Distributed photovoltaic optimization method, device, medium and program in remote area

Abstract

The invention discloses a distributed photovoltaic optimization method for a remote area, and relates to the technical field of power grid optimization, and the method comprises the steps: obtaining the first bearing data of a target transformer and the second bearing data of each target connection line, determining the first photovoltaic capacity constraint data corresponding to the target transformer according to the first bearing data, and obtaining the second bearing data of each target connection line; determining second photovoltaic capacity constraint data corresponding to each target connecting line according to the second bearing data; obtaining transformer voltage data of the target transformer and line voltage data of each target connecting line, determining first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and determining second voltage constraint data corresponding to each target connecting line according to the line voltage data; and according to the photovoltaic capacity constraint data and the voltage constraint data, determining quantity data and configuration data of the distributed photovoltaic connected with each target connecting line. The photovoltaic acceptance capability of a remote area can be improved, and the photovoltaic utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid optimization, and in particular to a distributed photovoltaic optimization method, device, medium and program for remote areas. Background Art

[0002] With the continuous development of distributed photovoltaic technology, remote areas with difficulty in electricity use can achieve self-sufficiency in electricity by setting up distributed photovoltaics. On the basis of self-sufficiency in electricity, excess electricity can also be connected to the grid to obtain income through photovoltaic power generation.

[0003] However, the power supply radius of the substation in remote areas is large, the grid foundation is weak, and the voltage change from the transformer outlet to the user end is large. When the distributed power source is connected, the power return in some periods is easy to cause the terminal voltage to rise beyond the limit, and even make the line and transformer fully loaded or overloaded, resulting in operational risks, which greatly limits the access capacity of distributed power generation. Secondly, the grid in remote areas is mainly radial, with fewer sections, no power transfer capability, a single operation mode, and many weak power distribution nodes and lines. Once an overload or failure occurs, there is no backup circuit for power supply. Therefore, a more stringent evaluation system that comprehensively considers all weak power supply nodes and lines in the substation is needed to deal with the difficulty of absorption.

[0004] The existing technology generally evaluates the carrying capacity by considering only heavy overload or voltage deviation, and then adjusts the distributed photovoltaic according to the evaluation results. However, this evaluation and adjustment method is too simplified, which is easy to cause safety hazards, and the utilization rate of distributed photovoltaic is low. Summary of the invention

[0005] The present invention provides a distributed photovoltaic optimization method, device, medium and program for remote areas, which can improve the photovoltaic acceptance capacity of remote areas and enhance the photovoltaic utilization rate while ensuring the safe and stable operation of photovoltaics.

[0006] According to one aspect of the present invention, a distributed photovoltaic optimization method in remote areas is provided, characterized by comprising:

[0007] Acquire first load data of a target transformer, determine first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and acquire second load data of each target connection line connecting the target transformer and photovoltaic, and determine second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data;

[0008] Obtain transformer voltage data of a target transformer, determine first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and line voltage data of each target connection line connecting the target transformer and photovoltaic, and determine second voltage constraint data corresponding to each target connection line according to the line voltage data;

[0009] The quantity data and configuration data of the distributed photovoltaic connected to each of the target connection lines are determined according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data.

[0010] According to another aspect of the present invention, there is provided a distributed photovoltaic optimization device in a remote area, comprising:

[0011] A photovoltaic capacity constraint data determination module is used to obtain first load data of a target transformer, determine first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and obtain second load data of each target connection line connecting the target transformer and photovoltaic, and determine second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data;

[0012] A voltage constraint data determination module is used to obtain transformer voltage data of a target transformer, determine first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and line voltage data of each target connection line connecting the target transformer and photovoltaic, and determine second voltage constraint data corresponding to each target connection line according to the line voltage data;

[0013] The distributed photovoltaic optimization module is used to determine the quantity data and configuration data of the distributed photovoltaics connected to each of the target connecting lines according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distributed photovoltaic optimization method for remote areas described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distributed photovoltaic optimization method for remote areas described in any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the distributed photovoltaic optimization method for remote areas described in any embodiment of the present invention is implemented.

[0020] The technical solution of the embodiment of the present invention obtains the first load data of the target transformer and the second load data of each target connection line connecting the target transformer and the photovoltaic, determines the first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and determines the second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data. By constraining the photovoltaic capacity of the target connection line and the target transformer through the photovoltaic capacity constraint data, the transformer and line safety problems caused by excessive photovoltaic capacity can be avoided. By obtaining the transformer voltage data of the target transformer and the line voltage data of each target connection line connecting the target transformer and the photovoltaic, the first voltage constraint data corresponding to the target transformer is determined according to the transformer voltage data, and the second voltage constraint data corresponding to each target connection line is determined according to the line voltage data. The voltage constraint data can constrain the voltage of the target connection line and the target transformer, avoiding safety problems caused by excessive voltage of the target transformer and the target connection line. According to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data, the quantity data and configuration data of the distributed photovoltaic connected to each target connection line are determined, which can improve the utilization rate of photovoltaic on the basis of safe and stable operation of photovoltaic.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a distributed photovoltaic optimization method in remote areas provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of another distributed photovoltaic optimization method in remote areas provided according to the second embodiment of the present invention;

[0025] Figure 3 is a schematic structural diagram of a distributed photovoltaic optimization device in a remote area provided according to Embodiment 3 of the present invention;

[0026] Figure 4 It is a structural schematic diagram of an electronic device for implementing the distributed photovoltaic optimization method in remote areas according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below 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 of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 A flowchart of a distributed photovoltaic optimization method for remote areas is provided for the first embodiment of the present invention. This embodiment can be applied to determine the quantity data and configuration data of distributed photovoltaics connected to each target connection line according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data. The method can be executed by a distributed photovoltaic optimization device in a remote area. The distributed photovoltaic optimization device in a remote area can be implemented in the form of hardware and / or software. The distributed photovoltaic optimization device in a remote area can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110, obtaining first carrying data of the target transformer, determining first photovoltaic capacity constraint data corresponding to the target transformer according to the first carrying data, and obtaining second carrying data of each target connecting line connecting the target transformer and the photovoltaic, and determining second photovoltaic capacity constraint data corresponding to each target connecting line according to the second carrying data.

[0032] Among them, the target transformer can be a distribution transformer in a remote area that is difficult to consume electricity and needs to be constrained by photovoltaic capacity, determined based on the photovoltaic output data and photovoltaic load data of each transformer from multiple transformers in remote areas. For example, there are five distribution transformers in a remote area, each of which is connected to multiple distributed photovoltaic power generation devices and multiple electricity users in remote areas. The distribution transformer in the area with the most difficult power consumption can be selected from the five transformers to be determined as the target transformer. The first carrying data can be parameters related to power transmission and carrying capacity directly related to the target transformer. The first carrying data can include data related to the target transformer, such as the transformer rated capacity, the transformer maximum reverse load rate, and the transformer power factor of the target transformer.

[0033] The first photovoltaic capacity constraint data may be data determined based on the first load data and used to impose a heavy load constraint on the target transformer, and the heavy load constraint on the target transformer may be a constraint on the maximum load that the target transformer can bear. When the photovoltaic capacity carried by the target transformer is less than or equal to the first photovoltaic capacity constraint data, it means that the target transformer can safely and effectively access and absorb the electric energy generated by all distributed photovoltaic power generation devices connected to the target transformer.

[0034] The second load-bearing data may be parameters related to the power transmission and load-bearing capacity of each target connection line connecting the target transformer and the photovoltaic. The second load-bearing data of the target connection line may include the maximum transmission capacity of the target connection line, the maximum reverse load rate of the line, and the line power factor.

[0035] The second photovoltaic capacity constraint data may be data determined based on the second load bearing data and used to perform heavy load constraints on each target power line connected to the target transformer, and the heavy load constraint on the target power line may be a constraint on the maximum load that the target power line can bear. When the photovoltaic capacity carried by the target power line is less than or equal to the second photovoltaic capacity constraint data, it means that the target power line can safely and effectively access and absorb the electric energy generated by the distributed photovoltaic power generation device connected to the target power line.

[0036] Optionally, determining the first photovoltaic capacity constraint data corresponding to the target transformer based on the first load data may include: determining the first photovoltaic capacity constraint data of the target transformer based on the transformer rated capacity, transformer maximum reverse load rate and transformer power factor of the target transformer.

[0037] Optionally, the second photovoltaic capacity constraint data corresponding to each target connecting line may be determined according to the maximum transmission capacity of the line, the maximum reverse load rate of the line, and the line power factor corresponding to each target connecting line by the following formula:

[0038] A xl，i =S xl，i T xl η xl，i，t

[0039] Among them, A xl，i is the second photovoltaic capacity constraint data corresponding to the i-th target connection line, S xL，i is the maximum transmission capacity of the i-th target link, T xl is the maximum reverse load rate of the line, η xl，i，t is the line power factor of the i-th target connection line at time t.

[0040] Optionally, determining the second photovoltaic capacity constraint data corresponding to each target connecting line based on the second carrying data may include: determining the second photovoltaic capacity constraint data corresponding to each target connecting line based on the maximum transmission capacity of the line, the maximum reverse load rate of the line and the line power factor corresponding to each target connecting line.

[0041] Optionally, the first photovoltaic capacity constraint data of the target transformer may be determined according to the transformer rated capacity, the transformer maximum reverse load rate and the transformer power factor of the target transformer by the following formula:

[0042] A z =S z T z η z，t

[0043] Among them, A z is the first photovoltaic capacity constraint data of the target transformer, S z is the transformer rated capacity of the target transformer, T z is the maximum reverse load rate of the target transformer, η z，t is the transformer power factor of the target transformer at time t.

[0044] Exemplarily, multiple groups of distributed photovoltaic output curves and load curves under each transformer in remote areas can be obtained, and the photovoltaic power consumption of each transformer can be determined according to the multiple groups of distributed photovoltaic output curves and load curves, and then one or more transformers with the most difficult consumption can be determined as target transformers according to each photovoltaic power consumption situation. After the target transformer is determined, the first load-bearing data corresponding to the target transformer and the second load-bearing data of each target connection line connecting the target transformer and photovoltaic can be obtained. The first photovoltaic capacity constraint data for overloading the target transformer is determined according to the first load-bearing data. The second photovoltaic capacity constraint data for overloading each target power line connected to the target transformer is determined according to the second load-bearing data. By overloading the target transformer with the first photovoltaic capacity constraint data, the safe and stable operation of the target transformer can be guaranteed. By overloading each target power line with the second photovoltaic capacity constraint data, each target power line can be guaranteed to operate safely and stably.

[0045] S120, obtaining transformer voltage data of the target transformer, determining first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and line voltage data of each target connection line connecting the target transformer and photovoltaic, and determining second voltage constraint data corresponding to each target connection line according to the line voltage data.

[0046] The transformer voltage data may be the rated voltage data of the target transformer. The first voltage constraint data may be constraint data determined according to the rated voltage data of the target transformer, and used to constrain the load voltage of the target transformer. The line voltage data may be load rated voltage data of each target connecting line load connected to the target transformer. The second voltage constraint data may be constraint data determined according to the load rated voltage data corresponding to the target connecting line, and used to constrain the load voltage of each target connecting line connected to the target transformer.

[0047] Exemplarily, after determining the target transformer from multiple transformers in remote areas, the rated voltage data of the target transformer and the load rated voltage data of each target connection line connected to the target transformer are obtained. According to the rated voltage data of the target transformer, the constraint data for constraining the load voltage of the target transformer is determined. According to the load rated voltage data corresponding to the target connection line, the constraint data for constraining the load voltage of each target connection line connected to the target transformer is determined. By constraining the load voltage of the target transformer through the first voltage constraint data, the safety hazards caused by the excessively high load voltage of the target transformer can be avoided, and the safe and stable operation of the target transformer can be ensured. By constraining the load voltage of the target power line connected to the target transformer through the second voltage constraint data, the safety hazards caused by the excessively high load voltage of the target connection line can be avoided, and the safe and stable operation of the target connection line can be ensured, thereby smoothly completing the transmission of electricity.

[0048] S130, determining the quantity data and configuration data of the distributed photovoltaics connected to each target connection line according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data.

[0049] The quantity data of distributed photovoltaics may be the total number of distributed photovoltaics connected to the target transformer through the target power line, and the number of distributed photovoltaics connected to each target power line. The configuration data of distributed photovoltaics may be photovoltaic configuration data of distributed photovoltaics connected to each target power line. The configuration data of distributed photovoltaics may include, but is not limited to, information such as the type, quantity, power, installation angle, and orientation of photovoltaic modules.

[0050] Exemplarily, it may be based on the photovoltaic output data, load data of each target connection line connected to the target transformer, the node voltage offset data of each target connection line and the transformer voltage offset data of the target transformer, and the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data are used as constraint conditions to solve and determine the quantity data and configuration data of the distributed photovoltaic connected to each target connection line.

[0051] The technical solution of the embodiment of the present invention obtains the first load data of the target transformer and the second load data of each target connection line connecting the target transformer and the photovoltaic, determines the first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and determines the second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data. By constraining the photovoltaic capacity of the target connection line and the target transformer through the photovoltaic capacity constraint data, the transformer and line safety problems caused by excessive photovoltaic capacity can be avoided. By obtaining the transformer voltage data of the target transformer and the line voltage data of each target connection line connecting the target transformer and the photovoltaic, the first voltage constraint data corresponding to the target transformer is determined according to the transformer voltage data, and the second voltage constraint data corresponding to each target connection line is determined according to the line voltage data. The voltage constraint data can constrain the voltage of the target connection line and the target transformer, avoiding safety problems caused by excessive voltage of the target transformer and the target connection line. According to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data, the quantity data and configuration data of the distributed photovoltaic connected to each target connection line are determined, which can maximize the photovoltaic carrying capacity of the target transformer on the basis of safe and stable operation of photovoltaic.

[0052] Embodiment 2

[0053] Figure 2 This is a flow chart of a distributed photovoltaic optimization method for remote areas provided by the second embodiment of the present invention. Based on the above embodiment, this embodiment refines the determination of the quantity data and configuration data of distributed photovoltaics connected to each target connection line according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data, and the second voltage constraint data. Figure 2 As shown, the method includes:

[0054] S210, obtaining first carrying data of the target transformer, determining first photovoltaic capacity constraint data corresponding to the target transformer according to the first carrying data, and obtaining second carrying data of each target connecting line connecting the target transformer and the photovoltaic, and determining second photovoltaic capacity constraint data corresponding to each target connecting line according to the second carrying data.

[0055] Optionally, before obtaining the first load-bearing data and the second load-bearing data of the target transformer, it also includes: obtaining the historical distributed photovoltaic output coefficient initial curve and the historical load initial curve of the target connection line access node of each distributed photovoltaic load in the remote areas where distributed photovoltaic optimization is required, determining the target transformer according to the historical distributed photovoltaic output coefficient initial curve and the historical load initial curve of the target connection line access node of each distributed photovoltaic and load, and determining the distributed photovoltaic output coefficient curve and load curve of the 380V access node by the distributed photovoltaic curve group corresponding to the target transformer.

[0056] S220, obtaining transformer voltage data of the target transformer, determining first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and line voltage data of each target connection line connecting the target transformer and photovoltaic, and determining second voltage constraint data corresponding to each target connection line according to the line voltage data.

[0057] Optionally, determining the second voltage constraint data corresponding to each target connection line based on the line voltage data may include: obtaining a preset line voltage offset ratio of the target connection line, and determining the voltage constraint data corresponding to each target connection line based on the preset line voltage offset ratio and the line rated voltage data.

[0058] The preset line voltage offset ratio may be a ratio range in which the voltage of the target power line is allowed to deviate from its rated voltage when the target power line is operating normally, for example, not more than 10% of the rated voltage. The preset line voltage offset ratio may be an offset ratio pre-set based on factors such as the transmission capacity of the line, load changes, and line impedance. The preset line voltage offset ratio can ensure that the voltage level of the line can be kept within the allowable range when transmitting electric energy, thereby avoiding damage to the equipment and load caused by excessively high or low voltage.

[0059] Optionally, determining first voltage constraint data corresponding to the target transformer based on transformer voltage data may include: obtaining a preset transformer voltage offset ratio of the target transformer, and determining transformer voltage constraint data corresponding to the target transformer based on the preset transformer voltage offset ratio and the transformer rated voltage data.

[0060] The preset transformer voltage offset ratio may be a ratio range in which the output voltage of the target transformer is allowed to deviate from its rated voltage when the target transformer is operating normally, for example, not more than 10% of the rated voltage. The voltage offset ratio may be preset and may be determined based on factors such as the safe and stable operation requirements of the power system, the rated voltage of the equipment, and the design characteristics of the transformer. The setting of the preset transformer voltage offset ratio can ensure that the transformer can provide a stable output voltage under various load conditions, thereby ensuring the stability of the power system and the normal operation of the power equipment.

[0061] S230, obtaining photovoltaic output coefficient data and load data of the distributed photovoltaic connected to each target connection line, as well as node voltage offset data of each target connection line and transformer voltage offset data of the target transformer.

[0062] The photovoltaic output coefficient data can be the ratio of the actual power generation of the distributed photovoltaic connected to the target connection line to the theoretical power generation in actual operation, which is used to characterize the power generation efficiency of the distributed photovoltaic connected to the target connection line. The load data can be the power consumption data of the user end or power-consuming equipment of the distributed photovoltaic connected to the target connection line.

[0063] The node voltage offset data may be the difference between the actual operating voltage of each target connection line node and the rated voltage of the node. The transformer voltage offset data may be the difference between the actual operating voltage at one end of the target transformer and the rated voltage of the transformer.

[0064] S240, with the goal of maximizing the photovoltaic installed capacity of the target transformer, with the second photovoltaic capacity constraint data and the first photovoltaic capacity constraint data as the first constraint condition, with the second voltage constraint data and the first voltage constraint data as the second constraint condition, according to the photovoltaic output coefficient data and load data of the distributed photovoltaics connected to the target connecting line, as well as the node voltage offset data of each target connecting line and the transformer voltage offset data of the target transformer, determine the quantity data and configuration data of the distributed photovoltaics connected to each target connecting line.

[0065] Among them, the first constraint condition may include the second photovoltaic capacity constraint data and the first photovoltaic capacity constraint data. The first photovoltaic capacity constraint data may be the maximum photovoltaic capacity that the target transformer can carry. If the photovoltaic capacity of the target transformer exceeds this capacity, the transformer may be overloaded, thereby affecting the stable operation of the power system. The second photovoltaic capacity constraint data may be the maximum photovoltaic capacity that each target connecting line can carry. If the photovoltaic capacity of the target connecting line exceeds this capacity, the connecting line may overheat or be damaged. Maximizing the photovoltaic installed capacity of the target transformer means maximizing the installation of distributed photovoltaics to fully utilize the carrying capacity of the target transformer under the premise that the target transformer meets the first constraint condition and the second constraint condition. Maximizing the photovoltaic installed capacity of the target transformer can be obtained by solving the improved genetic algorithm, a distributed photovoltaic configuration scheme, so that the total installed capacity of photovoltaics reaches the maximum without exceeding the carrying capacity of the transformer and the line.

[0066] Specifically, it can be based on a pre-trained genetic algorithm, with the goal of maximizing the photovoltaic installed capacity of the target transformer, with the second photovoltaic capacity constraint data and the first photovoltaic capacity constraint data as the first constraint condition, and with the second voltage constraint data and the first voltage constraint data as the second constraint condition, according to the photovoltaic output coefficient data and load data of the distributed photovoltaic connected to the target connection line, as well as the node voltage offset data of each target connection line and the transformer voltage offset data of the target transformer, to solve the quantity data and configuration data of the distributed photovoltaic connected to each target connection line. Then, according to the quantity data and configuration data of the distributed photovoltaic connected to each target connection line, the photovoltaic connected to each target connection line under the target transformer is optimized, and the photovoltaic carrying capacity of the target transformer is maximized on the basis of ensuring the safe and stable operation of the target transformer and the target connection line.

[0067] In a specific application scenario, the distributed photovoltaic optimization method in remote areas provided by the embodiment of the present invention may include the following process:

[0068] Step 1: Extract the initial distributed photovoltaic output coefficient curve and load initial curve of each 380V-class power line node connected to the remote area where distributed photovoltaic optimization is required. With a step length of 15 minutes, average the historical data of photovoltaic output coefficient and load history of each node connected to the power line to obtain the initial photovoltaic output coefficient. and initial load curve.

[0069] Step 2: Generate typical distributed photovoltaic output coefficient curves and load curves for remote areas with difficult consumption based on data statistical characteristics. Specifically, for each node connected to the power line, statistical characteristic curves of photovoltaic and load error distribution under the power line can be calculated to obtain the probability value corresponding to the interval [-3σ, 3σ] of its power error distribution, where σ is the standard value of the power error. Randomly generate multiple groups of photovoltaic output coefficient errors in [-3σ, 3σ] and The distributed photovoltaic output coefficient curve And load curve The formula can be Preliminary estimation of distributed photovoltaic capacity C pv，0 ,Depend on The distributed photovoltaic output curve is obtained. Then, according to multiple groups of distributed photovoltaic output curves and load curves, the distribution transformer corresponding to the curve group with the most difficulty in absorbing among the multiple groups of distributed photovoltaics is determined as the target transformer, and the distributed photovoltaic output coefficient curve and load curve of the 380V access node are determined according to the distributed photovoltaic curve group corresponding to the target transformer, and then the distribution network carrying capacity of the target distribution transformer is evaluated according to the distributed photovoltaic output coefficient curve and load curve.

[0070] Step 3: Consider the maximum reverse overload of all lines and transformers in the weak grid area, and establish overload constraints for each 380V target connecting line and 380V~10kV target transformer.

[0071] Due to the weak regional grid, all 380v target connection lines and their connected distribution transformers should be subject to heavy overload constraints to avoid power over-limit problems caused by large-scale photovoltaic power generation. For example: the power of a single 380v target connection line exceeds the limit, or the power of the target transformer distribution transformer exceeds the limit due to the overall high reverse power of the 380v target connection line. Based on the above considerations, all 380v lines can be subject to reverse heavy overload constraints: if there are I 380v lines in the weak grid area, i∈I, then the distributed photovoltaic capacity connected to each 380v target connection line is The following overload constraints should be met:

[0072]

[0073] in, is the distributed photovoltaic capacity corresponding to the i-th target connection line, is the photovoltaic output coefficient of the i-th target connection line at time t, is the load of the i-th target connection line at time t, A xl，i is the second photovoltaic capacity constraint data corresponding to the i-th target connection line, S xl，i is the maximum transmission capacity of the i-th target link, T xl is the maximum reverse load rate of the line, which can be 80%, η xl，i，t is the line power factor of the i-th target connection line at time t.

[0074] The target transformer's distribution transformer reverse overload constraint, that is, the 380v~10kv transformer connected to the distributed photovoltaic capacity should meet the following constraints:

[0075]

[0076] Among them, A z is the first photovoltaic capacity constraint data of the target transformer, S z is the transformer rated capacity of the target transformer, T zis the maximum reverse load rate of the target transformer, which can be 80%, η z，t is the transformer power factor of the target transformer at time t.

[0077] Step 4: Considering the voltage quality of the load nodes and transformer nodes of all target connecting lines under the target transformer, establish the voltage offset constraints of the load nodes of each target connecting line and the 380V~10kV target transformer.

[0078] Affected by the radial weak grid in remote areas, voltage offset constraints should be imposed on the target transformer's distribution transformer and the load nodes of all target power lines at the end of the target transformer's distribution transformer to avoid voltage offset problems caused by long lines and large power supply radius: such as voltage offset exceeding the limit of a single remote load node, or voltage offset of the distribution transformer in the distribution transformer due to power reverse transmission.

[0079] For the voltage offset constraint of the load nodes at the end of all target power lines under the target transformer, suppose there are a total of I target power line load supply nodes in the area, and consider the load node voltage offset of the i-th target power line. The voltage deviation constraint of the load node at the end of the target power line station is established, which does not exceed 10% of the rated voltage of the node.

[0080]

[0081] The load node voltage deviation of the i-th target power line It can be determined by the following formula:

[0082]

[0083] in, is the load node voltage offset data of the i-th target power line, is the node voltage of the i-th target power line node at time t, is the rated voltage of the i-th target power line node.

[0084] The target transformer distribution transformer voltage deviation constraint: Consider the target transformer node voltage deviation C of 380V~10kV VEI Not exceeding 10% of the node rated voltage, establishing distributed photovoltaic capacity constraints connected to the target transformer.

[0085] C VEI ≤10%U n

[0086] The distributed photovoltaic capacity constraint of the target transformer can be determined by the following formula:

[0087]

[0088] Among them, C VEI is the target transformer node voltage offset value, U t is the transformer node voltage at time t, U n is the node rated voltage of the target transformer.

[0089] Step 5: Based on the constraints of step 3 and step 4, the carrying capacity is solved based on the improved genetic algorithm with the goal of maximizing the total capacity of distributed photovoltaic access on the distribution network side of the target transformer.

[0090] Due to the nonlinearity of the model and the large number of grid weak point constraints of the target transformer, an improved genetic algorithm is used to solve it, which has the characteristics of random adaptation and multi-path parallel search. The algorithm solution mainly includes the following steps:

[0091] Step 1: For a target transformer with n target connection lines and a photovoltaic accessible node, let the n-order matrix Represents the photovoltaic installed capacity connected to each target connection line node, where: That is, the photovoltaic capacity connected to the target connection line node is greater than or equal to 0, so the installed capacity of each node in the matrix C is represents the decision variable, and C is the corresponding chromosome. Then determine the n-order matrix Each element Each row represents the output coefficient of the i-th target connection line node at each time from t=1 to t=T, and each column represents the output coefficient of nodes 1 to n at time t. The matrix C·k is the photovoltaic output of different target connection line nodes of the target transformer at different times.

[0092] Step 2: Generate an initial population. Randomly generate an initial population that satisfies all constraints. If the generated individual does not meet any constraint, the individual will be discarded and regenerated until an initial population that meets the constraints is formed to ensure the feasibility of the initial population.

[0093] Step 3: Set the fitness function. Set the objective function is the fitness function.

[0094] Step 4: Use the elite retention strategy to perform the selection operation. Arrange all individuals in the current population in descending order of fitness value, and then directly use the first few individuals as new individuals in the new generation population.

[0095] Step 5: With a certain crossover probability P cPerform diagonal interpolation and crossover. Interpolate between the parent diagonal elements to generate new intermediate values ​​as the child elements. Mix the gene information by using interpolation to generate new solutions in the solution space. Specifically, for the parent chromosomes A and B, the child chromosome 1 can be expressed as: The daughter chromosome two can be expressed as: Where γ is a parameter that controls the interpolation position (0<γ<1).

[0096] Step 6: Dynamically adjust the mutation probability P according to the change of population fitness m When the fitness of the population converges slowly, increase P appropriately. m To introduce more mutations. Specifically, mutations can be made using the following formula: Specifically, the mutation method can be Gaussian noise mutation. Where N(0, σ 2 ) has a mean of 0 and a variance of σ 2 Gaussian distribution.

[0097] Step 7, termination and output. In the process of solving the problem through the genetic algorithm, if the genetic algorithm solves the problem to meet the predetermined number of iterations, the fitness does not change significantly in consecutive generations, or reaches any one of the target fitness values, the solution is terminated and the solution result is output.

[0098] The technical solution of the embodiment of the present invention obtains the first load data of the target transformer and the second load data of each target connection line connecting the target transformer and the photovoltaic, determines the first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and determines the second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data. By constraining the photovoltaic capacity of the target connection line and the target transformer through the photovoltaic capacity constraint data, the transformer and line safety problems caused by excessive photovoltaic capacity can be avoided. By obtaining the transformer voltage data of the target transformer and the line voltage data of each target connection line connecting the target transformer and the photovoltaic, the first voltage constraint data corresponding to the target transformer is determined according to the transformer voltage data, and the second voltage constraint data corresponding to each target connection line is determined according to the line voltage data. The voltage constraint data can constrain the voltage of the target connection line and the target transformer, avoiding safety problems caused by excessive voltage of the target transformer and the target connection line. According to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data, the quantity data and configuration data of the distributed photovoltaic connected to each target connection line are determined, which can maximize the photovoltaic carrying capacity of the target transformer on the basis of safe and stable operation of photovoltaic.

[0099] Embodiment 3

[0100] Figure 3 This is a schematic diagram of the structure of a distributed photovoltaic optimization device in a remote area provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: a photovoltaic capacity constraint data determination module 310, a voltage constraint data determination module 320 and a distributed photovoltaic optimization module 330.

[0101] Furthermore, the photovoltaic capacity constraint data determination module 310 can be used to obtain the first load data of the target transformer, determine the first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and obtain the second load data of each target connection line connecting the target transformer and the photovoltaic, and determine the second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data; the voltage constraint data determination module 320 can be used to obtain the transformer voltage data of the target transformer, determine the first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and the line voltage data of each target connection line connecting the target transformer and the photovoltaic, and determine the second voltage constraint data corresponding to each target connection line according to the line voltage data; the distributed photovoltaic optimization module 330 can be used to determine the quantity data and configuration data of the distributed photovoltaic connected to each target connection line according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data.

[0102] Furthermore, the distributed photovoltaic optimization module 330 is specifically used to: obtain photovoltaic output coefficient data and load data of the distributed photovoltaics connected to each target connection line, as well as the node voltage offset data of each target connection line and the transformer voltage offset data of the target transformer; with the goal of maximizing the photovoltaic installed capacity of the target transformer, with the second photovoltaic capacity constraint data and the first photovoltaic capacity constraint data as the first constraint condition, with the second voltage constraint data and the first voltage constraint data as the second constraint condition, according to the photovoltaic output coefficient data and load data of the distributed photovoltaics connected to the target connection line, as well as the node voltage offset data of each target connection line and the transformer voltage offset data of the target transformer, determine the quantity data and configuration data of the distributed photovoltaics connected to each target connection line.

[0103] Furthermore, the second carrying data includes the maximum transmission capacity of the line, the maximum reverse load rate of the line and the line power factor; the first carrying data includes the rated capacity of the transformer, the maximum reverse load rate of the transformer and the power factor of the transformer.

[0104] Furthermore, the photovoltaic capacity constraint data determination module 310 is specifically used to determine the second photovoltaic capacity constraint data corresponding to each target connecting line according to the maximum transmission capacity of the line, the maximum reverse load rate of the line and the line power factor corresponding to each target connecting line.

[0105] Furthermore, the photovoltaic capacity constraint data determination module 310 is further specifically used to determine first photovoltaic capacity constraint data of the target transformer according to the transformer rated capacity, the transformer maximum reverse load rate and the transformer power factor of the target transformer.

[0106] Furthermore, the second photovoltaic capacity constraint data corresponding to each target connecting line can be determined according to the maximum transmission capacity of the line, the maximum reverse load rate of the line and the line power factor corresponding to each target connecting line by the following formula:

[0107] A xl，i =S xl，i T xl η xl，i，t

[0108] Among them, A xl，i is the second photovoltaic capacity constraint data corresponding to the i-th target connection line, S xl，i is the maximum transmission capacity of the i-th target link, T xl is the maximum reverse load rate of the line, η xl，i，t is the line power factor of the i-th target connection line at time t.

[0109] Furthermore, the first photovoltaic capacity constraint data of the target transformer can be determined according to the transformer rated capacity, the transformer maximum reverse load rate and the transformer power factor of the target transformer by the following formula:

[0110] A z =S z T z η z，t

[0111] Among them, A z is the first photovoltaic capacity constraint data of the target transformer, S z is the transformer rated capacity of the target transformer, T z is the maximum reverse load rate of the target transformer, η z，t is the transformer power factor of the target transformer at time t.

[0112] Furthermore, the line voltage data includes line rated voltage data; and the transformer voltage data includes transformer rated voltage data.

[0113] Furthermore, the voltage constraint data determination module 320 is specifically used to: obtain a preset line voltage offset ratio of the target connection line, and determine the voltage constraint data corresponding to each target connection line according to the preset line voltage offset ratio and the line rated voltage data.

[0114] Furthermore, the voltage constraint data determination module 320 is specifically used to: obtain a preset transformer voltage offset ratio of the target transformer, and determine the transformer voltage constraint data corresponding to the target transformer according to the preset transformer voltage offset ratio and the transformer rated voltage data.

[0115] The distributed photovoltaic optimization device for remote areas provided by the embodiment of the present invention can execute the distributed photovoltaic optimization method for remote areas provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0116] Embodiment 4

[0117] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0118] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0119] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a distributed photovoltaic optimization method in remote areas.

[0121] In some embodiments, the distributed photovoltaic optimization method in remote areas may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the distributed photovoltaic optimization method in remote areas described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the distributed photovoltaic optimization method in remote areas by any other appropriate means (e.g., by means of firmware).

[0122] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0127] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0129] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed photovoltaic optimization method in remote areas, characterized in that: include: Acquire first load data of a target transformer, determine first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data, and acquire second load data of each target connection line connecting the target transformer and photovoltaic, and determine second photovoltaic capacity constraint data corresponding to each target connection line according to the second load data; Obtain transformer voltage data of a target transformer, determine first voltage constraint data corresponding to the target transformer according to the transformer voltage data, and line voltage data of each target connection line connecting the target transformer and photovoltaic, and determine second voltage constraint data corresponding to each target connection line according to the line voltage data; The quantity data and configuration data of the distributed photovoltaic connected to each of the target connection lines are determined according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data and the second voltage constraint data.

2. The method according to claim 1, characterized in that The step of determining the quantity data and configuration data of distributed photovoltaics connected to each of the target connection lines according to the first photovoltaic capacity constraint data, the second photovoltaic capacity constraint data, the first voltage constraint data, and the second voltage constraint data includes: Acquire photovoltaic output coefficient data and load data of the distributed photovoltaic connected to each of the target connection lines, as well as node voltage offset data of each of the target connection lines and transformer voltage offset data of the target transformer; With the goal of maximizing the photovoltaic installed capacity of the target transformer, with the second photovoltaic capacity constraint data and the first photovoltaic capacity constraint data as the first constraint condition, with the second voltage constraint data and the first voltage constraint data as the second constraint condition, according to the photovoltaic output coefficient data and load data of the distributed photovoltaic connected to the target connecting line, as well as the node voltage offset data of each of the target connecting line and the transformer voltage offset data of the target transformer, determine the quantity data and configuration data of the distributed photovoltaic connected to each of the target connecting lines.

3. The method according to claim 1, characterized in that The second load-bearing data includes the maximum transmission capacity of the line, the maximum reverse load rate of the line, and the power factor of the line; and determining the second photovoltaic capacity constraint data corresponding to each of the target connecting lines according to the second load-bearing data includes: The second photovoltaic capacity constraint data corresponding to each target connection line is determined according to the maximum transmission capacity of the line, the maximum reverse load rate of the line and the line power factor corresponding to each target connection line.

4. The method according to claim 1, characterized in that: The first load data includes the rated capacity of the transformer, the maximum reverse load rate of the transformer, and the power factor of the transformer; and determining the first photovoltaic capacity constraint data corresponding to the target transformer according to the first load data includes: The first photovoltaic capacity constraint data of the target transformer is determined according to the transformer rated capacity, the transformer maximum reverse load rate and the transformer power factor of the target transformer.

5. The method according to claim 3, characterized in that: The second photovoltaic capacity constraint data corresponding to each target connecting line is determined according to the maximum transmission capacity of the line, the maximum reverse load rate of the line and the power factor of the line corresponding to each target connecting line by the following formula: A xl，i =S xl，i T xl or xl，i，t Among them, A xl，j is the second photovoltaic capacity constraint data corresponding to the i-th target connection line, S xl，i is the maximum transmission capacity of the target connection line of the i-th line, T xl is the maximum reverse load rate of the line, η xl，i， is the line power factor of the i-th target connecting line at time t.

6. The method according to claim 4, characterized in that The first photovoltaic capacity constraint data of the target transformer is determined according to the transformer rated capacity, the transformer maximum reverse load rate and the transformer power factor of the target transformer through the following formula: A z =S z T z or z，t Among them, A z is the first photovoltaic capacity constraint data of the target transformer, S z is the transformer rated capacity of the target transformer, T z is the maximum reverse load rate of the target transformer, η z，t is the transformer power factor of the target transformer at time t.

7. The method according to claim 1, characterized in that The line voltage data includes line rated voltage data; The determining, according to the line voltage data, second voltage constraint data corresponding to each target connection line comprises: A preset line voltage offset ratio of the target connection line is obtained, and voltage constraint data corresponding to each of the target connection lines is determined according to the preset line voltage offset ratio and the line rated voltage data.

8. The method according to claim 1, characterized in that in, The transformer voltage data includes transformer rated voltage data; and determining the first voltage constraint data corresponding to the target transformer according to the transformer voltage data includes: A preset transformer voltage offset ratio of the target transformer is obtained, and transformer voltage constraint data corresponding to the target transformer is determined according to the preset transformer voltage offset ratio and the transformer rated voltage data.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distributed photovoltaic optimization method for remote areas as described in any one of claims 1-8 when executed.

10. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by a processor, the distributed photovoltaic optimization method for remote areas according to any one of claims 1 to 8 is implemented.

Images