A micro-grid investment benefit evaluation method and system taking low-carbon value into account

Abstract

The application discloses a micro-grid investment benefit evaluation method and system considering low-carbon value, comprising the following steps: S1, constructing a multi-dimensional investment benefit index system; S2, determining the comprehensive weight of each index in the comprehensive evaluation index system based on improved AHP according to the multi-dimensional investment benefit index system; and S3, performing investment benefit evaluation based on grey fuzzy comprehensive evaluation according to the comprehensive weight. The application can more scientifically and comprehensively reflect the real investment value of the micro-grid, and effectively supports investment decision and policy incentive of the micro-grid project.

Description

Technical Field

[0001] This invention belongs to the field of microgrid technology, specifically relating to a method and system for evaluating the investment benefits of microgrids that incorporates low-carbon value. Background Technology

[0002] Microgrids, as an important carrier for the efficient utilization of distributed energy, play an increasingly crucial role in promoting the consumption of renewable energy and reducing carbon emissions. Currently, some progress has been made in evaluating the investment benefits of microgrids, but existing research still has significant shortcomings: most evaluation models only focus on economic benefit indicators, such as net present value and internal rate of return, failing to reasonably incorporate low-carbon value, resulting in an incomplete assessment of the overall benefits of microgrids. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a method and system for evaluating the investment benefits of microgrids that incorporates low-carbon value.

[0004] To achieve the above objectives, the present invention provides the following solution: A method for evaluating the investment benefits of microgrids that incorporates low-carbon value, comprising: Step S1: Construct a multi-dimensional investment benefit indicator system; Step S2: Based on the multidimensional investment benefit index system, determine the comprehensive weight of each index in the comprehensive evaluation index system based on the improved AHP. Step S3: Based on the comprehensive weight, conduct an investment benefit assessment using grey fuzzy comprehensive evaluation.

[0005] As a preferred approach, the multi-dimensional investment benefit indicator system includes: low-carbon indicators, economic indicators, and technical indicators. Among them, low-carbon indicators include: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction. Economic indicators include: total investment in microgrid projects, total cost of microgrid projects, microgrid project revenue, and return on equity. Technical indicators include: power supply reliability, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

[0006] Preferably, step S2 includes: Construct a hierarchical analysis diagram; The indicators at each level are compared pairwise to form a judgment matrix; Based on the judgment matrix, a consistency check of the indicator weights is performed to obtain the weight matrix; The scores are calculated and sorted based on the weight matrix, and the comprehensive weight of the indicator is obtained by multiplying the weights of each level of indicators.

[0007] This invention also provides a microgrid investment benefit assessment system that incorporates low-carbon value, comprising: The first processing module is used to construct a multi-dimensional investment benefit indicator system; The second processing module uses the multidimensional investment benefit index system and the improved AHP to determine the comprehensive weight of each index in the comprehensive evaluation index system. The third processing module performs investment benefit assessment based on grey fuzzy comprehensive evaluation according to the comprehensive weights. As a preferred approach, the multi-dimensional investment benefit indicator system includes: low-carbon indicators, economic indicators, and technical indicators. Among them, low-carbon indicators include: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction. Economic indicators include: total investment in microgrid projects, total cost of microgrid projects, microgrid project revenue, and return on equity. Technical indicators include: power supply reliability, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

[0008] Preferably, the second processing module includes: The first processing unit is used to construct the hierarchical analysis diagram; The second processing unit is used to compare the indicators of each layer pairwise to form a judgment matrix. The third processing unit is used to perform consistency checks on the indicator weights based on the judgment matrix to obtain the weight matrix. The fourth processing unit is used to calculate and sort the scores based on the weight matrix, and multiply the weights of each level of indicators to obtain the comprehensive weight of the indicator.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, based on a multi-dimensional investment benefit index system, determines the comprehensive weight of each indicator in the comprehensive evaluation index system using an improved AHP (Analytic Hierarchy Process). Based on these comprehensive weights, it conducts investment benefit assessment using a grey fuzzy comprehensive evaluation method. This invention can more scientifically and comprehensively reflect the true investment value of microgrids, helping to optimize the returns of microgrid projects, effectively quantify carbon emission reduction effects, and efficiently allocate clean energy. Furthermore, it promotes the orderly adoption of microgrids in new power systems, increases market activity in microgrid investment, and reduces the risk of underestimating project returns due to neglecting low-carbon value. Attached Figure Description

[0010] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the microgrid investment benefit assessment method that incorporates low-carbon value in an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] Example 1 like Figure 1 As shown, this invention provides a method for evaluating the investment benefits of microgrids that incorporates low-carbon value, comprising: Step S1: Construct a multi-dimensional investment benefit indicator system; Step S2: Based on the multidimensional investment benefit index system, determine the comprehensive weight of each index in the comprehensive evaluation index system based on the improved AHP. Step S3: Based on the comprehensive weight, conduct an investment benefit assessment using grey fuzzy comprehensive evaluation.

[0015] As one embodiment of the present invention, the multi-dimensional investment benefit index system includes: low-carbon indicators, economic indicators, and technical indicators; wherein, the low-carbon indicators include: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction; the economic indicators include: total investment in microgrid projects, total cost of microgrid projects, revenue of microgrid projects, and return on equity of projects; the technical indicators include: power supply reliability rate, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

[0016] Furthermore, 1) Low-carbon indicators ① Total carbon emissions First, based on the fundamental theories of relevant accounting guidelines and standards, this paper studies the carbon verification methods for microgrids, focusing on their structure and function. Carbon emissions from microgrid projects originate from the power generation and distribution components.

[0017] In terms of energy production, the main focus is on the release of greenhouse gases caused by the combustion of fossil fuels and the purchase and use of electricity. The main energy sources of microgrids include small-scale gas turbine engines and renewable resources such as wind and solar power. In terms of power supply, the focus is on a range of greenhouse gas emissions generated by microgrid power supply systems. The calculation scope also includes SF6 emissions caused by the maintenance or disposal of sulfur hexafluoride (SF6) equipment, as well as the total amount of CO2 emissions due to losses during transmission and distribution.

[0018] (1) (2) (3) (4) (5) (6) For the energy production equipment of the project, the resulting electricity emissions are denoted as Emissions caused by the combustion of fossil fuels are denoted as... The consumption of each type of fossil fuel is denoted as The carbon content of the i-th fossil fuel is denoted as . The carbon oxidation rate of the i-th fossil fuel is denoted as . The relative molecular mass ratio between carbon dioxide and carbon is 44 / 12; emissions from purchased and used electricity are recorded as... The total electricity purchased and used is recorded as follows: Finally, the average emission factor of the national power grid was recorded as follows: .

[0019] Energy consumption of the power grid portion is denoted as When repairing and disposing of old SF6 equipment The amount of SF6 released is denoted as Decommissioned equipment i SF6 emissions in the data are denoted as Equipment during maintenance j The amount of SF6 emitted in China is denoted as The greenhouse gas impact factor of SF6 is denoted as . The total CO2 emissions caused by transportation and distribution are recorded as follows: Finally, the power loss caused by transmission and distribution losses is denoted as... .

[0020] The total carbon emissions of a microgrid project over its entire life cycle are the sum of the carbon emissions from the power generation and distribution components, as shown in equation (7): (7) In the formula: The total annual carbon emissions (tCO2) of the microgrid project. Carbon emissions from the power generation portion; This refers to the carbon emissions from the power distribution sector.

[0021] ② Renewable energy penetration rate The proportion of renewable energy generation in total power generation within a region is used to measure the cleanliness of the region's energy structure and the level at which new energy sources replace traditional thermal power.

[0022] ③ Number of CCERs and green certificates that can be certified The indicator represents the scale of emission reduction assets and green electricity certificates that can be issued and traded for regional renewable energy projects.

[0023] ④ System carbon emission reduction Compared to traditional all-thermal power generation scenarios, the annual reduction in carbon dioxide emissions after introducing renewable energy sources such as wind and solar power is a core indicator for directly quantifying low-carbon benefits. The specific formula is as follows: (8) ΔC Annual carbon emission reduction (tCO2). Baseline emission factor for thermal power plants Carbon emissions throughout the entire life cycle of renewable energy.

[0024] 2) Economic indicators ① Total investment of microgrid projects The total investment in a microgrid reflects its scale and consists of three parts: construction costs, interest during the construction process, and working capital required to ensure the early operation of the project. As shown in equation (9): (9) In the formula: Represents construction costs, Represents the interest in the construction process, while This is to ensure the liquidity needed for the early operation of the project.

[0025] This includes investment in the construction of power sources, other energy facilities, power distribution network projects, and energy storage projects.

[0026] ② Total cost of microgrid projects The total cost of the project can be broken down into fixed costs and variable costs, as shown in equation (10): (10) In the formula: C is the total project cost, R is the fixed cost, and F is the variable cost.

[0027] Fixed costs refer to expenses that remain constant within a specific range, unrelated to changes in output. They typically include depreciation, maintenance costs, wages and benefits, amortization, finance expenses, and various other costs. In microgrid operations, they primarily encompass the depreciation of fixed investment facilities. Variable costs, on the other hand, are costs that change with varying output. In microgrid projects, these mainly include the costs of providing services such as heat, cooling, and electricity, and are primarily composed of the microgrid's material costs, internal raw material consumption, and the cost of purchasing electricity from external sources.

[0028] ③ Benefits of microgrid projects The main revenue streams from microgrid projects include electricity sales, distribution services, ancillary services, value-added services, and low-carbon services, as shown in equation (11): (11) In the formula: For revenue from electricity sales services, For revenue from power distribution services, For ancillary service revenue, For value-added service revenue, To assess the low-carbon benefits of microgrid projects.

[0029] If the microgrid project supplies electricity to key emission enterprises participating in the carbon market, the low-carbon value benefits are as shown in equation (12): (12) In the formula: The price of carbon allowances in the national carbon market; This refers to the electricity generated and consumed by the microgrid project itself. If the microgrid company supplies electricity to non-key emission users, the low-carbon value benefits of the microgrid project can be realized through applying for CCERs or obtaining green certificates.

[0030] If a microgrid project meets the relevant methodological requirements, it can apply for CCER. According to the relevant methodology, the low-carbon value benefit of the project under this method is shown in equation (13): (13) In the formula: For CCER price, The baseline emissions for the emission reduction project in year y. The actual emissions of the emission reduction project in year y.

[0031] If the power source for the microgrid project is an onshore wind and solar power project included in the national renewable energy electricity price surcharge subsidy program, then the onshore wind and solar power project is eligible for a green certificate. The low-carbon benefits of the project are shown in equation (14): (14) In the formula: For the price of green certificates, The amount of electricity for green certificates approved for the project.

[0032] ④ Project return on equity This invention uses the return on equity (ROE) as a primary indicator for evaluating the investment benefits of microgrid projects. The ROE is calculated as the ratio of the annual net profit margin or the average annual profit margin during the operating period to the project's equity after the project reaches its design capacity. The calculation method is shown in equation (15): (15) In the formula: NP represents the project's annual profit in a normal year or the average net income during the operating period, in RMB 10,000; EC refers to the project's equity, in RMB 10,000. If the project's net return on equity exceeds the industry benchmark, it indicates that its profitability has reached the preset standard.

[0033] 3) Technical Specifications ① Power supply reliability It refers to the proportion of effective power supply time of the microgrid to the total time of the statistical period. It measures the uninterrupted power supply capability and power supply reliability level of the microgrid. The closer the value is to 100%, the higher the reliability.

[0034] ② Overall network loss rate The proportion of total active power loss generated by a microgrid during power transmission, distribution, inversion, and line transmission to total input active power reflects the energy efficiency loss level of the overall microgrid structure, converters, and lines.

[0035] ③ Voltage qualification rate The proportion of the total monitoring time during which the operating voltage of each monitoring node of the microgrid is within the allowable deviation range represents the voltage stability of the microgrid's power quality.

[0036] ④ Energy storage charging and discharging efficiency The ratio of the total discharge capacity to the total charging capacity of an energy storage system includes comprehensive efficiency factors such as battery losses and converter losses, reflecting the energy conversion and utilization capabilities of a microgrid energy storage system.

[0037] As one embodiment of the present invention, in step S2, the proportional scaling method in the traditional AHP comparison process is improved to obtain the comprehensive weight of each indicator in the comprehensive evaluation index system. Specifically, this includes: 1) Construct a hierarchical analysis diagram. First, establish level 1, level 2, and level 3 indicators sequentially to construct the hierarchical analysis diagram. The specific hierarchical steps are as follows: Step 1: Determine Level 1 Indicators (Target Layer) The sole Level 1 core evaluation indicator is set as the microgrid investment benefit assessment, which serves as the overall objective of this comprehensive evaluation and governs all evaluation dimensions.

[0038] Step 2: Determine Level 2 Indicators (Criteria Level) Based on the overall goal of Level 1 evaluation, three Level 2 criteria indicators are established by breaking down the project's comprehensive benefits into three core dimensions: low-carbon indicators, economic indicators, and technical indicators.

[0039] Step 3: Subdivide the indicators into 3 levels (indicator layer) Based on the evaluation connotations of each Level 2 indicator, specific and quantifiable Level 3 basic evaluation indicators are refined and implemented. These are the smallest calculation units of the evaluation system, and are specifically divided as follows: ① The three-level indicators corresponding to the two-level low-carbon indicators are: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction.

[0040] ② The third-level indicators corresponding to the second-level economic indicators are: total investment in microgrid projects, total cost of microgrid projects, revenue of microgrid projects, and return on equity of projects.

[0041] ③ The corresponding Level 3 indicators for Level 2 technical indicators: power supply reliability, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

[0042] Through the above three-step hierarchical construction, a three-level evaluation index system with clear hierarchy is formed, providing a standardized hierarchical framework for subsequent index weight calculation and comprehensive benefit evaluation, resulting in a hierarchical analysis diagram.

[0043] 2) Constructing the Judgment Matrix. Each level of indicators is compared pairwise to form a judgment matrix. Based on the Thomas experiment analysis method, to balance the accuracy of judgments from different experts under different standards, a 1-9 proportional scale method is used to quantify the judgment comparison standards. The 1-9 proportional scale method involves too many divisions, which may lead to small differences in the importance of each indicator, unclear decision-making characteristics, and larger errors due to the subjectivity of the scoring experts. To facilitate scoring and ensure quick and easy subsequent calculations, this invention will use the improved 1-4 indicator assignment scale method shown in Table 1 to organize the expert scores, thereby obtaining the proportion of each benefit evaluation indicator.

[0044] Table 1 For n evaluation elements to be compared in the same layer, the general form of their pairwise comparison judgment matrix is ​​shown in formula (1), where Represents element Relative to element The importance ratios and their meanings are shown in Table 1.

[0045] (16) 3) Consistency test of indicator weights.

[0046] To avoid excessive matrix calculation errors, a consistency check needs to be performed on matrix A. The specific steps are as follows: ① Normalization and single-level sorting. Calculate the largest eigenvalue of the judgment matrix A, denoted as λ. max The normalized matrix, denoted as W, yields the weight vector of the previous level corresponding to each element at that level. ② Consistency check. Calculate the consistency index C of matrix A. I The calculation formula is shown in formula (17). Single-level ranking consistency index C I The calculation formula is: (17) In the formula: a Let C be the order of the matrix. I The closer C is to 0, the higher the satisfaction of the consistency test results. I Introducing the random consistency index R I .

[0047] (18) If the test coefficient C RS If the value is less than 0.1, the matrix passes the consistency test; otherwise, its consistency does not meet the requirements and the weights must be redefined. 4) Obtain the overall weight by ranking the levels. Calculate the scores from the weight matrix and rank them. Multiply the weights of each level's indicators to obtain the overall weight for that indicator. The specific steps are as follows: ① Calculate the final weights of the level 2 indicators. Based on the constructed judgment matrix of the two-level indicators (low-carbon, economic, and technological) relative to the target layer, the weight vector of this judgment matrix is ​​obtained. That is, the final weights of these three level 2 indicators.

[0048] ② The single ranking weight of a Level 3 indicator (indicator layer) relative to its Level 2 indicator. For the third-level indicators under the second-level indicators, construct judgment matrices respectively, and calculate the weight vectors respectively. , , .

[0049] ③ The final combined weight of the three-level indicators Using a weighted summation method, the single ranking weight of the third-level indicator is multiplied by the weight of its corresponding second-level indicator, i.e.: in It is the first i The weights of each level 2 indicator, It is the second level indicator. j Single ranking weight for each level 3 indicator.

[0050] After calculating the weights of all three levels of indicators, the sum of the final weights of all three levels of indicators is 1, thus obtaining the weight vector set of the entire indicator system. .

[0051] As one embodiment of the present invention, step S3 specifically includes: 1) Determine the set of indicators for benefit evaluation 2) Determine the set of indicator comments The corresponding values ​​can be quantified as {1, 2, 3, 4, 5}.

[0052] 3) Conduct single-indicator evaluation, and construct the membership vector and membership matrix R for each indicator. The specific steps are as follows: ① For each qualitative indicator, the membership vector of the corresponding comment set is generally obtained by expert scoring: K experts vote on the level of each qualitative indicator based on the actual project situation, and the proportion of the indicator belonging to each comment level is obtained: (19) Obtain the membership vector .

[0053] ② For each quantitative indicator, the membership vector of the corresponding comment set is solved using the grey relational analysis algorithm. Let the corresponding comment set be... V The evaluation grading standard is γ={ c 1, c 2, ..., c m},set up c j and c j+1 They are adjacent Level 2 standards, and c j+1 > c j The membership vector is then solved using the grey relational algorithm as follows: for , , Finally, the membership vector can be obtained. .

[0054] Arrange the membership vectors of all 12 indicators by row to form a 12×5 fuzzy relation matrix: (20) 4) Determine the weight vector of the evaluation index set. The specific weights are determined by the improved AHP.

[0055] 5) Calculate the fuzzy comprehensive evaluation (fuzzy comprehensive membership degree) vector. .

[0056] In the formula It is a fuzzy synthesis operator.

[0057] 6) Make a comprehensive judgment or calculate the investment benefit assessment value according to the weighted comprehensive scoring method.

[0058] Simply using the "maximum membership principle" would lose information about the differences between levels. Therefore, this embodiment of the invention uses a weighted comprehensive scoring method to obtain continuous evaluation values: Quantify and score the comment set V: The final investment benefit assessment value can be calculated: .

[0059] This invention analyzes a microgrid project in an industrial park in East China as an example. The project constructs a 35kV grid-connected new energy microgrid, along with a 40MW distributed photovoltaic power station, a 10kV switching station, and a 5MW / 10MWh energy storage system. After completion, the project will supply approximately 150 million kWh of electricity annually. The region's annual photovoltaic power generation is 70 million kWh, with 60 million kWh fed into the grid and 1 million kWh supplied to the grid annually. The grid connection price is 0.60 yuan / kWh, while the supply price varies from 0.30 to 0.68 yuan / kWh depending on peak and off-peak periods.

[0060] The renewable energy generated by this project is not included in the national renewable energy subsidy supplement catalog. Furthermore, due to the large scale of the photovoltaic installation, it does not meet the applicable conditions for small microgrid projects using the relevant CCER methodology. Therefore, its low-carbon value cannot be directly reflected through green certificates or CCERs. In this project, 10 million kWh of renewable energy power is used for self-consumption. Assuming that the power users of this microgrid project are key emission enterprises already included in the carbon market, the project can achieve an annual low-carbon service revenue of approximately RMB 356,000, reaching an excellent level. Based on the above, an investment benefit assessment was conducted, and the comparative results are shown in Table 2.

[0061] Table 2 Example 2 This invention also provides a microgrid investment benefit assessment system that incorporates low-carbon value, comprising: The first processing module is used to construct a multi-dimensional investment benefit indicator system; The second processing module uses the multidimensional investment benefit index system and the improved AHP to determine the comprehensive weight of each index in the comprehensive evaluation index system. The third processing module performs investment benefit assessment based on grey fuzzy comprehensive evaluation according to the comprehensive weights. As one embodiment of the present invention, the multi-dimensional investment benefit index system includes: low-carbon indicators, economic indicators, and technical indicators; wherein, the low-carbon indicators include: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction; the economic indicators include: total investment in microgrid projects, total cost of microgrid projects, revenue of microgrid projects, and return on equity of projects; the technical indicators include: power supply reliability rate, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

[0062] As one embodiment of the present invention, the second processing module includes: The first processing unit is used to construct the hierarchical analysis diagram; The second processing unit is used to compare the indicators of each layer pairwise to form a judgment matrix. The third processing unit is used to perform consistency checks on the indicator weights based on the judgment matrix to obtain the weight matrix. The fourth processing unit is used to calculate and sort the scores based on the weight matrix, and multiply the weights of each level of indicators to obtain the comprehensive weight of the indicator.

[0063] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for evaluating the investment benefits of microgrids by incorporating low-carbon value, characterized in that, include: Step S1: Construct a multi-dimensional investment benefit indicator system; Step S2: Based on the multidimensional investment benefit index system, determine the comprehensive weight of each index in the comprehensive evaluation index system based on the improved AHP. Step S3: Based on the comprehensive weight, conduct an investment benefit assessment using grey fuzzy comprehensive evaluation.

2. The microgrid investment benefit assessment method incorporating low-carbon value as described in claim 1, characterized in that, The multi-dimensional investment benefit indicator system includes: low-carbon indicators, economic indicators, and technical indicators. Among them, low-carbon indicators include: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction. Economic indicators include: total investment in microgrid projects, total cost of microgrid projects, microgrid project revenue, and return on equity. Technical indicators include: power supply reliability, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

3. The microgrid investment benefit assessment method incorporating low-carbon value as described in claim 2, characterized in that, Step S2 includes: Construct a hierarchical analysis diagram; The indicators at each level are compared pairwise to form a judgment matrix; Based on the judgment matrix, a consistency check of the indicator weights is performed to obtain the weight matrix; The scores are calculated and sorted based on the weight matrix, and the comprehensive weight of the indicator is obtained by multiplying the weights of each level of indicators.

4. A microgrid investment benefit evaluation system that incorporates low-carbon value, characterized in that, include: The first processing module is used to construct a multi-dimensional investment benefit indicator system; The second processing module uses the multidimensional investment benefit index system and the improved AHP to determine the comprehensive weight of each index in the comprehensive evaluation index system. The third processing module uses a gray fuzzy comprehensive evaluation method based on the comprehensive weight to assess the investment benefits.

5. The microgrid investment benefit evaluation system incorporating low-carbon value as described in claim 4, characterized in that, The multi-dimensional investment benefit indicator system includes: low-carbon indicators, economic indicators, and technical indicators. Among them, low-carbon indicators include: total carbon emissions, renewable energy penetration rate, number of certified CCERs and green certificates, and system carbon emission reduction. Economic indicators include: total investment in microgrid projects, total cost of microgrid projects, microgrid project revenue, and return on equity. Technical indicators include: power supply reliability, comprehensive network loss rate, voltage qualification rate, and energy storage charging and discharging efficiency.

6. The microgrid investment benefit evaluation system incorporating low-carbon value as described in claim 5, characterized in that, The second processing module includes: The first processing unit is used to construct the hierarchical analysis diagram; The second processing unit is used to compare the indicators of each layer pairwise to form a judgment matrix. The third processing unit is used to perform consistency checks on the indicator weights based on the judgment matrix to obtain the weight matrix. The fourth processing unit is used to calculate and sort the scores based on the weight matrix, and multiply the weights of each level of indicators to obtain the comprehensive weight of the indicator.

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