Distributed photovoltaic access power distribution network carrying capacity hierarchical grading probability evaluation method

Abstract

The application discloses a distributed photovoltaic access power distribution network carrying capacity layered and graded probability evaluation method. Wherein, the method comprises the following steps: obtaining the historical operation data corresponding to each of a plurality of devices of a target area power distribution network; determining the static photovoltaic carrying capacity evaluation result corresponding to each of the plurality of devices according to the historical operation data corresponding to each of the plurality of devices; determining the photovoltaic carrying capacity probability evaluation result and the weak link classification evaluation result of the photovoltaic carrying capacity corresponding to each of the plurality of devices according to the static photovoltaic carrying capacity evaluation result corresponding to each of the plurality of devices; determining the photovoltaic carrying capacity layered and graded probability evaluation result of the target area power distribution network based on the photovoltaic carrying capacity probability evaluation result and the weak link classification evaluation result of the photovoltaic carrying capacity corresponding to each of the plurality of devices. The application solves the technical problem that the carrying capacity of the distributed photovoltaic access power distribution network cannot be probabilistically evaluated, and realizes the technical effect of providing a reference for the coordinated development of the distributed photovoltaic and the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of distributed power access, and in particular to a hierarchical and graded probability evaluation method for the carrying capacity of a distributed photovoltaic access distribution network. Background Art

[0002] The integration of a high proportion of distributed photovoltaic power into the distribution network has become an inevitable trend, currently characterized by widespread and locally high-density grid connection. The continuous expansion of distributed photovoltaic installations has highlighted issues that restrict the integration of distributed photovoltaic power in some regions, such as weak grid structures, excessive voltage deviations, and limited reverse power. This has brought new challenges to the safe and stable operation and peak load regulation of the distribution network. To ensure the coordinated development of distributed photovoltaic power, loads, and the power grid, it is necessary to assess the future capacity margin of distributed photovoltaic power that can be connected to each node and region based on the stable operating boundaries and actual operating status of the distribution network, providing guidance for the coordinated development and construction of distributed photovoltaic power and distribution networks. Therefore, to effectively solve the problem of distributed photovoltaic grid connection, give full play to the role of distributed photovoltaic power in ensuring power supply, and enhance the distribution network's ability to accommodate distributed photovoltaic power, research on how to scientifically and rationally assess the carrying capacity of distributed photovoltaic power connected to the distribution network, coordinate the coordinated operation of distributed photovoltaic power with adjustable loads, and enhance the safe carrying capacity of the distribution network will become key technical issues in promoting the high-quality development of distributed photovoltaic power. The carrying capacity of distributed photovoltaic access to the distribution network refers to the maximum distributed photovoltaic capacity that can be added to the distribution network under the conditions that the distribution equipment and lines are not overloaded, and the carrying capacity influencing factors such as voltage deviation, short-circuit current, and harmonics do not exceed the limit. Current research on the carrying capacity assessment of distributed PV access to distribution networks focuses on comprehensive assessment, simulation, and optimization modeling. In the comprehensive assessment of the carrying capacity of distributed PV access to distribution networks, researchers have conducted computational studies on distributed PV access capacity by verifying factors influencing PV carrying capacity, such as voltage deviation, harmonics, short-circuit current, three-phase imbalance, network losses, and investment costs. This type of assessment is generally used for rough estimates of extreme time-section scenarios, and regular and timely PV carrying capacity assessments are required. In the assessment of PV carrying capacity based on simulation software, MATLAB, PSCAD, OpenDSS, and other simulation software are often used to continuously increase the proportion of distributed PV access in the distribution network until the carrying capacity assessment index reaches a critical value, thereby achieving PV acceptance capacity assessment and analysis. This type of assessment can verify the safe operation constraints of the distribution network. The principle is relatively simple, but the assessment scenarios are limited. In the optimization modeling of PV carrying capacity, existing research mainly uses the idea of ​​maximizing PV carrying capacity as the objective function, taking into account safe operation constraints to establish an optimization model, and using different optimization algorithms to obtain the optimal solution. However, this approach suffers from complex modeling and solution, and limited applicable scenarios. In general, the conventional research on photovoltaic carrying capacity analysis technology currently mainly starts from the analysis of the impact of distributed photovoltaic grid connection on the power quality, relay protection and other aspects of the distribution network, and gives the carrying capacity under maximum or average load scenarios. It is an assessment of photovoltaic acceptance capacity that only considers extreme time sections or roughly average scenarios, making the assessed photovoltaic carrying capacity conservative or too rough, which limits the sustainable development of distributed photovoltaics.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiment of the present invention provides a hierarchical and graded probabilistic evaluation method for the carrying capacity of distributed photovoltaic access to a distribution network, so as to at least solve the technical problem that the probabilistic evaluation of the carrying capacity of distributed photovoltaic access to a distribution network cannot be performed.

[0005] According to one aspect of an embodiment of the present invention, a method for hierarchical and graded probability assessment of the carrying capacity of a distributed photovoltaic access distribution network is provided, including: obtaining historical operating data corresponding to each of multiple devices corresponding to the target area distribution network; determining static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections based on the historical operating data corresponding to each of the multiple devices; determining photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices in the target area distribution network in a hierarchical order from high voltage to low voltage based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple voltage levels in the target area distribution network; determining the hierarchical and graded probability assessment results of the photovoltaic carrying capacity of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices.

[0006] Optionally, based on the historical operating data corresponding to each of the multiple devices, the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections are determined, including: judging whether the historical operating data corresponding to each of the multiple devices conforms to the safety verification model of photovoltaic carrying capacity influencing factors; when the historical operating data corresponding to each of the multiple devices conforms to the safety verification model of photovoltaic carrying capacity influencing factors, determining the power corresponding to each of the multiple devices at multiple time sections based on the historical operating data corresponding to each of the multiple devices; determining the maximum newly added photovoltaic power corresponding to each of the multiple devices at multiple time sections based on the power corresponding to each of the multiple devices at multiple time sections. Capacity; determine whether the operating data of the areas to be assessed corresponding to multiple devices at multiple time sections after being connected to the distributed photovoltaic devices with their respective maximum newly added photovoltaic capacities meet the safety verification model of factors affecting photovoltaic carrying capacity; if the operating data of the areas to be assessed corresponding to multiple devices at multiple time sections after being connected to the distributed photovoltaic devices with their respective maximum newly added photovoltaic capacities do not meet the safety verification model of factors affecting photovoltaic carrying capacity, a binary evaluation model is used based on the maximum newly added photovoltaic capacities corresponding to the multiple devices at multiple time sections to determine the static photovoltaic carrying capacity evaluation results corresponding to the multiple devices at multiple time sections.

[0007] Optionally, the safety verification model of factors affecting photovoltaic carrying capacity is: Among them, △U H , △U L They are respectively the preset maximum positive voltage deviation limit and the maximum negative voltage deviation limit; δU H,i (t), δU L,i (t) are the calculated values ​​of the maximum positive voltage deviation and the maximum negative voltage deviation on bus i corresponding to device k at time t; I xz,i is the calculated value of the short-circuit current of busbar i; I m Specifies the limit value for the preset short-circuit current; I xb,h (t) is the calculated value of the hth harmonic current at time t; I xh is the preset harmonic current limit.

[0008] Optionally, based on the power corresponding to each of the multiple devices at the multiple time sections, determining the maximum newly added photovoltaic capacity corresponding to each of the multiple devices at the multiple time sections includes: using the reverse load rate formula according to the power corresponding to each of the multiple devices at the multiple time sections Determine the reverse load rate corresponding to multiple devices at multiple time sections, where λ k (t) is the reverse load rate of device k at time t, P Net,k (t) is the reverse power of device k at time t, S e,k is the maximum actual operating limit of device k, time t is one of the multiple time sections, and device k is one of the multiple devices; according to the reverse load rates corresponding to the multiple devices at the multiple time sections, based on the photovoltaic capacity formula Determine the maximum newly added photovoltaic capacity corresponding to multiple devices at multiple time sections, where S M,k (t) is the maximum newly added photovoltaic capacity in the area to be evaluated corresponding to device k at time t; r,k is the allowable reverse transmission margin of device k at time t, η PV (t) is the actual power per unit capacity of the distributed photovoltaic equipment corresponding to the target area distribution network at time t.

[0009] Optionally, based on the maximum newly added photovoltaic capacity corresponding to multiple devices at multiple time sections, a dichotomy evaluation model is used to determine the static photovoltaic carrying capacity evaluation results corresponding to multiple devices at multiple time sections, including: the steps of determining the static photovoltaic carrying capacity result corresponding to device k at time t based on the maximum newly added photovoltaic capacity corresponding to device k at time t are as follows, wherein time t is one of the multiple time sections, and device k is one of the multiple devices: setting the two ends of the dichotomy value interval to S a =0, S b =S M,k (t); Use dichotomous method to evaluate model Sc =(S a +S b ) / 2, obtain the area to be evaluated corresponding to device k with a photovoltaic capacity of S c The operation data of the distributed photovoltaic equipment after the device k is connected to the photovoltaic capacity S c Whether the operating data of the distributed photovoltaic equipment after the installation meets the safety verification model of the factors affecting the photovoltaic carrying capacity; in the area to be evaluated corresponding to the device k, the photovoltaic capacity S c When the operating data of the distributed photovoltaic equipment meets the safety verification model of the factors affecting photovoltaic carrying capacity, control S a =S c ; In the area to be evaluated corresponding to device k, the photovoltaic capacity connected is S c When the operating data of the distributed photovoltaic equipment does not meet the safety verification model of the factors affecting the photovoltaic carrying capacity, control S b =S c ;Judgement|S a -S b |<δ holds true, where δ is the preset evaluation accuracy; in |S a -S b When |<δ does not hold, repeat the above dichotomy evaluation model S c =(S a +S b ) / 2, obtain the area to be evaluated corresponding to device k with a photovoltaic capacity of S c The operation data of the distributed photovoltaic equipment after the photovoltaic capacity is connected is used to determine whether the operation data of the area to be evaluated corresponding to the device k after the distributed photovoltaic equipment with a photovoltaic capacity of is connected meets the safety verification model steps of the photovoltaic carrying capacity influencing factors, until |S a -S b |<δ holds; in |S a -S b When |<δ holds true, take S a and S b The minimum value among them is taken as the static photovoltaic carrying capacity evaluation result of device k at time t.

[0010] Optionally, according to the static photovoltaic carrying capacity assessment results corresponding to multiple devices at multiple time sections in the entire historical time set, based on multiple voltage levels in the target area distribution network, the photovoltaic carrying capacity probability assessment results corresponding to multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to multiple devices are determined in sequence from high voltage to low voltage, including: judging whether the target area distribution network has power flow reverse transmission to the preset voltage level power grid; in the case that the target area distribution network does not have power flow reverse transmission to the preset voltage level power grid, according to the hierarchical order, a graded assessment model is used to determine the photovoltaic carrying capacity weak link grading assessment results corresponding to multiple devices, and based on the percentile statistical model, the photovoltaic carrying capacity probability assessment results corresponding to multiple voltage levels are determined.

[0011] Optionally, when the target area distribution network does not reverse the flow to the preset voltage level grid, a graded assessment model is used according to the hierarchical order to determine the graded assessment results of the photovoltaic carrying capacity weak links corresponding to multiple devices, including: based on the static photovoltaic carrying capacity assessment results corresponding to multiple devices at multiple time sections in the entire historical time set, a safety verification probability assessment model is used to perform safety verification on the devices corresponding to multiple voltage levels; when there is a device corresponding to the target voltage level in multiple voltage levels that does not comply with the safety verification probability assessment model, it is determined that the target voltage level and the voltage levels below the target voltage level in the hierarchical order are all the highest level I of the photovoltaic carrying capacity weak link level.

[0012] Optionally, the safety check probability assessment model is These include the voltage deviation probability constraint check inequality model, the short-circuit current probability constraint check inequality model and the harmonic content probability constraint check inequality model, △U H , △U L are the preset maximum positive voltage deviation and maximum negative voltage deviation limits respectively; δU H,i (t), δU L,i (t) are the calculated values ​​of the maximum positive voltage deviation and the maximum negative voltage deviation on bus i corresponding to the voltage level at time t; I xz,i is the calculated value of the short-circuit current of busbar i set according to the preset rules; I m Specifies the limit for short-circuit current; I xb,h (t) is the actual obtained harmonic current value of the hth order at time t; I xh is the preset harmonic current limit; Pr{·} is the probability of the event being established; Ω is the entire historical time set; ε V , ε I , ε xThey are the allowable values ​​of voltage deviation verification probability level, short-circuit current verification probability level, and harmonic content verification probability level. The allowable value of short-circuit current verification probability level is the power supply reliability index in the target area.

[0013] Optionally, the grade assessment model is Among them, G B,k is the photovoltaic load-bearing capacity weak link level of equipment k, λ k (t) is the reverse load rate of device k at time t; λ r,k Characterizes the allowable reverse transmission margin of device k; λ c,k (t) is the controllable margin coefficient of device k at time t; Pr{·} is the probability of the event being established; Ω is the entire set of historical time; ε1, ε2, ε3, and ε4 are the preset probabilities respectively.

[0014] Optionally, ε1, ε2, ε3, and ε4 satisfy the following conditions:

[0015] Optionally, based on Determine the controllable margin coefficient of device k at time t, where P c,k (t) is the maximum load-type adjustable power of the area to be evaluated corresponding to device k at time t, S e,k is the maximum actual operating limit of device k. The maximum load type of the area to be evaluated corresponding to device k can be increased by power P c,k (t) is obtained based on the following model:

[0016]

[0017] Among them, △P load+,k,m (t) is the maximum adjustable power of the mth flexible load at time t in the evaluation area corresponding to device k; △P ess+,k,m (t) is the maximum adjustable power of the mth energy storage in the evaluation area corresponding to device k at time t; △P GT-,k,m (t) is the maximum adjustable power of the mth other adjustable distributed power source in the evaluation area corresponding to device k at time t; P load,max,k,m is the upper limit of the rated power of the mth flexible load in the evaluation area corresponding to device k; P load,k,m (t) is the power of the mth flexible load at time t in the evaluation area corresponding to device k; P ch,max,k,m is the maximum allowable charging power of the mth energy storage in the evaluation area corresponding to device k; P ess,k,m (t) is the power of the mth energy storage at time t in the evaluation area corresponding to device k, which is positive for charging and negative for discharging; E ess,k,m is the installed capacity of the mth energy storage in the evaluation area corresponding to device k; SOC max,k,mis the upper limit of the mth energy storage state of charge in the evaluation area corresponding to device k; SOC k,m (t) is the state of charge value of the mth energy storage at time t in the evaluation area corresponding to device k; η ch,k,m is the charging efficiency of the mth energy storage in the evaluation area corresponding to device k; △T is the control time interval in the historical scene statistics; P GT,k,m (t) is the output of the mth other adjustable distributed generation in the evaluation area corresponding to device k at time t; r down, k ,m N is the downward ramp rate of the mth other adjustable distributed power source in the evaluation area corresponding to device k; load,k 、N ess,k 、N GT,k are the total number of flexible loads, energy storage and other adjustable distributed power sources in the evaluation area corresponding to device k.

[0018] Optionally, the percentile statistical model is S ε,i =max{S a |Pr{S a ≤S M,i (t)}≥ε a},t∈Ω,ε a S is the percentile of the distribution network carrying capacity assessment for distributed photovoltaic access in the target area; ε,i The distribution network bus i in the target area is at percentile ε a Maximum bearing capacity when S M,i (t) is the photovoltaic carrying capacity of the target area distribution network bus i at time t, which is given by the formula S M,i (t) = min(S' M,i (t),S M,i-1 (t)) is determined, where S' M,i (t) = S M,k (t), S' M,i (t) is the photovoltaic carrying capacity assessment result of the adjacent busbar i under the positive power flow at time t for device k, S M,i-1 (t) is the photovoltaic carrying capacity assessment result of the adjacent upper-level busbar i-1 under the positive power flow at time t.

[0019] According to another aspect of an embodiment of the present invention, a distributed photovoltaic access distribution network carrying capacity hierarchical and graded probability assessment system is also provided, including: an acquisition module for acquiring historical operating data corresponding to each of multiple devices corresponding to the target area distribution network; a first determination module for determining the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections based on the historical operating data corresponding to each of the multiple devices; a second determination module for determining, based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections within the entire historical time set, the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices in the target area distribution network in a hierarchical order from high voltage to low voltage; a third determination module for determining the photovoltaic carrying capacity hierarchical and graded probability assessment results of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices.

[0020] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is also provided, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned distributed photovoltaic access distribution network carrying capacity hierarchical and graded probability assessment methods.

[0021] According to another aspect of an embodiment of the present invention, a computer device is provided, comprising a processor for running a program, wherein when the program is run, any one of the above-mentioned methods for hierarchical and graded probability assessment of the carrying capacity of distributed photovoltaic access to a distribution network is executed.

[0022] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for hierarchical and graded probability assessment of the carrying capacity of distributed photovoltaic access to a distribution network.

[0023] In an embodiment of the present invention, a carrying capacity assessment method for distributed photovoltaic access to a distribution network is adopted, by obtaining historical operating data corresponding to each of a plurality of devices corresponding to the target area distribution network; determining static photovoltaic carrying capacity assessment results corresponding to each of the plurality of devices at a plurality of time sections based on the historical operating data corresponding to each of the plurality of devices; determining photovoltaic carrying capacity probability assessment results corresponding to each of the plurality of voltage levels and photovoltaic carrying capacity weak link grading assessment results corresponding to each of the plurality of devices in the target area distribution network based on the static photovoltaic carrying capacity assessment results corresponding to each of the plurality of devices at a plurality of time sections within the entire historical time set; determining photovoltaic carrying capacity probability assessment results corresponding to each of the plurality of voltage levels and photovoltaic carrying capacity weak link grading assessment results corresponding to each of the plurality of devices based on the plurality of voltage levels in the target area distribution network; determining photovoltaic carrying capacity layered and graded probability assessment results of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to each of the plurality of voltage levels and photovoltaic carrying capacity weak link grading assessment results corresponding to each of the plurality of devices, thereby achieving the purpose of accurately probabilistically assessing photovoltaic carrying capacity, thereby achieving the technical effect of providing a reference for the development of distributed photovoltaic and distribution networks, and further solving the technical problem of being unable to perform probabilistic assessment of the carrying capacity of distributed photovoltaic access to the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 A hardware structure block diagram of a computer terminal for implementing a hierarchical and graded probabilistic evaluation method for the carrying capacity of distributed photovoltaic access to a distribution network is shown;

[0026] Figure 2 1. It is a flowchart of a hierarchical and graded probability evaluation method for distributed photovoltaic access to a distribution network carrying capacity according to an embodiment of the present invention;

[0027] Figure 3 This is a structural block diagram of a hierarchical and graded probability assessment system for the carrying capacity of distributed photovoltaic access to a distribution network provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description 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 numbers 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] According to an embodiment of the present invention, an embodiment of a method for hierarchical and graded probability assessment of the carrying capacity of a distributed photovoltaic access distribution network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0031] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a hierarchical and graded probability evaluation method for distributed photovoltaic access to a distribution network is shown. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0032] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0033] Memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the distributed photovoltaic access distribution network carrying capacity hierarchical and graded probabilistic assessment method in the embodiments of the present invention. The processor executes the software programs and modules stored in memory 104 to perform various functional applications and data processing, thereby implementing the distributed photovoltaic access distribution network carrying capacity hierarchical and graded probabilistic assessment method for the aforementioned application. Memory 104 can include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 can further include memory remotely located from the processor, which can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0034] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0035] Insufficient load-absorbing capacity and high solar irradiance can result in load output lower than that of distributed photovoltaic systems. This can affect some upstream lines and transformers, leading to reverse power flow. Further additions of distributed photovoltaic systems will significantly increase their transmission power and reverse load factor. To keep the reverse load factor of lines and transformers within a safe margin, it is necessary to assess the carrying capacity of distributed photovoltaic systems connected to the distribution network. Figure 2 FIG. 1 is a flow chart of a hierarchical and graded probability evaluation method for distributed photovoltaic access to a distribution network carrying capacity according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0036] Step S201: Obtain historical operation data corresponding to multiple devices corresponding to the target area distribution network.

[0037] In this step, historical data corresponding to multiple devices corresponding to the target area's distribution network is obtained. This data may include time-series data such as load power, voltage data, short-circuit current, harmonic current, distributed photovoltaic and other power output of each device in the distribution network within the target measurement time. The devices in the distribution network may include lines, transformers, and other equipment. Each transformer or line in the target area has its own corresponding power supply area, which is the area to be evaluated. Specifically, based on the historical operating data of the distribution network in multiple historical scenarios, a time-section power flow calculation can be performed to obtain the power flowing on the corresponding lines or transformers in the distribution network.

[0038] Step S202 : determining static photovoltaic carrying capacity assessment results corresponding to each of the plurality of devices at a plurality of time sections based on the historical operation data corresponding to each of the plurality of devices.

[0039] In this step, the factors affecting the assessment of the carrying capacity of distributed photovoltaic access to the distribution network mainly include the thermal stability of the distribution equipment, voltage deviation exceeding the limit, power quality exceeding the standard, and protection failure. In the static assessment process of the carrying capacity of distributed photovoltaic access to the distribution network, the voltage deviation verification, short-circuit current verification and harmonic content safety verification can be performed first. After the safety verification, the static photovoltaic carrying capacity assessment results corresponding to the multiple devices at multiple time sections can be determined based on the historical operation data corresponding to the multiple devices. Specifically, the target newly added distributed photovoltaic capacity corresponding to the multiple devices can be determined based on the historical operation data corresponding to the multiple devices. The target newly added distributed photovoltaic capacity is the photovoltaic capacity that the corresponding device can still carry. Among them, the load in the power supply area of ​​the line or transformer and the total output of distributed photovoltaic can be obtained first. According to the distributed photovoltaic output and the output of other distributed power sources, the reverse load rate corresponding to the line or transformer can be determined. Then, the reverse load rate at this moment is used as the evaluation object to calculate the maximum accessible capacity in the power supply area corresponding to the line or transformer. The dichotomy method can be used to determine the newly added distributed photovoltaic capacity results corresponding to multiple devices based on the maximum newly added distributed photovoltaic capacity obtained, and then the static photovoltaic carrying capacity evaluation results corresponding to multiple devices can be determined.

[0040] Step S203: Based on the static photovoltaic carrying capacity assessment results corresponding to multiple devices at multiple time sections in the entire historical time set, and based on the multiple voltage levels in the target area distribution network, determine the photovoltaic carrying capacity probability assessment results corresponding to multiple voltage levels and the photovoltaic carrying capacity weak link graded assessment results corresponding to multiple devices in order from high voltage to low voltage.

[0041] In this step, the complete set of historical time can be a combination of multiple time sections. There will be multiple voltage levels in the target area distribution network, such as high voltage, medium voltage, and low voltage, and different voltage levels will correspond to different equipment. According to the static photovoltaic carrying capacity assessment results corresponding to multiple devices, the photovoltaic carrying capacity probability assessment results corresponding to each voltage level and the photovoltaic carrying capacity weak link grading assessment results corresponding to each device can be determined in sequence from high voltage to low voltage based on the order of voltage levels. Among them, the photovoltaic carrying capacity probability assessment result of the voltage level is related to the photovoltaic carrying capacity of the equipment within the voltage level. The higher the level in the weak link grading assessment result, the less weak the photovoltaic carrying capacity of the equipment can be considered. The evaluation is performed in the order of high voltage to low voltage because if the photovoltaic carrying capacity of the high voltage level is weak, the photovoltaic carrying capacity below the high voltage level will not exceed the high voltage level.

[0042] Step S204 , based on the photovoltaic carrying capacity probability assessment results corresponding to the multiple voltage levels and the photovoltaic carrying capacity weak link graded assessment results corresponding to the multiple devices, a photovoltaic carrying capacity hierarchical probability assessment result of the target area distribution network is determined.

[0043] In this step, based on the PV capacity probability assessment results for each of the multiple voltage levels and the PV capacity vulnerability assessment results for each of the multiple devices, the overall capacity assessment of the distribution network in the target area is determined. This assessment, in turn, determines the target area's capacity for distributed PV. This result provides an overview of the distribution network in the target area and provides a reference for integrating distributed PV into the target area.

[0044] Through the above steps, the accuracy of the probabilistic assessment of photovoltaic carrying capacity is achieved, thereby achieving the technical effect of providing a reference for the development of distributed photovoltaic and distribution networks, and further solving the technical problem of being unable to perform probabilistic assessment of the carrying capacity of distributed photovoltaic access to the distribution network.

[0045] As an optional embodiment, based on the historical operation data corresponding to each of the multiple devices, the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections are determined, including: judging whether the historical operation data corresponding to each of the multiple devices conforms to the safety verification model of photovoltaic carrying capacity influencing factors; when the historical operation data corresponding to each of the multiple devices conforms to the safety verification model of photovoltaic carrying capacity influencing factors, determining the power corresponding to each of the multiple devices at multiple time sections based on the historical operation data corresponding to each of the multiple devices; determining the maximum renewable power supply corresponding to each of the multiple devices at multiple time sections based on the power corresponding to each of the multiple devices at multiple time sections. Increase photovoltaic capacity; determine whether the operating data of the areas to be assessed corresponding to multiple devices at multiple time sections after being connected to the distributed photovoltaic devices with their respective maximum newly added photovoltaic capacities meet the safety verification model of factors affecting photovoltaic carrying capacity; if the operating data of the areas to be assessed corresponding to multiple devices at multiple time sections after being connected to the distributed photovoltaic devices with their respective maximum newly added photovoltaic capacities do not meet the safety verification model of factors affecting photovoltaic carrying capacity, based on the maximum newly added photovoltaic capacities corresponding to multiple devices at multiple time sections, a dichotomy evaluation model is used to determine the static photovoltaic carrying capacity evaluation results corresponding to multiple devices at multiple time sections.

[0046] Optionally, a safety check can be performed based on the historical operating data corresponding to each of the multiple devices, that is, to determine whether the historical operating data conforms to the safety check model of factors affecting photovoltaic carrying capacity. The safety check can include voltage deviation check, short-circuit current check, and harmonic check. The safety check model of factors affecting photovoltaic carrying capacity is Among them, △U H , △U L They are respectively the preset maximum positive voltage deviation limit and the maximum negative voltage deviation limit; δU H,i (t), δU L,i (t) are the calculated values ​​of the maximum positive voltage deviation and the maximum negative voltage deviation on bus i corresponding to device k at time t; I xz,i is the calculated value of the short-circuit current of busbar i; I m Specifies the limit value for the preset short-circuit current; I xb,h (t) is the calculated value of the hth harmonic current at time t; I xh The default harmonic current limit is the harmonic current limit specified in the relevant standards. If the historical operating data of a device fails the safety check, it can be considered that the PV load capacity of the power supply area corresponding to the device is weak. If the historical operating data of the device conforms to the safety check model of factors affecting PV load capacity, that is, if it passes the safety check, the power corresponding to multiple devices at multiple time intervals can be determined based on the historical operating data corresponding to each of the multiple devices at multiple time intervals.

[0047] Specifically, we can calculate the power flow of the distribution network in time sections based on the historical operation data of the distribution network in multiple historical scenarios, and obtain the power flowing through multiple lines or transformer devices in the distribution network. Then, we can use the reverse load rate formula based on the power corresponding to each device in multiple time sections. Determine the reverse load rate corresponding to multiple devices at multiple time sections, where λ k (t) is the reverse load rate of device k at time t, P Net,k (t) is the reverse power of device k at time t, S e,k is the maximum actual operating limit of device k, time t is one of the multiple time sections, and device k is one of the multiple devices; according to the reverse load rates corresponding to the multiple devices at the multiple time sections, based on the photovoltaic capacity formula Determine the maximum newly added photovoltaic capacity corresponding to multiple devices at multiple time sections, where S M,k (t) is the maximum newly added photovoltaic capacity in the area to be evaluated corresponding to device k at time t; r,k is the allowable reverse transmission margin of device k at time t, η PV (t) is the actual power per unit capacity of the distributed PV device corresponding to the target area's distribution network at time t. The operating data of the distributed PV devices with the maximum PV capacity that can be added to the target area's corresponding areas at multiple time intervals can be used to determine whether they meet the safety verification model for factors affecting PV capacity. This means determining whether the operating data passes the safety verification. If the operating data passes the safety verification, the maximum PV capacity that can be added can be used as the PV capacity that can be added to the distributed PV device, representing the static PV capacity assessment result for the device.

[0048] If the operating data of the areas to be assessed corresponding to multiple devices at multiple time sections after being connected to the distributed photovoltaic devices with the maximum newly added photovoltaic capacity do not meet the safety verification model of the factors affecting photovoltaic carrying capacity, the binary evaluation model can be used based on the maximum newly added photovoltaic capacity corresponding to the multiple devices at multiple time sections to determine the static photovoltaic carrying capacity evaluation results corresponding to the multiple devices at multiple time sections. Specifically, the binary value interval can be set to S a =0, S b =S M,k (t), then the dichotomy method is used to evaluate the model S c =(S a +S b ) / 2, obtain the corresponding device in the area to be evaluated when the photovoltaic capacity is S cThe operation data of the distributed photovoltaic equipment after the device is connected; determine the area to be evaluated corresponding to the device when the photovoltaic capacity is S c Whether the operating data of the distributed photovoltaic equipment after installation complies with the safety verification model of factors affecting photovoltaic carrying capacity, that is, whether the operating data can pass the safety verification; in the area to be evaluated corresponding to the equipment, when the photovoltaic capacity is S c If the operating data of the distributed photovoltaic equipment meets the safety verification model of the factors affecting photovoltaic carrying capacity, S can be controlled. a =S c If it does not meet the requirements, that is, the operating data does not pass the safety check, S b =S c Then judge |S a -S b |<δ holds true, where δ is the preset evaluation accuracy; in |S a -S b When |<δ does not hold, repeat the above dichotomy evaluation model S c =(S a +S b ) / 2, obtain the area to be evaluated corresponding to device k with a photovoltaic capacity of S c The operating data of the distributed photovoltaic equipment after the operation is completed, and it is judged again whether it passes the safety check until |S a -S b |<δ holds; in |S a -S b When |<δ holds, we can take S a and S b The minimum value among them is taken as the static photovoltaic carrying capacity evaluation result of the equipment at time t.

[0049] As an optional embodiment, according to the static photovoltaic carrying capacity assessment results corresponding to multiple devices at multiple time sections in the entire historical time set, based on multiple voltage levels in the target area distribution network, the photovoltaic carrying capacity probability assessment results corresponding to multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to multiple devices are determined in sequence from high voltage to low voltage, including: judging whether the target area distribution network has power flow reverse transmission to the preset voltage level power grid; in the case that the target area distribution network does not have power flow reverse transmission to the preset voltage level power grid, according to the hierarchical order, a graded assessment model is adopted to determine the photovoltaic carrying capacity weak link grading assessment results corresponding to multiple devices, and based on the percentile statistical model, the photovoltaic carrying capacity probability assessment results corresponding to multiple voltage levels are determined.

[0050] Optionally, to determine the probability assessment results of the photovoltaic carrying capacity corresponding to each of the multiple voltage levels, it is possible to first determine whether there is a case where the target area distribution network performs a reverse flow of power to the preset voltage level power grid. The preset voltage level here can be 220KV and above. If so, it proves that the target area distribution network is no longer able to withstand the access of excess distributed photovoltaics, that is, the overall assessment level of the weak link of the distributed photovoltaic carrying capacity of the target area distribution network is the highest level, that is, the photovoltaic carrying capacity is the weakest at this time. In the case that there is no reverse flow in the target area distribution network, a graded assessment model can be used to perform a graded assessment of the weak links of the photovoltaic carrying capacity of multiple devices based on the hierarchical order. At the same time, the photovoltaic carrying capacity assessment results corresponding to each of the multiple voltage levels can be determined based on the percentile statistical model.

[0051] According to the hierarchical order, a hierarchical assessment model is used to determine the graded assessment results of the photovoltaic carrying capacity weaknesses corresponding to multiple devices. Based on the static photovoltaic carrying capacity assessment results corresponding to multiple devices at multiple time sections in the entire historical time set, the safety verification probability assessment model can be used to first perform safety verification on the devices corresponding to multiple voltage levels. In the case that the equipment corresponding to the target voltage level in multiple voltage levels does not meet the safety verification probability assessment model, that is, it fails the safety verification, it can be determined that the target voltage level and the voltage levels below the target voltage level in the hierarchical order are the highest level of the photovoltaic carrying capacity weaknesses. Among them, the safety verification probability assessment model is These include the voltage deviation probability constraint check inequality model, the short-circuit current probability constraint check inequality model and the harmonic content probability constraint check inequality model, △U H , △U L are the preset maximum positive voltage deviation and maximum negative voltage deviation limits respectively; δU H,i (t), δU L,i (t) are the calculated values ​​of the maximum positive voltage deviation and the maximum negative voltage deviation on bus i corresponding to the voltage level at time t; I xz,i is the actual calculated value of busbar i short-circuit current according to standards and guidelines; I m Specifies the limit for short-circuit current; I xb,h (t) is the actual obtained harmonic current value of the hth order at time t; I xh is the preset harmonic current limit; Pr{·} is the probability of the event being established; Ω is the entire historical time set; ε V , ε I , ε x They are the allowable values ​​of voltage deviation verification probability level, short-circuit current verification probability level, and harmonic content verification probability level. The allowable value of short-circuit current verification probability level is the local power supply reliability index.

[0052] As an optional embodiment, the grade evaluation model is Among them, G B,k is the photovoltaic load-bearing capacity weak link level of equipment k, λ k (t) is the reverse load rate of device k at time t; λ r,k Characterizes the allowable reverse transmission margin of device k; λ c,k (t) is the controllable margin coefficient of device k at time t; Pr{·} is the probability of the event being established; Ω is the entire set of historical time; ε1, ε2, ε3, and ε4 are the preset probabilities, where ε1, ε2, ε3, and ε4 meet the following conditions:

[0053]

[0054] Optionally, the values ​​of ε1, ε2, ε3, and ε4 can be determined according to actual conditions. For example, if the area to be evaluated does not allow reverse flow before and after regulation, then the probability level allowable value is set to ε1 = ε2 = ε3 = 0, and the evaluation level is divided into two levels: the highest level and the lowest level. k (t)≥0}>0, the assessment level is the highest level, that is, the photovoltaic carrying capacity is the weakest, otherwise the assessment level is the lowest level. If the area to be assessed does not allow the flow to be reversed only after regulation, then the probability level allowable value is set to ε1=ε2=0, if Pr{λ k (t)≥0}>0 or Pr{λ c,k (t)≤λ k (t)<λ r,k}>0, the assessment level is I, the highest level. If the area to be assessed does not allow the situation where the thermal stability margin of the power distribution equipment capacity is exceeded, then the probability level allowable value is set to ε1=0. If Pr{λ k (t)≥λ r,k}>0, the evaluation level is Ⅰ, which is the highest level.

[0055] The above mentioned adjustable margin coefficient can be calculated according to the formula To determine, where P c,k (t) is the maximum load-type adjustable power of the area to be evaluated corresponding to device k at time t, S e,k is the maximum practical operating limit of equipment k. c,k (t), that is, the maximum load-type adjustable power of the area to be evaluated corresponding to device k at time t can be determined according to the following formula Among them, △P load+,k,m (t) is the maximum adjustable power of the mth flexible load at time t in the evaluation area corresponding to device k; △P ess+,k,m (t) is the maximum adjustable power of the mth energy storage at time t in the evaluation area corresponding to device k (charging load type); △PGT-,k,m (t) is the maximum adjustable power of the mth other adjustable distributed power source in the evaluation area corresponding to device k at time t (reducing the power of the power source is equivalent to increasing the load power); P load,max,k,m is the upper limit of the rated power of the mth flexible load in the evaluation area corresponding to device k; P load,k,m (t) is the power of the mth flexible load at time t in the evaluation area corresponding to device k; P ch,max,k,m is the maximum allowable charging power of the mth energy storage in the evaluation area corresponding to device k; P ess,k,m (t) is the power of the mth energy storage at time t in the evaluation area corresponding to device k, which is positive for charging and negative for discharging; E ess,k,m is the installed capacity of the mth energy storage in the evaluation area corresponding to device k; SOC max,k,m is the upper limit of the mth energy storage state of charge in the evaluation area corresponding to device k; SOC k,m (t) is the state of charge value of the mth energy storage at time t in the evaluation area corresponding to device k; η ch,k,m is the charging efficiency of the mth energy storage in the evaluation area corresponding to device k; △T is the control time interval in the historical scene statistics; P GT,k,m (t) is the output of the mth other adjustable distributed generation in the evaluation area corresponding to device k at time t; r down,k,m N is the downward ramp rate of the mth other adjustable distributed power source in the evaluation area corresponding to device k; load,k 、N ess,k 、N GT,k are the total number of flexible loads, energy storage and other adjustable distributed power sources in the evaluation area corresponding to device k.

[0056] As an optional embodiment, the percentile statistical model is S ε,i =max{S a |Pr{S a ≤S M,i (t)}≥ε a},t∈Ω,ε a S is the percentile of the distribution network carrying capacity assessment for distributed photovoltaic access in the target area; ε,i The distribution network bus i in the target area is at percentile ε a Maximum bearing capacity when S M,i (t) is the photovoltaic carrying capacity of the target area distribution network bus i at time t, which is given by the formula S M,i (t) = min(S' M,i (t),S M,i-1 (t)) is determined, where S' M,i (t) = S M,k (t), S' M,i(t) is the photovoltaic carrying capacity assessment result of the adjacent busbar i under the positive power flow at time t for device k, S M,i-1 (t) is the photovoltaic carrying capacity assessment result of the adjacent upper-level busbar i-1 under the positive power flow at time t.

[0057] Alternatively, S can be calculated based on the percentile statistical model. ε,i =max{S a |Pr{S a ≤S M,i (t)}≥ε a}, t∈Ω, to determine the PV load capacity assessment results corresponding to multiple voltage levels. Percentile statistics is a mathematical statistical method that sorts sample statistical data and calculates the percentile of each data position. Here, the load capacity values ​​in a set of n historical scenarios can be sorted by size, and the load capacity value at the p% position is called the PV load capacity at the pth percentile. This statistical method comprehensively considers all historical scenario conditions, sorting the PV load capacity values ​​from large to small, and providing load capacity statistics based on a probability distribution. By evaluating the load capacity of distributed PV at different percentiles, it is possible to avoid deviations from extreme cases and improve the credibility of the load capacity assessment under the historical scenario set. For example, if there are 20 PV carrying capacity values ​​(in MW) in the statistics, the order from largest to smallest is: 10, 9.8, 9.8, 9.5, 9.4, 9.4, 9.3, 9.1, 9.0, 9.0, 9.0, 8.8, 8.6, 8.6, 7.5, 6.5, 6.4, 6.3, 6.2, 2.1. When the carrying capacity percentile is set to 95%, the 20th × 95% = 19th value 6.2 is the maximum carrying capacity when the percentile is 95%, that is, S ε,i ,From the above formula, we can see that in the historical statistical scenario set, 95% of the scenarios have an assessed carrying capacity value of no less than 6.2MW.

[0058] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0059] Through the description of the above implementation methods, those skilled in the art can clearly understand that the hierarchical and graded probability assessment method for the carrying capacity of distributed photovoltaic access to the distribution network according to the above embodiment can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0060] According to an embodiment of the present invention, a distributed photovoltaic access distribution network carrying capacity hierarchical probability evaluation system for implementing the above-mentioned distributed photovoltaic access distribution network carrying capacity hierarchical probability evaluation method is also provided. Figure 3 is a structural block diagram of a hierarchical and graded probability evaluation system for distributed photovoltaic access to a distribution network carrying capacity according to an embodiment of the present invention. Figure 3 As shown, the distributed photovoltaic access distribution network carrying capacity hierarchical probability assessment system includes: an acquisition module 31, a first determination module 32, a second determination module 33 and a third determination module 34. The distributed photovoltaic access distribution network carrying capacity hierarchical probability assessment system is described below.

[0061] The acquisition module 31 is used to obtain historical operation data corresponding to multiple devices corresponding to the target area distribution network.

[0062] The first determination module 32 is configured to determine static photovoltaic carrying capacity assessment results corresponding to each of the plurality of devices at a plurality of time sections based on the historical operation data corresponding to each of the plurality of devices.

[0063] The second determination module 33 is used to determine the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link classification assessment results corresponding to each of the multiple devices in the multiple time sections within the entire historical time set, based on the multiple voltage levels in the target area distribution network, in order from high voltage to low voltage.

[0064] The third determination module 34 is used to determine the photovoltaic carrying capacity layered and graded probability assessment results of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to multiple voltage levels and the photovoltaic carrying capacity weak link graded assessment results corresponding to multiple devices.

[0065] It should be noted that the acquisition module 31, first determination module 32, second determination module 33, and third determination module 34 correspond to steps S201 to S204 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the system, can be run on the computer terminal 10 provided in the embodiment.

[0066] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0067] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the distributed photovoltaic access distribution network carrying capacity hierarchical and graded probabilistic assessment method and system in the embodiments of the present invention. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the above-mentioned distributed photovoltaic access distribution network carrying capacity hierarchical and graded probabilistic assessment method. The memory can include high-speed random access memory and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include a memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0068] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain the historical operation data corresponding to each of the multiple devices corresponding to the target area distribution network; determine the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections based on the historical operation data corresponding to each of the multiple devices; based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections in the entire historical time set, determine the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices in the target area distribution network in order from high voltage to low voltage; based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices, determine the photovoltaic carrying capacity layered and graded probability assessment results of the target area distribution network.

[0069] By adopting an embodiment of the present invention, a method for evaluating the carrying capacity of distributed photovoltaic access to a distribution network is provided, which obtains historical operating data corresponding to each of multiple devices corresponding to the distribution network in a target area; determines static photovoltaic carrying capacity evaluation results corresponding to each of the multiple devices at multiple time sections based on the historical operating data corresponding to each of the multiple devices; determines photovoltaic carrying capacity probability evaluation results corresponding to each of the multiple voltage levels and photovoltaic carrying capacity weak link grading evaluation results corresponding to each of the multiple devices in the distribution network in the target area in a hierarchical order from high voltage to low voltage based on the static photovoltaic carrying capacity evaluation results corresponding to each of the multiple voltage levels; determines the photovoltaic carrying capacity layered and graded probability evaluation results of the distribution network in the target area based on the photovoltaic carrying capacity probability evaluation results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading evaluation results corresponding to each of the multiple devices, thereby achieving the purpose of accurately evaluating the photovoltaic carrying capacity probabilistically, thereby realizing the technical effect of providing a reference for the development of distributed photovoltaic and distribution networks, and further solving the technical problem of being unable to perform probabilistic evaluation of the carrying capacity of distributed photovoltaic access to the distribution network.

[0070] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0071] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the method for evaluating the carrying capacity of distributed photovoltaic access to a power distribution network provided in the embodiment above.

[0072] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0073] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining historical operating data corresponding to each of the multiple devices corresponding to the target area distribution network; determining the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections based on the historical operating data corresponding to each of the multiple devices; determining the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices in the target area distribution network in order from high voltage to low voltage based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple voltage levels in the target area distribution network; determining the photovoltaic carrying capacity stratification and grading probability assessment results of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices.

[0074] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: obtaining historical operating data corresponding to each of the multiple devices corresponding to the target area distribution network; determining the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections based on the historical operating data corresponding to each of the multiple devices; based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections in the entire historical time set, based on the multiple voltage levels in the target area distribution network, determine the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices in order from high voltage to low voltage; based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices, determine the photovoltaic carrying capacity layered and graded probability assessment results of the target area distribution network.

[0075] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0076] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0078] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0079] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0081] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A hierarchical and graded probability evaluation method for the carrying capacity of distributed photovoltaic access distribution networks, characterized in that: include: Obtain historical operating data corresponding to multiple devices in the target area distribution network; Determining static photovoltaic load capacity assessment results corresponding to each of the multiple devices at multiple time sections based on historical operation data corresponding to each of the multiple devices; Based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections within the entire historical time set, and based on multiple voltage levels in the target area distribution network, determine the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weakness link graded assessment results corresponding to each of the multiple devices in descending order from high voltage; Determine a tiered and graded probability assessment result of the photovoltaic carrying capacity of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link graded assessment results corresponding to each of the multiple devices; Among them, according to the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections in the entire historical time set, based on the multiple voltage levels in the target area distribution network, the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices are determined in sequence from high voltage to low voltage, including: judging whether the target area distribution network has a power flow reverse transmission to the preset voltage level power grid; in the case that the target area distribution network does not have a power flow reverse transmission to the preset voltage level power grid, according to the hierarchical order, a graded assessment model is used to determine the photovoltaic carrying capacity weak link grading assessment results corresponding to each of the multiple devices, and based on the percentile statistical model, the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels are determined; The grade evaluation model is Among them, G B,k is the photovoltaic load-bearing capacity weak link level of equipment k, λ k (t) is the reverse load rate of device k at time t; λ r,k Characterizes the allowable reverse transmission margin of device k; λ c,k (t) is the controllable margin coefficient of device k at time t; is the probability of the event being established; Ω is the entire set of historical time; ε1, ε2, ε3, and ε4 are respectively the preset probabilities; Among them, based on Determine the controllable margin coefficient of device k at time t, where P c,k (t) is the maximum load-type adjustable power of the area to be evaluated corresponding to device k at time t, S e,k is the maximum actual operating limit of device k, and the maximum load type of the area to be evaluated corresponding to device k can be increased by power P c,k (t) is obtained based on the following model: Where ΔP load+,k,m (t) is the maximum adjustable power of the mth flexible load at time t in the evaluation area corresponding to device k; ΔP ess+,k,m (t) is the maximum adjustable power of the mth energy storage in the evaluation area corresponding to device k at time t; ΔP GT-,k,m (t) is the maximum adjustable power of the mth other adjustable distributed power source in the evaluation area corresponding to device k at time t; P load,max,k,m is the upper limit of the rated power of the mth flexible load in the evaluation area corresponding to device k; P load,k,m (t) is the power of the mth flexible load at time t in the evaluation area corresponding to device k; P ch,max,k,m is the maximum allowable charging power of the mth energy storage in the evaluation area corresponding to device k; P ess,k,m (t) is the power of the mth energy storage at time t in the evaluation area corresponding to device k, which is positive for charging and negative for discharging; E ess,k,m is the installed capacity of the mth energy storage in the evaluation area corresponding to device k; SOC max,k,m is the upper limit of the mth energy storage state of charge in the evaluation area corresponding to device k; SOC k,m (t) is the state of charge value of the mth energy storage at time t in the evaluation area corresponding to device k; η ch,k,m is the charging efficiency of the mth energy storage in the evaluation area corresponding to device k; ΔT is the control time interval in the historical scene statistics; P GT,k,m (t) is the output of the mth other adjustable distributed generation in the evaluation area corresponding to device k at time t; r down,k,m N is the downward ramp rate of the mth other adjustable distributed power source in the evaluation area corresponding to device k; load,k 、N ess,k 、N GT,k are the total number of flexible loads, energy storage and other adjustable distributed power sources in the evaluation area corresponding to device k.

2. The method according to claim 1, characterized in that The determining, based on the historical operation data corresponding to each of the plurality of devices, the static photovoltaic carrying capacity assessment results corresponding to each of the plurality of devices at a plurality of time sections includes: Determining whether the historical operating data corresponding to each of the plurality of devices conforms to a safety verification model of factors affecting photovoltaic load capacity; When the historical operating data corresponding to each of the plurality of devices conforms to the safety verification model of the photovoltaic load capacity influencing factors, determining the power corresponding to each of the plurality of devices at the plurality of time sections according to the historical operating data corresponding to each of the plurality of devices; Determining, based on the power corresponding to each of the plurality of devices at the plurality of time sections, the maximum newly addable photovoltaic capacity corresponding to each of the plurality of devices at the plurality of time sections; Determining whether the operating data of the to-be-assessed areas corresponding to the multiple devices at the multiple time sections after being connected to the distributed photovoltaic devices with the maximum newly added photovoltaic capacity respectively, conform to the photovoltaic carrying capacity influencing factor safety verification model; When the operating data of the areas to be evaluated corresponding to the multiple devices at the multiple time sections after being connected to the distributed photovoltaic devices with the maximum newly added photovoltaic capacity corresponding to each of the multiple devices at the multiple time sections do not meet the safety verification model of the photovoltaic carrying capacity influencing factors, a dichotomy evaluation model is used based on the maximum newly added photovoltaic capacity corresponding to each of the multiple devices at the multiple time sections to determine the static photovoltaic carrying capacity evaluation results corresponding to each of the multiple devices at the multiple time sections.

3. The method according to claim 2, characterized in that The safety verification model of factors affecting photovoltaic carrying capacity is: Among them, ΔU H , ΔU L They are respectively the preset maximum positive voltage deviation limit and the maximum negative voltage deviation limit; δU H,i (t), δU L,i (t) are the calculated values ​​of the maximum positive voltage deviation and the maximum negative voltage deviation on bus i corresponding to device k at time t; I xz,i is the calculated value of the short-circuit current of busbar i; I m Specifies the limit value for the preset short-circuit current; I xb,h (t) is the calculated value of the hth harmonic current at time t; I xh is the preset harmonic current limit.

4. The method according to claim 2, characterized in that The determining, based on the powers corresponding to the plurality of devices at the plurality of time sections, the maximum newly addable photovoltaic capacities corresponding to the plurality of devices at the plurality of time sections, includes: According to the power corresponding to each of the multiple devices at the multiple time sections, the reverse load rate formula is used Determine the reverse load rate corresponding to each of the multiple devices at the multiple time sections, where λ k (t) is the reverse load rate of device k at time t, P Net,k (t) is the reverse power of device k at time t, S e,k is the maximum practical operating limit of device k, time t is one of the multiple time sections, and device k is one of the multiple devices; According to the reverse load rates corresponding to the multiple devices at the multiple time sections, based on the photovoltaic capacity formula Determine the maximum newly added photovoltaic capacity corresponding to each of the multiple devices at the multiple time sections, where S M,k (t) is the maximum newly added photovoltaic capacity in the area to be evaluated corresponding to device k at time t; r,k is the allowable reverse transmission margin of device k at time t, η PV (t) is the actual power per unit capacity of the distributed photovoltaic equipment corresponding to the target area distribution network at time t.

5. The method according to claim 2, characterized in that The step of determining the static photovoltaic carrying capacity evaluation results corresponding to each of the multiple devices at the multiple time sections using a dichotomy evaluation model based on the maximum newly added photovoltaic capacity corresponding to each of the multiple devices at the multiple time sections includes: determining the static photovoltaic carrying capacity result corresponding to device k at time t based on the maximum newly added photovoltaic capacity corresponding to device k at time t, wherein time t is one of the multiple time sections, and device k is one of the multiple devices: Set the two ends of the binary value interval to S a =0, S b =S M,k (t); The dichotomous method was used to evaluate the model S c =(S a +S b ) / 2, obtain the area to be evaluated corresponding to device k with a photovoltaic capacity of S c The operation data of distributed photovoltaic equipment; Determine whether the area to be evaluated corresponding to the device k has a photovoltaic capacity of S c Whether the operating data of the distributed photovoltaic equipment after testing complies with the safety verification model of the photovoltaic carrying capacity influencing factors; In the area to be evaluated corresponding to the device k, the photovoltaic capacity is S c When the operating data of the distributed photovoltaic equipment meets the safety verification model of the photovoltaic carrying capacity influencing factors, control S a =S c ; In the area to be evaluated corresponding to the device k, the photovoltaic capacity is S c When the operating data of the distributed photovoltaic equipment does not meet the safety verification model of the photovoltaic carrying capacity influencing factors, control S b =S c ; Judgment|S a -S b |<δ holds true, where δ is the preset evaluation accuracy; In|S a -S b When |<δ does not hold, repeat the above dichotomy evaluation model S c =(S a +S b ) / 2, obtain the area to be evaluated corresponding to device k with a photovoltaic capacity of S c The operation data of the distributed photovoltaic equipment after the device k is connected is used to determine whether the area to be evaluated corresponding to the device k has a photovoltaic capacity of S c Whether the operating data of the distributed photovoltaic equipment after the installation meets the steps of the photovoltaic carrying capacity influencing factor safety verification model until |S a -S b |<δ holds; In|S a -S b When |<δ holds true, take S a and S b The minimum value among them is taken as the static photovoltaic carrying capacity evaluation result of device k at time t.

6. The method according to claim 1, characterized in that In the case where the target area distribution network does not reverse power flow to the preset voltage level grid, a hierarchical assessment model is used according to the hierarchical order to determine the hierarchical assessment results of the photovoltaic carrying capacity weak links corresponding to the multiple devices, including: Based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections in the entire historical time set, a safety verification probability assessment model is used to perform safety verification on the devices corresponding to each of the multiple voltage levels; When there is a device corresponding to the target voltage level in the multiple voltage levels that does not comply with the safety verification probability assessment model, it is determined that the target voltage level and the voltage levels below the target voltage level in the hierarchical order are all the highest level I of the photovoltaic carrying capacity weak link level.

7. The method according to claim 6, characterized in that The safety check probability assessment model is: These include the voltage deviation probability constraint check inequality model, the short-circuit current probability constraint check inequality model and the harmonic content probability constraint check inequality model, ΔU H , ΔU L are the preset maximum positive voltage deviation and maximum negative voltage deviation limits respectively; δU H,i (t), δU L,i (t) are the calculated values ​​of the maximum positive voltage deviation and the maximum negative voltage deviation on bus i corresponding to the voltage level at time t; I xz,i is the calculated value of the short-circuit current of busbar i set according to the preset rules; I m Specifies the limit for short-circuit current; I xb,h (t) is the actual obtained harmonic current value of the hth order at time t; I xh is the preset harmonic current limit; is the probability of the event being established; Ω is the entire set of historical time; ε V , ε I , ε x They are respectively the allowable value of the voltage deviation verification probability level, the allowable value of the short-circuit current verification probability level, and the allowable value of the harmonic content verification probability level. The allowable value of the short-circuit current verification probability level is the power supply reliability index in the target area.

8. The method according to claim 1, characterized in that ε1, ε2, ε3, and ε4 meet the following conditions:

9. The method according to claim 1, characterized in that The percentile statistical model is S ε,i =max{S α |Pr{S α ≤S M,i (t)}≥ε α },t∈Ω,ε α S is the percentile of the distribution network carrying capacity assessment for distributed photovoltaic access in the target area; ε,i The distribution network bus i in the target area is at percentile ε α Maximum bearing capacity when S M,i (t) is the photovoltaic carrying capacity of the target area distribution network bus i at time t, which is expressed by the formula S M,i (t) = min(S' M,i (t),S M,i-1 (t)) is determined, where S' M,i (t) = S M,k (t), S' M,i (t) is the photovoltaic carrying capacity assessment result of the adjacent busbar i under the positive power flow at time t for device k, S M,i-1 (t) is the photovoltaic carrying capacity assessment result of the adjacent upper-level busbar i-1 under the positive power flow at time t.

10. A hierarchical and graded probability assessment system for distributed photovoltaic access to distribution network carrying capacity, characterized in that: include: An acquisition module is used to obtain historical operation data corresponding to multiple devices corresponding to the target area distribution network; A first determining module is configured to determine static photovoltaic carrying capacity assessment results corresponding to each of the plurality of devices at a plurality of time sections based on historical operation data corresponding to each of the plurality of devices; A second determination module is configured to determine, based on the static photovoltaic carrying capacity assessment results corresponding to each of the multiple devices at multiple time sections within the entire historical time set, the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels in the target area distribution network, and the photovoltaic carrying capacity weakness link graded assessment results corresponding to each of the multiple voltage levels in descending order from high voltage to low voltage; A third determination module is configured to determine a tiered and graded probability assessment result of the photovoltaic carrying capacity of the target area distribution network based on the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels and the photovoltaic carrying capacity weak link graded assessment results corresponding to each of the multiple devices; The second determination module is further configured to determine whether the target area distribution network performs reverse power transmission to the preset voltage level grid; if the target area distribution network does not perform reverse power transmission to the preset voltage level grid, the hierarchy assessment model is used to determine the photovoltaic carrying capacity weak link graded assessment results corresponding to each of the multiple devices based on the hierarchy order, and the photovoltaic carrying capacity probability assessment results corresponding to each of the multiple voltage levels are determined based on the percentile statistical model; The grade evaluation model is Among them, G B,k is the photovoltaic load-bearing capacity weak link level of equipment k, λ k (t) is the reverse load rate of device k at time t; λ r,k Characterizes the allowable reverse transmission margin of device k; λ c,k (t) is the controllable margin coefficient of device k at time t; is the probability of the event being established; Ω is the entire set of historical time; ε1, ε2, ε3, and ε4 are respectively the preset probabilities; Among them, based on Determine the controllable margin coefficient of device k at time t, where P c,k (t) is the maximum load-type adjustable power of the area to be evaluated corresponding to device k at time t, S e,k is the maximum actual operating limit of device k, and the maximum load type of the area to be evaluated corresponding to device k can be increased by power P c,k (t) is obtained based on the following model: Where ΔP load+,k,m (t) is the maximum adjustable power of the mth flexible load at time t in the evaluation area corresponding to device k; ΔP ess+,k,m (t) is the maximum adjustable power of the mth energy storage in the evaluation area corresponding to device k at time t; ΔP GT-,k,m (t) is the maximum adjustable power of the mth other adjustable distributed power source in the evaluation area corresponding to device k at time t; P load,max,k,m is the upper limit of the rated power of the mth flexible load in the evaluation area corresponding to device k; P load,k,m (t) is the power of the mth flexible load at time t in the evaluation area corresponding to device k; P ch,max,k,m is the maximum allowable charging power of the mth energy storage in the evaluation area corresponding to device k; P ess,k,m (t) is the power of the mth energy storage at time t in the evaluation area corresponding to device k, which is positive for charging and negative for discharging; E ess,k,m is the installed capacity of the mth energy storage in the evaluation area corresponding to device k; SOC max,k,m is the upper limit of the mth energy storage state of charge in the evaluation area corresponding to device k; SOC k,m (t) is the state of charge value of the mth energy storage at time t in the evaluation area corresponding to device k; η ch,k,m is the charging efficiency of the mth energy storage in the evaluation area corresponding to device k; ΔT is the control time interval in the historical scene statistics; P GT,k,m (t) is the output of the mth other adjustable distributed generation in the evaluation area corresponding to device k at time t; r down,k,m N is the downward ramp rate of the mth other adjustable distributed power source in the evaluation area corresponding to device k; load,k 、N ess,k 、N GT,k are the total number of flexible loads, energy storage and other adjustable distributed power sources in the evaluation area corresponding to device k.

11. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the hierarchical and graded probability assessment method for the carrying capacity of distributed photovoltaic access to the distribution network as described in any one of claims 1 to 9.

12. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is run, the processor executes the hierarchical and graded probability assessment method for the carrying capacity of distributed photovoltaic access to a distribution network as claimed in any one of claims 1 to 9.

13. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for hierarchical and graded probability assessment of the carrying capacity of distributed photovoltaic access to a distribution network as claimed in any one of claims 1 to 9 is implemented.

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