Distribution network side configuration optimization method based on photovoltaic output characteristics

Abstract

The invention relates to the technical field of electric power, in particular to a distribution network side configuration optimization method based on photovoltaic output characteristics, and the method comprises the steps: carrying out the weighted optimization of photovoltaic power generation data through environment parameters, and carrying out the real-time prediction of the photovoltaic output characteristics and a power grid load based on an optimization result, determining the optimal capacity of the energy storage device according to the prediction results of the two and the charge and discharge efficiency of the current energy storage device, and finally optimizing the scheduling strategy of the energy storage system according to the characteristic difference between the power grid load and the photovoltaic output. According to the invention, the prediction precision of the photovoltaic output characteristics is improved, the capacity configuration and scheduling strategy of the energy storage device is optimized, the maximum utilization of the energy storage device is ensured, and the stability of the power distribution system is improved.

Description

Technical Field

[0001] This application relates to the field of power technologies, and particularly to a method for optimizing the configuration on the distribution network side based on the photovoltaic output characteristics. Background Art

[0002] As a power grid system close to the user side and with high flexibility, the distribution network side of the power grid can absorb photovoltaic power supply to the greatest extent. However, due to the volatility of the output power of photovoltaic, in the method for optimizing the configuration on the distribution network side based on the photovoltaic output characteristics, in order to ensure the reliability of power supply, it is necessary to configure a reasonable capacity of energy storage devices in the system to achieve regulation during peak shaving and valley filling. Although introducing energy storage will reduce the photovoltaic power abandonment rate, the cost of energy storage is relatively high, and excessive configured capacity will affect the economy of the microgrid. Therefore, it is crucial to configure an appropriate energy storage capacity and dispatching strategy.

[0003] Currently, the method for optimizing the configuration on the distribution network side usually relies on environmental data and historical data to predict photovoltaic power generation from two perspectives: short-term and long-term. In the historical data prediction method, the short-term characteristics of environmental data and the long-term characteristics of historical data are inconsistent in the time dimension. Therefore, if the two are directly combined for photovoltaic power generation prediction, a large prediction error will occur, which also leads to deviations in the configuration of energy storage devices. In addition, the prediction of load and photovoltaic output itself has double uncertainties, which further exacerbates the error in the optimal configuration of energy storage devices. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method for optimizing the configuration on the distribution network side based on the photovoltaic output characteristics to solve the above problems.

[0005] An embodiment of this application provides a method for optimizing the configuration on the distribution network side based on the photovoltaic output characteristics, and the method includes: Analyze all environmental parameters at each sampling moment to obtain an environmental action factor; analyze the change trend of the environmental action factor to predict the environmental action factor at the next moment; obtain a theoretical predicted value of photovoltaic power generation based on the historical power of photovoltaic power generation, and compare the environmental action factor with its predicted value to obtain an actual predicted value of photovoltaic power generation; Determine a predicted value of the power generation amount at the corresponding sampling moment based on the actual predicted value of photovoltaic power generation; predict the power grid load value at the next moment based on the historical power grid load demand data, and compare it with the predicted value of the power generation amount to determine the theoretical capacity demand of the energy storage device at the next moment; calculate the comprehensive charge-discharge efficiency of the energy storage device based on the electric energy distribution during the charge and discharge process of the energy storage device; analyze the distribution of the theoretical capacity demand and the comprehensive charge-discharge efficiency within a preset effective period to obtain the optimal energy storage capacity; Based on the theoretical capacity requirement and charge-discharge power of the energy storage device, establish an objective function; based on the optimal energy storage capacity, determine the constraint conditions of the objective function, and solve the objective function to optimize the distribution network side configuration.

[0006] Preferably, the environmental parameters specifically include light intensity, temperature, and cloud thickness.

[0007] Preferably, the obtaining of the environmental action factor is specifically as follows: Obtain the positive fusion result of the light intensity and temperature at each sampling moment, and take the product of the negative correlation mapping of the cloud thickness at each sampling moment and the positive fusion result as the environmental action factor at each sampling moment.

[0008] Preferably, the prediction of the environmental action factor at the next moment is specifically as follows: Arrange the environmental action factors at the current sampling moment and all previous sampling moments in chronological order to form an environmental action sequence, and obtain the element mean of the first-order difference sequence of the environmental action sequence; take the sum value of the environmental action factor at the current sampling moment and the element mean as the predicted value of the environmental action factor at the next sampling moment.

[0009] Preferably, the actual predicted value of the photovoltaic power generation at the next moment is specifically as follows: Calculate the ratio of the predicted value of the environmental action factor at the next moment to the environmental action factor at the current moment; take the positive fusion result of the ratio and the theoretical predicted value of the photovoltaic power generation at the next moment as the actual predicted value of the photovoltaic power generation at the next moment.

[0010] Preferably, the actual predicted value of the photovoltaic power generation is specifically the product of the ratio and the theoretical predicted value.

[0011] Preferably, the determination of the theoretical capacity requirement of the energy storage device at the next moment is specifically the difference between the predicted value of the power generation of the power grid at the next moment and the predicted value of the power grid load.

[0012] Preferably, the process of calculating the comprehensive charge-discharge efficiency of the energy storage device is as follows: Take the ratio between the actually stored electric energy and the actually input electric energy of the energy storage device as the charging efficiency of the energy storage device; Take the ratio between the effective electric energy provided by the energy storage device to the power grid and the total released electric energy as the discharge efficiency of the energy storage device; Take the product between the charging efficiency and the discharge efficiency of the energy storage device as the comprehensive charge-discharge efficiency of the energy storage device.

[0013] Preferably, the specific process of obtaining the optimal energy storage capacity is as follows: The maximum value obtained by positively integrating the theoretical capacity demand of the energy storage device at each sampling moment within the effective period with the comprehensive charge-discharge efficiency is used as the optimal energy storage capacity within the effective period.

[0014] Preferably, the formula for the optimal energy storage capacity is specifically: ; where represents the optimal energy storage capacity of the power grid within the effective period; is the theoretical capacity demand at the t-th sampling moment within the effective period, is the comprehensive charge-discharge efficiency of the energy storage device, and max{} represents the maximum value function, represents the number of sampling moments within the effective period.

[0015] Preferably, the specific formula for establishing the objective function is: ; where represents the objective function; is the theoretical capacity demand at the t-th sampling moment within the effective period, is the discharge power of the energy storage device at the -th sampling moment, is the charge power of the energy storage device at the -th sampling moment,

[0016] Preferably, the constraint condition is specifically that the power of the energy storage device is less than or equal to the optimal energy storage capacity.

[0017] This application has at least the following beneficial effects: In response to the challenges in photovoltaic power generation analysis, traditional methods directly integrate long-term and short-term characteristics, often resulting in inaccurate prediction of photovoltaic changes, which in turn affects the optimal configuration of the energy storage system. By improving the prediction accuracy of photovoltaic output characteristics, the capacity configuration and scheduling strategy of the energy storage device are optimized, not only ensuring the maximum utilization of the energy storage device, but also enhancing the stability of the distribution system. First, all environmental parameters at each sampling moment are analyzed to obtain the environmental action factor, which helps to analyze the degree of influence of the environmental situation on photovoltaic power generation; since environmental factors have a significant impact on the short-term changes in photovoltaic output characteristics, the prediction results of photovoltaic power generation can be adjusted according to the future environmental change trend, so analyzing the change trend of the environmental action factor and predicting the environmental action factor at the next moment; based on the historical power of photovoltaic power generation, the theoretical prediction value of photovoltaic power generation is obtained, and by comparing the environmental action factor with its prediction value, the actual prediction value of photovoltaic power generation is obtained. When predicting the photovoltaic power generation power, considering the influence of the environment on photovoltaic power generation makes the prediction results more accurate.

[0018] Further, based on historical grid load demand data, predict the grid load value at the next moment, compare it with the predicted power generation value, determine the theoretical capacity demand of the energy storage device at the next moment. According to the theoretical capacity demand, the charging and discharging strategies of the energy storage device can be scheduled more reasonably to ensure the minimization of the difference between the grid load and the energy storage system, thereby avoiding power surplus or shortage and improving the stability and efficiency of the power system; calculate the comprehensive charging and discharging efficiency of the energy storage device based on the electrical energy distribution during the charging and discharging process of the energy storage device; analyze the distribution of the theoretical capacity demand and the comprehensive charging and discharging efficiency within a preset effective period to obtain the optimal energy storage capacity. The analysis of the comprehensive charging and discharging efficiency can help identify the efficiency differences of the energy storage system under different working conditions, reduce energy losses by adjusting the operation strategy, and thus improve the overall energy utilization rate of the system.

[0019] Finally, based on the theoretical capacity demand and the charging and discharging power of the energy storage device, establish an objective function; determine the constraint conditions of the objective function based on the optimal energy storage capacity, and solve the objective function to optimize the distribution network side configuration; the process of optimizing the objective function improves the prediction accuracy of the photovoltaic output characteristics, optimizes the capacity configuration and scheduling strategy of the energy storage device, not only ensures the maximum utilization of the energy storage device, but also improves the stability of the distribution system. The optimized energy storage device can more efficiently meet the grid load demand and reduce energy waste. Description of the Drawings

[0020] Figure 1 It is a flowchart of the distribution network side configuration optimization method based on the photovoltaic output characteristics provided by this application; Figure 2 It is a flowchart for obtaining the optimal energy storage capacity of the energy storage device provided by this application. Detailed Embodiments

[0021] In the description of the embodiments of this application, words such as "exemplary", "or", "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example" is intended to present relevant concepts in a specific manner.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0023] In addition, it should be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or shown in the flowcharts, which include one or more steps for implementing the methods, without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0025] This application proposes an optimization method for distribution network side configuration based on photovoltaic output characteristics, which is applied to the field of power technology. Referring to the attached Figure 1 , the method includes the following steps: S1: Collect environmental parameters and grid power generation data at each sampling moment, and monitor the charge and discharge capacity of the energy storage device.

[0026] Generally, a photovoltaic distribution network system mainly consists of a photovoltaic array, an energy storage device, an inverter, a photovoltaic and energy storage transformer, a distribution network, and a supporting central control system and user-side demand management system. Among them, the central control system manages and coordinates the operation of all relevant devices and systems through centralized data collection, processing, and analysis, so as to ensure the safety, stability, and efficiency of the system. The user demand management system can dynamically adjust power consumption, respond to grid dispatching and user demands, so as to achieve coordinated and optimized operation of the grid and the photovoltaic system.

[0027] Currently, when optimizing the configuration of the photovoltaic distribution network side, due to the different time dimensions corresponding to the short-term characteristics of environmental data and the long-term characteristics of photovoltaic power generation historical data, the error of the optimized configuration of the energy storage device will be further increased. Then, in order to improve the optimization accuracy of the energy storage device, this application first obtains the power generation data of the grid at each sampling moment in the past month and the grid load data at the corresponding sampling moment through the central control system. Among them, the power generation data in this application includes but is not limited to instantaneous power, frequency, etc.

[0028] At the same time, external weather environment factors will also affect the stability of photovoltaic power generation and thus affect the scheduling strategy of the energy storage device. Therefore, the environmental parameters at the sampling moment in the past three days can be obtained through the sensors on the energy storage device. The environmental parameters in this application include but are not limited to light intensity, temperature, cloud thickness, etc. In this embodiment, all data is collected synchronously, and the sampling interval is set to 2 hours to monitor the charge and discharge capacity of the energy storage device, so as to facilitate subsequent optimization of the capacity and scheduling strategy of the energy storage device on the distribution network side according to the correlation of different data.

[0029] S2: Analyze all environmental parameters at each sampling moment to obtain the environmental impact factor; analyze the change trend of the environmental impact factor to predict the environmental impact factor at the next moment; obtain the theoretical prediction value of the photovoltaic power generation based on the historical power of photovoltaic power generation, and compare the environmental impact factor with its predicted value to obtain the actual prediction value of the photovoltaic power generation.

[0030] In a photovoltaic distribution network system, the stable operation of the distribution network side energy storage device generally depends on the characteristics of photovoltaic power output. Due to the influence of environmental factors, the characteristics of photovoltaic power output are intermittent and volatile, resulting in a large error between the configuration of the energy storage device and the actual demand. To reduce this error, this application predicts the characteristics of photovoltaic power output based on the fluctuation characteristics of environmental factors.

[0031] The efficiency of photovoltaic power generation is affected by environmental weather. When the light intensity is high and the cloud thickness is low, the photovoltaic will receive more light energy and the power generation efficiency is high. On the contrary, the photovoltaic power generation efficiency is poor. However, due to the strong real-time and irregularity of environmental weather, to improve the prediction accuracy of the characteristics of photovoltaic power output, first analyze all environmental parameters at each sampling moment to obtain the environmental impact factor. Specifically: obtain the positive fusion result of the light intensity and temperature at each sampling moment, and take the product of the negative correlation mapping of the cloud thickness at each sampling moment and the positive fusion result as the environmental impact factor at each sampling moment. In this embodiment, ; where is the environmental impact factor at the i-th sampling moment; is the light intensity at the i-th sampling moment; is the temperature at the i-th sampling moment; is the cloud thickness at the i-th sampling moment. It should be understood that the more favorable the environmental conditions for photovoltaic power generation at a sampling moment, the larger the corresponding environmental impact factor, and vice versa.

[0032] The real-time trend of the environmental impact factor represents the short-term environmental change result. Therefore, based on the change trend of the environmental impact factor at the current sampling moment and the previous sampling moments, predict the environmental impact factor at the next moment. Specifically: arrange the environmental impact factors at the current sampling moment and all previous sampling moments in chronological order to form an environmental impact sequence, and obtain the element mean of the first-order difference sequence of the environmental impact sequence; take the sum of the environmental impact factor at the current sampling moment and the element mean as the predicted value of the environmental impact factor at the next sampling moment. It should be understood that the element mean represents the environmental change trend within the sampling period. Then, combining this trend with the environmental impact factor at the current moment can obtain the predicted value of the environmental impact at the next moment. The larger the value, the more favorable the environmental trend for photovoltaic power generation.

[0033] Since environmental factors have a significant impact on the short-term changes in the characteristics of photovoltaic power output, the prediction results of photovoltaic power generation can be adjusted according to the future environmental change trend. Specifically, when the environmental change trend is favorable for photovoltaic power generation, the predicted power generation value for the future should be appropriately increased; when the environmental change trend is unfavorable, the predicted value should be appropriately decreased.

[0034] Here, based on the historical power of photovoltaic power generation, the theoretical predicted value of photovoltaic power generation is obtained through the Long Short-Term Memory (LSTM) network. By comparing the environmental impact factor at the current moment with the predicted value of the environmental impact factor at the next moment, and combining the theoretical predicted value of the photovoltaic power generation power at the next moment, the actual predicted value of the photovoltaic power generation power is obtained. Specifically: calculate the ratio of the predicted value of the environmental impact factor at the next moment to the environmental impact factor at the current moment; use the positive fusion result of the ratio and the theoretical predicted value of the photovoltaic power generation power at the next moment as the actual predicted value of the photovoltaic power generation power at the next moment. In this embodiment, the positive fusion between multiple variables adopts a multiplication calculation method.

[0035] It should be understood that the larger the ratio, the more favorable the predicted trend of environmental change is for photovoltaic power generation, and the predicted power of photovoltaic power generation needs to be increased; otherwise, the predicted power is decreased. This method combines the change trend of environmental weather to gradually predict the photovoltaic power output characteristics at future moments, improving the accuracy of the prediction results.

[0036] S3: Determine the predicted power generation value corresponding to the sampling moment based on the actual predicted value of the photovoltaic power generation power; predict the grid load value at the next moment based on the historical grid load demand data, and compare it with the predicted power generation value to determine the theoretical capacity demand of the energy storage device at the next moment; calculate the comprehensive charge-discharge efficiency of the energy storage device based on the electrical energy distribution during the charge-discharge process of the energy storage device; analyze the distribution of the theoretical capacity demand and the comprehensive charge-discharge efficiency within the preset effective period to obtain the optimal energy storage capacity.

[0037] This application considers optimizing the configuration of the energy storage device through the prediction results of the photovoltaic power output characteristics to ensure the operation stability of the distribution network side system. The energy storage device includes the energy storage device and the corresponding power scheduling strategy. Therefore, this application optimizes the optimal energy storage capacity and its scheduling strategy of the energy storage device based on the prediction results of the photovoltaic power output characteristics and the load data.

[0038] First, determine the optimal capacity of the energy storage device. Generally, the purpose of the energy storage device for photovoltaic power generation is to balance the gap between photovoltaic power generation and the grid load: when the photovoltaic power generation is excessive, the energy storage device will absorb the excess electric energy and store it; when the photovoltaic power generation is insufficient, the energy storage device will release the stored electric energy to balance the grid load and ensure the stable operation of the grid. Then, to make the energy storage device of this grid operate stably, its capacity needs to cover the excess part of photovoltaic power generation and the shortage part of the grid load.

[0039] Since the power generation is positively correlated with the power generation amount, the predicted power generation amount at the corresponding sampling moment is determined based on the actual predicted value of the real-time photovoltaic power generation. At the same time, the historical grid load demand data is collected. Since the grid load data is generally relatively stable, the autoregressive integrated moving average (ARIMA) model can be used to obtain the predicted grid load value at the next moment. Further, based on the difference between the predicted power generation amount at the next moment of the grid and the predicted grid load value, the theoretical capacity requirement of the energy storage device at the next moment is determined. In this embodiment, the difference between variables is calculated by taking the difference. It should be understood that the closer the theoretical capacity requirement is to 0, the closer the power generation amount and consumption amount of electric energy are to balance at the corresponding moment; on the contrary, if the capacity requirement is far from 0, it indicates that there is a large difference between the power generation and consumption of electric energy, and this unbalanced part of the electric energy needs to be adjusted and supplied by the energy storage device.

[0040] At the same time, the charge-discharge efficiency of the energy storage device also determines the size of its optimal capacity. If its charge-discharge efficiency is larger, it means that the electric energy lost by the energy storage device during the energy storage and release process is less, and then the actual capacity requirement of the energy storage device is closer to the theoretical capacity requirement. Calculate the comprehensive charge-discharge efficiency of this energy storage device specifically: take the ratio between the actually stored electric energy and the actually input electric energy of the energy storage device as the charge efficiency of the energy storage device; take the ratio between the effective electric energy provided by the energy storage device to the grid and the total released electric energy as the discharge efficiency of the energy storage device; take the product between the charge efficiency and the discharge efficiency of the energy storage device as the comprehensive charge-discharge efficiency of the energy storage device.

[0041] It should be understood that the larger the value of the charge efficiency, the less electric energy is lost when the energy storage device is charging, and the same is true for the discharge efficiency. Then, the closer the value of the comprehensive charge-discharge efficiency is to 1, the better the charge-discharge efficiency of the energy storage device.

[0042] In this embodiment, the effective period is set to the next week, denoted as , the implementer can adjust it according to the actual situation; determine the optimal energy storage capacity within the effective period according to the maximum value after the forward fusion of the theoretical capacity requirements of the energy storage device at each sampling moment within the effective period and the comprehensive charge-discharge efficiency. In this embodiment, the forward fusion between variables adopts a multiplication calculation method, and its specific calculation formula is: ; In the formula, represents the optimal energy storage capacity of the power grid within the effective period; is the theoretical capacity requirement at the t-th sampling moment within the effective period, is the comprehensive charge-discharge efficiency of the energy storage device. Then can represent the maximum value of the theoretical capacity requirement of the energy storage device after excluding power loss within the effective period, and is used as the optimal energy storage capacity of the energy storage device within the effective period.

[0043] Among them, the flowchart for obtaining the optimal energy storage capacity of the energy storage device is as Figure 2 shown.

[0044] When this effective period ends, the theoretical capacity requirement within the next effective period can continue to be predicted based on the PV output characteristics, and then the optimal energy storage capacity can be determined. For this distribution network side energy storage device, the purpose of dynamically determining the optimal energy storage capacity within different future effective periods is achieved.

[0045] S4: Establish an objective function based on the theoretical capacity requirement and charge-discharge power of the energy storage device; determine the constraint conditions of the objective function based on the optimal energy storage capacity, and solve the objective function to optimize the distribution network side configuration.

[0046] For the distribution network side, the generation, storage, and consumption of electric energy are a real-time dynamic process. Then, the dispatching strategy of the energy storage device is a key link to ensure the efficient peak shaving and valley filling of the energy storage device and the stability of the power system.

[0047] In the problem of optimal dispatching of energy storage devices, load balance optimization is an important goal in the dispatching optimization of energy storage devices, aiming to balance the difference between the power grid load and the power generation, so that the load demand of the power grid is balanced, while minimizing the burden on the power grid as much as possible and ensuring the matching of power supply and demand.

[0048] This application sets a load balance optimization objective function. The core of this objective function is to minimize the difference between the power grid load and the PV power generation and the charge-discharge power of the energy storage device. This difference represents the load level that the power grid system needs to meet at a certain future time point. Minimizing the difference means that the power grid can operate in a nearly ideal balanced state.

[0049] For the effective cycle of the power grid, the objective function can be divided into three parts: ① the remaining load demand of the power grid, i.e., the theoretical capacity demand; ② the rated discharge power of the energy storage device; ③ the rated charge power of the energy storage device.

[0050] The remaining load demand of the power grid, which is the difference between the load in the power grid and the actual power generation, then there are three situations: When it indicates that the power grid load is greater than the power generation of the system, and more power is needed to supplement; when it indicates that the power grid load is too low, which may cause power waste, and the energy storage device can absorb the excess power through charging; when the power grid load is balanced with the power generation, and in this case, no energy storage scheduling is required.

[0051] The ultimate goal of this application is to balance the difference between the power grid load and the energy storage device, and the charge and discharge power of the energy storage device directly reflects the magnitude of this difference. Therefore, in the objective function, only the difference in the remaining load demand of the power grid needs to be concerned, and it is not necessary to distinguish whether it is excess power (low power grid load) or insufficient power (high power grid load); the key is to make the difference between the remaining load demand of the power grid and the charge and discharge power of the energy storage device as small as possible, so as to achieve the balance of the power grid load. Then the corresponding objective function can be expressed as: ; where, represents the objective function; is the theoretical capacity demand at the t-th sampling moment within the effective cycle, is for the discharge power of the energy storage device at the -th sampling moment, is the charge power of the energy storage device at the -th sampling moment,

[0052] By solving this objective function through the dynamic programming method, making the optimal values at each sampling moment when the satisfied value is the smallest, it should be noted that before this, the optimal energy storage capacity of the energy storage device of the power grid has been determined. Therefore, in the solving process, the constraint condition needs to add that the power of the energy storage device is less than or equal to the optimal energy storage capacity, that is , and then optimize its scheduling strategy. Among them, solving the objective function by dynamic programming is a well-known prior art, and this application will not elaborate on it.

[0053] By the above method, the optimal energy storage capacity and dispatching strategy of the distribution network side energy storage device for this power grid are determined, so as to reduce the power loss of the entire system, realize the optimal configuration of the rated capacity of the energy storage device, and achieve the maximum utilization rate of the energy storage device. At the same time, the energy storage device should not only be optimized with the photovoltaic power generation system, but also be co-optimized with other power equipment of the distribution network (such as wind energy, traditional power generation facilities, etc.). Through the joint dispatching of multiple energy sources and energy storage devices, the overall economy and reliability of the power grid can be optimized.

[0054] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the descriptions. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0055] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for optimizing the configuration on the distribution network side based on the characteristics of photovoltaic power output, characterized in that, The method includes the following steps: Analyze all environmental parameters at each sampling moment to obtain an environmental impact factor; analyze the change trend of the environmental impact factor to predict the environmental impact factor at the next moment; obtain a theoretical predicted value of the photovoltaic power generation based on the historical power of the photovoltaic power generation, and compare the environmental impact factor with its predicted value to obtain an actual predicted value of the photovoltaic power generation; Determine the predicted power generation value at the corresponding sampling moment based on the actual predicted value of the photovoltaic power generation; predict the grid load value at the next moment based on the historical grid load demand data, and compare it with the predicted power generation value to determine the theoretical capacity demand of the energy storage device at the next moment; calculate the comprehensive charge-discharge efficiency of the energy storage device based on the electric energy distribution during the charge and discharge process of the energy storage device; analyze the distribution of the theoretical capacity demand and the comprehensive charge-discharge efficiency within a preset effective period to obtain the optimal energy storage capacity; Establish an objective function based on the theoretical capacity demand and the charge-discharge power of the energy storage device; determine the constraint conditions of the objective function based on the optimal energy storage capacity, and solve the objective function to optimize the distribution network side configuration.

2. The distribution network side configuration optimization method based on the photovoltaic output characteristics according to claim 1, wherein, The environmental parameters specifically include light intensity, temperature, and cloud thickness.

3. The optimization method for distribution network side configuration based on photovoltaic output characteristics according to claim 2, characterized in that, The obtaining of the environmental impact factor is specifically as follows: Obtain the positive fusion result of the light intensity and temperature at each sampling moment, and take the product of the negative correlation mapping of the cloud thickness at each sampling moment and the positive fusion result as the environmental impact factor at each sampling moment.

4. The distribution network side configuration optimization method based on the photovoltaic output characteristics according to claim 1, wherein The prediction of the environmental impact factor at the next moment is specifically as follows: Arrange the environmental impact factors at the current sampling moment and all previous sampling moments in chronological order to form an environmental impact sequence, and obtain the element mean of the first-order difference sequence of the environmental impact sequence; take the sum value of the environmental impact factor at the current sampling moment and the element mean as the predicted value of the environmental impact factor at the next sampling moment.

5. The optimization method for distribution network side configuration based on photovoltaic output characteristics according to claim 1, wherein The actual predicted value of the photovoltaic power generation at the next moment is specifically: Calculate the ratio of the predicted value of the environmental impact factor at the next moment to the environmental impact factor at the current moment; take the positive fusion result of the ratio and the theoretical predicted value of the photovoltaic power generation at the next moment as the actual predicted value of the photovoltaic power generation at the next moment.

6. The method for optimizing the distribution network side configuration based on the photovoltaic output characteristics according to claim 5, characterized in that, The actual predicted value of the photovoltaic power generation is specifically the product of the ratio and the theoretical predicted value.

7. The method for optimizing the distribution network side configuration based on the photovoltaic output characteristics according to claim 1, wherein The determination of the theoretical capacity demand of the energy storage device at the next moment is specifically the difference between the predicted power generation value of the power grid at the next moment and the predicted value of the grid load.

8. The distribution network side configuration optimization method based on the photovoltaic output characteristics according to claim 1, characterized in that, The process of calculating the comprehensive charge-discharge efficiency of the energy storage device is as follows: Take the ratio between the electric energy actually stored in the energy storage device and the electric energy actually input as the charge efficiency of the energy storage device; Take the ratio between the effective electric energy provided by the energy storage device to the power grid and the total released electric energy as the discharge efficiency of the energy storage device; Take the product between the charge efficiency and the discharge efficiency of the energy storage device as the comprehensive charge-discharge efficiency of the energy storage device.

9. The optimization method for distribution network side configuration based on the characteristics of photovoltaic output as described in claim 1, characterized in that, The specific process of obtaining the optimal energy storage capacity is as follows: Take the maximum value after the positive fusion of the theoretical capacity demand and the comprehensive charge-discharge efficiency of the energy storage device at each sampling moment within the effective period as the optimal energy storage capacity within the effective period.

10. The method for optimizing the distribution network side configuration based on the photovoltaic output characteristics according to claim 9, wherein The formula for the optimal energy storage capacity is specifically as follows: ; In the formula, represents the optimal energy storage capacity of the power grid within the effective period; is the theoretical capacity demand at the t-th sampling moment within the effective period, is the comprehensive charge-discharge efficiency of the energy storage device, and max{} represents the maximum value function, represents the number of sampling moments within the effective period.

11. The method for optimizing the distribution network side configuration based on the photovoltaic output characteristics according to claim 1, wherein The specific formula for establishing the objective function is as follows: ; where represents the objective function; is the theoretical capacity demand at the t-th sampling moment within the effective period, is for the discharge power of the energy storage device at the sampling moment, is for the charging power of the energy storage device at the sampling moment, is the number of samplings within the effective period.

12. The method for optimizing the distribution network side configuration based on the photovoltaic output characteristics according to claim 1, wherein The constraint condition is specifically that the power of the energy storage device is less than or equal to the optimal energy storage capacity.

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