Micro-grid full-life-cycle low-carbon operation evaluation model construction method considering stepped carbon transaction

Abstract

The invention discloses a micro-grid full-life-cycle low-carbon operation evaluation model construction method considering stepped carbon transactions. The method comprises the following steps: constructing a micro-grid low-carbon operation evaluation comprehensive benefit system; constructing a micro-grid low-carbon operation evaluation cost index system; constructing a micro-grid low-carbon operation evaluation income index system; and constructing a micro-grid comprehensive evaluation model based on the EGAM method. The evaluation model constructed by the method can completely and effectively reflect the whole-process operation state and comprehensive benefits of the power industry from energy development, power production, transmission and distribution to terminal consumption, the limitation of a traditional evaluation mode is broken, and the relevance of different stages in the power industry is disclosed.

Description

Technical Field

[0001] This invention relates to the field of microgrid comprehensive evaluation technology, specifically to a method for constructing a microgrid full life-cycle low-carbon operation assessment model that takes into account tiered carbon trading. Background Technology

[0002] In current technologies, energy optimization and low-carbon assessment have become hot research topics. The effective utilization of distributed energy can alleviate problems such as energy shortages, environmental pollution, and high carbon emissions. Microgrids, as a good solution for integrating distributed energy, can effectively solve various problems brought about by distributed generation, and are currently a research hotspot and key trend in the power industry. Scholars Huang Wei, Chen Xue, Lin Huaide, and others have constructed an economic benefit evaluation index system for power distribution network operation from the perspectives of cycle cost and cycle benefit. Based on the expected service life of the power equipment in the distribution network and basic data, they calculated the index values ​​to complete the assessment of the economic benefits of distribution network operation. Scholars Liu Hanxiao, Shan Sike, Wei Shuzhou, and others used the life cycle method to assess the carbon footprint of a coal-fired power plant, collecting on-site data of coal-fired power generation and combining it with the carbon emission factors of each coal-fired power generation behavior to conduct process carbon emission analysis, completing the "cradle-to-gate" carbon footprint assessment. Currently, scholars are conducting assessment research, but there is a lack of research on low-carbon operation assessment using microgrids as the research object.

[0003] An assessment of the power industry from a full life-cycle perspective can comprehensively and effectively demonstrate the operational status and overall benefits of the power industry from energy development, power production, transmission and distribution to end-user consumption. This not only breaks the limitations of traditional assessment methods but also reveals the interconnections between different stages in the power industry. Summary of the Invention

[0004] To address the economic needs of energy conservation, emission reduction, and low-carbon operation in microgrids, this invention provides a method for constructing a low-carbon operation assessment model for the entire life cycle of microgrids, taking into account tiered carbon trading. The assessment model constructed by this method can fully and effectively reflect the operational status and comprehensive benefits of the power industry from energy development, power production, transmission and distribution to end-user consumption. It not only breaks through the limitations of traditional assessment methods but also reveals the correlation between different stages in the power industry.

[0005] The technical solution adopted in this invention is as follows: A method for constructing a microgrid lifecycle low-carbon operation assessment model that takes into account tiered carbon trading includes: 1) Construct a comprehensive benefit assessment system for low-carbon operation of microgrids; 2): Construct a cost indicator system for evaluating the low-carbon operation of microgrids; 3): Construct a benefit indicator system for low-carbon operation assessment of microgrids; 4): Construct a comprehensive evaluation model for microgrids based on the EGAM method.

[0006] In section 1), the distributed energy sources selected for the microgrid include wind turbines, photovoltaic units, and micro gas turbines, with battery storage as the energy storage unit. Among these, the micro gas turbines and the carbon emissions from electricity purchases (all from thermal power plants) are considered as carbon sources in the research. Establishment as follows... Figure 1 The diagram shows the structure of a low-carbon microgrid system.

[0007] In step 1), the economic feasibility of the proposed solution is analyzed by evaluating its expected benefits and costs. This establishes indicators for total revenue benefit, total expenditure benefit, and comprehensive benefit value, thus completing the construction of a comprehensive benefit system. The economic evaluation formula for the microgrid's lifespan is defined as follows: 1.1: Total Revenue Benefits The calculation expression is: (1); In formula (1): This represents the total revenue and benefit value of the microgrid over its entire life cycle over N years; T represents the lifespan, in years. The value represents the annual revenue benefit of the microgrid in year t during its entire life cycle; C represents the discount rate, which is 8% in this invention; N represents the number of years the microgrid operates during its entire life cycle.

[0008] 1.2: Total Expenditure Efficiency The calculation expression is: (2); In formula (2): This represents the total expenditure benefit value of a microgrid over its entire lifecycle over N years. This represents the annual expenditure benefit value of a microgrid throughout its entire life cycle.

[0009] 1.3: Comprehensive Benefit Value The calculation expression is: (3); exist The scheme with the greatest overall benefit is selected from the schemes with a value greater than 1.

[0010] In step 2), a cost index system for evaluating the low-carbon operation of microgrids is constructed, including: 2.1: Investment cost of microgrid equipment: Calculate the total life-cycle investment of distributed energy equipment and energy storage equipment in the initial stage of microgrid construction, and discount it to the present value of a single year; 2.2: Maintenance Costs: Calculate the annual maintenance costs for various types of equipment based on their installed capacity. 2.3: Operating Costs: Includes the operating costs of energy storage devices and the fuel costs for generating electricity from controllable distributed energy sources such as micro gas turbines; 2.4: Carbon Trading Costs: Based on the difference between the actual carbon emissions of the microgrid and the free carbon emission allowances, carbon trading-related expenditures or benefits are calculated in different intervals to reflect the impact of the tiered carbon trading mechanism on costs.

[0011] In section 2.1, the microgrid equipment investment cost refers to the distributed energy equipment and energy storage equipment invested in the initial stage of microgrid construction. The total cost of equipment investment throughout the entire life cycle of a microgrid is expressed as follows: (4); In equation (4): n represents the n devices put into the microgrid; This represents the number of devices of type i in the microgrid; This represents the unit power investment cost of the i-th type of equipment; The i-th device represents the installed capacity; r represents the device's inflation rate. This indicates the year of installation for the i-th type of equipment. If the equipment is invested in batches, time-sharing calculation is required.

[0012] The total cost of equipment investment throughout the microgrid's lifecycle is discounted to its present value over one year. for: (5); In equation (5): T represents the economic life cycle of the equipment; t represents the target year. .

[0013] In section 2.2, the maintenance cost in a microgrid is generally proportional to the installed capacity of the equipment, which can be expressed as: (6); In formula (6): This represents the unit capacity maintenance cost of the i-th device.

[0014] In section 2.3, the operating cost of a microgrid mainly includes the operating cost of energy storage devices and the power generation cost of controllable distributed energy sources. The power generation cost of distributed energy sources mainly includes fuel costs during the power generation process.

[0015] Annual operating cost of microgrids The calculation expression is: (7); In equation (7): This indicates the unit operating cost of the battery; This indicates the charging and discharging efficiency of the battery. This indicates the fuel cost per unit output of a micro gas turbine. and These represent the annual charge and annual discharge capacity of the battery, respectively. This represents the total annual output of the micro gas turbine.

[0016] In section 2.4, the carbon trading cost of microgrids mainly involves the relationship between the carbon emission quota of microgrids and the actual carbon emission. To calculate the tiered carbon trading cost, it is necessary to set the corresponding range for calculation. If actual carbon emissions are lower than carbon allowances, the excess carbon allowances can be traded; otherwise, additional carbon allowances need to be purchased through carbon trading. The formula for calculating free carbon allowances is as follows: (8); (9); (10); In the above formula, , , These are represented as free carbon emission allowances for microgrids, purchased electricity, and micro gas turbines, respectively, in tons (t). , These represent the free carbon emission allowances per unit of electricity and per unit of heat, respectively, in t / kWh. This represents the purchased power at time t, in kW. This indicates the thermal power output value of the micro gas turbine, in kW. This indicates the electrical power of a micro gas turbine, measured in kW. Indicates the conversion factor; The carbon emission allowance per unit of heat output is expressed in t / MJ. This indicates a time interval, expressed in hours.

[0017] The formula for calculating the actual carbon emissions of a microgrid is as follows: (11); In equation (11): , , These represent the actual carbon emissions from microgrids, purchased electricity, and micro gas turbines, respectively, in tons (t); their calculation method is the same as the formula for calculating uncompensated carbon emission quotas. (12); (13); In the formula: Indicates the tiered carbon trading cost; This represents the difference between actual carbon emissions and carbon emission allowances. Indicates the interval length; Indicates the benchmark price for carbon trading; This indicates the price growth rate.

[0018] In step 3), a microgrid low-carbon operation assessment benefit index system is constructed, which includes five key benefits: 3.1: Power generation revenue: Based on the peak-valley electricity pricing mechanism on the load side, the annual power generation revenue is calculated according to the electricity consumption during peak hours, valley hours, and normal times and the corresponding electricity prices. 3.2: Carbon emission reduction benefits: Calculate the carbon emission reduction caused by carbon-free distributed power generation replacing purchased thermal power and micro gas turbine power generation, and then convert it into carbon emission reduction benefits; 3.3: Discharge subsidy revenue, calculate the revenue obtained by the microgrid from selling the surplus electricity back to the grid after meeting the local load; 3.4: Network Loss Revenue: Based on the characteristic of distributed generation being consumed locally to reduce transmission losses, the revenue generated is calculated. 3.5: Residual value of equipment: After the life cycle of microgrid equipment ends, the recovery income is calculated based on the residual value rate of different equipment and deferred to the first year of the assessment period.

[0019] In section 3.1, the electricity generated by distributed energy resources in the microgrid is first used to meet its own load-side demand. This invention uses a peak-valley electricity price mechanism for calculation, and the annual power generation revenue expression of the microgrid can be obtained as follows: (14); In equation (14): , , These represent the peak-hour electricity price, valley-hour electricity price, and normal-hour electricity price on the load side of the microgrid, respectively. , , These represent the peak electricity consumption, valley electricity consumption, and normal electricity consumption on the load side of the microgrid each year.

[0020] In section 3.2, the annual carbon emission reduction benefit of a microgrid mainly refers to the carbon emission reduction resulting from the replacement of some micro gas turbines and purchased electricity with carbon-free electricity generated by distributed energy sources in the microgrid. The final formula for calculating the annual carbon emission reduction of a microgrid is as follows: (15); In equation (15): The carbon emission factor representing purchased thermal power is taken as 0.944. ; The carbon emission factor of the micro gas turbine is taken as 0.4792. ; This represents the purchased electricity that was replaced by carbon-free distributed power sources at time t. This represents the amount of electricity generated by the external gas turbine that is replaced by carbon-free distributed power sources at time t.

[0021] After obtaining the final annual carbon emission reduction Then, substitute it into formula (13) to obtain the annual carbon emission reduction benefit value of the microgrid, as follows: Obtain the annual carbon emission reduction amount, and according to formula (13) according to the set step interval length, substitute the annual carbon emission reduction amount into the formula corresponding to the interval length, and calculate the step carbon trading cost, which is the annual carbon emission reduction benefit value of the microgrid.

[0022] In section 3.3, the microgrid discharge subsidy revenue mainly refers to the revenue obtained by the microgrid from transmitting distributed power generation to the grid for sale after meeting local load requirements. The specific calculation expression is as follows: (16); In equation (16): , , These represent the peak-hour electricity price, off-peak electricity price, and normal-hour electricity price on the load side of the microgrid, respectively. , , These represent the annual peak-hour electricity sales, annual valley-hour electricity sales, and annual normal-hour electricity sales on the load side of the microgrid, respectively.

[0023] In section 3.4, the distributed energy generation of the microgrid reduces transmission losses during local consumption, thus generating network loss benefits. This invention assumes zero transmission losses from distributed generation, resulting in the following expression for the network loss benefits of the microgrid: (17); In equation (17): This represents the average electricity price calculated by the power grid under peak-valley pricing. This represents the proportion of long-distance power transmission losses from centralized power generation to total power generation; in this invention, the value is 5.6%. This represents the annual power generation of the i-th device; This represents the annual transaction volume between the microgrid and the power grid.

[0024] In section 3.5, the residual value of equipment refers to the cost or income required to clean up and recycle the microgrid equipment at the end of its life cycle. Photovoltaic panels, wind turbines, micro gas turbines, and batteries, among other equipment, can all be recycled after their life cycle, yielding varying amounts of residual value income. The formula for calculating the total residual value income of microgrid equipment is as follows: (18); In equation (18): This represents the residual value rate of the i-th type of equipment, which is determined by the equipment type.

[0025] The formula for converting total income to first-year income is: (19).

[0026] In step 4), a comprehensive evaluation model for microgrids based on the EGAM method is constructed. The EGAM method combines the entropy weight method and the grey relational analysis method. The grey relational analysis method calculates the correlation degree of each evaluation indicator, and the entropy weight method is then introduced to assign weights to each evaluation indicator, ultimately resulting in an objective and effective evaluation system process. This includes the following steps: First, the weights of the indicators are determined using the entropy weight method. An evaluation matrix is ​​then established based on the evaluation indicators and the proposed solutions. After normalizing the indicators, the information entropy of each indicator is calculated. The smaller the information entropy, the more information the indicator contains, and the greater its weight. The sum of the weights of all indicators is 1. Next, the correlation degree of the indicators is analyzed using the grey relational analysis method. A reference sequence and a comparison sequence are determined and normalized. The absolute difference between the two and the correlation coefficient are calculated. The closer the correlation coefficient is to 1, the higher the correlation degree of the indicators. Finally, the indicator weights and correlation degrees are combined to calculate the comprehensive evaluation value of each solution.

[0027] Specifically, it includes the following: 4.1: The entropy weight method is an objective weighting method based on information content. The core idea of ​​this method is to analyze the entropy of the information involved in the evaluation indicators to determine the weights involved. The weight of the indicators depends only on the dispersion of the data, and no expert scoring is required during the evaluation process, resulting in low computational complexity. Therefore, the entropy weight method is an objective and simple evaluation method. The basic steps are as follows: An evaluation matrix Z is established based on u evaluation indicators and m evaluation schemes. The expression for the evaluation matrix Z is: (20); In equation (20): the matrix , , .

[0028] Considering that the evaluation indicators include various indicators such as cost indicators and revenue indicators, which differ in units, they need to be normalized for a unified evaluation. The specific calculation formula is as follows: (twenty one); In equation (21): This represents the result of the i-th object after normalization under the j-th metric.

[0029] Objective evaluation indicator information is extracted by calculating the information entropy of each different evaluation indicator. The specific calculation formula is shown below: (twenty two); In equation (22): Let represent the information entropy of the j-th evaluation index.

[0030] The calculated information entropy is used to determine the weight value of each indicator. The indicator weight value is inversely proportional to the information entropy value. The smaller the information entropy value of an indicator, the more information it contains. The sum of the weight values ​​of all indicators is 1. (twenty three); In equation (23): This represents the weight value of the j-th evaluation indicator.

[0031] 4.2: Grey relational analysis is a method used to analyze the degree of correlation between various evaluation indicators. The core idea of ​​this method is to calculate the geometric curve similarity between different indicators to rank their merits. The higher the degree of correlation between indicators, the more obvious their grey relational degree, thus revealing the influence of the indicators on the target. It has the advantages of low data requirements and simple calculation. The basic steps are as follows: First, the analysis sequence is determined to obtain the reference sequence for which grey relational analysis is required. Comparison of sequences ; The obtained sequence is normalized to become dimensionless numerical values, and the calculation expression is as follows: (twenty four); In equation (24): This represents the dimensionless value of the i-th comparison sequence after normalization at the k-th data point; This represents the original value of the i-th comparison sequence at the k-th data point; This represents the original value of the i-th comparison sequence at the first data point.

[0032] After obtaining the normalized sequence, calculate and compare the sequences. and reference sequence The absolute difference indicates a stronger correlation between the two. The smaller the absolute difference, the stronger the correlation between them. The specific calculation expression is: (25); In equation (25): It represents the normalized absolute difference between the i-th comparison sequence and the reference sequence at the k-th data point; This represents the dimensionless value of the reference sequence at the k-th data point after normalization.

[0033] The correlation coefficient between two sequences is calculated by the absolute difference between them. The closer the correlation coefficient is to 1, the higher the degree of correlation between the two sequences.

[0034] (26); In equation (26): This represents the correlation coefficient between the i-th comparison sequence and the reference sequence at the k-th data point; This represents the minimum absolute difference between the i-th comparison sequence and the reference sequence after normalization among all data points; This represents the maximum absolute difference between the i-th comparison sequence and the reference sequence after normalization among all data points; The resolution coefficient is mainly used to adjust the difference between correlation coefficients, thereby controlling the influence of the environment on the comparison results. It is generally taken in the range of 0.1-0.5.

[0035] Based on the above calculation expression, the final formula for calculating the correlation degree can be obtained as follows: (27); In equation (27): This indicates the number of data points in the sequence.

[0036] 4.3: Considering that the grey relational analysis method may result in equal weights for all indicators during calculation, the entropy weight method is introduced to assign weights to the indicators, making the evaluation method more objective and effective. Therefore, the following results were obtained: Figure 3 The example shown is a comprehensive evaluation model for microgrids based on the EGAM method.

[0037] The specific steps of the microgrid comprehensive evaluation model based on the EGAM method are as follows: First, based on the comprehensive benefit method, we establish input cost and benefit indicators for microgrids, construct an evaluation system, and set different scheme types according to the target scenarios of microgrids; According to the entropy weight method, an evaluation matrix is ​​established based on the number of indicators and the number of schemes. The evaluation indicators are normalized, and the calculated information entropy determines the weight of the indicators. Based on the grey relational analysis method, the analysis series are determined, the absolute difference of the series is calculated, and the correlation coefficients between different indicators are obtained. Finally, the results of the two methods are combined to calculate the comprehensive evaluation value of the scheme, the specific expression of which is shown below: (28).

[0038] This invention provides a method for constructing a low-carbon operation assessment model for the entire life cycle of a microgrid that takes into account tiered carbon trading. The technical effects are as follows: 1) This invention takes a life-cycle perspective and uses a discount rate to uniformly calculate the revenue and expenditure benefits of different years, breaking through the limitations of traditional short-term assessments, truly reflecting the long-term economic feasibility of microgrids, providing an intuitive and quantifiable decision-making basis for optimal scheme selection, and reducing subjective interference.

[0039] 2) This invention covers four core cost categories: equipment investment, maintenance, operation, and tiered carbon trading. It includes both the initial one-time investment and long-term operating costs as well as policy-related carbon trading costs, without omitting any key costs. It innovatively integrates a tiered carbon trading mechanism, calculates costs in different intervals, responds to the national low-carbon policy orientation, enhances the timeliness and practicality of the assessment method, and can also guide microgrids to optimize their carbon emission behavior.

[0040] 3) This invention constructs a revenue system from multiple dimensions, covering five categories of revenue: power generation, carbon emission reduction, discharge subsidies, grid losses, and residual value of equipment. It fully captures the entire revenue potential of low-carbon operation of microgrids, including not only traditional electricity revenue, but also highlighting the additional revenue brought by low-carbon policies and the characteristics of distributed generation.

[0041] 4) This invention combines the entropy weight method and the grey relational analysis method, which complement each other. The entropy weight method objectively assigns weights based on the dispersion of indicator data, eliminating the need for expert scoring and reducing the interference of subjective factors on weight allocation, thus ensuring the fairness and objectivity of the evaluation. The grey relational analysis method calculates the geometric curve similarity between indicators, quantifies the correlation, and clearly identifies key indicators that have a significant impact on the overall benefits, providing a clear direction for improvement in the optimal configuration of microgrids. Attached Figure Description

[0042] The present invention will be further described below with reference to the accompanying drawings and examples; Figure 1 Diagram of a low-carbon microgrid system structure.

[0043] Figure 2 This is a schematic diagram of the evaluation index system for low-carbon operation of microgrids.

[0044] Figure 3 This is a schematic diagram of a comprehensive evaluation model for microgrids based on the EGAM method.

[0045] Figure 4 This is the monthly average electricity load curve for the microgrid.

[0046] Figure 5 Typical daily load curves for different seasons.

[0047] Figure 6 The predicted power generation curves for a single wind turbine on a typical day in different seasons are shown.

[0048] Figure 7 The forecast curves for single photovoltaic power generation on typical days in different seasons are shown.

[0049] Figure 8 The results are the weight values ​​of the indicators for the full life cycle assessment of microgrids.

[0050] Figure 9 The evaluation indicators for different schemes are normalized. Detailed Implementation

[0051] To meet the economic requirements of energy conservation, emission reduction, and low-carbon operation of microgrids, this invention proposes an economic evaluation method for the low-carbon operation of microgrids throughout their entire lifecycle, taking into account tiered carbon trading. First, an economic evaluation index system incorporating the input costs and operating benefits of low-carbon operation of microgrids is constructed based on the comprehensive benefit method. Second, an EGAM evaluation model combining entropy weight and grey relational analysis is proposed to determine the weights of the indicators and analyze the correlation between multiple indicators. Finally, multiple microgrid configurations and operation scenarios are set up to verify the effectiveness of the proposed evaluation method.

[0052] A simulation was conducted using a microgrid in a southern region as the research object. Its system structure is as follows: Figure 1 As shown in Table 1, the main equipment parameter settings are included in this document.

[0053]

[0054] The microgrid's annual power generation load is divided into four periods based on seasons. Typical daily load data for each season are used for calculations, resulting in the following... Figure 4 The microgrid monthly average load curve shown is as follows: Figure 5 The curves showing the typical daily load comparison for different seasons are shown.

[0055] Based on the characteristics of different seasons, the following are given: Figure 6 , Figure 7 The image shows the power generation forecasts for a single wind turbine and a single photovoltaic unit on a typical day under different seasons. From... Figure 6 , Figure 7 As can be seen, due to seasonal climate influences, the average daily load is highest in summer, while winter electricity consumption is similar to that in autumn, both exceeding the typical daily load in spring. Due to seasonal wind and sunlight influences, wind turbines have the strongest average output in winter, while photovoltaic (PV) turbines have the strongest average output in summer.

[0056] To analyze the impact of different devices on the economic operation of the system, the microgrid lifespan was set at 40 years, and four different operating scenarios were analyzed and compared: (1) Option 1: Power supply by micro gas turbine and purchased power; (2) Option 2: Power supply from wind turbines and micro gas turbines, and purchased electricity; (3) Option 3: Photovoltaic power generation, micro gas turbine power supply and purchased power; (4) Option 4: Power supply from wind turbines, photovoltaic power generation, micro gas turbines, and purchased electricity; (5) Option 5: Power supply from wind turbines, photovoltaic power generation, storage batteries, micro gas turbines and purchased electricity.

[0057]

[0058] The final values ​​of various indicators for the annual low-carbon operation of the microgrid under the five schemes are shown in Table 3.

[0059] Table 3 shows that when a microgrid only uses micro gas turbines and purchased electricity, although it has the lowest equipment investment cost, the tiered carbon trading cost is the highest, while the annual carbon emission reduction benefits and discharge subsidy benefits are the lowest, which does not conform to the concept of low-carbon operation of microgrids. After adding different distributed energy devices and energy storage devices, the tiered carbon trading cost of microgrid operation gradually decreases, and the benefits gradually increase, which makes full use of the low-carbon operation of microgrids and meets the concept of environmental protection.

[0060]

[0061] The results of the case studies show that the EGAM method can effectively identify the optimal system configuration scheme, and that the positive adoption of multiple types of distributed power sources and energy storage in combination can achieve better economic efficiency and low carbon benefits throughout the entire life cycle.

[0062] The evaluation indicators were processed using the EGAM evaluation method to obtain the weight values ​​of the microgrid life cycle evaluation indicators and the normalized results of the evaluation indicators under different schemes, such as... Figure 8 , Figure 9 As shown.

[0063] Depend on Figure 8 It can be seen that after processing with the entropy weight method, the indicators obtained different weight values. Considering the low-carbon operation of the microgrid, the weight value of power generation revenue is the maximum value among the revenue indicators, followed by carbon emission reduction revenue. The weight value of carbon trading costs is the maximum value among the cost indicators. Furthermore, the sum of the weights of all indicators is 1, which conforms to the definition of the entropy weight method. Figure 9 It can be seen that by processing the five schemes with the grey relational analysis method, the normalized results of different indicators can be obtained. It can be found that scheme 1 only has an advantage in terms of equipment investment cost (cost indicators are inversely proportional to the normalized results), while scheme 5 is basically the optimal solution in terms of revenue indicators.

[0064] The two methods were combined, and the final score results after evaluation by the EGAM method are shown in Table 4.

[0065]

[0066] As shown in Table 4, Scheme 5 achieves the highest low-carbon operation score across the entire microgrid lifecycle. Combined with the data in Table 3, it can be concluded that, excluding its highest equipment investment cost, its overall benefits are higher than the other four schemes, while its operation and maintenance costs and tiered carbon trading costs are lower. In conclusion, the coordinated operation of multiple distributed energy devices and energy storage batteries can bring considerable economic benefits, achieve low-carbon operation of the microgrid, reduce carbon dioxide emissions, and protect the environment.

[0067] To verify the performance of the EGAM evaluation method proposed in this invention, the coefficient of variation method and the TOPSIS method are compared, and the evaluation results are shown in Table 5.

[0068]

[0069] As shown in Table 5, the ranking of the method proposed in this invention is consistent with the other two methods, and the score ranking is also basically consistent, which verifies that the EGAM method proposed in this invention has a certain scientific validity. Compared with the other two evaluation methods, the score difference of the coefficient of variation method is too extreme. If there are extreme values ​​in the indicators, it will lead to weight distortion and ignore the correlation between different schemes. The TOPSIS method is easily affected by the scale and correlation of the indicators, and sometimes fails to reflect the performance of the scheme on specific indicators, resulting in a score of 0 for a certain scheme. The EGAM method, by combining combined weighting and correlation analysis, effectively improves the stability and reliability of the evaluation results, reduces the bias that may be caused by a single method, and provides a more comprehensive and accurate decision-making basis for the selection of low-carbon operation schemes for microgrids. In addition, this method has low data requirements, does not require the sample to follow a specific distribution, and is suitable for small samples and incomplete information systems, with strong applicability and flexibility.

Claims

1. A method for constructing a low-carbon operation assessment model for the entire life cycle of a microgrid, taking into account tiered carbon trading, characterized in that... include: 1) Construct a comprehensive benefit assessment system for low-carbon operation of microgrids; 2): Construct a cost indicator system for evaluating the low-carbon operation of microgrids; 3): Construct a benefit indicator system for low-carbon operation assessment of microgrids; 4): Construct a comprehensive evaluation model for microgrids based on the EGAM method.

2. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 1, characterized in that: In the first part, the distributed energy sources selected for the microgrid include wind turbines, photovoltaic units and micro gas turbines, and the energy storage unit is battery energy storage; among them, the micro gas turbine and the carbon emissions from electricity purchase are taken as carbon source research objects to establish a low-carbon operation microgrid system structure.

3. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 2, characterized in that: In step 1), the economic feasibility of the plan is analyzed by evaluating the expected benefits and costs of the target plan, establishing total revenue benefit indicators, total expenditure benefit indicators, and comprehensive benefit value indicators, thus completing the construction of the comprehensive benefit system, defined as follows: 1.1: Total Revenue Benefits The calculation expression is: （1）； In formula (1): This represents the total revenue benefit of a microgrid over its entire lifecycle over N years; T represents the lifespan. This represents the annual revenue benefit of the microgrid in year t during its entire life cycle; C represents the discount rate; N represents the number of years the microgrid operates during its entire life cycle. 1.2: Total Expenditure Efficiency The calculation expression is: （2）； In formula (2): This represents the total expenditure benefit value of a microgrid over its entire lifecycle over N years. This represents the annual expenditure benefit value of a microgrid throughout its entire life cycle. 1.3: Comprehensive Benefit Value The calculation expression is: （3）； exist The scheme with the greatest overall benefit is selected from the schemes with a value greater than 1.

4. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 3, characterized in that: In step 2), a cost index system for evaluating the low-carbon operation of microgrids is constructed, including: 2.1: Investment cost of microgrid equipment: Calculate the total life-cycle investment of distributed energy equipment and energy storage equipment in the initial stage of microgrid construction, and discount it to the present value of a single year; 2.2: Maintenance Costs: Calculate the annual maintenance costs for various types of equipment based on their installed capacity. 2.3: Operating Costs: Includes the operating costs of energy storage devices and the fuel costs for generating electricity from controllable distributed energy sources such as micro gas turbines; 2.4: Carbon Trading Costs: Based on the difference between the actual carbon emissions of the microgrid and the free carbon emission allowances, carbon trading-related expenditures or benefits are calculated in different intervals to reflect the impact of the tiered carbon trading mechanism on costs.

5. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 4, characterized in that: In section 2.1, the microgrid equipment investment cost refers to the distributed energy equipment and energy storage equipment invested in the initial stage of microgrid construction. The total cost of equipment investment throughout the entire life cycle of a microgrid is expressed as follows: （4）； In equation (4): n represents the n devices put into the microgrid; This represents the number of devices of type i in the microgrid; This represents the unit power investment cost of the i-th type of equipment; The i-th device represents the installed capacity; r represents the device's inflation rate. This indicates the year of installation for the i-th type of equipment. If the equipment is invested in batches, time-sharing calculation is required. The total cost of equipment investment throughout the microgrid's lifecycle is discounted to its present value over one year. for: （5）； In equation (5): T represents the economic life cycle of the equipment; t represents the target year. ; In section 2.2, the maintenance cost of a microgrid is proportional to the installed capacity of the equipment, expressed as: （6）； In formula (6): This represents the unit capacity maintenance cost of the i-th device; In section 2.3, the operating cost of a microgrid includes the operating cost of energy storage devices and the power generation cost of controllable distributed energy sources; the power generation cost of distributed energy sources includes fuel costs during the power generation process. Annual operating cost of microgrids The calculation expression is: （7）； In equation (7): This indicates the unit operating cost of the battery; This indicates the charging and discharging efficiency of the battery. This indicates the fuel cost per unit output of a micro gas turbine. and These represent the annual charge and annual discharge capacity of the battery, respectively. This represents the total annual output of the micro gas turbines; In section 2.4, the carbon trading cost of microgrids involves the relationship between the carbon emission quota of microgrids and the actual carbon emission. To calculate the tiered carbon trading cost, it is necessary to set the corresponding range for calculation. If actual carbon emissions are lower than carbon allowances, the excess carbon allowances can be traded; otherwise, additional carbon allowances need to be purchased through carbon trading. The formula for calculating free carbon allowances is as follows: （8）； （9）； （10）； In the above formula, , , These are respectively represented as free carbon emission allowances for microgrids, purchased electricity, and micro gas turbines; , These represent the free carbon emission allowances per unit of electricity and per unit of heat, respectively. This represents the purchased power at time t; This indicates the thermal power output value of the micro gas turbine; This indicates the electrical power of the micro gas turbine; Indicates the conversion factor; This indicates the carbon emission amount per unit of heat generated. Indicates a time interval; The formula for calculating the actual carbon emissions of a microgrid is as follows: （11）； In equation (11): , , These represent the actual carbon emissions from microgrids, purchased electricity, and micro gas turbines, respectively. （12）； （13）； In the formula: Indicates the tiered carbon trading cost; This represents the difference between actual carbon emissions and carbon emission allowances. Indicates the interval length; Indicates the benchmark price for carbon trading; This indicates the price growth rate.

6. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 5, characterized in that: In step 3), a microgrid low-carbon operation assessment benefit index system is constructed, which includes five key benefits: 3.1: Power generation revenue: Based on the peak-valley electricity pricing mechanism on the load side, the annual power generation revenue is calculated according to the electricity consumption during peak hours, valley hours, and normal times and the corresponding electricity prices. 3.2: Carbon emission reduction benefits: Calculate the carbon emission reduction caused by carbon-free distributed power generation replacing purchased thermal power and micro gas turbine power generation, and then convert it into carbon emission reduction benefits; 3.3: Discharge subsidy revenue, calculate the revenue obtained by the microgrid from selling the surplus electricity back to the grid after meeting the local load; 3.4: Network Loss Revenue: Based on the characteristic of distributed generation being consumed locally to reduce transmission losses, the revenue generated is calculated. 3.5: Residual value of equipment: After the life cycle of microgrid equipment ends, the recovery income is calculated based on the residual value rate of different equipment and deferred to the first year of the assessment period.

7. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 6, characterized in that: In section 3.1, the electricity generated by distributed energy resources in the microgrid is first used to meet its own load-side demand. The load-side electricity price is calculated using a peak-valley pricing mechanism, resulting in the following expression for the annual power generation revenue of the microgrid: （14）； In equation (14): , , These represent the peak-hour electricity price, valley-hour electricity price, and normal-hour electricity price on the load side of the microgrid, respectively. , , These represent the peak-hour electricity consumption, valley-hour electricity consumption, and normal-hour electricity consumption on the load side of the microgrid, respectively. In section 3.2, the annual carbon emission reduction benefit of a microgrid refers to the carbon emission reduction resulting from the replacement of some micro gas turbines and purchased electricity with carbon-free electricity generated by distributed energy sources in the microgrid. The final formula for calculating the annual carbon emission reduction of a microgrid is as follows: （15）； In equation (15): Indicates the carbon emission factor of purchased thermal power; Indicates the carbon emission factor of a micro gas turbine; This represents the purchased electricity that was replaced by carbon-free distributed power sources at time t. This represents the amount of electricity generated by the external gas turbine that is replaced by a carbon-free distributed power source at time t. After obtaining the final annual carbon emission reduction Then, substituting into equation (13) yields the annual carbon emission reduction benefit value of the microgrid; In section 3.3, the microgrid discharge subsidy revenue refers to the revenue obtained by a microgrid from reversing the flow of distributed generation to the grid for sale after meeting local load requirements. The specific calculation expression is as follows: （16）； In equation (16): , , These represent the peak-hour electricity price, off-peak electricity price, and normal-hour electricity price on the load side of the microgrid, respectively. , , These represent the annual peak-hour electricity sales, annual valley-hour electricity sales, and annual normal-hour electricity sales on the load side of the microgrid, respectively. In section 3.4, the distributed energy generation of the microgrid reduces transmission losses during local consumption, thus generating network loss revenue. Assuming zero transmission losses from distributed generation, the expression for the network loss revenue of the microgrid is as follows: （17）； In equation (17): This represents the average electricity price calculated by the power grid under peak-valley pricing. This indicates the proportion of long-distance power transmission losses from centralized power generation to total power generation. This represents the annual power generation of the i-th device; This represents the annual transaction volume between the microgrid and the power grid; In section 3.5, the residual value of the equipment refers to the cost or income required to clean up and recycle the microgrid equipment at the end of its life cycle. Photovoltaic panels, wind turbines, micro gas turbines, and batteries can all be recycled after their life cycle, yielding different residual value incomes. The formula for calculating the total residual value income of microgrid equipment is as follows: （18）； In equation (18): This represents the residual value rate of the i-th type of equipment, which is determined by the equipment type; The formula for converting total income to first-year income is: （19）。 8. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 7, characterized in that: In step 4), constructing a comprehensive evaluation model for microgrids based on the EGAM method includes the following steps: First, the weights of the indicators are determined by the entropy weight method. An evaluation matrix is ​​established based on the evaluation indicators and the scheme. After normalizing the indicators, the information entropy of each indicator is calculated. The smaller the information entropy, the more information the indicator contains and the greater its weight. The sum of the weights of all indicators is 1. Then, the correlation degree of the indicators is analyzed by grey relational analysis, the reference series and comparison series are determined and normalized, and the absolute difference between the two and the correlation coefficient are calculated. The closer the correlation coefficient is to 1, the higher the correlation degree of the indicators. Finally, the indicator weights and correlation degrees are combined to calculate the comprehensive evaluation value of each scheme.

9. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 8, characterized in that: The steps of the entropy weight method are as follows: An evaluation matrix Z is established based on u evaluation indicators and m evaluation schemes. The expression for the evaluation matrix Z is: （20）； In equation (20): the matrix , , ; Considering the differences in various indicators and units among the evaluation criteria, normalization is necessary for a unified evaluation. The specific calculation formula is as follows: （21）； In equation (21): This represents the result of the i-th object after normalization under the j-th metric; Objective evaluation indicator information is extracted by calculating the information entropy of each different evaluation indicator. The specific calculation formula is shown below: （22）； In equation (22): This represents the information entropy of the j-th evaluation indicator; The calculated information entropy is used to determine the weight value of each indicator. The weight value of an indicator is inversely proportional to the information entropy value. The smaller the information entropy value of an indicator, the more information it contains. The sum of the weight values ​​of all indicators is 1. （23）； In equation (23): This represents the weight value of the j-th evaluation indicator.

10. The method for constructing a microgrid life-cycle low-carbon operation assessment model considering tiered carbon trading as described in claim 9, characterized in that: Grey relational analysis is used to analyze the degree of correlation between various evaluation indicators. The core of this method is to calculate the geometric curve similarity between different indicators to rank their merits. The higher the degree of correlation between indicators, the more obvious the grey relational degree, thus revealing the influence of the indicators on the target. The basic steps are as follows: First, the analysis sequence is determined to obtain the reference sequence for which grey relational analysis is required. Comparison of sequences ; The obtained sequence is normalized to become dimensionless numerical values, and the calculation expression is as follows: （24）； In equation (24): This represents the dimensionless value of the i-th comparison sequence after normalization at the k-th data point; This represents the original value of the i-th comparison sequence at the k-th data point; This represents the original value of the i-th comparison sequence at the first data point; After obtaining the normalized sequence, calculate and compare the sequences. and reference sequence The absolute difference indicates a stronger correlation between the two. The smaller the absolute difference, the stronger the correlation between them. The specific calculation expression is: （25）； In equation (25): It represents the normalized absolute difference between the i-th comparison sequence and the reference sequence at the k-th data point; This represents the dimensionless value of the reference sequence at the k-th data point after normalization. The correlation coefficient between two sequences is calculated by the absolute difference between them. The closer the correlation coefficient is to 1, the higher the degree of correlation between the two sequences. （26）； In equation (26): This represents the correlation coefficient between the i-th comparison sequence and the reference sequence at the k-th data point; This represents the minimum absolute difference between the i-th comparison sequence and the reference sequence after normalization among all data points; This represents the maximum absolute difference between the i-th comparison sequence and the reference sequence after normalization among all data points; The resolution coefficient is mainly used to adjust the difference between correlation coefficients, thereby controlling the influence of the environment on the comparison results. Based on the above calculation expression, the final formula for calculating the correlation degree is: （27）； In equation (27): This indicates the number of data points in the sequence; Considering that the grey relational method will make the index weights equal during the calculation process, the entropy weight method is introduced to assign weights to the indexes, making the evaluation method more objective and effective. Therefore, a microgrid comprehensive evaluation model based on the EGAM method is obtained.

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