Power supply system and method of optical storage fusion power distribution station

Abstract

The invention discloses a power supply system and method for an optical storage fusion power distribution station, relates to the field of optical storage power distribution station power supply, and aims to solve the problems that electric energy in an energy storage module battery cannot be released, the resource utilization rate is reduced, the scheduling frequency is increased and the power consumption is reduced due to intermittency and volatility of photovoltaic power generation and introduction of an external power grid back frequency adjustment discharge mode. According to the method, logistic regression calculation is carried out according to the spare quantity of an energy storage battery and a current power supply quantity estimated value, the scheduling rationality is determined, and the receiving strength of solar radiation to a photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel and a short-term load predicted value are obtained according to the scheduling rationality; prediction is carried out by establishing a time sequence model, a scheduling adjustment scheme is determined according to a prediction result, a simple scheduling strategy is obtained according to the similarity of similar historical prediction scheduling adjustment schemes and the implementation rate of the historical prediction scheduling adjustment schemes, excessive scheduling is reduced, and maximization of photovoltaic power generation resources is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of power supply for a photovoltaic power generation and storage distribution station, and more specifically, to a power supply system and method for a photovoltaic power generation and storage fusion distribution station. Background Art

[0002] With the rapid development of renewable energy, photovoltaic power generation systems are gradually being used in distribution stations to achieve efficient use of green energy. Photovoltaic-storage fusion refers to the use of photovoltaic power generation and energy storage technology, that is, converting solar energy into electrical energy and then performing battery energy storage operations to achieve efficient and stable power supply, which can effectively improve the system's power supply reliability and energy utilization rate.

[0003] The prior art has the following deficiencies: At present, due to the intermittent and volatile nature of photovoltaic power generation, a single photovoltaic system is difficult to meet the stable demand of the load. After the external power grid is introduced to ensure the stability of the power supply system, it is often necessary to frequently adjust the discharge mode, increase the dispatch frequency, and there is a hidden danger of energy loss. Obviously, when adjusting to discharge the external power grid, photovoltaics continue to store energy, resulting in the inability to release the energy in the battery of the energy storage module, reducing the utilization rate of resources, increasing the dispatch frequency, and wasting production resources. Therefore, a power supply system and method for a photovoltaic storage fusion distribution station is proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a power supply system and method for a photovoltaic-storage integrated distribution station, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0006] To achieve the above object, the present invention provides the following technical solution: a power supply method for a photovoltaic-storage integrated distribution station, comprising S1: collecting the remaining capacity of the energy storage battery and the estimated value of the current power supply, and performing normalization processing, performing logistic regression calculation on the normalized data to obtain a reasonable probability, and comparing it with a preset reasonable threshold value to determine the unreasonable result of the current scheduling; S2: Obtain the unreasonable results of the current scheduling, collect weather forecast information and load information, obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value; S3: Based on the intensity of solar radiation received by the photovoltaic panels, the difference between the current air temperature and the surface temperature of the photovoltaic panels, and the short-term load forecast value, the photovoltaic power generation is predicted and analyzed through the time series model to further refine and optimize the scheduling adjustment plan; S4: Based on the scheduling adjustment plan, similarity calculation is performed with the historical prediction scheduling adjustment plan, and based on the similarity calculation, the realization rate of the selected historical prediction scheduling adjustment plan and the similarity of the corresponding similar historical prediction scheduling adjustment plan are calculated; S5: Obtain the similarity of similar historical prediction scheduling adjustment plans and the realization rate of historical prediction scheduling adjustment plans for cluster analysis, determine the realization rate of the scheduling adjustment plan, and obtain a simple scheduling strategy.

[0007] In a preferred embodiment, the remaining amount of the energy storage battery is obtained by real-time monitoring of the voltage and current of the battery and combining the discharge characteristics and capacity curve of the battery; By taking load data within the current total sampling time and using linear regression with temperature, humidity and sunshine, and combining historical load data with meteorological data, a historical load curve is obtained, and a weighted average calculation is performed based on the load data at the corresponding sampling time on the curve to obtain the current power supply estimate; Substituting the estimated value of the remaining energy storage battery capacity and the current power supply into the logistic regression calculation, the specific formula is expressed as follows: ; In the formula, L is the result of logistic regression calculation, that is, reasonable probability, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as: ; In the formula, is the bias term, and are the regression coefficients of the estimated energy storage battery capacity and current power supply, is the remaining amount of energy storage battery, Provide an estimate of the current power supply; Compare the reasonable probability with the preset reasonable threshold, specifically: If the reasonable probability is greater than or equal to the reasonable threshold, the current schedule is evaluated as reasonable, the current schedule rationality result is obtained, and it returns to the starting stage. If the reasonable probability is less than the reasonable threshold, the current schedule is evaluated as unreasonable, the current schedule unreasonable result is obtained, and it is transmitted to the next layer.

[0008] In a preferred embodiment, the weather forecast information includes the intensity of solar radiation received by the photovoltaic panel and the difference between the current air temperature and the surface temperature of the photovoltaic panel, and the load information includes a short-term load forecast value; The radiation intensity of the atmosphere and the ground is obtained by connecting to satellite remote sensing and multiplying it with the incident angle between the photovoltaic panel and the sun's rays and the photoelectric conversion efficiency of the photovoltaic panel to obtain the intensity of solar radiation received by the photovoltaic panel; The surface temperature of the photovoltaic panel and the external environment temperature are obtained by the temperature sensor installed on the surface of the photovoltaic panel and the temperature sensor installed in the external environment, and the difference between the current air temperature and the surface temperature of the photovoltaic panel is obtained by subtracting the temperature. Through the sensors installed on the surface of the distribution station, the historical load duration, external power grid output value, and temperature change value are obtained and substituted into the decision tree to obtain the short-term load forecast value.

[0009] In a preferred embodiment, photovoltaic power generation is predicted and analyzed by a time series model, and the time series model adopts an ARIMAX model. The specific steps of performing photovoltaic power generation prediction and analysis by a time series model are as follows: Step A1, obtaining prediction data; Step A2, establishing an ARIMAX model; Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters; Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis; Step A5, using the fitted model to predict the future scheduling adjustment plan, and taking the maximum value of the prediction result as the maximum probability value of photovoltaic power generation in the future time period.

[0010] In a preferred embodiment, the exogenous variables in the ARIMAX model include the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value. The ARIMAX model formula is: ; In the formula, is the probability of photovoltaic power generation meeting the requirements at the current sampling time, is the photovoltaic power generation with lag i, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is the white noise term with lag j, is a white noise term, representing random error, is an exogenous variable The coefficient of is the lag order of the exogenous variable, is an exogenous variable lagged by k periods; In step A3, , , , , It is calculated by the maximum likelihood estimation method.

[0011] In a preferred embodiment, all sampling times corresponding to the short-term prediction are substituted into the formula to calculate and obtain the maximum probability value of photovoltaic power generation; The maximum probability value of photovoltaic power generation is compared with the preset satisfaction threshold to determine the scheduling adjustment plan. If it is greater than or equal to the satisfaction threshold, it means that the photovoltaic power generation system can meet future load demand and is adjusted to give priority to photovoltaic power generation to reduce dependence on the external power grid. Otherwise, the output status of the external power grid is maintained.

[0012] In a preferred embodiment, the characteristics of the scheduling adjustment scheme include the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, the short-term load forecast value, and the maximum probability value of photovoltaic power generation; The characteristics of the historical prediction scheduling adjustment plan include the historical basis of the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, the short-term load forecast value, and the maximum probability value of photovoltaic power generation; The similarity between the scheduling adjustment plan and the historical prediction scheduling adjustment plan is obtained by similarity calculation, and they are sorted in order from large to small, and several historical prediction scheduling adjustment plans with the highest similarity are selected as the similarity of similar historical prediction scheduling adjustment plans; The photovoltaic power generation duration determined according to the historical forecast scheduling adjustment plan and its corresponding actual photovoltaic power generation duration are calculated using relative error to obtain the realization rate of the historical forecast scheduling adjustment plan.

[0013] In a preferred embodiment, the scheduling adjustment scheme realization rate is obtained using k-means cluster analysis; The similarity of similar historical forecast scheduling adjustment plans and the realization rate of historical forecast scheduling adjustment plans are used as input data to generate multiple data points; Then use K-means clustering to cluster multiple data points into k clusters. The specific steps are: Step B1: Randomly select k initial cluster centers; Step B2: Assign each data point to the nearest initial cluster center; Step B3: Calculate the mean of the data points in each cluster and update the cluster center; Step B4: Repeat the steps of assigning and updating cluster centers until the cluster centers no longer change.

[0014] In a preferred embodiment, the average realization rate of the designated cluster is used as the realization rate of the current scheduling adjustment plan, and a simple scheduling strategy is set according to the realization rate of the current scheduling adjustment plan.

[0015] A power supply system of a photovoltaic-storage integrated distribution station includes a rational analysis module, a data acquisition module, a scheduling prediction module and a scheme evaluation module; The rational analysis module is used to collect the remaining capacity of the energy storage battery and the estimated current power supply, and perform normalization processing, perform logistic regression calculation on the normalized data, and compare it with the preset reasonable threshold value to determine the unreasonable result of the current scheduling and send it to the data acquisition module; The data acquisition module is used to obtain unreasonable results of the current scheduling, collect weather forecast information and load information, obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value, and send them to the scheduling prediction module; The scheduling prediction module is used to perform prediction analysis on photovoltaic power generation based on the intensity of solar radiation received by the photovoltaic panels, the difference between the current temperature and the surface temperature of the photovoltaic panels, and the short-term load forecast value, and further refine and optimize the scheduling adjustment plan, and send it to the plan evaluation module; The scheme evaluation module is used to calculate the similarity between the scheduling adjustment scheme and the historical prediction scheduling adjustment scheme, and to perform cluster analysis based on the realization rate of the selected historical prediction scheduling adjustment scheme and the similarity of the corresponding similar historical prediction scheduling adjustment schemes, so as to determine the realization rate of the scheduling adjustment scheme and obtain a simple scheduling strategy.

[0016] Technical effects and advantages of the present invention: 1. The present invention determines the rationality of scheduling by preferentially performing logistic regression calculations based on the remaining capacity of the energy storage battery and the estimated current power supply, and obtains the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value based on the rationality of scheduling. Prediction is performed by establishing a time series model, and a scheduling adjustment plan is determined based on the prediction results to reduce over-scheduling, improve power supply continuity, and ensure the maximization of photovoltaic power generation resources.

[0017] 2. The present invention performs similarity calculation based on the scheduling adjustment plan and the historical predicted scheduling adjustment plan, and performs cluster analysis based on the realization rate of the historical predicted scheduling adjustment plan selected after the similarity calculation and the similarity between the corresponding scheduling adjustment plan and the historical predicted scheduling adjustment plan, determines the realization rate of the scheduling adjustment plan, obtains a simple scheduling strategy, improves the accuracy and rationality of scheduling decisions, reduces resource waste in scheduling adjustments, and determines the feasibility of scheduling adjustment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a method flow chart of a power supply method for a photovoltaic-storage integrated distribution station of the present invention.

[0019] Figure 2This is a module schematic diagram of a power supply system of a photovoltaic energy storage fusion distribution station of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 See also Figure 1 , a power supply method for a photovoltaic and energy storage integrated distribution station, the specific operation process is as follows: S1: Collect the remaining capacity of the energy storage battery and the estimated value of the current power supply, and perform normalization processing, perform logistic regression calculation on the normalized data to obtain a reasonable probability, and compare it with the preset reasonable threshold to determine the unreasonable result of the current scheduling; For setting the start collection conditions, the current power supply method of the photovoltaic and energy storage integrated distribution station is external power supply. When photovoltaic and energy storage are integrated to store electricity, the current free capacity of the energy storage battery and the current continuous power supply are collected; It should be noted that there can be more than one condition for setting the start of collection. The experimenter can limit it according to the current remaining battery capacity or the ratio of light energy to electric energy, etc., which is not limited here; The remaining capacity of the energy storage battery refers to the current remaining power of the energy storage battery, which is expressed as the battery charging state or the remaining available power. The acquisition logic is to obtain the remaining capacity of the energy storage battery by real-time monitoring of the battery voltage and current, combined with the battery discharge characteristics and capacity curve; Specifically, the calculation formula of the remaining capacity of the energy storage battery is expressed as follows: ; In the formula, is the remaining amount of energy storage battery, is the remaining amount of energy storage battery at the previous sampling time, is the current of the battery, is the sampling time interval, is the rated capacity of the battery, where t=1, 2, 3…T, and t is the sampling time, and T is the total sampling time; Specifically, there is no limitation on the sampling time interval and the number of sampling times, but they are set by the experimenter according to the charging and discharging frequency of the energy storage battery or the scheduling frequency of the power supply equipment, which will not be elaborated here; The current power supply estimate refers to the power supply demand expected by the system within the current total sampling time. The acquisition logic is to use the historical load curve for weighted average calculation to obtain the current power supply estimate. Specifically, the selection of the historical load curve includes taking the load data within the current total sampling time and using linear regression with temperature, humidity and sunshine, combining the historical load data with meteorological data to obtain the historical load curve, and performing weighted average calculation based on the load data at the corresponding sampling time on the curve to obtain the current power supply estimate. ; The remaining amount of energy storage battery and the estimated current power supply are normalized, and all input variables are converted to the same range to ensure that the contribution of each input to the model is balanced. Specifically, the normalization method is to normalize the interval [0,1], and the specific formula is expressed as: ; In the formula, is the remaining amount of energy storage battery, is the normalized free capacity of the energy storage battery, is the minimum value of the remaining amount of energy storage battery, The maximum value of the remaining capacity of the energy storage battery; The current power supply estimate is also normalized using the above formula, which will not be elaborated here. After normalization, the values ​​of the remaining capacity of the energy storage battery and the estimated current power supply are both within the range of [0, 1]. The system can make comparisons and decisions on a unified scale, thereby improving the accuracy and efficiency of scheduling. Substituting the estimated value of the remaining energy storage battery capacity and the current power supply into the logistic regression calculation, the specific formula is expressed as follows: ; In the formula, L is the result of logistic regression calculation, that is, reasonable probability, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as: ; In the formula, is the bias term, and are the regression coefficients of the estimated remaining capacity of the energy storage battery and the current power supply capacity; Compare the logistic regression calculation result, i.e. the reasonable probability, with the preset reasonable threshold, specifically: If the reasonable probability is greater than or equal to the reasonable threshold, the current schedule is evaluated as reasonable, the current schedule rationality result is obtained, and it returns to the starting stage; if the reasonable probability is less than the reasonable threshold, the current schedule is evaluated as unreasonable, the current schedule unreasonable result is obtained, and it is transmitted to the next layer; Specifically, the preset reasonable threshold is set by the experimenter based on historical data analysis and system tolerance, which will not be elaborated here; The start stage indicates a return to the system initialization operation stage, which requires manual confirmation before the operation of step S1 is performed. Otherwise, the system will not operate. Specifically, the next layer refers to the operation method of the next step or the transfer of current information to the next module, which will not be described in detail here; S2: Obtain the unreasonable results of the current scheduling, collect weather forecast information and load information, obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value; Among them, weather forecast information includes the intensity of solar radiation received by photovoltaic panels and the difference between the current air temperature and the surface temperature of photovoltaic panels, and load information includes short-term load forecast values; Specifically, weather forecast information and load information are both used as short-term forecasts. The specific short-term forecast setting can be collected every hour or every three hours, which will not be described in detail here; The solar radiation received by the photovoltaic panel refers to the intensity of the solar radiation energy received by the photovoltaic panel surface, which is usually directly related to the output power of the photovoltaic panel. Its acquisition logic is to obtain the radiation intensity of the atmosphere and the surface through satellite remote sensing and multiply it by the incident angle between the photovoltaic panel and the sun's rays and the photoelectric conversion efficiency of the photovoltaic panel to obtain the solar radiation received by the photovoltaic panel. Specifically, the calculation formula for obtaining the intensity of solar radiation received by the photovoltaic panel is as follows: ; In the formula, is the intensity of solar radiation received by the photovoltaic panel, is the solar radiation intensity, is the incident angle between the photovoltaic panel and the sunlight, is the photoelectric conversion efficiency of photovoltaic panels; It should be noted that the connected satellite remote sensing refers to the solar radiation data provided by meteorological satellites, including satellite remote sensing technology using sensors carried by satellites to observe the earth's surface, thereby obtaining information about solar radiation, surface cover, etc. Among them, the incident angle between the photovoltaic panel and the sun's rays is obtained based on the latitude of the photovoltaic panel installation location, the installation angle and the zenith angle of the sun. The photovoltaic panel photoelectric conversion efficiency is determined by the photovoltaic panel's technical specifications or the results obtained by the experimenter in the laboratory test. The difference between the current air temperature and the surface temperature of the photovoltaic panel refers to the difference between the ambient air temperature and the surface temperature of the photovoltaic panel. The acquisition logic is to obtain the surface temperature of the photovoltaic panel and the external environment temperature through the temperature sensor installed on the surface of the photovoltaic panel and the temperature sensor installed in the external environment, and then subtract them to obtain the difference between the current air temperature and the surface temperature of the photovoltaic panel. Specifically, the location where the temperature sensor is installed is not limited, but is set by the experimenter according to the location of the light energy absorbing element corresponding to the photovoltaic panel, and will not be described in detail here; The short-term load forecast value refers to the forecast of future short-term load demand. The acquisition logic is to substitute the historical load duration, external power grid output value, and temperature change value into the decision tree to obtain the short-term load forecast value; Among them, historical load duration, external grid output value, and temperature change value are all obtained through sensors installed on the surface of the distribution station; Among them, decision tree refers to a machine learning method. In load forecasting, decision tree is usually used for regression tasks, that is, predicting a continuous value based on input features (such as historical load, seasonal factors, temperature, etc.), that is, the short-term load forecast value. Specifically, decision tree is an existing technology, and the steps for obtaining the short-term load forecast value are not described in detail; S3: Based on the intensity of solar radiation received by the photovoltaic panels, the difference between the current air temperature and the surface temperature of the photovoltaic panels, and the short-term load forecast value, the photovoltaic power generation is predicted and analyzed through the time series model to further refine and optimize the scheduling adjustment plan; As can be seen from the above, the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value are continuous values ​​within a period of time. Therefore, for the time series model, this embodiment adopts the ARIMAX model. Refining and optimizing the current scheduling adjustment plan means that when the unreasonable results of the current scheduling are determined, through further analysis and adjustment, the power supply distribution plan is made more scientific and reasonable, thereby realizing the control of photovoltaic power supply; Furthermore, the specific steps for predicting and analyzing photovoltaic power generation through the time series model are as follows: Step A1, obtaining prediction data; Step A2, establishing an ARIMAX model; Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters; Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis; Step A5, using the fitted model to predict the future scheduling adjustment plan, and taking the maximum value of the prediction result as the maximum probability value of photovoltaic power generation in the future time period; The probability of meeting the requirements of photovoltaic power generation refers to the historical probability of meeting the requirements of photovoltaic power generation, and its data is used as the main variable of the time series; Furthermore, the basic form of the ARIMAX model is: ; In the formula, is the probability of photovoltaic power generation meeting the requirements at the current sampling time, is the photovoltaic power generation with lag i, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is the white noise term with lag j, is a white noise term, representing random error, is an exogenous variable The coefficient of is the lag order of the exogenous variable, is an exogenous variable lagged by k periods; It should be noted that the exogenous variable part can integrate the impact of other relevant variables (including the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value) on the probability of photovoltaic power generation meeting the requirements;

[0022] It should be noted that in step A3, , , , , It is calculated by the maximum likelihood estimation (MLE) method. The specific steps are as follows: Error term Normal distribution , then the likelihood function is: ; Taking the logarithm of the likelihood function gives the log-likelihood function: ; By maximizing the log-likelihood function, we can obtain the parameter estimates , , , , ; Substitute all sampling times corresponding to the short-term forecast into the formula to calculate and obtain the maximum probability value of photovoltaic power generation; Compare the maximum probability value of photovoltaic power generation with the preset satisfaction threshold to determine the scheduling adjustment plan. If it is greater than or equal to the satisfaction threshold, it means that the photovoltaic power generation system can meet the future load demand, and it is adjusted to give priority to photovoltaic power generation and reduce dependence on the external power grid. Otherwise, the output state of the external power grid is maintained; Among them, the threshold value is set by the experimenters based on the historical photovoltaic power generation duration and probability, which will not be elaborated here; The present invention determines the rationality of scheduling by preferentially performing logistic regression calculations based on the remaining capacity of energy storage batteries and the estimated current power supply, and obtains the intensity of solar radiation received by photovoltaic panels, the difference between the current air temperature and the surface temperature of photovoltaic panels, and the short-term load forecast value based on the rationality of scheduling. Prediction is performed by establishing a time series model, and a scheduling adjustment plan is determined based on the prediction results to reduce over-scheduling, improve power supply continuity, and ensure the maximization of photovoltaic power generation resources.

[0023] Example 2 In Example 1 of the present invention, an example is given to perform a logistic regression calculation based on the remaining amount of the energy storage battery and the estimated value of the current power supply to determine the rationality of the scheduling, and obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value based on the rationality of the scheduling, and predict by establishing a time series model. According to the prediction results, the operation strategy of the scheduling adjustment plan is determined; however, in Example 1, only the adjustment plan is started from, and the feasibility of the plan is not analyzed. Obviously, since the probability of photovoltaic power generation being satisfied is continued to be predicted after the load is predicted, this will lead to the accumulation of data prediction errors and excessive reliance on historical data and model assumptions, thereby reducing the feasibility of the scheduling adjustment plan; in view of the above problems, Example 2 of the present invention is further refined; S4: Based on the scheduling adjustment plan, similarity calculation is performed with the historical prediction scheduling adjustment plan, and based on the similarity calculation, the realization rate of the selected historical prediction scheduling adjustment plan and the similarity of the corresponding similar historical prediction scheduling adjustment plan are calculated; Specifically, the scheduling adjustment plan includes the maximum probability value of photovoltaic power generation; The characteristics of the scheduling adjustment scheme include the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, the short-term load forecast value, and the maximum probability value of photovoltaic power generation described in the above embodiment 1, wherein the logic of obtaining the specific data is not described in detail here; The historical prediction scheduling adjustment plan refers to the scheduling adjustment plan obtained by predicting the historical intensity of solar radiation received by photovoltaic panels, the difference between the current temperature and the surface temperature of photovoltaic panels, and the short-term load forecast value through a time series model. Its characteristics include the historical intensity of solar radiation received by photovoltaic panels, the difference between the current temperature and the surface temperature of photovoltaic panels, the short-term load forecast value, and the maximum probability value of photovoltaic power generation; From the above, we can see that the number of features of the scheduling adjustment plan and the historical prediction scheduling adjustment plan is consistent, and the features correspond to each other; The characteristics of the scheduling adjustment plan and the historical prediction scheduling adjustment plan are vectorized. The specific vectorization expression is: as well as ; In the formula, the solar radiation intensity received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, the short-term load forecast value, and the maximum probability value of photovoltaic power generation under the scheduling adjustment scheme and the historical prediction scheduling adjustment scheme are expressed as and , and , and as well as and ; Where A is the vectorized representation of the scheduling adjustment plan, and B is the vectorized representation of the historical prediction scheduling adjustment plan; Substituting the vectorized representation of the above data into the similarity formula, this embodiment expresses the similarity between the scheduling adjustment scheme and the historical prediction scheduling adjustment scheme based on cosine similarity. The specific formula is expressed as follows: ; In the formula, is the similarity between the scheduling adjustment plan and the historical forecast scheduling adjustment plan, is the dot product of two vectors, and Represent the modulus of vectors A and B respectively; and i is the similarity of the i-th comparison with the historical forecast scheduling adjustment plan; i=1, 2, 3...n, and i is the number of comparisons, and n is the total number of comparisons; Specifically, the calculation formula for the dot product of two vectors is: ; Specifically, and The calculation formulas are: and

[0024] The similarities between the scheduling adjustment schemes obtained by similarity calculation and the historical prediction scheduling adjustment schemes are sorted from large to small, and a data set is obtained: , select several historical prediction scheduling adjustment plans with the highest similarity as the similarity of similar historical prediction scheduling adjustment plans; For example, the top ten historical prediction scheduling adjustment plans with the highest similarity are selected as similar historical prediction scheduling adjustment plans; It should be noted that the number of selected historical prediction scheduling adjustment plans with the highest similarity can be determined by the experimenter, or the corresponding historical prediction scheduling adjustment plans with a specified similarity or above can be determined by setting a similarity threshold. The specific number and implementation method are not limited, but are implemented by the experimenter according to the specific plan, and will not be elaborated here. Obtain the realization rate corresponding to similar historical forecast scheduling adjustment plans, that is, the realization rate of the historical forecast scheduling adjustment plan refers to the final realization degree of the historical forecast scheduling adjustment plan. Its acquisition logic is to use the relative error calculation between the photovoltaic power generation duration determined by the historical forecast scheduling adjustment plan and its corresponding actual photovoltaic power generation duration to obtain the realization rate of the historical forecast scheduling adjustment plan; S5: Obtain the similarity of similar historical prediction scheduling adjustment plans and the realization rate of historical prediction scheduling adjustment plans for cluster analysis, determine the realization rate of the scheduling adjustment plan, and obtain a simple scheduling strategy; It should be noted that this embodiment uses k-means cluster analysis to obtain the scheduling adjustment scheme realization rate; The similarity of similar historical forecast scheduling adjustment plans and the realization rate of historical forecast scheduling adjustment plans are used as input data to generate multiple data points, each of which consists of a pair of values ​​(similarity and realization rate); Then the input data is normalized, and the specific normalization process is a prior art and is not limited here; Use K-means clustering to cluster the data into k clusters. The specific steps of K-means clustering are as follows: Step B1: Randomly select k initial cluster centers; Step B2: Assign each data point to the nearest initial cluster center; Step B3: Calculate the mean of the data points in each cluster and update the cluster center; Step B4: Repeat the steps of assigning and updating the cluster center until the cluster center no longer changes; Specifically, it is performed before starting the K-means clustering algorithm, and the optimal number of clusters k is determined by the elbow rule to determine the number of clusters of the data structure; When running K-means clustering, try different values ​​of k, calculate the sum of squared errors of the clusters for each value of k, and then plot the SSE as it changes with k. Find the "elbow" in the graph, that is, the inflection point, which is the point where the rate of decrease of the sum of squared errors slows down significantly. The k value corresponding to this point is usually the optimal number of clusters; Step B5: Obtain the realization rate of each cluster through K-means cluster analysis, and the cluster center represents the average realization rate of the category. Then substitute the similarity of the current scheduling adjustment plan into the cluster model to determine the cluster to which it belongs; Step B6: the current scheduling adjustment plan is classified into a specified cluster according to the similarity value with the historical predicted scheduling adjustment plan, and the result is output; Specifically, the average realization rate of the specified cluster is used as the realization rate of the current scheduling adjustment plan; Set a simple scheduling strategy based on the current scheduling adjustment plan realization rate; For example, when the scheduling adjustment plan realization rate is 80%, the current photovoltaic power generation strategy is maintained without subsequent adjustments. When the scheduling adjustment plan realization rate is 40%, it is changed to external power generation and only one adjustment is made; The specific simplified dispatching strategy is not limited, but is set by the experimenter based on the realization rate of the dispatching adjustment plan and the number of times the power supply mode is changed in the historical prediction dispatching adjustment plan, so it will not be described in detail here; The present invention performs similarity calculation based on the scheduling adjustment scheme and the historical prediction scheduling adjustment scheme, and performs cluster analysis based on the realization rate of the historical prediction scheduling adjustment scheme selected after the similarity calculation and the similarity between the corresponding scheduling adjustment scheme and the historical prediction scheduling adjustment scheme, determines the realization rate of the scheduling adjustment scheme, obtains a simple scheduling strategy, improves the accuracy and rationality of scheduling decisions, reduces resource waste in scheduling adjustment, and determines the feasibility of the scheduling adjustment scheme.

[0025] Example 3 See also Figure 2 , a power supply system of a photovoltaic storage integrated distribution station, including a reasonable analysis module, a data acquisition module, a scheduling prediction module and a scheme evaluation module; The rational analysis module is used to collect the remaining capacity of the energy storage battery and the estimated current power supply, and perform normalization processing, perform logistic regression calculation on the normalized data, and compare it with the preset reasonable threshold value to determine the unreasonable result of the current scheduling and send it to the data acquisition module; The data acquisition module is used to obtain unreasonable results of the current scheduling, collect weather forecast information and load information, obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value, and send them to the scheduling prediction module; The scheduling prediction module is used to perform prediction analysis on photovoltaic power generation based on the intensity of solar radiation received by the photovoltaic panels, the difference between the current temperature and the surface temperature of the photovoltaic panels, and the short-term load forecast value, and further refine and optimize the scheduling adjustment plan, and send it to the plan evaluation module; The scheme evaluation module is used to calculate the similarity between the scheduling adjustment scheme and the historical prediction scheduling adjustment scheme, and to perform cluster analysis based on the realization rate of the selected historical prediction scheduling adjustment scheme and the similarity of the corresponding similar historical prediction scheduling adjustment schemes, so as to determine the realization rate of the scheduling adjustment scheme and obtain a simple scheduling strategy.

[0026] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0027] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0028] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0029] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0030] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0031] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0032] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0033] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0034] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0035] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A power supply method for a photovoltaic and energy storage fusion distribution station, characterized in that: include: S1: Collect the remaining capacity of the energy storage battery and the estimated value of the current power supply, and perform normalization processing, perform logistic regression calculation on the normalized data to obtain a reasonable probability, and compare it with the preset reasonable threshold to determine the unreasonable result of the current scheduling; S2: Obtain the unreasonable results of the current scheduling, collect weather forecast information and load information, obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value; S3: Based on the intensity of solar radiation received by the photovoltaic panels, the difference between the current air temperature and the surface temperature of the photovoltaic panels, and the short-term load forecast value, the photovoltaic power generation is predicted and analyzed through the time series model to further refine and optimize the scheduling adjustment plan; S4: Based on the scheduling adjustment plan, similarity calculation is performed with the historical prediction scheduling adjustment plan, and based on the similarity calculation, the realization rate of the selected historical prediction scheduling adjustment plan and the similarity of the corresponding similar historical prediction scheduling adjustment plan are calculated; S5: Obtain the similarity of similar historical prediction scheduling adjustment plans and the realization rate of historical prediction scheduling adjustment plans for cluster analysis, determine the realization rate of the scheduling adjustment plan, and obtain a simple scheduling strategy.

2. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 1 is characterized in that: By real-time monitoring of the battery voltage and current, combined with the battery discharge characteristics and capacity curve, the remaining amount of energy storage battery can be obtained; By taking load data within the current total sampling time and using linear regression with temperature, humidity and sunshine, and combining historical load data with meteorological data, a historical load curve is obtained, and a weighted average calculation is performed based on the load data at the corresponding sampling time on the curve to obtain the current power supply estimate; Substituting the estimated value of the remaining energy storage battery capacity and the current power supply into the logistic regression calculation, the specific formula is expressed as follows: ； In the formula, L is the result of logistic regression calculation, that is, reasonable probability, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as: ； In the formula, is the bias term, and are the regression coefficients of the estimated energy storage battery capacity and current power supply, is the remaining amount of energy storage battery, Provide an estimate of the current power supply; Compare the reasonable probability with the preset reasonable threshold, specifically: If the reasonable probability is greater than or equal to the reasonable threshold, the current schedule is evaluated as reasonable, the current schedule rationality result is obtained, and it returns to the starting stage. If the reasonable probability is less than the reasonable threshold, the current schedule is evaluated as unreasonable, the current schedule unreasonable result is obtained, and it is transmitted to the next layer.

3. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 2 is characterized in that: Weather forecast information includes the intensity of solar radiation received by the photovoltaic panel and the difference between the current air temperature and the surface temperature of the photovoltaic panel. Load information includes short-term load forecast values. The radiation intensity of the atmosphere and the ground is obtained by connecting to satellite remote sensing and multiplying it with the incident angle between the photovoltaic panel and the sun's rays and the photoelectric conversion efficiency of the photovoltaic panel to obtain the intensity of solar radiation received by the photovoltaic panel; The surface temperature of the photovoltaic panel and the external environment temperature are obtained by the temperature sensor installed on the surface of the photovoltaic panel and the temperature sensor installed in the external environment, and the difference between the current air temperature and the surface temperature of the photovoltaic panel is obtained by subtracting the temperature. Through the sensors installed on the surface of the distribution station, the historical load duration, external power grid output value, and temperature change value are obtained and substituted into the decision tree to obtain the short-term load forecast value.

4. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 3 is characterized in that: The photovoltaic power generation is predicted and analyzed by the time series model. The time series model adopts the ARIMAX model. The specific steps of the photovoltaic power generation prediction and analysis by the time series model are as follows: Step A1, obtaining prediction data; Step A2, establishing an ARIMAX model; Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters; Step A4, verify the effect of the fitting model and check the goodness of fit of the model by residual analysis; Step A5, using the fitted model to predict the future scheduling adjustment plan, and taking the maximum value of the prediction result as the maximum probability value of photovoltaic power generation in the future time period.

5. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 4 is characterized in that: The exogenous variables in the ARIMAX model include the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value. The ARIMAX model formula is: ； In the formula, is the probability of photovoltaic power generation meeting the requirements at the current sampling time, is the photovoltaic power generation with lag i, is a constant term, is the i-th order autoregressive parameter, is the order of the autoregressive term, is the j-th order moving average parameter, is the order of the moving average term, is the white noise term with lag j, is a white noise term, representing random error, is an exogenous variable The coefficient of is the lag order of the exogenous variable, is an exogenous variable lagged by k periods; In step A3, , , , , It is calculated by the maximum likelihood estimation method.

6. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 5 is characterized in that: Substitute all sampling times corresponding to the short-term forecast into the formula to calculate and obtain the maximum probability value of photovoltaic power generation; The maximum probability value of photovoltaic power generation is compared with the preset satisfaction threshold to determine the scheduling adjustment plan. If it is greater than or equal to the satisfaction threshold, it means that the photovoltaic power generation system can meet future load demand and is adjusted to give priority to photovoltaic power generation to reduce dependence on the external power grid. Otherwise, the output status of the external power grid is maintained.

7. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 6 is characterized in that: The characteristics of the scheduling adjustment plan include the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, the short-term load forecast value, and the maximum probability value of photovoltaic power generation; The characteristics of the historical prediction scheduling adjustment plan include the historical basis of the intensity of solar radiation received by the photovoltaic panel, the difference between the current air temperature and the surface temperature of the photovoltaic panel, the short-term load forecast value, and the maximum probability value of photovoltaic power generation; The similarity between the scheduling adjustment plan and the historical prediction scheduling adjustment plan is obtained by similarity calculation, and they are sorted in order from large to small, and several historical prediction scheduling adjustment plans with the highest similarity are selected as the similarity of similar historical prediction scheduling adjustment plans; The photovoltaic power generation duration determined according to the historical forecast scheduling adjustment plan and its corresponding actual photovoltaic power generation duration are calculated using relative error to obtain the realization rate of the historical forecast scheduling adjustment plan.

8. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 7 is characterized in that: Use k-means cluster analysis to obtain the scheduling adjustment plan realization rate; The similarity of similar historical forecast scheduling adjustment plans and the realization rate of historical forecast scheduling adjustment plans are used as input data to generate multiple data points; Then use K-means clustering to cluster multiple data points into k clusters. The specific steps are: Step B1: Randomly select k initial cluster centers; Step B2: Assign each data point to the nearest initial cluster center; Step B3: Calculate the mean of the data points in each cluster and update the cluster center; Step B4: Repeat the steps of assigning and updating cluster centers until the cluster centers no longer change.

9. The power supply method of the photovoltaic energy storage fusion distribution station according to claim 8 is characterized in that: The average realization rate of the specified cluster is used as the realization rate of the current scheduling adjustment plan, and a simple scheduling strategy is set according to the realization rate of the current scheduling adjustment plan.

10. A power supply system for a photovoltaic-storage fusion distribution station, used to implement the power supply method for a photovoltaic-storage fusion distribution station according to any one of claims 1 to 9, characterized in that: It includes rational analysis module, data collection module, scheduling prediction module and scheme evaluation module; The rational analysis module is used to collect the remaining capacity of the energy storage battery and the estimated current power supply, and perform normalization processing, perform logistic regression calculation on the normalized data, and compare it with the preset reasonable threshold value to determine the unreasonable result of the current scheduling and send it to the data acquisition module; The data acquisition module is used to obtain unreasonable results of the current scheduling, collect weather forecast information and load information, obtain the intensity of solar radiation received by the photovoltaic panel, the difference between the current temperature and the surface temperature of the photovoltaic panel, and the short-term load forecast value, and send them to the scheduling prediction module; The scheduling prediction module is used to perform prediction analysis on photovoltaic power generation based on the intensity of solar radiation received by the photovoltaic panels, the difference between the current temperature and the surface temperature of the photovoltaic panels, and the short-term load forecast value, and further refine and optimize the scheduling adjustment plan, and send it to the plan evaluation module; The scheme evaluation module is used to calculate the similarity between the scheduling adjustment scheme and the historical prediction scheduling adjustment scheme, and to perform cluster analysis based on the realization rate of the selected historical prediction scheduling adjustment scheme and the similarity of the corresponding similar historical prediction scheduling adjustment schemes, so as to determine the realization rate of the scheduling adjustment scheme and obtain a simple scheduling strategy.

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