. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 1, characterized in that, Step S1 includes: Step S11, determining the cost parameters, including total project investment, capital ratio, fixed operation and maintenance costs, and charge / discharge operation and maintenance costs, based on the project investment estimate and operation and maintenance plan; Step S12, obtaining the technical parameters, including installed capacity, rated energy storage capacity, charge / discharge efficiency, maximum charge / discharge rate, initial state of charge range, and cycle life, based on the technical specifications and performance test reports of the energy storage equipment; Step S13, fitting an empirical attenuation curve based on the attenuation characteristics of the actual technical route to establish a capacity attenuation model; Step S14, analyzing and determining the grid connection constraint parameters, including maximum allowable charge / discharge power, maximum charge / discharge rate, grid access point capacity limit, minimum duration required to participate in different markets, and response speed, based on grid connection technology regulations and target market rules; Step S15, integrating the parameters and models determined in steps S11 to S14 to construct the basic parameter model.

3. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 2, characterized in that, Step S2 includes: Step S21, extracting key statistical features and typical price curve patterns based on the fluctuation characteristics, peak-valley characteristics and seasonality of the historical price series of the electricity market; Step S22, performing time clustering based on the key statistical features and typical price curve patterns to obtain several market operation scenarios; Step S23, mapping and verifying the compatibility of each market operation scenario with the grid connection constraint parameters and technical parameters in the basic parameter model to obtain a set of operation scenarios.

4. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 3, characterized in that, The market operation scenarios in step S22 include at least high-frequency fluctuation scenarios, typical peak and trough scenarios, extreme price event scenarios, multiple market coupling scenarios, and long-term capacity signal scenarios.

5. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 4, characterized in that, Step S3 includes: Step S31, matching scenario types according to the time-series price signals of each market operation scenario and the preset charging and discharging rule library to obtain a charging and discharging strategy combination; Step S32, obtaining the charging and discharging power command at each moment in each market operation scenario according to the time-series simulation results of each market operation scenario and the charging and discharging strategy combination, forming a charging and discharging power trajectory; Step S33, based on the charging and discharging power trajectory, charging and discharging efficiency and initial state of charge, obtaining the state of charge change sequence according to energy conservation, forming an energy conversion path.

6. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 5, characterized in that, Step S31 includes: Step S311, extracting market feature vectors based on the time-series price signals of each market operation scenario; Step S312, obtaining the strategy type identifier of the market operation scenario based on the similarity between the market feature vectors and all preset standard scenario vectors in the preset charging and discharging rule base; Step S313, obtaining a charging and discharging strategy combination that includes at least a charging price trigger threshold and a discharging price trigger threshold based on the strategy type identifier and the corresponding basic threshold parameters in the preset charging and discharging rule base.

7. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 6, characterized in that, Step S4 includes: Step S41, calculating energy arbitrage revenue, ancillary service revenue, and capacity compensation revenue by performing dot product and integral operations and market rule settlement based on the charging and discharging power trajectory and the time-series price signal corresponding to the market operation scenario; Step S42, calculating the direct lifetime loss share based on the discharge depth data determined by the charging and discharging power trajectory and the state of charge sequence, and thereby calculating the lifetime loss cost; Step S43, performing positional superposition of the energy arbitrage revenue, ancillary service revenue, capacity compensation revenue, and lifetime loss cost mapped to the corresponding calculation period to obtain the scenario-level revenue decomposition result.

8. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 7, characterized in that, Step S5 includes: Step S51, performing scenario probability weighted calculation based on the scenario-level revenue decomposition results of each market operation scenario to obtain the expected net cash flow for each year of the entire life cycle; Step S52, collecting and allocating the initial investment, operation and maintenance costs, replacement costs of the energy storage system, and the expected net cash flow for each year to form a cross-year net cash flow distribution sequence; Step S53, using the net present value obtained by period discounting calculation based on the net cash flow distribution sequence and the preset discount rate, the internal rate of return obtained by iterative calculation of the net present value, and the investment payback period obtained by summing the net cash flow of each period and performing linear interpolation calculation as the financial evaluation indicators.

9. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 8, characterized in that, Step S6 includes: Step S61, calculating the mean, standard deviation, and P90 quantile of each financial evaluation indicator based on the net present value sequence, internal rate of return sequence, and investment payback period sequence under each market operation scenario; Step S62, performing life cycle consistency verification based on the distribution characteristics of the financial evaluation indicators to obtain verification results; Step S63, if any two verification results pass, taking each financial evaluation indicator as the final calculation result and outputting a revenue calculation report; and if any two verification results fail, generating an adjustment instruction to adjust the charging and discharging strategy mapping rules.

10. The method for calculating the revenue of the energy storage market based on a full life-cycle financial model according to claim 9, characterized in that, Step S62 includes: Step S621, Net Present Value (NPV) consistency check, calculating the coefficient of variation based on the mean and standard deviation of the NPV, and determining that the check passes when the coefficient of variation is less than a preset first risk threshold; Step S622, Internal Rate of Return (IRR) consistency check, calculating the probability that the IRR is less than a preset benchmark rate of return based on the IRR, and determining that the check passes when the probability is less than or equal to a preset second risk threshold; Step S623, Investment Recovery Period (IPR) consistency check, determining that the check passes when the P90 quantile of the IPR is less than a preset maximum recovery period.

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