An integrated energy system evaluation method, device and medium

Abstract

The present application relates to a kind of integrated energy system evaluation method, equipment and medium, the method includes establishing the integrated energy system evaluation index system of multiple primary indicators, selecting the secondary index related to each primary indicator;For secondary index, determine calculation method;According to the specific calculation formula of each secondary index, the index value of each secondary index is calculated;Based on entropy weight method and analytic hierarchy process, the weight of each secondary index is calculated, wherein entropy weight method is used to calculate the objective weight of each secondary index, and analytic hierarchy process is used to evaluate the subjective weight of each secondary index;Subjective weight and objective weight are combined according to predetermined proportion, to obtain the maximum weight of each secondary index;The comprehensive score of integrated energy system is calculated using the calculated index value and the obtained maximum weight, and the evaluation of integrated energy system is completed.The present application can provide more objective evaluation for the running state of integrated energy system, and provide theoretical guidance for subsequent planning and design and optimization operation.

Description

Technical Field

[0001] This invention relates to the field of integrated energy planning and optimization, specifically to an integrated energy system evaluation method, equipment, and medium. Background Technology

[0002] Integrated Energy Systems (IES), as an important component of new power systems, have become a key technological path for achieving energy transition and improving energy efficiency. By efficiently integrating various energy forms, IES can not only improve energy utilization efficiency but also optimize the energy structure and reduce environmental pollution.

[0003] However, due to the complexity and diversification of integrated energy systems, their evaluation and optimization face numerous challenges. Existing evaluation methods often focus on economic and environmental benefits, neglecting key performance indicators such as system reliability and stability. Furthermore, existing evaluation systems are often overly simplistic in their weighting, failing to fully reflect the actual importance of each evaluation indicator. This limits the scientific rigor and accuracy of system planning, design, and operational optimization.

[0004] Currently, most research on the evaluation of integrated energy systems still relies on single methods or models for system analysis and optimization design. For example, some patents and papers focus on data-driven evaluation systems, such as the patent "Data-Driven Method for Energy Efficiency Evaluation and Improvement of Integrated Energy Systems." While these methods have shown certain advantages in practical applications, the comprehensiveness and objectivity of their evaluation results need improvement due to the lack of comprehensive consideration of the coupling relationships between different energy networks. Summary of the Invention

[0005] This invention provides a method, device, and medium for evaluating integrated energy systems. Its purpose is to address the problems of insufficient consideration of the coupling relationship between different energy networks, single evaluation dimensions, and simplified weight allocation in existing integrated energy system evaluation methods, so as to provide a more comprehensive and scientific evaluation system.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method, apparatus, and medium for evaluating a comprehensive energy system, comprising the following steps:

[0007] Establish an evaluation index system for integrated energy systems. This evaluation index system includes multiple primary indicators, and select secondary indicators related to each primary indicator.

[0008] Specific calculation methods are determined for each selected secondary indicator;

[0009] Calculate the value of each secondary indicator based on its specific calculation formula.

[0010] The weights of each secondary indicator are calculated based on the entropy weight method and the analytic hierarchy process (AHP). The entropy weight method is used to calculate the objective weights of each secondary indicator, while the AHP is used to evaluate the subjective weights of each secondary indicator.

[0011] Subjective weights and objective weights are combined according to a predetermined ratio to obtain the final weight of each secondary indicator;

[0012] Using the calculated index values ​​and the obtained final weights, the comprehensive score of the integrated energy system is calculated, and the evaluation of the integrated energy system is completed.

[0013] Furthermore, an evaluation index system for integrated energy systems is established. This evaluation index system includes multiple primary indicators, and the methods for selecting secondary indicators related to each primary indicator include:

[0014] Establish a comprehensive energy system evaluation system, with the primary indicators being technical evaluation indicators, economic evaluation indicators, environmental evaluation indicators, and reliability evaluation indicators;

[0015] The technical evaluation indicators are defined as the secondary indicators, including energy storage unit efficiency, energy storage unit capacity retention rate, relative failure number of energy storage unit battery clusters, energy storage equipment status evaluation, photovoltaic system efficiency, and photovoltaic system power generation prediction accuracy.

[0016] Define the secondary indicators under the economic evaluation indicators, including the benefits of energy storage systems, photovoltaic systems, charging systems, and system demand control.

[0017] Define the secondary indicators under environmental protection indicators, including pollutant emission reduction and pollutant emission reduction;

[0018] Define secondary indicators under the reliability index, including system functional reliability rate and distribution network loss rate.

[0019] Furthermore, methods for calculating the objective weights of each secondary indicator based on the entropy weight method include:

[0020] Receive the indicator data to be evaluated, normalize each indicator, and obtain the normalized data;

[0021] Based on the obtained normalized data, a data matrix is ​​set up for each year / month. Under the same indicator, the proportion of data in each year / month is calculated.

[0022] Based on the definition of information entropy, the information entropy of each secondary indicator is calculated;

[0023] Based on the calculated information entropy, calculate the information redundancy of each indicator;

[0024] The objective weights of each indicator are calculated based on information redundancy.

[0025] Furthermore, the formula for calculating the proportion of data for each year / month is as follows:

[0026]

[0027] In the formula, P ij Y represents the proportion of the j-th indicator data value in the i-th year / month to the total value of that indicator. ij This represents the normalized data value of the j-th indicator in the i-th year / month, where n represents the total number of years or months observed, and m represents the total number of indicators.

[0028] The formula for calculating the information entropy of each secondary indicator is as follows:

[0029]

[0030] In the formula, E j The information entropy of the j-th indicator;

[0031] Based on the calculated information entropy, the formula for calculating the information redundancy of each indicator is as follows:

[0032] D j =1-E j

[0033] In the formula, D j This represents the information redundancy of the j-th indicator;

[0034] The formula for calculating the objective weights of each indicator based on information redundancy is as follows:

[0035]

[0036] In the formula, w j This represents the objective weight of the j-th indicator calculated based on information redundancy.

[0037] Furthermore, methods for calculating the subjective weights of each secondary indicator based on the analytic hierarchy process (AHP) include:

[0038] A hierarchical evaluation model is constructed based on different levels of evaluation indicators, which is divided into target layer, criterion layer and sub-criterion layer.

[0039] Using Saaty's 1-9 scaling method, a judgment matrix is ​​constructed through pairwise comparisons between each sub-criterion:

[0040] A = (a ij ) m×n

[0041] Among them, a ijThis indicates the importance of index i relative to index j within the criterion layer or sub-criterion layer, where the matrix elements satisfy a. ij >0, a ij =1 / a ji a ii =1;

[0042] The judgment matrix is ​​sorted hierarchically, and the square root method is used to calculate the m-th power of the product of each row of the judgment matrix, resulting in an m-dimensional vector:

[0043]

[0044] in, Indicates the original weights;

[0045] Original vector Standardization yields subjective weights, namely:

[0046]

[0047] In the formula, w i This indicates subjective weighting.

[0048] Furthermore, the method for calculating the weights of each secondary indicator based on the analytic hierarchy process (AHP) also includes a consistency test, the method of which includes:

[0049] Calculate the largest eigenvalue of the judgment matrix:

[0050]

[0051] In the formula, n represents the dimension of the judgment matrix; AW i This represents the product of vector W and matrix A;

[0052] Calculate the consistency index:

[0053]

[0054] Consult the random consistency index RI corresponding to the order n of the judgment matrix, and calculate the consistency ratio:

[0055]

[0056] If CR < 1, then the consistency of the judgment matrix is ​​within the acceptable range; otherwise, the judgment matrix needs to be reconstructed.

[0057] Furthermore, the subjective weights and objective weights are combined according to a predetermined ratio to obtain the final weight calculation formula for each secondary indicator, as follows:

[0058] w c =w j *M+w i*N, c=i=j=1...m

[0059] In the formula, w c w represents the final weight of each secondary indicator. j w represents the objective weight of the j-th indicator calculated based on information redundancy. i The subjective weight is represented by M, which is the proportional factor of the objective weight, representing the proportion of objective evaluation in the total weight. N is the proportional factor of the subjective weight, representing the proportion of subjective data in the total weight. c = i = j = 1...m represents the index of the indicator, from 1 to m, where m is the total number of indicators.

[0060] Furthermore, the comprehensive score of the integrated energy system is calculated using the following formula:

[0061]

[0062] In the formula, S c w represents the calculated overall score for each solution. c This represents the final weight of each secondary indicator, x. m This represents the value of each secondary indicator, where m is the total number of indicators, and j and c represent the index of the indicator.

[0063] To achieve the above objectives, a second aspect of the present invention provides an electronic device including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the steps of the integrated energy system evaluation method.

[0064] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the integrated energy system evaluation method.

[0065] The beneficial effects of this invention are:

[0066] Compared with existing technologies, this invention provides a comprehensive energy system evaluation method, equipment, and medium. By introducing a weight allocation mechanism combining an improved entropy weighting method and the analytic hierarchy process (AHP), it comprehensively considers multi-dimensional evaluation indicators such as technology, economy, environmental protection, and reliability. This method first objectively reflects the importance of each indicator and the variability of data through an improved entropy weighting method. Then, it combines this with the AHP to consider expert opinions and practical needs, making subjective weight adjustments. This dual weight allocation mechanism not only improves the scientific rigor and adaptability of the evaluation system but also refines the complex coupling relationships between different energy networks, thereby more accurately assessing the performance and efficiency of the comprehensive energy system and providing more accurate theoretical support and decision-making basis for system planning, design, and operational optimization. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0068] Figure 1 This is a flowchart of a comprehensive energy system evaluation method disclosed in an embodiment of the present invention.

[0069] Figure 2 This is a framework diagram of an entropy weight method disclosed in an embodiment of the present invention for calculating the objective weights of each secondary indicator.

[0070] Figure 3 This is a framework diagram of the analytic hierarchy process (AHP) disclosed in an embodiment of the present invention for evaluating the subjective weights of each secondary indicator. Detailed Implementation

[0071] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0072] According to embodiments of the present invention, it should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the following manufacturing method, in some cases the steps shown or described may be performed in a different order than that shown here.

[0073] To address the problem of overly concentrated evaluation dimensions in existing technologies, this invention provides a comprehensive energy system evaluation method that can provide a more comprehensive assessment of comprehensive energy systems and offer theoretical support for system planning, design, and optimized operation.

[0074] While some patents offer broad evaluation dimensions, their evaluation depth is somewhat lacking. For example, patents evaluating energy storage systems, due to their singular energy supply and the fact that the core component being evaluation is energy storage, while other distributed generation units are not yet covered, suffice as evaluation systems using primary indicators such as technology and economics to meet application requirements. However, current integrated energy systems, with their diverse energy sources, exhibit complex energy flow patterns, leading to significant differences in their comprehensive evaluation theories compared to traditional energy systems. Evaluation must comprehensively consider the coupling relationships between different energy supply networks. To address this, this patent adds relevant secondary indicators, refining the evaluation system and comprehensively considering the coupling relationships between various energy sources, effectively improving the adaptability of the evaluation system.

[0075] Furthermore, addressing the shortcomings of the current entropy weight method as an objective evaluation method, such as its over-reliance on data and algorithms, neglect of individual differences and specific needs of those being evaluated, relatively fixed evaluation methods and standards that are difficult to adjust according to specific circumstances, and the need for substantial data support (which can affect the accuracy of evaluation results if the data quality is low or incomplete), this paper introduces the Analytic Hierarchy Process (AHP) to subjectively evaluate the weights. This AHP is then combined with the original weights of the entropy weight method to create a subjective-objective combined evaluation method, thereby optimizing the evaluation system.

[0076] This invention is achieved using the following technical solution:

[0077] like Figure 1 As shown, the present invention provides a comprehensive energy system evaluation method, comprising the following steps:

[0078] Step S100: Establish an evaluation index system for a comprehensive energy system. This evaluation index system for a comprehensive energy system includes multiple primary indicators, and select secondary indicators related to each primary indicator.

[0079] Step S200: Determine the specific calculation method for each selected secondary indicator;

[0080] Step S300: Calculate the value of each secondary indicator according to the specific calculation formula of each secondary indicator;

[0081] Step S400: Calculate the weight of each secondary indicator based on the entropy weight method and the analytic hierarchy process (AHP). The entropy weight method is used to calculate the objective weight of each secondary indicator, and the AHP is used to evaluate the subjective weight of each secondary indicator.

[0082] Step S500: Combine subjective weights and objective weights according to a predetermined ratio to obtain the final weight of each secondary indicator;

[0083] Step S600: Using the calculated index values ​​and the obtained final weights, calculate the comprehensive score of the integrated energy system to complete the evaluation of the integrated energy system.

[0084] In this embodiment, as described in steps S100-S300 above, a comprehensive energy system evaluation index system is proposed. This system comprises four primary indicators: technology, economy, environmental protection, and reliability. Secondary indicators are selected under each primary indicator, and the calculation method for each secondary indicator is determined (see...). Figure 2 and Figure 3 The specific steps are as follows:

[0085] Step S11: Establish a comprehensive energy system evaluation system, with each primary indicator being a technical evaluation indicator, an economic evaluation indicator, an environmental evaluation indicator, and a reliability evaluation indicator.

[0086] Step S12: Define secondary indicators under the technical evaluation indicators, such as energy storage unit efficiency ηb reflecting the charge and discharge efficiency of the energy storage system, energy storage unit capacity retention rate δb reflecting the health status of the energy storage battery, energy storage unit battery cluster relative failure count RTOP, energy storage equipment status evaluation S, and photovoltaic system efficiency PR. T The accuracy of photovoltaic system power generation prediction (APV) reflects the operating efficiency of the photovoltaic system, and the above indicators comprehensively reflect the system's operating conditions from a technical perspective.

[0087] In this embodiment, the calculation method for each secondary indicator is determined, and the final weight of the secondary indicator is set to x. i (i = 1, ..., m; m is the number of secondary indicators).

[0088] The formula for calculating the efficiency of an energy storage unit is:

[0089] η b =Discharge amount / Charge amount = x1

[0090] The formula for calculating the capacity retention rate of an energy storage unit is:

[0091]

[0092] Among them, E p E represents the actual discharge capacity of the energy storage unit, expressed in kWh. f The rated capacity of the energy storage unit is expressed in kWh.

[0093] The formula for calculating the relative failure count (RTOP) of a battery cluster in an energy storage unit is as follows:

[0094] RTOP = FTOP / BPN × 100% = x3

[0095] Where FTOP is the number of battery cluster failures, in times, and BPN is the total number of battery clusters in the unit, in clusters.

[0096] The formula for calculating the condition assessment S of energy storage equipment is:

[0097]

[0098] Where, k i F represents the weight of the evaluation index for the i-th type of energy storage device. i denoted as the score of the evaluation index for the i-th type of energy storage device.

[0099] The formula for calculating the efficiency of a photovoltaic system is:

[0100]

[0101] Among them, E T P represents the electricity output of the photovoltaic power station during time period T, expressed in kWh.e The nominal installed capacity of a photovoltaic power plant is expressed in kWp; h. T The peak solar radiation on the azimuth surface during time period T is expressed in W / m². 2 / sky.

[0102] The formula for calculating the accuracy of photovoltaic system power generation prediction is as follows:

[0103] APV = |1-α| = x6

[0104] Where α is (actual power generation - predicted power generation) / actual power generation.

[0105] Step S13: Define secondary indicators under the economic evaluation indicators, with energy storage system benefit BF as an example. b Photovoltaic system benefits BF p Charging system benefits BF c System demand control benefits BF d The above indicators are used to comprehensively reflect the system's operating conditions from an economic perspective.

[0106] The formula for calculating the benefits of an energy storage system (based on a two-charge, two-discharge strategy and peak-valley electricity pricing) is as follows:

[0107] BF b = Power generation / 2 × Electricity price (peak) + Power generation / 2 × Electricity price (peak) - Electricity consumption × Electricity price (off-peak)

[0108] The formula for calculating the benefits of a photovoltaic system is:

[0109] BF c = Photovoltaic power generation × Peak-valley electricity price = x7

[0110] The formula for calculating the efficiency of a charging system is:

[0111] BF c =Charging amount × Service fee = x8

[0112] The formula for calculating the benefits of system demand control is:

[0113]

[0114] in, and These represent the maximum monthly load (kW) of the energy storage system before and after the output of the energy storage system in month n of year x.

[0115] The above secondary economic evaluation indicators are calculated using the Z-Score normalization principle.

[0116] Step S14: Define secondary indicators under the environmental protection indicators, such as pollutant emission reduction (photovoltaic) CO2 / SO2 / NOx, and pollutant emission reduction (charging pile) CO2, to comprehensively reflect the system's operating conditions from an environmental perspective.

[0117] CO2 = E 电 ×EF 电 =x 10

[0118] Step S15: Define secondary indicators under the reliability index, with the system functional reliability rate R as an example. S Distribution network loss rate R D Assess system operational stability from a reliability perspective:

[0119]

[0120]

[0121] The environmental protection and reliability secondary evaluation indicators above are calculated using the Z-Score normalization principle.

[0122] In this embodiment, as described in step S400 above, an improved entropy weight method based on a combination of subjective and objective factors is proposed to evaluate the weight of each indicator. The entropy weight method is used to calculate the objective weight of each secondary indicator, while the analytic hierarchy process (AHP) is used to evaluate the subjective weight of each secondary indicator. The traditional entropy weight method calculates the weight of relevant indicators using the following formula:

[0123]

[0124] Where H is the information entropy, q is the number of source messages, and p(x) is the number of source messages. i ) is message x i The probability of occurrence.

[0125] The traditional entropy weighting method provides a standard definition of information entropy, a theoretical foundation describing the uncertainty and expected value of information. It is commonly used to explain the general concept of information entropy and sets the framework for the entire methodology.

[0126] It's important to note that entropy weighting is a measure of the degree of order in a system, while entropy is a measure of the degree of disorder. According to the definition of information entropy, for a given indicator, the entropy value can be used to determine its degree of dispersion. The smaller the information entropy value, the greater the dispersion of the indicator, and the greater its influence (i.e., weight) on the overall evaluation. If all values ​​of an indicator are equal, then that indicator has no effect in the overall evaluation. Therefore, information entropy can be used to calculate the weights of each indicator, providing a basis for multi-indicator comprehensive evaluation.

[0127] The improved entropy weight method will be explained in detail below:

[0128] The indicators to be evaluated are then dedimensionalized. Assume there are m indicators:

[0129] X1,X2,...,X m

[0130] Assume the normalized values ​​for each indicator are:

[0131] Y1,Y2,...,Y m

[0132] Suppose a first-level indicator has m second-level indicators, and data for n years / months has been obtained, denoted as matrix Y. ij Under the same indicator, the proportion of each year / month's value to the total value is calculated using the following formula:

[0133]

[0134] In the formula, P ij Y represents the proportion of the j-th indicator data value in the i-th year / month to the total value of that indicator. ij This represents the normalized data value of the j-th indicator in the i-th year / month, where n represents the total number of years or months observed, and m represents the total number of indicators.

[0135] According to the definition of information entropy in information theory, the information entropy of a set of data is:

[0136]

[0137] In the formula, E j Let p be the information entropy of the j-th index. ij＝0 Define E j =0.

[0138] Based on the formula for calculating information entropy, the information entropy of each indicator is calculated as E1, E2, ..., E m The objective weights of each indicator are calculated using information entropy.

[0139]

[0140] In the formula, w j This represents the objective weight of the j-th indicator calculated based on information redundancy, where k represents the indicator format, i.e., k = m.

[0141] Then, the objective weight is calculated by calculating the information redundancy:

[0142] D j =1-E j

[0143] In the formula, D j This represents the information redundancy of the j-th indicator.

[0144] Then calculate the objective weight value of the indicator:

[0145]

[0146] The improved analytic hierarchy process will be explained in detail below:

[0147] First, a hierarchical evaluation model is constructed based on the different levels of evaluation indicators, which is divided into the target layer, the criterion layer, and the sub-criterion layer.

[0148] The judgment matrix was constructed using Santy's 1-9 scaling method (see Table 1). The weights of each sub-criteria layer to the target layer were determined by comparing each sub-criteria pairwise.

[0149] Table 1: 1-9 Scale Method

[0150]

[0151] For the sub-criteria layer, the following formula can be constructed:

[0152] A = (a ij ) m×n

[0153] Among them, a ij This indicates the importance of index i relative to index j within the criterion layer or sub-criterion layer, where the matrix elements satisfy a. ij >0, a ij =1 / a ji a ii =1.

[0154] Based on matrix A, a hierarchical single sort is performed. Using the square root method, the m-th power of the product of each row of matrix A is calculated to obtain an m-dimensional vector:

[0155]

[0156] Among them, w i This represents the original weights. Standardizing the original vector yields the weight vector:

[0157]

[0158] In the formula, w i This indicates subjective weighting.

[0159] Then, the method for calculating the weights of each secondary indicator based on the analytic hierarchy process (AHP) also includes a consistency test, the method of which includes:

[0160] Calculate the largest eigenvalue of the judgment matrix:

[0161]

[0162] In the formula, n represents the dimension of the judgment matrix; AW i This represents the product of vector W and matrix A;

[0163] Calculate the consistency index:

[0164]

[0165] Consult the random consistency index RI corresponding to the order n of the judgment matrix, and calculate the consistency ratio:

[0166]

[0167] If CR < 1, then the consistency of the judgment matrix is ​​within the acceptable range; otherwise, the judgment matrix needs to be reconstructed.

[0168] Assume that the sum of the two parameters, M and N, representing the subjective and objective weight ratios, is 1 and both are greater than 0.

[0169] In this embodiment, as described in step S500 above, the subjective weight and objective weight are combined according to a predetermined ratio to obtain the following formula for calculating the final weight of each secondary indicator:

[0170] w c =w j *M+w i *N, c=i=j=1...m

[0171] In the formula, w c w represents the final weight of each secondary indicator. j w represents the objective weight of the j-th indicator calculated based on information redundancy. i The subjective weight is represented by M, which is the proportional factor of the objective weight, representing the proportion of objective evaluation in the total weight. N is the proportional factor of the subjective weight, representing the proportion of subjective data in the total weight. c = i = j = 1...m represents the index of the indicator, from 1 to m, where m is the total number of indicators.

[0172] In this embodiment, as described in step S600 above, the comprehensive score of the integrated energy system is calculated using the following formula based on the calculated index values ​​and the obtained final weights:

[0173]

[0174] In the formula, S c w represents the calculated overall score for each solution. c This represents the final weight of each secondary indicator, x. mThis represents the value of each secondary indicator, where m is the total number of indicators, and j and c represent the index of the indicator.

[0175] The improved entropy weighting method based on the analytic hierarchy process combines objective and subjective evaluation. It determines the weight by processing and calculating actual data, and incorporates subjective guiding factors to provide a comprehensive weight sequence.

[0176] By establishing a comprehensive energy system evaluation model, decision-makers can not only make more rational choices during the planning stage, but also monitor the system in real time during operation to ensure its efficient operation, and evaluate its economic and environmental benefits in the later stages.

[0177] In summary, this invention analyzes the operating conditions based on actual system data and constructs a multi-dimensional comprehensive evaluation system based on four dimensions: computation, economy, environmental protection, and reliability. It also expands the evaluation system with secondary indicators for integrated energy systems, refining the evaluation framework. Secondly, an improved entropy weight method based on the analytic hierarchy process (AHP) is used to allocate subjective and objective weights to each evaluation indicator. Compared to existing entropy weight methods, this method incorporates subjective weight allocation, reducing biases caused by single evaluation methods. It comprehensively considers expert experience, data, and algorithms, providing a holistic evaluation of the integrated energy system and offering theoretical guidance for subsequent planning, design, and optimized operation. Furthermore, a subjective-objective weight ratio factor is proposed. By adjusting this factor, the evaluation system can be adapted to various operating conditions, such as subjective evaluation methods for energy-related policy planning assessments, objective evaluation methods for economic benefit analysis, and combined subjective-objective evaluation methods for integrated energy system evaluations.

[0178] According to another aspect of the embodiments of this application, an electronic device is also provided, including a processor and a memory, wherein the processor is configured to implement the steps of the method when executing a computer program stored in the memory.

[0179] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0180] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0181] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0182] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0183] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A comprehensive energy system evaluation method, characterized in that, Includes the following steps: Establish an evaluation index system for integrated energy systems. This evaluation index system includes multiple primary indicators, and select secondary indicators related to each primary indicator. Specific calculation methods are determined for each selected secondary indicator; Calculate the value of each secondary indicator based on its specific calculation formula. The weights of each secondary indicator are calculated based on the entropy weight method and the analytic hierarchy process (AHP). The entropy weight method is used to calculate the objective weights of each secondary indicator, while the AHP is used to evaluate the subjective weights of each secondary indicator. Subjective weights and objective weights are combined according to a predetermined ratio to obtain the final weight of each secondary indicator; Using the calculated index values ​​and the obtained final weights, the comprehensive score of the integrated energy system is calculated, and the evaluation of the integrated energy system is completed.

2. The comprehensive energy system evaluation method as described in claim 1, characterized in that, An evaluation index system for integrated energy systems is established, comprising multiple primary indicators. The methods for selecting secondary indicators related to each primary indicator include: Establish a comprehensive energy system evaluation system, with each primary indicator being a technical evaluation indicator, an economic evaluation indicator, an environmental evaluation indicator, and a reliability evaluation indicator; The technical evaluation indicators are defined as the secondary indicators, including energy storage unit efficiency, energy storage unit capacity retention rate, relative failure number of energy storage unit battery clusters, energy storage equipment status evaluation, photovoltaic system efficiency, and photovoltaic system power generation prediction accuracy. Define the secondary indicators under the economic evaluation indicators, including the benefits of energy storage systems, photovoltaic systems, charging systems, and system demand control. Define the secondary indicators under environmental protection indicators, including pollutant emission reduction and pollutant emission reduction; Define secondary indicators under the reliability index, including system functional reliability rate and distribution network loss rate.

3. The comprehensive energy system evaluation method as described in claim 1, characterized in that, Methods for calculating the objective weights of each secondary indicator based on the entropy weight method include: Receive the indicator data to be evaluated, normalize each indicator, and obtain the normalized data; Based on the obtained normalized data, a data matrix is ​​set up for each year / month. Under the same indicator, the proportion of data in each year / month is calculated. Based on the definition of information entropy, the information entropy of each secondary indicator is calculated; Based on the calculated information entropy, calculate the information redundancy of each indicator; The objective weights of each indicator are calculated based on information redundancy.

4. The comprehensive energy system evaluation method as described in claim 3, characterized in that, The formula for calculating the proportion of data for each year / month is as follows: In the formula, P ij Y represents the proportion of the j-th indicator data value in the i-th year / month to the total value of that indicator. ij This represents the normalized data value of the j-th indicator in the i-th year / month, where n represents the total number of years or months observed, and m represents the total number of indicators. The formula for calculating the information entropy of each secondary indicator is as follows: In the formula, E j The information entropy of the j-th indicator; Based on the calculated information entropy, the formula for calculating the information redundancy of each indicator is as follows: D j =1-E j In the formula, D j This represents the information redundancy of the j-th indicator; The formula for calculating the objective weights of each indicator based on information redundancy is as follows: In the formula, w j This represents the objective weight of the j-th indicator calculated based on information redundancy.

5. The comprehensive energy system evaluation method as described in claim 1, characterized in that, Methods for calculating the subjective weights of each secondary indicator based on the analytic hierarchy process (AHP) include: A hierarchical evaluation model is constructed based on different levels of evaluation indicators, which is divided into target layer, criterion layer and sub-criterion layer. Using Saaty's 1-9 scaling method, a judgment matrix is ​​constructed through pairwise comparisons between each sub-criterion: A=(a ij ) m×n Among them, a ij This indicates the importance of index i relative to index j within the criterion layer or sub-criterion layer, where the matrix elements satisfy a. ij >0, a ij =1 / a ji a ii =1; The judgment matrix is ​​sorted hierarchically, and the square root method is used to calculate the m-th power of the product of each row of the judgment matrix, resulting in an m-dimensional vector: in, Indicates the original weights; Original vector Standardization yields subjective weights, namely: In the formula, w i This indicates subjective weighting.

6. The comprehensive energy system evaluation method as described in claim 5, characterized in that, The method for calculating the weights of each secondary indicator based on the analytic hierarchy process (AHP) also includes a consistency test, the method of which includes: Calculate the largest eigenvalue of the judgment matrix: In the formula, n represents the dimension of the judgment matrix; AW i This represents the product of vector W and matrix A; Calculate the consistency index: Consult the random consistency index RI corresponding to the order n of the judgment matrix, and calculate the consistency ratio: If CR < 1, then the consistency of the judgment matrix is ​​within the acceptable range; otherwise, the judgment matrix needs to be reconstructed.

7. The comprehensive energy system evaluation method as described in claim 1, characterized in that, The subjective and objective weights are combined according to a predetermined ratio to obtain the final weight of each secondary indicator. The calculation formula is as follows: w c =w j *M+w i *N,c=i=j=1...m In the formula, w c w represents the final weight of each secondary indicator. j w represents the objective weight of the j-th indicator calculated based on information redundancy. i The subjective weight is represented by M, which is the proportional factor of the objective weight, representing the proportion of objective evaluation in the total weight. N is the proportional factor of the subjective weight, representing the proportion of subjective data in the total weight. c = i = j = 1...m represents the index of the indicator, from 1 to m, where m is the total number of indicators.

8. The comprehensive energy system evaluation method as described in claim 1, characterized in that, The overall score of the integrated energy system is calculated using the following formula: In the formula, S c w represents the calculated overall score for each solution. c This represents the final weight of each secondary indicator, x. m This represents the value of each secondary indicator, where m is the total number of indicators, and j and c represent the index of the indicator.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to implement the steps of the integrated energy system evaluation method as described in any one of claims 1 to 8 when executing a computer program stored in the memory.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program, when run by a processor, executes the steps of the integrated energy system evaluation method according to any one of claims 1 to 8.

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