New energy project optimization method and system based on comprehensive evaluation index system

Abstract

The invention discloses a new energy project optimization method and system based on a comprehensive evaluation index system, and belongs to the technical field of power systems. The method comprises the following steps: acquiring multi-source data related to energy project operation; calculating a quantized value and a standardized score matrix of each index in a pre-constructed multi-dimensional operation benefit evaluation index system according to the multi-source data; constructing a fuzzy judgment matrix according to the quantized value of each index, and calculating a comprehensive weight vector of each index according to the fuzzy judgment matrix; calculating a comprehensive evaluation result according to the standardized score matrix and the comprehensive weight vector of each index; the comprehensive evaluation result is subjected to differential rolling updating according to the characteristics of the new energy project; and generating a new energy project optimization strategy according to the comprehensive evaluation result, and generating a new energy project optimization result according to the new energy project optimization strategy. According to the method, dynamic benefit evaluation and closed-loop optimization of the new energy project in the operation stage can be realized, and the scientificity of evaluation and the rationality of optimization decision are improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for optimizing new energy projects based on a comprehensive evaluation index system. Background Technology

[0002] With the rapid growth of new energy installed capacity, the power system structure is gradually evolving towards cleaner, smarter, and more diversified directions. The coordinated operation of multiple energy forms, such as wind power, photovoltaics, energy storage, thermal power, and pumped storage, places higher demands on efficiency management during the operation phase. Existing energy project evaluations primarily rely on static economic indicators, which are insufficient to reflect the dynamic efficiency characteristics that change over time during project operation.

[0003] Current energy project operation evaluations face several prominent problems, primarily manifested in a singular evaluation system, limitations in data processing, insufficient dynamic adaptability, and weak decision support capabilities. Existing evaluation methods largely focus on static economic indicators, lacking comprehensive consideration of multi-dimensional indicators and failing to fully reflect the complex characteristics of multi-energy collaborative operation under the new power system. Furthermore, traditional methods employ a rather crude approach to data collection frequency and processing, failing to adequately consider the data fluctuation characteristics of intermittent power sources such as wind and solar power, as well as the differentiated data requirements of different types of energy projects. In addition, existing evaluation models lack a rolling update mechanism, making them unable to adapt to dynamic changes in market electricity prices, policy environment, equipment status, and other factors. This results in evaluation results lagging behind actual operating conditions, a disconnect between evaluation results and operational optimization, and a lack of effective mechanisms to translate evaluation results into specific optimization strategies, hindering continuous project improvement. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for optimizing new energy projects based on a comprehensive evaluation index system. This method and system can realize dynamic benefit evaluation and closed-loop optimization of new energy projects during the operation phase, thereby improving the scientific nature of the evaluation and the rationality of the optimization decision.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] On the one hand, this invention provides a method for optimizing new energy projects based on a comprehensive evaluation index system, including:

[0007] Acquire multi-source data related to the operation of energy projects;

[0008] Based on the multi-source data, calculate the quantitative value and standardized score matrix of each indicator in the pre-constructed multi-dimensional operational efficiency evaluation index system;

[0009] A fuzzy judgment matrix is ​​constructed based on the quantified values ​​of each indicator, and the comprehensive weight vector of each indicator is calculated based on the fuzzy judgment matrix.

[0010] The comprehensive evaluation result is calculated based on the standardized score matrix and comprehensive weight vector of each indicator; the comprehensive evaluation result is updated in a differentiated and rolling manner according to the characteristics of the new energy project.

[0011] Based on the comprehensive evaluation results, an optimization strategy for new energy projects is generated, and based on the optimization strategy, the optimization results for new energy projects are generated.

[0012] Optionally, the multi-source data related to the operation of new energy projects includes equipment operating parameters, power load demand, meteorological data, energy storage status data, grid connection point data, market electricity price data, and policy constraint data.

[0013] Optionally, the standardized score matrix is ​​generated by standardizing each indicator in the multi-dimensional operational efficiency evaluation index system to eliminate dimensional differences.

[0014] The multi-dimensional operational efficiency evaluation index system includes technical efficiency indicators, energy efficiency indicators, environmental efficiency indicators, economic efficiency indicators, and social efficiency indicators.

[0015] Optionally, fuzzy hierarchical analysis is used to construct a fuzzy judgment matrix for each indicator based on its quantified value, and to calculate the comprehensive weight vector for each indicator based on the fuzzy judgment matrix, including:

[0016] Fuzzy trigonometric numbers As a measure of fuzziness, the quantified values ​​of each indicator are compared pairwise based on the fuzziness to construct a fuzzy judgment matrix. ;in, This indicates the importance of the i-th indicator relative to the j-th indicator; These represent the lower limit, most likely value, and upper limit of the weight of the i-th indicator relative to the j-th indicator, respectively. This indicates the order of the fuzzy judgment matrix;

[0017] Calculate the fuzzy weights of the fuzzy judgment matrix, and defuzzify the fuzzy weights to obtain the relative weight vector of each index.

[0018] The relative weight vectors of each indicator are validated for consistency, and a comprehensive weight vector for each indicator is generated.

[0019] Optionally, the relative weight vector of each indicator is represented as follows:

[0020] ;

[0021] in, This represents the relative weight vector of the i-th indicator; Let represent the lower limit, most likely value, and upper limit of the weight of the i-th indicator, respectively.

[0022] Optionally, a consistency check is performed on the relative weight vectors of each indicator to generate a comprehensive weight vector for each indicator, including:

[0023] If the relative weight vectors of each indicator meet the consistency verification condition, then the comprehensive weight vector of each indicator is generated.

[0024] If the relative weight vectors of each indicator do not meet the consistency verification conditions, the fuzzy triangular number is readjusted.

[0025] The consistency verification condition is expressed as follows:

[0026] ;

[0027] ;

[0028] in, Indicates the consistency ratio; Indicates consistency index; Indicates the random consistency index; This represents the largest eigenvalue of the fuzzy judgment matrix.

[0029] Optionally, the comprehensive evaluation result is expressed as:

[0030] ;

[0031] in, This indicates the overall evaluation result; This represents the combined weight vector of each indicator; This represents the standardized score matrix for each indicator.

[0032] Optionally, the comprehensive evaluation results are updated on a rolling basis according to the characteristics of new energy projects, including:

[0033] Sensitivity analysis was used to compare the comprehensive evaluation results with the historical benchmark values ​​of the comprehensive evaluation results, and the key influencing factors of the comprehensive evaluation results were identified.

[0034] Based on the influencing factors of the comprehensive evaluation results, a gradual correction strategy is adopted to correct the comprehensive weight vector, resulting in an updated comprehensive evaluation result.

[0035] Secondly, this invention provides a new energy project optimization system based on a comprehensive evaluation index system, comprising:

[0036] The data acquisition module is used to acquire multi-source data related to the operation of energy projects.

[0037] The quantitative indicator module is used to: calculate the quantitative value and standardized score matrix of each indicator in the pre-constructed multi-dimensional operational efficiency evaluation indicator system based on the multi-source data;

[0038] The qualitative indicator module is used to: construct a fuzzy judgment matrix based on the quantitative values ​​of each indicator, and calculate the comprehensive weight vector of each indicator based on the fuzzy judgment matrix;

[0039] The comprehensive evaluation module is used to calculate the comprehensive evaluation results based on the standardized score matrix and comprehensive weight vector of each indicator.

[0040] The project optimization module is used to: generate optimization strategies for new energy projects based on comprehensive evaluation results, and generate optimization results for new energy projects based on the optimization strategies.

[0041] Thirdly, the present invention provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the new energy project optimization method based on a comprehensive evaluation index system described in the first aspect.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0043] This invention employs a scientific method for determining indicator weights and a comprehensive evaluation method, combined with intelligent data processing technology and a dynamic optimization mechanism. This effectively addresses the limitations of traditional evaluation methods, ensuring the scientific validity of weight determination while achieving unified processing of qualitative and quantitative indicators. The constructed fuzzy judgment matrix effectively reduces the uncertainty of subjective judgment and improves the reliability of evaluation results. Regarding the dynamic mechanism, rolling updates and sensitivity analysis are introduced, enabling the evaluation system to have self-learning and adaptive capabilities. The system can dynamically adjust indicator weights based on real-time data, providing targeted suggestions for operational optimization. This achieves full-process automation from data collection to decision support, forming a closed-loop data evaluation and optimization mechanism to ensure the timeliness and effectiveness of operational management. Attached Figure Description

[0044] Figure 1 The diagram shown is a flowchart of one embodiment of the new energy project optimization method based on a comprehensive evaluation index system of the present invention.

[0045] Figure 2 The diagram shown is a framework schematic of the multi-dimensional operational efficiency evaluation index system of the present invention in one embodiment. Detailed Implementation

[0046] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0047] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0048] Example 1

[0049] like Figure 1 As shown in the figure, this embodiment introduces a method for optimizing new energy projects based on a comprehensive evaluation index system, including the following steps:

[0050] Step 1: Collect multi-source data related to the operation of new energy projects, specifically:

[0051] Collect multi-source data related to the operation of new energy projects, including equipment operating parameters, electricity load demand, meteorological data, energy storage status data, grid connection point data, market electricity price data, and policy constraint data. Collect this information in real time through monitoring devices and an energy management system, and then perform data cleaning, noise reduction, and formatting through a data management module to form a standardized basic database.

[0052] Step Two: Construct a multi-dimensional operational efficiency evaluation indicator system, specifically as follows:

[0053] Construct a multi-dimensional operational efficiency evaluation index system, including five primary indicators: technical efficiency indicators, energy efficiency indicators, environmental efficiency indicators, economic efficiency indicators, and social efficiency indicators. Figure 2 As shown, the technical benefit indicators reflect the reliability and technological maturity of the system operation, including technological maturity, safety and reliability, equipment and facility maintainability, and land area; energy benefit indicators reflect energy utilization efficiency and energy storage regulation capacity, including comprehensive energy utilization rate, clean energy consumption rate, energy intensity, equipment utilization rate, and peak shaving and valley filling power; environmental benefit indicators reflect the impact of project operation on carbon emissions and pollution, including the proportion of green electricity use, carbon dioxide emission reduction rate, gaseous pollutant emission reduction rate, and noise impact; economic benefit indicators reflect the cost-benefit relationship and capital recovery period, including dynamic investment recovery period, internal rate of return, and unit energy supply cost; and social benefit indicators reflect policy responsiveness and industrial driving effect, including policy support, industrial benefits, and employment benefits. By scientifically establishing indicators at each level, the comprehensiveness and rationality of the evaluation process can be ensured.

[0054] Step 3: Data analysis and indicator calculation, specifically:

[0055] The collected multi-source data is denoised, completed, and standardized. Based on the multi-source data, the quantitative values ​​and standardized score matrices of each indicator in the pre-constructed multi-dimensional operational efficiency evaluation index system are calculated. For example, economic indicators use financial models to calculate internal rate of return and net present value; energy indicators use real-time power data to calculate unit energy consumption and energy storage utilization rate; and environmental indicators use emission coefficients to calculate carbon emission reduction and pollutant emission levels.

[0056] In the process of constructing the standardized score matrix, differentiated processing methods are adopted for different types of indicators. By standardizing each indicator in the multi-dimensional operational efficiency evaluation indicator system, the differences in dimensions are eliminated, and a standardized score matrix is ​​generated. Specifically, the range method or the Z-score method is used for data normalization. For example, for continuous numerical indicators such as equipment availability rate in technical efficiency and comprehensive energy utilization rate in energy efficiency, range standardization is used to transform them into a unified numerical range; while for indicators such as investment payback period in economic efficiency, the Z-score method is used for standardization to ensure the comparability of data for each indicator.

[0057] Step 4: Assign the comprehensive weight vector for each indicator, specifically as follows:

[0058] The Fuzzy Analytic Hierarchy Process (FAHP) is adopted. This method introduces fuzzy mathematical principles on the basis of the traditional Analytic Hierarchy Process (AHP) and uses fuzzy judgment matrices to replace precise pairwise comparisons, thereby effectively reducing the uncertainty and fuzziness of expert subjective judgment.

[0059] First, a hierarchical model is constructed, decomposing the target layer, criterion layer, and indicator layer according to logical relationships. Then, a fuzzy judgment matrix is ​​obtained through expert consultation or the Delphi method, and the judgment strength is represented using triangular fuzzy numbers. Next, fuzzy consistency tests and membership function calculations are used to obtain the fuzzy weights of each indicator. Finally, after normalization, the clarified weights are obtained. This process not only improves the scientific rigor of weight calculation but also maintains the stability of the overall evaluation system when there are biases in the opinions of multiple experts.

[0060] Specifically, several experts were invited to revise the fuzzy trigonometric numbers based on their experience and the actual project situation. Ambiguity represents the uncertainty in expert judgment;

[0061] A fuzzy judgment matrix is ​​constructed by comparing the quantified values ​​of each indicator pairwise based on the fuzziness. ;in, This indicates the importance of the i-th indicator relative to the j-th indicator; These represent the lower limit, most likely value, and upper limit of the weight of the i-th indicator relative to the j-th indicator, respectively. This indicates the order of the fuzzy judgment matrix;

[0062] The fuzzy weights of the fuzzy judgment matrix are calculated, and the fuzzy weights are defuzzified using the centroid method to obtain the relative weight vector of each index, expressed as:

[0063] ;

[0064] in, This represents the relative weight vector of the i-th indicator; Let represent the lower limit, most likely value, and upper limit of the weight of the i-th indicator, respectively.

[0065] To ensure the consistency of the judgment matrix, a consistency check is performed on the relative weight vectors of each indicator, generating a comprehensive weight vector for each indicator, and consistency check conditions are set:

[0066] ;

[0067] ;

[0068] in, This represents the consistency ratio, used to determine whether the weights are reasonable. A value less than 0.1 indicates acceptable consistency in the weights. Indicates consistency index; The random consistency index is a fixed lookup table value, determined by the order of the fuzzy judgment matrix. Determine, for example =3 =0.58, =4 =0.90; This represents the largest eigenvalue of the fuzzy judgment matrix;

[0069] If the relative weight vectors of each indicator meet the consistency verification condition, then the comprehensive weight vector of each indicator is generated.

[0070] If the relative weight vectors of each indicator do not meet the consistency verification conditions, the fuzzy triangular number is readjusted.

[0071] Finally, the overall weight of each secondary indicator is calculated through hierarchical overall ranking.

[0072] Step 5: Comprehensive evaluation calculation and optimization, specifically as follows:

[0073] In the comprehensive evaluation stage, this invention uses the fuzzy comprehensive evaluation (FCE) method to process qualitative and quantitative indicators in a unified manner, construct a complete evaluation result, and achieve a scientific quantitative assessment of the operational benefits of energy projects through systematic data processing and matrix operations.

[0074] Quantitative indicators are standardized to eliminate dimensional differences and generate a standardized score matrix. Specifically, the range method or Z-score method is used for data normalization.

[0075] Qualitative indicators are quantified by combining expert scoring with fuzzy membership transformation. Based on the actual project situation, domain experts use the Delphi method to score qualitative indicators such as policy support and social benefits in multiple rounds. Then, membership functions such as triangular fuzzy numbers or trapezoidal fuzzy numbers are used to transform the linguistic evaluation into numerical results.

[0076] Regarding weight allocation, a comprehensive weight vector is formed based on the calculation results of the aforementioned FAHP. This comprehensive weight vector undergoes consistency verification and normalization to ensure the scientific validity and rationality of the weights for each indicator. The weight determination process fully considers the characteristic differences of different energy projects; for example, wind power projects focus more on wind energy utilization coefficient, photovoltaic projects emphasize irradiance utilization rate, and energy storage projects focus on cycle efficiency, making the weight allocation more targeted.

[0077] The core operation of the comprehensive evaluation is achieved through matrix synthesis. Based on the standardized score matrix of each indicator and the comprehensive weight vector, the comprehensive evaluation result is calculated and expressed as follows:

[0078] ;

[0079] in, This indicates the overall evaluation result; This represents the combined weight vector of each indicator; This represents the standardized score matrix for each indicator.

[0080] The comprehensive evaluation results are updated on a rolling basis, taking into account the characteristics of new energy projects:

[0081] Sensitivity analysis was used to compare the comprehensive evaluation results with the historical benchmark values ​​of the comprehensive evaluation results, and the key influencing factors of the comprehensive evaluation results were identified.

[0082] Based on the influencing factors of the comprehensive evaluation results, a gradual correction strategy is adopted to correct the comprehensive weight vector, resulting in an updated comprehensive evaluation result.

[0083] By organically integrating a multi-level, multi-indicator evaluation system through matrix operations, the overall operational efficiency of various energy projects is systematically reflected, and the comprehensive evaluation results are presented. It is a standardized value, ranging from 0 to 1. The higher the value, the better the overall operational efficiency of the project.

[0084] The comprehensive evaluation results can also be combined with sensitivity analysis to deeply analyze the impact of changes in each indicator on the overall evaluation results and identify key influencing factors. For example, by analyzing the impact of external factors such as electricity price fluctuations and fuel cost changes on the comprehensive evaluation results, it can help operation managers develop more targeted risk response strategies. At the same time, the comprehensive evaluation results can also be compared with the industry's historical comprehensive evaluation benchmark values ​​to help the project clarify its own positioning in the industry and provide direction for continuous improvement.

[0085] Step Six: Rolling Updates and Closed-Loop Feedback, specifically:

[0086] Continuous optimization is achieved through a dynamic data update mechanism. Different update cycles are set according to the characteristics of energy projects: wind power, photovoltaic, energy storage, and thermal power projects adopt regular updates to ensure data timeliness.

[0087] By using sensitivity analysis, the comprehensive evaluation results are compared with the historical benchmark values ​​to quantitatively identify the key influencing factors of the comprehensive evaluation results. The impact of variables such as electricity price fluctuations, fuel costs, and equipment efficiency on the comprehensive evaluation results is monitored. Based on the analysis results, the index weights are automatically adjusted, and a gradual correction strategy is adopted to maintain the stability of the evaluation system.

[0088] Establish a closed-loop mechanism for monitoring, evaluation, feedback, and optimization, and compare operational data with expected targets in real time. When anomalies are detected, the system automatically generates optimization suggestions, such as adjusting operating strategies and optimizing maintenance plans, and verifies the effectiveness of the improvements in the next cycle.

[0089] The system's built-in knowledge base continuously accumulates and optimizes case studies, providing references for new decisions through intelligent matching. This dynamic optimization mechanism enables energy projects to quickly adapt to market changes and continuously improve operational efficiency.

[0090] Step 7: Decision support and continuous improvement, specifically:

[0091] Based on the rolling comprehensive evaluation results, operational optimization suggestions and improvement strategies are generated, including operation scheduling optimization, energy storage control strategy adjustment and maintenance cycle optimization.

[0092] By continuously analyzing and generating optimization strategies for new energy projects, and then generating optimization results based on these strategies, dynamic optimization and sustainable development of energy project operational efficiency can be achieved.

[0093] This embodiment achieves unified calculation of qualitative and quantitative indicators through comprehensive evaluation results, improving the scientific rigor and objectivity of the comprehensive evaluation; it introduces a rolling data update and sensitivity analysis mechanism, enabling real-time tracking of changes in project benefits and achieving closed-loop management of evaluation and optimization; the method is universal and applicable to comparative analysis of the operational benefits of various energy types such as wind power, photovoltaic, thermal power, energy storage, and pumped storage; it can provide dynamic decision support for energy companies and management departments, promoting the economy, stability, and sustainability of project operations.

[0094] This embodiment achieves scientific evaluation and optimization decision-making during the operation phase of energy projects by constructing a multi-dimensional comprehensive evaluation system, introducing a hybrid evaluation system, and implementing a rolling update mechanism. Compared with existing static evaluation methods, this embodiment has the advantages of dynamism, scalability, and strong decision-making guidance capabilities, effectively improving the economic efficiency, reliability, and sustainable operation level of energy projects.

[0095] Example 2

[0096] Based on Example 1, this example introduces a new energy project optimization system based on a comprehensive evaluation index system, including:

[0097] The data acquisition module is used to acquire multi-source data related to the operation of energy projects.

[0098] The quantitative indicator module is used to: calculate the quantitative value and standardized score matrix of each indicator in the pre-constructed multi-dimensional operational efficiency evaluation indicator system based on the multi-source data;

[0099] The qualitative indicator module is used to: construct a fuzzy judgment matrix based on the quantitative values ​​of each indicator, and calculate the comprehensive weight vector of each indicator based on the fuzzy judgment matrix;

[0100] The comprehensive evaluation module is used to calculate the comprehensive evaluation results based on the standardized score matrix and comprehensive weight vector of each indicator.

[0101] The project optimization module is used to: generate optimization strategies for new energy projects based on comprehensive evaluation results, and generate optimization results for new energy projects based on the optimization strategies.

[0102] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0103] Example 3

[0104] This embodiment introduces a computer-readable storage medium storing a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the steps of the new energy project optimization method based on a comprehensive evaluation index system described in Embodiment 1.

[0105] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing new energy projects based on a comprehensive evaluation index system, characterized in that, include: Acquire multi-source data related to the operation of energy projects; Based on the multi-source data, calculate the quantitative value and standardized score matrix of each indicator in the pre-constructed multi-dimensional operational efficiency evaluation index system; A fuzzy judgment matrix is ​​constructed based on the quantified values ​​of each indicator, and the comprehensive weight vector of each indicator is calculated based on the fuzzy judgment matrix. The comprehensive evaluation result is calculated based on the standardized score matrix and comprehensive weight vector of each indicator. The comprehensive evaluation results are updated on a rolling basis according to the characteristics of new energy projects; Based on the comprehensive evaluation results, an optimization strategy for new energy projects is generated, and based on the optimization strategy, the optimization results for new energy projects are generated.

2. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 1, characterized in that, The multi-source data related to the operation of new energy projects includes equipment operating parameters, power load demand, meteorological data, energy storage status data, grid connection point data, market electricity price data, and policy constraint data.

3. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 1, characterized in that, The standardized score matrix is ​​generated by standardizing each indicator in the multi-dimensional operational efficiency evaluation index system to eliminate dimensional differences. The multi-dimensional operational efficiency evaluation index system includes technical efficiency indicators, energy efficiency indicators, environmental efficiency indicators, economic efficiency indicators, and social efficiency indicators.

4. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 1, characterized in that, Using fuzzy hierarchical analysis, a fuzzy judgment matrix for each indicator is constructed based on its quantified value. The comprehensive weight vector for each indicator is then calculated based on the fuzzy judgment matrix, including: Fuzzy trigonometric numbers As a measure of fuzziness, the quantified values ​​of each indicator are compared pairwise based on the fuzziness to construct a fuzzy judgment matrix. ;in, This indicates the importance of the i-th indicator relative to the j-th indicator; These represent the lower limit, most likely value, and upper limit of the weight of the i-th indicator relative to the j-th indicator, respectively. This indicates the order of the fuzzy judgment matrix; Calculate the fuzzy weights of the fuzzy judgment matrix, and defuzzify the fuzzy weights to obtain the relative weight vector of each index. The relative weight vectors of each indicator are validated for consistency, and a comprehensive weight vector for each indicator is generated.

5. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 4, characterized in that, The relative weight vectors of the indicators are represented as follows: ； in, This represents the relative weight vector of the i-th indicator; Let represent the lower limit, most likely value, and upper limit of the weight of the i-th indicator, respectively.

6. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 4, characterized in that, The relative weight vectors of each indicator are validated for consistency, and a comprehensive weight vector for each indicator is generated, including: If the relative weight vectors of each indicator meet the consistency verification condition, then the comprehensive weight vector of each indicator is generated. If the relative weight vectors of each indicator do not meet the consistency verification conditions, the fuzzy triangular number is readjusted. The consistency verification condition is expressed as follows: ； ； in, Indicates the consistency ratio; Indicates consistency index; Indicates the random consistency index; This represents the largest eigenvalue of the fuzzy judgment matrix.

7. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 1, characterized in that, The comprehensive evaluation result is expressed as follows: ； in, This indicates the overall evaluation result; This represents the combined weight vector of each indicator; This represents the standardized score matrix for each indicator.

8. The optimization method for new energy projects based on a comprehensive evaluation index system according to claim 1, characterized in that, The comprehensive evaluation results are updated on a rolling basis according to the characteristics of new energy projects, including: Sensitivity analysis was used to compare the comprehensive evaluation results with the historical benchmark values ​​of the comprehensive evaluation results, and the key influencing factors of the comprehensive evaluation results were identified. Based on the influencing factors of the comprehensive evaluation results, a gradual correction strategy is adopted to correct the comprehensive weight vector, resulting in an updated comprehensive evaluation result.

9. A new energy project optimization system based on a comprehensive evaluation index system, characterized in that, include: The data acquisition module is used to acquire multi-source data related to the operation of energy projects. The quantitative indicator module is used to: calculate the quantitative value and standardized score matrix of each indicator in the pre-constructed multi-dimensional operational efficiency evaluation indicator system based on the multi-source data; The qualitative indicator module is used to: construct a fuzzy judgment matrix based on the quantitative values ​​of each indicator, and calculate the comprehensive weight vector of each indicator based on the fuzzy judgment matrix; The comprehensive evaluation module is used to calculate the comprehensive evaluation results based on the standardized score matrix and comprehensive weight vector of each indicator. The project optimization module is used to: generate optimization strategies for new energy projects based on comprehensive evaluation results, and generate optimization results for new energy projects based on the optimization strategies.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the new energy project optimization method based on a comprehensive evaluation index system as described in any one of claims 1-8.

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