Reliable capacity calculation method and related equipment for photovoltaic power plants considering sandstorm weather

Abstract

This invention relates to the field of solar photovoltaic power generation technology, particularly to the calculation of reliable capacity of photovoltaic power plants considering sandstorm weather and related devices. It establishes a solar irradiance model and a photovoltaic output model; and divides different output scenarios of photovoltaic power plants under sandstorm weather into typical and extreme scenarios. By conducting stochastic production simulations on typical and extreme scenarios respectively, the reliability indicators of the actual system, the reliability indicators of the equivalent system, and the reliable capacity of the photovoltaic power plant for each scenario are obtained. The invention fully considers the impact of sand and dust particle accumulation on the received solar radiation after a sandstorm. By obtaining the reliability indicators of the actual system and the equivalent system for each scenario, the reliable capacity of the photovoltaic power plant is used for system planning and scheduling, improving the reliability of the power system under extreme weather conditions such as sandstorms. This solves the problem that the reliable capacity calculation of photovoltaic power plants cannot meet the reliability and stability assessment requirements under sandstorm weather conditions.

Description

Technical Field

[0001] This invention relates to the field of solar photovoltaic power generation technology, specifically to a method and related apparatus for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather, and more particularly to a method and related apparatus for calculating the reliable capacity of a photovoltaic power station considering the uncertainty of sandstorm weather. Background Technology

[0002] Photovoltaic power generation is a technology that directly converts light energy into electrical energy using the photovoltaic effect at semiconductor interfaces. Its principle is as follows: when light shines on a solar cell, the light is absorbed at the interface layer. Photons with sufficient energy can excite electrons from covalent bonds in P-type and N-type silicon, generating electron-hole pairs. Before recombination, the electrons and holes near the interface layer are separated by the electric field of the space charge; electrons move towards the positively charged N-region, and holes move towards the negatively charged P-region. This charge separation at the interface layer generates a measurable outward voltage between the P- and N-regions. Electrodes can be added to both sides of the silicon wafer and a voltmeter connected to generate a current. Photovoltaic power generation technology has three major advantages: permanence, cleanliness, and flexibility. It has a long lifespan; as long as the sun exists, solar cells can be used for a long time with a one-time investment. Furthermore, it does 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 energy source, photovoltaic power generation is being widely applied in various fields. From residential to commercial, industrial, public utilities, and remote areas, photovoltaic (PV) power generation systems have demonstrated unique advantages and value. With continuous technological advancements and sustained policy support, the application prospects of PV power generation will become even broader.

[0003] However, with global climate change, extreme weather events are becoming more frequent, especially sandstorms, extreme heat without wind, and extreme cold without sunlight. These extreme and abnormal weather events have a serious impact on new energy power plants such as photovoltaic and wind power. The output of new energy power plants is strongly affected by changes in meteorological factors during extreme weather events. For example, extreme heat without wind will cause the surface temperature of photovoltaic modules to rise, thereby reducing their power generation efficiency. In addition, high temperatures will also accelerate the aging of photovoltaic module encapsulation materials and cables, further affecting power generation performance. Extreme cold without sunlight will lead to insufficient sunlight intensity, causing frost heave in the foundation of photovoltaic power plants, which can damage them. In addition, low temperatures can also affect the performance and lifespan of photovoltaic modules. Especially under sandstorm weather conditions, solar irradiance will be severely reduced, causing short-term and drastic fluctuations in the output of photovoltaic power generation. This can lead to an imbalance between power supply and demand in the power system, which may result in serious grid accidents such as damage to various units and large-scale power outages. This is because under sandstorm weather conditions, the air is filled with a large amount of sand and dust aerosols, which will reduce the solar radiation received by the photovoltaic panels. At the same time, after the sandstorm passes, sand and dust particles will accumulate on the photovoltaic panels, reducing the light transmittance of the photovoltaic panels and causing a severe reduction in photovoltaic output.

[0004] To address the inherent limitation of new energy power generation being dependent on weather conditions, sufficient thermal power units and energy storage equipment are needed to ensure power balance. By calculating the credible capacity of new energy power plants, new energy units can be replaced by conventional units that are not severely affected by weather conditions in subsequent calculations. In other words, the calculation of the credible capacity of photovoltaic power plants equivalences the random new energy model with the deterministic conventional unit model, which is of great significance in subsequent power balance calculations.

[0005] The reliable capacity of a photovoltaic (PV) power plant refers to the stable electrical power output of the plant under specific conditions. This concept fully considers various factors affecting the power generation capacity of the plant, such as solar energy resources, PV module quality, and module layout. Reliable capacity is a crucial indicator for measuring the stable power generation capability of a PV power plant under specific conditions. Due to the severe impact of sandstorms on PV power output, which causes significant fluctuations, traditional methods for calculating the reliable capacity of PV power plants, which rely on statistical analysis to establish a probability distribution model of PV power output, primarily consider average weather conditions and do not adequately account for the impact of extreme weather events. Therefore, they cannot accurately reflect the performance of PV power plants under extreme conditions and cannot meet the requirements for assessing the reliability and stability of PV power generation in the power system during sandstorms.

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

[0007] To address the problem that existing technologies for calculating the reliable capacity of photovoltaic power plants cannot meet the requirements for assessing the reliability and stability of photovoltaic power generation in power systems under sandstorm weather conditions, this invention provides a method and related apparatus for calculating the reliable capacity of photovoltaic power plants that takes sandstorm weather conditions into account.

[0008] To achieve the above objectives, the present invention employs the following technical solution:

[0009] This invention provides a method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather, including:

[0010] A solar irradiance model is established; the solar irradiance model includes a solar irradiance model under clear atmospheric conditions, and the aerosol absorption radiation coefficient in the solar irradiance model under clear atmospheric conditions is corrected by utilizing the absorption, reflection and scattering effects of dust aerosol particles on solar radiation during dust storms.

[0011] A photovoltaic power output model is established based on the solar irradiance model;

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

[0013] Based on the reliability indicators of the actual system and the reliability indicators of the equivalent system in the corresponding scenario, the reliable capacity of the photovoltaic power station in the corresponding scenario is obtained.

[0014] Furthermore, the method for establishing the solar irradiance model is as follows:

[0015]

[0016] Where, τ d τ represents the light transmittance of the photovoltaic panel glass under dust accumulation conditions. c The transmittance of the 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 The intensity of solar radiation received by the dust-accumulating photovoltaic panel; I s This represents the intensity of solar radiation received by the surface of the photovoltaic panel under normal climatic conditions.

[0017] Furthermore, the method for establishing the photovoltaic output model based on the solar irradiance model is as follows:

[0018] The solar radiation intensity received by the dust-accumulating photovoltaic panel was obtained based on the solar irradiance model.

[0019] Based on the solar radiation intensity received by the dust-accumulating photovoltaic panel, a photovoltaic output model is established, specifically as follows:

[0020]

[0021] CF = η T η i n n n l (4)

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

[0023] Furthermore, the method for classifying different output scenarios of photovoltaic power plants under sandstorm weather conditions into typical scenarios and extreme scenarios based on the photovoltaic output model is as follows:

[0024] Based on the photovoltaic power output model, the membership degree of the relative cluster centers is calculated using an improved fuzzy C-means clustering algorithm.

[0025] The output scenarios where the membership degree of the relative cluster center is higher than a certain threshold are classified as typical scenarios.

[0026] Output scenarios where the membership degree of the relative cluster center is lower than a certain threshold are classified as extreme scenarios.

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

[0028]

[0029] in, x is the center vector of the sample; i For the i-th sample; u ikLet be the membership degree of the i-th sample to the k-th cluster; c be the number of clusters; L(c) be the adaptive factor, where the numerator represents the inter-cluster distance and the denominator represents the intra-cluster distance. The convergence condition for the adaptive factor is: L(c) > L(c-1) and L(c) > L(c+1), thus determining the optimal number of clusters c; n be the number of samples; m be the fuzzy weighting parameter, set to 2; v i Let d be the i-th cluster center; ik Let be the Euclidean distance between the i-th sample and the cluster centers of the k-th cluster;

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

[0031]

[0032] x ij v represents the j-th data point of the i-th sample; kj The j-th data point is the k-th cluster center;

[0033] Calculate the cosine distance of the included angle And together with the Euclidean distance, they form a combined distance ρ ik :

[0034]

[0035] Where, d ik x is the Euclidean distance between the i-th sample and the k-th cluster center; ij v represents the j-th data point of the i-th sample; kj The j-th data point is the k-th cluster center; ρ is the cosine distance of the included angle; i k is the combined distance of the i-th sample to the k-th cluster; β is the scaling factor; 1-σ is the weight of the Euclidean distance, σ is the weight of the cosine distance, and β is the factor that reduces the Euclidean distance by a certain factor so that the cosine distance and the Euclidean distance are on the same order of magnitude.

[0036] Based on the calculated comprehensive distance ρ ik Update the membership matrix:

[0037]

[0038] Where, ρ ik Let be the combined distance between the i-th sample and the k-th cluster.

[0039] Furthermore, the method for obtaining the reliability index of the actual system and the reliability index of the equivalent system in the corresponding scenarios by performing random production simulations on typical and extreme scenarios respectively is as follows:

[0040] Typical and extreme scenarios are extracted separately to perform serialized modeling of system components, and the available margin sequence and load consumption margin sequence of photovoltaic power plants for the corresponding scenarios are obtained respectively; the available margin sequence of photovoltaic power plants includes the available margin sequence of generator sets and the available margin sequence of energy storage; the load consumption margin sequence of photovoltaic power plants includes the load consumption margin sequence of generator sets and the consumption margin sequence of energy storage.

[0041] Based on the available margin sequence and load consumption margin sequence of the photovoltaic power station for the corresponding scenario, supply and demand balance calculations are performed, and random production simulations are conducted to calculate the reliability index of the actual system for the corresponding scenario.

[0042] Based on the reliability indicators of the actual system in the corresponding scenario, obtain the reliability indicators of the equivalent system in the corresponding scenario.

[0043] Furthermore, the method for calculating the reliability index of the actual system in the corresponding scenario by performing supply and demand balance calculations based on the photovoltaic power station availability margin sequence and photovoltaic power station load consumption margin sequence, and by random production simulation, is as follows:

[0044]

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

[0046] This invention provides a reliable capacity calculation system for photovoltaic power plants that takes into account sandstorm weather, comprising:

[0047] Solar Irradiance Model Establishment Module: Used to establish a solar irradiance model; the solar irradiance model includes a solar irradiance model under clear atmospheric conditions, and uses the absorption, reflection and scattering effects of dust aerosol particles in the air on solar radiation during dust storms to correct the aerosol absorption radiation coefficient in the solar irradiance model under clear atmospheric conditions;

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

[0049] Scene segmentation module: This module is used to classify different output scenarios of photovoltaic power plants under sandstorm weather conditions into typical scenarios and extreme scenarios based on the photovoltaic output model.

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

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

[0052] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method described above.

[0053] A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described above.

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

[0055] This invention provides a method for calculating the reliable capacity of a photovoltaic (PV) power plant considering sandstorm weather. The method establishes a solar irradiance model and a PV output model. Based on the PV output model, different output scenarios of the PV power plant under sandstorm weather are divided into typical scenarios and extreme scenarios. Random production simulations are performed on both typical and extreme scenarios to obtain the reliability indices of the actual system and the equivalent system for each scenario. Based on the reliability indices of the actual system and the equivalent system for each scenario, the reliable capacity of the PV power plant for each scenario is obtained. This method fully considers the impact of dust particles accumulating on photovoltaic panels after a sandstorm, reducing the light transmittance of the photovoltaic panel glass and thus affecting the solar radiation received. By modifying the light transmittance model of the photovoltaic panel glass, a solar radiation model considering the impact of dust accumulation is established, laying the foundation for the reliable capacity of photovoltaic power plants under sandstorm weather conditions and providing a basis for subsequent scenario classification. By effectively classifying different output conditions under sandstorm weather conditions into typical and extreme scenarios, more accurate input data can be provided for reliable capacity assessment, obtaining the reliability indicators of the actual system and the equivalent system for the corresponding scenarios. The reliable capacity of photovoltaic power plants can be used for system planning and scheduling, improving the reliability of the power system under extreme weather conditions such as sandstorms.

[0056] Furthermore, the scene clustering extraction in this invention employs an improved fuzzy C-means clustering algorithm. As an unsupervised clustering algorithm combining k-means and fuzzy theory, it measures the similarity between sample points and clusters through fuzzy membership calculation, overcoming the deficiency of traditional k-means clustering methods that fix sample points to a certain cluster. Traditional fuzzy C-means clustering uses Euclidean distance as the standard for judging the membership degree of a sample to a certain cluster. Considering that meteorological information and photovoltaic output are strongly correlated with time series, and that Euclidean distance has certain limitations in mining the similarity of morphological changes in time series, the cosine distance is not affected by the numerical value and can correctly reflect the curve shape and direction, making it a commonly used indicator for characterizing time series similarity. Therefore, the cosine distance is introduced when measuring membership degree. The comprehensive distance composed of Euclidean distance and cosine distance is used as the standard for judging the membership degree of a sample to a certain cluster. The cosine distance, as an indicator for characterizing time series similarity, makes up for the limitations of Euclidean distance in mining the similarity of morphological changes in time series. Therefore, the high and low membership degrees obtained by the improved fuzzy C-means clustering can represent typical and extreme scenarios of dust storms, respectively, and are more suitable for analyzing abnormal dust storm weather.

[0057] Furthermore, the present invention employs serialized modeling for the calculation of reliability indicators for actual systems, which reduces computational complexity and consumes less time than the traditional Monte Carlo method.

[0058] This invention provides a reliable capacity calculation system for photovoltaic power plants considering sandstorm weather. The system establishes a solar irradiance model and a photovoltaic output model, and then divides the scene into modules for solar irradiance model establishment, photovoltaic output model establishment, scene segmentation, reliability index acquisition, and reliable capacity acquisition. It performs the following steps: establishing a solar irradiance model and a photovoltaic output model; dividing the photovoltaic power plant into typical and extreme scenarios under sandstorm weather based on the photovoltaic output model; conducting random production simulations for both typical and extreme scenarios to obtain the reliability indices of the actual system and the equivalent system for each scenario; and finally, obtaining the reliable capacity of the photovoltaic power plant for each scenario based on the reliability indices of the actual and equivalent systems. The system features a simple structure, clear functions, and ease of expansion and maintenance. Through modular design, the system can efficiently process data, improving computational efficiency and accuracy, while also enhancing its flexibility and adaptability. These advantages make the system widely applicable and of significant practical value in the planning, design, operation, and maintenance of photovoltaic power plants.

[0059] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the above-described method. The device has a simple structure, low modification cost, and minimal resource consumption.

[0060] A computer-readable storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the method described above. This storage medium is highly portable and versatile. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating a reliable capacity calculation method for photovoltaic power plants that takes into account sandstorm weather, according to the present invention.

[0062] Figure 2 This is a flowchart of the membership calculation method in a reliable capacity calculation method for photovoltaic power plants that takes into account sandstorm weather, according to the present invention.

[0063] Figure 3 This is a clustering result diagram of the membership degree calculation method in the reliable capacity calculation method for photovoltaic power plants that takes into account sandstorm weather of the present invention.

[0064] Figure 4 This is a power-time comparison curve of typical scenarios and original data obtained from the clustering method of the membership degree calculation method in the reliable capacity calculation method of photovoltaic power plants considering sandstorm meteorology of the present invention.

[0065] Figure 5 This is a flowchart illustrating a method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather, according to the present invention, to obtain the reliability index of the actual system, the reliability index of the equivalent system, and the reliable capacity for the corresponding scenario.

[0066] Figure 6 This is a structural diagram of a reliable capacity calculation system for photovoltaic power plants that takes into account sandstorm weather, according to the present invention. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0069] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for illustrative purposes only and not for limiting the scope of the invention. See also... Figure 1 This invention discloses a method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather, including:

[0070] S1: Establish a solar irradiance model; the solar irradiance model includes a solar irradiance model under clear atmospheric conditions. After a dust storm, dust particles accumulate on the photovoltaic panels, reducing the light transmittance of the photovoltaic glass and thus affecting the solar radiation received. Therefore, by utilizing the absorption, reflection, and scattering effects of dust aerosol particles on solar radiation during a dust storm, the aerosol absorption radiation coefficient in the solar irradiance model under clear atmospheric conditions is corrected to obtain a solar irradiance model considering the effects of dust accumulation. This provides an accurate basis for the subsequent establishment of the photovoltaic power output model. Specifically:

[0071]

[0072] Where, τ d τ represents the light transmittance of the photovoltaic panel glass under dust accumulation conditions. c I represents the light transmittance of the photovoltaic panel glass under clean conditions; erf is the error function; w is the dust density; e1, e2, and e3 are empirical coefficients, taken as e1 = 34.37, e2 = 0.17, and e3 = 0.8473 respectively; j The intensity of solar radiation received by the dust-accumulating photovoltaic panel; I s S2: The intensity of solar radiation received by the photovoltaic panel surface under normal climatic conditions. Based on the solar irradiance model, a photovoltaic output model is established, specifically as follows:

[0073] The photovoltaic power output model includes the photovoltaic power output model under clear atmospheric conditions before a sandstorm passes:

[0074]

[0075] The photovoltaic output model under dust accumulation conditions on photovoltaic panels after a sandstorm is as follows:

[0076]

[0077] CF = η T η i n n n l (5)

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

[0079] S3: Based on the photovoltaic power output model, different power output scenarios of photovoltaic power plants under sandstorm weather conditions are divided into typical scenarios and extreme scenarios. An improved fuzzy C-means (FCM) clustering algorithm combining Euclidean distance and cosine distance is used to perform cluster analysis on the photovoltaic power output data. Then, based on the clustering results, data with high membership to cluster centers are selected as typical scenarios, and data with low membership are selected as extreme scenarios. Through cluster analysis, different power output situations of photovoltaic power plants under sandstorm weather conditions are effectively divided into typical and extreme scenarios, providing accurate input data for reliability assessment. Specifically:

[0080] Based on the photovoltaic output model, the membership degree of relative cluster centers is calculated using an improved fuzzy C-means clustering algorithm. (See [link to relevant documentation]). Figure 2 Specifically:

[0081]

[0082] in, x is the center vector of the sample; i For the i-th sample; uik Let be the membership degree of the i-th sample to the k-th cluster; c be the number of clusters; L(c) be the adaptive factor, where the numerator represents the inter-cluster distance and the denominator represents the intra-cluster distance. The convergence condition for the adaptive factor is: L(c) > L(c-1) and L(c) > L(c+1), thus determining the optimal number of clusters c; n be the number of samples; m be the fuzzy weighting parameter, set to 2; v i Let d be the i-th cluster center; ik Let be the Euclidean distance between the i-th sample and the cluster centers of the k-th cluster;

[0083] First, initialize the data so that c = 2 and L(c) = 0. Randomly initialize the membership matrix U. Then, determine the cluster centers v according to formulas (6) to (8). 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 Based on the Euclidean distance d from each sample point to the cluster center of each cluster ik and cosine distance Calculate the composite distance ρ i k, and update the membership matrix U again, check if ||U(k+1)-U(k)||<ε holds. If not, let n=n+1, and re-initialize the membership matrix randomly. If yes, check if the adaptive factor converges. If the adaptive factor does not converge, let c=c+1 to redetermine the cluster center V and the center vector of the sample. If the adaptive factor converges, the clusters and the membership degrees of each sample to the cluster centers are obtained; where ε is the condition for determining convergence.

[0084] Specifically, the Euclidean distance D = {d} from each sample point to the cluster center of each cluster is calculated. i1 ,d i2 ,…,d ik ,…,d ic The method is as follows:

[0085]

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

[0087] Calculate the cosine distance of the included angle And together with the Euclidean distance, they form a combined distance ρ ik :

[0088]

[0089] Where, d ik x is the Euclidean distance between the i-th sample and the k-th cluster center; ij v represents the j-th data point of the i-th sample; kj The j-th data point is the k-th cluster center; ρ is the cosine distance of the included angle; i k is the combined distance of the i-th sample to the k-th cluster; β is the scaling factor; 1-σ is the weight of the Euclidean distance, σ is the weight of the cosine distance, and β is the factor that reduces the Euclidean distance by a certain factor so that the cosine distance and the Euclidean distance are on the same order of magnitude.

[0090] Based on the calculated comprehensive distance ρ ik Update the membership matrix:

[0091]

[0092] Where, ρ ik Let be the combined distance between the i-th sample and the k-th cluster;

[0093] Finally, output scenarios with a membership degree relative to the cluster centers higher than a certain threshold are classified as typical scenarios; output scenarios with a membership degree relative to the cluster centers lower than a certain threshold are classified as extreme scenarios. See also Figure 3 The membership degree of each sample represents its relative distance to the nearest cluster center, forming a distribution like this: Figure 3 The clustering results show that the more similar a data point is to its cluster center, the higher its membership degree and the closer it is to the cluster center. Data with high membership degrees to cluster centers are considered typical scenarios, while data with low membership degrees are considered extreme scenarios. One type of data with high membership degrees relative to the cluster centers is extracted as a typical sandstorm scenario, resulting in the following: Figure 4 The power-time curve;

[0094] S4: Perform stochastic production simulations for typical and extreme scenarios to obtain the reliability indices of the actual system and the equivalent system for each scenario. The actual system includes photovoltaic units, energy storage devices, and loads. Resources and demand are defined as follows: generator units and energy storage devices are considered resources, and loads and energy storage devices are considered demands. Calculate availability margin and consumption margin: the probability distribution of available power is the availability margin, and the probability distribution of power consumed by demand is the consumption margin. Stochastic production simulations are performed on the actual system under both typical and extreme scenarios to calculate the reliability index, i.e., the expected value of electricity deficit (EENS). Specifically:

[0095] Typical and extreme scenarios are extracted separately to perform serialized modeling of system components, obtaining the photovoltaic power station availability margin sequence and photovoltaic power station load consumption margin sequence for the corresponding scenarios. The photovoltaic power station availability margin sequence includes the generator set availability margin sequence and the energy storage availability margin sequence; the photovoltaic power station load consumption margin sequence includes the generator set load consumption margin sequence and the energy storage consumption margin sequence. Serialized modeling includes generator set serialization, energy storage device serialization, and load serialization. The generator set serialization involves discretizing the output of the photovoltaic generator set, defining the discretization step size, and obtaining the photovoltaic unit... The output power probability distribution sequence; the energy storage device serialization considers the charging and discharging constraints of the energy storage device, performs probability statistics on its historical output, and obtains the probability distribution of equivalent power; the load serialization analyzes historical load data, considers the load peak-valley difference, and obtains the load consumption margin sequence. The following definitions are made for the sequence operation: generator sets and energy storage devices are resources; loads and energy storage devices are demands; the probability distribution of available power of resources is the availability margin; the probability distribution of power consumed by demand is the consumption margin; when the availability margin equals the consumption margin, the system is in power balance, i.e., supply and demand balance. Specifically:

[0096] Photovoltaic power generation is greatly affected by weather. Its output is zero at night when there is no sunlight, but can reach its rated output under clear weather conditions. Its output ranges from 0 to its rated output. A discretization step size ΔC is defined, and the output value of the photovoltaic power station is discretized into a sequence S according to the step size ΔC. The i-th number in sequence S is:

[0097]

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

[0099] Considering the constraints of charge / discharge rate, charge / discharge capacity, and charge / discharge power of the energy storage device, probabilistic statistics are performed on the historical output of the energy storage device to obtain the probability distribution F of the equivalent power of the energy storage device. st_gene and F st_load :

[0100]

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

[0102] Considering the variation in load peak-valley difference, the upper and lower load limits are obtained. A sampling analysis of the historical load data of 8760 hours in the previous year is conducted to obtain the load consumption margin sequence.

[0103] Based on the available margin sequence and load consumption margin sequence of the photovoltaic power station for the corresponding scenario, supply and demand balance calculations are performed, and randomized production simulations are conducted to calculate the reliability index of the actual system for the corresponding scenario, specifically:

[0104] In various typical scenarios, stochastic production simulations are performed by calculating the supply and demand balance between the available margin sequence and the consumption margin sequence to obtain the expected power shortage value (EENS) of the actual system reliability index. EENS represents the amount of electricity required for the system to reach supply and demand balance; that is, the lower the value, the higher the system reliability. Specifically:

[0105]

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

[0107] See Figure 5 First, the system components are serialized and random production simulations are performed to calculate the reliability index EENS of the actual system. Let C be the reliability index of the actual system. max =C pv C min =0, where C pv The capacity of the photovoltaic (PV) unit is given; the PV units in the actual system are removed and replaced with equivalent conventional units (such as thermal power units), with the equivalent conventional unit capacity C. ge n=(C max +C min The system is then subjected to a random production simulation (the system after the unit replacement is called the equivalent system), and the reliability index EENS' of the equivalent system is calculated. Subsequently, it is determined whether the error between the reliability index of the actual system and the equivalent system is within the allowable range. If so, the reliable capacity of the actual system is obtained, which is the equivalent conventional unit capacity of the equivalent system. Otherwise, the binary iterative method is used to update C. max and C min The value of is used to replace the equivalent conventional unit and conduct another random production simulation 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: Based on the reliability indicators of the actual system and the equivalent system in the corresponding scenario, obtain the reliable capacity of the photovoltaic power station for that scenario. (See [link]) Figure 5 Specifically:

[0109] The reliability indicators of the actual system in the corresponding scenario are compared with the reliability indicators of the equivalent system in the corresponding scenario. The equivalent conventional unit capacity of the equivalent system is adjusted by using the bisection method, so that the reliability indicators of the equivalent system are iteratively calculated until the difference between the reliability indicators of the two systems is within the error range and the reliability of the two systems is the same. At this time, the total capacity of the equivalent conventional unit of the equivalent system is the reliable capacity of the actual system.

[0110] See Figure 6 This invention provides a reliable capacity calculation system for photovoltaic power plants that takes into account sandstorm weather, comprising:

[0111] Solar Irradiance Model Establishment Module: Used to establish a solar irradiance model; the solar irradiance model includes a solar irradiance model under clear atmospheric conditions, and uses the absorption, reflection and scattering effects of dust aerosol particles in the air on solar radiation during dust storms to correct the aerosol absorption radiation coefficient in the solar irradiance model under clear atmospheric conditions;

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

[0113] Scene segmentation module: This module is used to classify different output scenarios of photovoltaic power plants under sandstorm weather conditions into typical scenarios and extreme scenarios based on the photovoltaic output model.

[0114] Reliability index acquisition module: used to perform random production simulations on typical and extreme scenarios to obtain the reliability index of the actual system and the reliability index of the equivalent system in 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 and the reliability index of the equivalent system in the corresponding scenario.

[0115] This invention provides a terminal device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above. The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete this invention.

[0116] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

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

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

[0119] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed 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 electrical carrier signals and telecommunication signals.

[0120] In summary, this invention provides a method, system, equipment, and storage medium for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather. The method first establishes a solar irradiance model under clear atmospheric conditions and corrects the aerosol absorption radiation coefficient in the model to account for the impact of sandstorm aerosol particles on solar radiation. Using an improved fuzzy C-means clustering algorithm, combined with parameters such as photovoltaic output, irradiance, and visibility, the scenario set is classified to determine typical and extreme scenarios. Based on these scenarios, this invention sequentially models the system components of the photovoltaic power station, builds an actual system model, and conducts random production simulations under different meteorological conditions to calculate the expected value of insufficient power consumption, a reliability indicator of the system. An iterative approximation method is used to compare the expected value of insufficient power consumption between the actual system and the equivalent system until the difference in their reliability indicators is within the error range, thereby determining the reliable capacity of the photovoltaic power station under sandstorm weather conditions. This effectively solves the problem of photovoltaic power stations participating in power system power balance under extreme sandstorm weather conditions, allowing new energy units to be equivalently converted into conventional units for subsequent calculations.

[0121] The above description is merely a preferred embodiment of the present invention and is not intended to limit the technical solution of the present invention in any way. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can be modified and replaced in several simple ways, and these modifications and replacements are all within the scope of protection covered by the claims.

Claims

1. A method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather, characterized in that, include: A solar irradiance model is established; the solar irradiance model includes a solar irradiance model under clear atmospheric conditions, and the aerosol absorption radiation coefficient in the solar irradiance model under clear atmospheric conditions is corrected by utilizing the absorption, reflection and scattering effects of dust aerosol particles on solar radiation during dust storms. A photovoltaic power output model is established based on the solar irradiance model; Based on the photovoltaic power output model, different power output scenarios of photovoltaic power stations under sandstorm weather are divided into typical scenarios and extreme scenarios; Randomized production simulations were conducted for typical and extreme scenarios to obtain the reliability indices of the actual system and the equivalent system for each scenario. Based on the reliability indicators of the actual system and the equivalent system in the corresponding scenario, the reliable capacity of the photovoltaic power station in the corresponding scenario is obtained, specifically including: The system components are serialized and randomized production simulations are performed to calculate the reliability index of the actual system. Let... C max = C pv , C min =0, where C pv The capacity of the photovoltaic (PV) units; remove the PV units from the actual system and replace them with equivalent conventional units, the capacity of which is... C gen =( C max + C min The system is then divided by 2, and a random production simulation is performed again. The system after the unit replacement is called the equivalent system, and the reliability index EENS' of the equivalent system is calculated. Then, it is determined whether the error between the reliability index of the actual system and the equivalent system is within the allowable range. If so, the reliable capacity of the actual system is obtained, which is the equivalent conventional unit capacity of the equivalent system; otherwise, a binary iterative method is used to update the reliability index. C max and C min The value of EENS is used to replace the equivalent conventional unit and another random production simulation is performed to obtain a new round of equivalent system reliability index EENS'. C max and C min All are intermediate variables, among which the reliability index is the expected value of insufficient power consumption.

2. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather as described in claim 1, characterized in that, The method for establishing the solar irradiance model is as follows: （1） （2） in, The transmittance of the photovoltaic panel glass under dust accumulation conditions; The light transmittance of the photovoltaic panel glass under clean conditions; erf It is the error function; E1 represents the dust density; E2 and E3 are empirical coefficients. The intensity of solar radiation received by the dust-accumulating photovoltaic panel; This represents the intensity of solar radiation received by the surface of the photovoltaic panel under normal climatic conditions.

3. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather as described in claim 1, characterized in that, The method for establishing the photovoltaic power output model based on the solar irradiance model is as follows: The solar radiation intensity received by the dust-accumulating photovoltaic panel was obtained based on the solar irradiance model. Based on the solar radiation intensity received by the dust-accumulating photovoltaic panel, a photovoltaic output model is established, specifically as follows: （3） （4） in, The output value of the dust-accumulating photovoltaic power station; The conversion efficiency of photovoltaic modules in converting solar energy into electrical energy; The intensity of solar radiation received by the dust-accumulating photovoltaic panel; The solar radiation intensity is under standard conditions, which refers to a photovoltaic power station cell temperature of 25°C and an air quality factor (AM) of 1.

5. This refers to the total installed capacity of the photovoltaic power station; This is a temperature correction factor; Correction factor for the azimuth and tilt angle of photovoltaic modules; The inverter efficiency of the photovoltaic system is taken as 0.8 to 0.95; This is the line correction factor for the photovoltaic system, ranging from 0.95 to 0.

98.

4. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather as described in claim 1, characterized in that, The method for classifying different power output scenarios of photovoltaic power plants under sandstorm weather into typical and extreme scenarios based on the photovoltaic power output model is as follows: Based on the photovoltaic power output model, the membership degree of the relative cluster centers is calculated using an improved fuzzy C-means clustering algorithm. The output scenarios where the membership degree of the relative cluster center is higher than a preset threshold are classified as typical scenarios. Output scenarios where the membership degree of the relative cluster center is lower than a preset threshold are classified as extreme scenarios.

5. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather as described in claim 4, characterized in that, The method for calculating the membership degree of relative cluster centers using an improved fuzzy C-means clustering algorithm based on the photovoltaic output model is as follows: （5） （6） （7） in, The center vector of the sample; For the i-th sample; For the first i The nth sample pair k The membership degree of each cluster set; c This represents the number of clusters. Let be the adaptive factor, where the numerator represents the inter-class distance and the denominator represents the intra-class distance. The convergence condition for determining the adaptive factor is: > and > Determine the number of clusters c To determine the optimal number of clusters; n The number of samples; m The fuzzy weighting parameter is set to 2; For the first i Cluster centers; For the first i The nth sample pair k The Euclidean distance between the cluster centers of each cluster set; Calculate the Euclidean distance from each sample point to the cluster center of each cluster. The method is as follows: （8） For the first i The first sample j One data point; For the first k The first cluster center j One data point; Calculate the cosine distance of the included angle And combine with Euclidean distance to form a comprehensive distance : （9） （10） （11） in, For the first i The nth sample pair k The Euclidean distance between the cluster centers; For the first i The first sample j One data point; For the first k The first cluster center j One data point; The distance is the cosine of the included angle; For the first i The nth sample pair k The combined distance of each cluster; This is the proportionality coefficient; The weights are the Euclidean distance values. The weight of the cosine distance of the included angle. To reduce the Euclidean distance by a certain factor so that the cosine distance and the Euclidean distance are on the same order of magnitude; Based on the calculated comprehensive distance Update the membership matrix: （12） in, For the first i The nth sample pair k The combined distance of each cluster.

6. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather as described in claim 1, characterized in that, The method for obtaining the reliability indices of the actual system and the equivalent system in the corresponding scenarios by performing random production simulations on typical and extreme scenarios respectively is as follows: Typical and extreme scenarios are extracted separately to perform serialized modeling of system components, and the available margin sequence and load consumption margin sequence of photovoltaic power plants for the corresponding scenarios are obtained respectively; the available margin sequence of photovoltaic power plants includes the available margin sequence of generator sets and the available margin sequence of energy storage. The photovoltaic power plant load consumption margin sequence includes the generator load consumption margin sequence and the energy storage consumption margin sequence. Based on the available margin sequence and load consumption margin sequence of the photovoltaic power station for the corresponding scenario, supply and demand balance calculations are performed, and random production simulations are conducted to calculate the reliability index of the actual system for the corresponding scenario. Based on the reliability indicators of the actual system in the corresponding scenario, obtain the reliability indicators of the equivalent system in the corresponding scenario.

7. The method for calculating the reliable capacity of a photovoltaic power station considering sandstorm weather as described in claim 6, characterized in that, The method for calculating the reliability index of the actual system in the corresponding scenario by performing supply and demand balance calculations based on the available margin sequence and load consumption margin sequence of the photovoltaic power station for the corresponding scenario, and by random production simulation, is as follows: （13） in, For the reliability indicators of the actual system; The system electrical load power is the system power in state b. This represents the frequency value of the system in state b. The duration of state b in the system; Let be the probability value when the system is in state b; A represents all states in which the system cannot meet the load within a given time period; T is time.

8. A reliable capacity calculation system for photovoltaic power plants that takes into account sandstorm weather, characterized in that, include: Solar Irradiance Model Establishment Module: Used to establish a solar irradiance model; the solar irradiance model includes a solar irradiance model under clear atmospheric conditions, and uses the absorption, reflection and scattering effects of dust aerosol particles in the air on solar radiation during dust storms to correct the aerosol absorption radiation coefficient in the solar irradiance model under clear atmospheric conditions; Photovoltaic power output model building module: used to build a photovoltaic power output model based on the solar irradiance model; Scene segmentation module: This module is used to classify different output scenarios of photovoltaic power plants under sandstorm weather conditions into typical scenarios and extreme scenarios based on the photovoltaic output model. Reliability index acquisition module: used to perform random production simulations on typical and extreme scenarios to obtain the reliability index of the actual system and the reliability index of the equivalent system in the corresponding scenario. Trusted Capacity Acquisition Module: Used to obtain the trusted capacity of the photovoltaic power station for the corresponding scenario based on the reliability indicators of the actual system and the equivalent system for the corresponding scenario. Specifically, it includes: The system components are serialized and randomized production simulations are performed to calculate the reliability index of the actual system. Let... C max = C pv , C min =0, where C pv The capacity of the photovoltaic (PV) units; remove the PV units from the actual system and replace them with equivalent conventional units, the capacity of which is... C gen =( C max + C min The system is then divided by 2, and a random production simulation is performed again. The system after the unit replacement is called the equivalent system, and the reliability index EENS' of the equivalent system is calculated. Then, it is determined whether the error between the reliability index of the actual system and the equivalent system is within the allowable range. If so, the reliable capacity of the actual system is obtained, which is the equivalent conventional unit capacity of the equivalent system; otherwise, a binary iterative method is used to update the reliability index. C max and C min The value of EENS is used to replace the equivalent conventional unit and another random production simulation is performed to obtain a new round of equivalent system reliability index EENS'. C max and C min All are intermediate variables, among which the reliability index is the expected value of insufficient power consumption.

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, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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