CN117220286B - Risk assessment method, device and medium for water-wind-solar multi-energy complementary system - Google Patents
Risk assessment method, device and medium for water-wind-solar multi-energy complementary system Download PDFInfo
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Abstract
The invention relates to the technical field of power system planning, and discloses a risk assessment method, a risk assessment device and a risk assessment medium for a water-wind-solar multi-energy complementary system. Further, on the basis of quantitatively analyzing wind power and photovoltaic output prediction uncertainty, corresponding wind power and photovoltaic output scenes are obtained, and further, a risk quantization method is adopted to quantitatively evaluate peak shaving risks of the water-wind-solar multi-energy complementary system by combining the obtained hydroelectric output planning scheme and a preset load curve. Therefore, by implementing the method, the influence of the randomness of the wind power photovoltaic output on the water-wind-solar multi-energy complementary system is considered, the peak regulation risk of the water-wind-solar multi-energy complementary system is quantitatively evaluated, and scientific decision support can be provided for a decision maker.
Description
Technical Field
The invention relates to the technical field of power system planning, in particular to a risk assessment method, a risk assessment device and a risk assessment medium for a water-wind-solar multi-energy complementary system.
Background
In a water-wind-solar multi-energy complementary system with wind-solar large-scale access, due to the existence of uncertainty output characteristics of new energy sources such as wind power, photovoltaic and the like, a peak regulation strategy established in the power grid dispatching department in the future is likely to have a certain quantitative risk, in the prior art, most of power grid coordination peak regulation optimization methods based on a deterministic modeling thought are provided, the risk characteristics of residual load after the water-wind-solar multi-energy system participates in the power grid peak regulation are not quantitatively analyzed, and short-term peak regulation risk of the water-wind-solar multi-energy complementary system is difficult to evaluate.
Disclosure of Invention
In view of the above, the invention provides a risk assessment method, a risk assessment device and a risk assessment medium for a water-wind-solar multi-energy complementary system, so as to solve the problem that the existing grid coordination peak shaving optimization method cannot accurately quantitatively assess the short-term peak shaving risk of the water-wind-solar multi-energy complementary system.
In a first aspect, the invention provides a risk assessment method for a water-wind-solar multi-energy complementary system, which comprises the following steps:
acquiring a wind power photovoltaic historical output data set of a water-wind-solar multi-energy complementary system to be evaluated; based on the wind power photovoltaic historical output data set, rolling prediction is carried out on wind power photovoltaic output of a water-wind-solar multi-energy complementary system to be evaluated by utilizing a time sequence prediction method, so as to obtain a wind power photovoltaic prediction output data set; obtaining a wind power output scene and a photovoltaic output scene through a preset processing method based on a wind power photovoltaic historical output data set and a wind power photovoltaic prediction output data set; solving a preset wind-solar multi-energy complementary combined peak regulation model by using a group intelligent optimization algorithm to obtain a hydroelectric power plan scheme of a water-wind-solar multi-energy complementary system to be evaluated; based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power planning scheme, carrying out quantitative evaluation on peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
According to the risk assessment method for the water-wind-solar multi-energy complementary system, a wind-photovoltaic prediction force data set is utilized, prediction is carried out by using a time sequence prediction method, and a corresponding water-electricity output planning scheme is obtained through a built preset water-wind-solar multi-energy complementary combined peak regulation model. Further, on the basis of quantitatively analyzing wind power and photovoltaic output prediction uncertainty, corresponding wind power and photovoltaic output scenes are obtained, and further, a risk quantization method is adopted to quantitatively evaluate peak shaving risks of the water-wind-solar multi-energy complementary system by combining the obtained hydroelectric output planning scheme and a preset load curve. Therefore, by implementing the method, the influence of the randomness of the wind power photovoltaic output on the water-wind-solar multi-energy complementary system is considered, the peak regulation risk of the water-wind-solar multi-energy complementary system is quantitatively evaluated, and scientific decision support can be provided for a decision maker.
In an alternative embodiment, obtaining a wind power output scene and a photovoltaic output scene based on a wind power photovoltaic historical output data set and a wind power photovoltaic predicted output data set through a preset processing method includes:
based on the wind photovoltaic historical output data set and the wind photovoltaic predicted output data set, the wind photovoltaic output probability distribution characteristic is obtained through processing by a preset calculation method and a statistical analysis method; based on wind power photovoltaic prediction force data set and wind power photovoltaic output probability distribution characteristics, obtaining a wind power output scene and a photovoltaic output scene through a preset scene analysis method.
The method utilizes the wind-electricity photovoltaic prediction force data set, predicts by using a time sequence prediction method and obtains a corresponding water-electricity output planning scheme through a constructed preset water-wind-light multi-energy complementary combined peak regulation model. Further, on the basis of quantitatively analyzing wind power and photovoltaic output prediction uncertainty, corresponding wind power and photovoltaic output scenes are obtained.
In an alternative embodiment, based on the wind photovoltaic historical output data set and the wind photovoltaic predicted output data set, the wind photovoltaic output probability distribution characteristics are obtained through processing by a preset calculation method and a statistical analysis method, and the method comprises the following steps:
based on the wind photovoltaic historical output data set and the wind photovoltaic predicted output data set, obtaining a wind photovoltaic output prediction deviation result through a preset calculation method; based on the wind photovoltaic predicted force data set and the wind photovoltaic force predicted deviation result, obtaining wind photovoltaic force probability distribution characteristics through a statistical analysis method.
In an alternative embodiment, based on a wind power photovoltaic predicted power data set and wind power photovoltaic power output probability distribution characteristics, a wind power output scene and a photovoltaic power output scene are obtained through a preset scene analysis method, including:
Based on the wind-electricity photovoltaic output probability distribution characteristics, determining wind-electricity photovoltaic output prediction deviation randomness through a preset scene analysis method; and determining a wind power output scene and a photovoltaic output scene based on the wind power photovoltaic predicted power data set and wind power photovoltaic output prediction deviation randomness.
According to the method, the influence of wind-electricity-photovoltaic prediction output randomness on the water-wind-light multi-energy complementary system is considered, and the wind-electricity-photovoltaic output probability distribution characteristic is combined, so that a corresponding wind-electricity output scene and a corresponding photovoltaic output scene can be obtained.
In an alternative embodiment, before acquiring the hydroelectric power plan scheme of the water-wind-solar multi-energy complementary system to be evaluated based on the preset wind-solar multi-energy complementary combined peak shaving model, the method further comprises:
and establishing a preset wind-solar multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference of the to-be-evaluated water-wind-solar multi-energy complementary system as a target.
The invention establishes a corresponding wind-solar multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference as a target, and provides support for the subsequent acquisition of an optimal hydroelectric power plan scheme.
In an alternative embodiment, based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power planning scheme, a preset risk quantification method is utilized to quantitatively evaluate peak shaving risk of a water-wind-solar multi-energy complementary system to be evaluated, so as to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated, and the method comprises the following steps:
Determining the residual load peak-valley difference statistical characteristics of the water-wind-solar multi-energy complementary system to be evaluated based on a preset load curve, a wind power output scene, a photovoltaic output scene and a hydroelectric power output plan scheme; based on the residual load peak-valley difference statistical characteristics, carrying out quantitative evaluation on peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
According to the invention, the residual load peak Gu Chafen distribution characteristics of the wind power photovoltaic output randomness-influenced launch wind-light multi-energy complementary system after peak regulation are statistically analyzed on the basis of the wind power photovoltaic predicted output data set.
In an alternative embodiment, based on the statistical characteristics of peak-valley differences of residual loads, a preset risk quantization method is used for quantitatively evaluating peak-shaving risks of the water-wind-solar multi-energy complementary system to be evaluated to obtain risk evaluation results of the water-wind-solar multi-energy complementary system to be evaluated, and the method comprises the following steps:
based on the residual load peak-valley difference statistical characteristics, quantifying peak-shaving risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method to obtain a plurality of peak-shaving risk quantification values; and determining a risk assessment result of the water-wind-solar multi-energy complementary system to be assessed based on the peak shaving risk quantification values.
In an optional implementation manner, based on the peak-valley difference statistical characteristics of the residual load, the peak-shaving risk of the water-wind-solar multi-energy complementary system to be evaluated is quantified by using a preset risk quantification method to obtain a plurality of peak-shaving risk quantification values, including: quantification is performed by the relationship of:
wherein:indicating a confidence level of +.>Peak shaving risk quantization value; />Representing a confidence level;representing peak-to-valley difference of residual loadXAnd corresponding peak shaving risks.
In a second aspect, the present invention provides a risk assessment device for a water-wind-solar multi-energy complementary system, the device comprising:
the acquisition module is used for acquiring a wind power photovoltaic historical output data set of the water, wind and light multi-energy complementary system to be evaluated; the prediction module is used for carrying out rolling prediction on the wind power photovoltaic output of the water-wind-solar multi-energy complementary system to be evaluated by utilizing a time sequence prediction method based on the wind power photovoltaic historical output data set to obtain a wind power photovoltaic prediction output data set; the processing module is used for obtaining a wind power output scene and a photovoltaic output scene through a preset processing method based on the wind power photovoltaic historical output data set and the wind power photovoltaic prediction output data set; the solving module is used for solving a preset wind-solar multi-energy complementary combined peak regulation model by utilizing a group intelligent optimization algorithm to obtain a hydropower output planning scheme of the water-wind-solar multi-energy complementary system to be evaluated; the quantitative evaluation module is used for quantitatively evaluating peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by utilizing a preset risk quantification method based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power planning scheme to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
In an alternative embodiment, a processing module includes:
the processing sub-module is used for obtaining the wind-electricity photovoltaic output probability distribution characteristic based on the wind-electricity photovoltaic historical output data set and the wind-electricity photovoltaic prediction output data set through processing of a preset calculation method and a statistical analysis method; and the analysis submodule is used for obtaining a wind power output scene and a photovoltaic output scene through a preset scene analysis method based on the wind power photovoltaic prediction output data set and wind power photovoltaic output probability distribution characteristics.
In an alternative embodiment, a processing sub-module includes:
the calculation unit is used for obtaining a wind-electricity photovoltaic output prediction deviation result through a preset calculation method based on the wind-electricity photovoltaic historical output data set and the wind-electricity photovoltaic prediction output data set; and the analysis unit is used for obtaining the wind-electricity photovoltaic output probability distribution characteristic through a statistical analysis method based on the wind-electricity photovoltaic output prediction output data set and the wind-electricity photovoltaic output prediction deviation result.
In an alternative embodiment, the analysis sub-module includes:
the analysis and determination unit is used for determining the randomness of the prediction deviation of the wind-electricity photovoltaic output through a preset scene analysis method based on the wind-electricity photovoltaic output probability distribution characteristic; the first determining unit is used for determining a wind power output scene and a photovoltaic output scene based on the wind power photovoltaic predicted output data set and wind power photovoltaic output prediction deviation randomness.
In an alternative embodiment, the apparatus further comprises:
the building module is used for building a preset wind-light multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference of the to-be-evaluated wind-light multi-energy complementary system as a target.
In a third aspect, the present invention provides a computer device comprising: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the water-wind-solar multi-energy complementary system risk assessment method according to the first aspect or any corresponding implementation mode.
In a fourth aspect, the present invention provides a computer readable storage medium, where computer instructions are stored on the computer readable storage medium, where the computer instructions are configured to cause a computer to perform the method for risk assessment of a water-wind-solar multi-energy complementary system according to the first aspect or any one of its corresponding embodiments.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a risk assessment method of a water-wind-solar multi-energy complementary system according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of another risk assessment method of a water-wind-solar multi-energy complementary system according to an embodiment of the invention;
FIG. 3 is a schematic flow chart of a risk assessment method of a water-wind-solar multi-energy complementary system according to an embodiment of the invention;
FIG. 4 is a block diagram of a risk assessment device of a water-wind-solar multi-energy complementary system according to an embodiment of the invention;
fig. 5 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a risk assessment method for a water-wind-solar multi-energy complementary system, which improves the accuracy of peak shaving risk quantification assessment by considering the influence of wind-photovoltaic output randomness on the water-wind-solar multi-energy complementary system in the assessment process.
According to an embodiment of the present invention, there is provided an embodiment of a risk assessment method for a water-wind-solar multi-energy complementary system, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.
In this embodiment, a risk assessment method for a water-wind-solar multi-energy complementary system is provided, fig. 1 is a flowchart of a risk assessment method for a water-wind-solar multi-energy complementary system according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, acquiring a wind power photovoltaic historical output data set of a water, wind and light multi-energy complementary system to be evaluated.
The wind power photovoltaic historical output data set can comprise a plurality of wind power historical output data and a plurality of photovoltaic historical output data of the water-wind-solar multi-energy complementary system to be evaluated.
Step S102, rolling prediction is carried out on wind power photovoltaic output of a water-wind-solar multi-energy complementary system to be evaluated by utilizing a time sequence prediction method based on a wind power photovoltaic historical output data set, and a wind power photovoltaic prediction output data set is obtained.
The time sequence prediction method is a regression prediction method, belongs to quantitative prediction, and has the following basic principle: on one hand, the continuity of the development of things is acknowledged, the past time series data is used for statistical analysis, and the development trend of the things is estimated; on the other hand, the randomness caused by the influence of accidental factors is fully considered, in order to eliminate the influence caused by random fluctuation, the historical data is utilized for carrying out statistical analysis, and the data is properly processed for carrying out trend prediction.
Specifically, based on the obtained wind power photovoltaic historical output data set, a time sequence prediction method is applied to obtain a wind power photovoltaic prediction output data set of the water-wind-solar multi-energy complementary system to be evaluated.
Further, the influence of wind power photovoltaic output randomness on a water-wind-solar multi-energy complementary system is considered through a time sequence prediction method.
In one example, the wind power photovoltaic output on the n+1th day is rolled and predicted by using the wind power photovoltaic output data on the n days before the wind power photovoltaic by using a time sequence prediction method to form a prediction data set.
Step S103, obtaining a wind power output scene and a photovoltaic output scene through a preset processing method based on the wind power photovoltaic historical output data set and the wind power photovoltaic prediction output data set.
Specifically, the obtained wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set are output by using a preset processing method, so that corresponding wind power output scenes and photovoltaic output scenes can be obtained.
And step S104, solving a preset wind-solar multi-energy complementary combined peak regulation model by using a group intelligent optimization algorithm to obtain a hydropower output planning scheme of the water-wind-solar multi-energy complementary system to be evaluated.
The group intelligent optimization algorithm (Swarm Intelligence OptimizationAlgorithm) is a common algorithm in computation intelligence, and the basic theory is to simulate the behaviors of animal groups such as fish groups, bird groups, bee groups, wolves, bacteria groups and the like in nature, and achieve the aim of optimization through simple and limited interaction among individuals by utilizing information communication and cooperation among groups.
Specifically, a preset wind-solar multi-energy complementary combined peak regulation model is solved by using a group intelligent optimization algorithm, so that a hydropower output planning scheme corresponding to the water-wind-solar multi-energy complementary system to be evaluated can be obtained.
Step S105, quantitatively evaluating peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power planning scheme to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
The preset risk quantification method may be a CVaR condition risk value quantification method.
Specifically, on the basis of a preset load curve, an obtained wind power output scene, a photovoltaic output scene and a water power planning scheme, a preset risk quantification method is utilized to quantitatively evaluate the peak shaving risk of the water-wind-solar multi-energy complementary system to be evaluated.
According to the risk assessment method for the water-wind-solar multi-energy complementary system, a wind-photovoltaic prediction force data set is utilized, prediction is carried out by using a time sequence prediction method, and a corresponding water-electricity output planning scheme is obtained through a built preset water-wind-solar multi-energy complementary combined peak regulation model. Further, on the basis of quantitatively analyzing wind power and photovoltaic output prediction uncertainty, corresponding wind power and photovoltaic output scenes are obtained, and further, a risk quantization method is adopted to quantitatively evaluate peak shaving risks of the water-wind-solar multi-energy complementary system by combining the obtained hydroelectric output planning scheme and a preset load curve. Therefore, by implementing the method, the influence of the randomness of the wind power photovoltaic output on the water-wind-solar multi-energy complementary system is considered, the peak regulation risk of the water-wind-solar multi-energy complementary system is quantitatively evaluated, and scientific decision support can be provided for a decision maker.
In this embodiment, a risk assessment method for a water-wind-solar multi-energy complementary system is provided, fig. 2 is a flowchart of a risk assessment method for a water-wind-solar multi-energy complementary system according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
step S201, acquiring a wind power photovoltaic historical output data set of a water, wind and light multi-energy complementary system to be evaluated. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, rolling prediction is carried out on wind power photovoltaic output of a water-wind-solar multi-energy complementary system to be evaluated by utilizing a time sequence prediction method based on a wind power photovoltaic historical output data set, and a wind power photovoltaic prediction output data set is obtained. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S203, obtaining a wind power output scene and a photovoltaic output scene through a preset processing method based on the wind power photovoltaic historical output data set and the wind power photovoltaic prediction output data set.
Specifically, the step S203 includes:
step S2031, based on the wind photovoltaic historical output data set and the wind photovoltaic predicted output data set, obtaining wind photovoltaic output probability distribution characteristics through processing of a preset calculation method and a statistical analysis method.
Specifically, the wind power photovoltaic historical output data set may be reflected in a distribution characteristic of wind power photovoltaic output over a historical period of time; the wind power photovoltaic can reflect the distribution characteristic of the predicted wind power photovoltaic output, so that the wind power photovoltaic output probability distribution characteristic can be obtained by combining the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set, outputting the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set through a preset calculation method and a statistical analysis method.
Step S2032, obtaining a wind power output scene and a photovoltaic output scene through a preset scene analysis method based on a wind power photovoltaic predicted output data set and wind power photovoltaic output probability distribution characteristics.
Specifically, according to the description of the step S102, the obtained wind power photovoltaic predicted power data set considers the influence of wind power photovoltaic power output randomness on the water-wind-solar multi-energy complementary system, and further, the wind power output scene and the photovoltaic output scene of the water-wind-solar multi-energy complementary system to be evaluated under the influence of the wind power photovoltaic power output randomness can be obtained by combining the influence of the obtained wind power photovoltaic power output randomness on the water-wind-solar multi-energy complementary system and applying the influence of the wind power photovoltaic power output randomness on the water-wind-solar multi-energy complementary system.
In some optional embodiments, step S2031 includes:
and a step a1, obtaining a wind-electricity photovoltaic output prediction deviation result through a preset calculation method based on the wind-electricity photovoltaic historical output data set and the wind-electricity photovoltaic prediction output data set.
And a2, obtaining the wind-electricity photovoltaic output probability distribution characteristic through a statistical analysis method based on the wind-electricity photovoltaic predicted output data set and the wind-electricity photovoltaic output prediction deviation result.
Specifically, according to the obtained wind power photovoltaic historical output data set and wind power photovoltaic predicted output data set, wind power photovoltaic sunrise output prediction deviation can be calculated;
furthermore, the wind-electricity photovoltaic output probability distribution characteristic can be obtained through statistics by combining the wind-electricity photovoltaic prediction output data set and using a statistical analysis method.
In some optional embodiments, step S2032 includes:
and b1, determining the wind-electricity photovoltaic output prediction deviation randomness through a preset scene analysis method based on the wind-electricity photovoltaic output probability distribution characteristics.
And b2, determining a wind power output scene and a photovoltaic output scene based on the wind power photovoltaic predicted output data set and wind power photovoltaic output prediction deviation randomness.
The preset scene analysis method may be a multi-scene analysis method.
Specifically, according to the obtained wind power photovoltaic output probability distribution characteristics, describing the randomness of wind power photovoltaic output deviation by using a multi-scene analysis method. Further, a wind power output scene and a photovoltaic output scene corresponding to the water-wind-solar-energy multi-energy complementary system to be evaluated can be obtained by combining the wind power photovoltaic prediction output data set.
And S204, solving a preset wind-solar multi-energy complementary combined peak regulation model by using a group intelligent optimization algorithm to obtain a hydropower output planning scheme of the water-wind-solar multi-energy complementary system to be evaluated. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S205, quantitatively evaluating peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power planning scheme to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated. Please refer to step S105 in the embodiment shown in fig. 1 in detail, which is not described herein.
According to the risk assessment method for the water-wind-solar multi-energy complementary system, a wind-photovoltaic prediction force data set is utilized, prediction is carried out by using a time sequence prediction method, and a corresponding water-electricity output planning scheme is obtained through a built preset water-wind-solar multi-energy complementary combined peak regulation model. Further, on the basis of quantitatively analyzing wind power and photovoltaic output prediction uncertainty, corresponding wind power and photovoltaic output scenes are obtained, and further, a risk quantization method is adopted to quantitatively evaluate peak shaving risks of the water-wind-solar multi-energy complementary system by combining the obtained hydroelectric output planning scheme and a preset load curve. Therefore, by implementing the method, the influence of the randomness of the wind power photovoltaic output on the water-wind-solar multi-energy complementary system is considered, the peak regulation risk of the water-wind-solar multi-energy complementary system is quantitatively evaluated, and scientific decision support can be provided for a decision maker.
In this embodiment, a risk assessment method for a water-wind-solar multi-energy complementary system is provided, fig. 3 is a flowchart of the risk assessment method for the water-wind-solar multi-energy complementary system according to an embodiment of the present invention, as shown in fig. 3, the flowchart includes the following steps:
step S301, acquiring a wind power photovoltaic historical output data set of a water, wind and light multi-energy complementary system to be evaluated. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, rolling prediction is carried out on wind power photovoltaic output of a water-wind-solar multi-energy complementary system to be evaluated by utilizing a time sequence prediction method based on a wind power photovoltaic historical output data set, and a wind power photovoltaic prediction output data set is obtained. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, obtaining a wind power output scene and a photovoltaic output scene through a preset processing method based on the wind power photovoltaic historical output data set and the wind power photovoltaic prediction output data set. Please refer to step S203 in the embodiment shown in fig. 2 in detail, which is not described herein.
And S304, establishing a preset wind-solar multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference of the to-be-evaluated wind-solar multi-energy complementary system as a target.
The residual load peak-valley difference represents the difference between the maximum load and the minimum load of the water-wind-solar multi-energy complementary system to be evaluated in a certain time period, and the following relation (1) is shown:
（1）
wherein:representing the peak-valley difference of the residual load; />Representing the maximum residual load; />Representing the minimum residual load.
Specifically, the peak-valley difference of the residual load of the water-wind-solar multi-energy complementary system to be evaluated is the minimum, and a corresponding preset wind-solar multi-energy complementary combined peak regulation model is constructed.
And step S305, solving a preset wind-solar multi-energy complementary combined peak regulation model by using a group intelligent optimization algorithm to obtain a hydropower output planning scheme of the water-wind-solar multi-energy complementary system to be evaluated. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S306, quantitatively evaluating peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power planning scheme to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
Specifically, the step S306 includes:
step 3061, determining the residual load peak-valley-difference statistical characteristics of the water-wind-solar multi-energy complementary system to be evaluated based on a preset load curve, a wind power output scene, a photovoltaic output scene and a hydroelectric power output planning scheme.
Specifically, on the basis of the obtained preset load curve, wind power output scene, photovoltaic output scene and water power planning scheme, residual load peak-valley difference is taken as a statistical object, and the statistical characteristics of the residual load peak-valley difference of the water-wind-solar energy multi-energy complementary system to be evaluated can be obtained through statistical analysis.
Step 3062, quantitatively evaluating peak regulation risk of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method based on residual load peak-valley difference statistical characteristics, and obtaining a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
Specifically, based on the obtained residual load peak-valley difference statistical characteristics, a preset risk quantification method (CVaR condition risk value) is adopted to quantitatively evaluate the peak regulation risk of the water-wind-solar energy multi-energy complementary system to be evaluated, so that a risk evaluation result of the water-wind-solar energy multi-energy complementary system to be evaluated can be obtained.
In some alternative embodiments, step S3062 includes:
and c1, quantifying peak shaving risks of a water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method based on residual load peak-valley difference statistical characteristics to obtain a plurality of peak shaving risk quantification values.
And c2, determining a risk assessment result of the water-wind-solar multi-energy complementary system to be assessed based on the peak shaving risk quantification values.
Specifically, a CVaR condition risk value quantification method is adopted to quantify peak shaving risk of the water-wind-solar multi-energy complementary system to be evaluated, and the peak shaving risk is represented by the following relational expression (2):
（2）
wherein:indicating a confidence level of +.>Peak shaving risk quantization value; />Representing a confidence level;representing peak-to-valley difference of residual loadXAnd corresponding peak shaving risks.
Further, according to the obtained peak shaving risk quantification values, a risk assessment result of the water-wind-solar multi-energy complementary system to be assessed can be determined.
In one example, assume that the peak-to-valley difference of residual load after water power peak shaving is，/>Is confidence level +.>Is->The corresponding peak shaver risk quantization value +.>Can be calculated according to the following relation (3):
（3）
further, the calculation results are shown in the following table 1:
TABLE 1
Wherein, taking the serial number 1 as an example,when (I)>Indicating that the confidence level was 95% at the time of dayYu Fuhe the peak-valley difference is less than->The conditional expectation of the value is +.>。
According to the risk assessment method for the water-wind-solar multi-energy complementary system, a wind-photovoltaic prediction force data set is utilized, prediction is carried out by using a time sequence prediction method, and a corresponding water-electricity output planning scheme is obtained through a built preset water-wind-solar multi-energy complementary combined peak regulation model. Further, on the basis of quantitatively analyzing wind power and photovoltaic output prediction uncertainty, corresponding wind power and photovoltaic output scenes are obtained, and further, a risk quantization method is adopted to quantitatively evaluate peak shaving risks of the water-wind-solar multi-energy complementary system by combining the obtained hydroelectric output planning scheme and a preset load curve. Therefore, by implementing the method, the influence of the randomness of the wind power photovoltaic output on the water-wind-solar multi-energy complementary system is considered, the peak regulation risk of the water-wind-solar multi-energy complementary system is quantitatively evaluated, and scientific decision support can be provided for a decision maker.
In an example, in order to quantitatively evaluate risk characteristics of residual load after the water-wind-solar multi-energy system participates in peak shaving of a power grid, based on uncertainty characteristics of wind power and photovoltaic output prediction, a short-term peak shaving risk evaluation method of the water-wind-solar multi-energy complementary system is provided, and the specific implementation process comprises the following steps:
1. wind-electricity photovoltaic historical output data prediction and analysis:
based on the wind power photovoltaic historical output data, the wind power photovoltaic output on the n-day before wind power photovoltaic is rolled and predicted by using a time sequence prediction method to form a prediction data set.
2. Wind power and photovoltaic sunrise force scene acquisition:
2.1 wind power photovoltaic prediction deviation statistical characteristic analysis: calculating the wind-electricity photovoltaic sunrise power prediction deviation by combining wind-electricity photovoltaic historical output data and a prediction data set, and carrying out statistics by using a statistical analysis method to obtain wind-electricity photovoltaic output probability distribution characteristics;
2.2 wind power photovoltaic prediction bias uncertainty description: based on the probability distribution characteristics obtained in the step 2.1, describing the randomness of the wind power photovoltaic output deviation by using a multi-scene analysis method, and obtaining wind power and photovoltaic daily output scenes by combining the daily output prediction result.
3. And (3) hydropower daily output plan acquisition:
3.1, constructing a wind-solar multi-energy complementary combined peak regulation model: constructing a wind-solar multi-energy complementary combined peak regulation model by taking the minimum peak-valley difference of the residual load of the power grid as a target;
3.2 model solving based on a group intelligent optimization algorithm: and (3) solving the model in the step (3.1) by using a group intelligent optimization algorithm to obtain a hydropower sunrise planning scheme.
4. And (3) analyzing peak-valley difference statistical characteristics of residual load of the power grid:
based on a typical daily load curve of the power grid, combining the wind power and photovoltaic daily output scene obtained in the step 2 and the hydro-electric daily output planning scheme obtained in the step 3, taking the peak-valley difference of the residual load as a statistical object, and statistically analyzing the peak-valley difference statistical characteristic of the residual load of the power grid.
5. Peak regulation risk quantification of water-wind-solar multi-energy system:
and (3) quantifying the short-term peak regulation risk of the water-wind-solar hybrid system by adopting a risk quantification method (CVaR condition risk value) based on the peak-valley difference statistical characteristics of the residual load of the power grid obtained in the step (4).
According to the short-term peak regulation risk assessment method for the water-wind-light multi-energy complementary system, the influence of wind-light output randomness on the power grid is considered from the power grid peak regulation angle, the distribution characteristics of the residual load peak Gu Chafen after the peak regulation of the water-wind-light multi-energy system under the influence of the wind-light output randomness are statistically analyzed, the peak regulation risk is quantitatively analyzed by using a risk quantitative analysis means, the short-term peak regulation risk of the water-wind-light multi-energy complementary system is accurately and quantitatively assessed, and scientific decision support can be provided for a decision maker.
The embodiment also provides a risk assessment device for the water-wind-solar multi-energy complementary system, which is used for realizing the embodiment and the preferred implementation mode, and is not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The embodiment provides a risk assessment device for a water-wind-solar multi-energy complementary system, as shown in fig. 4, including:
the acquisition module 401 is configured to acquire a wind power photovoltaic historical output data set of the water, wind and solar energy multi-energy complementary system to be evaluated.
The prediction module 402 is configured to perform rolling prediction on a wind power photovoltaic output of the water-wind-solar multi-energy complementary system to be evaluated by using a time sequence prediction method based on the wind power photovoltaic historical output data set, so as to obtain a wind power photovoltaic prediction output data set.
The processing module 403 is configured to obtain a wind power output scene and a photovoltaic output scene through a preset processing method based on the wind power photovoltaic historical output data set and the wind power photovoltaic prediction output data set.
And the solving module 404 is used for solving a preset wind-solar multi-energy complementary combined peak regulation model by using a group intelligent optimization algorithm to obtain a hydropower output planning scheme of the water-wind-solar multi-energy complementary system to be evaluated.
The quantitative evaluation module 405 is configured to quantitatively evaluate peak-shaving risk of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method based on a preset load curve, a wind power output scene, a photovoltaic output scene and a water power plan, so as to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
In some alternative embodiments, processing module 403 includes:
and the processing sub-module is used for obtaining the wind-electricity photovoltaic output probability distribution characteristic based on the wind-electricity photovoltaic historical output data set and the wind-electricity photovoltaic prediction output data set through processing of a preset calculation method and a statistical analysis method.
And the analysis submodule is used for obtaining a wind power output scene and a photovoltaic output scene through a preset scene analysis method based on the wind power photovoltaic prediction output data set and wind power photovoltaic output probability distribution characteristics.
In some alternative embodiments, the processing sub-module includes:
the calculation unit is used for obtaining a wind-electricity photovoltaic output prediction deviation result through a preset calculation method based on the wind-electricity photovoltaic historical output data set and the wind-electricity photovoltaic prediction output data set.
And the analysis unit is used for obtaining the wind-electricity photovoltaic output probability distribution characteristic through a statistical analysis method based on the wind-electricity photovoltaic output prediction output data set and the wind-electricity photovoltaic output prediction deviation result.
In some alternative embodiments, the analysis sub-module includes:
the analysis and determination unit is used for determining the wind-electricity photovoltaic output prediction deviation randomness through a preset scene analysis method based on the wind-electricity photovoltaic output probability distribution characteristics.
The first determining unit is used for determining a wind power output scene and a photovoltaic output scene based on the wind power photovoltaic predicted output data set and wind power photovoltaic output prediction deviation randomness.
In some alternative embodiments, the apparatus includes:
the building module is used for building a preset wind-light multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference of the to-be-evaluated wind-light multi-energy complementary system as a target.
In some alternative embodiments, the quantitative evaluation module 405 includes:
the determining submodule is used for determining the residual load peak-valley difference statistical characteristic of the water-wind-solar multi-energy complementary system to be evaluated based on a preset load curve, a wind power output scene, a photovoltaic output scene and a hydroelectric power output plan scheme.
The quantitative evaluation sub-module is used for quantitatively evaluating peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method based on residual load peak-valley difference statistical characteristics to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated.
In some alternative embodiments, the quantitative evaluation sub-module includes:
and the quantization unit is used for quantizing peak shaving risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method based on the residual load peak-valley difference statistical characteristics to obtain a plurality of peak shaving risk quantization values.
And the second determining unit is used for determining a risk assessment result of the water-wind-solar multi-energy complementary system to be assessed based on the peak shaving risk quantification values.
In some alternative embodiments, the quantization unit includes:
a quantization subunit, configured to perform quantization according to a relation of the following formula:
wherein:indicating a confidence level of +.>Peak shaving risk quantization value; />Representing a confidence level;representing peak-to-valley difference of residual loadXAnd corresponding peak shaving risks.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The risk assessment device of the water-wind-solar multi-energy complementary system in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit ) circuits, processors and memories executing one or more software or fixed programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the risk assessment device of the water-wind-solar multi-energy complementary system shown in the figure 4.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 5, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 5.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (11)
1. The risk assessment method of the water-wind-solar multi-energy complementary system is characterized by comprising the following steps of:
acquiring a wind power photovoltaic historical output data set of a water-wind-solar multi-energy complementary system to be evaluated;
based on the wind power photovoltaic historical output data set, rolling prediction is carried out on the wind power photovoltaic output of the water-wind-solar-energy multi-energy complementary system to be evaluated by using a time sequence prediction method, and a wind power photovoltaic prediction output data set is obtained;
based on the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set, obtaining a wind power output scene and a photovoltaic output scene through a preset processing method;
establishing a preset wind-solar multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference of the to-be-evaluated water-wind-solar multi-energy complementary system as a target;
solving a preset wind-solar multi-energy complementary combined peak regulation model by using a group intelligent optimization algorithm to obtain a hydroelectric power plan scheme of the to-be-evaluated water-wind-solar multi-energy complementary system;
Based on a preset load curve, the wind power output scene, the photovoltaic output scene and the hydroelectric power output planning scheme, quantitatively evaluating peak shaving risk of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantification method to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated, wherein the risk evaluation result comprises the following steps:
determining the residual load peak-valley difference statistical characteristics of the water-wind-solar-energy multi-energy complementary system to be evaluated based on a preset load curve, the wind power output scene, the photovoltaic output scene and the hydroelectric power output planning scheme;
based on the residual load peak-valley difference statistical characteristics, carrying out quantitative evaluation on peak regulation risk of the water-wind-solar multi-energy complementary system to be evaluated by using the preset risk quantization method to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated;
based on the residual load peak-valley difference statistical characteristics, quantitatively evaluating peak regulation risk of the water-wind-solar multi-energy complementary system to be evaluated by using the preset risk quantification method to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated, wherein the method comprises the following steps:
based on the residual load peak-valley difference statistical characteristics, quantifying peak-shaving risks of the water-wind-solar multi-energy complementary system to be evaluated by using the preset risk quantification method to obtain a plurality of peak-shaving risk quantification values;
And determining the risk assessment result of the water-wind-solar multi-energy complementary system to be assessed based on the peak shaving risk quantification values.
2. The method of claim 1, wherein obtaining a wind power output scene and a photovoltaic output scene based on the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set through a preset processing method comprises:
based on the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set, processing by a preset calculation method and a statistical analysis method to obtain wind power photovoltaic output probability distribution characteristics;
and obtaining the wind power output scene and the photovoltaic output scene through a preset scene analysis method based on the wind power photovoltaic predicted output data set and the wind power photovoltaic output probability distribution characteristic.
3. The method of claim 2, wherein obtaining a wind-powered photovoltaic output probability distribution characteristic based on the wind-powered photovoltaic historical output dataset and the wind-powered photovoltaic predicted output dataset through a preset calculation method and a statistical analysis method comprises:
based on the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set, obtaining a wind power photovoltaic output prediction deviation result through the preset calculation method;
And obtaining the wind power photovoltaic output probability distribution characteristic through the statistical analysis method based on the wind power photovoltaic predicted output data set and the wind power photovoltaic output predicted deviation result.
4. The method of claim 2, wherein obtaining the wind power output scene and the photovoltaic output scene through a preset scene analysis method based on the wind power photovoltaic predicted force data set and the wind power photovoltaic output probability distribution characteristic comprises:
determining wind power photovoltaic output prediction deviation randomness through a preset scene analysis method based on the wind power photovoltaic output probability distribution characteristics;
and determining the wind power output scene and the photovoltaic output scene based on the wind power photovoltaic predicted output data set and the wind power photovoltaic output prediction deviation randomness.
5. The method according to claim 1, wherein quantifying peak shaving risk of the water-wind-solar multi-energy complementary system to be evaluated by using the preset risk quantification method based on the residual load peak-valley difference statistical characteristic, to obtain a plurality of peak shaving risk quantification values, includes: quantification is performed by the relationship of:
wherein: Indicating a confidence level of +.>Peak shaving risk quantization value; />Representing a confidence level;representing peak-to-valley difference of residual loadXAnd corresponding peak shaving risks.
6. A risk assessment device for a water-wind-solar multi-energy complementary system, the device comprising:
the acquisition module is used for acquiring a wind power photovoltaic historical output data set of the water, wind and light multi-energy complementary system to be evaluated;
the prediction module is used for carrying out rolling prediction on the wind power photovoltaic output of the water, wind and light multi-energy complementary system to be evaluated by utilizing a time sequence prediction method based on the wind power photovoltaic historical output data set to obtain a wind power photovoltaic prediction output data set;
the processing module is used for obtaining a wind power output scene and a photovoltaic output scene through a preset processing method based on the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set;
the building module is used for building a preset wind-solar multi-energy complementary combined peak regulation model by taking the minimum residual load peak-valley difference of the to-be-evaluated water-wind-solar multi-energy complementary system as a target;
the solving module is used for solving a preset wind-solar multi-energy complementary combined peak regulation model by utilizing a group intelligent optimization algorithm to obtain a hydropower output planning scheme of the to-be-evaluated water-wind-solar multi-energy complementary system;
The quantitative evaluation module is used for quantitatively evaluating peak regulation risk of the water-wind-solar multi-energy complementary system to be evaluated by utilizing a preset risk quantization method based on a preset load curve, the wind power output scene, the photovoltaic output scene and the hydroelectric output plan scheme to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated;
wherein the quantitative evaluation module comprises:
the determining submodule is used for determining the residual load peak-valley difference statistical characteristics of the water-wind-solar multi-energy complementary system to be evaluated based on a preset load curve, a wind power output scene, a photovoltaic output scene and a hydroelectric output plan scheme;
the quantitative evaluation sub-module is used for quantitatively evaluating peak regulation risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method based on residual load peak-valley difference statistical characteristics to obtain a risk evaluation result of the water-wind-solar multi-energy complementary system to be evaluated;
wherein the quantization evaluation submodule includes:
the quantization unit is used for quantizing peak shaving risks of the water-wind-solar multi-energy complementary system to be evaluated by using a preset risk quantization method based on the residual load peak-valley difference statistical characteristics to obtain a plurality of peak shaving risk quantization values;
And the second determining unit is used for determining a risk assessment result of the water-wind-solar multi-energy complementary system to be assessed based on the peak shaving risk quantification values.
7. The apparatus of claim 6, wherein the processing module comprises:
the processing sub-module is used for obtaining wind power photovoltaic output probability distribution characteristics based on the wind power photovoltaic historical output data set and the wind power photovoltaic predicted output data set through processing of a preset calculation method and a statistical analysis method;
and the analysis submodule is used for obtaining the wind power output scene and the photovoltaic output scene through a preset scene analysis method based on the wind power photovoltaic predicted output data set and the wind power photovoltaic output probability distribution characteristic.
8. The apparatus of claim 7, wherein the processing sub-module comprises:
the calculation unit is used for obtaining a wind power photovoltaic output prediction deviation result through the preset calculation method based on the wind power photovoltaic historical output data set and the wind power photovoltaic prediction output data set;
and the analysis unit is used for obtaining the wind power photovoltaic output probability distribution characteristic through the statistical analysis method based on the wind power photovoltaic predicted output data set and the wind power photovoltaic output predicted deviation result.
9. The apparatus of claim 7, wherein the analysis sub-module comprises:
the analysis and determination unit is used for determining the randomness of the wind power photovoltaic output prediction deviation through a preset scene analysis method based on the wind power photovoltaic output probability distribution characteristics;
and the first determining unit is used for determining the wind power output scene and the photovoltaic output scene based on the wind power photovoltaic predicted output data set and the wind power photovoltaic output prediction deviation randomness.
10. A computer device, comprising:
the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the risk assessment method of the water-wind-solar multi-energy complementary system is executed by the processor.
11. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the water-wind-solar multi-energy complementary system risk assessment method according to any one of claims 1 to 5.
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