Underwater compressed air energy storage system site selection evaluation method

Abstract

The invention relates to a renewable energy source energy storage technology, and particularly discloses an underwater compressed air energy storage system site selection evaluation method, which comprises the steps of S1, establishing a multi-level dynamic coupling site selection evaluation framework; s2, carrying out candidate region preliminary screening and data acquisition quantification, obtaining candidate regions, and establishing an evaluation matrix for indexes of the candidate regions to obtain a standardized evaluation matrix; s3, performing dynamic simulation and trend analysis; obtaining an offshore wind power-UWCAES coupling efficiency factor of each point in the candidate area; and S4, an entropy weight method is adopted to automatically distribute objective weights of the indexes according to the data dispersion degree, then the importance order of the indexes of all levels is calculated according to an improved fuzzy analytic hierarchy process, subjective weights are obtained, finally, comprehensive weights are calculated according to a dynamic weight distribution rule, and all site selection schemes are scored. The method has the advantages that the comprehensive evaluation of dimensions and the dynamic evaluation of time scales are realized, and the evaluation result is more scientific, foresight and high in decision support value.

Description

Technical Field

[0001] This invention relates to renewable energy storage technology, and in particular to a site selection assessment method for underwater compressed air energy storage systems. Background Technology

[0002] To mitigate fluctuations in wind power grid connection, supporting energy storage systems have become an indispensable technology. Among these, compressed air energy storage has attracted significant attention due to its advantages such as high capacity scalability and long lifespan. Traditional compressed air energy storage relies on underground salt caverns or hard rock caverns to store high-pressure air, but the lack of such geological conditions in the offshore environment has driven the rapid development of underwater compressed air energy storage technology. This technology uses deep-sea hydrostatic pressure to replace the pressure of the surrounding rock in caverns, storing compressed air in flexible underwater gas storage devices. It combines environmental adaptability with engineering flexibility and is suitable for offshore wind farms.

[0003] The deployment of Underwater Compressed Air Energy Storage (UWCAES) systems relies heavily on scientific site selection. On one hand, marine geological and hydrological conditions directly impact storage safety: a hard seabed reduces foundation reinforcement costs, and a stable ocean current environment helps extend equipment lifespan. On the other hand, the need for coordinated planning of wind power and energy storage is urgent, requiring consideration of multiple factors such as offshore distance and operational economics. However, current technologies generally treat wind farms and energy storage systems as independent entities, failing to quantitatively couple and evaluate the dynamic characteristics of wind power output with the physical response capabilities of the underwater energy storage system during the site selection phase. This results in site selection outcomes that cannot guarantee the energy storage system's ability to effectively track wind power output and achieve efficient energy absorption. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a site selection evaluation method for underwater compressed air energy storage systems.

[0005] The objective of this invention is achieved through the following technical solution: a site selection evaluation method for underwater compressed air energy storage systems, the method comprising,

[0006] S1. Establish a multi-level dynamic coupling location evaluation framework, which includes basic layer evaluation indicators, coupling layer evaluation indicators and value layer evaluation indicators.

[0007] S2. Conduct preliminary screening of candidate regions, collect and quantify data, obtain candidate regions and establish an evaluation matrix for their indicators, obtain a standardized evaluation matrix, and perform data processing on the basic layer evaluation indicators and the value layer evaluation indicators.

[0008] S3. Perform dynamic simulation and trend analysis; obtain the offshore wind power-UWCAES coupling efficiency factor at each point in the candidate area, and evaluate the dynamic performance;

[0009] S4. The objective weights of the indicators are automatically assigned according to the degree of data dispersion using the entropy weight method. Then, the importance ranking of each level of indicators is calculated and the subjective weights are obtained according to the improved fuzzy hierarchical analysis method. Finally, the comprehensive weights are calculated according to the dynamic weight allocation rule, and each site selection scheme is scored to obtain the optimal site.

[0010] Specifically, the basic layer evaluation indicators are divided into primary and secondary indicators. Primary indicators include geological conditions, marine environment, and power grid access conditions. Secondary indicators under geological conditions include fault zone distance and seismic activity frequency. Secondary indicators under marine environment conditions include water depth, maximum ocean current velocity, annual water temperature variation range, and seawater pH value. Secondary indicators under power grid access conditions include straight-line distance to the nearest power grid access point and topographic complexity of the submarine cable corridor. The coupling layer evaluation indicators include the offshore wind power-UWCAES coupling efficiency factor. The value layer evaluation indicators are divided into primary and secondary indicators. Primary indicators include economic and environmental benefits. Secondary indicators under economic benefits include the levelized cost of electricity over the entire life cycle and the investment payback period. Secondary indicators under environmental benefits include annual carbon emission reduction and long-term impact score on the marine ecosystem.

[0011] In step S2, the Z-score standardization method is used to normalize the dimensions of each indicator to obtain a standardized evaluation matrix. First, the indicator data of all secondary indicators are used to obtain the following evaluation matrix:

[0012] ;

[0013] In the formula, This represents the data for the j-th indicator of the i-th site selection scheme; m is the number of site selection schemes to be evaluated; n represents the total number of secondary indicators to be evaluated;

[0014] Then, standardization is performed using the Z-score method:

[0015] ;

[0016] In the formula, This refers to the data for the j-th index of the i-th location selection scheme after standardization; Let be the average of the j-th metric across all site selection options; Let be the standard deviation of the j-th index of all site selection schemes;

[0017] The indicators are then normalized. The normalized data for positive indicators is the original data, and the normalized data for negative indicators is the opposite of the original data.

[0018] Specifically, the calculation of the coupling efficiency factor includes:

[0019] First, set the initial energy storage capacity of the underwater compressed air energy storage system:

[0020] ;

[0021] In the formula, The initial energy storage for the underwater compressed air energy storage system; This represents the maximum energy storage capacity.

[0022] Initial energy storage power:

[0023] ;

[0024] In the formula, and These are the initial UWCAES charging power and discharging power, respectively;

[0025] Then the loop calculation process begins. For each time step (t = 1, 2, ..., T), the following loop calculation process occurs:

[0026] S110. Determine system availability;

[0027] Determine whether UWCAES is usable during time period t. If it is not usable, perform the following steps:

[0028] ;

[0029] ;

[0030] ;

[0031] In the formula, The energy stored in the underwater compressed air energy storage system during time period t; The charging power of UWCAES during the time period t; The discharge power of UWCAES during time period t; then jump to S150;

[0032] If it is usable, then proceed to S120;

[0033] S120, Calculate power balance:

[0034] ;

[0035] In the formula, For excess wind power; Let t be the wind power output at time t; The load at time t;

[0036] ;

[0037] In the formula, This is due to a power deficit.

[0038] S130, Calculate charging power:

[0039] ;

[0040] In the formula, The maximum charging power at time t is limited by the ramp rate. Maximum power gradeability;

[0041] ;

[0042] In the formula, This is the maximum charging power limited by energy storage capacity. For charging efficiency;

[0043] The final charging power is:

[0044] ;

[0045] S140, Calculate the discharge power:

[0046] ;

[0047] In the formula, The maximum discharge power is limited by the ramp rate;

[0048] ;

[0049] In the formula, This is the maximum discharge power limited by the stored energy. For discharge efficiency;

[0050] The final discharge power is:

[0051] ;

[0052] S150, Upgraded UWCAES energy storage:

[0053] ;

[0054] Finally, calculate the coupling efficiency factor:

[0055] ;

[0056] In the formula, This refers to the total excess wind power energy that can be theoretically absorbed.

[0057] ;

[0058] In the formula, This refers to the net discharge energy actually delivered to the power grid.

[0059] ;

[0060] In the formula, This is the coupling efficiency factor.

[0061] Specifically, the steps in S4 for automatically assigning objective weights to indicators based on the degree of data dispersion using the entropy weight method are as follows:

[0062] The contribution of each indicator is calculated based on the standardized evaluation matrix. and information entropy value :

[0063] ;

[0064] ;

[0065] The objective weight of the j-th indicator is calculated from its information entropy value. :

[0066] ;

[0067] In the formula, Subscript This indicates the cumulative count.

[0068] The steps for obtaining subjective weights in S4 are as follows:

[0069] Construct a judgment matrix; perform pairwise comparisons between indicators at each level and determine the scale of the judgment matrix elements; establish a triangular fuzzy evaluation index matrix based on expert evaluations of each level of indicators, with the importance of the indicators represented by triangular fuzzy numbers:

[0070] ;

[0071] In the formula, The importance of the indicator will not be less than this value. The importance of the indicator is roughly this value. The importance of the indicator will not exceed this value;

[0072] Defuzzification is performed using the expectation formula of the triangular fuzziness number:

[0073] ;

[0074] In the formula, The expected value of the triangular fuzzy number; This represents the risk propensity value.

[0075] Based on the expected value, the judgment matrix A can be obtained:

[0076] ;

[0077] In the formula, This represents the comparison result of the importance of the i-th and j-th primary indicators;

[0078] Perform a consistency check; use the largest eigenvalue of the judgment matrix A. Calculate the consistency index (CI) of the matrix:

[0079] CI= ;

[0080] In the formula, Indicates the dimension of the judgment matrix;

[0081] Calculate the random consistency ratio of judgment matrix A:

[0082] ;

[0083] In the formula, RI is the average random consistency index;

[0084] If CR < 0.1, then the judgment matrix A passes the consistency test; otherwise, it fails, and the judgment matrix A needs to be reconstructed and the consistency test needs to be performed again on the new judgment matrix.

[0085] Subjective weights are calculated using the eigenvalue method; the eigenvector corresponding to the largest eigenvalue of the judgment matrix A is calculated. Then, normalization is performed to obtain the subjective weight vector of the primary indicator. Furthermore, the weights of the secondary indicators under the primary indicators are the same as those of the primary indicators. After uniformizing all secondary indicators, the subjective weights of the secondary indicators determined by the analytic hierarchy process are obtained. .

[0086] Specifically, the steps for calculating the comprehensive weight based on the dynamic weight allocation rule and scoring each site selection scheme are as follows:

[0087] The combined weights are derived from the results calculated using the analytic hierarchy process (AHP) and the entropy weight method.

[0088] ;

[0089] Define the power output volatility of a wind farm:

[0090] ;

[0091] In the formula, This represents the power output of a typical wind farm during the t-hour period of a day. This represents the average daily output of a typical offshore wind farm.

[0092] when When defined as a high-fluctuation working condition, it is necessary to increase the weight of geological conditions and marine environment, and reduce the weight of construction cost;

[0093] For indicator j that needs to be dynamically adjusted, its adjusted weight is:

[0094] ;

[0095] In the formula, This is the matrix before adjustment; Benchmark volatility; The adjustment amplitude coefficient represents the maximum allowable change in the weight. The maximum design volatility is the maximum allowable volatility for offshore wind power design.

[0096] right Normalization is performed to obtain the final indicator weights;

[0097] when When defined as a low-fluctuation operating condition, the original combined weight result is maintained;

[0098] Each site selection scheme is scored based on a standardized evaluation matrix and weighted combinations of indicators, and the scheme with the highest score is selected as the optimal site selection scheme.

[0099] The present invention has the following advantages:

[0100] 1. By quantitatively coupling the dynamic output of wind power with the physical response of underwater energy storage during the site selection phase, the fundamental defect of traditional methods in being unable to assess the dynamic matching degree of "source-storage" is solved, and the ability of the site selection scheme to absorb offshore wind power is significantly improved.

[0101] 2. Through the system architecture of the foundation layer, coupling layer, and value layer, as well as the evaluation of short-term dynamics and long-term trends, the evaluation dimensions are made more comprehensive and the time scale is more dynamic, making the evaluation results more scientific, forward-looking, and valuable for decision support.

[0102] 3. The evaluation framework proposed in this invention is not only applicable to underwater compressed air energy storage, but its dynamic coupling adaptability concept can also be extended to the site selection evaluation of other offshore energy storage technologies, which has important theoretical significance and broad application prospects. Attached Figure Description

[0103] Figure 1 This is a schematic diagram of the evaluation method of the present invention. Detailed Implementation

[0104] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0105] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0106] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0107] The present invention will be further described below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0108] like Figure 1 As shown, a site selection evaluation method for underwater compressed air energy storage systems is presented, which includes:

[0109] S1. Establish a multi-level dynamic coupling site selection evaluation framework, which includes basic layer evaluation indicators, coupling layer evaluation indicators, and value layer evaluation indicators. The basic layer evaluation indicators are divided into primary and secondary indicators. The primary indicators include geological conditions, marine environment, and power grid access conditions. The secondary indicators under geological conditions are fault zone distance and seismic activity frequency. The secondary indicators under marine environment conditions are water depth, maximum ocean current velocity, annual water temperature variation range, and seawater pH value. The secondary indicators under power grid access conditions are the straight-line distance to the nearest power grid access point and the topographic complexity of the submarine cable corridor. The coupling layer evaluation indicators include the offshore wind power-UWCAES coupling efficiency factor. The value layer evaluation indicators are divided into primary and secondary indicators. The primary indicators include economic and environmental benefits. The secondary indicators under economic benefits are the levelized cost of electricity over the entire life cycle and the investment payback period. The secondary indicators under environmental benefits are annual carbon emission reduction and long-term impact score on the marine ecosystem.

[0110] S2. Initial screening of candidate regions and quantification of data collection are conducted. Candidate regions are identified, and an evaluation matrix is ​​established for their indicators to obtain a standardized evaluation matrix. Data processing is performed on the basic layer evaluation indicators and the value layer evaluation indicators. First, geographical input data is needed to delineate candidate regions. Then, indicator data within the evaluation indicator system are collected, and the Z-score standardization method is used to normalize the dimensions of each indicator to obtain standardized judgment moments. Geological data is initially screened based on the geological conditions in the basic layer. The initial screening principle is mainly based on geological conditions and their subordinate secondary indicators: to ensure the long-term stable operation of the underwater compressed air energy storage system, priority is given to selecting areas far away from active fault zones and with low historical seismic activity frequency to form a candidate region set. Then, indicator data within the candidate regions are collected, and the Z-score standardization method is used to normalize the dimensions of each indicator to obtain a standardized evaluation matrix.

[0111] By statistically analyzing the data of all secondary indicators for all site selection schemes, the following evaluation matrix is ​​obtained:

[0112] ;

[0113] In the formula, This represents the data for the j-th indicator of the i-th site selection scheme; m is the number of site selection schemes to be evaluated; n represents the total number of secondary indicators to be evaluated;

[0114] Then, standardization is performed using the Z-score method:

[0115] ;

[0116] In the formula, This refers to the data for the j-th index of the i-th location selection scheme after standardization; Let be the average of the j-th metric across all site selection options; Let be the standard deviation of the j-th index of all site selection schemes;

[0117] The indicators are then normalized. The normalized data for positive indicators is the original data, and the normalized data for negative indicators is the opposite of the original data.

[0118] S3. Perform dynamic simulation and trend analysis; obtain the offshore wind power-UWCAES coupling efficiency factor at each point in the candidate area, and evaluate the dynamic performance; the dynamic evaluation is obtained by calculating the coupling efficiency factor, and some key parameters required in the calculation process are as follows: charging efficiency. and discharge efficiency These two parameters primarily depend on the water depth of the candidate point. According to the thermodynamic principles of underwater compressed air energy storage, in adiabatic or near-adiabatic processes, the theoretical isentropic efficiency is related to the compression / expansion stage and pressure ratio. Those skilled in the art can, based on the preset technical scheme of the target UW-CAES system, use standard thermodynamic cycle efficiency formulas or performance curves provided by the manufacturer to query or calculate the corresponding water depth. and value.

[0119] Maximum power gradeability This parameter is affected by the maximum ocean current velocity and the device design. High current velocities increase the hydrodynamic load on the device, potentially limiting its rapid start-up / shutdown or load-changing capabilities. Those skilled in the art can evaluate and determine a safe and reliable design based on ocean current data from candidate points, combined with the dynamic response characteristics of similar marine engineering equipment, through hydrodynamic simulation or by consulting engineering design specifications / experience databases. value.

[0120] Maximum energy storage capacity This parameter is determined by both the water depth and seabed geological conditions at the candidate site. Water depth affects the design pressure and structural strength requirements of the gas storage chamber; geological conditions determine whether large gas storage devices can be safely anchored and the number that can be deployed. Those skilled in the art can, based on pre-defined gas storage chamber unit dimensions and materials, utilize structural mechanics and geotechnical engineering analysis methods to assess the maximum total capacity that can be safely deployed under given water depth and geological conditions. .

[0121] Power transmission loss coefficient This parameter primarily depends on the distance from the grid connection point and the type of cable selected. It can be determined using the DC or AC line loss formulas used in power engineering. To make an estimate, among which For distance, This is the loss coefficient per unit length.

[0122] The calculation of the coupling efficiency factor includes:

[0123] First, set the initial energy storage capacity of the underwater compressed air energy storage system:

[0124] ;

[0125] In the formula, The initial energy storage for the underwater compressed air energy storage system; This represents the maximum energy storage capacity.

[0126] Initial energy storage power:

[0127] ;

[0128] In the formula, and These are the initial UWCAES charging power and discharging power, respectively;

[0129] Then the loop calculation process begins. For each time step (t = 1, 2, ..., T), the following loop calculation process occurs:

[0130] S110. Determine system availability;

[0131] This is the maintenance interruption flag for time period t, and its value is generated by ship navigation frequency, equipment maintenance cycle, and extreme sea state predictions. This means that UWCAES cannot be used during this period, and the following steps will be taken:

[0132] ;

[0133] ;

[0134] ;

[0135] In the formula, The energy stored in the underwater compressed air energy storage system during time period t; The charging power of UWCAES during the time period t; The discharge power of UWCAES during time period t; then jump to S150;

[0136] If it is usable, then proceed to S120;

[0137] S120, Calculate power balance:

[0138] ;

[0139] In the formula, For excess wind power; Let t be the wind power output at time t; The load at time t;

[0140] ;

[0141] In the formula, This is due to a power deficit.

[0142] S130, Calculate charging power:

[0143] ;

[0144] In the formula, The maximum charging power at time t is limited by the ramp rate. Maximum power gradeability;

[0145] ;

[0146] In the formula, This is the maximum charging power limited by energy storage capacity. For charging efficiency;

[0147] The final charging power is:

[0148] ;

[0149] S140, Calculate the discharge power:

[0150] ;

[0151] In the formula, The maximum discharge power is limited by the ramp rate;

[0152] ;

[0153] In the formula, This is the maximum discharge power limited by the stored energy. For discharge efficiency;

[0154] The final discharge power is:

[0155] ;

[0156] S150, Upgraded UWCAES energy storage:

[0157] ;

[0158] Finally, calculate the coupling efficiency factor:

[0159] ;

[0160] In the formula, This refers to the total excess wind power energy that can be theoretically absorbed.

[0161] ;

[0162] In the formula, This refers to the net discharge energy actually delivered to the power grid.

[0163] ;

[0164] In the formula, This is the coupling efficiency factor.

[0165] S4. The objective weights of the indicators are automatically assigned according to the degree of data dispersion using the entropy weight method. Then, the importance ranking of each level of indicators is calculated and the subjective weights are obtained according to the improved fuzzy hierarchical analysis method. Finally, the comprehensive weights are calculated according to the dynamic weight allocation rule, and each site selection scheme is scored to obtain the optimal site.

[0166] First, based on the entropy weight method, objective weights are automatically assigned to the basic layer and value indicators according to the degree of data dispersion. Then, the contribution of each indicator is calculated based on the standardized evaluation matrix. and information entropy value :

[0167] ;

[0168] ;

[0169] Then, the objective weight of the j-th indicator can be calculated from its information entropy value. :

[0170] ;

[0171] In the formula, Subscript This indicates the cumulative count.

[0172] Then, the importance ranking of the three levels is calculated and subjective weights are obtained according to the improved fuzzy hierarchical analysis method; a judgment matrix is ​​constructed; for the application scenario of offshore wind power consumption, pairwise comparisons are made between the indicators of each level, and the scale of the judgment matrix elements is determined according to the table below.

[0173] Table 1. Methods for determining the scaling of matrix elements

[0174] Scale meaning 1 This indicates that the two indicators are equally important. 3 This indicates that, compared to the two indicators, the former is slightly more important than the latter. 5 This indicates that, compared to the two indicators, the former is significantly more important than the latter. 7 This indicates that, compared to the two indicators, the former is significantly more important than the latter. 9 This indicates that, compared to the latter, the former is extremely important. 2,4,6,8 The intermediate value of the two adjacent judgments above reciprocal The judgment of comparing indicators i and j is Then the judgment of comparing index j with i

[0175] A triangular fuzzy evaluation index matrix is ​​established based on expert evaluations of indicators at each level. If experts believe that the importance of two indicators will not exceed [a certain threshold], then [the matrix is ​​considered]. The scale will not be lower than The scale, most likely value is The importance of this indicator in the scheme can be represented by a triangular fuzzy number as follows:

[0176] ;

[0177] In the formula, The importance of the indicator will not be less than this value; This value roughly represents the importance of the indicator; The importance of the indicator will not exceed this value;

[0178] Defuzzification is performed using the expectation formula of the triangular fuzziness number:

[0179] ;

[0180] In the formula, The expected value of the triangular fuzzy number; This is the risk propensity value, which ranges between 0 and 1. A higher value indicates that the decision-maker is more inclined to the upper limit of the expert assessment.

[0181] Obtain the judgment matrix A based on the expected value:

[0182] ;

[0183] In the formula, This represents the comparison result of the importance of the i-th and j-th primary indicators;

[0184] Perform a consistency check; use the largest eigenvalue of the judgment matrix A. Calculate the consistency index (CI) of the matrix:

[0185] CI= ;

[0186] In the formula, Indicates the dimension of the judgment matrix;

[0187] The average random consistency index RI is obtained from the table below:

[0188] k 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49

[0189] Calculate the random consistency ratio of judgment matrix A:

[0190] ;

[0191] In the formula, RI is the average random consistency index;

[0192] If CR < 0.1, then the judgment matrix A passes the consistency test; otherwise, it fails, and the judgment matrix A needs to be reconstructed and the consistency test needs to be performed again on the new judgment matrix.

[0193] Subjective weights are calculated using the eigenvalue method; the eigenvector corresponding to the largest eigenvalue of the judgment matrix A is calculated. Then, normalization is performed to obtain the subjective weight vector of the primary indicator. Furthermore, the weights of the secondary indicators under the primary indicators are the same as those of the primary indicators. After uniformizing all secondary indicators, the subjective weights of the secondary indicators determined by the analytic hierarchy process are obtained. .

[0194] The specific steps for calculating the comprehensive weight based on the dynamic weight allocation rule and scoring each site selection scheme are as follows:

[0195] The combined weights are derived from the results calculated using the analytic hierarchy process (AHP) and the entropy weight method.

[0196] ;

[0197] Define the power output volatility of a wind farm:

[0198] ;

[0199] In the formula, This represents the power output of a typical wind farm during the t-hour period of a day. This represents the average daily output of a typical offshore wind farm.

[0200] when When defined as a high-fluctuation working condition, it is necessary to increase the weight of geological conditions and marine environment, and reduce the weight of construction cost;

[0201] For indicator j that needs to be dynamically adjusted, its adjusted weight is:

[0202] ;

[0203] In the formula, This is the matrix before adjustment; Benchmark volatility; The adjustment amplitude coefficient represents the maximum allowable change in the weight. The maximum design volatility is the maximum allowable volatility for offshore wind power design.

[0204] right Normalization is performed to obtain the final indicator weights;

[0205] when When defined as a low-fluctuation operating condition, the original combined weight result is maintained;

[0206] Each site selection scheme is scored based on a standardized evaluation matrix and weighted combinations of indicators, and the scheme with the highest score is selected as the optimal site selection scheme.

[0207] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention, or modify it into equivalent embodiments, without departing from the scope of the present invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technology of the present invention without departing from the scope of the present invention are within the protection scope of the present invention.

Claims

1. A site selection evaluation method for underwater compressed air energy storage systems, characterized in that: The method includes, S1. Establish a multi-level dynamic coupling location evaluation framework, which includes basic layer evaluation indicators, coupling layer evaluation indicators and value layer evaluation indicators. S2. Conduct preliminary screening of candidate regions, collect and quantify data, obtain candidate regions and establish an evaluation matrix for their indicators, obtain a standardized evaluation matrix, and perform data processing on the basic layer evaluation indicators and the value layer evaluation indicators. S3. Perform dynamic simulation and trend analysis; obtain the offshore wind power-UWCAES coupling efficiency factor at each point in the candidate area, and evaluate the dynamic performance; S4. The objective weights of the indicators are automatically assigned according to the degree of data dispersion using the entropy weight method. Then, the importance ranking of each level of indicators is calculated and the subjective weights are obtained according to the improved fuzzy hierarchical analysis method. Finally, the comprehensive weights are calculated according to the dynamic weight allocation rule, and each site selection scheme is scored to obtain the optimal site.

2. The underwater compressed air energy storage system site selection evaluation method according to claim 1, characterized in that: The basic layer evaluation indicators are divided into primary and secondary indicators. Primary indicators include geological conditions, marine environment, and power grid access conditions. Secondary indicators under geological conditions include fault zone distance and seismic activity frequency. Secondary indicators under marine environment conditions include water depth, maximum ocean current velocity, annual water temperature variation range, and seawater pH value. Secondary indicators under power grid access conditions include straight-line distance to the nearest power grid access point and topographic complexity of the submarine cable corridor. The coupling layer evaluation indicators include the offshore wind power-UWCAES coupling efficiency factor. The value layer evaluation indicators are also divided into primary and secondary indicators. Primary indicators include economic and environmental benefits. Secondary indicators under economic benefits include the levelized cost of electricity over the entire life cycle and the investment payback period. Secondary indicators under environmental benefits include annual carbon emission reduction and long-term impact score on the marine ecosystem.

3. The underwater compressed air energy storage system site selection evaluation method according to claim 1, characterized in that: In step S2, the Z-score standardization method is used to normalize the dimensions of each indicator to obtain a standardized evaluation matrix. First, the indicator data of all secondary indicators are used to obtain the following evaluation matrix: ； In the formula, This represents the data for the j-th indicator of the i-th site selection scheme; m is the number of site selection schemes to be evaluated; n represents the total number of secondary indicators to be evaluated; Then, standardization is performed using the Z-score method: ； In the formula, For the standardized data of the j-th index of the i-th location scheme, Let be the average of the j-th metric across all site selection options; Let be the standard deviation of the j-th index of all site selection schemes; The indicators are then normalized. The normalized data for positive indicators is the original data, and the normalized data for negative indicators is the opposite of the original data.

4. The underwater compressed air energy storage system site selection evaluation method according to claim 1, characterized in that: The calculation of the coupling efficiency factor includes: First, set the initial energy storage capacity of the underwater compressed air energy storage system: ； In the formula, The initial energy storage for the underwater compressed air energy storage system; This represents the maximum energy storage capacity. Initial energy storage power: ； In the formula, and These are the initial UWCAES charging power and discharging power, respectively; Then the loop calculation process begins. For each time step (t = 1, 2, ..., T), the following loop calculation process occurs: S110. Determine system availability; Determine whether UWCAES is usable during time period t. If it is not usable, perform the following steps: ； ； ； In the formula, The charging power of UWCAES during the time period t; Let t be the discharge power of UWCAES during the time period t; The energy stored in the underwater compressed air energy storage system during time period t; Jump to S150; If it is usable, then proceed to S120; S120, Calculate power balance: ； In the formula, For excess wind power; Let t be the wind power output at time t; The load at time t; ； In the formula, This is due to a power deficit. S130, Calculate charging power: ； In the formula, The maximum charging power at time t is limited by the ramp rate. Maximum power gradeability; ； In the formula, This is the maximum charging power limited by energy storage capacity. For charging efficiency; The final charging power is: ； S140. Calculate the discharge power: ； In the formula, The maximum discharge power is limited by the ramp rate; ； In the formula, This is the maximum discharge power limited by the stored energy. For discharge efficiency; The final discharge power is: ； S150, Upgrade UWCAES Energy Storage: ； Finally, calculate the coupling efficiency factor: ； In the formula, This refers to the total excess wind power energy that can be theoretically absorbed. ； In the formula, This refers to the net discharge energy actually delivered to the power grid. ； In the formula, This is the coupling efficiency factor.

5. The underwater compressed air energy storage system site selection evaluation method according to claim 1, characterized in that: The specific steps in S4 for automatically assigning objective weights to indicators based on the degree of data dispersion using the entropy weight method are as follows: The contribution of each indicator is calculated based on the standardized evaluation matrix. and information entropy value : ； ； The objective weight of the j-th indicator is calculated from its information entropy value. : ； In the formula, Subscript This indicates the cumulative count.

6. The underwater compressed air energy storage system site selection evaluation method according to claim 5, characterized in that: The steps for obtaining subjective weights in S4 are as follows: Construct a judgment matrix; perform pairwise comparisons between indicators at each level and determine the scale of the judgment matrix elements; establish a triangular fuzzy evaluation index matrix based on expert evaluations of each level of indicators, with the importance of the indicators represented by triangular fuzzy numbers: ； In the formula, The importance of the indicator will not be less than this value; This value roughly represents the importance of the indicator; The importance of the indicator will not exceed this value; Defuzzification is performed using the expectation formula of the triangular fuzziness number: ； In the formula, The expected value of the triangular fuzzy number; This represents the risk propensity value. Obtain judgment matrix A based on the expected value: ； In the formula, To determine the elements in the matrix, we represent the comparison results of the importance of the i-th and j-th primary indicators; Perform a consistency check; use the largest eigenvalue of the judgment matrix A. Calculate the consistency index (CI) of the matrix: CI= ; In the formula, Indicates the dimension of the judgment matrix; Calculate the random consistency ratio of judgment matrix A: ； In the formula, RI is the average random consistency index; If CR < 0.1, then matrix A is considered to have passed the consistency test. Otherwise, it will fail, and the judgment matrix A needs to be reconstructed, and the consistency check of the new judgment matrix needs to be performed again. Subjective weights are calculated using the eigenvalue method; Calculate the eigenvector corresponding to the largest eigenvalue of the decision matrix A. Then, normalization is performed to obtain the subjective weight vector of the primary indicator. Furthermore, the weights of the secondary indicators under the primary indicators are the same as those of the primary indicators. After uniformizing all secondary indicators, the subjective weights of the secondary indicators determined by the analytic hierarchy process are obtained. .

7. The underwater compressed air energy storage system site selection evaluation method according to claim 5, characterized in that: The specific steps for calculating the comprehensive weight based on the dynamic weight allocation rule and scoring each site selection scheme are as follows: The combined weights are derived from the results calculated using the analytic hierarchy process (AHP) and the entropy weight method. ； Define the power output volatility of a wind farm: ； In the formula, This represents the power output of a typical wind farm during the t-hour period of a day. This represents the average daily output of a typical offshore wind farm. when When defined as a high-fluctuation working condition, it is necessary to increase the weight of geological conditions and marine environment, and reduce the weight of construction cost; For indicator j that needs to be dynamically adjusted, its adjusted weight is: ； In the formula, This is the matrix before adjustment. Benchmark volatility; The adjustment amplitude coefficient represents the maximum allowable change in the weight. The maximum design volatility is the maximum allowable volatility for offshore wind power design. right Normalization is performed to obtain the final indicator weights; when When defined as a low-fluctuation operating condition, the original combined weight result is maintained; Each site selection scheme is scored based on a standardized evaluation matrix and weighted combinations of indicators, and the scheme with the highest score is selected as the optimal site selection scheme.

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