Multi-element energy storage benefit evaluation method based on FAHP and TOPSIS methods

Abstract

The invention discloses a multivariate energy storage benefit evaluation method based on FAHP and TOPSIS methods, and belongs to the technical field of benefit evaluation. Comprising the following steps: establishing a multi-element energy storage system comprehensive benefit evaluation index system, establishing a mathematical model of each index, and determining the weight of each index through an FAHP method; constructing an evaluation matrix and standardizing the evaluation matrix, and obtaining a weighted standardized decision matrix in combination with the weight determined by the FAHP method; and positive and negative ideal schemes are determined through a TOPSIS method, distances and closeness between the schemes and the ideal solutions are obtained, and results are sorted to obtain an optimal scheme. According to the method, the FAHP and the TOPSIS method are combined, the problems can be considered more comprehensively, the limitation of a single method is avoided, the result is more objective and credible, and the whole decision problem is considered more systematically and comprehensively.

Description

Technical Field

[0001] The invention relates to the technical field of benefit evaluation, and in particular to a multivariate energy storage benefit evaluation method based on FAHP and TOPSIS methods. Background Art

[0002] With the large-scale grid connection of new energy sources such as distributed photovoltaics, the power grid flow has undergone major changes, causing the power grid distribution transformer to be reversely overloaded and causing changes in the UHV AC flow, posing a huge challenge to the safety of the power grid. In order to maintain the safe and stable operation of the new power system and promote the consumption of new energy, it is urgent to incorporate flexible resources such as energy storage into the power grid. The application scenarios of energy storage systems in power systems are diverse, including peak shaving and valley filling, peak and frequency regulation, and power supply guarantee. However, different energy storage technologies and application scenarios have different requirements for costs, benefits, technical parameters, etc. How to scientifically and reasonably evaluate the benefits of energy storage systems has become an important challenge facing decision makers and investors.

[0003] For the comprehensive benefit evaluation of energy storage, it is necessary to assign weights to various indicators and evaluate their economic efficiency based on the establishment of a comprehensive benefit index system. Commonly used analysis methods include analytic hierarchy process (AHP), data envelopment analysis (DEA), fuzzy logic method, entropy weight method, TOPSIS, etc. These methods can comprehensively evaluate the benefits of energy storage systems from multiple dimensions and levels. However, using only one of the evaluation methods makes the results more limited. In order to consider the problem more comprehensively and objectively, a multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method is needed to analyze the economic efficiency of multivariate energy storage. Summary of the invention

[0004] The purpose of the present invention is to propose a multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method, comprising the following steps:

[0005] Establish a comprehensive benefit evaluation index system for multi-element energy storage systems and a mathematical model for each index, and determine the weight of each index using the FAHP method;

[0006] The evaluation matrix was constructed and standardized, and the weighted standardized decision matrix was obtained by combining the weights determined by the FAHP method;

[0007] The positive and negative ideal solutions are determined by the TOPSIS method, and the distance and closeness of each solution to the ideal solution are obtained. The results are sorted to obtain the optimal solution.

[0008] The comprehensive benefit evaluation indicators of the diversified energy storage system include construction cost, operation and maintenance cost, charging cost, battery replacement cost, reduction of standby capacity, new energy absorption capacity, peak regulation income, frequency regulation income, delaying grid construction investment, reducing power outage losses, and reducing carbon emissions.

[0009] The formula for calculating construction cost is:

[0010] C inv =C(r,n)·(δ P P+δ E E)

[0011]

[0012] Where P is the rated charging and discharging power of the energy storage station, kW; E is the rated capacity of the energy storage station, kWh; δ P is the cost coefficient related to the peak power input and output of the energy storage power station, RMB / kW; δ E is the cost coefficient related to the capacity of the energy storage power station, RMB / kWh; r is the discount rate, %; n is the operating year of the energy storage power station, year.

[0013] The calculation formula for operation and maintenance costs is:

[0014]

[0015] In the formula, δ P,OM is the annual operation and maintenance cost coefficient per unit power, RMB / kW; δ E,OM is the annual operation and maintenance cost coefficient per unit capacity, RMB / kWh; r is the discount rate, %; n is the operating years of the energy storage power station, including the year after the battery is replaced.

[0016] The calculation formula for charging cost is:

[0017]

[0018] Where P c is the charging electricity price, yuan; θ DOD is the charge and discharge depth of the energy storage battery, %; T n It is the annual cycle number of the energy storage power station.

[0019] The calculation formula for battery replacement cost is:

[0020]

[0021] Where, τ is the average annual reduction rate of battery cost; t is the year of battery replacement; δ res is the unit capacity replacement battery cost coefficient, RMB / kWh.

[0022] Frequency regulation revenue includes capacity compensation revenue and mileage compensation revenue.

[0023] The beneficial effects of the present invention are:

[0024] 1. By combining FAHP and TOPSIS methods, the present invention can consider the problem more comprehensively and avoid the limitations of a single method;

[0025] 2. The present invention makes the results more objective and credible, and also makes the entire decision-making problem more systematic and comprehensive;

[0026] 3. The method of the present invention can provide a simple, easy and effective decision support tool for practical decision-making problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method of the present invention;

[0028] Figure 2 This is a diagram of the comprehensive benefit evaluation index system of the multi-energy storage system;

[0029] Figure 3 This is a diagram of the comprehensive scoring results of the energy storage power station. DETAILED DESCRIPTION

[0030] The present invention proposes a multivariate energy storage benefit evaluation method based on FAHP and TOPSIS methods, which will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0031] Figure 1 The flowchart of the multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method of the present invention comprises the following steps:

[0032] Step 1: Establish a comprehensive benefit evaluation index system for multivariate energy storage systems and a mathematical model for each index, and determine the weight of each index using the FAHP method.

[0033] Figure 2 This is a diagram of the comprehensive benefit evaluation index system of the multi-energy storage system. The specific index descriptions and definitions are as follows:

[0034] (1) Construction cost: The initial construction cost of an energy storage power station refers to the initial construction cost of an energy storage system with a given capacity. The initial investment cost is mainly related to the storage capacity and transmission power of the system. The calculation formula for the initial construction investment cost is as follows:

[0035] C inv =δ P P+δ E E (1)

[0036] Where: P is the rated charging and discharging power of the energy storage station, kW; E is the rated capacity of the energy storage station, kWh; δ P is the cost coefficient related to the peak power input and output of the energy storage power station, RMB / kW; δE is the cost coefficient related to the capacity of the energy storage power station, RMB / kWh.

[0037] The equal annual value coefficient C(r,n) is expressed as:

[0038]

[0039] Where r is the discount rate, %; n is the operating years (lifespan) of the energy storage power station, years.

[0040] Considering the time value of money, the investment cost of energy storage is corrected and converted into annual cost C inv Can be corrected to:

[0041] C inv =C(r,n)·(δ P P+δ E E) (3)

[0042] (2) Operation and maintenance cost: The operation and maintenance cost of an energy storage power station refers to the cost required to keep the energy storage power station in a continuously operational state. The total operation and maintenance cost of an energy storage power station mainly includes capacity operation and maintenance cost, power operation and maintenance cost, and labor cost. The sum of the present value of the annual operation and maintenance cost of power and capacity is expressed as follows:

[0043]

[0044] In the formula, δ P,OM is the annual operation and maintenance cost coefficient per unit power, RMB / kW; δ E,OM is the annual operation and maintenance cost coefficient per unit capacity, RMB / kWh; r is the discount rate, %; n is the operating year of the energy storage power station (1, 2, 3, …, N), including the year after the battery is replaced.

[0045] If the annual operation and maintenance costs per unit power and capacity are difficult to determine, the total operation and maintenance costs of the energy storage power station can be estimated as a certain proportion of the initial investment cost. The present value of the total operation and maintenance costs of the energy storage power station can be expressed as:

[0046]

[0047] Where μ represents the operation and maintenance cost coefficient of energy storage.

[0048] (3) Charging cost: The charging cost of an energy storage station is an important component of its life cycle cost, especially for electrochemical energy storage technologies such as lithium batteries. The charging cost mainly depends on factors such as charging electricity price, charge and discharge depth, and battery cycle life. The present value of the charging cost of an energy storage station is expressed as:

[0049]

[0050] Where Pc is the charging electricity price, yuan; θ DOD is the charge and discharge depth of the energy storage battery, %; T n It is the annual cycle number of the energy storage power station.

[0051] (4) Battery replacement cost: When the performance of energy storage batteries cannot meet the requirements of energy storage power stations, replacing batteries in energy storage power stations can extend the service life of energy storage power stations, improve the investment utilization efficiency of energy storage power stations, and shorten the investment recovery period. When the battery energy storage life cycle is shorter than the actual project cycle, it needs to be replaced, and the replacement cost mainly comes from the battery itself. The replacement cost of battery energy storage is:

[0052]

[0053] Where, τ is the average annual reduction rate of battery cost; t is the year of battery replacement; δ res is the unit capacity replacement battery cost coefficient, RMB / kWh.

[0054] (5) Reduce the reserve capacity: Due to the significant randomness and uncertainty of distributed power generation, a large reserve capacity is required to quickly adjust the volatility of distributed power generation. Instead of using large-capacity allocation capacity, a storage system with flexible "throughput" characteristics is used. It charges at low points in the load curve and discharges at high points. While completing peak shaving and valley filling, it plays the role of distributed generation reserve capacity, improving the economy and safety of system operation. By using the normal distribution to fit the new energy generation, the expected value of the energy storage system used to reduce the required reserve capacity can be obtained, as shown in the following formula:

[0055]

[0056] Furthermore, the benefits of reducing the reserve capacity of the energy storage system are:

[0057] S spare =e r P RC (9)

[0058] In the formula, P α Indicates the limit of the power grid's ability to absorb renewable energy generation without requiring backup capacity; P μ Represents the average value of the power emitted by new energy; P δ Represents the fluctuation deviation of renewable energy power generation; e r Indicates the price per unit of power reserve capacity.

[0059] (6) New energy absorption capacity: Energy storage systems can effectively alleviate this problem by storing excess electricity during the peak period of new energy generation and releasing electricity during the trough period of new energy generation, thereby smoothing the fluctuations of new energy generation and improving the grid's absorption capacity for new energy.

[0060] Assuming that the power fluctuation of renewable energy generation follows a normal distribution, the expected value of the energy storage system's ability to improve renewable energy consumption can be calculated as shown in the following formula:

[0061]

[0062] Among them, E storage Represents the capacity of the energy storage system, V lim Represents the grid-connected capacity limit of new energy, P μ It represents the average value of renewable energy power generation, and σ represents the standard deviation of renewable energy power generation.

[0063] The benefits of energy storage systems improving the capacity to absorb new energy are:

[0064] R RE =E storage ×C RE (11)

[0065] Among them, C RE Represents the income per unit of new energy consumption capacity.

[0066] (7) Peak load shaving benefits: The load changes in the power system require the power grid to provide sufficient power at peak load times, while still maintaining power balance at low load times. The energy storage system can store electricity during low load times and release electricity during peak load times, thereby effectively shaving peak loads and filling valleys, reducing the need for backup capacity and improving the economic efficiency of the power grid.

[0067] The benefits of energy storage system in peak load regulation can be expressed as:

[0068]

[0069] Among them, S p1 The annual energy storage peak-shaving price compensation income; E i is the peak-shaving discharge amount of energy storage on the i-th day; e is the contract electricity price of the power plant; N p The number of days that energy storage is used for peak load regulation in a year.

[0070] (8) Frequency regulation income: The income from energy storage participating in the frequency regulation market is divided into capacity compensation income and mileage compensation income. The mathematical model of energy storage frequency regulation income is as follows:

[0071] S F =S C +S M (13)

[0072] In the formula, S F Frequency modulation compensation income; S C is the capacity compensation income; S M Compensate for mileage income.

[0073] ①Capacity compensation

[0074] Capacity compensation is based on the energy storage frequency regulation capacity quota compensation, and the calculation method is:

[0075] S C =P AGC ·C C (14)

[0076] Where: P AGC C is the adjustable capacity of the energy storage automatic generation control AGC (Automatic Generation Control), which is the difference between the upper and lower limits of the adjustable capacity of the energy storage that can be put into AGC operation (MW); C The frequency regulation capacity compensation price (yuan / MW·month). The regulation rate of the AGC command issued to the energy storage device should be the power increase and decrease rate required by the power grid during the period. The upper and lower limits of the energy storage AGC command regulation are the rated charge and discharge power of the energy storage.

[0077] ② Mileage compensation

[0078] The benefits of energy storage system mileage compensation are:

[0079]

[0080] where k f is the average number of calls per dispatch cycle (15 minutes); m is the average frequency modulation mileage coefficient of each call of energy storage; P is the rated power of energy storage; C F is the mileage price of energy storage frequency regulation, and N is the number of days that energy storage participates in frequency regulation.

[0081] (9) Delaying investment in grid construction: Energy storage systems are used in distribution networks to reduce peak load and fill valley loads, which can reduce the construction of transmission and distribution network capacity, reduce the probability of system congestion, and further delay the upgrade and expansion of grid equipment, thereby improving power quality, increasing power supply reliability, and reducing costs. Since energy storage systems have the characteristics of rapid charging and discharging, they can be used to charge and store energy during low-load periods and discharge and release energy during high-load periods, thereby delaying the investment in the construction of transmission and distribution lines and distribution stations, achieving an economical solution.

[0082] The annual benefits of energy storage systems delaying grid equipment investment are as follows:

[0083]

[0084] In the formula, C inv Represents the one-time investment cost required for power grid upgrade; i t represents the inflation rate; i drepresents the discount rate; N represents the operational life of the invested energy storage system; ε is the annual growth rate of the load, α is the ratio of the rated power of the energy storage to the peak power of the load, and 1-α is defined here as the peak shaving coefficient of the energy storage device.

[0085] (10) Reduce power outage losses: Power outages not only cause inconvenience to residents' lives, but also cause huge economic losses to industrial production and commercial activities. When a power grid fails or the load increases suddenly, the energy storage system can quickly release the stored energy and provide emergency power supply as a backup power source, thereby reducing the duration and frequency of power outages and thus reducing the economic losses caused by power outages.

[0086] The benefit of energy storage system in reducing power outage loss can be expressed as:

[0087] S outage =R rel E ENS λ S [1-P(W i <E ENS )] (18)

[0088] in:

[0089] E ENS =T S (1-A S ) m (19)

[0090]

[0091] Where: R rel It represents the power shortage loss evaluation rate of important users of the distribution station; E ENS It represents the expected value of power shortage of important users caused by each power outage of the power grid; S represents the power outage rate of the distribution station; T S Indicates the number of hours of electricity used by the user per year; A S To indicate the reliability of power supply; P m Indicates the power value required to ensure the power supply to users; W i Represents the remaining capacity of the energy storage system in the i-th period.

[0092] (11) Reducing carbon emissions: With the improvement of environmental awareness and increasingly stringent carbon emission regulations, reducing carbon emissions has become an important goal of the power industry. Energy storage systems can effectively reduce carbon emissions by optimizing grid dispatch, improving the utilization rate of renewable energy, and reducing the demand for fossil fuel power generation. Especially during the peak period of renewable energy generation, energy storage systems can absorb excess clean energy and release it during peak electricity consumption, reducing dependence on high-carbon emission power sources such as thermal power.

[0093] The benefits of energy storage systems in reducing carbon emissions can be expressed as:

[0094] B7=Q c ρ c N day (twenty one)

[0095] Where: Q c The daily carbon emission reduction after configuring energy storage; ρ c is the carbon trading price, N day is the annual operating days of energy storage.

[0096] Step 2: Construct the evaluation matrix and standardize it, and combine the weights determined by the FAHP method to obtain the weighted standardized decision matrix.

[0097] Fuzzy analytic hierarchy process (FAHP) is a comprehensive evaluation method that combines qualitative analysis and quantitative analysis by integrating fuzzy mathematics into the analytic hierarchy process. By using fuzzy numbers to express expert judgment, the inaccuracy of single numerical evaluation in the traditional AHP method is avoided. The process of fuzzy analytic hierarchy process is as follows:

[0098] (1) Construct a hierarchical model. Decompose the top-level goals layer by layer, and decompose the evaluation indicators at each level into several sub-indicators based on the actual situation of the research object, and finally form an evaluation indicator system that progresses from the lowest level research indicators to the research goals.

[0099] (2) Construct a fuzzy judgment matrix. The core of constructing a fuzzy judgment matrix is ​​to compare the indicators at each level in pairs according to the opinions of experts, and to reflect the relationship between the indicators at each level through the fuzzy matrix. If the fuzzy judgment matrix A meets two basic conditions: a ii =0.5, a ij +a ji =1, then A is called a fuzzy complementary judgment matrix. When comparing indicators, a 0.1-0.9 scale suitable for constructing additive consistent complementary judgment matrices is used. The scaling method is shown in Table 1.

[0100] Table 1 Fuzzy analytic hierarchy process 0.1~0.9 scaling method

[0101] Scale significance 0.5 Factors i and j are equally important 0.6 Factor i is slightly more important than factor j 0.7 Factor i is significantly more important than factor j 0.8 Factor i is more important than factor j 0.9 Factor i is extremely more important than factor j 0.1-0.4 The above factors are compared

[0102] By comparing each element in the matrix pairwise, the fuzzy complementary judgment matrix A can be obtained;

[0103]

[0104] The weight calculation formula of the fuzzy complementary judgment matrix is ​​as follows:

[0105]

[0106] (3) Check the consistency of the fuzzy judgment matrix.

[0107] When the offset consistency is too large, it means that the calculation result of the weight vector has a large deviation and cannot be used as a basis for decision making. The compatibility of the fuzzy judgment matrix is ​​used to test its consistency principle. The specific method is as follows:

[0108] Assume that the weight vector of the fuzzy judgment matrix A is W = (W1, W2, ... W n ) T ,but make Then it is called W * =(W ij ) n×n is the characteristic matrix of the judgment matrix A.

[0109] Let matrix A = (a ij ) n×n and B=(b ij ) n×n are all fuzzy judgment matrices, called is the compatibility index between A and B.

[0110] Assuming that α is the attitude of the decision maker, when there is a relationship of I(A, W) ≤ α with the compatibility index, the matrix consistency is satisfied. Generally speaking, the decision maker's requirement for matrix consistency is affected by α, and its value is negatively correlated with the requirement, usually taking α = 0.1. When the compatibility index I(A, W)> α, it indicates that the consistency of the matrix is ​​not satisfied, and the judgment needs to be repeated until it is consistent.

[0111] (4) Hierarchical sorting

[0112] The sorting vector of A is obtained by calculating the fuzzy judgment matrix, whose value W = (W1, W2, ... W n ) T , and then the relative weights of the elements in adjacent layers can be calculated. By calculating the weights layer by layer until the bottom layer, the weight ranking of the bottom layer relative to the target layer can be obtained.

[0113] Step 3: Determine the positive and negative ideal solutions through the TOPSIS method, and obtain the distance and closeness of each solution to the ideal solution, and sort the results to obtain the optimal solution.

[0114] TOPSIS is a commonly used comprehensive evaluation method. It scores and ranks the evaluation schemes by calculating the relative closeness between the positive and negative ideal solutions. It can make full use of the information of the original data and accurately reflect the gap between the schemes. The calculation steps of the TOPSIS method are as follows:

[0115] (1) Decision matrix forward transformation. Based on the indicator data of each evaluation scheme, the decision matrix Y = (y ij )m×n , where y ij is the value of scheme i with respect to evaluation index j, m is the number of evaluation schemes, and n is the number of evaluation indicators

[0116]

[0117] Convert all evaluation indicators into benefit indicators, that is, the larger the indicator value, the better the indicator is, and get the positive decision matrix Y'=(y ij ') m×n The method of positive index is as follows:

[0118] For cost-type indicators, that is, indicators with smaller values, the better, formula (22) is used for positive processing:

[0119] y ij '=max|Y j |-y ij (twenty three)

[0120] Where: y ij ' is the element of the forward decision matrix Y'; max|Y j | is the index vector Y j For interval indicators, that is, indicators whose values ​​fall within a certain interval, the forward processing is performed using equations (24) and (25).

[0121] M=max{a-min|Y j |,max|Y j |-b} (24)

[0122]

[0123] Where: a, b are the lower and upper limits of the optimal interval of interval indicator j; min|Y j |,max|Y j | is the index vector Y j The minimum and maximum values ​​of the elements in the column.

[0124] (2) Normalization of the forward matrix. In order to eliminate the influence of different dimensions of each evaluation index, the forward decision matrix Y′ is normalized to obtain the standardized decision matrix Z = (z ij ) m×n , the commonly used standardization processing method for TOPSIS is:

[0125]

[0126] (3) Calculate the weighted normalized decision matrix. The normalized decision matrix is ​​multiplied by its associated weights. ij The weighted normalized value is:

[0127] V ij =W ij ·Z ij (27)

[0128] (4) Calculate the score. Based on the weighted decision matrix V, the maximum value of each column index is used to form a positive ideal solution The minimum value of each column index constitutes a negative ideal solution Define the distance from solution i to the positive ideal solution as Define the distance from solution i to the negative ideal solution as The calculation formula is:

[0129]

[0130] Where: is the maximum and minimum value of the index in the jth column.

[0131] The score of each solution is calculated by formula (29):

[0132]

[0133] Where: s i is the score of the i-th evaluation scheme, 0≤s i ≤1. The higher the score, the closer the evaluation scheme is to the positive ideal solution, and the better the evaluation result of the scheme is.

[0134] Table 2 Cost-benefit index data of energy storage power station

[0135]

[0136]

[0137] Table 2 Energy storage power station cost-benefit indicator data, Figure 3 This is the comprehensive scoring result of the energy storage power station. The three energy storage power stations perform differently in different indicators, but overall the energy storage system has significant positive benefits in peak and frequency regulation, reducing power outage losses, delaying grid investment, and reducing carbon emissions. Among them, the first energy storage power station scored the highest, showing advantages in economy and comprehensive benefits, indicating that under the condition of reasonable configuration of energy storage capacity and power, the energy storage system can provide effective support for the power grid.

[0138] By combining FAHP and TOPSIS methods, the present invention can consider the problem more comprehensively and avoid the limitations of a single method, making the result more objective and credible, and making the consideration of the entire decision-making problem more systematic and comprehensive.

Claims

1. A multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method, characterized in that: The following steps are involved: Establish a comprehensive benefit evaluation index system for multi-element energy storage systems and a mathematical model for each index, and determine the weight of each index using the FAHP method; The evaluation matrix was constructed and standardized, and the weighted standardized decision matrix was obtained by combining the weights determined by the FAHP method; The positive and negative ideal solutions are determined by the TOPSIS method, and the distance and closeness of each solution to the ideal solution are obtained. The results are sorted to obtain the optimal solution.

2. The multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method according to claim 1 is characterized in that: The comprehensive benefit evaluation indicators of the diversified energy storage system include construction cost, operation and maintenance cost, charging cost, battery replacement cost, reduction of standby capacity, new energy absorption capacity, peak regulation income, frequency regulation income, delaying grid construction investment, reducing power outage losses, and reducing carbon emissions.

3. The multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method according to claim 2 is characterized in that: The calculation formula of the construction cost is: C inv =C(r,n)·(δ P P+d E E) Where P is the rated charging and discharging power of the energy storage station, kW; E is the rated capacity of the energy storage station, kWh; δ P is the cost coefficient related to the peak power input and output of the energy storage power station, RMB / kW; δ E is the cost coefficient related to the capacity of the energy storage power station, RMB / kWh; r is the discount rate, %; n is the operating year of the energy storage power station, year.

4. The multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method according to claim 3 is characterized in that: The calculation formula for the operation and maintenance cost is: In the formula, δ P,OM is the annual operation and maintenance cost coefficient per unit power, RMB / kW; δ E,OM is the annual operation and maintenance cost coefficient per unit capacity, RMB / kWh; r is the discount rate, %; n is the operating years of the energy storage power station, including the year after the battery is replaced.

5. The multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method according to claim 4 is characterized in that: The calculation formula of the charging cost is: Where P c is the charging electricity price, yuan; θ DOD is the charge and discharge depth of the energy storage battery, %; T n It is the annual cycle number of the energy storage power station.

6. The multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method according to claim 5 is characterized in that: The calculation formula of the battery replacement cost is: Where, τ is the average annual reduction rate of battery cost; t is the year of battery replacement; δ res is the unit capacity replacement battery cost coefficient, RMB / kWh.

7. The multivariate energy storage benefit evaluation method based on FAHP and TOPSIS method according to claim 6 is characterized in that: The frequency regulation income includes capacity compensation income and mileage compensation income.

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