Distributed photovoltaic area self-balancing load scheduling method and system

Abstract

The invention relates to a distributed photovoltaic transformer area self-balancing load scheduling method and system. The method comprises the following steps: acquiring load data of a distributed photovoltaic transformer area; calculating the net load of each area in the distributed photovoltaic transformer area based on the load data; dividing the distributed photovoltaic district into a first subarea and a second subarea based on a size relationship between the net load and a preset load threshold value; generating a load guiding strategy based on the divided areas and the load data; and applying the load guiding strategy to a central scheduling platform in the distributed photovoltaic transformer area to realize self-balancing load scheduling of the distributed photovoltaic transformer area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic district management, and mainly relates to a distributed photovoltaic district self-balancing load scheduling method and system. BACKGROUND

[0002] The large-scale access of distributed photovoltaics has changed the traditional passive distribution network into an active distribution network with bidirectional flow characteristics: during the day when the light is sufficient, the power generation of distributed photovoltaics may be much higher than the power load at that time, resulting in a large amount of excess power being transmitted to the upper-level power grid in reverse through the distribution transformer, that is, "reverse flow". This phenomenon not only causes the voltage of the district line to be too high and run out of limits, but may also trigger the action of the protection device and cause the off-grid accident. At the same time, as a flexible load with high power and high randomness, the centralized charging behavior of electric vehicles (usually occurring during the night peak load) is naturally out of phase with the photovoltaic power generation period, further exacerbating the peak-valley difference of the district load within a day, resulting in problems such as transformer overload and voltage drop. The above problems have caused serious load imbalance within the district.

[0003] To address the above challenges, the following technical means exist in the prior art: 1. Increase the capacity of the transformer and replace the line with a thicker cross section to improve the power supply capacity of the district. However, this method has a huge investment cost and is a global upgrade to solve the short-term peak load, resulting in low equipment utilization and poor economics, and is not an intelligent solution. 2. Deploy an energy storage system in the district to smooth the load curve through "low storage and high generation". However, existing applications are mostly limited to simple time sequence energy transfer and lack precise coordination with photovoltaic output and load characteristics. The control strategy is usually based on fixed thresholds or simple logic and cannot dynamically respond to the imbalance of some areas within the district. 3. Through time-of-use pricing or direct load control to guide user behavior. This method can shave the peak and fill the valley to some extent, but the regulation object is usually limited to the load side and cannot effectively link with the generation side (photovoltaic) and the energy storage side. 4. Install reactive power compensation devices or voltage regulating transformers at voltage limit nodes to stabilize the voltage. This method only compensates after the voltage problem occurs and does not solve the problem of load imbalance caused by the spatial mismatch between active power supply and demand.

[0004] Therefore, there is an urgent need for a method that can automatically balance the distribution of electrical energy within the district according to demand and on-site. SUMMARY

[0005] To solve the problems existing in the prior art, the present application provides a distributed photovoltaic district self-balancing load scheduling method and system.

[0006] The technical scheme of the present application is as follows: In one aspect, the present application provides a distributed photovoltaic district self-balancing load scheduling method, which comprises: obtaining load data of the distributed photovoltaic district; calculating the net load of each region in the distributed photovoltaic district based on the load data; dividing the distributed photovoltaic district into a first subregion and a second subregion based on the size relationship between the net load and a preset load threshold; generating a load guiding strategy based on the divided regions and the load data; applying the load guiding strategy to a central scheduling platform in the distributed photovoltaic district to realize self-balancing load scheduling of the distributed photovoltaic district.

[0007] Preferably, the load data includes total load data and photovoltaic power generation power of the distributed photovoltaic district; calculating the net load of each region in the distributed photovoltaic district based on the load data.

[0008] Preferably, the central scheduling platform is used to cooperatively control the energy storage system, virtual power plant and demand response mechanism in the distributed photovoltaic district.

[0009] Preferably, the first subregion is a region with a net load less than the preset load threshold; the second subregion is a region with a net load greater than the preset load threshold.

[0010] Preferably, the load guiding strategy includes controlling the energy storage system, and the specific steps are: in the first subregion, the net load of the distributed photovoltaic district is in surplus or deficit, and the energy storage system is controlled to charge; in the second subregion, the net load of the distributed photovoltaic district is in surplus, and the energy storage system is controlled to discharge.

[0011] Preferably, the load guiding strategy includes cross-region load scheduling of the virtual power plant, and the specific steps are: the virtual power plant receives the net load of the first subregion and the second subregion; based on the transmission capacity limit between regions, the net load of the first subregion and the second subregion, and a preset power distribution coefficient, the scheduling load from the first subregion to the second subregion is calculated; based on the scheduling load, the excess part of the electric energy in the first subregion is scheduled to the second subregion.

[0012] Preferably, the load guiding strategy includes adjusting the charging behavior of the distributed photovoltaic district by the demand response mechanism, and the specific steps are: obtaining the current basic electricity price; based on the basic electricity price, the net load and the average load of the second subregion, and a preset price adjustment coefficient, a dynamic electricity price signal sent to the charging users in the second subregion is generated.

[0013] Preferably, the adjusting the charging behavior of the distributed photovoltaic subdistrict according to the demand response mechanism further comprises: Based on the preset load reduction coefficient, the net load of the second subdistrict, and the rated power of the charging pile, a dynamic charging power upper limit instruction sent to the second subdistrict charging user is generated.

[0014] In another aspect, the present application also provides a distributed photovoltaic subdistrict self-balancing load scheduling system, the system comprising: The data acquisition module is used to acquire load data of the distributed photovoltaic subdistrict; and the net load of each region in the distributed photovoltaic subdistrict is calculated based on the load data; The region division module is used to divide the distributed photovoltaic subdistrict into a first subdistrict and a second subdistrict based on the load data; The strategy generation module is used to generate a load guiding strategy based on the divided regions and the load data; The load guiding strategy is applied to the central scheduling platform to realize self-balancing load scheduling of the distributed photovoltaic subdistrict.

[0015] In another aspect, the present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the method as described in the present application.

[0016] In another aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to realize the method as described in the present application.

[0017] The present application has the following beneficial effects: 1. The present application provides a distributed photovoltaic subdistrict self-balancing load scheduling method and system, which unifies and cooperatively controls originally isolated energy storage systems, photovoltaic power generation, and electric vehicle charging loads through a central scheduling platform, realizes cross-time and space linkage of “source-load-storage” resources, enhances the coordination and complementarity of various distributed resources, improves the self-balancing ability and operation efficiency of the photovoltaic subdistrict, and avoids the limitations of single technical means. 2. The present application provides a distributed photovoltaic subdistrict self-balancing load scheduling method and system, which dynamically identifies power surplus areas (first subdistrict) and power shortage areas (second subdistrict) in the subdistrict by calculating the net load and setting a threshold value, realizes accurate positioning of problem areas, improves the pertinence and accuracy of load regulation, improves the efficiency of surplus power guiding to load centers, and avoids resource waste caused by “one-size-fits-all” regulation. 3. The application provides a distributed photovoltaic substation self-balancing load scheduling method and system, which suppresses the voltage out-of-limit problem caused by power fluctuation and reverse power flow through energy storage charging and discharging and cross-regional scheduling, improves the substation voltage stability, reduces the risk of high or low voltage, enhances the anti-interference ability of the power grid to photovoltaic and load fluctuation, and improves the power supply reliability and safety. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The specific flowchart of the embodiment of the application is as follows. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0020] It should be understood that the step numbers used herein are only for the convenience of description, and are not intended to limit the execution sequence of the steps.

[0021] It should be understood that the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in the specification and the appended claims of the application, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0022] The terms "comprise" and "include" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] The term "and / or" means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0024] Embodiment one: Referring to Figure 1 The application provides a distributed photovoltaic substation self-balancing load scheduling method, which comprises: S1, obtaining load data of a distributed photovoltaic substation; The load data includes total load data and photovoltaic power generation power of the distributed photovoltaic substation; The distributed photovoltaic substation is a designated power supply area in a power distribution system, which contains a high proportion of distributed photovoltaic power generation and has significant load fluctuation (especially caused by electric vehicle charging), and includes: Distributed photovoltaic power generation systems, including photovoltaic modules and inverters installed on the user side (such as industrial and commercial rooftops and residential rooftops), are the main source of excess electricity in the distribution area. Electric vehicle charging load clusters, including public charging stations, community charging piles, and private charging piles, are the main factors causing peak loads and drastic fluctuations in the distribution area. Distributed energy storage systems, with lithium batteries and other energy storage devices installed at key nodes (such as photovoltaic access points, charging stations, and line ends), are the core of realizing the spatial and temporal transfer of electrical energy. Power grid infrastructure includes distribution transformers, lines, switchgear, etc., and serves as the channel for transmitting electrical energy. Control equipment includes reactive power compensation devices (SVG, capacitors, etc.), voltage regulating transformers, and flexible AC transmission devices (such as static var generators) to ensure voltage quality and controllable power flow. The load data includes the total load data and photovoltaic power generation of the distributed photovoltaic area; The net load of each area in a distributed photovoltaic (PV) system is calculated based on load data, and expressed by the formula: ; In the formula, Indicates the first Net load of each region; Indicates the first Total load of each region; Indicates the first Photovoltaic power generation capacity of each region; S2. The central dispatch platform is used to coordinate the control of energy storage systems, virtual power plants and demand response mechanisms within the distributed photovoltaic area. The energy storage system consists of multiple energy storage units, which refer to electrochemical energy storage units composed of lithium-ion batteries, flow batteries, or supercapacitors. The virtual power plant specifically integrates distributed energy sources such as electric vehicle charging piles, photovoltaic power generation, and energy storage equipment to form a virtual power plant; The demand response mechanism is a mechanism for adjusting user charging behavior; S3. Based on load data, the distributed photovoltaic area is divided into the first zone and the second zone; The first partition is the area where the net load is less than a preset load threshold. ,in This indicates the preset load threshold. The second zone is the area where the net load is greater than a preset load threshold. ; S4. Generate a load steering strategy based on the region division and load data; S41. The load steering strategy includes controlling the energy storage system, and the specific steps are as follows: Within the first zone, if the distributed photovoltaic (PV) area experiences a net load surplus (i.e., excess electricity), the energy storage system is controlled to charge, as expressed by the formula: ; In the formula, express The charging power of the instantaneous energy storage system; Indicates the maximum power of the energy storage system; This indicates the preset charging coefficient; Within the second zone, if there is a net load surplus in the distributed photovoltaic (PV) distribution area, i.e., overload, the energy storage system is controlled to discharge, as expressed by the formula: ; In the formula, express The discharge power of the energy storage system at any given time; This indicates the preset charging coefficient; S42. The load steering strategy includes cross-regional load dispatching of virtual power plants, with the following specific steps: The virtual power plant receives the net load of the first and second zones; Based on the inter-regional transmission capacity limitations, the net load of the first and second zones, and the preset power allocation coefficient, the scheduling load from the first zone to the second zone is calculated, expressed by the formula: ; In the formula, express Time First Division To the second partition Dispatch load; express Time First Division To the second partition Transmission capacity limitations; Indicates the first partition Net load; Indicates the second partition Net load; Indicates the first partition The power allocation factor; Indicates the second partition The power allocation factor; Based on the aforementioned scheduling load, the excess power in the first zone will be scheduled to the second zone; S43. The load steering strategy includes adjusting the charging behavior of distributed photovoltaic power stations through the demand response mechanism. The specific steps are as follows: Obtain the current base electricity price; Based on the basic electricity price, the net load and the average load of the second sub-area, and the preset price adjustment coefficient, a dynamic electricity price signal sent to the user of the second sub-area is generated, which is expressed by the formula as follows: ; In the formula, represents the dynamic electricity price signal at the moment t; represents the basic electricity price; represents the preset price adjustment coefficient; represents the average load of the second sub-area; represents the average load of the second sub-area; represents the average load of the second sub-area; The demand response mechanism adjustment of the charging behavior of the distributed photovoltaic sub-area also includes: Based on the preset load reduction coefficient, the net load of the second sub-area, and the rated power of the charging pile, a dynamic charging power upper limit instruction sent to the user of the second sub-area is generated, which is expressed by the formula as follows: ; In the formula, represents the dynamic charging power at the moment t; represents the rated power of the charging pile; represents the preset load reduction coefficient; S5, the load guiding strategy is applied to the central scheduling platform to realize the self-balancing load scheduling of the distributed photovoltaic sub-area. Embodiment two:

[0025] The embodiment provides a self-balancing load scheduling system of a distributed photovoltaic sub-area, and the system comprises: A data acquisition module is configured to acquire load data of the distributed photovoltaic sub-area; and based on the load data, the net load of each region in the distributed photovoltaic sub-area is calculated. A region division module is configured to divide the distributed photovoltaic sub-area into a first sub-area and a second sub-area based on the load data. A strategy generation module is configured to generate a load guiding strategy based on the divided regions and the load data. The load guiding strategy is applied to the central scheduling platform to realize the self-balancing load scheduling of the distributed photovoltaic sub-area.

[0026] Embodiment three: The embodiment provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements a self-balancing load scheduling method of a distributed photovoltaic sub-area according to any one of the embodiments.

[0027] Embodiment four: ​The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the distributed photovoltaic substation self-balancing load scheduling method according to any one of the embodiment 1.

[0028] In the embodiments of the present application, "at least one" refers to one or more, and "multiple" refers to two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents that the associated objects before and after it are in an "or" relationship. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, c can be single or multiple.

[0029] Those skilled in the art can realize that the units and algorithm steps described in the embodiments disclosed in the present application can be realized by electronic hardware, computer software and combination of electronic hardware and computer software. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0030] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0031] In several embodiments provided in the present application, any function realized in the form of a software function unit and sold or used as an independent product can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM for short), a random access memory (Random Access Memory, RAM for short), a magnetic disk or an optical disk and various program code storage media.

[0032] The above merely illustrates the embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which are made by using the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A distributed photovoltaic substation self-balancing load scheduling method, characterized in that, The method comprises: obtaining load data of the distributed photovoltaic area; calculating the net load of each area in the distributed photovoltaic area based on the load data; dividing the distributed photovoltaic area into a first subarea and a second subarea based on the size relationship between the net load and a preset load threshold; generating a load guidance strategy based on the divided areas and the load data; applying the load guidance strategy to a central scheduling platform in the distributed photovoltaic area to realize self-balancing load scheduling of the distributed photovoltaic area. 2.The distributed photovoltaic substation self-balancing load scheduling method according to claim 1, wherein, The central scheduling platform is used to cooperatively control the energy storage system, the virtual power plant and the demand response mechanism in the distributed photovoltaic area. 3.The distributed photovoltaic substation self-balancing load scheduling method according to claim 2, characterized in that, The first subarea is an area with a net load less than the preset load threshold. The second subarea is an area with a net load greater than the preset load threshold.

4. The distributed photovoltaic substation self-balancing load scheduling method according to claim 3, characterized in that, The load guidance strategy includes controlling the energy storage system, and the specific steps are: In the first subarea, the net load of the distributed photovoltaic area is in surplus or deficit, and the energy storage system is controlled to charge; In the second subarea, the net load of the distributed photovoltaic area is in surplus, and the energy storage system is controlled to discharge.

5. The distributed photovoltaic substation self-balancing load scheduling method according to claim 4, characterized in that, The load guidance strategy includes cross-area load scheduling of the virtual power plant, and the specific steps are: The virtual power plant receives the net load of the first subarea and the second subarea; Based on the transmission capacity limit between areas, the net load of the first subarea and the second subarea, and a preset power distribution coefficient, the scheduling load from the first subarea to the second subarea is calculated; Based on the scheduling load, the excess part of the electric energy in the first subarea is scheduled to the second subarea.

6. The distributed photovoltaic substation self-balancing load scheduling method according to claim 5, characterized in that, The load guidance strategy includes adjusting the charging behavior of the distributed photovoltaic area by the demand response mechanism, and the specific steps are: obtaining the current basic electricity price; Based on the basic electricity price, the net load and the average load of the second subarea, and a preset price adjustment coefficient, a dynamic electricity price signal is generated and sent to the charging users in the second subarea.

7. The distributed photovoltaic substation self-balancing load scheduling method according to claim 6, characterized in that, Adjusting the charging behavior of the distributed photovoltaic area by the demand response mechanism further includes: Based on the preset load reduction coefficient, the net load of the second subarea and the rated power of the charging pile, a dynamic charging power upper limit instruction is generated and sent to the charging users in the second subarea.

8. A distributed photovoltaic substation self-balancing load scheduling system, characterized in that, The system comprises: The data acquisition module is used to obtain the load data of the distributed photovoltaic area; and calculate the net load of each area in the distributed photovoltaic area based on the load data; The area division module is used to divide the distributed photovoltaic area into a first subarea and a second subarea based on the load data; The strategy generation module is used to generate a load guidance strategy based on the divided areas and the load data; The load guidance strategy is applied to the central scheduling platform to realize self-balancing load scheduling of the distributed photovoltaic area.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the method of any one of claims 1 to 7.

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