Photovoltaic power station credible capacity calculation method considering sand storm weather and related device

Abstract

The invention relates to the technical field of solar photovoltaic power generation, in particular to a photovoltaic power station credible capacity calculation and related device considering sand storm weather. Different output scenes of the photovoltaic power station under the sand storm weather are divided into typical scenes and extreme scenes; random production simulation is carried out on the typical scene and the extreme scene to obtain the reliability index of the actual system, the reliability index of the equivalent system and the credible capacity of the photovoltaic power station of the corresponding scene. According to the method, the influence of sand dust particle accumulation on solar radiation receiving after the sand storm passes is fully considered, the reliability index of an actual system and the reliability index of an equivalent system of a corresponding scene are obtained, the credible capacity of a photovoltaic power station is used for system planning and scheduling, and the reliability of a power system under extreme meteorological conditions such as the sand storm is improved. The problem that the credible capacity calculation of the photovoltaic power station cannot meet the reliability and stability evaluation requirements under the sand storm weather is solved.

Description

Technical Field

[0001] The present invention relates to the field of solar photovoltaic power generation technology, and specifically to a method for calculating the credible capacity of a photovoltaic power station taking into account sandstorm weather and related devices, and in particular to a method for calculating the credible capacity of a photovoltaic power station taking into account the uncertainty of sandstorm weather and related devices. Background Art

[0002] Photovoltaic power generation is a technology that uses the photovoltaic effect of the semiconductor interface to directly convert light energy into electrical energy. Its power generation principle is: when light shines on a solar cell, the light is absorbed in the interface layer, and photons with sufficient energy can excite electrons from covalent bonds in P-type silicon and N-type silicon to generate electron-hole pairs. Before the electrons and holes near the interface layer recombine, they will be separated from each other by the electric field of the space charge, and the electrons will move to the positively charged N area, and the holes will move to the negatively charged P area. Through the charge separation of the interface layer, an outward testable voltage will be generated between the P area and the N area. At this time, electrodes can be added on both sides of the silicon wafer and connected to a voltmeter to form a current. Photovoltaic power generation technology has three advantages: permanence, cleanliness, and flexibility. It has a long life. As long as the sun exists, solar cells can be invested once and used for a long time; and it will not cause environmental pollution. In recent years, with the increasing prominence of the global energy crisis and air pollution problems, photovoltaic power generation technology has developed rapidly. As a clean and sustainable way to obtain energy, photovoltaic power generation is being widely used in various fields. Photovoltaic power generation systems have demonstrated their unique advantages and value in special application scenarios ranging from households to commercial, industrial, public facilities and remote areas. With the continuous advancement of technology and continued policy support, the application prospects of photovoltaic power generation will be broader.

[0003] However, with the global climate change, extreme weather conditions are becoming more frequent, especially sandstorms, extreme heat and no wind, extreme cold and no light, and other extreme abnormal weather conditions, which have a serious impact on new energy power stations such as photovoltaic and wind power. The output of new energy power stations is strongly affected by the changes in meteorological factors of extreme weather. For example, extremely hot and windless weather will cause the surface temperature of photovoltaic modules to rise, thereby reducing their power generation efficiency. In addition, high temperature will also accelerate the aging of the packaging materials and cables of photovoltaic modules, further affecting the power generation effect; extreme cold and no light will lead to insufficient light intensity, causing frost heaving of the foundation of photovoltaic power stations, destroying At the same time, low temperature may also affect the performance and life of photovoltaic components; especially under sandstorm weather conditions, the solar irradiance will be severely attenuated, causing the output value of photovoltaic power generation to fluctuate violently in a short time, resulting in an imbalance between supply and demand of the power system, which may lead to damage to each unit and large-scale power outages and other serious grid accidents. This is because under sandstorm weather conditions, the air is filled with a large amount of sand and dust aerosols, which will attenuate the solar radiation received by the photovoltaic panels. At the same time, after the sandstorm passes, the sand and dust particles will accumulate on the photovoltaic panels, reducing the transmittance of the photovoltaic panels, resulting in serious attenuation of photovoltaic output.

[0004] In order to cope with the inherent defect of renewable energy power generation that it depends on the weather, it is necessary to equip a sufficient number of thermal power units and energy storage equipment to ensure power balance. By calculating the credible capacity of new energy power stations, the new energy units can be replaced by conventional units that are not severely affected by the weather to participate in subsequent calculations. That is, the calculation of the credible capacity of photovoltaic power stations equates the random new energy model with the deterministic conventional unit model, which is of great significance in the subsequent power balance calculation.

[0005] The credible capacity of a photovoltaic power station refers to the stable electric power that a photovoltaic power station can output under specific conditions. This concept fully considers various factors that affect the power generation capacity of a power station, such as solar energy resources, photovoltaic module quality, and module layout. The credible capacity of a photovoltaic power station is an important indicator to measure the stable power generation capacity of a photovoltaic power station under specific conditions. Since sandstorms seriously affect photovoltaic power generation output and photovoltaic power generation output fluctuates severely, the traditional credible capacity calculation method of a photovoltaic power station, which establishes a probability distribution model of photovoltaic power generation output through statistical analysis, mainly considers average meteorological conditions, and does not take into account the impact of extreme weather events. It cannot accurately reflect the performance of photovoltaic power stations under extreme conditions and cannot meet the evaluation requirements for the reliability and stability of photovoltaic power generation in the power system under sandstorms.

[0006] Therefore, there is an urgent need for a photovoltaic reliable capacity calculation that takes into account the uncertainty of sandstorm meteorology to address the inherent defect of new energy power generation that "depends on the weather". This is of great significance for optimizing the operation of photovoltaic power generation systems and improving power generation efficiency. Summary of the invention

[0007] In view of the problem that the credible capacity calculation of photovoltaic power stations in the prior art cannot meet the reliability and stability assessment requirements of photovoltaic power generation in the power system under sandstorm weather, the present invention provides a credible capacity calculation method and related devices for photovoltaic power stations taking sandstorm weather into consideration.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] The present invention provides a method for calculating the credible capacity of a photovoltaic power station taking into account sandstorm weather, comprising:

[0010] Establishing a solar radiation model; the solar radiation model includes a solar radiation model under clear atmospheric conditions, and using the absorption, reflection and scattering effects of sand and dust aerosol particles in the air during a sandstorm on solar radiation to correct the aerosol absorption radiation coefficient in the solar radiation model under clear atmospheric conditions;

[0011] According to the solar irradiation model, a photovoltaic output model is established;

[0012] According to the photovoltaic output model, different output scenarios of photovoltaic power stations under sandstorm weather are divided into typical scenarios and extreme scenarios; random production simulations are performed on typical scenarios and extreme scenarios respectively to obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario;

[0013] According to the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario, the reliable capacity of the photovoltaic power station of the corresponding scenario is obtained.

[0014] Furthermore, the method for establishing the solar radiation model is:

[0015]

[0016] Among them, τ d is the light transmittance of photovoltaic panel glass under dust accumulation conditions; τ c is the transmittance of photovoltaic panel glass under clean conditions; erf is the error function; w is the dust density; e1, e2 and e3 are all empirical coefficients; I j I is the solar radiation intensity received by the dust-accumulated photovoltaic panel; s It is the solar radiation intensity received by the photovoltaic panel surface under normal climate.

[0017] Furthermore, the method for establishing a photovoltaic output model according to the solar irradiation model is:

[0018] According to the solar radiation model, the solar radiation intensity received by the dust-accumulated photovoltaic panel is obtained;

[0019] According to the solar radiation intensity received by the dust-accumulated photovoltaic panels, a photovoltaic output model is established, specifically:

[0020]

[0021] CF=η T η i n n n l (4)

[0022] Among them, P PV is the output value of the dust-accumulating photovoltaic power station; CF is the conversion efficiency of photovoltaic modules in converting solar energy into electrical energy; I s is the solar radiation intensity received by the dust-accumulated photovoltaic panel; G is the sunshine intensity under standard conditions, which means that the temperature of the photovoltaic power station battery is 25℃ and the air quality coefficient AM is 1.5; P PVPeak is the total installed capacity of the photovoltaic power station; η T is the temperature correction coefficient; η i The azimuth tilt correction factor for the photovoltaic module installation; n n is the inverter efficiency of the photovoltaic system, ranging from 0.8 to 0.95; n l is the line correction coefficient of the photovoltaic system, which is taken as 0.95~0.98.

[0023] Furthermore, the method for dividing different output scenarios of photovoltaic power stations under sandstorm weather into typical scenarios and extreme scenarios according to the photovoltaic output model is as follows:

[0024] According to the photovoltaic output model, the improved fuzzy C-means clustering algorithm is used to calculate the membership degree of the relative cluster center.

[0025] The output scenarios whose membership degree relative to the cluster center is higher than a certain threshold are classified as typical scenarios;

[0026] The output scenarios whose membership relative to the cluster center is lower than a certain threshold are classified as extreme scenarios.

[0027] Furthermore, the method for calculating the membership degree of the relative cluster center based on the photovoltaic output model and using the improved fuzzy C-means clustering algorithm is as follows:

[0028]

[0029] in, is the center vector of the sample; x i is the i-th sample; u ikis the membership of the i-th sample to the k-th cluster; c is the number of clusters; L(c) is the adaptive factor, the numerator of the adaptive factor represents the distance between classes, and the denominator represents the distance within classes. The convergence condition of the adaptive factor is: L(c)>L(c-1) and L(c)>L(c+1), so the number of clusters c can be determined as the optimal number of clusters; n is the number of samples; m is the fuzzy weighting parameter, which is 2; v i is the i-th cluster center; d ik is the Euclidean distance between the i-th sample and the cluster center of the k-th cluster;

[0030] Calculate the Euclidean distance D from each sample point to the cluster center of each cluster. i1 ,d i2 ,…,d ik ,…,d ic} method is:

[0031]

[0032] x ij is the jth data point of the i-th sample; v kj is the jth data point of the kth cluster center;

[0033] Calculate the angle cosine distance And form a comprehensive distance ρ with the Euclidean distance ik :

[0034]

[0035] Among them, d ik is the Euclidean distance between the i-th sample and the k-th cluster center; x ij is the jth data point of the i-th sample; v kj is the jth data point of the kth cluster center; is the cosine distance of the angle; ρ i k is the comprehensive distance of the i-th sample to the k-th cluster; β is the proportional coefficient; 1-σ is the weight of the Euclidean distance, σ is the weight of the angle cosine distance, and β is to reduce the Euclidean distance by a certain multiple so that the angle cosine distance and the Euclidean distance are at the same order of magnitude;

[0036] According to the calculated comprehensive distance ρ ik , update the membership matrix:

[0037]

[0038] Among them, ρ ik is the comprehensive distance of the i-th sample to the k-th cluster.

[0039] Furthermore, the method of performing random production simulation on typical scenarios and extreme scenarios respectively to obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario is:

[0040] Typical scenarios and extreme scenarios are extracted respectively to serialize and model the system components, and the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station of the corresponding scenarios are obtained respectively; the available margin sequence of the photovoltaic power station includes the available margin sequence of the generator set and the available margin sequence of the energy storage; the load consumption margin sequence of the photovoltaic power station includes the load consumption margin sequence of the generator set and the consumption margin sequence of the energy storage;

[0041] According to the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station in the corresponding scenario, the supply and demand balance calculation is performed, and the random production simulation is performed to calculate the reliability index of the actual system in the corresponding scenario;

[0042] According to the reliability index of the actual system of the corresponding scenario, the reliability index of the equivalent system of the corresponding scenario is obtained.

[0043] Furthermore, the method of performing supply and demand balance calculation according to the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station in the corresponding scene, and performing random production simulation to calculate the reliability index of the actual system in the corresponding scene is:

[0044]

[0045] Among them, EENS is the reliability index of the actual system; D i is the system load shedding power when the system is in state i; F i is the frequency value of the system in state i; T i is the existence time of state i of the system; P i is the probability value when the system is in state i; A is all the states in which the system cannot meet the load within a given time; T is time.

[0046] The present invention provides a photovoltaic power station reliable capacity calculation system considering sandstorm weather, comprising:

[0047] Solar irradiation model establishment module: used to establish a solar irradiation model; the solar irradiation model includes a solar irradiation model under clear atmospheric conditions, and uses the absorption, reflection and scattering effects of sand and dust aerosol particles in the air during sandstorms on solar radiation to correct the aerosol absorption radiation coefficient in the solar irradiation model under clear atmospheric conditions;

[0048] Photovoltaic output model building module: used to build a photovoltaic output model based on the solar irradiation model;

[0049] Scenario division module: used to divide different output scenarios of photovoltaic power stations under sandstorm weather into typical scenarios and extreme scenarios according to the photovoltaic output model;

[0050] Reliability index acquisition module: used to perform random production simulations on typical scenarios and extreme scenarios respectively, and obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario;

[0051] Trusted capacity acquisition module: used to obtain the trusted capacity of the photovoltaic power station in the corresponding scenario based on the reliability index of the actual system in the corresponding scenario and the reliability index of the equivalent system in the corresponding scenario.

[0052] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0053] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention provides a method for calculating the credible capacity of a photovoltaic power station considering sandstorm weather. The method establishes a solar irradiation model and a photovoltaic output model; according to the photovoltaic output model, different output scenarios of the photovoltaic power station under sandstorm weather are divided into typical scenarios and extreme scenarios; random production simulations are performed on the typical scenarios and extreme scenarios respectively, and the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario are obtained. According to the reliability index of the actual system of the corresponding scenario, the reliability index of the equivalent system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario are obtained respectively, and the credible capacity of the photovoltaic power station of the corresponding scenario is obtained. This method fully considers the situation that after the sandstorm passes, dust particles accumulate on the photovoltaic panels, reducing the transmittance of the photovoltaic panel glass, thereby affecting the solar radiation it receives. By correcting the transmittance model of the photovoltaic panel glass, a solar irradiation model considering the influence of dust accumulation is established, laying the foundation for the credible capacity of photovoltaic power stations under sandstorm weather conditions, and providing a basis for subsequent scenario division. By effectively dividing different output conditions under sandstorm weather conditions into typical scenarios and extreme scenarios, more accurate input data can be provided for the evaluation of credible capacity, and the reliability indicators of the actual system corresponding to the scenario and the reliability indicators of the equivalent system can be obtained. The credible capacity of the photovoltaic power station is used for system planning and scheduling, thereby improving the reliability of the power system under extreme weather conditions such as sandstorms.

[0056] Furthermore, the scene clustering extraction of the present invention adopts an improved fuzzy C-means clustering algorithm, which is an unsupervised clustering algorithm that combines the k-means algorithm with the fuzzy theory. It measures the similarity between sample points and clusters by fuzzy membership calculation, and overcomes the defect of the traditional k-means clustering method that the sample points are fixed in a certain cluster. The traditional fuzzy C-means clustering algorithm uses Euclidean distance as a standard for judging the size of the membership of a sample to a certain cluster. Considering that meteorological information and photovoltaic output are strongly correlated with time series, and Euclidean distance has certain limitations in mining the similarity of morphological changes in time series, the angle cosine distance is not affected by the numerical value, can correctly reflect the shape and direction of the curve, and is a common indicator for characterizing the similarity of time series. Therefore, the angle cosine distance is introduced when measuring the membership. The comprehensive distance composed of the Euclidean distance and the angle cosine distance is used as a standard for judging the size of the membership of the sample to a certain cluster. The angle cosine distance is used as an indicator for characterizing the similarity of time series, which makes up for the limitations of the Euclidean distance in mining the similarity of morphological changes in time series. Therefore, the high and low memberships obtained by the improved fuzzy C-means clustering can represent the typical and extreme scenes of sandstorms, respectively, and are more suitable for analyzing abnormal sandstorm weather.

[0057] Furthermore, the present invention adopts serialized modeling of the method when calculating the reliability index of the actual system, which reduces the calculation complexity and takes less time than the traditional Monte Carlo method.

[0058] The invention provides a photovoltaic power station trusted capacity calculation system considering sandstorm weather. The system establishes a solar radiation model and a photovoltaic output model by setting a solar radiation model establishment module, a photovoltaic output model establishment module, a scene division module, a reliability index acquisition module and a trusted capacity acquisition module; according to the photovoltaic output model, different output scenes of the photovoltaic power station under sandstorm weather are divided into typical scenes and extreme scenes; random production simulations are respectively performed on the typical scenes and the extreme scenes to obtain the reliability index of the actual system of the corresponding scene and the reliability index of the equivalent system of the corresponding scene; according to the reliability index of the actual system of the corresponding scene, the reliability index of the equivalent system of the corresponding scene is respectively obtained; according to the reliability index of the actual system of the corresponding scene and the reliability index of the equivalent system of the corresponding scene, the trusted capacity process of the photovoltaic power station of the corresponding scene is obtained. The system has the advantages of simple structure, clear functions, easy expansion and maintenance, etc. Through modular design, the system can efficiently process data, improve calculation efficiency and accuracy, and enhance the flexibility and adaptability of the system. These advantages make the system have broad application prospects and important practical value in the planning, design, operation and maintenance of photovoltaic power stations.

[0059] The present invention also provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program. The device has a simple structure, low modification cost, and small resource occupation.

[0060] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor. The storage medium has good portability and strong versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The present invention is a flow chart of a method for calculating the credible capacity of a photovoltaic power station taking into account sandstorm weather.

[0062] Figure 2 The present invention is a flow chart of a method for calculating membership in a method for calculating credible capacity of a photovoltaic power station taking sandstorm weather into consideration.

[0063] Figure 3 This is a clustering result diagram of a membership calculation method in a method for calculating credible capacity of a photovoltaic power station taking sandstorm weather into consideration according to the present invention.

[0064] Figure 4 This is a power-time comparison curve diagram of typical scenes and original data obtained by clustering the membership calculation method in a method for calculating the credible capacity of a photovoltaic power station taking into account sandstorm weather in the present invention.

[0065] Figure 5 The present invention is a flow chart of a method for obtaining the reliability index of the actual system, the reliability index of the equivalent system and the reliable capacity of the corresponding scene in a reliable capacity calculation method of a photovoltaic power station considering sandstorm weather.

[0066] Figure 6 This is a structural diagram of a photovoltaic power station credible capacity calculation system taking sandstorm weather into consideration according to the present invention. DETAILED DESCRIPTION

[0067] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0068] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0069] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it. Figure 1 The present invention discloses a method for calculating the credible capacity of a photovoltaic power station considering sandstorm weather, comprising:

[0070] S1: Establish a solar irradiation model; the solar irradiation model includes a solar irradiation model under clear atmospheric conditions. After a sandstorm passes, dust particles accumulate on photovoltaic panels, reducing the transmittance of photovoltaic panel glass, thereby affecting the solar radiation received by them. Therefore, the aerosol absorption and radiation coefficient in the solar irradiation model under clear atmospheric conditions is corrected by using the absorption, reflection and scattering effects of dust aerosol particles in the air during the sandstorm on solar radiation to obtain a solar irradiation model that takes into account the influence of dust accumulation, providing an accurate basis for the establishment of the subsequent photovoltaic output model, specifically:

[0071]

[0072] Among them, τ d is the light transmittance of photovoltaic panel glass under dust accumulation conditions; τ c is the transmittance of photovoltaic panel glass under clean conditions; erf is the error function; w is the dust density; e1, e2 and e3 are all empirical coefficients, e1 = 34.37, e2 = 0.17, e3 = 0.8473 respectively; I j I is the solar radiation intensity received by the dust-accumulated photovoltaic panel; s is the solar radiation intensity received by the photovoltaic panel surface under normal climate. S2: According to the solar irradiation model, a photovoltaic output model is established, specifically:

[0073] The photovoltaic output model includes the photovoltaic output model in the clear atmosphere before the sandstorm passes:

[0074]

[0075] The photovoltaic output model under the condition of dust accumulation on photovoltaic panels after the sandstorm is:

[0076]

[0077] CF=η T η i n n n l (5)

[0078] Among them, P PV ' is the output value of the photovoltaic power station in the clear atmosphere before the sandstorm passes; P PV is the output value of the dust-accumulating photovoltaic power station; CF is the conversion efficiency of photovoltaic modules in converting solar energy into electrical energy; I s is the solar radiation intensity received by the dust-accumulated photovoltaic panel; G is the sunshine intensity under standard conditions, which means that the temperature of the photovoltaic power station battery is 25℃ and the air quality coefficient AM is 1.5; P PVPeak is the total installed capacity of the photovoltaic power station; η T is the temperature correction coefficient; η i The azimuth tilt correction factor for the photovoltaic module installation; n n is the inverter efficiency of the photovoltaic system, ranging from 0.8 to 0.95; n l is the line correction coefficient of the photovoltaic system, which is taken as 0.95 to 0.98. The temperature correction coefficient, the azimuth angle correction coefficient of the photovoltaic module installation, as well as the inverter efficiency and line correction coefficient are introduced to improve the accuracy of the photovoltaic output model;

[0079] S3: According to the photovoltaic output model, the different output scenarios of photovoltaic power stations under sandstorm weather conditions are divided into typical scenarios and extreme scenarios; the photovoltaic output data are clustered using the improved fuzzy C-means (FCM) clustering algorithm that combines Euclidean distance and angle cosine distance. Then, based on the clustering results, the data with high membership to the cluster center are selected as typical scenarios, and the data with low membership as extreme scenarios. Through clustering analysis, the different output situations of photovoltaic power stations under sandstorm weather conditions are effectively divided into typical scenarios and extreme scenarios, providing accurate input data for reliability assessment, specifically:

[0080] According to the photovoltaic output model, the improved fuzzy C-means clustering algorithm is used to calculate the membership degree relative to the cluster center, see Figure 2 , specifically:

[0081]

[0082] in, is the center vector of the sample; x i is the i-th sample; uik is the membership of the i-th sample to the k-th cluster; c is the number of clusters; L(c) is the adaptive factor, the numerator of the adaptive factor represents the distance between classes, and the denominator represents the distance within classes. The convergence condition of the adaptive factor is: L(c)>L(c-1) and L(c)>L(c+1), so the number of clusters c can be determined as the optimal number of clusters; n is the number of samples; m is the fuzzy weighting parameter, which is 2; v i is the i-th cluster center; d ik is the Euclidean distance between the i-th sample and the cluster center of the k-th cluster;

[0083] First, data is initialized, c = 2, L(c) = 0, and the membership matrix U is randomly initialized. According to formulas (6) to (8), the cluster center v is determined. i and the center vector of the sample Calculate the Euclidean distance d from each sample point to the cluster center of each cluster ik and cosine distance According to the Euclidean distance d from each sample point to the cluster center of each cluster ik and cosine distance Calculate the comprehensive distance ρ i k, and re-update the membership matrix U, determine whether ||U(k+1)-U(k)||<ε holds, if not, set n=n+1, re-initialize the membership matrix randomly, if so, determine whether the adaptive factor converges, if the adaptive factor does not converge, set c=c+1 to redetermine the cluster center V and the center vector of the sample If the adaptive factor converges, the membership of the cluster and each sample to the cluster center is obtained; where ε is the condition for judging convergence;

[0084] Among them, the Euclidean distance D = {d i1 ,d i2 ,…,d ik ,…,d ic} method is:

[0085]

[0086] x ij is the jth data point of the i-th sample; v k j is the jth data point of the kth cluster center;

[0087] Calculate the angle cosine distance And form a comprehensive distance ρ with the Euclidean distance ik :

[0088]

[0089] Among them, d ik is the Euclidean distance between the i-th sample and the k-th cluster center; x ij is the jth data point of the i-th sample; v kj is the jth data point of the kth cluster center; is the cosine distance of the angle; ρ i k is the comprehensive distance of the i-th sample to the k-th cluster; β is the proportional coefficient; 1-σ is the weight of the Euclidean distance, σ is the weight of the angle cosine distance, and β is to reduce the Euclidean distance by a certain multiple so that the angle cosine distance and the Euclidean distance are at the same order of magnitude;

[0090] According to the calculated comprehensive distance ρ ik , update the membership matrix,:

[0091]

[0092] Among them, ρ ik is the comprehensive distance of the i-th sample to the k-th cluster;

[0093] Finally, the output scenarios whose membership relative to the cluster center is higher than a certain threshold are classified as typical scenarios; the output scenarios whose membership relative to the cluster center is lower than a certain threshold are classified as extreme scenarios. Figure 3 , the membership degree of each sample represents its relative distance to the nearest cluster center, forming a Figure 3 The clustering results are as follows: the more similar the data is to the cluster center, the higher the membership is, and the closer it is to the cluster center; the data with high membership to the cluster center is taken as a typical scene, and the data with low membership to the cluster center is taken as an extreme scene. Extract one type of data with high membership to the cluster center as a typical scene of sandstorm, and get the following: Figure 4 The power-time curve of

[0094] S4: Perform random production simulations on typical scenarios and extreme scenarios respectively, and obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario; the actual system includes photovoltaic units, energy storage equipment and loads; the definition of resources and demand is to take the generator set and energy storage equipment as resources, and the load and energy storage equipment as demand; calculate the available margin and consumption margin: the probability distribution of the available power of the resource is the available margin, and the probability distribution of the demand consumption power is the consumption margin. Performing random production simulation is to perform random production simulation on the actual system in typical scenarios and extreme scenarios respectively, and calculate the reliability index, namely the expected value of electricity shortage (EENS), which is specifically:

[0095] Typical scenarios and extreme scenarios are extracted respectively to serialize and model the system components, and the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station of the corresponding scenarios are obtained respectively; the available margin sequence of the photovoltaic power station includes the available margin sequence of the generator set and the available margin sequence of the energy storage; the load consumption margin sequence of the photovoltaic power station includes the load consumption margin sequence of the generator set and the consumption margin sequence of the energy storage; the serialization modeling includes the serialization of the generator set, the serialization of the energy storage device and the load serialization; the serialization of the generator set is to discretize the output of the photovoltaic generator set, define the discretization step size, and obtain the photovoltaic unit The probability distribution sequence of output power; the energy storage device serialization is to consider the charging and discharging constraints of the energy storage device, perform probability statistics on its historical output, and obtain the probability distribution of equivalent power; the load serialization is to analyze the historical load data, consider the load peak-to-valley difference, and obtain the load consumption margin sequence, and make the following definition for the sequence operation: generator sets and energy storage devices are resources; loads and energy storage devices are demands; the probability distribution of resource available power is the available margin; the probability distribution of demand consumption power is the consumption margin; the available margin is equal to the consumption margin, that is, the system is in power balance, that is, supply and demand balance. Specifically:

[0096] Photovoltaic power generation is greatly affected by the weather. When there is no sunlight at night, the output is 0, and when the atmosphere is clear, it can be rated output. Its output is between 0 and rated output. The discretization step length ΔC is defined, and the output value of the photovoltaic power station is discretized into a sequence S according to the step length ΔC, where the i-th number of the sequence S is:

[0097]

[0098] Among them, S PV (i) is the probability that the output power of the photovoltaic unit is within the interval [(i-1)ΔC, iΔC]; P PV (x) is the output value of the dust photovoltaic power station;

[0099] Considering the charging and discharging rate constraints, charging and discharging capacity constraints, and charging and discharging power constraints of the energy storage equipment, the probability distribution F of the equivalent power of the energy storage equipment is obtained by performing probability statistics on the historical output of the energy storage equipment. st_gene and F st_load :

[0100]

[0101] Where X is the output power of the energy storage device, X>0 means energy storage discharge, and X<0 means energy storage charging; F st_gene is the probability distribution curve of energy storage equivalent generator, F st_load is the probability distribution curve of energy storage equivalent load;

[0102] Taking into account the changes in the load peak-to-valley difference, the upper and lower limit constraints of the load are obtained. A sample analysis of the load historical data of 8760 hours in the previous year is performed to obtain the load consumption margin sequence.

[0103] According to the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station in the corresponding scenario, the supply and demand balance calculation is performed, and the random production simulation is performed to calculate the reliability index of the actual system in the corresponding scenario, which is specifically:

[0104] In various typical scenarios, the supply and demand balance between the available margin sequence and the consumption margin sequence is calculated, and random production simulation is performed to calculate the expected power shortage value EENS, a reliability indicator of the actual system. EENS represents the amount of power that is lacking to achieve supply and demand balance in the system power. The lower the value, the higher the system reliability. Specifically:

[0105]

[0106] Among them, EENS is the reliability index of the actual system; D i is the system load shedding power when the system is in state i; F i is the frequency value of the system in state i; T i is the existence time of state i of the system; P i is the probability value when the system is in state i; A is all the states in which the system cannot meet the load within a given time; T is time.

[0107] See also Figure 5 First, the system components are serialized and random production simulation is performed to calculate the reliability index EENS of the actual system. Let C max =C pv , C min =0, where C pv is the capacity of the photovoltaic unit; remove the photovoltaic unit in the actual system and replace it with an equivalent conventional unit (such as a thermal power unit), the equivalent conventional unit capacity C ge n=(C max +C min ) / 2, and perform random production simulation again (the system after replacing the unit is called the equivalent system at this time), and calculate the reliability index EENS' of the equivalent system; then judge whether the reliability index error between the actual system and the equivalent system is within the allowable range. If so, the credible capacity of the actual system is obtained, which is the equivalent conventional unit capacity of the equivalent system; if not, use the binary iteration method to update C max and C min , replace the equivalent conventional unit and perform random production simulation again to obtain a new round of equivalent system reliability index EENS'; σ is the allowable error, C max and C minAll are intermediate variables.

[0108] S5: According to the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario, obtain the credible capacity of the photovoltaic power station of the corresponding scenario, see Figure 5 , specifically:

[0109] The reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario are compared, and the binary method is used to adjust the equivalent conventional unit capacity of the equivalent system, so that the reliability index of the equivalent system is iteratively calculated until the difference in the reliability index of the two systems is within the error range and the reliabilities of the two systems are the same. At this time, the total capacity of the equivalent conventional units of the equivalent system is the credible capacity of the actual system.

[0110] See also Figure 6 The present invention provides a photovoltaic power station reliable capacity calculation system considering sandstorm weather, comprising:

[0111] Solar irradiation model establishment module: used to establish a solar irradiation model; the solar irradiation model includes a solar irradiation model under clear atmospheric conditions, and uses the absorption, reflection and scattering effects of sand and dust aerosol particles in the air during sandstorms on solar radiation to correct the aerosol absorption radiation coefficient in the solar irradiation model under clear atmospheric conditions;

[0112] Photovoltaic output model building module: used to build a photovoltaic output model based on the solar irradiation model;

[0113] Scenario division module: used to divide different output scenarios of photovoltaic power stations under sandstorm weather into typical scenarios and extreme scenarios according to the photovoltaic output model;

[0114] Reliability index acquisition module: used to perform random production simulations for typical scenarios and extreme scenarios respectively, to obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario. Trustworthy capacity acquisition module: used to obtain the trustworthy capacity of the photovoltaic power station of the corresponding scenario based on the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario.

[0115] The present invention provides a terminal device including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented. The computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention.

[0116] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0117] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0118] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0119] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0120] In summary, the present invention provides a method, system, device and storage medium for calculating the credible capacity of a photovoltaic power station considering sandstorm weather. The method first establishes a solar irradiation model under clear atmospheric conditions, and corrects the aerosol absorption radiation coefficient in the model according to the influence of sand and dust aerosol particles on solar radiation during sandstorms. The improved fuzzy C-means clustering algorithm is used to classify the scene set in combination with parameters such as photovoltaic output, light intensity and visibility to determine typical scenes and extreme scenes. Based on these scenes, the present invention performs serial modeling on the system components of the photovoltaic power station, builds an actual system model, and performs random production simulation under different meteorological conditions to calculate the expected value of power shortage of the system's reliability index. The expected value of power shortage of the actual system and the equivalent system is compared by an iterative approximation method until the difference in the reliability index of the two is within the error range, thereby determining the credible capacity of the photovoltaic power station under sandstorm weather conditions. The problem of photovoltaic power stations participating in the power balance of the power system under extreme sandstorm weather conditions is effectively solved, so that new energy units can be equivalent to conventional units to participate in subsequent calculations.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to impose any limitation on the technical solution of the present invention. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can also be subjected to several simple modifications and substitutions, and these modifications and substitutions are also within the scope of protection covered by the claims.

Claims

1. A method for calculating the credible capacity of a photovoltaic power station considering sandstorm weather, characterized in that: include: Establishing a solar radiation model; the solar radiation model includes a solar radiation model under clear atmospheric conditions, and using the absorption, reflection and scattering effects of sand and dust aerosol particles in the air during a sandstorm on solar radiation to correct the aerosol absorption radiation coefficient in the solar radiation model under clear atmospheric conditions; According to the solar irradiation model, a photovoltaic output model is established; According to the photovoltaic output model, the different output scenarios of photovoltaic power stations under sandstorm weather are divided into typical scenarios and extreme scenarios; Perform random production simulations for typical scenarios and extreme scenarios respectively to obtain the reliability index of the actual system corresponding to the scenario and the reliability index of the equivalent system corresponding to the scenario; According to the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario, the reliable capacity of the photovoltaic power station of the corresponding scenario is obtained.

2. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather according to claim 1, characterized in that: The method for establishing the solar radiation model is: Among them, τ d is the light transmittance of photovoltaic panel glass under dust accumulation conditions; τ c is the transmittance of photovoltaic panel glass under clean conditions; erf is the error function; w is the dust density; e1, e2 and e3 are all empirical coefficients; I j I is the solar radiation intensity received by the dust-accumulated photovoltaic panel; s It is the solar radiation intensity received by the photovoltaic panel surface under normal climate.

3. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather according to claim 1, characterized in that: The method for establishing a photovoltaic output model according to the solar irradiation model is: According to the solar radiation model, the solar radiation intensity received by the dust-accumulated photovoltaic panel is obtained; According to the solar radiation intensity received by the dust-accumulated photovoltaic panels, a photovoltaic output model is established, specifically: CF=η T or i n n n l (4) Among them, P PV is the output value of the dust-accumulating photovoltaic power station; CF is the conversion efficiency of photovoltaic modules in converting solar energy into electrical energy; I s is the solar radiation intensity received by the dust-accumulated photovoltaic panel; G is the sunshine intensity under standard conditions, which means that the temperature of the photovoltaic power station battery is 25℃ and the air quality coefficient AM is 1.5; P PVPeak is the total installed capacity of the photovoltaic power station; η T is the temperature correction coefficient; η i The azimuth tilt correction factor for the photovoltaic module installation; n n is the inverter efficiency of the photovoltaic system, ranging from 0.8 to 0.95; n l is the line correction coefficient of the photovoltaic system, which is taken as 0.95~0.

98.

4. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather according to claim 1, characterized in that: The method for dividing different output scenarios of photovoltaic power stations under sandstorm weather into typical scenarios and extreme scenarios according to the photovoltaic output model is as follows: According to the photovoltaic output model, the improved fuzzy C-means clustering algorithm is used to calculate the membership degree of the relative cluster center. The output scenarios whose membership relative to the cluster center is higher than a certain threshold are classified as typical scenarios; the output scenarios whose membership relative to the cluster center is lower than a certain threshold are classified as extreme scenarios.

5. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather according to claim 4 is characterized in that: The method for calculating the membership degree of the relative cluster center based on the photovoltaic output model and using the improved fuzzy C-means clustering algorithm is as follows: in, is the center vector of the sample; x i is the i-th sample; u ik is the membership of the i-th sample to the k-th cluster; c is the number of clusters; L(c) is the adaptive factor, the numerator of the adaptive factor represents the distance between classes, and the denominator represents the distance within classes. The convergence condition of the adaptive factor is: L(c)>L(c-1) and L(c)>L(c+1), so the number of clusters c can be determined as the optimal number of clusters; n is the number of samples; m is the fuzzy weighting parameter, which is 2; v i is the i-th cluster center; d ik is the Euclidean distance between the i-th sample and the cluster center of the k-th cluster; Calculate the Euclidean distance D from each sample point to the cluster center of each cluster. i1 ,d i2 ,…,d ik ,…,d ic } method is: x ij is the jth data point of the i-th sample; v kj is the jth data point of the kth cluster center; Calculate the angle cosine distance And form a comprehensive distance ρ with the Euclidean distance ik : Among them, d ik is the Euclidean distance between the i-th sample and the k-th cluster center; x ij is the jth data point of the i-th sample; v kj is the jth data point of the kth cluster center; is the cosine distance of the angle; ρ ik is the comprehensive distance of the i-th sample to the k-th cluster; β is the proportional coefficient; 1-σ is the weight of the Euclidean distance, σ is the weight of the angle cosine distance, and β is to reduce the Euclidean distance by a certain multiple so that the angle cosine distance and the Euclidean distance are at the same order of magnitude; According to the calculated comprehensive distance ρ ik , update the membership matrix: Among them, ρ ik is the comprehensive distance of the i-th sample to the k-th cluster.

6. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather according to claim 1, characterized in that: The method of performing random production simulation on typical scenarios and extreme scenarios respectively to obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario is: Typical scenarios and extreme scenarios are extracted respectively to serialize and model the system components, and the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station of the corresponding scenarios are obtained respectively; the available margin sequence of the photovoltaic power station includes the available margin sequence of the generator set and the available margin sequence of the energy storage; The photovoltaic power station load consumption margin sequence includes a generator set load consumption margin sequence and an energy storage consumption margin sequence; According to the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station in the corresponding scenario, the supply and demand balance calculation is performed, and the random production simulation is performed to calculate the reliability index of the actual system in the corresponding scenario; According to the reliability index of the actual system of the corresponding scenario, the reliability index of the equivalent system of the corresponding scenario is obtained.

7. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather according to claim 6, characterized in that: The method of performing supply and demand balance calculation according to the available margin sequence of the photovoltaic power station and the load consumption margin sequence of the photovoltaic power station in the corresponding scene, and performing random production simulation to calculate the reliability index of the actual system in the corresponding scene is: Among them, EENS is the reliability index of the actual system; D i is the system load shedding power when the system is in state i; F i is the frequency value of the system in state i; T i is the existence time of state i of the system; P i is the probability value when the system is in state i; A is all the states in which the system cannot meet the load within a given time; T is time.

8. A photovoltaic power station reliable capacity calculation system considering sandstorm weather, characterized in that: include: Solar irradiation model establishment module: used to establish a solar irradiation model; the solar irradiation model includes a solar irradiation model under clear atmospheric conditions, and uses the absorption, reflection and scattering effects of sand and dust aerosol particles in the air during sandstorms on solar radiation to correct the aerosol absorption radiation coefficient in the solar irradiation model under clear atmospheric conditions; Photovoltaic output model establishment module: used to establish a photovoltaic output model based on the solar irradiation model; scenario division module: used to divide different output scenarios of photovoltaic power stations under sandstorm weather into typical scenarios and extreme scenarios based on the photovoltaic output model; Reliability index acquisition module: used to perform random production simulations on typical scenarios and extreme scenarios respectively, and obtain the reliability index of the actual system of the corresponding scenario and the reliability index of the equivalent system of the corresponding scenario; Trusted capacity acquisition module: used to obtain the trusted capacity of the photovoltaic power station in the corresponding scenario based on the reliability index of the actual system in the corresponding scenario and the reliability index of the equivalent system in the corresponding scenario.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing 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 7 are implemented.

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