Energy storage technology evaluation method based on dynamic duration view angle

Abstract

The invention discloses an energy storage technology evaluation method based on a dynamic time length visual angle, and the method comprises the steps: constructing six comprehensive evaluation scenes of different energy storage time length intervals, and recognizing the continuous time length characteristics of a power gap according to the wind and light resources and load characteristics of a region; determining the power continuous gap duration characteristic of the high-proportion new energy power system, and judging the required energy storage duration demand; constructing comprehensive evaluation indexes of various energy storage technologies; quantifying the index subjective weight in the multi-group decision problem through a Bayesian best-worst method; converting qualitative description into a quantitative form by using a cloud model, and calculating an objective weight in combination with a comprehensive determination model of an objective standard weight; the Bayesian best-worst method, the cloud model and the comprehensive determination model of the objective standard weight are adjusted through the game theory, and the comprehensive weight is calculated. And sorting the indexes through a multi-standard decision-making method based on advantages, and determining the optimal energy storage technology type selection under a dynamic duration view angle.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage system evaluation. Specifically, it relates to an evaluation method for energy storage technology from the perspective of dynamic duration. Background Art

[0002] With the rapid development of wind power and photovoltaic power, the power system faces increasingly high requirements for regulation capabilities, especially in dealing with the volatility and intermittency of new energy output. To ensure the safe and stable operation of the power system, energy storage technology has become an important solution. Through means such as peak shaving and valley filling, peak regulation and frequency modulation, and providing reserve capacity, energy storage technology has significantly improved the safety and stability of the power system. Electrochemical energy storage technology based on lithium batteries has become an effective means to cope with the daily power supply and demand fluctuations due to its advantages in short-term energy storage.

[0003] With the continuous increase in the proportion of new energy, the problem of seasonal output shortage will become particularly severe, and the demand for long-term energy storage technology will increase day by day. Dynamically dividing the optimal energy storage technology according to the energy storage duration will help select and deploy the most suitable energy storage technology solutions for different power gap durations, thereby enhancing the stability and sustainability of the power system. However, existing technologies mainly focus on the selection of energy storage technologies in scenarios such as peak regulation and frequency modulation, and a comprehensive evaluation framework for energy storage technologies with different discharge durations has not been formed. Summary of the Invention

[0004] In view of the problem that the existing energy storage technology evaluation method lacks a comprehensive evaluation of energy storage technologies with different discharge durations, the present invention provides an evaluation method for energy storage technology from the perspective of dynamic duration.

[0005] To achieve the above technical objectives, the technical solutions adopted by the present invention are as follows:

[0006] An evaluation method for energy storage technology from the perspective of dynamic duration, comprising the steps of:

[0007] S1. Construct six comprehensive evaluation scenarios for different energy storage duration intervals, and identify the characteristics of the power gap duration according to the wind and light resources and load characteristics of the region;

[0008] S2. Determine the characteristics of the power continuous gap duration of a high-proportion new energy power system, and judge the required energy storage duration demand;

[0009] S3. Construct comprehensive evaluation indicators for various energy storage technologies;

[0010] S4. Quantify the subjective weights of indicators in multi-group decision-making problems through the Bayesian Best-Worst Method (BBWM method);

[0011] S5. Use the Cloud model to convert qualitative descriptions into quantitative forms, and calculate the objective weights by combining with the comprehensive determination (IDOCRIW) model of objective standard weights.

[0012] S6. Adjust the comprehensive determination models of the Bayesian Best-Worst Method, Cloud model, and objective standard weights through game theory, and calculate the comprehensive weights.

[0013] S7. Rank each indicator through the Multi-Criteria Decision Making on the basis of Ratio Analysis plus an Ordered Weighted Averaging operator (MARCOS) to determine the optimal energy storage technology selection from the perspective of dynamic duration.

[0014] Furthermore, the detailed steps for constructing the comprehensive evaluation scenarios for six different energy storage duration intervals are as follows:

[0015] Divide six time scale intervals according to the duration of the power gap.

[0016] Construct six comprehensive evaluation scenarios based on dynamic duration by integrating the six time scale intervals.

[0017] Furthermore, the detailed steps for determining the power continuous gap duration characteristics of the high-proportion renewable energy power system and judging the required energy storage duration demand in step S2 are as follows:

[0018] Select a high-proportion renewable energy power system and summarize its existing power generation resource characteristics.

[0019] Simulate the power output situation of this area under the 100% high-proportion renewable energy scenario.

[0020] Analyze the source-load characteristic curves of typical days and months to determine the required energy storage duration interval.

[0021] Furthermore, the detailed steps for constructing the comprehensive evaluation indicators for various energy storage technologies in step S3 are as follows:

[0022] Search relevant literature to determine the core indicators for energy storage technology evaluation.

[0023] For the perspective of dynamic evaluation, subdivide and adjust the indicators collected from the literature.

[0024] Integrate the indicators in the dimensions of technology, economy, environment, and society to form the final evaluation framework.

[0025] Furthermore, the detailed steps for quantifying the subjective weights of indicators in the multi-group decision-making problem by using the BBWM method in step S4 are as follows:

[0026] Select the best and worst indicators in the comprehensive indicator evaluation system.

[0027] The optimal indicators are compared with the indicators in the technical, economic, environmental, and social dimensions to generate the corresponding optimal indicator preference vectors;

[0028] The worst indicators are compared with the indicators in the technical, economic, environmental, and social dimensions to generate the corresponding worst indicator preference vectors;

[0029] Through the Bayesian hierarchical model, a set of standard optimal indicator weights are determined according to different preferences preset in the system;

[0030] The trust ranking model is used to rank the priorities of various indicators.

[0031] Furthermore, the detailed steps of converting qualitative descriptions into quantitative forms by using the Cloud model and calculating the objective weights in combination with the IDOCRIW model in step S5 include:

[0032] Define the language set L for qualitative evaluation;

[0033] Convert each qualitative language variable into a quantitative Cloud model;

[0034] Sum up multiple quantitative Cloud models;

[0035] Calculate the total score of the quantitative Cloud model to obtain the quantitative value of the qualitative indicator;

[0036] Use the entropy weight method to assign weights to the decision matrix respectively, and use the criterion impact loss method (CILOS method) to correct the weights of the entropy weight method;

[0037] Obtain the IDOCRIW objective weight value.

[0038] Furthermore, the detailed steps of adjusting the comprehensive determination model of the Bayesian best-worst method, Cloud model, and objective standard weights through game theory in step S6 and calculating the comprehensive weights include:

[0039] Construct a comprehensive weight linear expression to obtain the optimized first derivative condition expression;

[0040] Solve the normalized weight coefficients, substitute them into the comprehensive weight linear expression, and obtain the comprehensive weights.

[0041] Furthermore, the detailed steps of ranking each indicator based on the MARCOS model and determining the optimal energy storage technology selection from the perspective of dynamic duration in step S7 include:

[0042] Construct a decision matrix and normalize the decision matrix;

[0043] After the decision matrix is normalized, multiply it by the weight of each indicator to obtain a weighted normalized matrix and calculate the weighted normalized matrix;

[0044] Then calculate the utility degrees of each energy storage scheme;

[0045] Calculate the utility function to determine the optimal energy storage technology selection from the perspective of dynamic duration.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] Divide different energy storage discharge durations into several scenarios, dynamically identify the optimal energy storage technology solution, aiming to achieve the supply-demand balance of the power system under a high proportion of new energy. The present invention provides a scientific energy storage technology selection scheme for independent energy storage investment operators, helps them evaluate the energy storage technology with the optimal comprehensive benefits under different power gap durations, and promotes the stability and sustainable development of the high-proportion new energy power grid.

[0048] First, divide through the power gap duration at multiple time scales to construct six comprehensive evaluation scenarios with different energy storage duration intervals. Then, taking the power system of a certain region in China as an example, analyze the characteristics of the power continuous gap duration in the 100% decarbonization scenario of this region. For different types of energy storage technologies, select the optimal energy storage scheme for each duration interval; combine relevant literature and data to establish a comprehensive comprehensive evaluation index system. Use the BBWM method to quantify the subjective weights in the multi-group decision-making problem to ensure that the personal preferences of each decision-maker are fully reflected. Introduce the Cloud-IDOCRIW model, transform qualitative descriptions into quantitative information based on the Cloud model, and calculate the objective weights in combination with the IDOCRIW model to effectively take into account the relationship between indicators and the discreteness of data. Optimize and adjust the BBWM-Cloud-IDOCRIW model through game theory to achieve the organic combination of subjective and objective weights; finally, rank each indicator based on the MARCOS model to ensure that the model remains robust in the complex scenario of multiple indicators and multiple schemes, and optimize the energy storage technology selection. Description of the Drawings

[0049] Figure 1 It is the overall flowchart of an energy storage technology evaluation method based on the perspective of dynamic duration in an embodiment of the present invention. Detailed Embodiments

[0050] For the convenience of understanding by those skilled in the art, the present invention will be further described below in conjunction with the embodiments and the drawings. The content mentioned in the embodiments does not limit the present invention.

[0051] As Figure 1 shown, this embodiment provides an energy storage technology evaluation method based on the perspective of dynamic duration, including the following steps:

[0052] S1: Construct six comprehensive evaluation scenarios with different energy storage duration intervals, specifically including:

[0053] This step aims to consider the randomness of the output of wind power, photovoltaic power, and load in a high-proportion new energy power system, and there may be differences in the power shortage duration in different regions.

[0054] Therefore, the present invention divides the continuous power gap into six time scales: <4 hours, 4 - 10 hours, 10 - 24 hours, 24 hours - 1 week, 1 week - 1 month, >1 month.

[0055] In the high-proportion new energy scenario, based on the wind and light resources and load characteristics of the region, the duration of the power gap can be identified and the corresponding optimal energy storage technology solutions can be deployed.

[0056] S2: Determine the duration characteristics of the continuous power gap of a certain high-proportion new energy power system, and judge the required energy storage duration demand.

[0057] This step is to analyze the energy storage demand of a high-proportion new energy power system in a certain region. Considering the randomness of the output of wind power, photovoltaic power, and load, the energy storage duration is divided according to the differences in the power shortage duration in different regions.

[0058] First, select a certain power system, summarize the characteristics of its existing power generation resources, and count the existing photovoltaic power generation P wt , wind power generation P pv , hydropower generation P h , other energy power generation P c , and the existing load demand Y;

[0059] Secondly, simulate the output situation of the region under the 100% high-proportion new energy scenario. Replace the power generation resources in the region with wind power, photovoltaic power, and hydropower to meet the load demand Y.

[0060] Under the assumption that the growth rates of wind power and photovoltaic power remain unchanged, the growth multiple k of the wind and light satisfies:

[0061] k×P wt +k×P pv +P h =Y

[0062] Solve for the growth multiple k:

[0063] k=(Y - P h ) / (P wt +P pv ) where Y - P h is the load that needs to be completely met by wind power and photovoltaic power in the future. P wt +P pv is the total current power generation of wind power and photovoltaic power.

[0064] Next, analyze the source-load characteristic curves of typical days and months to determine the required energy storage duration range.

[0065] First, analyze the time when the source-load output is insufficient within a typical day:

[0066] ΔG(t) = G(t) - L(t)

[0067] where L(t) is the hourly load demand, and G(t) is the total hourly power generation, and it satisfies:

[0068] G(t) = k × C wind (t) + k × C pv (t) + C hy (t)

[0069] where C wind (t), C pv (t), C hy (t) are the hourly output powers of wind power, photovoltaic power, and hydropower respectively. Then the time T when the output is insufficient within a typical day loss is the number of hours when ΔG(t) < 0 lasts.

[0070] Secondly, analyze the continuous time when the source-load output is insufficient on a monthly basis:

[0071] First, define the total monthly power generation Egen(m) and power consumption Eload(m):

[0072]

[0073] If Egen(m) < Eload(m), it means there is a power shortage in that month.

[0074] Calculate the number of months M when the output is insufficient on an annual time scale f , that is, the total number of months that satisfy Egen(m) < Eload(m).

[0075] S3: Detailed steps for constructing comprehensive evaluation indicators for various energy storage technologies:

[0076] First, review the existing relevant literature and determine the core indicators for evaluating energy storage technologies;

[0077] Secondly, for the dynamic evaluation perspective of the present invention, adjust the indicators in the literature, especially divide the cost indicator into power capacity cost (CHY / kW) and energy capacity cost (CHY / kWh), and incorporate the levelized cost of energy storage (LCOS) to better reflect the economy of energy storage technologies;

[0078] Finally, integrate the indicators in the technical, economic, environmental, and social dimensions to form the final evaluation framework.

[0079] S4: Quantify the subjective weights of indicators in the multi-group decision-making problem using the BBWM method:

[0080] First, in the comprehensive indicator evaluation system C = {c 1 , c 2 , …, c n}, according to the personal preferences of experts, select the optimal indicator c B and the worst indicator c W , which represent the most important and the least important items among all indicators considered by the experts respectively.

[0081] Secondly, the experts make pairwise comparisons between the optimal indicator and other indicators, using integers from 1 to 9 to represent the preference intensity, where 1 means the two indicators are equally important and 9 means the optimal indicator is extremely important. Finally, generate the "optimal indicator - other indicators" preference vector: A B = (a B1 , a B2 ,..., a Bn )

[0082] Then, the experts make pairwise comparisons between the worst indicator and other indicators, adopting the same scoring rule from 1 to 9, and generate the "other indicators - worst indicator" preference vector: A W = (a 1W , a 2W ,..., a nW ) T

[0083] Fourthly, use the Bayesian hierarchical model to calculate the weights of the optimal indicators under the different preferences of multiple experts. Assume there are k = 1, …, K experts, and each expert provides the "optimal indicator - other indicators" vector and the "other indicators - worst indicator" vector, which are respectively For each expert, calculate the individual optimal indicator weight w total respectively, and finally obtain the overall optimal weight through aggregation.

[0084] Based on the above data, construct the joint probability distribution of all random variables:

[0085]

[0086] where are independent of each other under the condition of the given parameter w k . Based on the joint probability distribution, apply Bayes' theorem to obtain the hierarchical model as follows:

[0087]

[0088] Then, model each item in the model: use the multinomial distribution to describe Parameterize the Dirichlet distribution in terms of the mean and concentration parameter. Additionally, introduce an uninformative Dirichlet distribution as the prior distribution:

[0089]

[0090] w total ~Dir(α)

[0091] Since the model lacks an analytical solution, sampling is performed using JAGS (Just Another Gibbs Sampler) to calculate the posterior distributions of the weights of each expert and the aggregated weights. Finally, the overall weighted value w of the optimal weights is represented by the mean of the Dirichlet distribution total .

[0092] Finally, use trust ranking to further judge the superiority and inferiority between different indicators.

[0093] First, rank each pair of evaluation indicators c i , c j to obtain their precedence relationship, and the formula is as follows:

[0094] O = (c i , c j , R, d)

[0095] Next, calculate the precedence confidence of each pair of indicators This confidence reflects the probability that indicator c i is more advantageous than indicator c j under the given expert preferences.

[0096] Finally, use the Markov chain Monte Carlo (MCMC) method to draw Q samples from the posterior distribution and further calculate the precedence confidence of each pair of indicators. The formula is:

[0097]

[0098] where is the q th th sample in the Markov chain Monte Carlo samples.

[0099] S5: Use the Cloud model to convert qualitative descriptions into quantitative forms and combine with the IDOCRIW model to calculate the objective weights.

[0100] In this step, the Cloud model - IDOCRIW algorithm is used to construct an objective weight model for qualitative indicators. First, based on the principles of fuzzy sets and probability theory, the Cloud model converts qualitative language variables into quantitative numerical values. Define the language set L = {l α∣α=-δ,…,0,…,δ}, such as {very unimportant, unimportant, average, important, very important}, and experts qualitatively evaluate the indicators of each energy storage technology based on this set to form a scoring matrix V k :

[0101]

[0102] where represents the k f th expert's linguistic variable evaluation of the c i th indicator of the e qj th energy storage technology. Then, each linguistic variable l α is converted into the corresponding numerical cloud model by the golden section method, and the numerical characteristics of the cloud model are calculated as follows:

[0103]

[0104] where X max and X min are the upper and lower limits of the score respectively, Ex is the expected value of the cloud model, En is the entropy value of the cloud model, and He represents the uncertainty of the entropy value En

[0105] Then, the cloud models of each expert are weighted and averaged to obtain a comprehensive evaluation cloud model:

[0106]

[0107] where, aA ij (aEx ij , aEn ij , aHe ij ) represents the average evaluation cloud model of all experts K when evaluating the qualitative index c i of the energy storage technology e qj

[0108] Finally, calculate the overall score of the cloud model The quantitative evaluation of different energy storage technologies is realized by comparing the scores of each cloud model

[0109] On this basis, the IDOCRIW model is used to assign objective weights to different indicators. First, the entropy weight method is used to assign weights to the decision matrix. Construct a decision matrix containing n indicators C = {c 1 , c 2 , …, c n} and m energy storage technologies E = {e 1 , e 2 ,..., e m}:

[0110] ​

[0111] Among them, v ij represents the energy storage e i For the index c j quantitative value. Then, normalize the quantitative value and calculate the entropy weight of each index:

[0112]

[0113] d j = 1 - E j

[0114] Among them, 0 ≤ E j ≤ 1. Finally, solve the index weight of the entropy weight method

[0115]

[0116] Secondly, use the criterion impact loss method to correct the weight of the entropy weight method. Through the formula

[0117] P = ||p ij ||

[0118]

[0119] Calculate the relative loss of each index relative to the optimal and worst values, and then generate the CILOS weight q j , and combine the entropy weight method weight to obtain the objective weight of each index

[0120] S6: Adjust the BBWM-Cloud-IDOCRIW model through game theory and calculate the comprehensive weight.

[0121] In order to take into account the subjective experience of experts and objective data, the game theory method is used to combine the subjective weight and the objective weight to calculate the comprehensive weight. This step is to first construct a linear combination expression of the comprehensive weight:

[0122] W = α 1 SW T + α 2 OW T

[0123] Among them, SW T represents the subjective weight vector calculated based on the BBWM method, OW T represents the objective weight vector calculated based on the Cloud-IDOCRIW method, α 1 and α 2 are the weight coefficients of the linear combination.

[0124] Next, to obtain the optimal combined weights, the optimal weight coefficients are determined by constructing an objective function with the goal of minimizing the deviation of the final weights:

[0125] minα 1 SW T +α 2 OW T -W i T i = 1, 2

[0126] W 1 T = SW T ,

[0127] Based on this objective function, the following optimized first derivative conditions are derived:

[0128]

[0129] The optimal solution can be obtained through this condition The normalized weight coefficients are calculated as:

[0130]

[0131] Finally, the obtained coefficients are substituted into the comprehensive weight calculation formula to obtain the final comprehensive weight.

[0132]

[0133] S7: Sort each index based on the MARCOS model to determine the optimal energy storage technology selection from the perspective of dynamic duration.

[0134] This step is as follows. First, construct the decision matrix as follows:

[0135]

[0136] Among them, C = {c 1 , c 2 , …, c n} is the energy storage evaluation index, E = {e 1 , e 2 ,..., e m} is the energy storage solution, and v ij represents the quantitative value of the energy storage solution e i for the index c j . eAI = (eAI 1 , eAI 2 ,..., eAI n ), eI = (eI 1 , eI 2 ,..., eIn ) represent the ideal solution and the negative ideal solution, respectively defined as:

[0137]

[0138] where Ω B and Ω C represent the benefit - type and cost - type index sets respectively.

[0139] Secondly, normalize the decision matrix:

[0140]

[0141] Next, calculate the weighted normalized matrix:

[0142] a ij = n ij × w j , i = 1, 2,..., m; j = 1, 2,..., n

[0143] where, a ij represents the weighted normalized matrix, and w j is the weight of each index.

[0144] Then, calculate the utility degrees of each scheme:

[0145]

[0146] where, S i represents the sum of the weighted matrix row of scheme a ij , S eAI , S eI represent the weighted values of the ideal solution and the negative ideal solution respectively, and satisfy:

[0147]

[0148] Finally, calculate the utility function f(e i ):

[0149]

[0150] represent the negative ideal and ideal utility functions respectively. The scheme with the highest utility function value f(e i ) is considered the optimal scheme.

[0151] Next, take the power system in a certain area in the northwest of China as an example to illustrate the energy storage evaluation method of this application.

[0152] (1) Comprehensive evaluation scenario construction: Considering the differences in the power shortage duration in this region, six different comprehensive evaluation scenarios were designed to select the optimal energy storage technology solutions under each duration scenario. The duration and scale characteristics of energy storage technologies under various scenarios are shown in Table 1:

[0153] Table 1 Energy storage duration and scale under different scenarios

[0154]

[0155] (2) Determine the characteristics of the power continuous gap duration under the high - proportion power system scenario in this region and judge the required energy storage duration demand:

[0156] First, count the characteristics of the existing power generation - consumption resources in this region. The total existing power demand is 101.83 billion kWh, among which wind power and photovoltaic power account for 44.3%, and hydropower accounts for 39.1%. Second, simulate the output situation of this region under the 100% high - proportion new - energy scenario. Assume that in the future, wind power, photovoltaic power, and hydropower will be the main components of the power system in this region, and no new hydropower will be added, and the wind and light will grow at a ratio of 1:2. Finally, when the power generation and power consumption are completely matched, the power grid in this region will achieve 100% decarbonization.

[0157] Under the high - proportion new - energy scenario, there are mismatches between the power generation and consumption structures on typical days in winter and summer in this region, resulting in over - generation at noon and shortage of output at night, forming a load gap. The energy storage system can play a role in daily peak regulation, and the required energy storage duration is 10 - 24 hours to achieve peak shaving and valley filling. In addition, the power generation is higher in winter and lower in summer, and the power load gap in October reaches 1.577 billion kWh, increasing the demand for long - duration energy storage. The required energy storage duration is more than 1 month, which can charge at low prices in winter and conduct peak arbitrage in summer.

[0158] (3) Construct comprehensive evaluation indicators for various energy storage technologies.

[0159] Adjust the indicators by integrating relevant literature and combining the characteristics of the present invention, and finally form an evaluation framework for energy storage technologies, as shown in Table 2:

[0160] Table 2 Energy storage index evaluation system table

[0161]

[0162] (3) Use the BBWM method to quantify the subjective weights of indicators in multi - group decision - making problems.

[0163] First, seven experts evaluated 15 evaluation indicators for six scenarios. Each expert selected the best and worst indicators and gave integer scores from 1 to 9. Next, the preference intensity was calculated through pairwise comparison and a preference vector was generated. Taking scenario S1 as an example, the judgment results of the "optimal indicator - other indicators" vector and the "other indicators - worst indicator" vector of the experts were input into the Bayesian hierarchical model, and after calculation by the JAGS solver, the group subjective weights were obtained. Through the trust ranking model, the subjective weight results under each scenario were visualized. The results showed that for scenarios with short energy storage discharge duration (S1, S2, S3), experts considered the energy storage power cost C21 to be a relatively important indicator (the indicator weight was second only to the cost per kilowatt-hour C25); in contrast, in scenarios with long-term energy storage (S4, S5, S6), having a low capacity cost C22 was considered to be relatively important in the above scenarios (its weight was comparable to the cost per kilowatt-hour C25). In addition, throughout all scenarios, the degree of promoting employment C41 was considered to be the indicator with the lowest weight.

[0164] (4) Use the Cloud model to convert qualitative descriptions into quantitative forms and combine with the IDOCRIW model to calculate the objective weights.

[0165] Since the evaluation includes qualitative indicators, the present invention uses the Cloud model to quantify the qualitative evaluation results. First, define the language variable set and let seven experts evaluate the qualitative indicators. For the two smallest indicator types C32 and C33, use the language inverse operation for adjustment. After the experts' scoring, a scoring matrix is formed and converted into a Cloud model. Then, based on the experts' experience weights, the Cloud models of all experts are integrated to obtain the final comprehensive evaluation. The results show that: in terms of technology maturity, lithium batteries and pumped storage have advantages, and hydrogen energy storage is poor; in terms of siting flexibility, lithium batteries are the best; in terms of soil and water damage, pumped storage is the most destructive; in terms of harmful substance leakage and pollution, pumped storage performs the best; in terms of promoting employment, lithium batteries and hydrogen energy storage contribute the most; in terms of technology safety, pumped storage and flow batteries perform the best. Finally, use the formula to calculate the comprehensive score of each energy storage technology, as shown in Table 3.

[0166] Table 3 Cloud model score table of each qualitative indicator of different energy storage technologies

[0167]

[0168] Next, after combining the Cloud model, all the indicator data are brought into the IDOCRIW model to calculate the objective weight results, as shown in Table 4.

[0169] Table 4 Objective weight result table of the IDOCRIW model under each scenario

[0170]

[0171] The results show that cost - related indicators are key decision - making factors in all scenarios, especially the capacity cost (C22) and the power cost (C21). Due to their significant differences among different energy storage technologies, they have higher weights. However, relying solely on objective weights has limitations and cannot reflect the dynamic changes in indicator weights under different discharge duration scenarios. The subjective weighting method of BBWM based on expert experience can adjust the relative importance of power cost and capacity cost according to the energy storage discharge duration, better reflecting the actual situation. Therefore, the comprehensive weighting method combining subjective and objective weights can better ensure data accuracy and improve the comprehensiveness and effectiveness of decision - making.

[0172] (5) Adjust the BBWM - Cloud - IDOCRIW model through game theory and calculate the comprehensive weights.

[0173] To balance the differences between subjective and objective weightings, this embodiment introduces game theory for comprehensive weight assignment. After adjustment by game theory, in scenarios S1 to S3, the levelized cost of electricity (C25) and the power cost (C21) obtain the highest weights, while the influence of the capacity cost (C22) weakens, indicating that subjective weights dominate. In scenarios S4 to S6, the weights of the capacity cost and the levelized cost of electricity are comparable, reflecting the importance of long - duration energy storage technologies. The final results show that in short - duration energy storage scenarios, the levelized cost of electricity and the power cost are key indicators, while in long - duration energy storage scenarios, the capacity cost and the levelized cost of electricity are more important.

[0174] (6) Rank each indicator based on the MARCOS model to determine the optimal energy storage technology selection from the perspective of dynamic duration.

[0175] After substituting the comprehensive weights into the MARCOS model, the results are shown in Table 5.

[0176] Table 5 Energy storage technology ranking results under each scenario

[0177]

[0178] The results show that in scenarios S1 to S2 with shorter energy storage durations, lithium - ion batteries are the first choice due to their lower levelized cost of electricity, followed by pumped - storage hydroelectricity. As the energy storage duration increases, the weights of the levelized cost of electricity and the capacity cost increase, making hydrogen energy storage and thermal energy storage technologies more preferred in long - term energy storage scenarios (S4 - S6) due to their low capacity costs. Due to their higher capacity costs, the comprehensive ranking of lithium - ion batteries decreases as the duration increases. The results indicate that the most suitable energy storage technologies vary under different energy storage duration demand scenarios, highlighting the importance of dynamically differentiating the optimal energy storage technology.

[0179] The above has introduced in detail a method for evaluating energy storage technologies from the perspective of dynamic duration provided by this application. The description of specific embodiments is only used to help understand the method and its core idea of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and modifications can still be made to this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for evaluating energy storage technology based on a dynamic duration perspective, characterized in that: Includes steps: S1. Construct six comprehensive evaluation scenarios with different energy storage duration intervals, and identify the duration characteristics of power shortage according to the wind and solar resources and load characteristics of the region; S2. Determine the duration characteristics of the power shortage in the high-proportion new energy power system and determine the required energy storage duration requirements; S3. Construct comprehensive evaluation indicators for various energy storage technologies; S4. Quantify the subjective weights of indicators in multi-group decision-making problems through Bayesian best-worst methods; S5. Use the cloud model to convert the qualitative description into quantitative form and calculate the objective weights in combination with the comprehensive determination model of objective standard weights; S6. Adjust the comprehensive determination model of the Bayesian best-worst method, cloud model and objective criterion weights through game theory to calculate the comprehensive weights; S7. The indicators are ranked by a multi-criteria decision-making method based on dominance to determine the optimal energy storage technology selection from a dynamic duration perspective.

2. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 1 is characterized in that: The detailed steps of constructing the comprehensive evaluation scenarios of six different energy storage duration intervals include: The electricity shortage is divided into six time scale intervals according to its duration; Six comprehensive evaluation scenarios based on dynamic duration are constructed by integrating the six time scale intervals.

3. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 2 is characterized in that: In step S2, the characteristics of the duration of the power shortage of the high-proportion new energy power system are determined, and the detailed steps of determining the required energy storage duration demand include: Select a high-proportion new energy power system and summarize its existing power generation resource characteristics; Simulate the region's output under a 100% high-proportion renewable energy scenario; Analyze the source-load characteristic curves of typical days and months to determine the required energy storage duration range.

4. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 3 is characterized in that: The detailed steps of constructing comprehensive evaluation indicators of various energy storage technologies in step S3 include: Search existing literature to identify core indicators for energy storage technology evaluation; In view of the dynamic evaluation perspective, the indicators collected in the literature are subdivided and adjusted; Integrate indicators from technical, economic, environmental and social dimensions to form the final evaluation framework.

5. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 4 is characterized in that: The detailed steps of quantifying the subjective weights of indicators in the multi-group decision-making problem by adopting the Bayesian best-worst method in step S4 include: Select the best and worst indicators in the comprehensive indicator evaluation system; The optimal indicator is compared with indicators in technical, economic, environmental and social dimensions to generate the corresponding optimal indicator preference vector; The worst indicator is compared with indicators in technical, economic, environmental and social dimensions to generate the corresponding worst indicator preference vector; Through the Bayesian hierarchical model, the optimal indicator weights for a set of standards are determined according to different preferences preset by the system; Use the trust ranking model to prioritize various indicators.

6. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 5 is characterized in that: In step S5, the cloud model is used to convert the qualitative description into a quantitative form, and the objective weight is calculated by combining the comprehensive determination model of the objective standard weight. The detailed steps include: Define the language set L and conduct qualitative evaluation; Convert each qualitative linguistic variable into a quantitative cloud model; Aggregate multiple quantitative cloud models; Calculate the total score of the quantitative cloud model and obtain the quantitative value of the qualitative indicator; The entropy weight method is used to weight the decision matrix, and the criterion influence loss method is used to modify the weight of the entropy weight method. Obtain the objective weight value of the comprehensive determination model of the objective standard weight.

7. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 6 is characterized in that: In step S6, the Bayesian best-worst method, the cloud model, and the comprehensive determination model of the objective standard weight are adjusted by game theory. The detailed steps of calculating the comprehensive weight include: Construct a comprehensive weighted linear expression; The optimal weight coefficient is determined by constructing an objective function, the goal is to minimize the deviation of the final weight; Based on the objective function, the expression of the optimized first-order derivative condition is derived; Solve the normalized weight coefficient and substitute it into the comprehensive weight linear expression to obtain the comprehensive weight.

8. The energy storage technology evaluation method based on a dynamic duration perspective according to claim 7 is characterized in that: In step S7, the multi-criteria decision-making method model based on advantage ranks each indicator, and the detailed steps of determining the optimal energy storage technology selection from the perspective of dynamic duration include: Construct a decision matrix and normalize the decision matrix; The decision matrix is ​​normalized and then multiplied by the weight of each indicator to obtain a weighted normalized matrix, and the weighted normalized matrix is ​​calculated; Then calculate the utility degree of each energy storage solution; Calculate the utility function and determine the optimal energy storage technology selection from a dynamic duration perspective.

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