Comprehensive evaluation method and device for power market transaction value of new energy power station

Abstract

The invention discloses a comprehensive evaluation method and device for the power market transaction value of a new energy power station, and relates to the technical field of new energy, and the method comprises the steps: obtaining various evaluation index data of different new energy stations in different time periods; determining a comprehensive weight of each piece of evaluation index data in the multiple pieces of evaluation index data, and screening target evaluation index data in each piece of evaluation index data according to the comprehensive weight; inputting the target evaluation index data into a diagnosis model, and outputting a diagnosis result of the target evaluation index data; and according to the diagnosis result, evaluating the power market transaction values of different new energy stations in different time periods to obtain an evaluation result. According to the invention, the transaction value of the new energy power station power market can be accurately and efficiently evaluated, so that the operation condition and transaction efficiency of the new energy power station power market can be effectively reflected.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a comprehensive evaluation method and device for the transaction value of a new energy power station in an electricity market. Background Art

[0002] At present, the development of new energy power stations is rapid, and they are characterized by intermittency and volatility, which brings great challenges to the operation and management of the power system. In order to better reflect the operation and transaction efficiency of the power market of new energy power stations, a comprehensive evaluation of the power market transaction value can be conducted.

[0003] The existing comprehensive evaluation methods for the transaction value of the power market of new energy power plants require a lot of data collection, collation and analysis, which is labor-intensive and inefficient. At the same time, these methods only rely on a certain type of data, lack comprehensive consideration of multiple influencing factors, and cannot effectively reflect the transaction situation of the power market of new energy power plants.

[0004] Currently, no effective solution has been proposed to the above problems. Summary of the invention

[0005] The embodiments of this specification provide a comprehensive evaluation method and device for the transaction value of electricity market of new energy power stations, so as to solve the problem that the existing technology cannot accurately and efficiently evaluate the transaction value of electricity market of new energy power stations.

[0006] In a first aspect, an embodiment of this specification provides a comprehensive evaluation method for the transaction value of a new energy power station power market, the method comprising:

[0007] Obtain various evaluation index data of different new energy sites at different time periods;

[0008] Determining a comprehensive weight of each evaluation indicator data among the plurality of evaluation indicator data, so as to screen target evaluation indicator data among the evaluation indicator data according to the comprehensive weight;

[0009] Inputting the target evaluation index data into a diagnostic model, and outputting a diagnostic result of the target evaluation index data;

[0010] According to the diagnosis results, the power market transaction values ​​of different new energy sites in different time periods are evaluated to obtain evaluation results.

[0011] In some embodiments, the multiple evaluation index data include but are not limited to: electricity index data, resource index data, and strategy index data. The electricity index data include but are not limited to: base electricity, medium- and long-term trading electricity, spot settlement electricity, actual online electricity, equivalent utilization hours, and electricity limit.

[0012] In some embodiments, determining the comprehensive weight of each evaluation indicator data in the plurality of evaluation indicator data includes:

[0013] Constructing a first matrix according to the multiple evaluation index data, solving the weight vector of each evaluation index data according to the first matrix, and obtaining the subjective weight vector of each evaluation index data;

[0014] Taking the power market transaction value as the evaluation object, taking the evaluation index data corresponding to the power market transaction value as the evaluation index variable, and constructing a second matrix according to the evaluation object and the evaluation index variable;

[0015] According to the first matrix, an objective weight vector of each evaluation index data is calculated;

[0016] The comprehensive weight of each evaluation index data is determined according to the subjective weight vector and the objective weight vector.

[0017] In some embodiments, the step of screening target evaluation index data among various evaluation index data according to the comprehensive weight includes:

[0018] Transforming the second matrix to obtain a decision matrix;

[0019] constructing a weighted normative matrix according to the comprehensive weights and the decision matrix;

[0020] Determine the merits and demerits of each evaluation index data according to the weighted normative matrix;

[0021] According to the merits, the evaluation index data are sorted, and the evaluation index data greater than a preset sorting threshold are selected from the sorting results as target evaluation index data.

[0022] In some embodiments, the diagnostic model is trained in the following manner:

[0023] Obtaining a variety of historical evaluation index data of different new energy sites in different time periods and abnormal labels corresponding to the historical evaluation index data;

[0024] Performing data preprocessing on the historical evaluation index data to obtain a training data set;

[0025] A support vector machine is trained based on the training data set, and a diagnosis model is obtained when the accuracy of the trained support vector machine meets a preset accuracy threshold.

[0026] In some embodiments, the power market transaction values ​​of different new energy sites in different time periods are evaluated according to the diagnosis results to obtain evaluation results, including:

[0027] When there are abnormal situations in the diagnosis results, the transaction values ​​of the electricity market of different new energy sites in different time periods are evaluated according to the abnormal situations to obtain evaluation results.

[0028] In some embodiments, the method further comprises:

[0029] According to the evaluation results, different new energy stations are ranked according to different time periods to obtain ranking results;

[0030] According to the ranking results, formulate an optimization strategy for evaluation index data of the different new energy sites in the corresponding time period;

[0031] The evaluation index data is optimized based on the evaluation index data optimization strategy.

[0032] In a second aspect, the embodiments of this specification also provide a comprehensive evaluation device for the transaction value of the power market of a new energy power station, the device comprising:

[0033] An acquisition module is used to obtain various evaluation index data of different new energy sites at different time periods;

[0034] A determination module, used to determine the comprehensive weight of each evaluation index data among the multiple evaluation index data, so as to screen the target evaluation index data among the evaluation index data according to the comprehensive weight;

[0035] A diagnosis module, used for inputting the target evaluation index data into a diagnosis model and outputting a diagnosis result of the target evaluation index data;

[0036] The evaluation module is used to evaluate the power market transaction value of different new energy sites in different time periods according to the diagnosis results to obtain evaluation results.

[0037] On the third aspect, the embodiments of this specification also provide a computer device, including a memory, a processor and a computer program / instructions stored in the memory, wherein the processor executes the computer program / instructions to implement the steps of the comprehensive evaluation method of the electricity market transaction value of the above-mentioned new energy power station.

[0038] In a fourth aspect, an embodiment of the present specification also provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for comprehensively evaluating the transaction value of electricity market of new energy power stations.

[0039] In a fifth aspect, the embodiments of this specification also provide a computer program product, which, when executed by a processor, implements the steps of the above-mentioned comprehensive evaluation method of the transaction value of electricity market of new energy power stations.

[0040] The embodiment of this specification provides a comprehensive evaluation method and device for the transaction value of the power market of a new energy power station. First, a variety of evaluation index data of different new energy sites in different time periods are obtained. Then, the comprehensive weight of each evaluation index data in the multiple evaluation index data is determined to screen the target evaluation index data in each evaluation index data according to the comprehensive weight. Then, the target evaluation index data is input into the diagnosis model, and the diagnosis result of the target evaluation index data is output. Finally, according to the diagnosis result, the transaction value of the power market of different new energy sites in different time periods is evaluated to obtain the evaluation result. In the embodiment of this specification, by obtaining a variety of evaluation index data of different new energy sites in different time periods, the problem of inaccurate and comprehensive evaluation of the power market value caused by a single evaluation index data can be avoided, and at the same time, the data basis can be provided for the subsequent rapid acquisition of the evaluation results of the power market transaction value of different new energy sites in different time periods. By introducing the diagnosis model, the diagnosis results of the target evaluation index data of different new energy sites in different time periods can be obtained quickly, so that the corresponding evaluation results of the power market transaction value can be quickly determined, which greatly improves the evaluation efficiency and avoids the problem that the existing evaluation method of the power market transaction value requires a large amount of data collection, collation and analysis work, which is heavy in workload and low in efficiency. By determining the comprehensive weight, the target evaluation index data can be accurately screened, thereby improving the efficiency and accuracy of the diagnostic model output diagnosis results. Through the above scheme, the transaction value of the new energy power station power market can be accurately and efficiently evaluated, thereby effectively reflecting the operation status and transaction efficiency of the new energy power station power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0042] Figure 1 It is a flow chart of a comprehensive evaluation method of the transaction value of a new energy power station power market provided in an embodiment of this specification;

[0043] Figure 2 It is a schematic diagram of an embodiment of a comprehensive evaluation method for the transaction value of the power market of a new energy power station provided by an embodiment of this specification, in a scenario example;

[0044] Figure 3 It is a schematic diagram of an embodiment of a comprehensive evaluation method for the transaction value of the power market of a new energy power station provided by an embodiment of this specification, in a scenario example;

[0045] Figure 4 It is a schematic diagram of an embodiment of a comprehensive evaluation method for the transaction value of the power market of a new energy power station provided by an embodiment of this specification, in a scenario example;

[0046] Figure 5 It is a schematic diagram of an embodiment of a comprehensive evaluation method for the transaction value of the power market of a new energy power station provided by an embodiment of this specification, in a scenario example;

[0047] Figure 6 It is a functional schematic diagram of a comprehensive evaluation system for the transaction value of a new energy power station in the power market provided by an embodiment of this specification;

[0048] Figure 7 It is a module schematic diagram of a comprehensive evaluation system for the transaction value of the power market of a new energy power station provided in an embodiment of this specification;

[0049] Figure 8 This is a schematic diagram of the structure of a comprehensive evaluation device for the transaction value of electricity market of a new energy power station provided in an embodiment of this specification;

[0050] Fig. 9 It is a schematic diagram of the structural composition of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0052] The field of new energy technology mainly involves the development and utilization of renewable energy such as wind energy, solar energy, and biomass energy. With the continuous improvement of global requirements for environmental protection and sustainable development, the development of new energy power stations is rapid. The main characteristics of new energy power stations are intermittency and volatility, which brings new challenges to the operation and management of power systems. At the same time, with the continuous advancement of power market reform, the degree of openness of the power trading market has become increasingly greater, and power market transactions have become an important source of income for new energy power stations.

[0053] The existing comprehensive evaluation methods for power market transaction value have the following problems: First, these methods have a large workload and low efficiency, and cannot meet the requirements of new energy investment and operation companies for the timeliness and accuracy of transaction evaluation. Secondly, these methods lack comprehensive consideration of the transaction strategy capabilities of new energy power stations and cannot effectively reflect the transaction status of new energy power stations. Finally, these methods often rely on single data and lack comprehensive consideration of other factors (such as market environment, etc.). Therefore, the existing comprehensive evaluation methods for power market transaction value have great limitations in practical applications and cannot meet the needs of new energy investment and operation companies.

[0054] In view of the above-mentioned problems existing in the existing methods and the specific reasons for the above-mentioned problems, this application considers introducing a comprehensive evaluation method and device for the transaction value of the electricity market of new energy power stations, which can accurately and efficiently evaluate the transaction value of the electricity market of new energy power stations, thereby effectively reflecting the operation status and transaction efficiency of the electricity market of new energy power stations.

[0055] It should be noted that the user-related information and data involved in the embodiments of this specification are all information and data authorized by the user or fully authorized by relevant parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users or relevant parties to choose to authorize or refuse.

[0056] It should also be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0057] Figure 1 It is a flow chart of a comprehensive evaluation method for the transaction value of electricity market of a new energy power station provided in an embodiment of this specification. Although this specification provides method operation steps or device structures as shown in the following embodiments or drawings, more or fewer operation steps or module units may be included in the method or device based on routine or no creative labor. In the steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the method or module structure described is applied in an actual device, server or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, a parallel processor or multi-threaded processing environment, or even distributed processing, server cluster implementation environment). For specific implementation, refer to Figure 1As shown, the method may include the following contents.

[0058] S101: Obtain various evaluation index data of different new energy sites at different time periods.

[0059] In this embodiment, different new energy stations may include: wind power type new energy stations, photovoltaic type new energy stations and energy storage type new energy stations, etc., and different time periods may include: single day, month, quarter and year, etc. That is, multiple evaluation index data of wind power type new energy stations, photovoltaic type new energy stations and energy storage type new energy stations in single day, month, quarter and year may be obtained.

[0060] By obtaining multiple evaluation index data of different new energy sites at different time periods, we can avoid the problem that a single data cannot effectively and comprehensively reflect the transaction status of the new energy power station power market. At the same time, we can rank different new energy sites according to different time periods in the future, and lay the foundation for a more intuitive understanding of the power market operation and transaction efficiency of different new energy sites at different time periods.

[0061] In some embodiments, the above-mentioned multiple evaluation index data may include but are not limited to: electricity index data, resource index data, and strategy index data. The electricity index data may include but are not limited to: base electricity, medium- and long-term trading electricity, spot settlement electricity, actual online electricity, equivalent utilization hours, and electricity limit.

[0062] In some embodiments, evaluation index data that affect the transaction value of the electricity market of new energy power stations can be collected from multiple aspects, such as taking into account systematicity and pertinence, comprehensiveness and adaptability, and screening out four categories of 18 main evaluation index data. Based on these collected evaluation index data, a complete database of electricity market value evaluation index data can be established. The database can store a variety of evaluation index data of different new energy sites in different time periods, so that when there is a need to obtain evaluation index data, the corresponding evaluation index data can be obtained from the database in a timely manner. Specifically, the electricity index data, resource index data, and strategy index data of different new energy sites in different time periods can be collected, and relevant data such as the market environment can also be collected, so as to construct a complete database of electricity market value evaluation index data.

[0063] Among them, the electricity index data can reflect the market trading electricity and actual power generation of new energy sites during the statistical period, and may include but are not limited to: base electricity, medium- and long-term trading electricity, spot settlement electricity, actual grid-connected electricity, equivalent utilization hours, and power limit. Resource index data can reflect the market trading electricity price and settlement electricity price of new energy sites during the statistical period, as well as the market trading income of new energy sites during the statistical period, and may include but are not limited to: medium- and long-term transaction weighted average price, spot settlement average price, kilowatt-hour average price, kilowatt-hour industry level, kilowatt-hour improvement level, settlement electricity fee, medium- and long-term settlement electricity fee, spot settlement electricity fee, market recovery electricity fee, and double detailed rules assessment expenses. Strategy indicator data can reflect the profit and loss of medium- and long-term trading decisions and spot trading decisions of new energy sites in power market transactions, and may include but are not limited to: medium- and long-term transaction revenue increase, spot transaction revenue increase.

[0064] Among them, base electricity: the sum of electricity purchased uniformly at the benchmark electricity price allocated to the station by the dispatcher, unit: MWh. Medium- and long-term transaction electricity: the sum of various medium- and long-term transaction electricity signed by the station (excluding base electricity), unit: MWh. Spot settlement electricity: the sum of electricity settled by the station in the spot market, unit: MWh. Actual grid electricity: all the electric energy transmitted by the station to the power grid, which can be read from the meter at the gateway between the station and the power grid, unit: MWh. Equivalent utilization hours: the number of hours of power generation converted from the power generation of the station to the full load operation condition of all installed capacity of the station, also known as equivalent full-load power generation hours, unit: h. Limited electricity: the sum of the station (the electricity reported the day before - the electricity cleared the day before), unit: MWh. Weighted average price of medium- and long-term transactions: the weighted average price of the medium- and long-term contracts signed by the station, unit: yuan / MWh. Spot settlement average price: the average price settled by the station in the spot market, unit: yuan / MWh. Average price per kilowatt-hour: the average settlement price per kilowatt-hour of the station, unit: RMB / MWh. Industry level per kilowatt-hour: the average price per kilowatt-hour of the station / the average market price of the same type of power source, unit: %. Level of improvement per kilowatt-hour: the industry level of the station / the industry level of the station in the same period in history, unit: %. Settlement electricity fee: the total settlement electricity fee of the station, unit: RMB. Medium- and long-term settlement electricity fee: the medium- and long-term contract settlement electricity fee of the station, unit: RMB. Spot settlement electricity fee Rspot: the electricity fee settled by the station in the spot market, unit: RMB. Market recovery electricity fee Rmarket recovery: the market recovery electricity fee of the station, unit: RMB. Double detailed rules assessment fee: the double detailed rules assessment fee of the station, unit: RMB. Increased income from medium- and long-term transactions (ΔR 中长期): The medium- and long-term transaction electricity price is benchmarked against the spot market electricity price the day before. The medium- and long-term transaction revenue increase is considered in two situations. One is that in the absence of medium- and long-term congestion, it refers to the revenue of the difference between the average medium- and long-term transaction price of the station during the statistical period and the average spot price the day before. The other is that in the presence of medium- and long-term congestion, it refers to the revenue of the difference between the average medium- and long-term transaction price of the station during the statistical period and the settlement reference point electricity price, unit: yuan. Spot transaction revenue increase: the revenue increase of the station spot declaration strategy compared with the original power forecast strategy, unit: yuan.

[0065]

[0066] S102: Determine a comprehensive weight of each evaluation index data among the plurality of evaluation index data, so as to screen target evaluation index data among the evaluation index data according to the comprehensive weight.

[0067] In this embodiment, the comprehensive weights of the evaluation index data of different new energy sites in different time periods can be determined. The comprehensive weight is a weight vector formed by integrating the subjective weight vector and the objective weight vector, which can reflect both subjective experience judgment and decision-making preferences, and objective bid evaluation information and scheme differences. On this basis, the target evaluation index data can be accurately screened out based on the comprehensive weight, thereby improving the efficiency and accuracy of the diagnostic model output of the diagnostic results.

[0068] In some embodiments, see Figure 2 As shown, the above determination of the comprehensive weight of each evaluation index data in the multiple evaluation index data may include:

[0069] S1: construct a first matrix according to multiple evaluation index data, solve the weight vector of each evaluation index data according to the first matrix, and obtain the subjective weight vector of each evaluation index data;

[0070] S2: Taking the power market transaction value as the evaluation object, taking the evaluation index data corresponding to the power market transaction value as the evaluation index variable, and constructing a second matrix according to the evaluation object and the evaluation index variable;

[0071] S3: Calculate the objective weight vector of each evaluation index data according to the second matrix;

[0072] S4: Determine the comprehensive weight of each evaluation index data according to the subjective weight vector and the objective weight vector.

[0073] Specifically, the process of determining the above subjective weight vector is as follows:

[0074] (1) Construct a pairwise comparison judgment matrix (i.e. the first matrix mentioned above)

[0075]

[0076] a ij A numerical 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. Different scales correspond to different relative importance levels. The larger the ratio, the higher the importance of i. This step can quantify qualitative judgments.

[0077] (2) Solving the weight vector

[0078] After obtaining the judgment matrix, the weight of each evaluation index data is solved according to the constructed judgment matrix. Specifically, the 1 / m power of the product of each row can be calculated to obtain an m-dimensional vector, as shown below:

[0079]

[0080] Then normalize the vector to get the weight vector:

[0081]

[0082] The final subjective weight vector of each evaluation index data is: W = (w1, w2, w3…w n ) T

[0083] (3) Solving the maximum characteristic root and consistency test

[0084] After obtaining the subjective weight vector, its maximum eigenvalue can be calculated as follows:

[0085]

[0086] 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:

[0087]

[0088] Among them, CI is the consistency index, λ max is the maximum eigenvalue, and n is the order of the judgment matrix.

[0089]

[0090] 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.

[0091] The determination process of the above objective weight vector is as follows:

[0092] (1) Construct the data matrix A = (a ij ) n×m (i.e. the second matrix mentioned above)

[0093] The power market transaction value can be used as the evaluation object, and the evaluation index data corresponding to the power market transaction value can be used as the evaluation index variable. Specifically, the power market transaction value of different new energy stations in different time periods can be used as the evaluation object, the number of which can be n, and the corresponding evaluation index data can be m. The value of the i-th evaluation object with respect to the j-th index variable is a ij (i=1,2,3...n,j=1,2,3...m).

[0094] (2) Calculate the weight of the evaluation index data based on the second matrix

[0095] Using the original data matrix A = (a ij ) n×m Calculate the weight of the i-th evaluation object with respect to the j-th evaluation index data value:

[0096]

[0097] (3) Calculate the entropy value of the jth evaluation index data

[0098]

[0099] (4) Calculate the coefficient of variation of the j-th evaluation index data

[0100] g j =1-e j, ,j=1,2,...,m

[0101] (5) Calculate the weight of the j-th evaluation index data (i.e., obtain the above objective weight vector)

[0102]

[0103] The process of determining the above comprehensive weights is as follows:

[0104] Assume that the subjective weight vector of each evaluation index data obtained above is The objective weight vector of each evaluation index data is β = (β1, β2, β3…β n ), the combined weighting method can be used to comprehensively integrate the subjective weight vector and the objective weight vector to obtain the comprehensive weight of each evaluation index data. The specific formula is as follows:

[0105]

[0106] By integrating the subjective weight vector and the objective weight vector through the combined weighting method, the determination of the weight of each evaluation index can focus on both experience preference and relative objectivity, thus ensuring the rationality of the weight distribution of each evaluation index.

[0107] In some embodiments, see Figure 3 As shown, the above-mentioned screening of target evaluation index data in each evaluation index data according to the comprehensive weight includes:

[0108] S1: transform the first matrix to obtain a decision matrix;

[0109] S2: constructing a weighted normative matrix according to the comprehensive weight and the decision matrix;

[0110] S3: Determine the merits and demerits of each evaluation index data according to the weighted normative matrix;

[0111] S4: sorting the evaluation index data according to the merits and demerits, and selecting the evaluation index data greater than a preset sorting threshold from the sorting results as the target evaluation index data.

[0112] Specifically, the process of screening target evaluation indicator data can be as follows:

[0113] (1) Data preprocessing

[0114] Since the numerical values ​​of the attribute values ​​of different evaluation index data vary greatly, in order to accurately evaluate, it is necessary to normalize the values ​​in the attribute value table, that is, to transform the values ​​to the interval [0, 1].

[0115] The first matrix A=(a ij ) m×n After transformation, the decision matrix B is obtained. ij ) m×n , i=1,2,3…m,j=1,2,3…n. Assume is the maximum value in the jth column of the decision matrix, is the minimum value in the jth column of the decision matrix.

[0116] In order to make the optimal value of each evaluation index data after transformation 1 and the worst value 0, a standard 0-1 transformation can be performed. Specifically, different standardized transformation methods can be used according to different types of evaluation index data:

[0117] For benefit-based indicators, the standardized transformation method is:

[0118] For cost-based indicators, the standardized transformation method is:

[0119] (2) Constructing a weighted normative matrix

[0120] Based on the above comprehensive weights and decision matrix, the weighted normative matrix can be constructed according to the following formula:

[0121] c ij =w j b ij ,i=1,2,...,n

[0122] (3) Determine positive and negative ideal solutions

[0123] The above-mentioned weighted normative matrix can be combined to determine the maximum and minimum values ​​of the corresponding evaluation index data according to the type of evaluation index data. Specifically, let the positive ideal solution c * The j-th attribute value of Negative ideal solution c 0 The j-th attribute value of but

[0124] Positive ideal solution:

[0125] Negative ideal solution:

[0126] Among them, the positive ideal solution can be the optimal value of the evaluation index data, and the negative ideal solution can be the worst value of the evaluation index data.

[0127] (4) Calculate the distance between each solution and the positive ideal solution and the negative ideal solution (that is, the advantages and disadvantages of each evaluation index data can be determined by the calculated distance)

[0128] The distance from the alternative solution (i.e. the weighted normative matrix mentioned above) to the positive ideal solution is (which can be used as the optimal value of each evaluation index data):

[0129]

[0130] The distance from the alternative solution to the negative ideal solution is (which can be used as the inferior value of each evaluation index data):

[0131]

[0132] (5) Sort the evaluation index data according to their merits and demerits

[0133] According to the merits and demerits of each evaluation index data, the ranking index value or comprehensive evaluation index can be determined according to the following formula:

[0134]

[0135] The evaluation index data can be sorted from large to small according to the sorting index value or the comprehensive evaluation index. The higher the sorting result, the better the evaluation index data. The evaluation index data greater than the preset sorting threshold can be screened out from the sorting result as the target evaluation index data. The preset sorting threshold can be set according to actual needs, and this manual does not make any specific restrictions on this.

[0136] By screening the target evaluation index data, the efficiency and accuracy of the diagnostic model's output of diagnostic results can be effectively improved.

[0137] S103: Input the target evaluation index data into a diagnosis model, and output a diagnosis result of the target evaluation index data.

[0138] In this embodiment, a diagnostic module can be pre-trained, and the diagnostic module can be used to identify abnormal conditions in the target evaluation index data, such as identifying problems with the trading strategy of a new energy power station based on the diagnostic model, etc. The screened target evaluation index data is input into the diagnostic model, and the diagnostic results of the target evaluation index data can be accurately and quickly output.

[0139] In some embodiments, see Figure 4 As shown, the above diagnostic model can be trained in the following way:

[0140] S1: Obtaining a variety of historical evaluation index data of different new energy sites in different time periods and abnormal labels corresponding to the historical evaluation index data;

[0141] S2: performing data preprocessing on the historical evaluation index data to obtain a training data set;

[0142] S3: Training a support vector machine based on the training data set, and obtaining a diagnosis model when the accuracy of the trained support vector machine meets a preset accuracy threshold.

[0143] Specifically, a variety of historical evaluation index data of different new energy sites in different time periods can be directly obtained from the above-established database. Each historical evaluation index data has a corresponding abnormal label. The historical evaluation index data can be preprocessed first (such as: data with collection loss, errors, etc. can be processed, and the data input to the model can be standardized, etc.), and the preprocessed data can be used as a training data set. After that, a support vector machine (SVM) can be trained based on the training data set, and then the training results and the abnormal labels are used to calculate the loss of the model training. If the loss value is too large, that is, the training accuracy does not meet the preset accuracy threshold, the model parameters need to be further adjusted until the accuracy of the training support vector machine meets the preset accuracy threshold, and a trained diagnostic model is obtained. Among them, the preset accuracy threshold can be set according to actual needs, and this specification does not make specific restrictions on this.

[0144] S104: Based on the diagnosis result, the power market transaction value of different new energy sites in different time periods is evaluated to obtain an evaluation result.

[0145] In this embodiment, the power market transaction value of different new energy sites in different time periods can be evaluated according to the diagnostic results output by the model to obtain corresponding evaluation results. The evaluation process is the process of scoring the power market transaction value of different new energy sites in different time periods. By combining the diagnostic results to evaluate the power market transaction value, the accuracy of the evaluation can be effectively improved, and the obtained evaluation results can more accurately reflect the transaction status of new energy power stations.

[0146] In some embodiments, the above-mentioned evaluation of the power market transaction value of different new energy sites in different time periods according to the diagnosis results to obtain the evaluation results may include:

[0147] When there are abnormal situations in the diagnosis results, the transaction values ​​of the electricity market of different new energy sites in different time periods are evaluated according to the abnormal situations to obtain evaluation results.

[0148] Specifically, when the diagnostic result of the target evaluation index data is that there are abnormal conditions in the target evaluation index data, the power market transaction values ​​of different new energy sites in different time periods can be evaluated or scored in combination with the abnormal conditions. For example, an abnormal threshold can be set according to actual needs. If the abnormal condition is greater than the abnormal threshold, a lower score is given. On the contrary, if the abnormal condition is less than the abnormal threshold, a relatively high score is given. Finally, an evaluation or scoring result can be obtained.

[0149] It should be noted that the scoring method is not limited to the above examples. Technical personnel in the relevant field may make other changes based on the technical essence of the embodiments of this specification. However, as long as the functions and effects achieved are the same or similar to those of the embodiments of this specification, they should be covered within the protection scope of the embodiments of this specification.

[0150] In some embodiments, see Figure 5 As shown, after obtaining the evaluation results, the specific implementation may also include:

[0151] S1: According to the evaluation results, different new energy stations are ranked according to different time periods to obtain ranking results;

[0152] S2: formulating an optimization strategy for evaluation index data of the different new energy sites in corresponding time periods according to the ranking results;

[0153] S3: Optimizing the evaluation index data based on the evaluation index data optimization strategy.

[0154] Specifically, different renewable energy sites can be ranked from low to high according to the scores in the evaluation results, and in different time periods. The higher the ranking, the more obvious the abnormality of the evaluation index data, and the lower the power market transaction value, that is, the power market transaction income. Finally, a corresponding evaluation index data optimization strategy can be formulated based on the ranking results to optimize the corresponding evaluation index data to improve the power market transaction income.

[0155] For example, wind power type new energy stations, photovoltaic type new energy stations and energy storage type new energy stations can be ranked on a daily basis, and wind power type new energy stations, photovoltaic type new energy stations and energy storage type new energy stations can be ranked on a monthly basis, etc. Assuming that the ranking results are: wind power type new energy stations on a single day, photovoltaic type new energy stations on a single day, and energy storage type new energy stations on a single day, then for the wind power type new energy stations on a single day, whose indicators are most abnormal, an optimization strategy for the corresponding evaluation index data can be formulated to optimize the corresponding evaluation index data. For example, if there are problems with the trading strategy of the wind power type new energy stations on a single day, the power station can adjust the trading window to increase the amount of electricity traded between provinces, and so on.

[0156] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For details, please refer to the description of the above-mentioned related processing related embodiments, and no further description is given here.

[0157] The above is an explanation of the present invention, however, it is worth noting that the specific embodiment is only for the purpose of better illustrating the present application and describing a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0158] In a specific implementation scenario, a comprehensive evaluation system for the transaction value of the power market of a new energy power station can also be established, and the comprehensive evaluation method for the transaction value of the power market of a new energy power station can be applied to the system. Figure 6 As shown, the above-mentioned comprehensive evaluation system for the transaction value of the electricity market of new energy power stations can have the following functions:

[0159] Scoring and ranking function for different types of new energy power stations: It mainly realizes the ranking and display of the comprehensive scores of power trading value of different types of new energy power stations (wind power, photovoltaic and energy storage, etc.), and can conduct horizontal comparison and ranking display according to different time dimensions and site installed capacity, regional power stations and other conditions.

[0160] New energy power station evaluation index weight setting function: mainly realizes the constraints of new energy power stations in different types of scenarios, regions and installed capacities, and sets the relevant algorithm input parameters (power generation, transaction power, electricity price per kilowatt-hour, etc.) and the algorithms used.

[0161] Basic information management function of new energy power stations: mainly realizes the management of relevant information of the operated new energy power stations, such as equipment installed type, installed capacity, unit model, power generation, real-time power and other information to obtain, store and display.

[0162] New energy power station transaction value statistical analysis function: mainly realizes data statistics and analysis of different types of electricity transaction values ​​of new energy power stations, such as spot, medium and long-term, ancillary services and other types of transactions.

[0163] New energy power station transaction strategy analysis function: mainly realizes the collection, review and attribution analysis of the strategy information of each transaction of the new energy power station, and forms a strategy experience diagnosis knowledge base by collecting the review results of experts, and gives approximate strategy problem suggestions based on the scoring ranking analysis function.

[0164] Evaluation and analysis report output function: mainly realizes the output of comparative evaluation and recommendation reports on the daily, monthly, quarterly and annual power trading conditions of different types of new energy power stations. It can also update data in real time and output corresponding evaluation reports.

[0165] User authority management function: mainly realizes the authority setting for managers in different areas, and views the information of different power stations according to the authority.

[0166] Among them, the above-mentioned comprehensive evaluation system of the transaction value of the power market of new energy power stations can integrate the above-mentioned established database (storing various evaluation index data of different new energy sites in different time periods), the determination method of comprehensive weights, the diagnostic model, and the evaluation ranking method, so as to accurately and efficiently evaluate the transaction value of the power market of new energy power stations, thereby effectively reflecting the operation and transaction efficiency of the power market of new energy power stations. The comprehensive evaluation system (or platform) of the transaction value of the power market of new energy power stations can be developed in Java language, deployed on the server, and support multi-user concurrent access. In addition, the system can also realize data visualization, such as: the transaction score and ranking can be displayed through bar charts, line charts and scatter charts, as well as the diagnostic results of the diagnostic model, so that the evaluation results are more intuitive and clear. The system can also improve optimization suggestions, such as: according to the ranking results, the evaluation index data optimization strategy for different new energy sites in the corresponding time period can be formulated, which can effectively improve the power market transaction income. The system can also set user authority management to ensure the security and confidentiality of data, and only authorized users can access and operate the system. Authorized users can include corporate marketing managers, traders, etc.

[0167] Through the above steps, we can effectively solve the problems of large workload and low efficiency of the existing comprehensive evaluation method of electricity market transaction value, lack of comprehensive consideration of the trading strategy capabilities of new energy power stations, and lack of comprehensive consideration of other factors (such as market environment, etc.). It can effectively improve the evaluation workload and evaluation efficiency and provide more valuable reference for new energy investment and operation companies.

[0168] See also Figure 7 As shown, the comprehensive evaluation system for the power market transaction value of the above-mentioned new energy power station can include the following modules: Data acquisition module: mainly realizes the acquisition of relevant data (electricity, power, transaction electricity price, income and strategy, etc.) of the provincial power trading system, power trading auxiliary strategy system and power production control system.

[0169] Data storage module: mainly used to store and save the data collected by the data acquisition module.

[0170] Data processing module: mainly realizes the processing of data with loss or error in collection, and standardizes the parameter data of the input model.

[0171] Data calculation module: mainly realizes the acquisition of processed data and obtains the diagnostic results of the data through the preset diagnostic model algorithm;

[0172] Weight setting module: mainly realizes the setting of new multi-dimensional weights such as the power trading market environment of different types of power plants;

[0173] Strategy analysis module: mainly realizes the knowledge and experience accumulated after reviewing and analyzing the power trading strategy, and analyzes the existing problems and suggestions of the strategy.

[0174] Data output module: mainly realizes the transmission of the calculated scoring data, and displays and ranks the scoring data of the power benefits or values ​​of different types of new energy power stations.

[0175] Through the comprehensive evaluation system of the transaction value of the power market of the new energy power station, the transaction scores and rankings of different types and different stations on a single day, month, quarter and year can be obtained quickly, which greatly improves the evaluation workload and the evaluation efficiency and quality. Compared with the existing technology that requires a large amount of data collection, collation and analysis, the present invention greatly reduces the workload and improves the efficiency. In addition, other factors such as the market environment are comprehensively considered to provide a more comprehensive evaluation result. Compared with the existing technology that only relies on a single data for evaluation, the evaluation result of the present invention is more accurate and can better reflect the real transaction situation of the new energy power station. In addition, the evaluation system can automatically give the possible problems of the trading strategy, and provide a basis for the targeted optimization and improvement of the trading strategy for the new energy investment and operation enterprises. Compared with the lack of comprehensive consideration of the trading strategy capabilities of the new energy power station in the existing technology, the evaluation system of the present invention can better meet the needs of the new energy investment and operation enterprises. In addition, the evaluation system can be applied to the evaluation of the transaction situation of different types and a large number of power stations, and has good flexibility and adaptability. Compared with the evaluation needs of different types and a large number of power stations that cannot adapt to the existing technology, the evaluation system of the present invention has a wider range of applications.

[0176] Although this specification provides examples such as the following embodiments or the attached Figure 8 The method operation steps or device structure shown in the figure, but based on routine or no creative labor, the method or device may include more or fewer operation steps or module units after partial merger. In the steps or structures that do not logically have a necessary causal relationship, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments of this specification or the drawings. When the method or module structure described is applied to an actual device, server or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, a parallel processor or multi-threaded processing environment, or even a distributed processing, server cluster implementation environment). Based on the above-mentioned comprehensive evaluation method for the power market transaction value of a new energy power station, the embodiments of this specification also propose an embodiment of a comprehensive evaluation device for the power market transaction value of a new energy power station. As Figure 8 As shown, the device may specifically include the following modules:

[0177] The acquisition module 801 can be used to obtain various evaluation index data of different new energy sites in different time periods;

[0178] The determination module 802 may be used to determine the comprehensive weight of each evaluation index data among the plurality of evaluation index data, so as to select the target evaluation index data among the evaluation index data according to the comprehensive weight;

[0179] The diagnosis module 803 may be used to input the target evaluation index data into a diagnosis model and output a diagnosis result of the target evaluation index data;

[0180] The evaluation module 804 can be used to evaluate the transaction value of the electricity market of different new energy sites in different time periods according to the diagnosis results to obtain evaluation results.

[0181] In some embodiments, the various evaluation index data in the above-mentioned acquisition module 801 may include but are not limited to: electricity index data, resource index data, and strategy index data. The electricity index data may include but are not limited to: base electricity, medium- and long-term trading electricity, spot settlement electricity, actual online electricity, equivalent utilization hours, and electricity limit.

[0182] In some embodiments, the above-mentioned determination module 802 can be specifically used to construct a first matrix based on multiple evaluation index data, solve the weight vector of each evaluation index data based on the first matrix, and obtain the subjective weight vector of each evaluation index data; take the power market transaction value as the evaluation object, and take the evaluation index data corresponding to the power market transaction value as the evaluation index variable, and construct a second matrix based on the evaluation object and the evaluation index variable; according to the second matrix, calculate the objective weight vector of each evaluation index data; determine the comprehensive weight of each evaluation index data based on the subjective weight vector and the objective weight vector.

[0183] In some embodiments, the above-mentioned determination module 802 can also be specifically used to transform the first matrix to obtain a decision matrix; construct a weighted specification matrix based on the comprehensive weight and the decision matrix; determine the pros and cons of each evaluation index data based on the weighted specification matrix; sort each evaluation index data based on the pros and cons values, and select evaluation index data greater than a preset sorting threshold from the sorting results as target evaluation index data.

[0184] In some embodiments, the diagnostic model in the above-mentioned diagnostic module 803 is trained in the following manner: obtaining a variety of historical evaluation index data of different new energy stations in different time periods and abnormal labels corresponding to the historical evaluation index data; performing data preprocessing on the historical evaluation index data to obtain a training data set; training a support vector machine based on the training data set, and obtaining a diagnostic model when the accuracy of the trained support vector machine meets a preset accuracy threshold.

[0185] In some embodiments, the evaluation module 804 can be specifically used to evaluate the power market transaction value of different new energy sites in different time periods according to the abnormal situation when there is an abnormal situation in the diagnosis result to obtain an evaluation result.

[0186] In some embodiments, the above-mentioned evaluation module 804 can also be used to rank different new energy stations according to different time periods based on the evaluation results to obtain ranking results; formulate an evaluation index data optimization strategy for the different new energy stations in the corresponding time periods based on the ranking results; and optimize the evaluation index data based on the evaluation index data optimization strategy.

[0187] It can be seen from the above that a comprehensive evaluation device for the transaction value of the electricity market of a new energy power station provided in the embodiment of this specification can accurately and efficiently evaluate the transaction value of the electricity market of a new energy power station, thereby effectively reflecting the operation status and transaction efficiency of the electricity market of a new energy power station.

[0188] The embodiments of this specification also provide an electronic device based on the comprehensive evaluation method of the electricity market transaction value of the above-mentioned new energy power station, including a processor and a memory for storing processor executable programs / instructions. When the processor is implemented, it can perform the following steps according to the program / instructions: obtain a variety of evaluation index data of different new energy sites in different time periods; determine the comprehensive weight of each evaluation index data in the multiple evaluation index data, and screen the target evaluation index data in each evaluation index data according to the comprehensive weight; input the target evaluation index data into the diagnosis model, and output the diagnosis result of the target evaluation index data; according to the diagnosis result, evaluate the electricity market transaction value of different new energy sites in different time periods to obtain the evaluation result.

[0189] In order to complete the above instructions more accurately, refer to Fig. 9 As shown, the embodiment of this specification also provides another specific electronic device, wherein the electronic device includes a network communication port 901, a processor 902 and a memory 903, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0190] The network communication port 901 can be used to obtain various evaluation index data of different new energy sites at different time periods;

[0191] The processor 902 may be specifically used to determine a comprehensive weight of each evaluation index data among the multiple evaluation index data, so as to screen target evaluation index data among the evaluation index data according to the comprehensive weight; input the target evaluation index data into the diagnosis model, and output a diagnosis result of the target evaluation index data; and evaluate the power market transaction value of different new energy sites in different time periods according to the diagnosis result to obtain an evaluation result;

[0192] The memory 903 may be specifically used to store corresponding instruction programs.

[0193] In this embodiment, the network communication port 901 can be a virtual port that is bound to different communication protocols so that different data can be sent or received. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0194] In this embodiment, the processor 902 may be implemented in any appropriate manner. For example, the processor may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not limit this.

[0195] In this embodiment, the memory 903 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function that has no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0196] The embodiments of this specification also provide a computer storage medium based on the comprehensive evaluation method of the electricity market transaction value of the above-mentioned new energy power station, and the computer storage medium stores a computer program / instruction, which, when executed, implements: obtaining a variety of evaluation index data of different new energy sites in different time periods; determining the comprehensive weight of each evaluation index data in the multiple evaluation index data, so as to screen the target evaluation index data in each evaluation index data according to the comprehensive weight; inputting the target evaluation index data into the diagnosis model, and outputting the diagnosis result of the target evaluation index data; and evaluating the electricity market transaction value of different new energy sites in different time periods according to the diagnosis result to obtain the evaluation result.

[0197] In this embodiment, the storage medium includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD), or a memory card. The memory may be used to store computer program instructions. The network communication unit may be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0198] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer storage medium can be explained in comparison with other implementations and will not be described in detail here.

[0199] The embodiments of this specification also provide a computer program product based on the comprehensive evaluation method of the electricity market transaction value of the above-mentioned new energy power station, including a non-transitory computer-readable storage medium storing a computer program / instruction, wherein the computer program / instruction can be operated to enable the computer to perform the following steps: obtaining a variety of evaluation index data of different new energy sites in different time periods; determining the comprehensive weight of each evaluation index data in the multiple evaluation index data, so as to screen the target evaluation index data in each evaluation index data according to the comprehensive weight; inputting the target evaluation index data into the diagnosis model, and outputting the diagnosis result of the target evaluation index data; and evaluating the electricity market transaction value of different new energy sites in different time periods according to the diagnosis result to obtain the evaluation result.

[0200] Although the present specification provides method operation steps as described in the embodiments or flow charts, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps, and does not represent a unique execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. The first, second, etc. words are used to represent the name, and do not represent any particular order.

[0201] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0202] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0203] Through the description of the above embodiments, it can be known that those skilled in the art can clearly understand that the present specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present specification can essentially be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present specification or some parts of the embodiments.

[0204] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. This specification can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0205] Although the present specification is described through embodiments, persons skilled in the art will appreciate that there are many variations of the present specification without departing from the spirit of the present specification, and it is intended that the appended claims include these variations without departing from the spirit of the present specification.

Claims

1. A comprehensive evaluation method for the transaction value of the power market of a new energy power station, characterized in that: include: Obtain various evaluation index data of different new energy sites at different time periods; Determining a comprehensive weight of each evaluation indicator data among the plurality of evaluation indicator data, so as to screen target evaluation indicator data among the evaluation indicator data according to the comprehensive weight; Inputting the target evaluation index data into a diagnosis model, and outputting a diagnosis result of the target evaluation index data; According to the diagnosis results, the power market transaction values ​​of different new energy sites in different time periods are evaluated to obtain evaluation results.

2. The method according to claim 1, characterized in that The multiple evaluation index data include but are not limited to: electricity index data, resource index data, and strategy index data. The electricity index data include but are not limited to: base electricity, medium- and long-term transaction electricity, spot settlement electricity, actual online electricity, equivalent utilization hours, and electricity limit.

3. The method according to claim 1, characterized in that Determining the comprehensive weight of each evaluation index data in the plurality of evaluation index data includes: Constructing a first matrix according to the multiple evaluation index data, solving the weight vector of each evaluation index data according to the first matrix, and obtaining the subjective weight vector of each evaluation index data; Taking the power market transaction value as the evaluation object, taking the evaluation index data corresponding to the power market transaction value as the evaluation index variable, and constructing a second matrix according to the evaluation object and the evaluation index variable; According to the second matrix, an objective weight vector of each evaluation index data is calculated; The comprehensive weight of each evaluation index data is determined according to the subjective weight vector and the objective weight vector.

4. The method according to claim 3, characterized in that The step of screening target evaluation index data from among the evaluation index data according to the comprehensive weight comprises: Transforming the first matrix to obtain a decision matrix; constructing a weighted normative matrix according to the comprehensive weights and the decision matrix; Determine the merits and demerits of each evaluation index data according to the weighted normative matrix; According to the merits, the evaluation index data are sorted, and the evaluation index data greater than a preset sorting threshold are selected from the sorting results as target evaluation index data.

5. The method according to claim 1, characterized in that The diagnostic model is trained in the following manner: Obtaining a variety of historical evaluation index data of different new energy sites in different time periods and abnormal labels corresponding to the historical evaluation index data; Performing data preprocessing on the historical evaluation index data to obtain a training data set; A support vector machine is trained based on the training data set, and a diagnosis model is obtained when the accuracy of the trained support vector machine meets a preset accuracy threshold.

6. The method according to claim 1, characterized in that According to the diagnosis results, the power market transaction values ​​of different new energy sites in different time periods are evaluated to obtain evaluation results, including: When there are abnormal situations in the diagnosis results, the transaction values ​​of the electricity market of different new energy sites in different time periods are evaluated according to the abnormal situations to obtain evaluation results.

7. The method according to claim 1, characterized in that The method further comprises: According to the evaluation results, different new energy stations are ranked according to different time periods to obtain ranking results; According to the ranking results, formulate an optimization strategy for evaluation index data of the different new energy sites in the corresponding time period; The evaluation index data is optimized based on the evaluation index data optimization strategy.

8. A comprehensive evaluation device for the transaction value of the power market of a new energy power station, characterized in that: include: An acquisition module is used to obtain various evaluation index data of different new energy sites at different time periods; A determination module, used to determine the comprehensive weight of each evaluation index data among the multiple evaluation index data, so as to screen the target evaluation index data among the evaluation index data according to the comprehensive weight; A diagnosis module, used for inputting the target evaluation index data into a diagnosis model and outputting a diagnosis result of the target evaluation index data; The evaluation module is used to evaluate the power market transaction value of different new energy sites in different time periods according to the diagnosis results to obtain evaluation results.

9. A computer device comprising a memory, a processor and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images