Energy storage system capacity configuration method integrating multiple typical weather scenes

Abstract

The invention relates to the technical field of photovoltaic energy storage, and discloses an energy storage system capacity configuration method integrating multiple typical weather scenes, comprising the following steps: clustering data samples according to historical data to obtain photovoltaic power generation power curves and demand power curves under n typical weather conditions; comparing the photovoltaic power generation power curve under each typical weather condition with the demand power curve under the corresponding typical weather condition to obtain a corrected capacity configuration scheme under the corresponding typical weather condition; obtaining predicted weather types of several days after the current moment; according to the voltage stability index of the power distribution network, a line fault model considering the weather influence is established, the fault probability of the photovoltaic power distribution line under different weather conditions is calculated, and a capacity configuration scheme is correspondingly adjusted and corrected; the flexibility of capacity configuration of the energy storage system is effectively improved, the energy storage loss and the operation cost are reduced, the stability of configuration scheme output is effectively ensured, and the reliability of capacity configuration of the energy storage system is improved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic energy storage technology, and specifically to a method for configuring the capacity of an energy storage system that integrates multiple typical weather scenarios. Background Art

[0002] Photovoltaic power generation has the advantages of clean energy, flexible installed capacity, and renewable energy. However, the inherent instability, intermittency, and reverse peak regulation of photovoltaics have seriously affected the safe and stable operation of the power system. Therefore, photovoltaic power stations in most areas are equipped with energy storage systems. As a fast-responding flexible power source, the energy storage system can effectively alleviate the impact of photovoltaic access to the distribution network and improve the absorption rate. It has a wide range of applications and high applicability, and can effectively promote the development of the power industry in the direction of science and technology. In the future, the proportion of new energy power generation will continue to increase, and the demand for energy storage will continue to increase. Therefore, research on energy storage capacity configuration technology is of great significance to the development of photovoltaic power stations.

[0003] Photovoltaic + energy storage will become the mainstream model for the development of photovoltaic power stations in the future, but the economic efficiency of energy storage is still not perfect, and the rigid requirement of configuring energy storage in photovoltaic power stations will inevitably bring additional costs. If energy storage is mandatory, whether its ratio is reasonable remains to be discussed. If the ratio is too high, it may cause project losses and waste resources; if the ratio is too low, it cannot meet the needs of the project, and the continuous reliability of power supply cannot be guaranteed in the event of a sudden power outage. Therefore, it is necessary to conduct in-depth research on the capacity configuration of the energy storage system from a scientific perspective, and comprehensively consider its economic efficiency and operating benefits to provide the optimal capacity configuration plan that can adapt to various weather conditions in various regions.

[0004] The existing energy storage system capacity configuration method lacks consideration of changes in supply and demand caused by various weather reasons, and ignores the impact of the probability of line failure caused by weather reasons on capacity configuration, resulting in inflexible capacity configuration of photovoltaic energy storage systems and unstable system power supply.

[0005] Application Contents

[0006] The purpose of this application is to provide a method for configuring the capacity of an energy storage system for comprehensive multiple typical weather scenarios, which effectively improves the flexibility of the capacity configuration of the energy storage system, reduces energy storage losses and operating costs, effectively ensures the stability of the configuration scheme output, and improves the reliability of the capacity configuration of the energy storage system.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for configuring the capacity of an energy storage system based on multiple typical weather scenarios according to the present invention includes:

[0009] S1: Based on the historical photovoltaic system daily power generation data, the data samples are clustered by the K-Means method to obtain the photovoltaic power generation curves under n typical weather conditions;

[0010] S2: Obtain demand power curves under n typical weather conditions based on historical data;

[0011] S3: Compare the photovoltaic power generation curve under each typical weather condition with the demand power curve under the corresponding typical weather condition to obtain a corrected capacity configuration plan under the corresponding typical weather condition;

[0012] S4: Get the forecast weather type for several days after the current moment;

[0013] S5: Based on the voltage stability index of the distribution network, a line fault model considering the influence of weather is established to calculate the probability of failure of the photovoltaic distribution line under different weather conditions;

[0014] S6: According to the predicted weather conditions and the probability of failure of photovoltaic distribution lines under different weather conditions, the capacity configuration plan is adjusted accordingly.

[0015] Further, the method of step S1 includes:

[0016] S1.1: Obtain the photovoltaic daily power generation data for 365 days in the past year. The sampling time is 1 hour. The i-th sample data is

[0017] i = 1, 2, ..., 365, where They represent the power generation from 1 to 24 hours on the i-th day, set the cluster number parameter n, randomly select n from the data sample, and form n initialized cluster centers. Each cluster center is j = 1, 2, ..., n;

[0018] S1.2: Calculate the distances between the remaining data samples and the centers of the n clusters, where the distance from the i-th sample to the k-th cluster center is Determine the distance between each sample and the center of the kth group, and assign it to the group closest to it;

[0019] S1.3: Update the cluster center according to the mean of all samples in each cluster. Where m k represents the number of samples in the kth population;

[0020] S1.4: Determine whether the clustering process has converged and whether the degree of change of the center of each group is less than the set threshold. If so, the clustering ends and the photovoltaic power generation curves under n typical weather conditions are obtained. If not, return to S1.2.

[0021] Furthermore, the method of step S3 includes: calculating the power difference between the required power and the photovoltaic power generation power corresponding to the same weather conditions; if the power difference is greater than a set threshold, determining the configuration capacity of the supercapacitor based on the power difference, and obtaining a corrected capacity configuration plan under the corresponding weather conditions.

[0022] Further, the method of step S5 includes: for a radial power grid including a plurality of nodes, any transmission route thereof is equivalent to a simplified model; defining a static voltage stability index α of the power grid transmission line VSI，j for: When 0<α VSI，j <1, the root's solvable discriminant Δ>0, at this time the power flow solution exists, the system static voltage is in a stable state, and α VSI，j The smaller the voltage, the better the static voltage stability of the circuit, and vice versa. VSI，j =1, the corresponding root discriminant Δ=0, indicating that the static voltage stability of the line is in a critical state; if the power of the node continues to increase at this time, voltage collapse will occur, resulting in voltage instability, and the transmission line will also lose the original balance node at this time; the voltage stability index of each line is calculated through this index.

[0023] Furthermore, the method of step S5 further includes:

[0024] Stochastic processes of climate state transition and geomorphic analysis;

[0025] Stochastic process of climate state transition: According to the impact of climate change on distribution network and distributed power source, the climate state is divided into three states: sunny, cloudy, rainy and snowy, denoted as a, b, c respectively; the concept of frequency equilibrium relationship is derived from the Markov state equation of steady state, and the following equation is obtained: Among them, p a 、p b 、p c are the probabilities of sunny, cloudy, and rainy and snowy weather conditions, satisfying p a +p b +p c =1;λ ab is the state transition probability from sunny to cloudy, λ ba is the state transition probability from cloudy to sunny, and the transition probabilities between multiple states can be obtained by analogy;

[0026] Geomorphic analysis: The terrain is divided into four types: flat land, mountainous land, hilly land, and high mountainous land. Assuming that the distribution network is located in the area with three meteorological conditions: rain and snow, cloudy and sunny, and assuming that a route crosses the four types of terrain, the probability of occurrence of each condition is obtained through the statistical data of the local meteorological department;

[0027] Line fault analysis: Based on the fact that the lines of the distribution network have no memory during operation, a power system fault probability model based on Poisson distribution is proposed; namely: Where h is the number of line failures per unit time, ω is the climate-topography factor, and ωλ is the line failure rate per unit time. The probability of line failure per unit time is: p(X≠0)=1-pX=0)=1-e -ωλ :

[0028] Construct a Monte Carlo random sampling fault analysis model for distribution network lines based on climate and terrain: According to the radial characteristics of the distribution network topology, a model consisting of different resistors in series is constructed; define X i is the percentage of the i-th line in the total line, then the unavailability of the i-th line under fault condition is defined as U i , the specific formula is as follows: i =p i X i / (p i X i +μ i ), where p i is the failure probability of the i-th line per unit time, μ i is the repair rate of the i-th line, i∈{1,…,N c}.

[0029] Furthermore, the method of step S6 includes: according to the predicted weather conditions, when the probability of failure of the photovoltaic distribution line under the corresponding weather conditions is greater than a set threshold, the corresponding photovoltaic distribution line is eliminated, and the output power of the energy storage system is completely used to supply the output demand; otherwise, the modified capacity configuration plan of step S3 is executed.

[0030] Compared with the prior art, the beneficial effects of this application are:

[0031] The present application obtains the photovoltaic power generation and the corresponding power demand under different weather conditions based on historical data, and obtains the corrected capacity configuration plan under the corresponding weather conditions based on the difference between the photovoltaic power generation and the power demand under the corresponding weather conditions. By predicting future weather types, different configurations can be adopted for the capacity of the energy storage system according to different weather types. Compared with the fixed configuration, the capacity of the energy storage system required to be put into use on the day can be effectively reduced, and the flexible configuration of the energy storage system can be achieved, thereby reducing energy storage loss and operating costs. Based on the obtained corrected capacity configuration plan, the corrected capacity configuration plan is adjusted according to the probability of line failure under different weather conditions, effectively ensuring the stability of the configuration plan output and improving the reliability of the energy storage system capacity configuration. The present application effectively improves the flexibility of the energy storage system capacity configuration, reduces energy storage loss and operating costs, effectively ensures the stability of the configuration plan output, and improves the reliability of the energy storage system capacity configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the implementation methods of the present application, the drawings required for use in the implementation methods will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a flow chart of the method of this application. DETAILED DESCRIPTION

[0034] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0035] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0036] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0037] See also Figure 1 , a method for configuring the capacity of an energy storage system for comprehensive multi-typical weather scenarios, including including S1: according to the historical photovoltaic system daily power generation data, clustering the data samples by the K-Means method to obtain the photovoltaic power generation curves under n typical weather conditions;

[0038] S1.1: Obtain the photovoltaic daily power generation data for 365 days in the past year. The sampling time is 1 hour. The i-th sample data is

[0039] i = 1, 2, ..., 365, where They represent the power generation from 1 to 24 hours on the i-th day, set the cluster number parameter n, randomly select n from the data sample, and form n initialized cluster centers. Each cluster center is j = 1, 2, ..., n;

[0040] S1.2: Calculate the distances between the remaining data samples and the centers of the n clusters, where the distance from the i-th sample to the k-th cluster center is Determine the distance between each sample and the center of the kth group, and assign it to the group closest to it;

[0041] S1.3: Update the cluster center according to the mean of all samples in each cluster. Where m k represents the number of samples in the kth population;

[0042] S1.4: Determine whether the clustering process has converged and whether the degree of change of the center of each group is less than the set threshold. If so, the clustering ends and the photovoltaic power generation curves under n typical weather conditions are obtained. If not, return to S1.2;

[0043] S2: Obtain demand power curves under n typical weather conditions based on historical data;

[0044] S3: Compare the photovoltaic power generation curve under each typical weather condition with the demand power curve under the corresponding typical weather condition to obtain a corrected capacity configuration plan under the corresponding typical weather condition;

[0045] Calculate the power difference between the required power and the photovoltaic power generation power under the same weather conditions; if the power difference is greater than a set threshold, determine the configuration capacity of the supercapacitor based on the power difference to obtain a corrected capacity configuration plan under the corresponding weather conditions;

[0046] The power difference is the absolute value of the difference between the photovoltaic power generation power corresponding to the weather type and the standard photovoltaic power generation power. By setting a threshold, it is determined whether the power difference that needs to be adjusted is too large. If the power fluctuation is too large, the capacity adjustment effect of the battery will be poor and the service life of the battery will be reduced, which will affect the stability of the photovoltaic energy storage system. Therefore, when the power difference is greater than the set threshold, the capacity of the supercapacitor is adjusted to obtain a corrected capacity configuration scheme including the supercapacitor corresponding to the weather type to complete the capacity configuration of the photovoltaic energy storage system.

[0047] S4: Get the forecast weather type for several days after the current moment;

[0048] S5: Based on the voltage stability index of the distribution network, a line fault model considering the influence of weather is established to calculate the probability of failure of the photovoltaic distribution line under different weather conditions;

[0049] For a radial power grid containing several nodes, any transmission line is equivalent to a simplified model; define the static voltage stability index α of the power grid transmission line VSI，j for: When 0<α VSI，j <1, the root's solvable discriminant Δ>0, at this time the power flow solution exists, the system static voltage is in a stable state, and α VSI，j The smaller the voltage, the better the static voltage stability of the circuit, and vice versa. VSI，j =1, the corresponding root discriminant Δ=0, indicating that the static voltage stability of the line is in a critical state; if the power of the node continues to increase at this time, voltage collapse will occur, resulting in voltage instability, and the transmission line will also lose the original balance node at this time; the voltage stability index of each line is calculated through this index.

[0050] Stochastic processes of climate state transition and geomorphic analysis;

[0051] Stochastic process of climate state transition: According to the impact of climate change on distribution network and distributed power source, the climate state is divided into three states: sunny, cloudy, rainy and snowy, denoted as a, b, c respectively; the concept of frequency equilibrium relationship is derived from the Markov state equation of steady state, and the following equation is obtained: Among them, p a 、p b 、p c are the probabilities of sunny, cloudy, and rainy and snowy weather conditions, satisfying p a +p b +p c =1;λ ab is the state transition probability from sunny to cloudy, λ bais the state transition probability from cloudy to sunny, and the transition probabilities between multiple states can be obtained by analogy;

[0052] Geomorphic analysis: The terrain is divided into four types: flat land, mountainous land, hilly land, and high mountainous land. Assuming that the distribution network is located in the area with three meteorological conditions: rain and snow, cloudy and sunny, and assuming that a route crosses the four types of terrain, the probability of occurrence of each condition is obtained through the statistical data of the local meteorological department;

[0053] Line fault analysis: Based on the fact that the lines of the distribution network have no memory during operation, a power system fault probability model based on Poisson distribution is proposed; namely: Where h is the number of line failures per unit time, ω is the climate-topography factor, and ωλ is the line failure rate per unit time. The probability of line failure per unit time is: p(X≠0)=1-p(X=0)=1-e -ωλ ;

[0054] Construct a Monte Carlo random sampling fault analysis model for distribution network lines based on climate and terrain: According to the radial characteristics of the distribution network topology, a model consisting of different resistors in series is constructed; define X i is the percentage of the i-th line in the total line, then the unavailability of the i-th line under fault condition is defined as U i , the specific formula is as follows: i =p i X i / (p i X i +μ i ), where p i is the failure probability of the i-th line per unit time, μ i is the repair rate of the i-th line, i∈{1,…,N c}.

[0055] S6: According to the predicted weather conditions and the probability of failure of photovoltaic distribution lines under different weather conditions, the capacity configuration plan is adjusted accordingly.

[0056] According to the predicted weather conditions, when the probability of failure of the photovoltaic distribution line under the corresponding weather conditions is greater than the set threshold, the corresponding photovoltaic distribution line is eliminated, and the output power of the energy storage system is used entirely to supply the output demand; otherwise, the modified capacity configuration plan of step S3 is executed.

[0057] According to the predicted weather conditions, when the probability of failure of the photovoltaic distribution line under the corresponding weather conditions is greater than the set threshold, it means that the corresponding photovoltaic distribution line has become a major unstable factor in the output. Eliminating the corresponding photovoltaic distribution line and completely relying on the output power of the energy storage system to supply the demand output can effectively ensure the stability of the configuration scheme output and improve the reliability of the energy storage system capacity configuration.

[0058] The present application obtains the photovoltaic power generation and the corresponding power demand under different weather conditions based on historical data, and obtains the corrected capacity configuration plan under the corresponding weather conditions based on the difference between the photovoltaic power generation and the power demand under the corresponding weather conditions. By predicting future weather types, different configurations can be adopted for the capacity of the energy storage system according to different weather types. Compared with the fixed configuration, the capacity of the energy storage system required to be put into use on the day can be effectively reduced, and the flexible configuration of the energy storage system can be achieved, thereby reducing energy storage loss and operating costs. Based on the obtained corrected capacity configuration plan, the corrected capacity configuration plan is adjusted according to the probability of line failure under different weather conditions, effectively ensuring the stability of the configuration plan output and improving the reliability of the energy storage system capacity configuration. The present application effectively improves the flexibility of the energy storage system capacity configuration, reduces energy storage loss and operating costs, effectively ensures the stability of the configuration plan output, and improves the reliability of the energy storage system capacity configuration.

[0059] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0060] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for configuring energy storage system capacity based on multiple typical weather scenarios, characterized by: include S1: Based on the historical photovoltaic system daily power generation data, the data samples are clustered by the K-Means method to obtain the photovoltaic power generation curves under n typical weather conditions; S2: Obtain demand power curves under n typical weather conditions based on historical data; S3: Compare the photovoltaic power generation curve under each typical weather condition with the demand power curve under the corresponding typical weather condition to obtain a corrected capacity configuration plan under the corresponding typical weather condition; S4: Get the forecast weather type for several days after the current moment; S5: Based on the voltage stability index of the distribution network, a line fault model considering the influence of weather is established to calculate the probability of failure of the photovoltaic distribution line under different weather conditions; S6: According to the predicted weather conditions and the probability of failure of photovoltaic distribution lines under different weather conditions, the capacity configuration plan is adjusted accordingly.

2. The method for configuring energy storage system capacity based on multiple typical weather scenarios according to claim 1, characterized in that: The method of step S1 comprises: S1.1: Obtain the photovoltaic daily power generation data for 365 days in the past year. The sampling time is 1 hour. The i-th sample data is in They represent the power generation from 1 to 24 hours on the i-th day, set the cluster number parameter n, randomly select n from the data sample, and form n initialized cluster centers. Each cluster center is S1.2: Calculate the distances between the remaining data samples and the centers of the n clusters, where the distance from the i-th sample to the k-th cluster center is Determine the distance between each sample and the center of the kth group, and assign it to the group closest to it; S1.3: Update the cluster center according to the mean of all samples in each cluster. Where m k represents the number of samples in the kth population; S1.4: Determine whether the clustering process has converged and whether the degree of change of the center of each group is less than the set threshold. If so, the clustering ends and the photovoltaic power generation curves under n typical weather conditions are obtained. If not, return to S1.

2.

3. The method for configuring energy storage system capacity based on multiple typical weather scenarios according to claim 1, characterized in that: The method of step S3 includes: calculating the power difference between the required power and the photovoltaic power generation power corresponding to the same weather conditions; if the power difference is greater than a set threshold, determining the configuration capacity of the supercapacitor based on the power difference, and obtaining a corrected capacity configuration plan under the corresponding weather conditions.

4. The method for configuring energy storage system capacity based on multiple typical weather scenarios according to claim 1, characterized in that: The method of step S5 comprises: for a radial power grid including a plurality of nodes, any transmission route thereof is equivalent to a simplified model; defining a static voltage stability index α of the power grid transmission line VSI，j for: When 0<α VSI，j <1, the root's solvable discriminant Δ>0, at this time the power flow solution exists, the system static voltage is in a stable state, and α VSI，j The smaller the voltage, the better the static voltage stability of the circuit, and vice versa. VSI，j =1, the corresponding root discriminant Δ=0, indicating that the static voltage stability of the line is in a critical state; if the power of the node continues to increase at this time, voltage collapse will occur, resulting in voltage instability, and the transmission line will also lose the original balance node at this time; the voltage stability index of each line is calculated through this index.

5. The method for configuring energy storage system capacity based on multiple typical weather scenarios according to claim 1, characterized in that: The method of step S5 further includes: Stochastic processes of climate state transition and geomorphic analysis; Stochastic process of climate state transition: According to the impact of climate change on distribution network and distributed power source, the climate state is divided into three states: sunny, cloudy, rainy and snowy, denoted as a, b, c respectively; the concept of frequency equilibrium relationship is derived from the Markov state equation of steady state, and the following equation is obtained: Among them, p a 、p b 、p c are the probabilities of sunny, cloudy, and rainy and snowy weather conditions, satisfying p a +p b +p c =1;λ ab is the state transition probability from sunny to cloudy, λ ba is the state transition probability from cloudy to sunny, and the transition probabilities between multiple states can be obtained by analogy; Geomorphic analysis: The terrain is divided into four types: flat land, mountainous land, hilly land, and high mountainous land. Assuming that the distribution network is located in the area with three meteorological conditions: rain and snow, cloudy and sunny, and assuming that a route crosses the four types of terrain, the probability of occurrence of each condition is obtained through the statistical data of the local meteorological department; Line fault analysis: Based on the fact that the lines of the distribution network have no memory during operation, a power system fault probability model based on Poisson distribution is proposed; namely: Where h is the number of line failures per unit time, ω is the climate-topography factor, and ωλ is the line failure rate per unit time. The probability of line failure per unit time is: p(X≠0)=1-p(X=0)=1-e -ωλ ; Construct a Monte Carlo random sampling fault analysis model for distribution network lines based on climate and terrain: According to the radial characteristics of the distribution network topology, a model consisting of different resistors in series is constructed; define X i is the percentage of the i-th line in the total line, then the unavailability of the i-th line under fault condition is defined as U i , the specific formula is as follows: i =p i X i / (p i X i +μ i ), where p i is the failure probability of the i-th line per unit time, μ i is the repair rate of the i-th line, i∈{1,…,N c }.

6. The method for configuring energy storage system capacity based on multiple typical weather scenarios according to claim 1, characterized in that: The method of step S6 includes: according to the predicted weather conditions, when the probability of failure of the photovoltaic distribution line under the corresponding weather conditions is greater than a set threshold, the corresponding photovoltaic distribution line is eliminated, and the output power of the energy storage system is completely used to supply the output demand; otherwise, the modified capacity configuration plan of step S3 is executed.

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