Electric energy coordinated distribution method and device based on distributed photovoltaic bearing capacity prediction and medium

Abstract

The invention relates to the technical field of distributed photovoltaic, in particular to an electric energy coordinated distribution method and device based on distributed photovoltaic bearing capacity prediction and a medium, and the method comprises the steps: obtaining all power supply lines of a current photovoltaic power generation base station, so as to construct a topological relation network; sequentially obtaining historical power flow section data of a load day and percentage gradient data of historical load change trends for many years from an N-level topological node at the tail end of the topological relation network upwards to a topological starting point; counting the historical power flow section data of the load day of each power supply line and the percentage gradient data of the historical load change trend for many years, and determining the electric energy state of each power supply line; and based on the electric energy state of each power supply line, redistributing the annual total photovoltaic power generation amount. Compared with the prior art, reasonable utilization of resources is facilitated, and waste and excessive consumption of electric energy can be avoided while it can be ensured that all production units obtain enough electric energy supply.

Description

Technical Field

[0001] The present invention relates to the field of distributed photovoltaic technology, and in particular to an electric energy coordination distribution scheme based on distributed photovoltaic carrying capacity prediction. Background Art

[0002] Since photovoltaic power generation has a time characteristic, that is, because photovoltaic power generation mainly relies on sunlight, its power generation time is usually concentrated in the daytime. The power generation gradually increases after sunrise, reaches a peak at noon, and then decreases as the sun sets. Therefore, photovoltaic power generation also relies on storage media, because power generation varies with time, so if a stable power supply is needed, the electricity generated by photovoltaic power generation needs to be stored, so as to adapt to human production and peak load regulation, so as to meet human production and life needs.

[0003] In the prior art, there are ways to coordinate and manage energy storage and power supply according to the characteristics of the power generation time period, such as a method and system for controlling the grid-connected operation of a distributed microgrid disclosed in Chinese patent CN102427249B. By monitoring real-time data such as environmental parameters such as light intensity and wind speed, input power and battery terminal voltage, and combining the predicted data of power generation power and power load, the photovoltaic power generation system, the total output power of the wind turbine, the load, and the battery energy storage system are coordinated and controlled to meet the requirements of system power balance. At the same time, according to the different power consumption in different time periods, the 24-hour load forecast curve is divided into load valley value, load flat value, load peak value and load peak value, and the discharge strategy of the energy storage system is arranged according to different time periods. The charging pile sets different charging prices according to different time periods; effective control is implemented on the power equipment involved by the user, so as to achieve the purpose of smoothing the power fluctuation of the power grid, reduce the investment in power equipment, and achieve the best economic effect and energy saving effect.

[0004] In the prior art including the above-mentioned patent, energy storage is coordinated according to the time characteristics of power generation to match electricity usage habits. However, electricity usage habits are determined by two factors: production and life. In particular, production cannot form fixed electricity usage habits. This causes the method provided by the above-mentioned technology to be passive in power coordination. It cannot be flexibly coordinated according to the actual production and life electricity usage in the power supply line. Because the carrying capacity of the power supply line under photovoltaic power generation is coordinated according to the storage end, that is, it is allocated according to the already counted production units and life electricity usage, rather than adjusted according to the actual production line. Therefore, when it is found that the power supply of the power supply line is tight, in order to meet the power demand of production on the power supply line, it is often compensated by limiting life electricity consumption. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method, device and medium for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] As a first aspect of the present invention, a method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction is provided, the steps comprising:

[0008] Obtain all power supply lines of the current photovoltaic power generation base station to build a topological relationship network;

[0009] From the N-level topological node at the end of the topological relationship network to the starting point of the topology, the flow section data of the daily history of the load and the percentage change data of the historical load change trend over many years are obtained in sequence;

[0010] The power flow section data of the daily history of the load of each power supply line and the percentage variation data of the multi-year historical load variation trend are statistically analyzed to determine the power state of each power supply line;

[0011] The total photovoltaic power generation throughout the year is redistributed based on the power state of each of the power supply lines, including coordinated allocation of each power supply line and coordinated allocation of each node in the power supply line.

[0012] As an optimal technical solution, the construction of the topological relationship network is specifically as follows: obtain the same-level main transformer of each power supply line, and use multiple main transformers of the same level as the topological starting point of the power supply line, use the secondary transformer of the next level as the secondary topological node, and push down in sequence to establish the topological relationship network.

[0013] As a preferred technical solution, the load daily history flow section data is calculated as follows:

[0014] Obtain the total power P obtained by the power supply line under the history and the total power X actually consumed in the power supply line, and calculate the power difference value X ′ =PX;

[0015] The energy difference value X ′ The average monthly power consumption is obtained by dividing the window into 12 months a year;

[0016] According to the power supply line, the total amount of electric energy consumption in the same month of a predetermined number of years is obtained, and the average value is obtained to obtain the average amount of electric energy consumption for each month;

[0017] The monthly power consumption mean is subtracted from the average power consumption quota of each month to obtain a negative value and a data set of the month corresponding to the negative value.

[0018] As a preferred technical solution, the percentage change data of the multi-year historical load change trend is calculated as follows:

[0019] Obtain the total annual electricity consumption C of the same unit in the power supply line within the continuous scheduled window period;

[0020] Calculate the annual total electricity consumption increase rate D of the power supply line each year:

[0021]

[0022] In the formula, Dn is the annual total electricity consumption rate of the power supply line in the nth year; Cn is the annual total electricity consumption of the same unit in the power supply line in the nth year;

[0023] The increasing rate of the predetermined window period is obtained, and the predicted annual total electricity consumption of the power supply line in the next year is obtained according to the increasing rate.

[0024] As a preferred technical solution, the determined power state of each power supply line includes the predicted annual total power consumption of each level of topological point, and the predicted annual total power consumption of the topological node is calculated as follows:

[0025] Obtain the total annual power consumption F of the same unit in each node in the topological nodes of each level under the power supply line within the continuous predetermined window period:

[0026] Calculate the annual total electricity consumption increase rate G of the same topological node each year:

[0027]

[0028] Where Gn is the annual total electricity consumption rate of the same topological point in the nth year; Fn is the annual total electricity consumption of the same unit at the same topological node in the nth year;

[0029] The annual total electricity consumption of the same topological node in a predetermined window period is obtained at an increasing rate, and the predicted annual total electricity consumption of each topological point in the next year is obtained according to the increasing rate.

[0030] As a preferred technical solution, the same units in the annual total electricity consumption of the same units are the same production units and the same living units under each node within the predetermined window period.

[0031] As a preferred technical solution, the coordinated allocation of the power supply lines is specifically as follows:

[0032] Coordinate based on the average monthly power consumption, count the power share that is not less than the preset safety threshold, and obtain the monthly adjustable power share;

[0033] According to the negative value of the average power consumption quota of each month and the month corresponding to the negative value, the data is classified to obtain the negative value data of each power supply line in the same month;

[0034] Based on the obtained negative value data of each power supply line in the same month, the available compensation amount of the monthly adjustable electric energy share is obtained:

[0035]

[0036] In the formula, U is the compensation amount and y is the negative value data of the power supply line.

[0037] As a preferred technical solution, the coordinated allocation of each node in the power supply line is specifically as follows: the compensation amount based on the obtained monthly adjustable electric energy share is evenly allocated to the production units of each node in the power supply line; if the negative value cannot be offset, the living unit of each node compensates the difference to the production unit.

[0038] As a second aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for coordinated electric energy distribution based on distributed photovoltaic carrying capacity prediction are implemented.

[0039] As a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction are implemented.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1) The present invention can understand the power demand and power flow of topological nodes at all levels by conducting in-depth analysis of the power consumption of these topological nodes. Thus, the power consumption of topological nodes at all levels in the power supply line can be intelligently coordinated to ensure the reasonable distribution and efficient use of power during the transmission process. Such a coordination mechanism helps to achieve the overall optimization and stable operation of the power supply line, and provide more reliable and high-quality power services for power users.

[0042] 2) The present invention comprehensively obtains the daily historical load flow section data of each power supply line that records the power flow conditions in different time periods of each day, as well as the percentage variation data of the historical load change trend over many years that reveals the long-term evolution law of power demand; through in-depth analysis of these data, the actual annual and monthly total power consumption of each power supply line can be accurately calculated. On this basis, the power supply lines are coordinated, and the excess power in those power supply lines is flexibly allocated to other lines with tight power demand, thereby achieving optimal allocation and efficient use of power.

[0043] 3) The present invention can accurately evaluate the electric energy lost in the power supply process by calculating the difference between the total electric energy obtained by the power supply line in history and the total electric energy actually consumed; and divide the electric energy difference value into 12 months a year, and calculate the monthly average electric energy consumption, which is helpful to understand the electric energy loss of each month and identify the impact of seasonal or cyclical factors on electric energy loss. Secondly, by obtaining the total electric energy consumption of the same month in a predetermined number of years and calculating the average value thereof to obtain the average electric energy consumption amount for each month, it is helpful to analyze the long-term trend of electric energy consumption and understand whether there is a trend of increasing or decreasing year by year, which is conducive to providing the premise for estimating future power supply distribution. By obtaining a data set of negative values ​​and the months corresponding to the negative values, it is possible to determine which months have the largest electricity consumption and the values ​​that exceed the monthly average electric energy consumption.

[0044] 4) The present invention coordinates and allocates based on the average monthly power consumption, calculates the monthly adjustable power share, optimizes the distribution of power while ensuring the safe and stable operation of the power grid, and ensures that each power supply line can obtain sufficient power supply. By classifying and compensating the negative power consumption values ​​of each power supply line in different months, the load differences between the lines can be balanced. This helps to reduce power grid losses and voltage fluctuations caused by uneven loads and improve the overall operating efficiency of the power grid. Coordinated allocation can ensure that the power grid can quickly adjust the power distribution strategy when facing load fluctuations or emergencies to ensure the stable operation of the power grid. This helps to reduce the occurrence of power grid failures and power outages and improve power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flow chart of the method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0047] Example 1

[0048] As one embodiment of the present invention, the present invention proposes a method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction. Figure 1 As shown, the method comprises the following steps:

[0049] S01. Obtain all power supply lines of the current photovoltaic power generation base station to build a topological relationship network;

[0050] S02, obtain the same level main transformer of each power supply line, and use multiple main transformers of the same level as the topological starting point of the power supply line, and the secondary transformers of the next level are the secondary topological nodes, and push down in sequence to establish

[0051] S03, from the N-level topological node at the end of the topological relationship network to the topological starting point, sequentially obtain the daily historical flow section data of the load and the percentage change data of the historical load change trend over many years;

[0052] S04, statistically analyzing the daily historical flow section data of each power supply line and the percentage variation data of the historical load variation trend over many years to determine the power state of each power supply line;

[0053] S05. Redistribute the total historical photovoltaic power generation for the whole year based on the power status of each power supply line.

[0054] Specifically, the main transformer and the secondary transformer in the embodiment are transformers. The so-called main transformer is the first transformer of the power supply line of the photovoltaic power generation base station. The secondary transformer is a transformer located at the next level of the above main transformer.

[0055] The above technology can fully obtain the daily historical flow section data of each power supply line, which records in detail the power flow in different time periods every day. At the same time, the system can also collect the percentage variation data of the historical load change trend over the years, which reveals the long-term evolution of power demand. Through in-depth analysis of these data, the actual annual and monthly total power consumption of each power supply line can be accurately calculated. On this basis, the management system can intelligently coordinate the power supply lines and flexibly allocate the excess power in those power supply lines to other lines with tight power demand, thereby achieving optimal configuration and efficient use of power.

[0056] Embodiment 2

[0057] Based on the above-mentioned embodiment 1, the calculation of the load daily history flow section data provided in this embodiment includes:

[0058] S31, the total electric energy obtained by the power supply line in the acquisition history is P, and the total electric energy actually consumed in the power supply line is X, then the electric energy difference value X ′ =PX;

[0059] S32, the electric energy difference value X ′ The average monthly power consumption is obtained by dividing the window into 12 months a year;

[0060] S33, then obtaining the total amount of power consumption in the same month of a predetermined number of years according to the power supply line, adding up the total amount and obtaining the average value, so as to obtain the average power consumption amount for each month;

[0061] S34, subtract the monthly power consumption mean from the average power consumption quota of each month to obtain a negative value and a data set of the month corresponding to the negative value.

[0062] Specifically, by calculating the difference between the total power P obtained by the power supply line in history and the total power X actually consumed, the power lost during the power supply process can be accurately evaluated, which is crucial for the energy efficiency management of the power grid and the formulation of energy-saving and consumption-reduction strategies. ′ Dividing the year into 12 months and calculating the average monthly power consumption helps to understand the power loss of each month. This helps to identify the impact of seasonal or cyclical factors on power loss. Secondly, by obtaining the total power consumption of the same month in a predetermined number of years and calculating its average, the average power consumption quota for each month can be obtained. This helps to analyze the long-term trend of power consumption and understand whether there is a trend of increasing or decreasing year by year, which is conducive to providing a premise for estimating future power supply distribution. By obtaining a data set of negative values ​​and the months corresponding to the negative values, it is possible to determine which months have the largest power consumption and the values ​​that exceed the average monthly power consumption.

[0063] Embodiment 3

[0064] Based on the above-mentioned first embodiment, the calculation of the percentage change data of the multi-year historical load change trend provided in this embodiment includes:

[0065] S35, obtaining the total annual electricity consumption C of the same unit in the power supply line ... within the continuous scheduled window period;

[0066] S36. Calculate the annual total electricity consumption rate D of the power supply line:

[0067] ...

[0069]

[0070] In the formula, Dn is the annual total electricity consumption rate of the power supply line in the nth year; Cn is the annual total electricity consumption of the same unit in the power supply line in the nth year;

[0071] S37. Obtaining the scheduled window period

[0072] S38. Obtain the predicted annual total electricity consumption of the power supply line for the next year according to the increasing rate.

[0073] It should be noted that, in step S03 of the above embodiment, the same units in the annual total electricity consumption of the same units are the same production units and the same living units under each node within the predetermined window period.

[0074] Specifically, by calculating consecutive years within a continuous window period, the total annual electricity consumption C of the same unit is obtained, thereby determining the growth rate of electricity consumption, thereby providing strong and reliable data support for the electricity consumption analysis of the next year.

[0075] In summary, based on Example 2 and Example 3, the daily historical load flow section data of each power supply line can be fully obtained. These data record in detail the power flow conditions in different time periods of the day. At the same time, the system can also collect percentage variation data of historical load change trends over the years. These data reveal the long-term evolution of electricity demand. Through in-depth analysis of these data, the actual annual and monthly total electricity consumption of each power supply line can be accurately calculated. On this basis, the power supply lines can be intelligently coordinated, and the excess electricity in those power supply lines can be flexibly allocated to other lines with tight electricity demand, thereby achieving optimal allocation and efficient utilization of electricity.

[0076] Embodiment 4

[0077] Based on the above embodiment 1, the power state of each power supply line determined includes the predicted annual total power consumption of each level of topological point, and the topological point is specifically a topological starting point, a second-level topological node, ..., an N-level topological node;

[0078] The calculation of the predicted annual total electricity consumption of the topological point includes:

[0079] S41, obtaining the total annual electricity consumption F of the same unit at the topological starting point, the secondary topological node, ... and each node in the N-level topological node of the power supply line within the continuous predetermined window period:

[0080] S42. Calculate the annual total electricity consumption increase rate G of the same topological point each year:

[0081] ...

[0083]

[0084] Where Gn is the annual total electricity consumption rate of the same topological point in the nth year; Fn is the annual total electricity consumption of the same unit under each node in the topological nodes at all levels in the nth year;

[0085] S43. Obtaining the scheduled window period

[0086] S44. Obtain the predicted annual total electricity consumption of each topological point in the next year according to the increasing rate.

[0087] Specifically, by calculating consecutive years within a continuous window period, the total annual electricity consumption F of the same unit at each topological point is obtained, so as to determine the growth rate of electricity consumption at each topological point in a power supply line, thereby providing strong and reliable data support for the electricity distribution of each topological point in the power supply line in the next year.

[0088] It should be noted that, in step S04 of the above embodiment, the same units in the annual total electricity consumption of the same units are the same production units and the same living units at each node within the predetermined window period.

[0089] Embodiment 5

[0090] Based on the above-mentioned first embodiment, the redistribution in step S05 includes coordinated allocation of each power supply line and coordinated allocation of each node in the power supply line.

[0091] Furthermore, the coordinated allocation of each power supply line includes:

[0092] S51, based on the monthly average power consumption obtained in step S03, coordinate and count the power share that is not less than the preset safety threshold to obtain the monthly adjustable power share;

[0093] S52, according to the negative value of the average power consumption quota of each month obtained in step S03 and the month corresponding to the negative value, the data is classified to obtain the negative value data of each power supply line in the same month;

[0094] S53. Based on the obtained negative value data of each power supply line in the same month, obtain the available compensation amount of the monthly adjustable electric energy share:

[0095]

[0096] In the formula, U is the compensation amount and y is the negative value data of the power supply line.

[0097] Furthermore, the coordinated allocation of each node in the power supply line includes allocating the compensation amount based on the obtained monthly adjustable electric energy share to the production units of each node in the power supply line on average. If the negative value cannot be offset, the living unit of each node compensates the difference to the production unit.

[0098] Specifically, by coordinating and allocating based on the average monthly energy consumption, it is possible to calculate the energy share that is not less than the preset safety threshold, that is, the monthly adjustable energy share. This helps to optimize the distribution of electricity and ensure that each power supply line can obtain sufficient electricity supply while ensuring the safe and stable operation of the power grid. By classifying and compensating for the negative energy consumption of each power supply line in different months, the load differences between the lines can be balanced. This helps to reduce grid losses and voltage fluctuations caused by uneven loads and improve the overall operating efficiency of the power grid. And through coordinated allocation, it can be ensured that the power grid can quickly adjust the energy distribution strategy when facing load fluctuations or emergencies to ensure the stable operation of the power grid. This helps to reduce the occurrence of power grid failures and power outages and improve power supply reliability.

[0099] Secondly, if the production unit of a node cannot completely offset the negative value, the living units of each node can compensate the difference to the production unit. This coordinated allocation method helps to balance the supply and demand relationship and ensure that the distribution of electricity between nodes is more fair and reasonable. By optimizing the distribution of electricity and balancing the supply and demand relationship, the loss and operating costs of the power grid can be reduced. This helps to improve the economy of the power grid and provide strong support for the sustainable development of power companies.

[0100] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0104] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0105] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments, and the electronic device specifically includes the following contents:

[0106] Processor, memory, communications interface and bus;

[0107] Wherein, the processor, memory, and communication interface communicate with each other via the bus;

[0108] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the method in the above embodiment are implemented.

[0109] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented.

[0110] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps described in the embodiment or flow chart, more or less operation steps can be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the process and / or box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. In the description of this specification, the description of the reference term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification.

[0112] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction, characterized in that the steps include: Obtain all power supply lines of the current photovoltaic power generation base station to build a topological relationship network; From the N-level topological node at the end of the topological relationship network to the starting point of the topology, the flow section data of the daily history of the load and the percentage change data of the historical load change trend over many years are obtained in sequence; The power flow section data of the daily history of the load of each power supply line and the percentage variation data of the multi-year historical load variation trend are statistically analyzed to determine the power state of each power supply line; The total photovoltaic power generation throughout the year is redistributed based on the power state of each of the power supply lines, including coordinated allocation of each power supply line and coordinated allocation of each node in the power supply line.

2. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 1, characterized in that: The construction of the topological relationship network is specifically as follows: obtain the same-level main transformer of each power supply line, and use multiple main transformers of the same level as the topological starting point of the power supply line, use the secondary transformer of the next level as the secondary topological node, and push downward in sequence to establish the topological relationship network.

3. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 1, characterized in that: The load daily history flow section data is calculated as follows: Obtain the total electric energy P obtained by the power supply line under the history and the total electric energy X actually consumed in the power supply line, and calculate the electric energy difference value X ′ =PX; The energy difference value X ′ The average monthly power consumption is obtained by dividing the window into 12 months a year; According to the power supply line, the total amount of electric energy consumption in the same month of a predetermined number of years is obtained, and the average value is obtained to obtain the average amount of electric energy consumption for each month; The monthly power consumption mean is subtracted from the average power consumption quota of each month to obtain a negative value and a data set of the month corresponding to the negative value.

4. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 1, characterized in that: The percentage change data of the multi-year historical load change trend is calculated as follows: Obtain the total annual electricity consumption C of the same unit in the power supply line within the continuous scheduled window period; Calculate the annual total electricity consumption increase rate D of the power supply line each year: In the formula, Dn is the annual total electricity consumption rate of the power supply line in the nth year; Cn is the annual total electricity consumption of the same unit in the power supply line in the nth year; The increasing rate of the predetermined window period is obtained, and the predicted annual total electricity consumption of the power supply line in the next year is obtained according to the increasing rate.

5. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 1, characterized in that: The determined power state of each power supply line includes the predicted annual total power consumption of each level of topological point, and the predicted annual total power consumption of the topological node is calculated as follows: Obtain the total annual power consumption F of the same unit in each node in the topological nodes of each level under the power supply line within the continuous predetermined window period: Calculate the annual total electricity consumption increase rate G of the same topological node each year: Where Gn is the annual total electricity consumption rate of the same topological point in the nth year; Fn is the annual total electricity consumption of the same unit at the same topological node in the nth year; The annual total electricity consumption of the same topological node in a predetermined window period is obtained at an increasing rate, and the predicted annual total electricity consumption of each topological point in the next year is obtained according to the increasing rate.

6. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 4 or 5, characterized in that: The same units in the annual total electricity consumption of the same units are the same production units and the same living units under each node within the predetermined window period.

7. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 1, characterized in that: The coordinated allocation of the power supply lines is as follows: Coordinate based on the average monthly power consumption, count the power share that is not less than the preset safety threshold, and obtain the monthly adjustable power share; According to the negative value of the average power consumption quota of each month and the month corresponding to the negative value, the data is classified to obtain the negative value data of each power supply line in the same month; Based on the obtained negative value data of each power supply line in the same month, the available compensation amount of the monthly adjustable electric energy share is obtained: In the formula, U is the compensation amount and y is the negative value data of the power supply line.

8. The method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction according to claim 1, characterized in that: The coordinated allocation of each node in the power supply line is specifically as follows: the compensation amount based on the obtained monthly adjustable electric energy share is evenly allocated to the production units of each node in the power supply line; If the negative value cannot be offset, the living unit of each node compensates the difference to the production unit.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for coordinated distribution of electric energy based on distributed photovoltaic carrying capacity prediction as described in any one of claims 1 to 8 are implemented.

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