Multi-type energy storage application value evaluation method and system

Abstract

The invention discloses a multi-type energy storage application value evaluation method and system, and relates to the technical field of energy storage, and the method comprises the steps: 1, building a multi-type energy storage operation cost and income model; step 2, defining a multi-dimensional evaluation criterion; step 3, establishing a multi-type energy storage application value evaluation mechanism, which comprises comparing importance between adjacent criteria and establishing an intuitive judgment matrix; determining each criterion weight by a variable weight fuzzy analytic hierarchy process; integrating each criterion weight to obtain a comprehensive value; and step 4, calculating comprehensive values under different schemes. And 5, comparing the comprehensive values, and taking the scheme corresponding to the highest comprehensive value as the optimal configuration scheme. The multi-type energy storage application value evaluation method has the effect of accurately evaluating the multi-type energy storage application value.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a method and system for evaluating the value of multiple types of energy storage applications. Background Art

[0002] In recent years, energy storage technologies based on different principles have developed rapidly, such as electrochemical energy storage, compressed air energy storage, flywheel energy storage, hydrogen energy storage, supercapacitor energy storage, etc. Evaluating indicators such as the economy, low carbon and reliability of various types of energy storage systems is of great significance for guiding the application of various types of energy storage systems.

[0003] Different types of energy storage systems have different costs and benefits. How to more accurately evaluate the value of multiple types of energy storage applications has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In order to more accurately evaluate the application value of multiple types of energy storage, the present application provides a method and system for evaluating the application value of multiple types of energy storage.

[0005] In the first aspect, the present application provides a method for evaluating the value of multi-type energy storage applications, using the following technical solutions:

[0006] A multi-type energy storage application value assessment method, including:

[0007] Step 1: Establish a cost and benefit model for multi-type energy storage operations;

[0008] Step 2: Define multi-dimensional evaluation criteria;

[0009] Step 3: Establish a multi-type energy storage application value assessment mechanism, which includes:

[0010] Compare the importance of adjacent criteria and establish an intuitive judgment matrix;

[0011] The variable weight fuzzy analytic hierarchy process is used to determine the weight of each criterion; and,

[0012] The comprehensive value is obtained by combining the weights of each criterion;

[0013] Step 4: Calculate the comprehensive value under different schemes;

[0014] Step 5: Compare the comprehensive values ​​and take the solution corresponding to the highest comprehensive value as the best configuration solution.

[0015] Optionally, the step 1 includes:

[0016] S11. Establish initial investment cost model, operation and maintenance cost model and waste disposal cost model;

[0017] S12. Establish energy storage peak load regulation benefit model, frequency regulation benefit model, network loss reduction benefit model and grid reliability improvement benefit model.

[0018] Optionally, the step 2 includes:

[0019] S21. Economic dimension:

[0020] Take cost criterion, time criterion and income criterion as first-level sub-criterion;

[0021] The second-level sub-criterion is to use the unit output cost as the cost criterion, the second-level sub-criterion is to use the investment recovery cost as the time criterion, and the second-level sub-criterion is to use the rate of return and the application value of the power market as the profit criterion;

[0022] The power source party, the grid party and the load party are used as the three-level sub-criteria of the rate of return, and the power overflow rate, the power utilization rate and the load missing rate are used as the three-level sub-criteria of the power market application value.

[0023] S22. Reliability dimension:

[0024] The energy supply reliability criterion and energy use reliability criterion are used as first-level sub-criterions;

[0025] Normal operation and fault status are used as the secondary sub-criteria of the functional reliability criterion, and power failure loss and power failure compensation are used as the secondary sub-criteria of the energy reliability criterion;

[0026] The fault-free working rate, power market inclusiveness and operating power fluctuation are taken as the three-level sub-criteria for normal operation, while the energy supply expectation, power market available power supply capacity and energy storage fault power supply failure rate are taken as the three-level sub-criteria for fault status.

[0027] S23, Environmental protection:

[0028] The low carbon principle and coal and energy conservation principle are used as the first-level sub-principles;

[0029] Taking low-carbon benefits as the secondary sub-criterion of the low-carbon criterion, taking coal-saving and energy-saving benefits as the secondary sub-criterion of the coal-saving and energy-saving criterion;

[0030] The three-level sub-criteria of low-carbon benefits are per capita carbon emissions, carbon emissions per unit GDP, clean energy acceptance, and unit power supply. The three-level sub-criteria of coal-fired and gas-fired power supply, energy consumption per unit GDP growth rate, decrease in the proportion of coal and gas, residual value of energy storage system, per capita energy consumption, and proportion of primary energy are used as the three-level sub-criteria of coal-saving and gas-saving benefits.

[0031] Optionally, the step three includes:

[0032] S31, compare the importance of two adjacent criteria to the criteria of the previous layer, and establish an intuitive judgment matrix;

[0033] S32, establishing a constant weight vector mathematical model for the intuitive judgment matrix;

[0034] S33, variable weight fuzzy analytic hierarchy process to determine the weight of each criterion;

[0035] S34, establishing a comprehensive weight mathematical model of each component of the criterion layer relative to the target layer according to the weight of each criterion;

[0036] S35. Establish a mathematical model for comprehensive evaluation of energy storage types based on the comprehensive weights of the criteria.

[0037] Optionally, the S11 includes:

[0038] Initial investment cost model:

[0039] Among them, C t is the initial investment cost, i is the discount rate; P es is the rated power of the energy storage power station, in MW; S es is the rated capacity of the energy storage power station; k p k is the cost coefficient related to the power of the energy storage station; q is the cost coefficient related to the capacity of the energy storage power station; n is the operating life of the energy storage power station;

[0040] Operation and maintenance cost model: C y =N p ×P es +N S ×S es ;

[0041] Among them, C y is the operation and maintenance cost, N p is the maintenance cost per unit power, N s is the maintenance cost per unit capacity;

[0042] Disposal cost model:

[0043] Among them, C f is the disposal cost, C en is the environmentally sound treatment cost, and γ is the battery body recovery factor.

[0044] Optionally, the S12 includes:

[0045] Energy storage peak load regulation profit model:

[0046] Among them, I1 is the peak load benefit of energy storage, is the peak load of the energy storage power station on the dth day, P pr The peak load compensation price, Dpr The number of days that the energy storage power station operates for peak load regulation in a year;

[0047] FM revenue model:

[0048] Among them, I2 is the frequency modulation gain, X fm M is the number of transaction cycles of energy storage power station in the frequency regulation market per month; fm (x) is the frequency regulation mileage that the energy storage power station can provide in the xth cycle; fm (x) is the frequency regulation mileage compensation that the energy storage power station can provide in the xth cycle; Kap fm (x) is the average value of the comprehensive frequency regulation performance index that the energy storage power station can provide in the xth cycle;

[0049] Reducing network loss profit model:

[0050] Among them, I3 is the profit of reducing network loss, P(h) is the electricity price at time h on a typical day, and P loss (h) and P loss_ess (h) are the grid loss power of the grid at time h on a typical day before and after the installation of energy storage;

[0051] Improve the benefit model of power grid reliability: I4=(O grid -O grid_ess )E grid ;

[0052] Among them, I4 is the benefit of improving grid reliability, O grid is the average annual power outage time of the power grid when no energy storage system is installed, O grid_ess is the average annual power outage time of the power grid after the energy storage system is installed, E grid The economic loss caused by power outage time.

[0053] Optionally, the S32 includes:

[0054]

[0055] Among them, μ ij is the membership degree of criterion i to criterion j, v ij is the non-membership degree of criterion i to criterion j, N is the order of the matrix, w i,0 is the constant weight.

[0056] Optionally, the S33 includes:

[0057]

[0058] Among them, w i,j For the further adjusted weights of each criterion, is an intuitionistic fuzzy operator, x ij is the i-th criterion among the j-th criterion, α ij is the equilibrium coefficient of the criterion, ω=(ω1,ω2,……,ω n ) T is considered as a constant weight vector, and k is considered as the degree of hesitation;

[0059] β is the adjustment coefficient of the criterion, x i,0 It is the preset standard value of the criterion status.

[0060] Optionally, the S34 includes:

[0061]

[0062] Among them, w i is the comprehensive weight of the criterion for the target layer, w p 、w q 、w ri They refer to the weights of the first-level sub-criteria, second-level sub-criteria, and third-level sub-criteria respectively; M1, M2, and M3 are the numbers of the first-level sub-criteria, second-level sub-criteria, and third-level sub-criteria respectively.

[0063] In the second aspect, the present application provides a multi-type energy storage application value assessment system, which adopts the following technical solutions:

[0064] A multi-type energy storage application value assessment system includes a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement any of the multi-type energy storage application value assessment methods described above.

[0065] In summary, the present application includes the following beneficial technical effects: by analyzing the application of various types of energy storage in the electricity market, establishing evaluation criteria that reflect economy, reliability and low carbon, and using this to establish a variety of mathematical models, constructing a method that can evaluate the application value of various types of energy storage from the perspectives of nonlinearity, coupling and dynamics, and using intuitive fuzzy theory in conjunction with variable weight methods to improve the problem of relatively single evaluation indicators, providing more accurate data support for how to determine various types of energy storage configuration plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the overall architecture of this application. DETAILED DESCRIPTION

[0067] The following is combined with Figure 1 This application is described in further detail.

[0068] The embodiment of the present application discloses a method for evaluating the value of multiple types of energy storage applications.

[0069] Reference Figure 1 , the value assessment methods for various types of energy storage applications include:

[0070] Step 1: Establish a cost and benefit model for multi-type energy storage operations, which includes:

[0071] S11. Establish initial investment cost model, operation and maintenance cost model and waste disposal cost model;

[0072] S12. Establish energy storage peak load regulation benefit model, frequency regulation benefit model, network loss reduction benefit model and grid reliability improvement benefit model.

[0073] Step 2: Define multi-dimensional evaluation criteria. This step analyzes the combined value of multiple types of energy storage from different scenarios by analyzing the power market, and defines evaluation criteria from the perspectives of economy, reliability, and environmental protection, including:

[0074] S21. Economic dimension:

[0075] Take cost criterion, time criterion and income criterion as first-level sub-criterion;

[0076] The second-level sub-criterion is to use the unit output cost as the cost criterion, the second-level sub-criterion is to use the investment recovery cost as the time criterion, and the second-level sub-criterion is to use the rate of return and the application value of the power market as the profit criterion;

[0077] The power source party, the grid party and the load party are used as the three-level sub-criteria of the rate of return, and the power overflow rate, the power utilization rate and the load missing rate are used as the three-level sub-criteria of the power market application value.

[0078] S22. Reliability dimension:

[0079] The energy supply reliability criterion and energy use reliability criterion are used as first-level sub-criterions;

[0080] Normal operation and fault status are used as the secondary sub-criteria of the functional reliability criterion, and power failure loss and power failure compensation are used as the secondary sub-criteria of the energy reliability criterion;

[0081] The fault-free working rate, power market inclusiveness and operating power fluctuation are taken as the three-level sub-criteria for normal operation, while the energy supply expectation, power market available power supply capacity and energy storage fault power supply failure rate are taken as the three-level sub-criteria for fault status.

[0082] S23, Environmental protection:

[0083] The low carbon principle and coal and energy conservation principle are used as the first-level sub-principles;

[0084] Taking low-carbon benefits as the secondary sub-criterion of the low-carbon criterion, taking coal-saving and energy-saving benefits as the secondary sub-criterion of the coal-saving and energy-saving criterion;

[0085] The three-level sub-criteria of low-carbon benefits are per capita carbon emissions, carbon emissions per unit GDP, clean energy acceptance, and unit power supply. The three-level sub-criteria of coal-fired and gas-fired power supply, energy consumption per unit GDP growth rate, decrease in the proportion of coal and gas, residual value of energy storage system, per capita energy consumption, and proportion of primary energy are used as the three-level sub-criteria of coal-saving and gas-saving benefits.

[0086] Step 3: Establish a multi-type energy storage application value assessment mechanism, which includes:

[0087] S31. Compare the importance of two adjacent criteria to the criteria of the previous layer and establish an intuitive judgment matrix.

[0088] The above is implemented based on the intuitionistic fuzzy theory. For example: Let non-empty X be the domain set, and let the matrix P = (p ij ) nXn is the intuitive judgment matrix, p ij It is an intuitive product number and can be easily understood as the membership, non-membership, and hesitation of criterion i to criterion j; n is the number of rows and columns of the matrix.

[0089] Regarding the degree of importance, example: Table 1, qualitative evaluation and intuitive fuzzy number; the first one in () is called membership, the second one is called non-membership, and the third one is called hesitation.

[0090] Qualitative evaluation Intuitionistic fuzzy numbers Extremely important (0.90,0.10,0) Very important (0.80,0.15,0.05) important (0.70,0.20,0.10) More important (0.60,0.25,0.15) Equally important (0.50,0.30,0.20) Less important (0.40,0.45,0.15) unimportant (0.30,0.50,0.10) Very unimportant (0.20,0.75,0.05) Extremely unimportant (0.10,0.90,0)

[0091] For example, if 10 people vote, 7 are in favor, 2 are against, and 1 abstains, which corresponds to the "important" in Table 1 above. Therefore, we can know that in addition to using the aforementioned cost and benefit model to calculate the corresponding numerical analysis importance, we should also obtain the corresponding data of each criterion in the process of establishing the matrix to improve the matrix.

[0092] S32, establishing a constant weight vector mathematical model for the intuitive judgment matrix, which includes:

[0093]

[0094] Among them, μ ij is the membership degree of criterion i to criterion j, v ij is the non-membership degree of criterion i to criterion j, N is the order of the matrix, w i,0 is the constant weight.

[0095] S33, variable weight fuzzy analytic hierarchy process to determine the weight of each criterion, which includes:

[0096]

[0097] Among them, w i,j For the further adjusted weights of each criterion, is an intuitionistic fuzzy operator, xij is the i-th criterion among the j-th criterion, α ij is the equilibrium coefficient of the criterion, ω=(ω1,ω2,……,ω n ) T is considered as a constant weight vector and k is considered as the degree of hesitation.

[0098] Above β is the adjustment coefficient of the criterion, x i,0 It is the preset standard value of the criterion status.

[0099] S34, according to the weights of each criterion, a comprehensive weight mathematical model of each component of the criterion layer relative to the target layer is established, which includes:

[0100]

[0101] Among them, w i is the comprehensive weight of the criterion for the target layer, w p 、w q 、w ri They refer to the weights of the first-level sub-criteria, second-level sub-criteria, and third-level sub-criteria respectively. M1, M2, and M3 are the numbers of the first-level sub-criteria, second-level sub-criteria, and third-level sub-criteria respectively.

[0102] S35. Establishing a mathematical model for comprehensive evaluation of energy storage types according to the comprehensive weight of the criteria, which includes:

[0103]

[0104] Among them, F i is the comprehensive evaluation value, X pqi Refers to the state standard value of each criterion, W pqi Refers to the combined weight of each criterion.

[0105] Step 4: Calculate the comprehensive value of different solutions, including:

[0106] Obtain multiple types of energy storage application solutions uploaded by users, and process them based on the multi-type energy storage application value evaluation mechanism to obtain a comprehensive evaluation value F i ;

[0107] Step 5: Compare the comprehensive values ​​and take the solution corresponding to the highest comprehensive value as the best configuration solution.

[0108] According to the above content, this method analyzes the application of multiple types of energy storage in the power market, establishes evaluation criteria that reflect economy, reliability and low carbon, and uses this to establish multiple mathematical models. It constructs a method that can evaluate the application value of multiple types of energy storage from the perspective of nonlinearity, coupling and dynamics. It uses intuitive fuzzy theory combined with variable weight methods to improve the problem of relatively single evaluation indicators, and provides more accurate data support for how to determine the configuration plan of multiple types of energy storage.

[0109] In one embodiment of the method, the initial investment cost model, the operation and maintenance cost model and the waste disposal cost model are established, which includes:

[0110] Initial investment cost model:

[0111] Among them, C t is the initial investment cost, i is the discount rate; P es is the rated power of the energy storage power station, in MW; S es is the rated capacity of the energy storage power station, in MWh; k p k is the cost coefficient related to the power of the energy storage power station, in units of 10,000 yuan / MW; q is the cost coefficient related to the capacity of the energy storage power station, in units of 10,000 yuan / MWh. n is the operating life of the energy storage power station, in years.

[0112] Operation and maintenance cost model: C y =N p ×P es +N S ×S es ;

[0113] Among them, C y is the operation and maintenance cost, N p is the unit power maintenance cost, in ten thousand yuan / MW; N s It is the unit capacity maintenance cost, and the unit is 10,000 yuan / MWh.

[0114] Disposal cost model:

[0115] Among them, C f is the disposal cost, C en is the environmentally sound treatment cost (10,000 yuan / MWh), and γ is the battery body recovery coefficient.

[0116] Energy storage peak load regulation profit model:

[0117] Among them, I1 is the peak load benefit of energy storage, is the peak load of the energy storage station on the dth day (MWh), P pris the peak load compensation price (yuan / kWh), D pr The number of days the energy storage power station operates for peak load regulation in a year.

[0118] FM revenue model:

[0119] Among them, I2 is the frequency modulation gain, X fm M is the number of transaction cycles of energy storage power station in the frequency regulation market per month; fm (x) is the frequency regulation mileage that the energy storage power station can provide in the xth cycle; fm (x) is the frequency regulation mileage compensation that the energy storage power station can provide in the xth cycle; Kap fm (x) is the average value of the comprehensive frequency regulation performance index that the energy storage power station can provide in the xth cycle.

[0120] Reducing network loss profit model:

[0121] Among them, I3 is the profit of reducing network loss, P(h) is the electricity price at time h on a typical day, and P loss (h) and P loss_ess (h) are the grid loss power at time h on a typical day before and after the installation of energy storage.

[0122] Improve the benefit model of power grid reliability: I4=(O grid -O grid_ess )E grid ;

[0123] Among them, I4 is the benefit of improving grid reliability, O grid is the average annual power outage time of the power grid when no energy storage system is installed, O grid_ess is the average annual power outage time of the power grid after the energy storage system is installed, E grid The economic loss caused by power outage time.

[0124] The embodiment of the present application also discloses a multi-type energy storage application value assessment system.

[0125] The multi-type energy storage application value assessment system includes a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement any of the multi-type energy storage application value assessment methods described above.

[0126] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for evaluating the value of multi-type energy storage applications, characterized in that: include: Step 1: Establish a cost and benefit model for multi-type energy storage operations; Step 2: Define multi-dimensional evaluation criteria; Step 3: Establish a multi-type energy storage application value assessment mechanism, which includes: Compare the importance of adjacent criteria and establish an intuitive judgment matrix; The variable weight fuzzy analytic hierarchy process is used to determine the weight of each criterion; The comprehensive value is obtained by combining the weights of each criterion; Step 4: Calculate the comprehensive value under different schemes; Step 5: Compare the comprehensive values ​​and take the solution corresponding to the highest comprehensive value as the best configuration solution.

2. The multi-type energy storage application value assessment method according to claim 1 is characterized in that: The step 1 comprises: S11. Establish initial investment cost model, operation and maintenance cost model and waste disposal cost model; S12. Establish energy storage peak load regulation benefit model, frequency regulation benefit model, network loss reduction benefit model and grid reliability improvement benefit model.

3. The multi-type energy storage application value assessment method according to claim 1 is characterized in that: The second step comprises: S21. Economic dimension: Take cost criterion, time criterion and income criterion as first-level sub-criterion; The second-level sub-criterion is to use the unit output cost as the cost criterion, the second-level sub-criterion is to use the investment recovery cost as the time criterion, and the second-level sub-criterion is to use the rate of return and the application value of the power market as the profit criterion; The power source party, the grid party and the load party are used as the three-level sub-criteria of the rate of return, and the power overflow rate, the power utilization rate and the load missing rate are used as the three-level sub-criteria of the power market application value. S22. Reliability dimension: The energy supply reliability criterion and energy use reliability criterion are used as first-level sub-criterions; Normal operation and fault status are used as the secondary sub-criteria of the functional reliability criterion, and power failure loss and power failure compensation are used as the secondary sub-criteria of the energy reliability criterion; The fault-free working rate, power market inclusiveness and operating power fluctuation are taken as the three-level sub-criteria for normal operation, while the energy supply expectation, power market available power supply capacity and energy storage fault power supply failure rate are taken as the three-level sub-criteria for fault status. S23, Environmental protection: The low carbon principle and coal and energy conservation principle are used as the first-level sub-principles; Taking low-carbon benefits as the secondary sub-criterion of the low-carbon criterion, taking coal-saving and energy-saving benefits as the secondary sub-criterion of the coal-saving and energy-saving criterion; The three-level sub-criteria of low-carbon benefits are per capita carbon emissions, carbon emissions per unit GDP, clean energy acceptance, and unit power supply. The three-level sub-criteria of coal-fired and gas-fired power supply, energy consumption per unit GDP growth rate, decrease in the proportion of coal and gas, residual value of energy storage system, per capita energy consumption, and proportion of primary energy are used as the three-level sub-criteria of coal-saving and gas-saving benefits.

4. The multi-type energy storage application value assessment method according to claim 1 is characterized in that: The step three comprises: S31, compare the importance of two adjacent criteria to the criteria of the previous layer, and establish an intuitive judgment matrix; S32, establishing a constant weight vector mathematical model for the intuitive judgment matrix; S33, variable weight fuzzy analytic hierarchy process to determine the weight of each criterion; S34, establishing a comprehensive weight mathematical model of each component of the criterion layer relative to the target layer according to the weight of each criterion; S35. Establish a mathematical model for comprehensive evaluation of energy storage types based on the comprehensive weights of the criteria.

5. The multi-type energy storage application value assessment method according to claim 2 is characterized in that: The S11 includes: Initial investment cost model: Among them, C t is the initial investment cost, i is the discount rate; P es is the rated power of the energy storage power station, in MW; S es is the rated capacity of the energy storage power station; k p k is the cost coefficient related to the power of the energy storage station; q is the cost coefficient related to the capacity of the energy storage power station; n is the operating life of the energy storage power station; Operation and maintenance cost model: C y =N p ×P es +N S ×S es ; Among them, C y is the operation and maintenance cost, N p is the maintenance cost per unit power, N s is the maintenance cost per unit capacity; Disposal cost model: Among them, C f is the disposal cost, C en is the environmentally sound treatment cost, and γ is the battery body recovery factor.

6. The multi-type energy storage application value assessment method according to claim 2 is characterized in that: The S12 includes: Energy storage peak load regulation profit model: Among them, I1 is the peak load benefit of energy storage, is the peak load of the energy storage power station on the dth day, P pr The peak load compensation price, D pr The number of days that the energy storage power station operates for peak load regulation in a year; FM revenue model: Among them, I2 is the frequency modulation gain, X fm M is the number of transaction cycles of energy storage power station in the frequency regulation market per month; fm (x) is the frequency regulation mileage that the energy storage power station can provide in the xth cycle; fm (x) is the frequency regulation mileage compensation that the energy storage power station can provide in the xth cycle; Kap fm (x) is the average value of the comprehensive frequency regulation performance index that the energy storage power station can provide in the xth cycle; Network loss reduction profit model: Among them, I3 is the profit of reducing network loss, P(h) is the electricity price at time h on a typical day, and P loss (h) and P loss_ess (h) are the grid loss power of the grid at time h on a typical day before and after the installation of energy storage; Improve the benefit model of power grid reliability: I4=(O grid -O grid_ess )E grid ; Among them, I4 is the benefit of improving grid reliability, O grid is the average annual power outage time of the power grid when no energy storage system is installed, O grid_ess is the average annual power outage time of the power grid after the energy storage system is installed, E grid The economic loss caused by power outage time.

7. The multi-type energy storage application value assessment method according to claim 4 is characterized in that: The S32 includes: Among them, μ ij is the membership degree of criterion i to criterion j, v ij is the non-membership degree of criterion i to criterion j, N is the order of the matrix, w i,0 is the constant weight.

8. The multi-type energy storage application value assessment method according to claim 7 is characterized in that: The S33 comprises: Among them, w i,j For the further adjusted weights of each criterion, is an intuitionistic fuzzy operator, x ij is the i-th criterion among the j-th criterion, α ij is the equilibrium coefficient of the criterion, ω=(ω1,ω2,……,ω n ) T is considered as a constant weight vector, and k is considered as the degree of hesitation; β is the adjustment coefficient of the criterion, x i,0 It is the preset standard value of the criterion status.

9. The multi-type energy storage application value assessment method according to claim 8 is characterized in that: The S34 comprises: Among them, w i is the comprehensive weight of the criterion for the target layer, w p 、w q 、w ri They refer to the weights of the first-level sub-criteria, second-level sub-criteria, and third-level sub-criteria respectively; M1, M2, and M3 are the numbers of the first-level sub-criteria, second-level sub-criteria, and third-level sub-criteria respectively.

10. A multi-type energy storage application value assessment system, characterized by: It comprises a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement the multi-type energy storage application value assessment method as described in any one of claims 1-9.

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