Distribution network side configuration optimization method based on photovoltaic output characteristics

Abstract

The present application relates to the field of electric power technology, and specifically to a method for optimizing the configuration of a distribution network based on photovoltaic output characteristics. The method comprises: weighted optimization of photovoltaic power generation data using environmental parameters, and real-time prediction of photovoltaic output characteristics and grid load based on the optimization results. The optimal capacity of the energy storage device is then determined based on the prediction results of the two combined with the current charge and discharge efficiency of the energy storage device. Finally, the scheduling strategy of the energy storage system is optimized based on the characteristic differences between the grid load and the photovoltaic output. The present application optimizes the capacity configuration and scheduling strategy of the energy storage device by improving the prediction accuracy of the photovoltaic output characteristics, which not only ensures the maximum utilization of the energy storage device, but also improves the stability of the distribution system.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a distribution network side configuration optimization method based on photovoltaic output characteristics. Background Art

[0002] The distribution side of the power grid, as a highly flexible grid system close to users, can maximize the absorption of photovoltaic power. However, due to the volatility of photovoltaic output power, distribution side configuration optimization methods based on photovoltaic output characteristics require the configuration of energy storage devices of reasonable capacity in the system to ensure power supply reliability and enable regulation during peak shaving and valley filling. While the introduction of energy storage can reduce the photovoltaic curtailment rate, energy storage costs are high, and excessive capacity configuration can affect the economic viability of microgrids. Therefore, configuring appropriate energy storage capacity and scheduling strategies is crucial.

[0003] Currently, configuration optimization methods for distribution networks typically rely on environmental and historical data to forecast PV power generation from both short-term and long-term perspectives. In historical data forecasting methods, the short-term characteristics of environmental data and the long-term characteristics of historical data are inconsistent in time. Therefore, directly combining the two to predict PV power generation will result in large prediction errors, which in turn leads to deviations in energy storage device configuration. Furthermore, the inherent uncertainty of load and PV output forecasts further exacerbates errors in the optimal configuration of energy storage devices. Summary of the Invention

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

[0005] One embodiment of the present application provides a method for optimizing distribution network configuration based on photovoltaic output characteristics, the method comprising:

[0006] Analyze all environmental parameters at each sampling moment to obtain environmental factors; analyze the changing trends of environmental factors and predict the environmental factors at the next moment; obtain the theoretical prediction value of photovoltaic power generation based on the historical power of photovoltaic power generation, and compare the environmental factors with the predicted values ​​to obtain the actual prediction value of photovoltaic power generation;

[0007] Determine the power generation forecast value at the corresponding sampling moment based on the actual predicted value of photovoltaic power generation; predict the grid load value at the next moment based on historical grid load demand data, and compare it with the power generation forecast value to determine the theoretical capacity requirement of the energy storage device at the next moment; calculate the comprehensive charge and discharge efficiency of the energy storage device based on the electric energy distribution of the energy storage device during the charging and discharging process; analyze the distribution of the theoretical capacity demand and the comprehensive charge and discharge efficiency within a preset effective period to obtain the optimal energy storage capacity;

[0008] Based on the theoretical capacity requirements and charge and discharge power of the energy storage device, an objective function is established; based on the optimal energy storage capacity, the constraints of the objective function are determined, and the objective function is solved to optimize the distribution network side configuration.

[0009] Preferably, the environmental parameters specifically include light intensity, temperature and cloud thickness.

[0010] Preferably, the environmental factors are:

[0011] The forward fusion result of the light intensity and temperature at each sampling moment is obtained, and the product of the negative correlation map of the cloud thickness at each sampling moment and the forward fusion result is used as the environmental action factor at each sampling moment.

[0012] Preferably, the prediction of the environmental factors at the next moment is specifically as follows:

[0013] The environmental factors at the current sampling moment and all previous sampling moments are arranged in time sequence to form an environmental action sequence, and the element mean of the first-order difference sequence of the environmental action sequence is obtained; the sum of the environmental factor at the current sampling moment and the element mean is used as the predicted value of the environmental factor at the next sampling moment.

[0014] Preferably, the actual predicted value of photovoltaic power generation at the next moment is specifically:

[0015] Calculate the ratio of the environmental action factor prediction value at the next moment to the environmental action factor at the current moment; and use the forward fusion result of the ratio and the theoretical prediction value of the photovoltaic power generation power at the next moment as the actual prediction value of the photovoltaic power generation power at the next moment.

[0016] Preferably, the actual predicted value of photovoltaic power generation power is specifically the product of the ratio and the theoretical predicted value.

[0017] Preferably, the determination of the theoretical capacity requirement of the energy storage device at the next moment is specifically the difference between the power generation forecast value of the power grid at the next moment and the power grid load forecast value.

[0018] Preferably, the process of calculating the comprehensive charge and discharge efficiency of the energy storage device is:

[0019] The ratio between the actual stored electric energy of the energy storage device and the actual input electric energy is taken as the charging efficiency of the energy storage device;

[0020] The ratio between the effective electric energy provided by the energy storage device to the grid and the total electric energy released is taken as the discharge efficiency of the energy storage device;

[0021] The product of the charging efficiency and discharging efficiency of the energy storage device is taken as the comprehensive charging and discharging efficiency of the energy storage device.

[0022] Preferably, the specific process of obtaining the optimal energy storage capacity is:

[0023] The maximum value after forward fusion of the theoretical capacity requirement of the energy storage device at each sampling moment within the effective period and the comprehensive charge and discharge efficiency is taken as the optimal energy storage capacity within the effective period.

[0024] Preferably, the formula for the optimal energy storage capacity is: Where, Indicates the optimal energy storage capacity of the power grid during the effective period; is the theoretical capacity requirement at the tth sampling moment within the effective period, is the comprehensive charge and discharge efficiency of the energy storage device, max{} represents the maximum value function, Indicates the number of sampling moments within the valid period.

[0025] Preferably, the specific formula for establishing the objective function is: Where, represents the objective function; is the theoretical capacity requirement at the tth sampling moment within the effective period, For the The discharge power of the energy storage device at each sampling moment, For the The charging power of the energy storage device at each sampling moment, is the number of samples within the valid period.

[0026] 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.

[0027] This application has at least the following beneficial effects:

[0028] This application addresses the challenges in photovoltaic power generation analysis. Traditional methods directly integrate long-term and short-term characteristics, which often leads to inaccurate predictions of photovoltaic changes, thereby affecting 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, which not only ensures the maximum utilization of the energy storage device, but also improves the stability of the distribution system. First, all environmental parameters at each sampling moment are analyzed to obtain environmental factors, which helps to analyze the degree of influence of environmental conditions on photovoltaic power generation; since environmental factors have a more significant impact on short-term changes in photovoltaic output characteristics, the prediction results of photovoltaic power generation can be adjusted according to the environmental change trend at future moments, so the changing trend of the environmental factor is analyzed and the environmental factor at the next moment is predicted; based on the historical power of photovoltaic power generation, the theoretical prediction value of photovoltaic power generation is obtained, and the environmental factor and its predicted value are compared to obtain the actual prediction value of photovoltaic power generation. When predicting photovoltaic power generation, the impact of the environment on photovoltaic power generation is considered to make the prediction result more accurate.

[0029] Furthermore, based on the historical grid load demand data, the grid load value at the next moment is predicted and compared with the power generation forecast value to determine the theoretical capacity demand of the energy storage device at the next moment. According to the theoretical capacity demand, the charging and discharging strategy of the energy storage device can be more reasonably dispatched to ensure that the difference between the grid load and the energy storage system is minimized, thereby avoiding power surplus or shortage and improving the stability and efficiency of the power system; based on the electric energy distribution of the energy storage device during the charging and discharging process, the comprehensive charging and discharging efficiency of the energy storage device is calculated; the distribution of the theoretical capacity demand and the comprehensive charging and discharging efficiency within a preset effective period is analyzed 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, and reduce energy loss by adjusting the operating strategy, thereby improving the overall energy utilization of the system.

[0030] Finally, an objective function is established based on the theoretical capacity requirements and charge and discharge power of the energy storage device. The constraints of the objective function are determined based on the optimal energy storage capacity, and the objective function is solved to optimize the distribution network configuration. The objective function optimization process optimizes the capacity configuration and scheduling strategy of the energy storage device by improving the prediction accuracy of photovoltaic output characteristics, which 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Flowchart of the distribution network side configuration optimization method based on photovoltaic output characteristics provided in this application;

[0032] Figure 2This is a flow chart for obtaining the optimal energy storage capacity of the energy storage device provided in this application. DETAILED DESCRIPTION

[0033] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. 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.

[0035] It should also 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 precedence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of protection of this application, the order of execution of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0037] This application proposes a distribution network side configuration optimization method based on photovoltaic output characteristics, which is applied to the field of power technology. Figure 1 , the method comprises the following steps:

[0038] S1: Collect environmental parameters and grid power generation data at each sampling moment and monitor the charging and discharging capacity of the energy storage device.

[0039] A typical photovoltaic distribution network system primarily consists of photovoltaic arrays, energy storage devices, inverters, photovoltaic and energy storage transformers, a distribution network, and a supporting central control system and user-side demand management system. The central control system manages and coordinates the operation of all relevant equipment and systems through centralized data collection, processing, and analysis, ensuring system safety, stability, and efficiency. The user demand management system dynamically adjusts power consumption in response to grid dispatch and user demand, enabling coordinated and optimized operation of the grid and photovoltaic system.

[0040] Currently, when optimizing the configuration of the photovoltaic distribution network, the difference in time dimensions between the short-term characteristics of environmental data and the long-term characteristics of historical photovoltaic power generation data can lead to further errors in optimizing the energy storage device configuration. To improve the accuracy of energy storage device optimization, this application first uses a central control system to obtain power generation data and grid load data at each sampling time over the past month. The power generation data in this application includes, but is not limited to, instantaneous power and frequency.

[0041] At the same time, external weather and environmental 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 time within 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 are collected synchronously, and the sampling interval is set to 2 hours to monitor the charging and discharging capacity of the energy storage device, so as to facilitate the subsequent optimization of the capacity and scheduling strategy of the energy storage device on the storage and distribution network side based on the correlation of different data.

[0042] S2: Analyze all environmental parameters at each sampling moment to obtain environmental factors; analyze the changing trends of environmental factors and predict the environmental factors at the next moment; obtain the theoretical prediction value of photovoltaic power generation based on the historical power of photovoltaic power generation, and compare the environmental factors with the predicted values ​​to obtain the actual prediction value of photovoltaic power generation.

[0043] In photovoltaic distribution networks, the stable operation of grid-side energy storage devices generally depends on the characteristics of photovoltaic output. However, due to environmental factors, photovoltaic output characteristics are intermittent and fluctuating, resulting in significant discrepancies between the energy storage device configuration and actual demand. To reduce this discrepancy, this application predicts photovoltaic output characteristics based on the fluctuating characteristics of environmental factors.

[0044] The efficiency of photovoltaic power generation is affected by the ambient weather. When the light intensity is high and the cloud thickness is low, the photovoltaic power generation will receive more light energy and the power generation efficiency will be higher. On the contrary, the photovoltaic power generation efficiency will be lower. However, due to the strong real-time and irregular nature of the ambient weather, in order to improve the prediction accuracy of photovoltaic output characteristics, all environmental parameters at each sampling moment are first analyzed to obtain the environmental effect factor. Specifically, the forward fusion result of the light intensity and temperature at each sampling moment is obtained, and the product of the negative correlation mapping of the cloud thickness at each sampling moment and the forward fusion result is used as the environmental effect factor at each sampling moment. In this embodiment, Where, is the environmental 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 if the environmental conditions at a sampling moment are more favorable for photovoltaic power generation, the corresponding environmental impact factor will be larger, and vice versa.

[0045] The real-time environmental factor trend represents the results of short-term environmental changes. Therefore, based on the changing trends of the environmental factors at the current sampling moment and the previous sampling moments, the environmental factors at the next moment are predicted. Specifically, the environmental factors at the current sampling moment and all previous sampling moments are arranged in time sequence to form an environmental effect sequence, and the element mean of the first-order difference sequence of the environmental effect sequence is obtained; the sum of the environmental factor at the current sampling moment and the element mean is used as the predicted environmental factor value at the next sampling moment. It should be understood that the element mean represents the trend of environmental changes within the sampling period. Therefore, combining this trend with the environmental factor at the current moment can produce the predicted environmental effect value at the next moment. The larger the value, the more favorable the environmental trend is for photovoltaic power generation.

[0046] Because environmental factors significantly influence short-term changes in PV output characteristics, PV power generation forecasts can be adjusted based on future environmental trends. Specifically, when environmental trends favor PV power generation, the future power generation forecast should be increased; when environmental trends are unfavorable, the forecast should be decreased.

[0047] Here, the Long Short-Term Memory (LSTM) network is used to obtain the theoretical prediction value of photovoltaic power generation based on the historical power of photovoltaic power generation. The current environmental impact factor is compared with the predicted environmental impact factor at the next moment, and combined with the theoretical predicted photovoltaic power generation value at the next moment, to obtain the actual predicted photovoltaic power generation value. Specifically, the ratio of the predicted environmental impact factor at the next moment to the current environmental impact factor is calculated; the ratio is forward-fused with the theoretical predicted photovoltaic power generation value at the next moment to obtain the actual predicted photovoltaic power generation value at the next moment. In this embodiment, the forward fusion of multiple variables uses a multiplication calculation method.

[0048] It should be understood that the larger the ratio is, the more favorable the predicted trend of environmental changes is for photovoltaic power generation, and the predicted power of photovoltaic power generation needs to be increased, and vice versa. This method combines the changing trend of environmental weather to gradually predict the photovoltaic output characteristics at future times, thereby improving the accuracy of the prediction results.

[0049] S3: Determine the power generation forecast value at the corresponding sampling moment based on the actual predicted value of photovoltaic power generation; predict the grid load value at the next moment based on historical grid load demand data, and compare it with the power generation forecast value to determine the theoretical capacity requirement of the energy storage device at the next moment; calculate the comprehensive charging and discharging efficiency of the energy storage device based on the electric energy distribution of the energy storage device during the charging and discharging process; 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.

[0050] This application considers optimizing the configuration of the energy storage device through the prediction results of photovoltaic output characteristics to ensure the operational 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 of the energy storage device and its scheduling strategy based on the prediction results of photovoltaic output characteristics and load data.

[0051] First, determine the optimal capacity of the energy storage device. The purpose of a typical photovoltaic energy storage device is to balance the gap between photovoltaic power generation and grid load. When photovoltaic power generation is excessive, the energy storage device absorbs and stores the excess energy. When photovoltaic power generation is insufficient, the energy storage device releases the stored energy to balance the grid load and ensure stable grid operation. Therefore, for this grid's energy storage device to operate stably, its capacity must cover both the excess photovoltaic power generation and the shortfall in grid load.

[0052] Since generated power is positively correlated with generated energy, the predicted power generation value at the corresponding sampling moment is determined based on the real-time actual predicted value of photovoltaic power generation. Simultaneously, historical grid load demand data is collected. Since grid load data is generally stable, the Autoregressive Integrated Moving Average (ARIMA) model can be used to obtain the grid load forecast value for the next moment. Furthermore, the theoretical capacity requirement of the energy storage device at the next moment is determined by the difference between the predicted power generation value and the predicted grid load value. In this embodiment, the difference between the variables is calculated using the difference. It should be understood that the closer the theoretical capacity demand is to 0, the closer the generation and consumption of electricity at the corresponding moment is to equilibrium. Conversely, if the capacity demand is far from 0, it indicates a large discrepancy between the generation and consumption of electricity, and this imbalance of electricity needs to be regulated and supplied by the energy storage device.

[0053] At the same time, the charge and discharge efficiency of the energy storage device also determines its optimal capacity. The higher the charge and discharge efficiency, the less energy the energy storage device loses during energy storage and release, and the closer the actual capacity requirement of the energy storage device is to the theoretical capacity requirement. The comprehensive charge and discharge efficiency of this energy storage device is calculated here. Specifically, the ratio of the actual stored energy to the actual input energy is used as the energy storage device's charge efficiency; the ratio of the effective energy provided to the grid to the total energy released is used as the energy storage device's discharge efficiency; and the product of the energy storage device's charge and discharge efficiency is used as the comprehensive charge and discharge efficiency of the energy storage device.

[0054] It should be understood that a higher charging efficiency indicates a lower energy loss during charging, and the same applies to discharge efficiency. Therefore, the closer the combined charge and discharge efficiency is to 1, the better the energy storage device's charging and discharging efficiency.

[0055] In this embodiment, the effective period is set to the next week, recorded as , the implementer can adjust it according to the actual situation; the optimal energy storage capacity within the effective period is determined based on the maximum value after forward fusion of the theoretical capacity demand of the energy storage device and the comprehensive charge and discharge efficiency at each sampling moment within the effective period. In this embodiment, the forward fusion between variables adopts the multiplication calculation method, and its specific calculation formula is: Where, Indicates the optimal energy storage capacity of the power grid during the effective period; is the theoretical capacity requirement at the tth sampling moment within the effective period, is the comprehensive charge and discharge efficiency of the energy storage device. It can be expressed as the maximum theoretical capacity requirement of the energy storage device after excluding power loss during the effective period, which is used as the optimal energy storage capacity of the energy storage device during the effective period.

[0056] The flow chart for obtaining the optimal energy storage capacity of the energy storage device is as follows: Figure 2 shown.

[0057] When this effective period ends, the theoretical capacity demand for the next effective period can be predicted based on the photovoltaic output characteristics, thereby determining the optimal energy storage capacity. This allows the optimal energy storage capacity for different future effective periods to be dynamically determined for this distribution network-side energy storage device.

[0058] S4: Establish an objective function based on the theoretical capacity requirement and charge and discharge power of the energy storage device; determine the constraints of the objective function based on the optimal energy storage capacity, and solve the objective function to optimize the distribution network side configuration.

[0059] For the distribution network side, the generation, storage and consumption of electric energy is a real-time dynamic process. Therefore, the scheduling strategy of the energy storage device is a key link to ensure that the energy storage device can efficiently shave peaks and fill valleys and ensure the stability of the power system.

[0060] In the optimal scheduling of energy storage devices, load balancing optimization is an important goal in the scheduling optimization of energy storage devices. It aims to balance the difference between grid load and power generation so that the load demand of the grid is balanced, while minimizing the burden on the grid and ensuring the matching of power supply and demand.

[0061] This application sets a load balancing optimization objective function. The core of this objective function is to minimize the difference between the grid load and the charge and discharge power of photovoltaic power generation and energy storage devices. This difference represents the load level that the grid system needs to meet at a certain point in the future. Minimizing this difference means that the grid can operate in a state close to the ideal balance.

[0062] For the effective period of the power grid, the objective function can be divided into three parts: ① the residual load demand of the power grid, that is, the theoretical capacity demand; ② the rated discharge power of the energy storage device; ③ the rated charging power of the energy storage device.

[0063] The residual load demand of the power grid, that is, the difference between the load in the power grid and the actual power generation, then there are three situations: when When the load on the power grid is greater than the power generation of the system, more power is needed to supplement it; when When the load on the power grid is too low, it may cause power waste. The energy storage device can absorb the excess power through charging. When the grid load and power generation are balanced, no energy storage scheduling is required in this case.

[0064] The ultimate goal of this application is to balance the difference between the grid load and the energy storage device, and the charging and discharging power of the energy storage device directly reflects the size of this difference. Therefore, in the objective function, we only need to focus on the difference in the grid's residual load demand, without distinguishing whether it is excess power (low grid load) or insufficient power (high grid load); the key is to minimize the difference between the grid's residual load demand and the charging and discharging power of the energy storage device as much as possible, so as to achieve grid load balance. The corresponding objective function can then be expressed as: Where, represents the objective function; is the theoretical capacity requirement at the tth sampling moment within the effective period, For the The discharge power of the energy storage device at each sampling moment, For the The charging power of the energy storage device at each sampling moment, is the number of samples in the valid period.

[0065] Solve this objective function through dynamic programming so that The optimal value at each sampling moment when the minimum value is satisfied. It should be noted that the optimal energy storage capacity of the energy storage device of the power grid has been determined before. Therefore, in the solution process, the constraint condition needs to be added 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, dynamic programming to solve the objective function is an existing well-known technology, and this application will not elaborate on it.

[0066] This approach determines the optimal energy storage capacity and scheduling strategy for the distribution-side energy storage devices of this power grid, thereby reducing power losses across the entire system, achieving the optimal configuration of the energy storage device's rated capacity, and maximizing its utilization. Furthermore, energy storage devices should be optimized not only with the photovoltaic power generation system but also with other power equipment in the distribution network (such as wind power and traditional power generation facilities). By coordinating multiple energy sources and energy storage devices, the overall economic and reliability of the power grid can be optimized.

[0067] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0068] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A distribution network side configuration optimization method based on photovoltaic output characteristics, characterized in that: The method comprises the following steps: Analyze all environmental parameters at each sampling moment to obtain environmental factors, which specifically include light intensity, temperature, and cloud thickness; analyze the changing trends of environmental factors and predict the environmental factors at the next moment; obtain a theoretical prediction value of photovoltaic power generation based on the historical power of photovoltaic power generation, and calculate the ratio of the predicted value of the environmental factor at the next moment to the environmental factor at the current moment; and forward-fuse the ratio with the theoretical prediction value of photovoltaic power generation at the next moment as the actual prediction value of photovoltaic power generation at the next moment; Determine the power generation forecast value at the corresponding sampling moment based on the actual predicted value of photovoltaic power generation; predict the grid load value at the next moment based on historical grid load demand data, and compare it with the power generation forecast value to determine the theoretical capacity requirement of the energy storage device at the next moment; calculate the comprehensive charge and discharge efficiency of the energy storage device based on the electric energy distribution of the energy storage device during the charging and discharging process; analyze the distribution of the theoretical capacity demand and the comprehensive charge and discharge efficiency within a preset effective period to obtain the optimal energy storage capacity; Based on the theoretical capacity requirements and charge and discharge power of the energy storage device, an objective function is established; based on the optimal energy storage capacity, the constraints of the objective function are determined, and the objective function is solved to optimize the distribution network side configuration.

2. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The environmental factors are specifically: The forward fusion result of the light intensity and temperature at each sampling moment is obtained, and the product of the negative correlation map of the cloud thickness at each sampling moment and the forward fusion result is used as the environmental action factor at each sampling moment.

3. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The prediction of the environmental factors at the next moment is specifically as follows: The environmental factors at the current sampling moment and all previous sampling moments are arranged in time sequence to form an environmental action sequence, and the element mean of the first-order difference sequence of the environmental action sequence is obtained; the sum of the environmental factor at the current sampling moment and the element mean is used as the predicted value of the environmental factor at the next sampling moment.

4. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The actual predicted value of photovoltaic power generation is specifically the product of the ratio and the theoretical predicted value.

5. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The theoretical capacity requirement of the energy storage device at the next moment is determined as the difference between the power generation prediction value of the power grid at the next moment and the power grid load prediction value.

6. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The process of calculating the comprehensive charge and discharge efficiency of the energy storage device is as follows: The ratio between the actual stored electric energy of the energy storage device and the actual input electric energy is taken as the charging efficiency of the energy storage device; The ratio between the effective electric energy provided by the energy storage device to the grid and the total electric energy released is taken as the discharge efficiency of the energy storage device; The product of the charging efficiency and discharging efficiency of the energy storage device is taken as the comprehensive charging and discharging efficiency of the energy storage device.

7. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The specific process of obtaining the optimal energy storage capacity is as follows: The maximum value after forward fusion of the theoretical capacity requirement of the energy storage device at each sampling moment within the effective period and the comprehensive charge and discharge efficiency is taken as the optimal energy storage capacity within the effective period.

8. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 7, wherein: The formula for the optimal energy storage capacity is specifically: t∈X; where E s represents the optimal energy storage capacity of the power grid within the effective period; S(t) is the theoretical capacity requirement at the tth sampling moment within the effective period, η is the comprehensive charging and discharging efficiency of the energy storage device, max{} represents the maximum value function, and X represents the number of sampling moments within the effective period.

9. The method for optimizing the distribution network configuration based on photovoltaic output characteristics according to claim 1, wherein: The specific formula for establishing the objective function is: Where f represents the objective function; S(t) is the theoretical capacity demand at the t-th sampling moment in the effective period, P dc (t) is the discharge power of the energy storage device at the tth sampling moment, P c (t) is the charging power of the energy storage device at the t-th sampling moment, and m is the number of sampling times in the effective period.

10. The method for optimizing distribution network configuration based on 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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