Electric quantity distribution system of low-efficiency photovoltaic power station based on multi-terminal analysis

Abstract

The invention relates to the technical field of power station electric quantity allocation, in particular to an electric quantity allocation system of a low-efficiency photovoltaic power station based on multi-terminal analysis, which comprises a supervision allocation platform, an electric quantity estimation unit, a fusion analysis unit, a feedback execution unit and a power grid allocation unit, according to the method, multi-terminal acquisition and fusion analysis of multi-source heterogeneous data are carried out in two directions of combination of power generation equipment and a meteorological environment, electric energy output of a power generation system of a photovoltaic power station is estimated, a power generation estimated value is obtained, and the power generation capacity is calculated according to the power generation estimated value and an actual power generation capacity value. The power generation equipment is comprehensively analyzed to judge whether the operation performance of the power generation equipment meets requirements or not, negative factors influencing the electric energy output efficiency are synchronously found out, the operation performance of the power generation equipment is optimized and dispatched, the operation efficiency of the power generation equipment is improved, and meanwhile demand analysis is carried out on a supply area of a photovoltaic power station. And electric quantity allocation control of the photovoltaic power station can be carried out according to the power generation pre-estimation condition and demand analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power station power allocation, and more specifically, to a power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis. Background Art

[0002] As the world's energy shortage problem becomes increasingly serious, solar energy, as a recognized alternative energy source, is being used more and more widely, and solar photovoltaic power generation is gradually becoming mainstream. However, current photovoltaic power stations are prone to power generation efficiency falling short of expectations due to equipment, environmental, management or technical problems. This is because the supply of solar energy is affected by factors such as weather and season, and power generation performance is affected by the operating status of the equipment.

[0003] Especially for inefficient photovoltaic power stations, if multi-source heterogeneous data cannot be collected and integrated for analysis in a timely manner, it will lead to defects in meteorological coupling prediction and insufficient perception of equipment status. As a result, it is impossible to perform differentiated power regulation based on the current characteristics of photovoltaic energy storage and the load demand of the power consumption area. The overall energy allocation is not reasonable and sufficient. To this end, the following technical solution is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an electricity allocation system for inefficient photovoltaic power stations based on multi-terminal analysis. It aims to solve the problem of power generation loss in inefficient photovoltaic power stations through multi-terminal data collection, intelligent analysis and optimized scheduling of power supply areas, thereby improving overall power generation efficiency and economic benefits.

[0005] The object of the present invention can be achieved by the following technical solutions: an electricity dispatching system for inefficient photovoltaic power stations based on multi-terminal analysis, comprising a supervision and dispatching platform, an electricity estimation unit, a fusion analysis unit, a feedback execution unit, and a grid dispatching unit; The supervision and coordination platform sets a supervision cycle of T days and marks the solar photovoltaic power generation equipment in the power station as power generation object i; The power estimation unit is used to collect the equipment operation data of the power generation object in the operating state, the meteorological impact data of the environment in which it is located, and the actual power generation capacity value during the supervision period, and perform power generation estimation analysis to obtain the power generation estimation value, and then send the power generation estimation value and the actual power generation capacity value to the fusion analysis unit; The fusion analysis unit conducts a comprehensive analysis on the power generation object to determine whether its operating performance meets the requirements, generates a normal operation signal and an inefficient operation signal, and sends the inefficient operation signal to the feedback execution unit; After receiving the inefficient operation signal, the feedback execution unit marks the power generation object corresponding to the inefficient operation signal as an inefficient operation object, collects the influencing factors of the inefficient operation object, analyzes the inefficient operation situation according to the influencing factors, and makes adjustments; The grid dispatch unit is used to collect the load data of the supply area corresponding to the power station stored in the supervision and dispatch platform, obtain the load forecast curve based on the load data and future meteorological data analysis, and simultaneously obtain the estimated power generation curve, and perform power optimization dispatch analysis based on the load forecast curve and the estimated power generation curve.

[0006] As a preferred embodiment of the present invention, the process of obtaining the power generation estimate includes: The power estimation unit collects the radiation intensity obtained by the power generation object on a single day during the supervision period, divides the radiation stage in which the radiation intensity exceeds the preset effective sunlight intensity threshold into the peak radiation stage, obtains the average radiation intensity in the peak radiation stage, marked as the peak radiation intensity, obtains the total duration in the peak radiation stage, marked as the peak radiation duration, and the component operating power value in the peak radiation stage. The radiation intensity and peak radiation duration are summarized as meteorological impact data, and the component operating power value is summarized as equipment operation data; The peak irradiation intensity, peak irradiation duration and component operating power values ​​are substituted into the preset power generation estimate ratio model to calculate the average daily estimated power generation. The sum of all daily average estimated power generation within the regulatory period is calculated to obtain the power generation estimate value.

[0007] As a preferred embodiment of the present invention, the process of performing a comprehensive operation analysis on a power generation object to determine whether its operating performance meets the requirements is as follows: The power estimation unit collects the actual power generation capacity value generated by the power generation object after a T-day supervision cycle, calculates the difference between the power generation estimation value and the actual power generation capacity value to obtain the actual floating deviation value, and compares the actual floating deviation value with the preset power floating threshold value; When the actual floating deviation value is less than the preset power floating threshold, it is judged that the operating performance of the power generation object within the supervision cycle meets the requirements and a normal operation signal is generated. Otherwise, it is judged that the operating performance of the power generation object within the supervision cycle does not meet the requirements and an inefficient operation signal is generated.

[0008] As a preferred embodiment of the present invention, the process of obtaining the influencing factors is as follows: The component temperature of the inefficient object and the ambient temperature of the environment are collected, and the ratio of the component temperature to the ambient temperature is calculated to obtain the component overheat coefficient. The inverter conversion efficiency of the inefficient object is collected, and the ratio of the inverter conversion efficiency to the preset inverter conversion efficiency threshold is calculated to obtain the inverter conversion floating coefficient. The invalid coverage rate of the inefficient object is also collected. The invalid coverage rate includes the dust coverage rate and the hot spot coverage rate, among which the ambient temperature, inverter conversion efficiency, and invalid coverage rate are summarized as influencing factors.

[0009] As a preferred embodiment of the present invention, the process of analyzing and regulating the inefficient operation is as follows: The component overheat coefficient, inverter conversion floating coefficient and invalid coverage rate are compared with the preset component overheat coefficient threshold, inverter conversion floating coefficient threshold and minimum invalid coverage rate threshold respectively. When the component overheat coefficient is greater than the component overheat coefficient threshold, a component overheat signal is generated. When the inverter conversion floating coefficient is less than the inverter conversion floating coefficient threshold, an inverter inefficient conversion signal is generated. When the invalid coverage rate is greater than the minimum invalid coverage rate threshold, a component invalid irradiation signal is generated.

[0010] As a preferred embodiment of the present invention, the process of the power grid dispatching unit performing power optimization dispatching analysis is as follows: During the regulatory period, load data for rigid load and adjustable load in the supply area are collected, and rigid load curves and adjustable load curves are obtained respectively. The load data within the regulatory period is used as historical data and combined with meteorological data to input the preset load demand model to predict the load demand of the next regulatory period, and load forecast data for rigid load and adjustable load are obtained, and rigid load forecast curves and adjustable load forecast curves are obtained respectively.

[0011] As a preferred embodiment of the present invention, the average daily estimated power generation of the future regulatory period is obtained, and the estimated power generation curve is drawn. The estimated power generation curve, the rigid load forecast curve and the adjustable load forecast curve are uniformly drawn on the horizontal axis in the same coordinate system. The load forecast data of the rigid load and the adjustable load in the regulatory time period and the average daily estimated power generation are compared. When the average daily estimated power generation is greater than the total value of the load forecast data of the rigid load and the adjustable load, an electric energy surplus signal is generated, otherwise an electric energy gap signal is generated.

[0012] As a preferred embodiment of the present invention, when an electric energy surplus signal is generated, the supervision period corresponding to the generation of the electric energy surplus signal is marked as a surplus period. During the surplus period, the excess electricity is stored in the energy storage system. The supervision period corresponding to the generation of the electric energy deficit signal is marked as a gap period. During the gap period, the stored energy is released to make up for the shortage, or electricity is purchased from the power grid, while giving priority to meeting rigid load demands.

[0013] Compared with the prior art, the advantages of the present invention are: 1. This solution focuses on the integration of power generation equipment and the meteorological environment. It collects and integrates multi-source heterogeneous data from multiple terminals, conducts multi-terminal analysis, and estimates the power output of the photovoltaic power station's power generation system to obtain an estimated power generation value. Based on the estimated power generation value and the actual power generation capacity, it conducts a comprehensive analysis of the power generation equipment to determine whether its operating performance meets requirements. It also identifies negative factors affecting power output efficiency and improves equipment status perception. This allows for optimized scheduling of power generation equipment performance, achieving a transformation and upgrade of photovoltaic power stations from passive response to active predictive scheduling, thereby improving the operating efficiency of power generation equipment and increasing power storage. 2. This solution also collects load data from the supply areas corresponding to power stations stored in the supervision and dispatching platform, integrates historical compliance data with future meteorological data, conducts demand analysis on the areas supplied by photovoltaic power stations, improves the accuracy of load forecasting, and conducts power dispatch and control of photovoltaic power stations based on photovoltaic energy storage estimates and load forecast analysis, thus solving the defects of traditional system data fragmentation and static regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is an overall principle block diagram of Example 1 of the present invention; Figure 2 This is a principle block diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work shall fall within the scope of protection of the present invention.

[0016] Example 1: The present invention discloses a power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis. Figure 1 , including the supervision and dispatching platform, the power estimation unit, the fusion analysis unit, the feedback execution unit and the power grid dispatching unit; The supervision and coordination platform sets a supervision cycle of T days and marks the solar photovoltaic power generation equipment in the power station as power generation object i.

[0017] The power estimation unit is used to collect the equipment operation data of the power generation object in the operating state, the meteorological impact data of the environment, and the actual power generation capacity value during the supervision period, and perform power generation estimation analysis to obtain the power generation estimation value; The process of obtaining power generation estimates includes: The power estimation unit collects the radiation intensity obtained by the power generation object on a single day during the supervision period, classifies the radiation stage in which the radiation intensity exceeds the preset effective sunlight intensity threshold as the peak radiation stage, obtains the average radiation intensity in the peak radiation stage, marked as the peak radiation intensity, obtains the total duration in the peak radiation stage, marked as the peak radiation duration, and the component operating power value in the peak radiation stage. The radiation intensity and peak radiation duration are summarized as meteorological impact data; Module operating power values ​​are summarized as equipment operating data. Solar irradiance is the primary factor affecting photovoltaic power generation and is positively correlated with the output power of photovoltaic modules. The greater the irradiance intensity, the more energy the photovoltaic cells of the power generation equipment absorb, and the higher the power generation. The peak irradiance duration (i.e., the average daily effective power generation time) is closely related to power generation. Research shows that power generation trends are highly consistent with peak sunshine hours, but less correlated with total sunshine hours. This is because peak sunshine hours more directly reflect the duration of periods of high irradiation. Therefore, peak irradiation intensity, peak irradiation duration, and module operating power values ​​are used as direct factors to ideally determine the estimated daily power generation. Substitute the peak irradiation intensity, peak irradiation duration, and module operating power values ​​into the preset power generation estimation ratio model to calculate the average daily estimated power generation. Sum all the daily average estimated power generation within the regulatory period to obtain the power generation estimate value. The power generation estimation proportional model is a standard model for power generation estimation that converts key factors affecting power generation efficiency into quantifiable proportional coefficients. For example, the standard daily power generation can be obtained based on the coefficient relationship of the collected peak irradiation intensity, peak irradiation duration and component operating power value. Therefore, the average daily estimated power generation can be obtained in real time through the common proportional model relationship, and the power generation estimate value and actual power generation capacity value are sent to the fusion analysis unit.

[0018] The fusion analysis unit receives the power generation estimate and actual power generation capacity value, and performs a comprehensive analysis on the power generation object to determine whether its operating performance meets the requirements. The specific analysis and judgment process is as follows: The power estimation unit collects the actual power generation capacity value generated by the power generation object after a T-day supervision cycle, calculates the difference between the power generation estimation value and the actual power generation capacity value to obtain the actual floating deviation value, and compares the actual floating deviation value with the preset power floating threshold value; When the actual floating deviation value is less than the preset power floating threshold, it is judged that the operating performance of the power generation object within the supervision period meets the requirements and a normal operation signal is generated. Otherwise, it is judged that the operating performance of the power generation object within the supervision period does not meet the requirements and an inefficient operation signal is generated. The larger the actual floating deviation value, the greater the difference between the actual power generation capacity value and the ideal power generation estimate value. The poor operating performance of the power generation object leads to a small actual power generation capacity value, and the power estimation unit sends the inefficient operation signal to the feedback execution unit.

[0019] After receiving the inefficient operation signal, the feedback execution unit marks the power generation object corresponding to the inefficient operation signal as an inefficient operation object, collects the influencing factors of the inefficient operation object, analyzes the inefficient operation situation according to the influencing factors, and makes adjustments; The process of obtaining the influencing factors is as follows: collecting the component temperature of the inefficient object and the ambient temperature of the environment in which it is located, calculating the ratio of the component temperature to the ambient temperature to obtain the component overheating coefficient, collecting the inverter conversion efficiency of the inefficient object, calculating the ratio of the inverter conversion efficiency to the preset inverter conversion efficiency threshold to obtain the inverter conversion floating coefficient, and collecting the invalid coverage rate of the inefficient object; Ineffective coverage includes dust coverage and hot spot coverage. Infrared thermal imaging devices are deployed to capture images of photovoltaic panels with low efficiency, identify areas covered by dust and hot spots, and perform area extraction and analysis to determine dust coverage and hot spot coverage. Ambient temperature, inverter conversion efficiency, and ineffective coverage are considered as influencing factors. The process of analyzing and regulating the inefficiency of operation is as follows: comparing the component overheating coefficient, the inverter conversion floating coefficient, and the invalid coverage rate with the preset component overheating coefficient threshold, the inverter conversion floating coefficient threshold, and the minimum invalid coverage rate threshold respectively; When the component overheat coefficient is greater than the component overheat coefficient threshold, a component overheat signal is generated; When the inverter conversion floating coefficient is less than the inverter conversion floating coefficient threshold, an inverter inefficient conversion signal is generated; When the invalid coverage rate is greater than the minimum invalid coverage rate threshold, a component invalid irradiation signal is generated; Collect the factors affecting inefficient operation, analyze each factor, identify the negative factors affecting power output efficiency, improve equipment status perception, and optimize the performance of power generation equipment to increase power storage; The process of optimizing the performance of power generation equipment can be specifically as follows: When a component overheating signal is generated, it indicates that the efficiency of the photovoltaic components of the inefficient power generation object decreases with increasing temperature, and the component overheats due to poor heat dissipation. The optimization method is to use external means (adding fans, cooling mechanisms) to improve the heat dissipation of the components and optimize the component efficiency. When an inverter inefficient conversion signal is generated, it indicates that the inverter conversion efficiency is low or faulty (such as poor heat dissipation, equipment aging), which directly affects energy storage. The optimization method is to replace the inverter. When a component invalid illumination signal is generated, the dust and hot spots on the photovoltaic panels of the inefficient power generation object are processed, the effective radiation area is expanded, and systematic optimization is carried out through multi-dimensional measures such as heat dissipation design, temperature control technology, and equipment operation and maintenance monitoring.

[0020] Example 2: Please refer to Figure 2 The grid dispatch unit is used to collect load data of the supply area corresponding to the power station stored in the supervision and dispatch platform, obtain the load forecast curve based on the load data and future meteorological data, and simultaneously obtain the estimated power generation curve, and perform power optimization and dispatch analysis based on the load forecast curve and the estimated power generation curve; During the regulatory period, load data for rigid loads (residential, hospital) and adjustable loads (industrial, commercial) in the supply area are collected to obtain rigid load curves and adjustable load curves respectively. The load data during the regulatory period is used as historical data and combined with meteorological data to input the preset load demand model to predict the load demand for the next regulatory period. Meteorological data includes radiation intensity, ambient temperature, etc. The load demand model here, such as a deep learning model, is a standard model for load demand estimation established by converting key factors affecting load demand into quantifiable proportional coefficients. It is suitable for processing the long-term dependence of load sequences and the temporal characteristics of meteorological data. It combines meteorological data as an exogenous variable to obtain load forecast data for rigid loads and adjustable loads. Based on multiple sets of load forecast data for rigid loads and multiple sets of load forecast data for adjustable loads, rigid load forecast curves and adjustable load forecast curves are obtained respectively. Obtain the average daily estimated power generation for the future regulatory period. The future average daily estimated power generation is calculated based on the peak irradiation intensity and peak irradiation duration obtained based on future meteorological data, combined with the component operating power value, and substituted into the preset power generation estimation ratio model. Draw the estimated power generation curve, and draw the estimated power generation curve, rigid load forecast curve, and adjustable load forecast curve on the same coordinate system on the horizontal axis. Compare the load forecast data of the rigid load and adjustable load within the regulatory period with the average daily estimated power generation. When the average daily estimated power generation is greater than the total value of the load forecast data of the rigid load and adjustable load, an energy surplus signal is generated; otherwise, an energy deficit signal is generated. When an energy surplus signal is generated, the regulatory period corresponding to the generation of the energy surplus signal will be marked as a surplus period. During the surplus period, the excess electricity will be stored in the energy storage system as a backup power source. The regulatory period corresponding to the generation of the energy deficit signal will be marked as a gap period. During the gap period, the stored energy will be released to make up for the shortfall, or electricity will be purchased from the grid, while giving priority to meeting rigid load demands.

[0021] This solution involves multiple parameter thresholds. It should be noted that the thresholds or preset values, preset ranges, etc. are set for result comparison and analysis in order to determine whether they are good or bad. The values ​​of these thresholds are set for entry and storage based on a combination of large-scale model analysis of sample data and manual experience. Appropriate adjustments can also be made based on seasonal or common-sense influencing conditions.

[0022] In summary, the system collects and integrates multi-source heterogeneous data from two perspectives: the power generation equipment itself and the meteorological environment. This allows for an estimate of the power output of the photovoltaic power station's power generation system, resulting in an estimated power generation value. Based on this estimated power generation value and the actual power generation capacity, a comprehensive analysis of the power generation equipment is conducted to determine whether its operating performance meets requirements. This also identifies negative factors that affect power output efficiency, improves equipment status perception, and optimizes the scheduling of power generation equipment performance. This enables the transformation and upgrade of photovoltaic power stations from passive response to active predictive scheduling, thereby improving the operating efficiency of power generation equipment and increasing power storage capacity. Collect and store load data of the supply area corresponding to the power station in the supervision and dispatching platform, integrate historical compliance data with future meteorological data, conduct demand analysis on the supply area of ​​the photovoltaic power station, improve the accuracy of load forecasting, and carry out power dispatch control of the photovoltaic power station based on the photovoltaic energy storage estimation and load forecast analysis, thus solving the defects of traditional system data fragmentation and static regulation.

[0023] The above description is only a preferred specific embodiment of the present invention; however, the protection scope of the present invention is not limited thereto; any technician familiar with the technical field within the technical scope disclosed by the present invention; any equivalent replacement or change based on the technical solution and improved conception of the present invention shall be covered within the protection scope of the present invention.

Claims

1. An energy dispatching system for inefficient photovoltaic power stations based on multi-terminal analysis, characterized by: It includes a supervision and dispatching platform, a power estimation unit, a fusion analysis unit, a feedback execution unit, and a power grid dispatching unit; The supervision and coordination platform sets a supervision cycle of T days and marks the solar photovoltaic power generation equipment in the power station as power generation object i; The power estimation unit is used to collect the equipment operation data of the power generation object in the operating state, the meteorological impact data of the environment in which it is located, and the actual power generation capacity value during the supervision period, perform power generation estimation analysis, obtain the power generation estimation value, and send the power generation estimation value and the actual power generation capacity value to the fusion analysis unit; The fusion analysis unit conducts a comprehensive analysis on the power generation object to determine whether its operating performance meets the requirements, generates a normal operation signal and an inefficient operation signal, and sends the inefficient operation signal to the feedback execution unit; After receiving the inefficient operation signal, the feedback execution unit marks the power generation object corresponding to the inefficient operation signal as an inefficient operation object, collects the influencing factors of the inefficient operation object, analyzes the inefficient operation situation according to the influencing factors, and makes adjustments; The grid dispatch unit is used to collect the load data of the supply area corresponding to the power station stored in the supervision and dispatch platform, obtain the load forecast curve based on the load data and future meteorological data analysis, and simultaneously obtain the estimated power generation curve, and perform power optimization and dispatch analysis based on the load forecast curve and the estimated power generation curve.

2. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 1 is characterized by: The process of obtaining power generation estimates includes: The power estimation unit collects the radiation intensity obtained by the power generation object on a single day during the supervision period, divides the radiation stage in which the radiation intensity exceeds the preset effective sunlight intensity threshold into the peak radiation stage, obtains the average radiation intensity in the peak radiation stage, marked as the peak radiation intensity, obtains the total duration in the peak radiation stage, marked as the peak radiation duration, and the component operating power value in the peak radiation stage. The radiation intensity and peak radiation duration are summarized as meteorological impact data, and the component operating power value is summarized as equipment operation data; The peak irradiation intensity, peak irradiation duration and component operating power values ​​are substituted into the preset power generation estimate ratio model to obtain the average daily estimated power generation. The sum of all the average daily estimated power generation within the regulatory period is calculated to obtain the power generation estimate value.

3. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 2 is characterized by: The process of conducting a comprehensive analysis of the operation of a power generation object to determine whether its operating performance meets the requirements is as follows: The power estimation unit collects the actual power generation capacity value generated by the power generation object after a T-day supervision cycle, calculates the difference between the power generation estimation value and the actual power generation capacity value to obtain the actual floating deviation value, and compares the actual floating deviation value with the preset power floating threshold value; When the actual floating deviation value is less than the preset power floating threshold, it is judged that the operating performance of the power generation object within the supervision cycle meets the requirements and a normal operation signal is generated. Otherwise, it is judged that the operating performance of the power generation object within the supervision cycle does not meet the requirements and an inefficient operation signal is generated.

4. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 3 is characterized by: The process of obtaining influencing factors is as follows: The feedback execution unit collects the component temperature of the inefficient object and the ambient temperature of the environment in which it is located, calculates the ratio of the component temperature to the ambient temperature to obtain the component overheating coefficient, collects the inverter conversion efficiency of the inefficient object, calculates the ratio of the inverter conversion efficiency to the preset inverter conversion efficiency threshold to obtain the inverter conversion floating coefficient, and collects the invalid coverage rate of the inefficient object, the invalid coverage rate includes the dust coverage rate and the hot spot coverage rate, among which the ambient temperature, inverter conversion efficiency, and invalid coverage rate are summarized as influencing factors.

5. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 4 is characterized by: The process of analyzing and regulating operational inefficiencies is as follows: The component overheat coefficient, inverter conversion floating coefficient and invalid coverage rate are compared with the preset component overheat coefficient threshold, inverter conversion floating coefficient threshold and minimum invalid coverage rate threshold respectively. When the component overheat coefficient is greater than the component overheat coefficient threshold, a component overheat signal is generated. When the inverter conversion floating coefficient is less than the inverter conversion floating coefficient threshold, an inverter inefficient conversion signal is generated. When the invalid coverage rate is greater than the minimum invalid coverage rate threshold, a component invalid irradiation signal is generated.

6. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 5 is characterized by: The process of power grid dispatching unit performing power optimization dispatching analysis is as follows: During the regulatory period, load data for rigid load and adjustable load in the supply area are collected, and rigid load curves and adjustable load curves are obtained respectively. The load data within the regulatory period is used as historical data and combined with meteorological data to input the preset load demand model to predict the load demand of the next regulatory period, and load forecast data for rigid load and adjustable load are obtained, and rigid load forecast curves and adjustable load forecast curves are obtained respectively.

7. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 6 is characterized by: Obtain the average daily estimated power generation for the future regulatory period and draw the estimated power generation curve. Draw the estimated power generation curve, rigid load forecast curve, and adjustable load forecast curve on the same coordinate system on the horizontal axis. Compare the load forecast data of the rigid load and adjustable load during the regulatory period with the average daily estimated power generation. When the average daily estimated power generation is greater than the total value of the load forecast data of the rigid load and adjustable load, an energy surplus signal is generated; otherwise, an energy deficit signal is generated.

8. The power allocation system for low-efficiency photovoltaic power stations based on multi-terminal analysis according to claim 7 is characterized by: When an energy surplus signal is generated, the regulatory period corresponding to the generation of the energy surplus signal will be marked as a surplus period. During the surplus period, the excess electricity will be stored in the energy storage system. The regulatory period corresponding to the generation of the energy deficit signal will be marked as a deficit period. During the deficit period, the energy storage electricity will be released to make up for the shortage, or electricity will be purchased from the power grid, while giving priority to meeting the rigid load demand.

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