A method and device for evaluating the suitability of a large-scale compressed air energy storage power station

Abstract

The application provides a large-scale compressed air energy storage power station suitability evaluation method and device, a novel large-scale compressed air energy storage power station suitability evaluation framework is specially constructed, the suitability evaluation method for the large-scale compressed air energy storage power station is filled, specific decision support and data support can be stably, efficiently and accurately provided for project function analysis, investment calculation, economic evaluation and other engineering needs, resource mismatch can be effectively avoided, and the decision scientificity of power station construction work is ensured.

Description

Technical Field

[0001] This application relates to the field of engineering, specifically to a method and apparatus for evaluating the suitability of a large-scale compressed air energy storage power station. Background Technology

[0002] Compressed-air energy storage (CAES), as a typical representative of long-term and large-scale energy storage technology, has core advantages such as large installed capacity, clean and environmentally friendly, safe and reliable, and is an important technical means to solve the grid connection problem of renewable energy.

[0003] However, the inventors of this application have found that, in stark contrast to the rapid implementation of compressed air energy storage projects, there is currently a lack of suitable evaluation methods for the construction of compressed air energy storage power stations in the existing technology, which cannot provide scientific and objective engineering guidance for the investment and construction of large-scale compressed air energy storage power stations. Summary of the Invention

[0004] This application provides a method and apparatus for evaluating the suitability of large-scale compressed air energy storage power stations. It is used to construct a novel framework for evaluating the suitability of large-scale compressed air energy storage power stations, filling the gap in suitability evaluation methods for large-scale compressed air energy storage power stations. It can stably, efficiently and accurately provide specific decision support and data support for engineering needs such as project functional analysis, investment calculation, and economic evaluation, effectively avoiding resource misallocation and ensuring the scientific nature of decision-making in power station construction.

[0005] Firstly, this application provides a method for evaluating the suitability of large-scale compressed air energy storage power stations, the method comprising: Determine the target area to be evaluated; Under the three-tiered evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations, the corresponding index data for the target area are obtained. Based on the indicator data, each evaluation indicator is scored to obtain the indicator score for each evaluation indicator. By combining the score of each evaluation indicator with the pre-configured indicator weight, the corresponding comprehensive score of the suitability of large-scale compressed air energy storage power stations is calculated to quantify the suitability of large-scale compressed air energy storage power stations in the target area.

[0006] Secondly, this application provides a suitability evaluation device for large-scale compressed air energy storage power stations, the device comprising: The defining unit is used to determine the target area to be evaluated. The acquisition unit is used to acquire the corresponding indicator data of the target area under the three-layer evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations; The scoring unit is used to score each evaluation indicator based on the indicator data, and obtain the indicator score for each evaluation indicator. The calculation unit is used to combine the index score of each evaluation indicator with the pre-configured index weight to calculate the corresponding comprehensive score of the suitability of large-scale compressed air energy storage power stations, so as to quantify the suitability of large-scale compressed air energy storage power stations in the target area.

[0007] Thirdly, this application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the method provided in the first aspect of this application when it invokes the computer program in the memory.

[0008] Fourthly, this application provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the method provided in the first aspect of this application.

[0009] From the above, it can be concluded that this application has the following beneficial effects: To address the suitability evaluation objectives of large-scale compressed air energy storage power plants, this application specifically constructs a novel suitability evaluation framework for large-scale compressed air energy storage power plants. This framework fills the gap in suitability evaluation methods for large-scale compressed air energy storage power plants and can stably, efficiently, and accurately provide specific decision support and data support for engineering needs such as project functional analysis, investment calculation, and economic evaluation. It effectively avoids resource misallocation and ensures the scientific nature of decision-making in power plant construction. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a method for evaluating the suitability of large-scale compressed air energy storage power stations as described in this application. Figure 2 This is a schematic diagram of an example of the three-tier evaluation index system of this application; Figure 3 This is a schematic diagram of an example of an expert questionnaire designed for the comprehensive evaluation system of this application.

[0012] Figure 4 This is a schematic diagram of a structural design for the suitability evaluation device for a large-scale compressed air energy storage power station in this application. Figure 5 This is a schematic diagram of one type of processing equipment used in this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. The naming or numbering of steps appearing in this application does not imply that the steps in the method flow must be performed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical purpose, as long as the same or similar technical effect is achieved.

[0015] The module division described in this application is a logical division. In practical applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual coupling, direct coupling, or communication connections may be through interfaces, and the indirect coupling or communication connections between modules may be electrical or other similar forms, none of which are limited in this application. Moreover, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed across multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this application.

[0016] Before introducing the suitability evaluation method for large-scale compressed air energy storage power plants provided in this application, we will first introduce the background content involved in this application.

[0017] The large-scale compressed air energy storage power station suitability evaluation method, device, and computer-readable storage medium provided in this application can be applied to processing equipment to construct a novel large-scale compressed air energy storage power station suitability evaluation framework. This fills the gap in suitability evaluation methods for large-scale compressed air energy storage power stations and can stably, efficiently, and accurately provide specific decision support and data support for engineering needs such as project functional analysis, investment calculation, and economic evaluation. It effectively avoids resource misallocation and ensures the scientific nature of power station construction decisions.

[0018] The large-scale compressed air energy storage power station suitability evaluation method mentioned in this application can be implemented by a large-scale compressed air energy storage power station suitability evaluation device, or by different types of processing devices such as servers, physical hosts, or user equipment (UE) that integrate the large-scale compressed air energy storage power station suitability evaluation device. The large-scale compressed air energy storage power station suitability evaluation device can be implemented in hardware or software. The UE can specifically be a terminal device such as a smartphone, tablet, laptop, desktop computer, or personal digital assistant (PDA). The processing devices can be configured in a device cluster.

[0019] It is understandable that the proposed solution is usually based on existing data or data that has already been collected. Therefore, the processing equipment that implements the proposed large-scale compressed air energy storage power station suitability evaluation method or is equipped with the corresponding application service of the proposed large-scale compressed air energy storage power station suitability evaluation method usually only needs to meet the required data processing capabilities. The specific equipment type and equipment deployment form are quite flexible.

[0020] If the direct collection of existing data mentioned above is also involved, then further hardware and software adaptation configurations are obviously required for the processing equipment to enable it to have the corresponding data collection capabilities. For example, if real-time collection of indicator data is required, the corresponding data collection devices / systems can be incorporated into the equipment cluster of the processing equipment, or the processing equipment itself can be the control part of these data collection devices / systems. Alternatively, these data collection devices / systems outside the processing equipment can be triggered by a third-party call to perform real-time data collection operations.

[0021] In addition, if there is a need to display the processing progress (including the processing results), the processing device itself can be configured with the required display screen (including touch screen) to display the specific content. Of course, the processing device can also display the specific content through an external display device or other devices with a display screen.

[0022] The following section introduces the suitability evaluation method for large-scale compressed air energy storage power plants provided in this application.

[0023] First, refer to Figure 1 , Figure 1 This paper illustrates a flowchart of a method for evaluating the suitability of a large-scale compressed air energy storage power station according to this application. The method for evaluating the suitability of a large-scale compressed air energy storage power station provided in this application may specifically include the following steps S101 to S104: Step S101: Determine the target area to be evaluated; Understandably, in order to address the issue of suitability assessment for large-scale compressed air energy storage power stations that needs to be addressed with high quality, this application first needs to determine the target area to be assessed in the current assessment work.

[0024] The target area is usually an administrative division at the provincial level, but in specific applications it can be adjusted to a lower level of administrative division according to actual needs.

[0025] In practical applications, the proposed solution is usually initiated along with the corresponding work task, namely the suitability assessment task for large-scale compressed air energy storage power stations. As for the target area, it can be directly indicated in the task information or indirectly indicated by a fixed area or other means.

[0026] Among them, the suitability evaluation task for large-scale compressed air energy storage power stations can be initiated manually or received by means of receiving tasks. For example, tasks can be received from online systems or initiated according to the autonomous task initiation strategy pre-configured on the system. Obviously, this is quite flexible.

[0027] At the same time, the target area involved here can be one or multiple, which can be flexibly configured according to the actual situation to meet diverse and batch evaluation needs.

[0028] Step S102: Under the three-layer evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations, obtain the corresponding index data for the target area. Understandably, this application has specifically established a three-tiered evaluation index system for the suitability assessment of large-scale compressed air energy storage power stations. This system is obviously composed of primary indicators, secondary indicators under the primary indicators, and tertiary indicators under the secondary indicators. In this way, the specific circumstances of each dimension that this application believes can effectively contribute to the suitability of large-scale compressed air energy storage power stations in the current region are precisely quantified through various indicators, laying a good foundation for subsequent scoring operations.

[0029] In this case, the indicator data of the target area to be processed can be obtained under the three-level evaluation indicator system.

[0030] The acquisition and processing of indicator data here usually involves the extraction of existing data, such as extracting indicator data from relevant local storage locations, receiving indicator data from online systems, or extracting indicator data from task information.

[0031] Of course, it is also possible that real-time collection and processing of indicator data will begin as the plan is implemented, until the amount of data that can be processed by the plan can be used to advance further plan processing.

[0032] Step S103: Based on the indicator data, score each evaluation indicator to obtain the indicator score for each evaluation indicator. Understandably, after obtaining the indicator data corresponding to each specific indicator, the preset scoring algorithm can be called to carry out the preliminary scoring operation for each specific indicator involved, so as to determine the indicator score of each evaluation indicator.

[0033] It is important to note that the evaluation indicators involved here can be used primarily for or only for the scoring of the three-level indicators.

[0034] Step S104: Combine the index score of each evaluation index with the pre-configured index weight to calculate the corresponding comprehensive score of the suitability of large-scale compressed air energy storage power stations, so as to quantify the suitability of large-scale compressed air energy storage power stations in the target area.

[0035] After obtaining the initial scores for each evaluation indicator, the weights of each indicator can be combined to perform a comprehensive scoring operation, thereby obtaining a comprehensive score for the suitability of large-scale compressed air energy storage power stations at the overall level.

[0036] In the detailed operation, it is understandable that the indicator weights involved here can be either pre-configured fixed indicator weights or indicator weights that can be adjusted in real time / dynamically according to the actual situation.

[0037] In this way, the comprehensive score of the suitability of large-scale compressed air energy storage power stations, which is represented by a single score, can conveniently and intuitively quantify the suitability of large-scale compressed air energy storage power stations in the current target area, and also facilitates the convenient retrieval of further relevant data.

[0038] As can be seen from the above scheme, this application has specifically constructed a novel evaluation framework for the suitability of large-scale compressed air energy storage power stations, which fills the gap in the evaluation method for the suitability of large-scale compressed air energy storage power stations. It can stably, efficiently and accurately provide specific decision support and data support for engineering needs such as project functional analysis, investment calculation, and economic evaluation, effectively avoid resource misallocation, and ensure the scientific nature of decision-making in power station construction.

[0039] Continue with the above Figure 1 The steps of the illustrated embodiment and their possible implementation methods in practical applications are described in detail.

[0040] As an exemplary embodiment, the three-tiered evaluation index system specifically constructed in this application may include three primary indicators: functional indicators, economic indicators, and social indicators.

[0041] For these three primary indicators, the following further configuration details are provided: 1) Functional indicators are mainly used to evaluate the functional role of compressed air energy storage power stations. Specifically, functional indicators can include three secondary indicators: 1.1) Energy security indicators, 1.2) Grid support indicators, and 1.3) Energy transition indicators. 1.1) Energy security indicators include two tertiary indicators: 1.1.1) Reserve capacity indicators and 1.1.2) Power supply reliability indicators. 1.2) Grid support indicators include two tertiary indicators: 1.2.1) Peak shaving and valley filling indicators and 1.2.2) Demand response indicators. 1.3) Energy transition indicators include two tertiary indicators: 1.3.1) Renewable energy penetration rate indicators and 1.3.2) Wind and solar power installed capacity indicators. 2) Functional indicators are mainly used to evaluate the economic benefits brought by compressed air energy storage power stations. Correspondingly, economic indicators can specifically include the secondary indicator 2.1) economic benefit indicators, which include the tertiary indicators 2.1.1) investment payback period indicators and 2.1.2) energy storage revenue indicators. 3) Social indicators are mainly used to evaluate the positive impact of compressed air energy storage power stations on the local area. Correspondingly, social indicators can include the secondary indicator 3.1) Regional policy indicators, which include the tertiary indicators 3.1.1) Policy quantity indicators and 3.1.2) Policy support intensity indicators.

[0042] For more details, please refer to this link. Figure 2 The diagram shown is an example of the three-tier evaluation index system of this application, for a more intuitive understanding.

[0043] As can be seen, the embodiments here specifically disclose the composition of the high-quality indicator system that this application ultimately determined to better characterize the various dimensions / factors of the suitability of a region for large-scale compressed air energy storage power stations.

[0044] Under the above-mentioned three-level evaluation index system design, for the scoring operation involved in step S103, as a corresponding exemplary embodiment, this application can have the following specific configuration contents: 1.1.1) For the reserve capacity indicator, it is specifically quantified by the reserve capacity gap rate. The score is 4 when it is 5%-10%, 6 when it is 10%-15%, 8 when it is 15%-20%, and 10 when it is >20%. Specifically, the reserve capacity shortfall ratio involved here can be expressed as: , in, This indicates the reserve capacity shortfall ratio. This represents the theoretical reserve capacity, in MW. This indicates the actual reserve capacity, measured in MW.

[0045] 1.1.2) For power supply reliability indicators, the power supply reliability rate is used for quantification. When it is >95%, the score is 4; when it is 90%-95%, the score is 6; when it is 85%-90%, the score is 8; and when it is <85%, the score is 10. Specifically, the power supply reliability involved here can be expressed as: , , in, Indicates power supply reliability. Indicates the equivalent power outage time. The duration of the statistics is expressed in hours (h), and n represents the number of power outage events, expressed in times. This represents the capacity of the i-th power outage, in MW. This indicates the total power supply capacity of the system, in MW. This represents the time of the i-th power outage, in hours (h).

[0046] 1.2.1) For the peak shaving and valley filling index, the peak-valley difference rate is used for quantification. When it is <30%, the score is 6; when it is 30%-40%, the score is 8; and when it is >40%, the score is 10. Specifically, the peak-to-valley difference rate involved here can be expressed as: , in, Indicates the peak-to-valley difference rate. This represents the actual maximum load within a specific statistical period, expressed in MW. This represents the actual minimum load within a certain statistical period, expressed in MW.

[0047] 1.2.2) For the demand response index, it is specifically quantified by the average number of daily peak shavings. The score is 4 when it is <1, 6 when it is 1-1.5, 8 when it is 1.5-2, and 10 when it is 2-3. 1.3.1) For the renewable energy penetration rate indicator, the score is 4 when <40%, 6 when 40%-60%, 8 when 60%-80%, and 10 when >80%; Specifically, the renewable energy penetration rate indicator here can be considered from the perspective of power generation, and can be expressed as: , in, Indicates the penetration rate of renewable energy. Indicates renewable energy generation. This indicates the total electricity generation of the region.

[0048] 1.3.2) For the wind and solar power installed capacity indicators, the score is 4 for wind power ≤ 2.1 million kW and solar power ≤ 4.5 million kWh, the score is 6 for wind power 2.11-4 million kW and solar power 4.5-6.5 million kWh, the score is 8 for wind power 4.01-7 million kW and solar power 6.5-16.5 million kWh, and the score is 10 for wind power 7.01-16.5 million kW and solar power 16.5-27 million kWh. 2.1) For the investment return cycle indicator, the score is 4 for >12 years, 6 for 10-12 years, 8 for 8-10 years, and 10 for <8 years; In practical operation, the investment payback period of pumped storage power stations can also be used as a reference for estimation. In this case, the maturity of technology and existing case data can be taken into account. Taking the Southwest region as an example, the investment payback period can be taken as 80% of that of local pumped storage power stations, while other regions can take 45%-55%.

[0049] 2.2) For the distribution and storage revenue indicator, the specific method is to quantify it using the deviation value of new energy power generation. The score is 4 when it is ≤10%, 6 when it is 10%-20%, 8 when it is 20%-30%, and 10 when it is 30%-40%. 3.1) For policy quantity indicators, the score is 4 for 0-50, 6 for 50-100, 8 for 100-150, and 10 for >150; 3.2) For the policy support strength indicator, it is specifically quantified through regional policy scores. The score is 4 when <150, 6 when 150-250, 8 when 250-350, and 10 when ≥350. The regional policy score is specifically quantified by combining the importance of the policy, the policy coverage, and the policy timeliness.

[0050] Specifically, the regional policy score here can be expressed as: , Where T represents the regional policy score, and n is the number of policies issued by the region. This represents the score for the i-th policy. This represents the score indicating the importance of the i-th policy. This represents the score for the i-th policy coverage segment. This represents the timeliness score of the i-th policy.

[0051] Furthermore, the importance of a policy can be specifically valued based on the number of items that meet the following criteria: (1) Targeting the core contradictions: Judging whether the policies directly address key issues such as energy storage profitability and grid connection; (2) Quantification of implementation efforts: The intensity of implementation is assessed by quantifiable factors such as subsidy amount and storage requirements. (3) Policy type relevance: verify whether it is a special policy for compressed air energy storage; (4) Clarity of development stage: It is clear that the policy is in the specific revision stage, such as soliciting opinions and implementing the plan; (5) Construction completeness, and examine the supporting guarantee situation such as regional energy storage construction coordination and implementation details; (6) Promote efficiency adaptability, identify the leading department and consider its implementation efficiency.

[0052] Correspondingly, there are: The score for the importance of policies is quantified based on compliance with the criteria: 0.3 for 1-2 items, 0.6 for 3-4 items, and 1 for 5-6 items. Regarding the score for policy coverage, the score is 0.6 when covering planning, standards, and macro policies; 0.8 when covering equipment R&D, encouragement of power distribution and storage, and demonstration projects; 0.9 when covering electricity supply, subsidy policies, and electricity pricing policies; and 1 when covering project construction and investment. For the timeliness score of policies, the score is 0.3 when the timeliness is ≥5 years, 0.7 when the timeliness is 3-5 years, and 1 when the timeliness is within 1-2 years.

[0053] At the same time, regarding the aforementioned data inputs, it is understandable that in specific operations, the data can be obtained from open data such as announcements on relevant official websites, from customized data products, from the local database of the solution application party, or from other sources, which is quite flexible.

[0054] Meanwhile, the specific configuration details of the above scoring operations can be further illustrated by referring to the following three example tables.

[0055] Table 1 - Quantitative Interval Division of Functional Indicators Table 2 - Quantitative Range Division of Economic and Social Indicators Table 3 - Quantitative Scores of Policy Importance, Coverage Aspects, and Timeliness Furthermore, regarding the pre-configured indicator weights involved in step S104, as an exemplary embodiment, this application can specifically determine them in conjunction with prior questionnaire operations. Correspondingly, the method of this application may further include: For each evaluation indicator, based on the expert questionnaire designed in the pre-constructed comprehensive evaluation system, the scaling method is used to assign weights to the expert questionnaire scores, and the weighted average score is calculated. The indicator weights are then calculated using the analytic hierarchy process.

[0056] Understandably, the comprehensive evaluation system design expert questionnaire aims to obtain expert scores on the importance scale of each indicator. The questionnaire design can follow the basic principles of rationality, generality, logic, clarity, non-inducement, and ease of data processing and analysis.

[0057] Specifically, this can also be combined with Figure 3 The diagram shown is an example of an expert questionnaire for the design of the comprehensive evaluation system of this application, which can be used to provide a more intuitive understanding of the questionnaire.

[0058] In addition, it is understandable that, besides referring to expert questionnaires, search algorithms such as whale optimization and simulated annealing can be used to automatically search for a more suitable weight configuration scheme that achieves the best configuration effect. Alternatively, related neural network methods such as multilayer perceptron (MLP) can be used to determine a suitable weight configuration scheme.

[0059] Furthermore, regarding the comprehensive score calculation operation involved in step S104, as an exemplary embodiment, the quantitative formula corresponding to the comprehensive score of the suitability of large-scale compressed air energy storage power stations can be specifically expressed as follows: , , in, This indicates the overall score for the suitability of large-scale compressed air energy storage power stations. This indicates the score for the primary indicator. Indicates the weight of the primary indicator. Indicates the weight of the primary indicator. This indicates the score for the three-level indicator. This indicates the weight of the three-level indicators.

[0060] Taking a certain region as an example, its reserve capacity is 10 points, power supply reliability is 9 points, peak shaving and valley filling is 8 points, demand response is 10 points, renewable energy penetration rate is 7 points, new energy installed capacity is 9 points, return on investment is 9 points, distribution and storage revenue is 8 points, policy quantity is 8 points, and policy quality is 10 points. Combining the indicator weights, the functional first-level indicator is 3.023 points, the economic first-level indicator is 3.043 points, and the social first-level indicator is 2.710 points, with a final comprehensive score of 8.776 points.

[0061] After calculating the comprehensive score reflecting the suitability of large-scale compressed air energy storage power stations in the current target area, as mentioned earlier, further data retrieval operations may be required as needed, such as typical result display, local storage, off-site storage, result forwarding, or further data analysis and processing.

[0062] Regarding the data analysis and processing, as an exemplary embodiment, this application may also involve a process for determining the suitability level, which can more intuitively and conveniently reflect the suitability situation. In this regard, the method of this application may further include: The suitability scores for large-scale compressed air energy storage power stations are used to classify the target areas into corresponding suitability levels.

[0063] Understandably, similar to the scoring operation mentioned earlier, the specific operation here may involve corresponding operations such as matching based on the mapping table to advance further determination processing.

[0064] Furthermore, this application also involves specific level establishment work. As an exemplary embodiment, the corresponding suitability level system may specifically include the following configuration content: A score of 0-5 indicates a weakly suitable area for development. A score of 5-6 indicates a relatively weak suitability area for construction. A score of 6-7 indicates a generally suitable area for construction. 7-8 points indicates a relatively strong suitability for construction. A score of 8 or higher indicates a highly suitable area for development.

[0065] As in the previous example, a region that achieves a final overall score of 8.776 is considered a highly suitable construction area.

[0066] At the same time, this application also considers that, based on the overall score and suitability rating obtained under the current circumstances, a further, more refined mechanism for adjusting suitable construction areas can be implemented.

[0067] In layman's terms, as mentioned earlier, the areas involved in this application are usually defined by administrative divisions, at most a certain province or city. However, this application takes into account the fixed attributes of the separable area range and considers the dynamic perspective to capture areas that are more suitable for building large-scale compressed air energy storage power stations under different circumstances.

[0068] In response, the target area involved in step S101 at the beginning can be different areas that can be divided under any strategy. These different areas can also contain or overlap, thus forming a variety of area division effects. In this case, subsequent area segmentation and merging of adjacent areas can be carried out based on the comprehensive score and suitability level of large-scale compressed air energy storage power stations in different areas. In this way, under the scenario of suitability evaluation of large-scale compressed air energy storage power stations, the area optimization results that can meet the specified optimization conditions such as increasing the number of strong suitability areas, the size of the strong suitability area, and optimizing the area based on the specified area range can be further determined. Under the idea of ​​mining higher quality area division under the condition of dynamic adjustment of area, a more delicate suitability area mining mechanism is created, thereby providing more diversified and high-quality suitability area decision support.

[0069] In conclusion, regarding the above solutions, this application offers the following beneficial effects: (1) A multi-indicator evaluation system covering three dimensions of functionality, economy and sociality was constructed, which broke through the limitations of the single-dimensional analysis of existing technologies and realized a comprehensive and systematic evaluation of the suitability of large-scale compressed air energy storage power stations, filling the gap in the evaluation method of the suitability of compressed air energy storage power stations.

[0070] (2) Differentiated calculation and quantification methods were designed based on the technical characteristics of different indicators. In addition, reference values ​​were given based on the current situation of missing data under the current technological development status. For example, in the case of some indicators without relevant compressed air energy storage power station data, relevant data of pumped storage can also be referenced, which improves the scientificity and reliability of the evaluation results.

[0071] (3) The weights of the indicators are determined by combining expert surveys and the analytic hierarchy process, which takes into account both the objectivity of expert experience and mathematical analysis, avoids the limitations of a single weighting method, and the weight allocation is more in line with the actual engineering needs.

[0072] (4) It has realized the zoning evaluation of suitability for construction, providing intuitive and scientific engineering guidance for the site selection and investment of large-scale compressed air energy storage power stations, effectively avoiding resource misallocation, and promoting the large-scale and standardized development of compressed air energy storage technology.

[0073] (5) The relevant data can be based on publicly available electricity consumption data, relevant standards and specifications and data of existing projects. The data sources are real and reliable, and the relevant datasets can be further expanded and improved with the development of technology.

[0074] The above is an introduction to the suitability evaluation method for large-scale compressed air energy storage power stations provided in this application. In order to facilitate better implementation of the suitability evaluation method for large-scale compressed air energy storage power stations provided in this application, this application also provides a suitability evaluation device for large-scale compressed air energy storage power stations from the perspective of functional modules.

[0075] See Figure 4 , Figure 4 This is a schematic diagram of a structure for the suitability evaluation device for a large-scale compressed air energy storage power station according to this application. In this application, the large-scale compressed air energy storage power station suitability evaluation device 400 may specifically include the following structure: Determining unit 401 is used to determine the target area to be evaluated; The acquisition unit 402 is used to acquire the corresponding indicator data of the target area under a three-layer evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations. The scoring unit 403 is used to score each evaluation indicator based on the indicator data to obtain the indicator score for each evaluation indicator. The calculation unit 404 is used to combine the index score of each evaluation index with the pre-configured index weight to calculate the corresponding comprehensive score of the suitability of the large-scale compressed air energy storage power station, so as to quantify the suitability of the large-scale compressed air energy storage power station in the target area.

[0076] In one exemplary embodiment, the three-tiered evaluation index system specifically includes three primary indicators: functional indicators, economic indicators, and social indicators. Functional indicators include three secondary indicators: energy security indicators, power grid support indicators, and energy transition indicators. Energy security indicators include two tertiary indicators: reserve capacity indicators and power supply reliability indicators. Power grid support indicators include two tertiary indicators: peak shaving and valley filling indicators and demand response indicators. Energy transition indicators include two tertiary indicators: renewable energy penetration rate indicators and wind and solar power installed capacity indicators. Economic indicators include a secondary indicator called economic benefit indicators, which in turn include two tertiary indicators: investment return cycle indicators and reserve allocation income indicators. Social indicators include a secondary indicator called regional policy indicators, which in turn include two tertiary indicators: policy quantity indicators and policy support intensity indicators.

[0077] In another exemplary embodiment, the reserve capacity indicator is specifically quantified by the reserve capacity gap rate, with a score of 4 for 5%-10%, a score of 6 for 10%-15%, a score of 8 for 15%-20%, and a score of 10 for >20%. For power supply reliability indicators, the specific quantification is the power supply reliability rate. When it is >95%, the score is 4; when it is 90%-95%, the score is 6; when it is 85%-90%, the score is 8; and when it is <85%, the score is 10. The peak shaving and valley filling index is specifically quantified by the peak-valley difference rate: <30% is scored as 6, 30%-40% is scored as 8, and >40% is scored as 10. For the demand response indicator, it is specifically quantified by the average number of peak shavings per day: <1 hour is scored as 4, 1-1.5 hours is scored as 6, 1.5-2 hours is scored as 8, and 2-3 hours is scored as 10. For the renewable energy penetration rate indicator, the score is 4 when it is <40%, 6 when it is 40%-60%, 8 when it is 60%-80%, and 10 when it is >80%. For wind and solar power installed capacity indicators, the score is 4 for wind power ≤ 2.1 million kilowatts and solar power ≤ 4.5 million kilowatt-hours, the score is 6 for wind power 2.11-4 million kilowatts and solar power 4.5-6.5 million kilowatt-hours, the score is 8 for wind power 4.01-7 million kilowatts and solar power 6.5-16.5 million kilowatt-hours, and the score is 10 for wind power 7.01-16.5 million kilowatts and solar power 16.5-27 million kilowatt-hours. For the investment return cycle indicator, the score is 4 for >12 years, 6 for 10-12 years, 8 for 8-10 years, and 10 for <8 years. For the distribution and storage revenue indicator, the deviation value of new energy power generation is used for quantification. The score is 4 when it is ≤10%, 6 when it is 10%-20%, 8 when it is 20%-30%, and 10 when it is 30%-40%. For policy quantity indicators, the score is 4 for 0-50, 6 for 50-100, 8 for 100-150, and 10 for >150; The policy support strength indicator is specifically quantified through regional policy scores: <150 is scored as 4, 150-250 as 6, 250-350 as 8, and ≥350 as 10. The regional policy score is quantified by combining the importance of the policy, the policy coverage, and the policy timeliness.

[0078] In yet another exemplary embodiment, the computing unit 404 is further configured to: For each evaluation indicator, based on the expert questionnaire designed in the pre-constructed comprehensive evaluation system, the scaling method is used to assign weights to the expert questionnaire scores, and the weighted average score is calculated. The indicator weights are then calculated using the analytic hierarchy process.

[0079] In yet another exemplary embodiment, the quantitative formula corresponding to the comprehensive suitability score of a large-scale compressed air energy storage power station is expressed as follows: , , in, This indicates the overall score for the suitability of large-scale compressed air energy storage power stations. This indicates the score for the primary indicator. Indicates the weight of the primary indicator. Indicates the weight of the primary indicator. This indicates the score for the three-level indicator. This indicates the weight of the three-level indicators.

[0080] In yet another exemplary embodiment, the apparatus further includes a dividing unit 405 for: The suitability scores for large-scale compressed air energy storage power stations are used to classify the target areas into corresponding suitability levels.

[0081] In yet another exemplary embodiment, the corresponding suitability rating system includes the following configuration: A score of 0-5 indicates a weakly suitable area for development. A score of 5-6 indicates a relatively weak suitability area for construction. A score of 6-7 indicates a generally suitable area for construction. 7-8 points indicates a relatively strong suitability for construction. A score of 8 or higher indicates a highly suitable area for development.

[0082] This application also provides a processing device from a hardware architecture perspective. As mentioned earlier, in practice, a processing device may exist as a device cluster. In this case, each device in the device cluster can also be referred to as a processing device. See [reference needed]. Figure 5 , Figure 5This diagram illustrates a structural schematic of the processing device of this application. Specifically, the processing device may include a processor 501, a memory 502, and an input / output device 503. The processor 501 executes the computer program stored in the memory 502 to implement, for example... Figure 1 The corresponding steps of the suitability evaluation method for large-scale compressed air energy storage power stations in the embodiments; or, when the processor 501 executes the computer program stored in the memory 502, it implements as follows: Figure 4 Corresponding to the functions of each unit in the embodiment, the memory 502 is used to store the functions executed by the processor 501 as described above. Figure 1 The computer program required for the suitability evaluation method of large-scale compressed air energy storage power stations in the corresponding embodiment.

[0083] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 502 and executed by processor 501 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a computer device.

[0084] The processing device may include, but is not limited to, processor 501, memory 502, and input / output device 503. Those skilled in the art will understand that the illustrations are merely examples of the processing device and do not constitute a limitation on the processing device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the processing device may also include network access devices, buses, etc., and processor 501, memory 502, input / output device 503, etc., are connected via a bus.

[0085] Processor 501 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the processing device, connecting various parts of the device through various interfaces and lines.

[0086] The memory 502 can be used to store computer programs and / or modules. The processor 501 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 502 and by calling data stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function, etc.; the data storage area may store data created according to the use of the processing device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0087] When processor 501 executes a computer program stored in memory 502, it can specifically perform the following functions: Determine the target area to be evaluated; Under the three-tiered evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations, the corresponding index data for the target area are obtained. Based on the indicator data, each evaluation indicator is scored to obtain the indicator score for each evaluation indicator. By combining the score of each evaluation indicator with the pre-configured indicator weight, the corresponding comprehensive score of the suitability of large-scale compressed air energy storage power stations is calculated to quantify the suitability of large-scale compressed air energy storage power stations in the target area.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described large-scale compressed air energy storage power station suitability evaluation device, processing equipment, and its corresponding units can be referred to as follows: Figure 1 The description of the suitability evaluation method for large-scale compressed air energy storage power stations in the corresponding embodiments will not be repeated here.

[0089] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0090] Therefore, this application provides a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute the present application. Figure 1 The steps of the suitability evaluation method for large-scale compressed air energy storage power stations in the corresponding embodiments can be found in the following examples. Figure 1The description of the suitability evaluation method for large-scale compressed air energy storage power stations in the corresponding embodiments will not be repeated here.

[0091] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0092] Because of the instructions stored in the computer-readable storage medium, the present application can be executed as described above. Figure 1 The steps of the suitability evaluation method for large-scale compressed air energy storage power stations in the corresponding embodiments can therefore achieve the results of this application. Figure 1 The beneficial effects that the suitability evaluation method for large-scale compressed air energy storage power stations can achieve in the corresponding embodiments are detailed in the preceding description and will not be repeated here.

[0093] The foregoing has provided a detailed description of the suitability evaluation method, apparatus, processing equipment, and computer-readable storage medium for large-scale compressed air energy storage power stations provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the core ideas of this application; furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the suitability of large-scale compressed air energy storage power stations, characterized in that, The method includes: Determine the target area to be evaluated; Under the three-layer evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations, the corresponding index data of the target area are obtained; Based on the aforementioned indicator data, a score is assigned to each evaluation indicator to obtain the indicator score for each evaluation indicator. By combining the index scores of each evaluation indicator with the pre-configured index weights, the corresponding comprehensive score for the suitability of large-scale compressed air energy storage power stations is calculated to quantify the suitability of large-scale compressed air energy storage power stations in the target area.

2. The method according to claim 1, characterized in that, The three-tiered evaluation index system specifically includes three primary indicators: functional indicators, economic indicators, and social indicators. The functional indicators include three secondary indicators: energy security indicators, power grid support indicators, and energy transition indicators. The energy security indicators include two tertiary indicators: reserve capacity indicators and power supply reliability indicators. The power grid support indicators include two tertiary indicators: peak shaving and valley filling indicators and demand response indicators. The energy transition indicators include two tertiary indicators: renewable energy penetration rate indicators and wind and solar power installed capacity indicators. The economic indicators include a secondary indicator called the economic benefit indicator, which in turn includes two tertiary indicators: the investment return cycle indicator and the allocation and storage revenue indicator. The social indicators include a secondary indicator called regional policy indicators, which in turn include two tertiary indicators: policy quantity indicators and policy support intensity indicators.

3. The method according to claim 2, characterized in that, The reserve capacity indicator is specifically quantified by the reserve capacity gap rate: 4 for 5%-10%, 6 for 10%-15%, 8 for 15%-20%, and 10 for >20%. The power supply reliability index is specifically quantified by power supply reliability rate: >95% is scored as 4, 90%-95% is scored as 6, 85%-90% is scored as 8, and <85% is scored as 10. The peak shaving and valley filling index is specifically quantified by the peak-valley difference rate: <30% is scored as 6, 30%-40% is scored as 8, and >40% is scored as 10. The demand response index is specifically quantified by the average number of peak shavings per day: <1 is scored as 4, 1-1.5 is scored as 6, 1.5-2 is scored as 8, and 2-3 is scored as 10. For the aforementioned renewable energy penetration rate indicator, the score is 4 when <40%, 6 when 40%-60%, 8 when 60%-80%, and 10 when >80%. For the aforementioned wind and solar power installed capacity indicators, the score is 4 for wind power ≤ 2.1 million kW and solar power ≤ 4.5 million kW, the score is 6 for wind power 2.11-4 million kW and solar power 4.5-6.5 million kW, the score is 8 for wind power 4.01-7 million kW and solar power 6.5-16.5 million kW, and the score is 10 for wind power 7.01-16.5 million kW and solar power 16.5-27 million kW. For the aforementioned investment return cycle indicator, the score is 4 for >12 years, 6 for 10-12 years, 8 for 8-10 years, and 10 for <8 years. The aforementioned distribution and storage revenue indicator is specifically quantified using the deviation value of new energy power generation. The score is 4 when it is ≤10%, 6 when it is 10%-20%, 8 when it is 20%-30%, and 10 when it is 30%-40%. For the policy quantity indicators, the score is 4 for 0-50, 6 for 50-100, 8 for 100-150, and 10 for >150; The policy support strength indicator is specifically quantified through regional policy scores: <150 is scored as 4, 150-250 as 6, 250-350 as 8, and ≥350 as 10. The regional policy score is quantified by combining the importance of the policy, the policy coverage, and the policy timeliness.

4. The method according to claim 3, characterized in that, The method further includes: For each evaluation indicator, based on the expert questionnaire designed in the pre-constructed comprehensive evaluation system, the scaling method is used to assign weights to the expert questionnaire scores, and the weighted average score is calculated. The weight of the indicator is then calculated using the analytic hierarchy process.

5. The method according to claim 1, characterized in that, The quantitative formula corresponding to the comprehensive score of the suitability of the large-scale compressed air energy storage power station is expressed as follows: ， ， in, This represents the overall suitability score for the aforementioned large-scale compressed air energy storage power station. This indicates the score for the primary indicator. Indicates the weight of the primary indicator. Indicates the weight of the primary indicator. This indicates the score for the three-level indicator. This indicates the weight of the three-level indicators.

6. The method according to claim 1, characterized in that, The method further includes: The target area is classified into corresponding suitability levels based on the comprehensive suitability score of the large-scale compressed air energy storage power station.

7. The method according to claim 6, characterized in that, The corresponding suitability rating system includes the following configuration items: A score of 0-5 indicates a weakly suitable area for development. A score of 5-6 indicates a relatively weak suitability area for construction. A score of 6-7 indicates a generally suitable area for construction. 7-8 points indicates a relatively strong suitability for construction. A score of 8 or higher indicates a highly suitable area for development.

8. A suitability evaluation device for large-scale compressed air energy storage power stations, characterized in that, The device includes: The defining unit is used to determine the target area to be evaluated. The acquisition unit is used to acquire the corresponding indicator data of the target area under a three-layer evaluation index system pre-constructed for the suitability evaluation of large-scale compressed air energy storage power stations. The scoring unit is used to score each evaluation indicator based on the indicator data to obtain the indicator score for each evaluation indicator. The calculation unit is used to combine the index score of each evaluation index with the pre-configured index weight to calculate the corresponding comprehensive score of the suitability of large-scale compressed air energy storage power stations, so as to quantify the suitability of large-scale compressed air energy storage power stations in the target area.

9. A processing device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the method as described in any one of claims 1 to 7 when it invokes the computer program in the memory.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the method of any one of claims 1 to 7.

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