Qualitative and quantitative combined green electricity transaction comprehensive benefit evaluation method

Abstract

The invention provides a qualitative and quantitative combined green electricity transaction comprehensive benefit evaluation method, which comprises the steps of establishing a transaction electric quantity model of a new energy station, and determining an electric energy transaction benefit and an environmental benefit of green electricity transaction based on the transaction electric quantity model, a new energy station income model is established based on the electric energy transaction benefit of the green electricity transaction and the environmental benefit; quantifying the evaluation index based on the relationship between the evaluation index of the green electricity transaction and the transaction electric quantity model and the new energy station income model to obtain a quantitative index model; calculating the index weight of the quantitative index model by adopting a three-scale analytic hierarchy process based on an optimal transfer matrix; and constructing a fuzzy comprehensive evaluation model based on the quantitative index model and the corresponding index weight, and performing quantitative processing on an evaluation result of the evaluation index by using the fuzzy comprehensive evaluation model to obtain a green electricity transaction comprehensive benefit evaluation result. By adopting the scheme, comprehensive evaluation of the participation of the new energy station in the green electricity transaction is realized.

Description

Technical Field

[0001] The present application relates to the field of new energy markets, and in particular to a method and device for evaluating the comprehensive benefits of green electricity transactions that combines qualitative and quantitative analysis. Background Art

[0002] In the context of global energy transformation, new energy technologies, as a key force in promoting sustainable economic and social development, are increasingly valued by governments and enterprises. Among them, green power trading, as an emerging market mechanism, has emerged. Green power trading aims to effectively connect the production and consumption of green energy through market mechanisms, allowing green power producers to sell their electricity to consumers according to certain rules and prices, thereby promoting the development and popularization of new energy.

[0003] In this case, the benefit evaluation of green power trading is crucial to the healthy development of the market. Without benefit evaluation, it will be impossible to accurately measure the market effect of green power trading, which may lead to the following impacts: unreasonable allocation of market resources, reduced confidence of investors in green energy projects, insufficient consumer awareness of green power, and reduced market transparency. These problems may hinder the long-term stable development of the green power trading market, and thus affect the overall progress of the new energy industry.

[0004] However, there is still a lack of systematic green power trading benefit evaluation methods. The lack of evaluation methods limits market participants' understanding and evaluation of the value of green power trading, and also restricts the effective formulation and implementation of relevant decisions. Therefore, it is urgent to develop a scientific and reasonable green power trading benefit evaluation method. Summary of the invention

[0005] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the first purpose of this application is to propose a comprehensive benefit evaluation method for green electricity trading that combines qualitative and quantitative methods, thereby achieving a comprehensive evaluation of new energy sites participating in green electricity trading.

[0007] The second objective of this application is to propose a green electricity trading comprehensive benefit evaluation device that combines qualitative and quantitative methods.

[0008] The third objective of the present application is to provide a computer device.

[0009] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, the first embodiment of the present application proposes a method for evaluating the comprehensive benefits of green electricity trading by combining qualitative and quantitative methods, including:

[0011] Establish a trading electricity model for new energy stations, determine the electricity trading benefits and environmental benefits of green electricity trading based on the trading electricity model, and establish a new energy station revenue model based on the electricity trading benefits and environmental benefits of green electricity trading;

[0012] Based on the relationship between the evaluation index of green electricity trading and the trading electricity model and the revenue model of new energy stations, the evaluation index is quantified to obtain a quantitative index model;

[0013] The three-scale analytic hierarchy process based on the optimal transfer matrix is ​​used to calculate the indicator weights of the quantitative indicator model;

[0014] A fuzzy comprehensive evaluation model is constructed based on the quantitative indicator model and the corresponding indicator weights, and the fuzzy comprehensive evaluation model is used to quantify the evaluation results of the evaluation indicators to obtain the comprehensive benefit evaluation results of green electricity trading.

[0015] Optionally, in one embodiment of the present application, establishing a transaction electricity model of a new energy station includes:

[0016] When all units participate in the third-level market or all units do not participate in the third-level market, the transaction volume of the new energy station participating in the third-level market in the a-th period of the t-th day is:

[0017]

[0018] Among them, Q ta is the power generation of the new energy station in the ath period on the tth day, Q is the power generation of the other stations in the ath period on the tth day, C represents the limit of the transaction channel capacity, Q poverty_ta is the power generation of the photovoltaic poverty alleviation power station in the a-th period on the t-th day;

[0019] When some units participate in the third-level market, if the new energy station participates in the third-level market, the transaction power of the new energy station in the a period on the tth day is:

[0020] If Q ta +Q′ ta ≤CQ poverty_ta ,

[0021] Δt_a=Q ta

[0022] Among them, Q′ ta is the power generation of the remaining units participating in the third-level market in the tth day and the ath period,

[0023] If Q ta +Q′ ta >CQ poverty_ta ,

[0024]

[0025] When some units participate in the third-level market, if the new energy station does not participate in the third-level market, the transaction volume of the new energy station participating in the fourth-level market in the a-th period of the t-th day is:

[0026] If Q′ ta >CQ poverty_ta ,

[0027] Δt_a′=0

[0028] Among them, Δt_a′ is the transaction volume of the new energy station in the fourth-level market in the ath period on the tth day,

[0029] If Q′ ta ≤CQ poverty_ta ,

[0030]

[0031] Among them, Q ta It is the power generation of other units participating in the fourth-level market in the a-th period on the t-th day.

[0032] Optionally, in one embodiment of the present application, determining the environmental benefits of green electricity trading based on the trading electricity model includes:

[0033] Calculate environmental benefits by comparing emissions between wind power and thermal power;

[0034] The new energy station revenue model includes the electric energy trading benefit model, the electric energy trading benefit model of green electricity trading and the electric energy trading benefit model are:

[0035] f(t)=Δt·π l +Δt′·π h

[0036]

[0037] Among them, f(t) is the power generation benefit of the new energy station on the tth day, Δt is the transaction volume of the new energy station in the third-level market on the tth day, π l represents the transaction value of electricity in the third-level market, Δt′ represents the transaction value of electricity in the fourth-level market of the new energy station on the tth day, π h It represents the value of electricity traded in the four-level market, and M is the number of days in the month.

[0038] Optionally, in one embodiment of the present application, the quantitative index model is a multi-level index system, and the index weights of the quantitative index model are calculated using a three-scale hierarchical analysis method based on an optimal transfer matrix, including:

[0039] According to the three-scale principle, the relative importance of each evaluation index at each level in the multi-level index system is compared, and a three-scale matrix for each level is established;

[0040] Calculate the optimal transfer matrix of the three-scale matrix at each level and transform the optimal transfer matrix into a judgment matrix;

[0041] The hierarchical single sorting is completed by using the square root method to calculate the eigenvector corresponding to the maximum eigenvalue in the judgment matrix of each level, and the calculated eigenvector is used as the weight vector of each evaluation index of the corresponding level.

[0042] Optionally, in one embodiment of the present application, the three-scale matrix is ​​expressed as:

[0043]

[0044] in,

[0045]

[0046] The optimal transfer matrix is ​​expressed as:

[0047]

[0048] in,

[0049]

[0050] The judgment matrix is ​​expressed as:

[0051]

[0052] in,

[0053]

[0054] The calculated eigenvector is:

[0055] W=[W 1 ,W 2 ,…,W n ] T

[0056] Where DW = λ max W,λ max is the largest characteristic root,

[0057]

[0058] The calculation formula for calculating the eigenvector using the square root method is:

[0059]

[0060] Among them, dik is the element in the judgment matrix.

[0061] Optionally, in one embodiment of the present application, a fuzzy comprehensive evaluation model is used to quantify the evaluation results of the evaluation indicators to obtain a comprehensive benefit evaluation result of green electricity trading, including:

[0062] Establish a comprehensive evaluation factor set and evaluation set, where the factor set is a set composed of evaluation indicators in the quantitative indicator model. The factor set U = (u 1 ,u 2 ,u 3 ,...,u m ),u m represents the mth factor, the evaluation set V = (v 1 ,v 2 ,v 3 ,...,v n ), v n Indicates the nth evaluation result;

[0063] Assume that the membership of the i-th factor in the factor set U to the first element in the evaluation set is r i1 The evaluation result of the i-th factor is based on the fuzzy set R i =(r i1 ,r i2 ,r i3 ,...,r in ) represents the result R evaluated by m factors. 1 ,R 2 ,...,R m The rows form the fuzzy comprehensive evaluation matrix R mn ;

[0064] Based on the calculated index weights, the factors in the corresponding factor set U are weighted, and the fuzzy set of each factor weight factor is A = (a 1 ,a 2 ,a 3 ,...,a m );

[0065] The fuzzy vector A on U is transformed into the fuzzy vector B on V through fuzzy transformation, B = A im ·R mn , determine the maximum membership degree of factor i corresponding to the evaluation set through the calculation results, and use it as the comprehensive benefit evaluation result of the corresponding evaluation index;

[0066] The comprehensive benefit evaluation results of green electricity trading are determined based on the comprehensive benefit evaluation results of all evaluation indicators.

[0067] To achieve the above-mentioned purpose, the second embodiment of the present invention proposes a green electricity transaction comprehensive benefit evaluation device combining qualitative and quantitative methods, including:

[0068] The revenue determination module is used to establish a transaction electricity model for new energy stations, determine the electricity transaction benefits and environmental benefits of green electricity transactions based on the transaction electricity model, and establish a revenue model for new energy stations based on the electricity transaction benefits and environmental benefits of green electricity transactions;

[0069] The indicator quantification module is used to quantify the evaluation indicators based on the relationship between the evaluation indicators of green electricity trading and the trading electricity model and the new energy station revenue model to obtain a quantitative indicator model;

[0070] An indicator weight determination module is used to calculate the indicator weight of the quantitative indicator model using a three-scale hierarchical analysis method based on an optimal transfer matrix;

[0071] The evaluation module is used to construct a fuzzy comprehensive evaluation model based on the quantitative indicator model and the corresponding indicator weights, and use the fuzzy comprehensive evaluation model to quantify the evaluation results of the evaluation indicators to obtain the comprehensive benefit evaluation results of green electricity trading.

[0072] To achieve the above-mentioned purpose, the third aspect of the present invention proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned qualitative and quantitative combined comprehensive benefit evaluation method for green electricity trading is implemented.

[0073] In order to achieve the above objectives, the fourth aspect of the present invention proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor, the above-mentioned qualitative and quantitative green electricity transaction comprehensive benefit evaluation method can be executed.

[0074] The qualitative and quantitative green electricity trading comprehensive benefit evaluation method of the embodiment of the present application, first, can establish a trading electricity model of the new energy station, and determine the electric energy trading benefit and environmental benefit of the green electricity transaction based on the model. Then, a new energy station revenue model is established based on the electric energy trading benefit and environmental benefit of the green electricity transaction, which can more accurately reflect the actual revenue of the new energy station in the green electricity transaction and provide a reliable revenue prediction tool for the new energy station. Furthermore, the evaluation index can be quantified by the relationship between the evaluation index of the green electricity transaction and the trading electricity model and the new energy station revenue model to form a quantitative index model. This allows a variety of benefits that were originally difficult to compare directly to be measured with a unified standard, which improves the objectivity and accuracy of the evaluation. Subsequently, the three-scale hierarchical analysis method based on the optimal transfer matrix is ​​used to calculate the indicator weights in the quantitative indicator model. This method can more scientifically and reasonably allocate the importance of different evaluation indicators, ensure the fairness of the evaluation results, and avoid the problem of unreasonable weight distribution due to too many evaluation factors. Finally, a fuzzy comprehensive evaluation model is constructed based on the quantitative index model and the corresponding index weights, and the evaluation results of the evaluation index are quantified using this model to obtain a comprehensive and accurate evaluation result of the comprehensive benefits of green electricity trading. Based on this evaluation result, it can help investors and market participants make more reasonable decisions in green electricity trading, thereby promoting the healthy development of the green electricity market.

[0075] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0077] Figure 1 A flowchart of a method for evaluating the comprehensive benefits of green electricity trading by combining qualitative and quantitative methods provided in Example 1 of the present application;

[0078] Figure 2 A schematic diagram of the hierarchical analysis structure of an embodiment of the present application.

[0079] Figure 3 A schematic diagram of the structure of a device for evaluating the comprehensive benefits of green electricity trading that combines qualitative and quantitative methods provided in an embodiment of the present application. DETAILED DESCRIPTION

[0080] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0081] The following describes the qualitative and quantitative green electricity transaction comprehensive benefit evaluation method and device of the embodiment of the present application with reference to the accompanying drawings.

[0082] Figure 1 A flowchart of a method for evaluating the comprehensive benefits of green electricity trading that combines qualitative and quantitative methods provided in Example 1 of the present application.

[0083] like Figure 1 As shown in the figure, the qualitative and quantitative green electricity trading comprehensive benefit evaluation method includes the following steps:

[0084] Step 101, establish a transaction electricity model for new energy stations, determine the electricity transaction benefits and environmental benefits of green electricity transactions based on the transaction electricity model, and establish a new energy station revenue model based on the electricity transaction benefits and environmental benefits of green electricity transactions.

[0085] In this step, in order to more accurately predict the power generation and market trading conditions of new energy stations, a trading electricity model for new energy stations can be established. Subsequently, the electric energy trading benefits and environmental benefits of green electricity trading can be determined based on the trading electricity model. Among them, the electric energy trading benefits can help new energy stations understand the economic benefits that can be obtained through green electricity trading under market conditions; and the quantification of environmental benefits can help new energy stations obtain additional benefits in the trading market and prove their contribution to environmental protection. Finally, a new energy station revenue model is established based on the electric energy trading benefits and environmental benefits, so that new energy stations can adjust their operating strategies according to the predicted benefits, thereby achieving higher economic and environmental benefits.

[0086] When establishing a trading electricity model, relevant power generation data of new energy sites can be collected, including power generation within a certain time period on a certain day, limitations on trading channel capacity, power generation of photovoltaic poverty alleviation power stations, and power generation data of other sites, such as power generation within a certain time period on a certain day. Then, a mathematical model is constructed based on the above data to simulate the power generation and trading electricity of new energy sites. It should be noted that after obtaining the trading electricity model, the accuracy of the model can also be verified by cross-validation and other methods to ensure that the model can reliably predict the actual power generation and trading electricity.

[0087] When determining the energy trading benefits and environmental benefits of green electricity trading, the following two parallel implementation methods can be used for evaluation:

[0088] First, the environmental benefits can be calculated by comparing the emission differences between wind power generation and thermal power generation, that is, by calculating how much pollutant emissions are reduced by wind power generation compared to thermal power generation, to measure its positive impact on the environment. Secondly, the environmental benefits of green electricity trading can also be determined by evaluating the impact of wind power stations on the surrounding ecological environment, including considering land use changes, biodiversity protection and other aspects.

[0089] In order to accurately evaluate the energy trading benefits of green electricity trading, calculations can be performed based on the trading electricity model. Specifically, the energy trading benefits of green electricity trading can be determined by collecting the power generation benefit data of new energy stations, the trading electricity calculated based on the trading electricity model, the value of the trading electricity, and the number of days in the month, and through a comprehensive analysis of these data. After obtaining the energy trading benefits and environmental benefits of green electricity trading, a new energy station revenue model can be established based on the two, which should take into account all relevant costs and revenue sources.

[0090] Step 102, quantifying the evaluation index based on the relationship between the evaluation index of green electricity trading and the trading electricity model and the new energy station revenue model to obtain a quantitative index model;

[0091] In the comprehensive benefit evaluation method of green electricity trading, multiple evaluation indicators are involved, and their impact on the benefits of green electricity trading varies. In order to comprehensively evaluate the overall benefits of green electricity trading, a quantitative indicator model can be constructed to combine the evaluation indicators of different dimensions. This method not only achieves a unified measurement of diversified benefits, ensuring that benefits that are difficult to directly compare can be evaluated under a common standard, but also improves the objectivity and accuracy of the evaluation process.

[0092] When establishing a quantitative indicator model, a series of indicators for evaluating green electricity trading can be determined. These indicators may include but are not limited to electricity trading benefits, environmental benefits, etc. Next, based on the established trading electricity model and new energy station revenue model, find out the relationship between these evaluation indicators and the model. For example, the electricity trading benefit can be calculated by the trading electricity and trading price in the trading electricity model, while the environmental benefit can be calculated by the power generation and emission factors in the model.

[0093] Each evaluation indicator can then be quantified, i.e. converted into a specific numerical value. For example, environmental benefits can be expressed in terms of reduced carbon dioxide emissions, and the benefits of electricity trading can be expressed in monetary units.

[0094] Finally, a comprehensive quantitative indicator model can be constructed. Specifically, on the basis of quantifying each evaluation indicator, a model containing all selected evaluation indicators is constructed, and the model can reflect the relationship and importance between these indicators.

[0095] Step 103, using a three-scale hierarchical analysis method based on an optimal transfer matrix to calculate the indicator weights of the quantitative indicator model;

[0096] Step 104: construct a fuzzy comprehensive evaluation model based on the quantitative index model and the corresponding index weights, and use the fuzzy comprehensive evaluation model to quantify the evaluation results of the evaluation index to obtain the comprehensive benefit evaluation results of green electricity trading.

[0097] In step 103 to step 104, in order to more scientifically and reasonably allocate the importance of different evaluation indicators and ensure the fairness and rationality of the evaluation results, the three-scale analytic hierarchy process based on the optimal transfer matrix (OTM-3S AHP) can be used to calculate the indicator weights of the quantitative indicator model. Subsequently, a fuzzy comprehensive evaluation model is constructed through the quantitative indicator model and the corresponding indicator weights, and the quantitative results of multiple evaluation indicators can be combined to obtain an evaluation result that fully reflects the comprehensive benefits of green electricity trading. The fuzzy comprehensive evaluation method can better deal with the ambiguity and uncertainty between evaluation indicators, thereby improving the accuracy and reliability of the evaluation.

[0098] The qualitative and quantitative green electricity trading comprehensive benefit evaluation method of the embodiment of the present application, first, can establish a trading electricity model of the new energy station, and determine the electric energy trading benefit and environmental benefit of the green electricity transaction based on the model. Then, a new energy station revenue model is established based on the electric energy trading benefit and environmental benefit of the green electricity transaction, which can more accurately reflect the actual revenue of the new energy station in the green electricity transaction and provide a reliable revenue prediction tool for the new energy station. Furthermore, the evaluation index can be quantified by the relationship between the evaluation index of the green electricity transaction and the trading electricity model and the new energy station revenue model to form a quantitative index model. This allows a variety of benefits that were originally difficult to compare directly to be measured with a unified standard, which improves the objectivity and accuracy of the evaluation. Subsequently, the three-scale hierarchical analysis method based on the optimal transfer matrix is ​​used to calculate the indicator weights in the quantitative indicator model. This method can more scientifically and reasonably allocate the importance of different evaluation indicators, ensure the fairness of the evaluation results, and avoid the problem of unreasonable weight distribution due to too many evaluation factors. Finally, a fuzzy comprehensive evaluation model is constructed based on the quantitative index model and the corresponding index weights, and the evaluation results of the evaluation index are quantified using this model to obtain a comprehensive and accurate evaluation result of the comprehensive benefits of green electricity trading. Based on this evaluation result, it can help investors and market participants make more reasonable decisions in green electricity trading, thereby promoting the healthy development of the green electricity market.

[0099] Embodiment 2 of the present application proposes a comprehensive benefit evaluation method for green electricity trading that combines qualitative and quantitative methods.

[0100] The method comprises: step A: establishing a transaction electricity model for a new energy station, determining the electricity transaction benefits and environmental benefits of green electricity transactions based on the transaction electricity model, and establishing a new energy station revenue model based on the electricity transaction benefits and environmental benefits of green electricity transactions:

[0101] Specifically, step A includes:

[0102] First, build a new energy station revenue model:

[0103] f(t)=Δt·π l +Δt′·π h (Formula 1)

[0104]

[0105] Where: f(t) is the power generation benefit of station i on the tth day; Δt represents the third priority market transaction power of station i on the tth day, Δt′ represents the fourth priority market transaction power of station i on the tth day, and the daily transaction power is the sum of the three daily periods of power; π l Represents the transaction power value of the third-level market, π hIt represents the transaction electricity value in the fourth-level market, and M is the number of days in the month.

[0106] New energy station i transaction electricity model:

[0107] (1) All units participate in the three-level market

[0108] On the first d days of the month, all units in the market participate in the third-level market, and all members have the same priority. At this time, the method for determining the amount of electricity Δt_a traded by the wind, solar and storage station in the third-level market during the a-th period on the t-th day is as follows:

[0109]

[0110] Among them, Q ta is the power generation of the new energy station in the ath period on the tth day, Q is the power generation of the other stations in the ath period on the tth day, C represents the limit of the transaction channel capacity, Q poverty_ta is the power generation of the photovoltaic poverty alleviation power station in the a-th period on the t-th day.

[0111] (2) Some units participate in the tertiary market

[0112] 1) Wind and solar storage stations participate in the tertiary market

[0113] The method for determining the transaction power Δt of wind-solar-storage station i in period a on day t is as follows:

[0114] If Q ta +Q′ ta ≤CQ poverty_ta , then Δt_a=Q ta (Formula 4)

[0115] Among them, Q′ ta It is the power generation of the other units participating in the tertiary market in the a-th period on the t-th day.

[0116] If Q ta +Q′ ta >CQ poverty_ta ,but

[0117] 2) Wind and solar storage stations do not participate in the tertiary market

[0118] If Q′ ta >CQ poverty_ta , then Δt_a′=0 (Formula 6)

[0119] Among them, Δt_a′ is the amount of electricity traded by the wind-solar-storage station in the fourth-level market in the ath period on the tth day. Priority is given to the remaining market members to participate in the third-level market. At this time, the channel capacity reaches the upper limit. Since the wind-solar-storage station is at a lower priority, the amount of electricity traded by the wind-solar-storage station in this period is 0, that is, no electricity can be generated.

[0120] If Q′ ta ≤CQ poverty_ta ,but

[0121] Among them, Q ta It is the power generation of the other units participating in the four-level market in the a-th period on the t-th day.

[0122] (3) All units do not participate in the tertiary market

[0123] At this time, all members have the same priority, and the method for determining the trading power of the wind, solar and storage units in period a on day t is consistent with the determination rule in (1).

[0124] Secondly, the environmental benefits can be calculated by comparing the emission differences between wind power generation and thermal power generation. For example, wind power generation can reduce carbon dioxide emissions by about 1,070 tons per gigawatt-hour. According to the environmental value assessment standard of 0.023 yuan per kilogram of carbon dioxide, the environmental value corresponding to this emission reduction is as high as 25,000 yuan, which clearly shows that green power projects have a great impact on reducing CO 2 Contribution to emissions.

[0125] Step B: Based on the indicator data of step A, an in-depth analysis is conducted on a systematic, large-scale object with a multi-level structure, which is decomposed layer by layer to finally form a multi-level, multi-dimensional target structure diagram. The structure can be divided into the target layer, the criterion layer and the program layer from shallow to deep. The final program layer includes several interrelated and interacting factors. Through pair-by-pair comparison, the relative importance of the elements at each level is quantified, a weight model is constructed, and mathematical methods such as linear algebra are used for analysis.

[0126] Specifically, step B includes:

[0127] The use of the three-scale AHP method for the optimal transfer matrix should include the following steps:

[0128] (1) Determine the research subject

[0129] The use of AHP should have a clear goal. First, a systematic analysis of the goal is required to determine the depth of evaluation of the research object, and the research scope of the research object is determined according to national and industry laws and regulations and other constraints. At the same time, the actual situation of the research object is widely collected, and supporting documents such as organizational structure, management process, financial data, technical data, etc. that may be used are collected and sorted, and the authenticity and validity of the data and materials are ensured to make full preparations for subsequent data analysis.

[0130] (2) Constructing a multi-level hierarchical structural model

[0131] After determining the research object, through systematic analysis, grasp the main influencing factors of the research object, analyze the research object layer by layer from coarse to fine, from shallow to deep, and form a multi-level hierarchical structure. Figure 2 , Figure 2 The schematic diagram of the hierarchical analysis structure of the embodiment of the present application is generally divided into a target layer, a criterion layer, and a solution layer. Figure 2 It is represented as goal A, criteria B1-B3, and solutions C1-C9. The goal layer is the goal or result that the system wants to achieve, and it is the primary criterion for system evaluation. The criterion layer is a smaller target unit established to achieve the goal layer. The solution layer, also known as the indicator layer, is a variety of plans and measures taken to achieve the goal. It is the lowest level indicator in the indicator system and cannot be further subdivided. The indicators at the same level are interconnected and distinguished from each other, and are organically combined into a whole, showing the overall picture of the evaluation object with clear logic and distinct levels. The final structure of the analytic hierarchy process is as follows: Figure 2 shown.

[0132] (3) Constructing a comparison judgment matrix

[0133] After building a multi-level indicator system, it is necessary to use the experience and knowledge of experts to compare the indicators at each level, judge the relative importance of each sub-indicator under the same indicator, and thus establish a judgment matrix. 1 -B n For example, the element B i With B j The ratio of the impact on target A.

[0134] Table 1 Judgment matrix

[0135]

[0136] According to Table 1, we construct a ij The judgment matrix is:

[0137]

[0138] The elements satisfy:

[0139] a ij >0

[0140]

[0141] a ii =1(i=1,2,3,...,n)

[0142] a ijThe value indicating the relative importance of two relative influencing factors. Saaty's 1-9 scale method is usually used to make pairwise judgments on the criteria layer indicators, as shown in Table 2. Different scales correspond to different relative importance levels. The larger the ratio, the higher the importance of i. This step can quantify qualitative judgments.

[0143] Table 2 Judgment matrix scale and definition

[0144]

[0145] (4) Solving the weight vector

[0146] After obtaining the judgment matrix, the weight of each indicator is solved according to the constructed judgment matrix. There are two methods to calculate the weight, the square root method and the sum method.

[0147] The calculation steps of the square root method are as follows:

[0148] 1) Calculate the product of each row To the power of , we get an m-dimensional vector.

[0149]

[0150] 2) Then normalize the vector to obtain the weight vector.

[0151]

[0152]

[0153] Calculate the weight vector W i =[W 1 ,W 2 ,…,W n ] T (Formula 12)

[0154] The calculation steps of the sum method are to first standardize each column of the judgment matrix, sum the standardized elements by row, and finally standardize the sum result. The obtained weight vector is consistent with the weight vector obtained by the square root method.

[0155] (5) Solving the maximum characteristic root and consistency test

[0156] After obtaining the weight vector through the above method, its maximum characteristic root can be calculated:

[0157]

[0158] Among them, n is the number of dimensions, that is, the order of the judgment matrix, thus, the maximum eigenvalue λ max Further calculate the consistency index CI value:

[0159]

[0160] The purpose of consistency test is to check whether there are logical problems in the constructed judgment matrix. For example, when comparing the relative importance of the three elements A, B, and C, if the importance of A relative to B is 5 and the importance of A relative to C is 0.25, when judging the importance of B relative to C, the importance of B should be less than that of C. If the importance of B relative to C is 3 when constructing the judgment matrix, it is logically infeasible, so consistency test is necessary. However, it is found from the above formula that the larger n is, the greater the error of CI value is. Therefore, the random consistency ratio CR is introduced in the consistency test to reduce the impact of the error:

[0161]

[0162] The RI value can be found by looking up the table. It is the random consistency index obtained by Satty simulation 1000 times, as shown in Table 3:

[0163] Table 3 Random consistency index value table

[0164]

[0165] When the random consistency ratio CR is less than 0.1, the calculated hierarchical ranking weights are considered correct and reasonable. Otherwise, the judgment matrix needs to be readjusted until the consistency test is qualified.

[0166] Step C: Based on the shortcomings of the method in step B, the three-scaling method of the optimal transfer matrix is ​​introduced into the hierarchical analysis method, and a rigorous mathematical proof of the improvement of the hierarchical analysis method is given. Through the improvement, the consistency requirements can be met at the initial stage, and the weight value can be directly calculated, avoiding the consistency test in the later stage.

[0167] Specifically, step C includes:

[0168] (1) First, experts compare the relative importance of each element at each level according to the three-scale principle and establish a three-scale matrix:

[0169]

[0170] The element a ij The value range and meaning of are:

[0171]

[0172] This is the three-scale principle.

[0173] (2) Calculate the optimal transfer matrix T of matrix A:

[0174]

[0175] The calculation method of the elements in the formula is:

[0176]

[0177] (3) Convert the matrix T into the judgment matrix D:

[0178]

[0179] Elements in the formula

[0180]

[0181] It can be mathematically proven that the matrix is ​​naturally consistent, so the matrix is ​​also called a consistency matrix.

[0182] (4) Hierarchical single sorting

[0183] The hierarchical single sorting can be completed by calculating the eigenvector corresponding to the largest eigenvalue in the matrix D, that is, satisfying DW = λ max The eigenvector of W = [W 1 ,W 2 ,…,W n ] T , as the weight vector of n elements at this level.

[0184] There are many methods for obtaining eigenvectors, such as the square root method, geometric mean method, and product method. Because the judgment matrix has a certain error, it is not necessary to pursue higher accuracy. In addition, the square root method is actually calculated by the hypergeometric mean method, which can make the eigenvector closer to the eigenvector within a certain range. Therefore, the eigenvector calculated by the square root method should be used. The calculation formula for applying the square root method to the matrix to obtain the eigenvector is as follows:

[0185]

[0186] (5) Total ranking of levels

[0187] After finding the eigenvectors of each judgment matrix, the calculation method of the total hierarchical ranking of the improved analytic hierarchy process is the same as that of the traditional analytic hierarchy process, which will not be described here.

[0188] The three-scale improved analytic hierarchy process requires simple and intuitive judgment information, and the judgments made are more accurate and easier for experts or decision makers to accept and master. The resulting judgment matrix naturally satisfies consistency, so there is no need to conduct a consistency test. The decision results obtained are consistent with the result weight ranking obtained by the original method.

[0189] Step D: Construct a fuzzy comprehensive evaluation model, and quantify the evaluation results based on the weights obtained in step C. The fuzzy comprehensive evaluation model can combine the quantitative results of multiple evaluation indicators to obtain an evaluation result that fully reflects the comprehensive benefits of green electricity trading. The fuzzy comprehensive evaluation method can better handle the fuzziness and uncertainty between evaluation indicators, thereby improving the accuracy and reliability of the evaluation.

[0190] Specifically, step D comprises:

[0191] In the multi-attribute comprehensive evaluation, the quality of many things cannot be described by a deterministic set. Due to the influence of knowledge and experience, everyone has different views on a certain thing, and it is difficult to give an accurate definition. Therefore, in order to describe the fuzzy concept relatively accurately and scientifically, the fuzzy mathematical method is used to quantify the fuzzy concept and facilitate processing and calculation. The fuzzy comprehensive evaluation model based on combined weighting is as follows:

[0192] (1) Establish a comprehensive evaluation factor set

[0193] The factor set is a general set composed of various factors that affect the evaluation object, usually represented by U, U = (u 1 ,u 2 ,u 3 ,...,u m ), where u i It represents the i-th factor affecting the evaluation object, that is, the evaluation index, which has different degrees of fuzziness.

[0194] (2) Establish a comprehensive evaluation set

[0195] The evaluation set is a set of various possible results that the evaluator may make on the evaluation object, represented by V, V = (v 1 ,v 2 ,v 3 ,...,v n ), where V j Represents the jth evaluation result, which can be expressed by different grades, comments or numbers.

[0196] (3) Perform single factor fuzzy evaluation to obtain the evaluation matrix

[0197] If the membership degree of the i-th element in the factor set U to the first element in the evaluation set V is r i1 , then the result of the single factor evaluation of the i-th element can be expressed by the fuzzy set R i =(r i1 ,r i2 ,r i3 ,...,r in ), with m single factor evaluation sets R 1 ,R2 ,...,R m The rows form the fuzzy comprehensive evaluation matrix R mn .

[0198] (4) Determine the factor weight vector

[0199] For each factor u i Weighting, the fuzzy set of each factor weight factor is represented by vector A, A = (a 1 ,a 2 ,a 3 ,...,a m ), the weight of each factor has been determined through the expert weighting of the post-evaluation index system based on the combined weighting in step C.

[0200] (5) Perform fuzzy synthesis and decision making

[0201] After determining the single factor evaluation matrix R and the factor weight vector A, the fuzzy vector A on U is transformed into the fuzzy vector B on V through fuzzy transformation, that is, B = A im ·R mn , the maximum membership degree of the evaluation set corresponding to the factor is determined by the calculation result, which is the final evaluation result of the indicator.

[0202] In order to implement the above-mentioned embodiment, the present application also proposes a green electricity transaction comprehensive benefit evaluation device that combines qualitative and quantitative methods.

[0203] Figure 3 A schematic diagram of the structure of a device for evaluating the comprehensive benefits of green electricity trading that combines qualitative and quantitative methods provided in an embodiment of the present application.

[0204] like Figure 3 As shown, the qualitative and quantitative green electricity trading comprehensive benefit evaluation device includes:

[0205] The revenue determination module is used to establish a transaction electricity model for new energy stations, determine the electricity transaction benefits and environmental benefits of green electricity transactions based on the transaction electricity model, and establish a revenue model for new energy stations based on the electricity transaction benefits and environmental benefits of green electricity transactions;

[0206] The indicator quantification module is used to quantify the evaluation indicators based on the relationship between the evaluation indicators of green electricity trading and the trading electricity model and the new energy station revenue model established by the revenue determination module to obtain a quantitative indicator model;

[0207] An indicator weight determination module is used to calculate the indicator weight of the quantitative indicator model obtained by the indicator quantification module using a three-scale hierarchical analysis method based on an optimal transfer matrix;

[0208] The evaluation module is used to construct a fuzzy comprehensive evaluation model based on the quantitative indicator model obtained by the indicator quantification module and the indicator weight corresponding to the quantitative indicator model obtained by the indicator weight determination module, and use the fuzzy comprehensive evaluation model to quantify the evaluation results of the evaluation indicators to obtain the comprehensive benefit evaluation results of green electricity trading.

[0209] It should be noted that the above explanation of the embodiment of the method for comprehensive benefit evaluation of green electricity trading combining qualitative and quantitative methods is also applicable to the device for comprehensive benefit evaluation of green electricity trading combining qualitative and quantitative methods of this embodiment, and will not be repeated here.

[0210] In order to implement the above embodiments, the present invention further proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above embodiments is implemented.

[0211] In order to implement the above embodiments, the present invention further proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method of the above embodiments is implemented.

[0212] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0213] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0214] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0215] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0216] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0217] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0218] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0219] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A qualitative and quantitative green electricity trading comprehensive benefit evaluation method, characterized in that: include: Establishing a transaction electricity model for new energy stations, determining the electricity energy transaction benefits and environmental benefits of green electricity transactions based on the transaction electricity model, and establishing a new energy station revenue model based on the electricity energy transaction benefits and environmental benefits of green electricity transactions; Quantifying the evaluation index based on the relationship between the evaluation index of green electricity trading and the trading electricity model and the new energy station revenue model to obtain a quantitative index model; The indicator weights of the quantitative indicator model are calculated using a three-scale hierarchical analysis method based on an optimal transfer matrix; A fuzzy comprehensive evaluation model is constructed based on the quantitative index model and the corresponding index weights, and the fuzzy comprehensive evaluation model is used to quantify the evaluation results of the evaluation indicators to obtain the comprehensive benefit evaluation results of green electricity trading.

2. The method according to claim 1, characterized in that Establish a trading electricity model for new energy stations, including: When all units participate in the third-level market or all units do not participate in the third-level market, the transaction volume of the new energy station participating in the third-level market in the a-th period of the t-th day is: Among them, Q ta is the power generation of the new energy station in the ath period on the tth day, Q is the power generation of the other stations in the ath period on the tth day, C represents the limit of the transaction channel capacity, Q poverty_ta is the power generation of the photovoltaic poverty alleviation power station in the a-th period on the t-th day; When some units participate in the third-level market, if the new energy station participates in the third-level market, the transaction power of the new energy station in the a period on the tth day is: Young Q ta +Q′ ta ≤CQ poverty_ta , Δt_a=Q ta Among them, Q′ ta is the power generation of the remaining units participating in the third-level market in the a-th period on the t-th day, Young Q ta +Q′ ta >CQ poverty_ta , When some units participate in the third-level market, if the new energy station does not participate in the third-level market, the transaction volume of the new energy station participating in the fourth-level market in the a-th period of the t-th day is: If Q′ ta >CQ poverty_ta , Δt_a′=0 Among them, Δt_a′ is the transaction volume of the new energy station in the fourth-level market in the ath period on the tth day, Young Q′ ta ≤CQ poverty_ta , Among them, Q ta It is the power generation of other units participating in the fourth-level market in the a-th period on the t-th day.

3. The method according to claim 2, characterized in that The environmental benefits of green electricity trading are determined based on the trading electricity model, including: Calculate environmental benefits by comparing emissions between wind power and thermal power; The new energy station revenue model includes an electric energy trading benefit model, and the electric energy trading benefit model of green electricity trading and the electric energy trading benefit model are respectively: f(t)=Δt·π l +Δt′·π h Among them, f(t) is the power generation benefit of the new energy station on the tth day, Δt is the transaction volume of the new energy station in the third-level market on the tth day, π l represents the transaction value of electricity in the third-level market, Δt′ represents the transaction value of electricity in the fourth-level market of the new energy station on the tth day, π h It represents the value of electricity traded in the four-level market, and M is the number of days in the month.

4. The method according to claim 1, characterized in that The quantitative index model is a multi-level index system. The three-scale hierarchical analysis method based on the optimal transfer matrix is ​​used to calculate the index weights of the quantitative index model, including: According to the three-scale principle, the relative importance of each evaluation index at each level in the multi-level index system is compared, and a three-scale matrix for each level is established; Calculate the optimal transfer matrix of the three-scale matrix at each level and transform the optimal transfer matrix into a judgment matrix; The hierarchical single sorting is completed by using the square root method to calculate the eigenvector corresponding to the maximum eigenvalue in the judgment matrix of each level, and the calculated eigenvector is used as the weight vector of each evaluation index of the corresponding level.

5. The method according to claim 4, characterized in that The three-scale matrix is ​​expressed as: in, The optimal transfer matrix is ​​expressed as: in, The judgment matrix is ​​expressed as: in, The calculated eigenvector is: W=[W1,W2,…,W n ] T Where DW = λ max W,λ max is the largest characteristic root, The calculation formula for calculating the eigenvector using the square root method is: Among them, d ik is the element in the judgment matrix.

6. The method according to claim 1, characterized in that The fuzzy comprehensive evaluation model is used to quantify the evaluation results of the evaluation indicators to obtain the comprehensive benefit evaluation results of green electricity trading, including: Establish a comprehensive evaluation factor set and evaluation set, wherein the factor set is a set composed of the evaluation indicators in the quantitative indicator model as factors, and the factor set U = (u1, u2, u3, ..., u m ),u m represents the mth factor, the evaluation set V = (v1, v2, v3, ..., v n ), v n Indicates the nth evaluation result; Assume that the membership of the i-th factor in the factor set U to the first element in the evaluation set is r i1 The evaluation result of the i-th factor is based on the fuzzy set R i =(r i1 , r i2 , r i3 , ..., r in ) represents the evaluation results of m factors R1, R2, ..., R m The rows form the fuzzy comprehensive evaluation matrix R mn ; Based on the calculated index weights, the factors in the corresponding factor set U are weighted. The fuzzy set of each factor weight factor is A = (a1, a2, a3, ..., a m ); The fuzzy vector A on U is transformed into the fuzzy vector B on V through fuzzy transformation, B = A im ·R mn , determine the maximum membership degree of factor i corresponding to the evaluation set through the calculation results, and use it as the comprehensive benefit evaluation result of the corresponding evaluation index; The comprehensive benefit evaluation results of green electricity trading are determined based on the comprehensive benefit evaluation results of all evaluation indicators.

7. A qualitative and quantitative green electricity trading comprehensive benefit evaluation device, characterized in that: include: A revenue determination module, used to establish a transaction electricity model for a new energy station, determine the electricity energy transaction benefits and environmental benefits of green electricity transactions based on the transaction electricity model, and establish a revenue model for a new energy station based on the electricity energy transaction benefits and environmental benefits of green electricity transactions; An indicator quantification module, used to quantify the evaluation indicator based on the relationship between the evaluation indicator of green electricity trading and the trading electricity model and the new energy station revenue model to obtain a quantitative indicator model; An indicator weight determination module, used to calculate the indicator weight of the quantitative indicator model by using a three-scale hierarchical analysis method based on an optimal transfer matrix; The evaluation module is used to construct a fuzzy comprehensive evaluation model based on the quantitative indicator model and the corresponding indicator weights, and use the fuzzy comprehensive evaluation model to quantify the evaluation results of the evaluation indicators to obtain the comprehensive benefit evaluation results of green electricity trading.

8. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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