Evaluation method and device of multi-energy complementary system, computer equipment and storage medium

Abstract

The embodiment of the invention provides an evaluation method and device of a multi-energy complementary system, computer equipment, a storage medium and a computer program product, and relates to the technical field of new energy power generation. The method comprises the following steps: acquiring a plurality of target evaluation indexes for a to-be-evaluated multi-energy complementary system; obtaining a subjective weight value of each target evaluation index; constructing a projection index function of the to-be-evaluated multi-energy complementary system according to the subjective weight value; obtaining an optimal objective weight value of each target evaluation index by using a projection index function and a particle swarm algorithm; and obtaining a system evaluation result of the to-be-evaluated multi-energy complementary system according to the optimal objective weight value and the subjective weight value. According to the method, the accuracy of system evaluation can be improved.

Description

Technical Field

[0001] The present application relates to the field of renewable energy power generation technology, and in particular to an evaluation method, device, computer equipment, storage medium and computer program product for a multi-energy complementary system. Background Art

[0002] For some multi-energy complementary systems, such as wind, solar, and water multi-energy complementary systems, wind, solar, and water multi-energy complementary systems achieve diversified utilization and complementarity of energy by integrating multiple energy sources, such as solar energy, wind energy, and water energy, and have higher reliability, sustainability, and flexibility. In order to give full play to the advantages of wind, solar, and water multi-energy complementary systems, it is necessary to evaluate and analyze their performance and capabilities.

[0003] There are some problems in the current multi-energy complementary system evaluation technology, such as incomplete evaluation indicators, unclear weights, and inconsistent standards, which may lead to inaccurate evaluation. Summary of the invention

[0004] Based on this, it is necessary to provide an evaluation method, device, computer equipment, storage medium and computer program product for a multi-energy complementary system in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating a multi-energy complementary system. The method comprises:

[0006] Obtain multiple target evaluation indicators for the multi-energy complementary system to be evaluated;

[0007] Obtaining the subjective weight value of each target evaluation indicator;

[0008] According to the subjective weight value, constructing the projection index function of the multi-energy complementary system to be evaluated;

[0009] Using the projection index function and particle swarm algorithm, the optimal objective weight value of each target evaluation index is obtained;

[0010] According to the optimal objective weight value and the subjective weight value, a system evaluation result of the multi-energy complementary system to be evaluated is obtained.

[0011] In one of the embodiments, there are multiple system setting schemes for the multi-energy complementary system to be evaluated; the system evaluation result of the multi-energy complementary system to be evaluated is obtained according to the optimal objective weight value and the subjective weight value, including: according to the optimal objective weight value and the subjective weight value, obtaining the system evaluation sub-result of each of the multiple system setting schemes; according to the system evaluation sub-results, obtaining the system evaluation result of the multi-energy complementary system to be evaluated.

[0012] In one of the embodiments, the obtaining of multiple target evaluation indicators for the multi-energy complementary system to be evaluated includes: obtaining multiple system evaluation indicators for the multi-energy complementary system to be evaluated; obtaining a normalized indicator value of each of the multiple system evaluation indicators; and obtaining the multiple target evaluation indicators for the multi-energy complementary system to be evaluated based on the normalized indicator value.

[0013] In one of the embodiments, the method of obtaining a plurality of target evaluation indicators for the multi-energy complementary system to be evaluated based on the normalized indicator value includes: obtaining a correlation between each system evaluation indicator and the multi-energy complementary system to be evaluated based on the normalized indicator value; and screening out a plurality of target evaluation indicators of the multi-energy complementary system to be evaluated from the plurality of system evaluation indicators based on the correlation.

[0014] In one embodiment, obtaining the subjective weight value of each of the target evaluation indicators includes: constructing a feature comparison matrix based on the relative importance between the multiple target evaluation indicators; performing a consistency check on the feature comparison matrix, and when the consistency check results in a passed check, obtaining the subjective weight value corresponding to each of the target evaluation indicators based on the feature comparison matrix.

[0015] In one of the embodiments, constructing the projection index function of the multi-energy complementary system to be evaluated based on the subjective weight value includes: constructing a one-dimensional projection value function of the multi-energy complementary system to be evaluated; constructing the projection index function of the multi-energy complementary system to be evaluated based on the subjective weight value and the one-dimensional projection value function.

[0016] In a second aspect, the present application provides an evaluation device for a multi-energy complementary system. The device comprises:

[0017] An acquisition module, used for acquiring multiple target evaluation indicators for the multi-energy complementary system to be evaluated;

[0018] A calculation module, used to obtain the subjective weight value of each target evaluation indicator;

[0019] A function construction module, used to construct a projection index function of the multi-energy complementary system to be evaluated according to the subjective weight value;

[0020] An optimization module, used to obtain the optimal objective weight value of each target evaluation index by using the projection index function and the particle swarm algorithm;

[0021] An evaluation module is used to obtain a system evaluation result of the multi-energy complementary system to be evaluated according to the optimal objective weight value and the subjective weight value.

[0022] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0023] Obtain multiple target evaluation indicators for the multi-energy complementary system to be evaluated;

[0024] Obtaining the subjective weight value of each target evaluation indicator;

[0025] According to the subjective weight value, constructing the projection index function of the multi-energy complementary system to be evaluated;

[0026] Using the projection index function and particle swarm algorithm, the optimal objective weight value of each target evaluation index is obtained;

[0027] According to the optimal objective weight value and the subjective weight value, a system evaluation result of the multi-energy complementary system to be evaluated is obtained.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0029] Obtain multiple target evaluation indicators for the multi-energy complementary system to be evaluated;

[0030] Obtaining the subjective weight value of each target evaluation indicator;

[0031] According to the subjective weight value, constructing the projection index function of the multi-energy complementary system to be evaluated;

[0032] Using the projection index function and particle swarm algorithm, the optimal objective weight value of each target evaluation index is obtained;

[0033] According to the optimal objective weight value and the subjective weight value, a system evaluation result of the multi-energy complementary system to be evaluated is obtained.

[0034] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0035] Obtain multiple target evaluation indicators for the multi-energy complementary system to be evaluated;

[0036] Obtaining the subjective weight value of each target evaluation indicator;

[0037] According to the subjective weight value, constructing the projection index function of the multi-energy complementary system to be evaluated;

[0038] Using the projection index function and particle swarm algorithm, the optimal objective weight value of each target evaluation index is obtained;

[0039] According to the optimal objective weight value and the subjective weight value, a system evaluation result of the multi-energy complementary system to be evaluated is obtained.

[0040] In the above-mentioned evaluation method, device, computer equipment, storage medium and computer program product for the multi-energy complementary system, first, multiple target evaluation indicators for the multi-energy complementary system to be evaluated can be obtained; then, the subjective weight value of each target evaluation indicator can be obtained; according to the subjective weight value, a projection index function of the multi-energy complementary system to be evaluated can be constructed; and then, the projection index function and the particle swarm algorithm can be used to obtain the optimal objective weight value of each target evaluation indicator; according to the optimal objective weight value and the subjective weight value, the system evaluation result of the multi-energy complementary system to be evaluated can be obtained. In the method provided in the embodiment of the present application, multiple system evaluation indicators of the multi-energy complementary system to be evaluated can be screened to obtain multiple target evaluation indicators, thereby improving the accuracy of indicator determination. In addition, based on the subjective weight value and the objective weight value of each target evaluation indicator, the multi-energy complementary system to be evaluated can be evaluated in multiple dimensions, and the objective weight value can also be optimized by using the projection index function and the particle swarm algorithm, thereby improving the accuracy of system evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of an evaluation method for a multi-energy complementary system provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a process for obtaining a target evaluation index provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of a process for obtaining target evaluation indicators using a grey correlation analysis method provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of a process for obtaining a subjective weight value provided in an embodiment of the present application;

[0046] Figure 5 A structural block diagram of an evaluation device for a multi-energy complementary system provided in an embodiment of the present application;

[0047] Figure 6 An internal structure diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] In an exemplary embodiment, Figure 1 As shown, a method for evaluating a multi-energy complementary system is provided. This embodiment uses the method applied to a server as an example for illustration. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0050] Step 102: obtaining a plurality of target evaluation indicators for the multi-energy complementary system to be evaluated.

[0051] Among them, in this step, first, multiple system evaluation indicators of the multi-energy complementary system to be evaluated (for example, a wind-solar-water multi-energy complementary system) can be obtained. The indicator types of the multiple system evaluation indicators may include but are not limited to natural resource potential evaluation indicators, system performance indicators, system reliability evaluation indicators, system flexibility evaluation indicators, and system economic evaluation indicators. The natural resource potential evaluation indicators of the multi-energy complementary system to be evaluated may include but are not limited to wind power resource evaluation indicators, photovoltaic resource evaluation indicators, hydropower resource evaluation indicators, wind-solar complementary evaluation indicators, photovoltaic-water complementary evaluation indicators, and water-wind complementary evaluation indicators. Specifically, the wind power resource evaluation indicators include wind energy size, wind energy utilization rate, and wind energy volatility. The size of wind energy is affected by many factors, among which the key factors are wind speed, air pressure, and ambient temperature. See equations (1) to (3). Equation (1) is used to obtain the average wind speed for one year; Equation (2) is used to obtain the average air pressure for one year; and Equation (3) is used to obtain the average ambient temperature for one year.

[0052]

[0053] in, is the average wind speed for the year; is the average air pressure for the year; is the average ambient temperature of the year; , and Represent hourly wind speed, air pressure and ambient temperature respectively.

[0054] The wind energy utilization rate is characterized by the effective utilization hours of wind energy. The effective utilization hours of wind energy are the hours when the wind speed is between the cut-in speed and the cut-out speed, which can be seen in formula (4):

[0055]

[0056] in, , are the cut-in and cut-out wind speeds, respectively.

[0057] Wind energy volatility The sum of wind speed differences over a year is used to characterize the wind speed variation characteristics, as shown in equation (5):

[0058]

[0059] The photovoltaic resource evaluation indicators include photovoltaic strength, photovoltaic utilization rate and photovoltaic volatility. The photovoltaic strength is affected by many factors, among which the key factors are light radiation intensity and ambient temperature. The average daily radiation intensity of a year is used here. and average ambient temperature To describe, we can refer to equations (6) and (7):

[0060]

[0061] in: and Represent the daily radiation intensity and ambient temperature respectively.

[0062] Photovoltaic utilization rate is the sum of effective sunshine time. To describe, we can refer to formula (8):

[0063]

[0064] in: Indicates the effective utilization time of sunshine every day.

[0065] Photovoltaic volatility The total daily radiation difference of one year is used to characterize the variation characteristics of light radiation intensity, as shown in formula (9):

[0066]

[0067] Hydropower resource assessment indicators include hydropower size, hydropower utilization rate and hydropower volatility. The size of hydropower depends mainly on the topography and precipitation of the region. The average annual precipitation is used here. To describe, please refer to formula (10):

[0068]

[0069] in: Indicates hourly precipitation.

[0070] The water energy utilization rate is calculated using the annual effective precipitation hours. To describe, please refer to formula (11):

[0071]

[0072] in: Indicates the number of hours of effective precipitation.

[0073] Hydropower volatility The sum of the precipitation differences of each month in a year is used to characterize the precipitation variation characteristics, as shown in formula (12):

[0074]

[0075] The wind-solar complementarity evaluation index reflects the mutual influence between wind power and photovoltaic power generation, using the wind speed difference between day and night Seasonal difference in wind speed To evaluate the complementary characteristics of wind and light, we can refer to equations (13) and (14):

[0076]

[0077] in: It is the average wind speed from sunset to sunrise on the next day. The average wind speed from sunrise to sunset on the day. is the average wind speed from the end of summer to the end of winter in the following year. It is the average wind speed from the end of winter to the end of summer of that year.

[0078] The photovoltaic-hydro complementarity evaluation index reflects the mutual influence between hydropower and photovoltaic power generation, using the day-night difference in precipitation. , seasonal difference in precipitation and cloudy precipitation rate To evaluate the complementary properties of light and water, we can refer to equations (15)-(17):

[0079]

[0080] in: It is the average precipitation from sunset to sunrise on the next day. It is the average precipitation from sunrise to sunset on the same day. It is the average precipitation from the end of summer to the end of winter in the following year. The average precipitation from the end of winter to the end of summer of the year; is the total number of days with annual precipitation; The total number of cloudy days in a year.

[0081] The water-wind complementarity evaluation index reflects the mutual influence between hydropower and wind power, using the dry season wind speed ratio Compared with the precipitation in the less windy period To evaluate the complementary characteristics of water and wind, we can refer to equations (18) and (19):

[0082]

[0083] in: is the average wind speed during the dry season; is the annual average wind speed; is the average rainfall during the period of low winds; The dry season is the three months with the least continuous rainfall in a year, and the windless season is the three months with the lowest continuous wind speed in a year.

[0084] The performance indicators of the wind, solar and hydropower multi-energy complementary system include wind power generation capacity indicators, photovoltaic power generation capacity indicators, and hydropower generation capacity indicators.

[0085] Wind power generation capacity index It is the ratio of the actual output power of the fan to the theoretical output power of the fan under the same working conditions. The actual output power data of the fan is taken from the fan data acquisition and monitoring control system.

[0086] The power generation performance of a wind turbine is closely related to the wind speed at its location. The wind turbine starts to operate and output electrical energy only when it reaches the cut-in wind speed. When the wind speed is lower than the rated wind speed, the power generation of the unit will change with the wind speed. When the wind speed exceeds the rated value, the unit output power will remain at the rated power level to achieve stable power supply. However, when the wind speed further increases to the cut-out wind speed, the wind turbine must be shut down for the sake of unit safety protection. Therefore, the calculation expression for the theoretical output power of the wind turbine is formula (20):

[0087]

[0088] in: is the fan output power; is the rated power of the fan; , , , are the current wind speed, rated wind speed, cut-in wind speed and cut-out wind speed of the fan respectively; A, B and C are the constant coefficient, first-order coefficient and second-order coefficient of the fan output respectively, and their calculation expressions are (21)-(23):

[0089]

[0090] The calculation expression of wind power generation capacity index is (24):

[0091]

[0092] Where: N is the statistical time; The actual output active power value of the wind turbine generator set within the statistical time length; It is the theoretical power value of the wind turbine within the statistical time length.

[0093] Photovoltaic power generation capacity indicators It is the ratio of the actual output power of the photovoltaic generator set to the theoretical output power of the photovoltaic generator set under the same working conditions. The actual output power data of the photovoltaic generator set is taken from the data acquisition and monitoring control system of the photovoltaic generator set.

[0094] The output power of a photovoltaic power generation system is mainly affected by the light intensity, the light receiving area of ​​the solar cell and the photoelectric conversion efficiency. The calculation expression for the theoretical output power of a photovoltaic system is (25):

[0095]

[0096] in: is the light intensity; is the light receiving area of ​​the solar cell; is the photoelectric conversion efficiency.

[0097] Beta distribution is generally used to simulate light intensity. The probability density function and cumulative distribution function of are (26) and (27):

[0098]

[0099] in: , is the shape parameter of the Beta distribution; is the maximum value of light intensity.

[0100] The calculation expression of photovoltaic power generation capacity index is:

[0101]

[0102] Where: N is the statistical time; The actual output active power value of the photovoltaic generator set within the statistical time length; It is the theoretical power value of the photovoltaic generator set within the statistical time length.

[0103] Hydropower Capacity Index It is the ratio of the actual output power of the hydropower station to the theoretical output power of the hydropower station under the same working conditions. The actual output power data of the hydropower station is taken from the data acquisition and monitoring control system of the hydropower station.

[0104] The calculation expression of the theoretical output power of the hydropower station is (29):

[0105]

[0106] in: is the density of water; is the acceleration due to gravity; is the height difference of the water from the reservoir to the turbine; is the amount of water flowing through the turbine per unit time; is the efficiency of the turbine.

[0107] The calculation expression of the hydropower generation capacity index is (30):

[0108]

[0109] Where: N is the statistical time; The actual output active power value of the hydropower station within the statistical time length; It is the theoretical power value of the hydropower station within the statistical time length.

[0110] The reliability indicators of wind, solar and hydropower multi-energy complementary systems include energy volatility, mean time between failures and power supply availability.

[0111] Energy volatility refers to the degree of power variation of renewable energy on a unit time scale, and its calculation expression is (31):

[0112]

[0113] in: is the output power of distributed renewable energy in the distribution network at the current moment; is the output power of distributed renewable energy in the distribution network at the previous moment.

[0114] The mean time between failures refers to the ratio of the total operating time to the number of failures during continuous operation, indicating how often a failure occurs on average when the system is operating continuously and normally. Its calculation expression is (32):

[0115]

[0116] in: is the number of generating units; is the running time; is the total number of faults that occurred in the tested unit.

[0117] Power supply availability is an indicator that measures the ability of a system or device to provide normal power services over a period of time. It reflects the proportion of the system or device in normal operation during the statistical time, and its calculation expression is (32):

[0118]

[0119] in: , Characterize the load points The number of users and the equivalent annual average failure downtime; T represents the statistical time.

[0120] The flexibility indicators of the wind, solar and hydropower multi-energy complementary system include energy consumption rate, system flexibility and energy complementarity rate.

[0121] Energy absorption rate refers to the ability of the power grid to absorb the output power of renewable energy, and its calculation expression is (33):

[0122]

[0123] in: The rated power of renewable energy connected to the grid; is the maximum load power of the power grid.

[0124] The system flexibility specifically includes upward flexibility margin, downward flexibility margin, upward flexibility deficiency and downward flexibility deficiency, and its calculation expressions are (34)-(37):

[0125]

[0126] in: , , , They are upward flexibility adequacy, downward flexibility adequacy, upward flexibility insufficiency, and downward flexibility insufficiency; , , and It is upward flexibility margin, downward flexibility margin, upward flexibility deficit, and downward flexibility deficit; , , , They are respectively adjusting upward the time for sufficient flexibility, adjusting downward the time for sufficient flexibility, adjusting upward the time for insufficient flexibility, and adjusting downward the time for insufficient flexibility.

[0127] The energy complementarity rate can evaluate the multi-energy complementarity effect of the system. It uses the difference between the output curve and the load curve to characterize this effect. Its calculation expression is (38):

[0128]

[0129] in: for arrive The output value of wind, solar and water multi-energy complementary system; for arrive The load value.

[0130] The economic evaluation indicators of wind, solar and water multi-energy complementary systems include direct economic benefits and green economic benefits , and its calculation expression is (39)-(43):

[0131]

[0132] in: Represents the income from renewable energy power generation, of which The on-grid electricity price of new energy It is the total annual power generation under the typical output of renewable energy; represents the total annual cost of new energy, where For Node Equipment investment costs; For Node Installation costs; For Node Operation and maintenance costs; is the rated installed capacity of renewable energy; is the total number of installed renewable energy sources; is the investment recovery factor; is the discount rate; The useful life of the new energy source; It is The environmental value of the pollutants; It is The amount of pollutants emitted; It is Fines for pollutants are the types of pollutants. The amount of pollutant emissions is calculated by the amount of pollutants emitted by coal-fired power generation when the power generation is the same as that of new energy.

[0133] In this step, a variety of system setting schemes for the multi-energy complementary system to be evaluated can be obtained. For each of the multiple system setting schemes, according to the degree of correlation between each system evaluation indicator among the multiple system evaluation indicators of the multi-energy complementary system to be evaluated and the multi-energy complementary system to be evaluated, a plurality of target evaluation indicators having a correlation degree greater than a correlation degree threshold value can be screened out from the multiple system evaluation indicators of the multi-energy complementary system to be evaluated. In a possible implementation, a grey correlation analysis method can be used to select multiple target evaluation indicators from the multiple system evaluation indicators. Specifically, the original system data of the multi-energy complementary system to be evaluated in a preset time period can be obtained, and the analysis basis data of each system evaluation indicator can be obtained based on the original system data; and the deviation standardization method is used to normalize the analysis basis data to obtain the normalized indicator value of each system evaluation indicator in the multiple system evaluation indicators; next, the indicator values ​​of multiple historical indicators of the multi-energy complementary system to be evaluated can be used; according to the indicator values ​​of the multiple historical indicators, a reference sequence of the multi-energy complementary system to be evaluated can be obtained, and according to the multiple normalized indicator values ​​corresponding to the multiple system evaluation indicators, a comparison sequence of the multi-energy complementary system to be evaluated can be obtained; thus, according to the reference sequence of the multi-energy complementary system to be evaluated and the comparison sequence of the multi-energy complementary system to be evaluated, the grey correlation coefficient between each system evaluation indicator in the multiple system evaluation indicators and the reference sequence can be obtained, which can be seen in formula (44), and then, according to the grey correlation coefficient between each system evaluation indicator and the reference sequence, the correlation degree of each system evaluation indicator can be obtained, which can be seen in formula (45).

[0134]

[0135] in, is the comparison sequence of the multi-energy complementary system to be evaluated; It is the absolute difference of the target evaluation index of the wind, solar and water multi-energy complementary system; is the grey correlation coefficient of the jth system evaluation index; This is the first System settings options.

[0136]

[0137] in, is the comparison sequence of the multi-energy complementary system to be evaluated; For the Grey correlation coefficient of system evaluation indicators; For the The correlation between the system evaluation index and the multi-energy complementary system to be evaluated; This is the first System settings options.

[0138] Step 104, obtaining the subjective weight value of each target evaluation indicator.

[0139] Among them, in a possible implementation method, obtaining the subjective weight value of each target evaluation indicator may include: constructing a feature comparison matrix according to the relative importance between multiple target evaluation indicators; performing a consistency check on the feature comparison matrix, and when the consistency check result is a passed check, obtaining the subjective weight value corresponding to each target evaluation indicator based on the feature comparison matrix. Specifically, trapezoidal fuzzy numbers are used to quantitatively represent the degree of importance of experts for pairwise comparisons of any two indicators among the multiple target evaluation indicators mentioned above, to construct a feature comparison matrix, which can also be called a judgment matrix, and to set expert weights using a peer consensus model. In the form of pairwise comparisons of expert opinions, the two experts with the smallest opinion differences are extracted, and then the judgment matrix of the two experts is corrected to ensure the consistency of expert opinions. Expert With experts The calculation of opinion consistency can be found in formula (46):

[0140]

[0141] in, is the number of evaluation indicators for wind, solar and water multi-energy complementary systems; is the identity matrix; is the judgment matrix; T is the transposed vector; Be an expert With experts Judgment matrix of opinions; respectively The judgment coefficient. The smaller the value, the higher the consistency. With experts The consensus of opinions is the minimum. is the maximum value, then use the following formula to correct it, see formula (47) and formula (48):

[0142]

[0143] in, is the correction parameter; Indicates the indicator number.

[0144] Furthermore, the consensus threshold of experts can be set ,generally, The value of is 1. Less than or equal to , it indicates that there is a consensus between the two experts and the consistency of opinions meets the application requirements. At this time, the subjective weight value of each target evaluation indicator can be obtained through formula (49).

[0145]

[0146] Step 106: construct a projection index function of the multi-energy complementary system to be evaluated according to the subjective weight value.

[0147] Among them, the projection index function of multiple target evaluation indicators of the multi-energy complementary system to be evaluated can be constructed first. Specifically, in the first step, a one-dimensional projection value function of multiple target evaluation indicators of the multi-energy complementary system to be evaluated can be constructed, which can be seen from formula (50):

[0148]

[0149] in, Can be the first The projection direction of the target evaluation index can represent the objective weight value of the target evaluation index; Can be the System settings, Normalized index value of each target evaluation index; Can be the first The subjective weight value of each target evaluation indicator; Can be the The one-dimensional projection value of the system setting scheme; the one-dimensional projection value can be used as the The system evaluation sub-results of the multi-energy complementary system to be evaluated under the system setting scheme; the system evaluation sub-results may include The comprehensive score of the multi-energy complementary system to be evaluated under the system setting scheme.

[0150] Next, the standard deviation and local density of the one-dimensional projection value function can be obtained, which can be seen in formulas (51) and (52) respectively.

[0151]

[0152] in, for The mean of .

[0153]

[0154] in, is the unit step function; for The window radius of the local density; is the sample distance. The setting of needs to ensure that the number of sample points in the window is moderate. When the number of sample points of the target evaluation index of the wind, solar and water multi-energy complementary system increases, the change of sample points in the window needs to be stable. Its value can be set to one tenth of the variance of the projection eigenvalues ​​of all samples.

[0155] Furthermore, the projection index function of multiple target evaluation indicators of the multi-energy complementary system to be evaluated can be constructed based on the standard deviation and local density of the one-dimensional projection value function and the subjective weight value, as shown in formula (53).

[0156]

[0157] The projection index function can be further optimized. The optimal direction of projection is set by the maximum value of the product of the standard deviation and the local density, as shown in equation (54):

[0158]

[0159] The penalty function method is used to transform the above equation into an objective function without constraints, as shown in equation (55):

[0160]

[0161] in: is the penalty vector. The solution of this equation belongs to nonlinear optimization problem, which is difficult to handle by conventional methods.

[0162] Step 108, using the projection index function and the particle swarm algorithm to obtain the optimal objective weight value of each target evaluation index.

[0163] In this step, the particle swarm algorithm can be used to achieve global optimization of the optimal projection direction and optimize the projection index function. Specifically, the projection direction population size of the wind-solar-water multi-energy complementary system evaluation index is set to ,set up Retrieve the optimal projection direction in the G-dimensional space. The projection direction of the evaluation index of the wind, solar and water multi-energy complementary system can be regarded as the The speed and position of particles are , Combined with the retrieval mechanism of the particle swarm algorithm, the update results of speed and position are as follows:

[0164]

[0165] in: It is a particle The speed of the moment; It is a particle The location at the moment; is the weight of the distance between the current position and the local optimal position when the speed changes, It is the weight of the distance between the current position and the global optimal position when the speed changes. They are the local optimal position and the global optimal position of the particle swarm, and their calculation formulas are equations (57) and (58) respectively:

[0166]

[0167] in: is the fitness function. Introducing the weight into the speed update process, we can get equation (59):

[0168]

[0169] When the weight changes, the global and local optimal retrieval capabilities of the algorithm are balanced. When the weight is adjusted using an adaptive scheme, the weight will decrease linearly with the change of the number of iterations. The weight calculation formula is (60):

[0170]

[0171] in: The maximum and minimum weights are in turn; They are the maximum number of iterations and the current number of iterations.

[0172] By using the above method, the optimal objective weight value of the multi-energy complementary system to be evaluated under various system setting schemes can be obtained.

[0173] Step 110, obtaining a system evaluation result of the multi-energy complementary system to be evaluated according to the optimal objective weight value and the subjective weight value.

[0174] Among them, when obtaining the optimal objective weight value of the multi-energy complementary system to be evaluated under various system setting schemes, the system evaluation sub-results of the multi-energy complementary system to be evaluated under various system setting schemes can be obtained according to formula (50), and the system setting scheme with the highest comprehensive score included in the system evaluation sub-results is determined as the target system setting scheme of the multi-energy complementary system to be evaluated, and the system evaluation sub-results of the multi-energy complementary system to be evaluated under the target system setting scheme are determined as the system evaluation results of the multi-energy complementary system to be evaluated.

[0175] In the method of this embodiment, first, multiple target evaluation indicators for the multi-energy complementary system to be evaluated can be obtained; then, the subjective weight value of each target evaluation indicator can be obtained; according to the subjective weight value, a projection index function of the multi-energy complementary system to be evaluated can be constructed; and then, the projection index function and the particle swarm algorithm can be used to obtain the optimal objective weight value of each target evaluation indicator; according to the optimal objective weight value and the subjective weight value, the system evaluation result of the multi-energy complementary system to be evaluated can be obtained. In the method provided by the embodiment of the present application, multiple system evaluation indicators of the multi-energy complementary system to be evaluated can be screened to obtain multiple target evaluation indicators, thereby improving the accuracy of indicator determination. In addition, based on the subjective weight value and the objective weight value of each target evaluation indicator, a multi-dimensional evaluation of the multi-energy complementary system to be evaluated can be performed, and the objective weight value can also be optimized by using the projection index function and the particle swarm algorithm, thereby improving the accuracy of system evaluation.

[0176] In an exemplary embodiment, step 110 may include:

[0177] According to the optimal objective weight value and the subjective weight value, a system evaluation sub-result of each system setting scheme in a plurality of system setting schemes is obtained; according to the system evaluation sub-result, a system evaluation result of the multi-energy complementary system to be evaluated is obtained.

[0178] Among them, when obtaining the optimal objective weight value of the multi-energy complementary system to be evaluated under various system setting schemes, the system evaluation sub-results of the multi-energy complementary system to be evaluated under various system setting schemes can be obtained according to formula (50), and the system setting scheme with the highest comprehensive score included in the system evaluation sub-results is determined as the target system setting scheme of the multi-energy complementary system to be evaluated, and the system evaluation sub-results of the multi-energy complementary system to be evaluated under the target system setting scheme are determined as the system evaluation results of the multi-energy complementary system to be evaluated.

[0179] In the method of this embodiment, multiple system setting schemes of the multi-energy complementary system to be evaluated can be obtained, and the system evaluation sub-results of the multi-energy complementary system to be evaluated under each system setting scheme can be obtained. Then, the system evaluation sub-results corresponding to the system setting scheme with the highest comprehensive score are used as the system evaluation results of the multi-energy complementary system to be evaluated, thereby improving the comprehensiveness, adequacy and accuracy of the system evaluation.

[0180] In an exemplary embodiment, Figure 2 As shown, step 102 may include steps 202 to 206. Among them:

[0181] Step 202: obtaining a plurality of system evaluation indicators for the multi-energy complementary system to be evaluated.

[0182] Step 204: Obtain a normalized index value of each system evaluation index among the multiple system evaluation indexes.

[0183] Among them, the original system data of the multi-energy complementary system to be evaluated in a preset time period can be obtained, and the analysis basis data of each system evaluation index can be obtained according to the original system data; and the deviation standardization method is used to normalize the analysis basis data to obtain the normalized index value of each system evaluation index in multiple system evaluation indexes. The scheme set corresponding to the multiple system setting schemes of the multi-energy complementary system to be evaluated is , the indicator set corresponding to the multiple system evaluation indicators of the multi-energy complementary system to be evaluated is , then System settings No. Target evaluation indicators The index value is recorded as , after normalization and dimensionless processing, we get the set The calculation formula is as follows:

[0184] If the indicator For a benefit-type indicator, the larger the indicator value, the better. The processing formula is shown in formula (61):

[0185]

[0186] If the indicator For a cost-type indicator, the smaller the indicator value, the better. The processing formula is as follows:

[0187]

[0188] Step 206: obtaining multiple target evaluation indicators for the multi-energy complementary system to be evaluated according to the normalized indicator value.

[0189] In the method of this embodiment, multiple system evaluation indicators of the multi-energy complementary system to be evaluated can be screened to obtain multiple target evaluation indicators, thereby improving the accuracy of indicator determination and further improving the accuracy of system evaluation.

[0190] In an exemplary embodiment, Figure 3 As shown, step 206 may include steps 302 to 304. Among them:

[0191] Step 302: Obtain the correlation between each system evaluation index and the multi-energy complementary system to be evaluated according to the normalized index value.

[0192] Step 304 , selecting multiple target evaluation indicators of the multi-energy complementary system to be evaluated from the multiple system evaluation indicators based on the degree of association.

[0193] In this step, a variety of system setting schemes for the multi-energy complementary system to be evaluated can be obtained. For each of the multiple system setting schemes, according to the degree of correlation between each system evaluation indicator among the multiple system evaluation indicators of the multi-energy complementary system to be evaluated and the multi-energy complementary system to be evaluated, a plurality of target evaluation indicators having a correlation degree greater than a correlation degree threshold value can be screened out from the multiple system evaluation indicators of the multi-energy complementary system to be evaluated. In a possible implementation, a grey correlation analysis method can be used to select multiple target evaluation indicators from the multiple system evaluation indicators. Specifically, the original system data of the multi-energy complementary system to be evaluated in a preset time period can be obtained, and the analysis basis data of each system evaluation indicator can be obtained based on the original system data; and the deviation standardization method is used to normalize the analysis basis data to obtain the normalized indicator value of each system evaluation indicator in the multiple system evaluation indicators; next, the indicator values ​​of multiple historical indicators of the multi-energy complementary system to be evaluated can be used; according to the indicator values ​​of the multiple historical indicators, a reference sequence of the multi-energy complementary system to be evaluated can be obtained, and according to the multiple normalized indicator values ​​corresponding to the multiple system evaluation indicators, a comparison sequence of the multi-energy complementary system to be evaluated can be obtained; thus, according to the reference sequence of the multi-energy complementary system to be evaluated and the comparison sequence of the multi-energy complementary system to be evaluated, the grey correlation coefficient between each system evaluation indicator in the multiple system evaluation indicators and the reference sequence can be obtained, which can be seen in formula (44), and then, according to the grey correlation coefficient between each system evaluation indicator and the reference sequence, the correlation degree of each system evaluation indicator can be obtained, which can be seen in formula (45).

[0194] In the method of this embodiment, a grey correlation analysis method can be used to screen out multiple target evaluation indicators from the multiple system evaluation indicators to improve the accuracy of indicator determination, and further, the accuracy of system evaluation can be improved.

[0195] In an exemplary embodiment, Figure 4 As shown, step 104 may include steps 402 to 404. Among them:

[0196] Step 402: construct a feature comparison matrix based on the relative importance of multiple target evaluation indicators.

[0197] Step 404 , performing a consistency check on the feature comparison matrix, and when the consistency check result is a pass, obtaining the subjective weight value corresponding to each target evaluation index based on the feature comparison matrix.

[0198] Specifically, trapezoidal fuzzy numbers are used to quantitatively represent the importance of experts in comparing any two of the above multiple target evaluation indicators. A feature comparison matrix, also called a judgment matrix, is constructed. The peer consensus model is used to set expert weights. The two experts with the smallest opinion difference are extracted in the form of pairwise comparison of expert opinions. The judgment matrix of the two experts is then corrected to ensure the consistency of expert opinions. With experts The calculation of opinion consistency can be found in equation (46). The smaller the value, the higher the consistency; if the expert With experts The consensus of opinions is the minimum. is the maximum value, then the following formula is used for correction, see formula (47) and formula (48); then, the expert consensus threshold can be set ,generally, The value of is 1. Less than or equal to , it indicates that there is a consensus between the two experts and the consistency of opinions meets the application requirements. At this time, the subjective weight value of each target evaluation indicator can be obtained through formula (49).

[0199] In the method of this embodiment, a multi-dimensional evaluation of the multi-energy complementary system to be evaluated can be performed based on the subjective weight value and objective weight value of each target evaluation index, and the objective weight value can also be optimized by using the projection index function and particle swarm algorithm, thereby improving the accuracy of system evaluation.

[0200] In an exemplary embodiment, step 106 may include:

[0201] A one-dimensional projection value function of the multi-energy complementary system to be evaluated is constructed; and a projection index function of the multi-energy complementary system to be evaluated is constructed according to the subjective weight value and the one-dimensional projection value function.

[0202] Among them, the projection index function of multiple target evaluation indicators of the multi-energy complementary system to be evaluated can be constructed first. Specifically, in the first step, a one-dimensional projection value function of multiple target evaluation indicators of the multi-energy complementary system to be evaluated can be constructed; next, the standard deviation and local density of the one-dimensional projection value function can be obtained; further, the projection index function of multiple target evaluation indicators of the multi-energy complementary system to be evaluated can be constructed according to the standard deviation and local density of the one-dimensional projection value function, as well as the subjective weight value.

[0203] In the method of this embodiment, the projection index function and the particle swarm algorithm can be used to optimize the objective weight value, thereby improving the accuracy of system evaluation.

[0204] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0205] Based on the same inventive concept, the embodiment of the present application also provides an evaluation device for a multi-energy complementary system for implementing the evaluation method for a multi-energy complementary system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the evaluation device for one or more multi-energy complementary systems provided below can refer to the limitations of the evaluation method for a multi-energy complementary system above, and will not be repeated here.

[0206] In one embodiment, Figure 5 As shown, an evaluation device for a multi-energy complementary system is provided, including: an acquisition module 502, a calculation module 504, a function construction module 506, an optimization module 508 and an evaluation module 510, wherein:

[0207] An acquisition module 502 is used to acquire multiple target evaluation indicators for the multi-energy complementary system to be evaluated;

[0208] A calculation module 504 is used to obtain the subjective weight value of each target evaluation indicator;

[0209] A function construction module 506 is used to construct a projection index function of the multi-energy complementary system to be evaluated according to the subjective weight value;

[0210] An optimization module 508 is used to obtain the optimal objective weight value of each target evaluation index by using the projection index function and the particle swarm algorithm;

[0211] The evaluation module 510 is used to obtain a system evaluation result of the multi-energy complementary system to be evaluated according to the optimal objective weight value and the subjective weight value.

[0212] In one embodiment, there are multiple system setting schemes for the multi-energy complementary system to be evaluated; the evaluation module 510 is also used to: obtain a system evaluation sub-result of each of the multiple system setting schemes according to the optimal objective weight value and the subjective weight value; and obtain the system evaluation result of the multi-energy complementary system to be evaluated according to the system evaluation sub-results.

[0213] In one embodiment, the acquisition module 502 is also used to: obtain multiple system evaluation indicators for the multi-energy complementary system to be evaluated; obtain a normalized indicator value of each of the multiple system evaluation indicators; and obtain multiple target evaluation indicators for the multi-energy complementary system to be evaluated based on the normalized indicator value.

[0214] In one embodiment, the acquisition module 502 is further used to: obtain the correlation between each of the system evaluation indicators and the multi-energy complementary system to be evaluated according to the normalized indicator value; and screen out multiple target evaluation indicators of the multi-energy complementary system to be evaluated from multiple system evaluation indicators according to the correlation.

[0215] In one embodiment, the calculation module 504 is also used to: construct a feature comparison matrix according to the relative importance between the multiple target evaluation indicators; perform a consistency check on the feature comparison matrix, and when the check result of the consistency check is passed, obtain the subjective weight value corresponding to each of the target evaluation indicators based on the feature comparison matrix.

[0216] In one embodiment, the function construction module 506 is further used to: construct a one-dimensional projection value function of the multi-energy complementary system to be evaluated; and construct the projection index function of the multi-energy complementary system to be evaluated according to the subjective weight value and the one-dimensional projection value function.

[0217] Each module in the evaluation device of the multi-energy complementary system can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0218] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store evaluation-related data of the multi-energy complementary system. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating a multi-energy complementary system is implemented.

[0219] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0220] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0221] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0222] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0223] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0224] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0225] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0226] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating a multi-energy complementary system, characterized in that: The method comprises: Obtain multiple target evaluation indicators for the multi-energy complementary system to be evaluated; Obtaining the subjective weight value of each target evaluation indicator; According to the subjective weight value, constructing the projection index function of the multi-energy complementary system to be evaluated; Using the projection index function and particle swarm algorithm, the optimal objective weight value of each target evaluation index is obtained; According to the optimal objective weight value and the subjective weight value, a system evaluation result of the multi-energy complementary system to be evaluated is obtained.

2. The method according to claim 1, characterized in that There are multiple system setting schemes for the multi-energy complementary system to be evaluated; The step of obtaining a system evaluation result of the multi-energy complementary system to be evaluated according to the optimal objective weight value and the subjective weight value includes: Obtaining a system evaluation sub-result of each of the system setting schemes in the plurality of system setting schemes according to the optimal objective weight value and the subjective weight value; According to the system evaluation sub-result, the system evaluation result of the multi-energy complementary system to be evaluated is obtained.

3. The method according to claim 1, characterized in that The obtaining of multiple target evaluation indicators for the multi-energy complementary system to be evaluated includes: Obtain multiple system evaluation indicators for the multi-energy complementary system to be evaluated; Obtaining a normalized index value of each of the system evaluation indicators; According to the normalized index value, a plurality of target evaluation indexes for the multi-energy complementary system to be evaluated are obtained.

4. The method according to claim 3, characterized in that The step of obtaining a plurality of target evaluation indicators for the multi-energy complementary system to be evaluated according to the normalized indicator value comprises: According to the normalized index value, obtaining the correlation between each of the system evaluation indexes and the multi-energy complementary system to be evaluated; According to the correlation degree, a plurality of the target evaluation indicators of the multi-energy complementary system to be evaluated are screened out from the plurality of the system evaluation indicators.

5. The method according to claim 1, characterized in that The obtaining of the subjective weight value of each target evaluation indicator includes: Constructing a feature comparison matrix according to the relative importance between the plurality of target evaluation indicators; A consistency check is performed on the feature comparison matrix, and when a check result of the consistency check is that the check passes, the subjective weight values ​​corresponding to the target evaluation indicators are obtained based on the feature comparison matrix.

6. The method according to any one of claims 1 to 5, characterized in that The step of constructing a projection index function of the multi-energy complementary system to be evaluated according to the subjective weight value comprises: Constructing a one-dimensional projection value function of the multi-energy complementary system to be evaluated; The projection index function of the multi-energy complementary system to be evaluated is constructed according to the subjective weight value and the one-dimensional projection value function.

7. An evaluation device for a multi-energy complementary system, characterized in that: The device comprises: An acquisition module, used for acquiring multiple target evaluation indicators for the multi-energy complementary system to be evaluated; A calculation module, used to obtain the subjective weight value of each target evaluation indicator; A function construction module, used to construct a projection index function of the multi-energy complementary system to be evaluated according to the subjective weight value; An optimization module, used to obtain the optimal objective weight value of each target evaluation index by using the projection index function and the particle swarm algorithm; An evaluation module is used to obtain a system evaluation result of the multi-energy complementary system to be evaluated according to the optimal objective weight value and the subjective weight value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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