Energy storage carbon asset evaluation model based on net present value-AHP-entropy weight method

Abstract

The invention discloses an energy storage carbon asset evaluation model based on a net present value-AHP-entropy weight method, and belongs to the technical field of carbon asset value evaluation. According to the model, an evaluation boundary and basic parameters are determined firstly, then the improvement amplitude of carbon assets on project financial indexes is calculated through a net present value method, then a project investment and carbon asset development evaluation index system is constructed, an AHP-entropy weight combination method is adopted to calculate index weights, a comprehensive score is obtained in combination with an interpolation method, and finally, the evaluation result is obtained. And finally, carbon asset development grades are divided through a two-dimensional evaluation matrix and sensitivity analysis. According to the method, the problems of single dimension, subjective weight and non-reproducible process of a traditional evaluation method are solved, accurate quantification of energy storage carbon assets is realized, a scientific basis is provided for carbon transaction and investment decision making, and the method is adaptive to CCER and carbon economic markets.

Description

Technical Field

[0001] This invention relates to the field of carbon asset valuation technology, specifically to a carbon asset valuation model for energy storage based on the net present value-AHP-entropy weight method. It is applicable to the quantitative assessment of the carbon asset development potential, investment decision support, and transaction pricing reference of various types and capacities of energy storage power stations, such as electrochemical energy storage and vanadium redox flow energy storage. It is particularly suitable for the carbon asset valuation scenario of energy storage projects under the my country CCER market and carbon inclusive market environment. Background Technology

[0002] The global transformation towards a low-carbon economy is driving carbon assets to become core environmental rights and new financial assets for enterprises. With its carbon reduction functions such as peak shaving and frequency regulation, smoothing grid fluctuations, and reducing dependence on fossil fuels, energy storage power stations are gradually showing their potential for carbon asset development and have become an important target for enterprises to participate in carbon trading.

[0003] Currently, the carbon asset assessment field mainly adopts traditional single assessment methods such as the income approach and the cost approach. Among them, the net present value approach, as the core application of the income approach, can quantify the present value of the project's future cash flows, but it only focuses on the financial dimension and does not include non-financial key factors such as policy adaptability and technological maturity, resulting in one-sided assessment results. The analytic hierarchy process (AHP) can integrate multi-dimensional qualitative and quantitative indicators, but it relies on expert subjective scoring, and the weight allocation is easily affected by experience bias. Although the entropy weight method can achieve objective weighting through data dispersion, it cannot reflect the industry policy guidance and practical experience value.

[0004] The existing technology has three major shortcomings: First, a unified carbon asset assessment index system and model standard for energy storage projects has not yet been established. Existing emission reduction methodologies (such as CMS-080-V01) have limited applicability, and the grid-connected energy storage system methodology published by Vera is still in the consultation phase, lacking assessment tools that can be directly implemented. Second, traditional assessment methods do not clearly define the data flow logic, and the connection between financial and non-financial indicators is unclear, making the assessment process difficult to reproduce. Third, the setting of key parameters lacks a basis. For example, the judgment matrix of the AHP method does not have clear construction rules, there is no unified standard for the upper and lower limits of indicator scores, the methodological hierarchy is vague, and the adjustment of the combined weight coefficients lacks specific guidance, making the scientificity and credibility of the assessment results insufficient and unable to meet the actual needs of corporate carbon asset development decisions and carbon trading market pricing. Summary of the Invention

[0005] (a) Purpose of the invention

[0006] The purpose of this invention is to provide an energy storage carbon asset assessment model based on the net present value-AHP-entropy weight method, which solves the problems of traditional assessment methods such as single dimension, unclear data flow, lack of basis for setting key parameters, and strong subjectivity in weight allocation. It realizes the accurate quantification of the carbon asset value of energy storage projects, the reproducibility of the assessment process, and the practicality of decision support, providing a scientific basis for enterprises to participate in carbon trading and formulate carbon asset development strategies.

[0007] (II) Technical Solution A carbon asset valuation model for energy storage based on the net present value-AHP-entropy weight method includes the following steps: (1) Determination of evaluation boundaries and basic parameters 1. In accordance with the requirements of Article 2 of Section IV, Accounting Treatment, in the Interim Provisions on Accounting Treatment Related to Carbon Emission Trading issued by the Ministry of Finance, the recognition and measurement standards for carbon assets should be clarified; the MRV (Monitorable, Reportable, Verifiable) principle should be strictly followed to define the carbon quota accounting boundary, emission reduction baseline parameters, and additionality demonstration standards for energy storage projects. The emission reduction baseline should refer to the core parameters of the CMS-080-V01 methodology in the National Greenhouse Gas Voluntary Emission Reduction Methodology (Sixth Batch) Filing List.

[0008] 2. Select typical energy storage project scenarios, including 10MW / 35MWh, 60MW / 120MWh, 300MW / 600MWh electrochemical energy storage projects and 5MW / 30MWh vanadium redox flow storage projects; collect basic data for each scenario, including technology maturity level, unit investment cost, feed-in tariff, project calculation period, discount rate, carbon price level, carbon verification cost, etc. The basic data are derived from industry database statistics, project feasibility study reports and publicly available data from the carbon trading market.

[0009] 3. Set upper and lower limits for indicator scores: For each secondary indicator, set upper and lower limits for its actual value (…). , ) and upper and lower limits of scores ( , Based on the domestic database of existing energy storage projects (covering measured data from over 200 energy storage projects) and the typical and extreme values ​​in the "White Paper on the Development of the Energy Storage Industry," the parameters are determined to ensure objectivity and industry suitability.

[0010] (2) Net Present Value Approach for Carbon Asset Financial Valuation 1. Construct a Net Present Value (NPV) calculation model, the formula is as follows:

[0011] In the formula: For the first Annual cash inflows include carbon asset trading revenue (annual emission reductions × carbon price) and electricity sales revenue (electricity generation × electricity price). For the first Annual cash outflow includes initial investment costs, annual operation and maintenance costs, and carbon verification and certification costs. The discount rate is risk-free (ranging from 4% to 8%, with the industry benchmark value of 7% used by default). The calculation period for the project is 20 years (the default setting for energy storage projects).

[0012] 2. Calculate the project's financial internal rate of return (FIRR) and payback period (…). The formula is as follows:

[0013]

[0014] In the formula: The number of years in which the cumulative net cash flow is positive or zero for the first time; the benchmark value for FIRR is set at 7% (referencing the financial feasibility standard for power grid projects).

[0015] 3. Calculate NPV and FIRR for both scenarios with and without carbon asset income. The improvement of project financial indicators by carbon assets is quantified. The IRR with carbon assets, IRR without carbon assets, and the improvement obtained from the above calculations will be used as the actual values ​​of the corresponding secondary indicators in the subsequent AHP-entropy weight combination method. .

[0016] (3) Multidimensional evaluation using the AHP-entropy weight combination method 1. Construct an evaluation indicator system: This system consists of two primary indicators: project investment and carbon asset development, with seven secondary indicators, as shown in the table below:

[0017] 2. Determine subjective weights using the Analytic Hierarchy Process (AHP): 1) Based on the 1-9 scale (1 = equally important, 3 = slightly important, 5 = significantly important, 7 = strongly important, 9 = extremely important, even numbers are median values), a judgment matrix of 7 secondary indicators is constructed, as shown in the table below (taking Example 1 as an example): 2) Calculate the largest eigenvalue of the judgment matrix. To perform a consistency check, the formula is: , ( The average random consistency index, hour ),when When the condition is met, the judgment matrix satisfies the consistency requirement.

[0018] 3) Subjective weights are obtained after normalization. .

[0019] 3. Determine objective weights using the entropy weight method: 1) Actual values ​​of the 7 secondary indicators Standardization process is performed to obtain (Positive indicator) or (Negative indicator).

[0020] 2) Calculate the information entropy of each indicator. The formula is as follows:

[0021] In the formula: To assess the number of project scenarios (this invention) );like Then define .

[0022] 3) Calculate objective weights The formula is as follows:

[0023] In the formula: Number of secondary indicators ( ).

[0024] 4. Calculate the portfolio weights: ,in , , The value is determined based on the assessment needs: when the focus is on expert experience and policy guidance, the value is set to... (like , When prioritizing data objectivity, set... (like , Balanced assignment is used by default. , .

[0025] 5. Calculate the individual scores of the indicators using interpolation, and then calculate the weighted total score by combining the combined weights: 1) Formula for calculating individual scores:

[0026] In the formula: This refers to the basic data collected in step 1.2 or the financial indicators calculated in step 2.3 (such as carbon-free asset IRR, IRR improvement rate, etc.). , The upper and lower limits of the actual values ​​of the indicators set in step 1.3; , These represent the upper and lower limits of the indicator score.

[0027] 2) Weighted total score calculation formula:

[0028] 3) By using the normalization method To convert to a percentage score, use the following formula: ,in , These represent the maximum and minimum weighted total scores across all evaluation scenarios.

[0029] (4) Comprehensive assessment and scenario analysis 1. Combining the financial indicators of the net present value method (NPV positive or negative, FIRR whether it is higher than the benchmark) with the percentage score of the AHP-entropy weight combination method, a two-dimensional evaluation matrix is ​​constructed to classify carbon asset development into three levels: high value (NPV>0 and FIRR≥8%). (Points), mid-value (NPV>0 and 7%≤FIRR<8% and 50 points≤) (Points), low value (NPV≤0 or FIRR<7%) point).

[0030] 2. Conduct sensitivity analysis: Select carbon price (fluctuation range 30-120 RMB / ton CO2), discount rate (fluctuation range 4%-10%), and methodology level (regional level → national level → international level) as key sensitivity factors. Use the single-factor variation method to analyze the impact of each factor on NPV and The impact range is determined, and the optimal carbon asset development strategy is output.

[0031] (III) Beneficial Effects The system clearly establishes a data flow chain of "basic data collection → financial indicator calculation → multi-dimensional indicator assignment → combined weight calculation → comprehensive scoring", and sets key details such as the construction of the AHP judgment matrix, consistency verification, and the basis for setting the upper and lower limits of indicators, thus solving the problem that the evaluation process of traditional methods is not reproducible.

[0032] By combining AHP-entropy weights, both expert experience and data objectivity are taken into account, and the adjustment rules for the combined weight coefficients are clearly defined. This ensures the integration of policy guidance and industry experience, avoids the bias of single weighting, and improves the credibility and adaptability of the evaluation results.

[0033] The criteria for classifying the "methodological levels" and the scoring rules are refined, and the industry data sources for the upper and lower limits of each indicator's score are clarified. This solves the shortcomings of traditional evaluation indicators being vague and parameter settings being unfounded, and ensures the fairness and comparability of the evaluation results.

[0034] Contextualized assessment schemes are designed for various types and capacities of energy storage projects. The improvement effect of carbon assets on project financial indicators is quantified. Combined with a two-dimensional assessment matrix and sensitivity analysis, precise development strategies are output, which are adapted to the actual needs of the CCER market and the carbon inclusive market, and provide direct support for corporate carbon asset trading and investment decisions.

[0035] It strictly adheres to national regulations on carbon emission rights accounting and the MRV principle, is compatible with existing emission reduction methodologies for energy storage projects, and can dynamically adjust indicator parameters as international and domestic methodologies are updated, thus possessing long-term applicability. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0037] To make the technical solution, objective, and beneficial effects of the present invention clearer, the following describes specific embodiments and... Figure 1 The present invention will be further described in detail below. This embodiment is only used to explain the present invention and is not intended to limit the scope of protection of the present invention.

[0038] The core of this invention lies in integrating the financial quantification capabilities of the Net Present Value (NPV) method with the multi-dimensional comprehensive evaluation advantages of the AHP-Entropy Weight combination method, constructing a two-dimensional evaluation system of "financial indicators + non-financial indicators." This addresses the technical shortcomings of traditional evaluation methods, such as single-dimensionality, subjective weighting, and non-reproducible processes. The implementation process strictly adheres to the Ministry of Finance's "Interim Provisions on Accounting Treatment Related to Carbon Emission Trading" and the MRV principle. Parameter settings are based on measured data from over 200 domestic energy storage projects and typical values ​​from the "Energy Storage Industry Development White Paper," ensuring the scientific validity and reproducibility of the evaluation results.

[0039] Example 1: Carbon Asset Assessment of a 10MW / 35MWh Electrochemical Energy Storage Project (1) Determination of evaluation boundaries and basic parameters 1. Accounting Boundaries: Refer to the CMS-080-V01 methodology to define the project's emission reduction baseline, clarify that the carbon emission reduction generated by the peak shaving and frequency regulation of energy storage power stations is an additional emission reduction and is included in the scope of carbon asset accounting; follow the MRV principle to determine the carbon emission reduction monitoring, reporting, and verification process.

[0040] 2. Basic Parameter Collection: The initial investment cost of the project is 20 million yuan, the unit investment cost is 1.71 yuan / Wh, the annual operation and maintenance cost is 500,000 yuan, the on-grid electricity price is 0.601 yuan / kWh, the carbon price is 60 yuan / ton CO2, the annual carbon emission reduction is 1,000 tons CO2, the carbon verification cost is 50,000 yuan / year, the project calculation period is 20 years, and the discount rate is taken as the industry benchmark value of 7%; the technology maturity level is "very mature", and the emission reduction methodology level is regional level.

[0041] 3. Setting upper and lower limits for indicators: The upper and lower limits for technology maturity score are 0-10 points, the upper and lower limits for actual unit investment cost are 1.2-2.5 yuan / Wh, the IRR benchmark value is 7%, the upper and lower limits for methodology level score are 0-25 points, and the upper and lower limits for IRR improvement score are 0-25 points. All the above parameters are determined based on typical values ​​and extreme values ​​in the industry database.

[0042] (2) Net Present Value Approach for Carbon Asset Financial Valuation 1. Cash Inflow Calculation Annual electricity sales revenue = 10MW × 8760h × 0.8 (utilization rate) × 0.601 yuan / kWh = 4.276 million yuan Annual carbon asset income = 1000 tons × 60 yuan / ton = 60,000 yuan Total annual cash inflow = 427.6 + 6 = 433.6 million yuan 2. Cash outflow calculation The initial investment cost is 20 million yuan (one-time investment in year 0), the annual operation and maintenance cost is 500,000 yuan, the annual carbon verification cost is 50,000 yuan, and the total annual cash outflow is 550,000 yuan.

[0043] 3. Calculation of financial indicators According to the net present value formula Calculate the financial indicators for scenarios with and without carbon assets: Carbon asset scenario: Ten thousand yuan, Investment recovery period Year; Carbon-free asset scenario: Ten thousand yuan, Investment recovery period Year; The improvement in IRR due to carbon assets = .

[0044] 4. Indicator Alignment: The IRR with carbon asset income, the IRR without carbon asset income, and the magnitude of IRR improvement will be used as the actual values ​​of the secondary indicators in the AHP-Entropy Weight Combination Method. .

[0045] (3) Multidimensional evaluation using the AHP-entropy weight combination method 1. Construct an evaluation index system: The primary indexes are project investment and carbon asset development, which are further divided into seven secondary indexes: technology maturity, unit investment cost, electricity price level, IRR of carbon assets, IRR of non-carbon assets, methodological level, and IRR improvement. The scoring standards for each index are strictly matched with the settings in the invention content.

[0046] 2. Subjective weight calculation using the AHP method 1) Constructing a judgment matrix: Based on the 1-9 scale method, experts score the relative importance of the seven secondary indicators to construct a judgment matrix.

[0047] 2) Consistency test: Calculate the largest eigenvalue of the judgment matrix. Consistency indicators Consistency ratio This satisfies the consistency requirements.

[0048] 3) Determination of subjective weights: After normalization, the subjective weights of each indicator are obtained. : Technology maturity 10%, unit investment cost 20%, electricity price level 20%, carbon asset IRR 10%, carbon-free asset IRR 10%, methodology level 20%, IRR improvement margin 10%.

[0049] 3. Calculation of Objective Weights Using the Entropy Weight Method 1) Standardization of indicators: This involves standardizing the actual values ​​of the seven secondary indicators. Perform positive / negative standardization to obtain the standardization matrix. .

[0050] 2) Calculate information entropy: based on the formula Calculate the information entropy of each indicator. The values ​​are 0.92, 0.88, 0.90, 0.91, 0.89, 0.87, and 0.93, respectively.

[0051] 3) Determination of objective weights: based on the formula The objective weights of each indicator were calculated. Technology maturity 13%, unit investment cost 18%, electricity price level 17%, carbon asset IRR 12%, carbon-free asset IRR 16%, methodology level 15%, IRR improvement margin 9%.

[0052] 4. Combined weight calculation: Balanced assignment is used. , According to the formula The resulting portfolio weights are: technology maturity 11.5%, unit investment cost 19%, electricity price level 18.5%, carbon asset IRR 11%, carbon-free asset IRR 13%, methodology level 17.5%, and IRR improvement margin 9.5%.

[0053] 5. Calculation of Overall Score 1) Calculation of individual scores: based on the interpolation formula The scores for each indicator were calculated as follows: technology maturity 9.6 points, unit investment cost 7.9 points, electricity price level 5.01 points, carbon asset IRR 5.73 points, carbon-free asset IRR 5.58 points, methodology level 15 points, and IRR improvement margin 15.88 points.

[0054] 2) Weighted total score calculation: based on the formula The weighted total score is calculated. point.

[0055] 3) Percentage Conversion: The weighted total score is normalized and converted into a percentage score. In this embodiment... point.

[0056] (4) Comprehensive evaluation and sensitivity analysis 1. Two-dimensional matrix assessment: under the scenario of carbon assets , It is in the 7%-8% range. The score is in the 50-70 range, indicating that the carbon asset development level of this project is medium value.

[0057] 2. Sensitivity Analysis: Carbon price, discount rate, and methodology level were selected as sensitive factors, and a single-factor variation analysis was conducted. When the carbon price rises to 80 yuan / ton, the annual income from carbon assets increases to 80,000 yuan. Increased to 2.8 million yuan Increased to 8.2%, The score has been raised to 72, upgrading the project to high value. When the discount rate drops to 5%, Increased to 2.1 million yuan Increased to 8.0%. Improved to 68 points; When the methodology level is upgraded to the national level, the individual score for the methodology level increases to 20 points. The score has been raised to 66.

[0058] 4.3 Development Strategy Recommendations: It is recommended that project owners prioritize promoting carbon price increases or methodological upgrades, and participate in carbon trading to improve project returns.

[0059] Example 25MW / 30MWh Vanadium Redox Flow Storage Project Carbon Asset Assessment (1) Determination of evaluation boundaries and basic parameters 1. Accounting Boundaries: Referring to the CMS-080-V01 methodology and MRV principles, the scope of carbon emission reduction accounting for vanadium redox flow storage projects is defined, and the requirements for monitoring and verifying emission reductions throughout the entire life cycle are clarified.

[0060] 2. Basic Parameter Collection: The initial investment cost of the project is RMB 18 million, the unit investment cost is RMB 2.0 / Wh, the annual operation and maintenance cost is RMB 400,000, the grid-connected electricity price is RMB 0.62 / kWh, the carbon price is RMB 60 / ton CO2, the annual carbon emission reduction is 800 tons CO2, the carbon verification cost is RMB 40,000 / year, the project calculation period is 20 years, and the discount rate is 7%; the technology maturity level is "mature", and the emission reduction methodology level is regional level.

[0061] 3. Setting upper and lower limits for indicators: consistent with Example 1 to ensure uniformity of evaluation standards.

[0062] (2) Net Present Value Approach for Carbon Asset Financial Valuation 1. Cash Inflow Calculation Annual electricity sales revenue = 5MW × 8760h × 0.75 (utilization rate) × 0.62 yuan / kWh = 1.945 million yuan Annual carbon asset income = 800 tons × 60 yuan / ton = 48,000 yuan Total annual cash inflow = 194.5 + 4.8 = 199.3 million yuan 2. Cash outflow calculation: Initial investment cost of RMB 18 million (Year 0), annual operation and maintenance cost of RMB 400,000, annual carbon verification cost of RMB 40,000, total annual cash outflow = RMB 440,000.

[0063] 3. Calculation of financial indicators Carbon asset scenario: Ten thousand yuan, Investment recovery period Year; Carbon-free asset scenario: Ten thousand yuan, Investment recovery period Year; The improvement in IRR due to carbon assets = .

[0064] (3) Multidimensional evaluation using the AHP-entropy weight combination method 1. Actual value of the indicator : Technology maturity (5 points, corresponding to "mature"), unit investment cost (2.0 yuan / Wh), electricity price level (0.62 yuan / kWh), carbon asset IRR (7.2%), carbon-free asset IRR (6.5%), methodology level (regional level, 15 points), IRR improvement (10.77%).

[0065] 2. Subjective weight calculation: The same judgment matrix as in Example 1 was used, and the consistency test was passed. The subjective weight is consistent with that in Example 1.

[0066] 3. Objective weight calculation: After standardization and information entropy calculation, the objective weights of each indicator are obtained, which are consistent with the objective weight logic in Example 1.

[0067] 4. Combined weight calculation: using , The balanced assignment is used to calculate the combined weights.

[0068] 5. Overall Score Calculation: Individual scores are calculated using interpolation, and the weighted total score is then normalized and converted to a percentage score. In this embodiment... point.

[0069] (4) Comprehensive evaluation and sensitivity analysis 1. Two-dimensional matrix assessment: under the scenario of carbon assets , It is in the 7%-8% range. The score is in the 50-70 range, indicating that the carbon asset development level of this project is medium to high value.

[0070] 2. Sensitivity Analysis: When the methodology level is upgraded to the national level, the score for the methodology level increases to 20 points. Improved to 71 points Increase to 7.8%, the project is upgraded to high value; when the carbon price increases to 90 yuan / ton, Increased to 1.5 million yuan Increased to 8.1%, The score was raised to 73.

[0071] 3. Development Strategy Recommendations: It is recommended that project owners promote the filing of emission reduction methodologies with the national level, while also paying attention to carbon price trends and participating in carbon trading when appropriate.

[0072] This specific implementation method solves the three core defects of traditional evaluation methods by organically combining the net present value method and the AHP-entropy weight combination method: To address the issue of traditional methods having only one dimension, a two-dimensional evaluation system of "financial indicators + non-financial indicators" is constructed. This system not only quantifies the financial contribution of carbon assets but also integrates non-financial factors such as technological maturity and methodological level to achieve a comprehensive evaluation. To address the issue of strong subjectivity in weight allocation, an AHP-entropy weight combination is adopted, which takes into account both expert experience and data objectivity. At the same time, the adjustment rules for the combined weight coefficients are clearly defined to avoid bias in single weighting. To address the issue of the non-reproducible evaluation process, the sources of basic parameters, the basis for setting upper and lower limits of indicators, the construction of judgment matrices, and the consistency verification process are clearly defined, forming a traceable and reproducible evaluation chain.

Claims

1. A carbon asset valuation model for energy storage based on the net present value-AHP-entropy weight method, characterized in that, Includes the following steps: (1) Determine the assessment boundary and basic parameters: Define the carbon quota accounting boundary, emission reduction baseline and additionality demonstration standard for energy storage projects, select typical energy storage project scenarios, collect the basic data required for assessment, and determine the actual value and upper and lower limits of each assessment indicator; (2) Net present value financial evaluation: Construct a net present value calculation model, calculate the financial indicators of the energy storage project under two scenarios: carbon asset income and carbon asset income, and quantify the improvement of the project's financial indicators by carbon assets; (3) Multi-dimensional evaluation using the AHP-entropy weight combination method: Construct an evaluation index system that includes indicators related to project investment and carbon asset development. Use the AHP method to determine subjective weights and the entropy weight method to determine objective weights. After calculating the combined weights, calculate the individual scores using the interpolation method. Convert the total score into a percentage score by normalizing the weighted total score. (4) Comprehensive assessment and scenario analysis: Combine financial assessment results with multi-dimensional comprehensive scores to construct an assessment matrix, classify carbon asset development levels, select key sensitive factors to conduct sensitivity analysis, and output carbon asset development strategies.

2. The energy storage carbon asset assessment model based on the net present value-AHP-entropy weight method according to claim 1, characterized in that, The determination of the assessment boundary and basic parameters in step (1) refers to the "Interim Provisions on Accounting Treatment Related to Carbon Emission Trading" issued by the Ministry of Finance to clarify the carbon asset recognition and measurement standards, and follows the MRV principle. The emission reduction baseline refers to the core parameters of the CMS-080-V01 methodology in the "National Greenhouse Gas Voluntary Emission Reduction Methodology (Sixth Batch) Filing List".

3. The energy storage carbon asset assessment model based on net present value-AHP-entropy weight method according to claim 1, characterized in that, The typical energy storage project scenarios mentioned in step (1) include electrochemical energy storage and vanadium redox flow storage projects. The basic data include technology maturity level, unit investment cost, grid connection price, project calculation period, discount rate, carbon price level, and carbon verification cost.

4. The energy storage carbon asset assessment model based on net present value-AHP-entropy weight method according to claim 1, characterized in that, The actual values ​​and upper and lower limits of scores for each evaluation indicator in step (1) are determined based on the database of domestic energy storage projects and the "White Paper on the Development of Energy Storage Industry".

5. The energy storage carbon asset assessment model based on net present value-AHP-entropy weight method according to claim 1, characterized in that, The formula for the net present value calculation model mentioned in step (2) is as follows: The financial indicators include the financial internal rate of return (FIRR) and the payback period. ,in For the first Annual cash inflow For the first Annual cash outflow For risk-free discount rate, The calculation period for the project.

6. The energy storage carbon asset assessment model based on net present value-AHP-entropy weight method according to claim 1, characterized in that, The evaluation index system described in step (3) includes 2 primary indicators and 7 secondary indicators. The primary indicators are project investment and carbon asset development. The secondary indicators include technology maturity, unit investment cost, electricity price level, IRR with carbon asset income, IRR without carbon asset income, methodology level, and improvement of internal rate of return by carbon assets.

7. The energy storage carbon asset assessment model based on the net present value-AHP-entropy weight method according to claim 6, characterized in that, The AHP method described in step (3) uses the 1-9 scale to construct the judgment matrix and determines the subjective weights after consistency verification; the entropy weight method determines the objective weights by standardizing the actual values ​​of the indicators and calculating the information entropy.

8. The energy storage carbon asset assessment model based on net present value-AHP-entropy weight method according to claim 1, characterized in that, The combined weights mentioned in step (3) are obtained through the formula The calculation is performed, where α+β=1, with the default values ​​of α=0.5 and β=0.

5. The values ​​of α and β can be adjusted based on expert experience and the need for data objectivity.

9. The energy storage carbon asset assessment model based on the net present value-AHP-entropy weight method according to claim 1, characterized in that, The interpolation method described in step (3) is used to calculate the individual item score, and the normalization method is used to convert the score to a percentage. The formula for the individual item score is as follows: The formula for a percentage score is: .

10. The energy storage carbon asset assessment model based on the net present value-AHP-entropy weight method according to claim 1, characterized in that, The key sensitive factors mentioned in step (4) include carbon price, discount rate, and methodology level. Carbon asset development levels are divided into high value, medium value, and low value. Sensitivity analysis is carried out using the single-factor variation method.

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