Power distribution network reactive power control method and device for suppressing photovoltaic uncertainty

Abstract

The invention relates to the technical field of power distribution network operation control, and particularly provides a power distribution network reactive power control method and device for suppressing photovoltaic uncertainty, and the method comprises the steps: substituting the sensitivity of the power of a power grid node to the voltage into a pre-constructed power distribution network operation control optimization model, and carrying out the solving, obtaining a linear reactive power control coefficient of the intelligent photovoltaic inverter on the power grid node; and adjusting the reactive power of the intelligent photovoltaic inverter on the power grid node based on the linear control coefficient of the intelligent photovoltaic inverter on the power grid node and the variable quantity of the injected photovoltaic active power of the power grid node. According to the technical scheme provided by the invention, the continuous uncertainty of distributed photovoltaic power generation is counteracted through the real-time reactive power control capability of the intelligent photovoltaic inverter, the optimal network voltage level is ensured, and random implementation of the photovoltaic power generation uncertainty in a random interval can be dealt with.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation control, and in particular to a distribution network reactive power control method and device for suppressing photovoltaic uncertainty. Background Art

[0002] With the vigorous promotion and application of distributed photovoltaics, the newly added distributed photovoltaic installed capacity has gradually exceeded that of centralized photovoltaics, and the distribution network operation control technology that adapts to large-scale distributed photovoltaic access has also become a current research hotspot. Among them, the safe operation of the distribution network focuses on voltage levels and line flows. The voltage / reactive power control of traditional distribution networks mainly relies on mechanical voltage regulation devices, such as on-load voltage-regulating transformers (transformer taps) and shunt capacitor groups. In addition, there are flexible AC voltage regulation devices based on flexible AC transmission technology (FACTS), such as static VAR compensators. However, the cost of flexible voltage regulation equipment is much higher than that of passive components such as shunt reactor groups, which limits its scope of application.

[0003] Distributed photovoltaic power generation output is characterized by continuous randomness, volatility and intermittency. In a short period of time (such as 1 minute), the output can vary by an order of magnitude of up to 10% of the rated power, causing large fluctuations in node voltage levels. Traditional mechanical voltage regulators are limited by factors such as operating life and response time, and their discrete voltage regulation capabilities are difficult to adapt to the continuous fluctuations in photovoltaic output in distribution networks. Although flexible AC voltage regulators can quickly adjust voltage, they are difficult to match with large-scale distributed photovoltaic power sources in terms of quantity and capacity due to cost constraints. Therefore, the issue of voltage / reactive power control of distribution networks containing distributed photovoltaics with uncertain output characteristics has become a hot topic.

[0004] It is noted that photovoltaics itself is a power source based on an inverter. Through power electronics technology, photovoltaic arrays are combined with passive reactive sources to realize distributed photovoltaic-static reactive compensator groups, which can take into account both grid-connected power generation and reactive output. The academic community defines it as a smart photovoltaic inverter. The latest IEEE 1547-2018 standard specifies the reactive voltage regulation function of smart inverters, including reactive mode, reactive-active mode, fixed power factor mode, etc. Smart photovoltaic inverters essentially increase the continuous voltage regulation resources in the distribution network. Accordingly, the voltage / reactive control of the distribution network considering the reactive control of smart photovoltaic inverters has become a new problem. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present invention proposes a distribution network reactive power control method and device for suppressing photovoltaic uncertainty.

[0006] In a first aspect, a distribution network reactive power control method for suppressing photovoltaic uncertainty is provided, wherein the distribution network reactive power control method for suppressing photovoltaic uncertainty comprises:

[0007] Substitute the power sensitivity of the grid node to the voltage into the pre-built distribution network operation control optimization model and solve it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node;

[0008] The reactive power of the smart photovoltaic inverter on the grid node is regulated based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node.

[0009] Preferably, the process of acquiring the sensitivity of the power of the grid node to the voltage includes:

[0010] The power flow equation of the power grid node is iteratively calculated by the Newton-Raphson method to obtain the sensitivity of the power of the power grid node to the voltage.

[0011] Furthermore, the power flow equation of the power grid node is as follows:

[0012]

[0013] In the above formula, θ is the phase angle of the grid node voltage, |V| is the amplitude of the grid node voltage, and g P (θ,|V) is the power flow equation corresponding to the active power, g Q (θ,|V) is the power flow equation corresponding to reactive power, Δ represents the change, P is the active power of the line, Q is the reactive power of the line, K Vp is the sensitivity of active power of the grid node to voltage, K Vq is the sensitivity of the reactive power of the grid node to the voltage, p is the active power injected into the grid node, and q is the reactive power injected into the grid node.

[0014] Preferably, the pre-built distribution network operation control optimization model is as follows:

[0015]

[0016] In the above formula, Ω N ,Ω sub and Ω PV are the set of grid nodes, the set of substation nodes and the set of distributed photovoltaic nodes respectively, W j is the voltage control weight of the jth node, is the sensitivity of the active power of the kth distributed photovoltaic node to the voltage of the jth node, is the sensitivity of the reactive power of the kth distributed photovoltaic node to the voltage of the jth node, α k is the linear reactive power control coefficient of the kth distributed photovoltaic node, is the change in the injected photovoltaic active power of the kth distributed photovoltaic node, Δp kis the change in injected active power of the kth distributed photovoltaic node, is the reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes, is the maximum reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0017] Furthermore, the voltage control weight of the j-th node is as follows:

[0018]

[0019] In the above formula, tr is the diagonal summation function, n is the number of buses, is the sensitivity of the active power of the jth node to the voltage of the jth node, K Vq is the sensitivity of the reactive power of the grid node to the voltage.

[0020] Furthermore, before substituting the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solving it, it includes: converting the pre-built distribution network operation control optimization model into a linear optimization model.

[0021] Furthermore, the linear optimization model is as follows:

[0022]

[0023] In the above formula, t j is the auxiliary variable of the jth node.

[0024] Furthermore, the reactive power of the smart photovoltaic inverter on the grid node is adjusted based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the change in the injected photovoltaic active power of the grid node, including:

[0025] Adjust the reactive power of the smart photovoltaic inverter on the grid node as follows:

[0026]

[0027] In the above formula, is the reactive power regulation value of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0028] In a second aspect, a distribution network reactive power control device for suppressing photovoltaic uncertainty is provided, wherein the distribution network reactive power control device for suppressing photovoltaic uncertainty comprises:

[0029] An analysis module is used to substitute the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solve it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node;

[0030] The regulating module is used to regulate the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node.

[0031] Preferably, the process of acquiring the sensitivity of the power of the grid node to the voltage includes:

[0032] The power flow equation of the power grid node is iteratively calculated by the Newton-Raphson method to obtain the sensitivity of the power of the power grid node to the voltage.

[0033] Furthermore, the power flow equation of the power grid node is as follows:

[0034]

[0035] In the above formula, θ is the phase angle of the grid node voltage, |V| is the amplitude of the grid node voltage, and g P (θ,|V) is the power flow equation corresponding to the active power, g Q (θ,|V) is the power flow equation corresponding to reactive power, Δ represents the change, P is the active power of the line, Q is the reactive power of the line, K Vp is the sensitivity of active power of the grid node to voltage, K Vq is the sensitivity of the reactive power of the grid node to the voltage, p is the active power injected into the grid node, and q is the reactive power injected into the grid node.

[0036] Preferably, the pre-built distribution network operation control optimization model is as follows:

[0037]

[0038] In the above formula, Ω N ,Ω sub and Ω PV are the set of grid nodes, the set of substation nodes and the set of distributed photovoltaic nodes respectively, W j is the voltage control weight of the jth node, is the sensitivity of the active power of the kth distributed photovoltaic node to the voltage of the jth node, is the sensitivity of the reactive power of the kth distributed photovoltaic node to the voltage of the jth node, α k is the linear reactive power control coefficient of the kth distributed photovoltaic node, is the change in the injected photovoltaic active power of the kth distributed photovoltaic node, Δp k is the change in injected active power of the kth distributed photovoltaic node, is the reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes, is the maximum reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0039] Furthermore, the voltage control weight of the j-th node is as follows:

[0040]

[0041] In the above formula, tr is the diagonal summation function, n is the number of buses, is the sensitivity of the active power of the jth node to the voltage of the jth node, K Vq is the sensitivity of the reactive power of the grid node to the voltage.

[0042] Furthermore, the device includes: a conversion module, specifically used to: convert the pre-built distribution network operation control optimization model into a linear optimization model.

[0043] Furthermore, the linear optimization model is as follows:

[0044]

[0045] In the above formula, t j is the auxiliary variable of the jth node.

[0046] Furthermore, the reactive power of the smart photovoltaic inverter on the grid node is adjusted based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the change in the injected photovoltaic active power of the grid node, including:

[0047] Adjust the reactive power of the smart photovoltaic inverter on the grid node as follows:

[0048]

[0049] In the above formula, is the reactive power regulation value of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0050] In a third aspect, a computer device is provided, comprising: one or more processors;

[0051] The processor is configured to execute one or more programs;

[0052] When the one or more programs are executed by the one or more processors, the distribution network reactive power control method for suppressing photovoltaic uncertainty is implemented.

[0053] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the distribution network reactive power control method for suppressing photovoltaic uncertainty is implemented.

[0054] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0055] The present invention provides a distribution network reactive power control method and device for suppressing photovoltaic uncertainty, including: substituting the sensitivity of power of a grid node to voltage into a pre-built distribution network operation control optimization model and solving it to obtain a linear reactive power control coefficient of an intelligent photovoltaic inverter on the grid node; adjusting the reactive power of the intelligent photovoltaic inverter on the grid node based on the linear control coefficient of the intelligent photovoltaic inverter on the grid node and the change in the injected photovoltaic active power of the grid node. The technical solution provided by the present invention offsets the continuous uncertainty of distributed photovoltaic power generation through the real-time reactive power control capability of the intelligent photovoltaic inverter, ensures the optimal network voltage level, and can cope with any realization of photovoltaic power generation uncertainty within its random interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic flow chart of main steps of a reactive power control method for a distribution network for suppressing photovoltaic uncertainty according to an embodiment of the present invention;

[0057] Figure 2 is a schematic diagram of an IEEE 33-node example system according to an embodiment of the present invention;

[0058] Figure 3 is a daily photovoltaic / load power curve diagram of an embodiment of the present invention;

[0059] Figure 4 is a linear reactive power control coefficient curve diagram of an embodiment of the present invention;

[0060] Figure 5 4 is a diagram comparing network voltage levels according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] As disclosed in the background technology, with the vigorous promotion and application of distributed photovoltaics, the newly added distributed photovoltaic installed capacity has gradually exceeded that of centralized photovoltaics, and the distribution network operation control technology that adapts to large-scale distributed photovoltaic access has also become a current research hotspot. Among them, the safe operation of the distribution network focuses on voltage levels and line currents. The voltage / reactive power control of traditional distribution networks mainly relies on mechanical voltage regulating devices, such as on-load tap-changing transformers (transformer taps) and shunt capacitor groups. In addition, there are flexible AC voltage regulating devices based on flexible AC transmission technology (FACTS), such as static VAR compensators. However, the cost of flexible voltage regulating equipment is much higher than that of passive components such as shunt reactor groups, which limits its scope of application.

[0064] Distributed photovoltaic power generation output is characterized by continuous randomness, volatility and intermittency. In a short period of time (such as 1 minute), the output can vary by an order of magnitude of up to 10% of the rated power, causing large fluctuations in node voltage levels. Traditional mechanical voltage regulators are limited by factors such as operating life and response time, and their discrete voltage regulation capabilities are difficult to adapt to the continuous fluctuations in photovoltaic output in distribution networks. Although flexible AC voltage regulators can quickly adjust voltage, they are difficult to match with large-scale distributed photovoltaic power sources in terms of quantity and capacity due to cost constraints. Therefore, the issue of voltage / reactive power control of distribution networks containing distributed photovoltaics with uncertain output characteristics has become a hot topic.

[0065] It is noted that photovoltaics itself is a power source based on an inverter. Through power electronics technology, photovoltaic arrays are combined with passive reactive sources to realize distributed photovoltaic-static reactive compensator groups, which can take into account both grid-connected power generation and reactive output. The academic community defines it as a smart photovoltaic inverter. The latest IEEE 1547-2018 standard specifies the reactive voltage regulation function of smart inverters, including reactive mode, reactive-active mode, fixed power factor mode, etc. Smart photovoltaic inverters essentially increase the continuous voltage regulation resources in the distribution network. Accordingly, the voltage / reactive control of the distribution network considering the reactive control of smart photovoltaic inverters has become a new problem.

[0066] In order to improve the above problems, the present invention provides a distribution network reactive power control method and device for suppressing photovoltaic uncertainty, including: substituting the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solving it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node; adjusting the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the change in the injected photovoltaic active power of the grid node. The technical solution provided by the present invention offsets the continuous uncertainty of distributed photovoltaic power generation through the real-time reactive power control capability of the smart photovoltaic inverter, ensures the optimal network voltage level, and can cope with any realization of photovoltaic power generation uncertainty within its random interval.

[0067] The above scheme is described in detail below.

[0068] Example 1

[0069] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a reactive power control method for a distribution network for suppressing photovoltaic uncertainty according to an embodiment of the present invention. Figure 1 As shown, the reactive power control method for suppressing photovoltaic uncertainty in the embodiment of the present invention mainly includes the following steps:

[0070] Step S101: Substituting the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solving it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node;

[0071] Step S102: adjusting the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node.

[0072] In this embodiment, the process of obtaining the sensitivity of the power of the grid node to the voltage includes:

[0073] The power flow equation of the power grid node is iteratively calculated by the Newton-Raphson method to obtain the sensitivity of the power of the power grid node to the voltage.

[0074] In one embodiment, the power flow equation of the power grid node is as follows:

[0075]

[0076]

[0077] In the above formula, θ is the phase angle of the grid node voltage, |V| is the amplitude of the grid node voltage, and g P (θ,|V) is the power flow equation corresponding to the active power, g Q (θ,|V) is the power flow equation corresponding to reactive power, Δ represents the change, P is the active power of the line, Q is the reactive power of the line, K Vp is the sensitivity of active power of the grid node to voltage, K Vq is the sensitivity of the reactive power of the grid node to the voltage, p is the active power injected into the grid node, and q is the reactive power injected into the grid node.

[0078] In this embodiment, the pre-built distribution network operation control optimization model is as follows:

[0079]

[0080] In the above formula, ΩN ,Ω sub and Ω PV are the set of grid nodes, the set of substation nodes and the set of distributed photovoltaic nodes respectively, W j is the voltage control weight of the jth node, is the sensitivity of the active power of the kth distributed photovoltaic node to the voltage of the jth node, is the sensitivity of the reactive power of the kth distributed photovoltaic node to the voltage of the jth node, α k is the linear reactive power control coefficient of the kth distributed photovoltaic node, is the change in the injected photovoltaic active power of the kth distributed photovoltaic node, Δp k is the change in injected active power of the kth distributed photovoltaic node, is the reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes, is the maximum reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0081] In one embodiment, the j-th node voltage control weight is as follows:

[0082]

[0083] In the above formula, tr is the diagonal summation function, n is the number of buses, is the sensitivity of the active power of the jth node to the voltage of the jth node, K Vq is the sensitivity of the reactive power of the grid node to the voltage.

[0084] In one embodiment, before substituting the sensitivity of power of the grid node to voltage into a pre-built distribution network operation control optimization model and solving it, the method includes: converting the pre-built distribution network operation control optimization model into a linear optimization model.

[0085] In one embodiment, the linear optimization model is as follows:

[0086]

[0087] In the above formula, t j is the auxiliary variable of the jth node.

[0088] In one embodiment, the method of adjusting the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the change amount of the injected photovoltaic active power of the grid node includes:

[0089] Adjust the reactive power of the smart photovoltaic inverter on the grid node as follows:

[0090]

[0091] In the above formula, is the reactive power regulation value of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0092] In a specific implementation, the present invention is analyzed by using an IEEE 33-node system. Figure 2 As shown. The normally open line is represented by a red dotted line. The substation node voltage is set to 1.0pu, and the network voltage safety range is 0.95-1.05. An on-load tap-changing transformer OLTC is set between nodes 1-2, with a transformation ratio range of 0.9-1.1 and a step size of 0.01. Shunt reactor groups are set at nodes 3 and 6, with an adjustment range of 0-0.06 and a step size of 0.01 (MVAR). Three scenarios are set to characterize the distributed photovoltaic penetration rate, including 30%, 60% and 90% of the rated load active power, represented as scenario 1 (S1 (Scenario1)), scenario 2 (S2 (Scenario2)) and scenario 3 (S3 (Scenario3)). Four distributed photovoltaic power sources (Distributed PV) are set at nodes 18, 22, 25 and 33, each with the same capacity. The photovoltaic inverter capacity is 110% of the rated active power. The load is divided into residential customer, commercial customer and industrial customer. The load curve is as follows: Figure 3 shown.

[0093] (1) Preliminary steps.

[0094] ① The network operation rated state is calculated through the optimal power flow model of the distribution network. The optimal power flow model takes into account network reconstruction and traditional reactive power optimization, which is recorded as VONR (VAR optimization and network reconfiguration). ② For the rated state obtained by the optimal power flow model, node sensitivity analysis is performed.

[0095] (2) Example analysis and verification

[0096] ① Linear reactive power control coefficient such as Figure 4 As shown in the figure, it shows that all smart photovoltaic inverters have obtained reactive power dispatch information. In the figure, PV@18, PV@22, PV@25, and PV@33 represent the photovoltaic connected to nodes 18, 22, 25, and 33 respectively. ② Comparison of network voltage levels after reactive power control is adopted or not Figure 5As shown. It can be seen that in scenarios S1, S2, and S3, when the smart photovoltaic inverter is used for reactive voltage optimization (VONR), the sum of the absolute values ​​of the voltage deviations of the network nodes is relatively small. When reactive power control is not considered (woQ (without reactive power control)), the sum of the absolute values ​​of the voltage deviations of the network nodes is significantly greater than the reactive power control parameters proposed by the present invention. Therefore, the advantages and effectiveness of the present invention have been verified.

[0097] Example 2

[0098] Based on the same inventive concept, the present invention also provides a distribution network reactive power control device for suppressing photovoltaic uncertainty, the distribution network reactive power control device for suppressing photovoltaic uncertainty comprises:

[0099] An analysis module is used to substitute the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solve it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node;

[0100] The regulating module is used to regulate the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node.

[0101] Preferably, the process of acquiring the sensitivity of the power of the grid node to the voltage includes:

[0102] The power flow equation of the power grid node is iteratively calculated by the Newton-Raphson method to obtain the sensitivity of the power of the power grid node to the voltage.

[0103] Furthermore, the power flow equation of the power grid node is as follows:

[0104]

[0105] In the above formula, θ is the phase angle of the grid node voltage, |V| is the amplitude of the grid node voltage, and g P (θ,|V) is the power flow equation corresponding to the active power, g Q (θ,|V) is the power flow equation corresponding to reactive power, Δ represents the change, P is the active power of the line, Q is the reactive power of the line, K Vp is the sensitivity of active power of the grid node to voltage, K Vq is the sensitivity of the reactive power of the grid node to the voltage, p is the active power injected into the grid node, and q is the reactive power injected into the grid node.

[0106] Preferably, the pre-built distribution network operation control optimization model is as follows:

[0107]

[0108] In the above formula, Ω N ,Ω sub and Ω PV are the set of grid nodes, the set of substation nodes and the set of distributed photovoltaic nodes respectively, W j is the voltage control weight of the jth node, is the sensitivity of the active power of the kth distributed photovoltaic node to the voltage of the jth node, is the sensitivity of the reactive power of the kth distributed photovoltaic node to the voltage of the jth node, α k is the linear reactive power control coefficient of the kth distributed photovoltaic node, is the change in the injected photovoltaic active power of the kth distributed photovoltaic node, Δp k is the change in injected active power of the kth distributed photovoltaic node, is the reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes, is the maximum reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0109] Furthermore, the voltage control weight of the j-th node is as follows:

[0110]

[0111] In the above formula, tr is the diagonal summation function, n is the number of buses, is the sensitivity of the active power of the jth node to the voltage of the jth node, K Vq is the sensitivity of the reactive power of the grid node to the voltage.

[0112] Furthermore, the device includes: a conversion module, specifically used to: convert the pre-built distribution network operation control optimization model into a linear optimization model.

[0113] Furthermore, the linear optimization model is as follows:

[0114]

[0115] In the above formula, t j is the auxiliary variable of the jth node.

[0116] Furthermore, the reactive power of the smart photovoltaic inverter on the grid node is adjusted based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the change in the injected photovoltaic active power of the grid node, including:

[0117] Adjust the reactive power of the smart photovoltaic inverter on the grid node as follows:

[0118]

[0119] In the above formula, is the reactive power regulation value of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

[0120] Example 3

[0121] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a distribution network reactive power control method for suppressing photovoltaic uncertainty in the above embodiment.

[0122] Example 4

[0123] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a distribution network reactive power control method for suppressing photovoltaic uncertainty in the above embodiment.

[0124] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A reactive power control method for a distribution network for suppressing photovoltaic uncertainty, characterized in that: The method comprises: Substitute the power sensitivity of the grid node to the voltage into the pre-built distribution network operation control optimization model and solve it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node; The reactive power of the smart photovoltaic inverter on the grid node is regulated based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node.

2. The method according to claim 1, characterized in that The process of obtaining the sensitivity of the power of the grid node to the voltage includes: The power flow equation of the power grid node is iteratively calculated by the Newton-Raphson method to obtain the sensitivity of the power of the power grid node to the voltage.

3. The method according to claim 2, characterized in that The power flow equation of the grid node is as follows: In the above formula, θ is the phase angle of the grid node voltage, |V| is the amplitude of the grid node voltage, and g P (θ,|V) is the power flow equation corresponding to the active power, g Q (θ,|V) is the power flow equation corresponding to reactive power, Δ represents the change, P is the active power of the line, Q is the reactive power of the line, K Vp is the sensitivity of active power of the grid node to voltage, K Vq is the sensitivity of the reactive power of the grid node to the voltage, p is the active power injected into the grid node, and q is the reactive power injected into the grid node.

4. The method according to claim 1, characterized in that The pre-built distribution network operation control optimization model is as follows: In the above formula, Ω N ,Ω sub and Ω PV are the set of grid nodes, the set of substation nodes and the set of distributed photovoltaic nodes respectively, W j is the voltage control weight of the jth node, is the sensitivity of the active power of the kth distributed photovoltaic node to the voltage of the jth node, is the sensitivity of the reactive power of the kth distributed photovoltaic node to the voltage of the jth node, α k is the linear reactive power control coefficient of the kth distributed photovoltaic node, is the change in the injected photovoltaic active power of the kth distributed photovoltaic node, Δp k is the change in injected active power of the kth distributed photovoltaic node, is the reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes, is the maximum reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

5. The method according to claim 4, characterized in that The j-th node voltage control weight is as follows: In the above formula, tr is the diagonal summation function, n is the number of buses, is the sensitivity of the active power of the jth node to the voltage of the jth node, K Vq is the sensitivity of the reactive power of the grid node to the voltage.

6. The method according to claim 4, characterized in that Before substituting the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solving it, it includes: converting the pre-built distribution network operation control optimization model into a linear optimization model.

7. The method according to claim 6, characterized in that The linear optimization model is as follows: In the above formula, t j is the auxiliary variable of the jth node.

8. The method according to claim 4, characterized in that The method of adjusting the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node includes: Adjust the reactive power of the smart photovoltaic inverter on the grid node as follows: In the above formula, is the reactive power regulation value of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

9. A reactive power control device for a distribution network for suppressing photovoltaic uncertainty, characterized in that: The device comprises: An analysis module is used to substitute the sensitivity of the power of the grid node to the voltage into the pre-built distribution network operation control optimization model and solve it to obtain the linear reactive power control coefficient of the smart photovoltaic inverter on the grid node; The regulating module is used to regulate the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node.

10. The device according to claim 9, characterized in that The process of obtaining the sensitivity of the power of the grid node to the voltage includes: The power flow equation of the power grid node is iteratively calculated by the Newton-Raphson method to obtain the sensitivity of the power of the power grid node to the voltage.

11. The device according to claim 10, characterized in that The power flow equation of the grid node is as follows: In the above formula, θ is the phase angle of the grid node voltage, |V| is the amplitude of the grid node voltage, and g P (θ,|V) is the power flow equation corresponding to the active power, g Q (θ,|V) is the power flow equation corresponding to reactive power, Δ represents the change, P is the active power of the line, Q is the reactive power of the line, K Vp is the sensitivity of active power of the grid node to voltage, K Vq is the sensitivity of the reactive power of the grid node to the voltage, p is the active power injected into the grid node, and q is the reactive power injected into the grid node.

12. The device according to claim 9, characterized in that The pre-built distribution network operation control optimization model is as follows: In the above formula, Ω N ,Ω sub and Ω PV are the set of grid nodes, the set of substation nodes and the set of distributed photovoltaic nodes respectively, W j is the voltage control weight of the jth node, is the sensitivity of the active power of the kth distributed photovoltaic node to the voltage of the jth node, is the sensitivity of the reactive power of the kth distributed photovoltaic node to the voltage of the jth node, α k is the linear reactive power control coefficient of the kth distributed photovoltaic node, is the change in the injected photovoltaic active power of the kth distributed photovoltaic node, Δp k is the change in injected active power of the kth distributed photovoltaic node, is the reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes, is the maximum reactive power of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

13. The device according to claim 12, characterized in that The j-th node voltage control weight is as follows: In the above formula, tr is the diagonal summation function, n is the number of buses, is the sensitivity of the active power of the jth node to the voltage of the jth node, K Vq is the sensitivity of the reactive power of the grid node to the voltage.

14. The device according to claim 12, characterized in that The device comprises: a conversion module, which is specifically used to: convert the pre-built distribution network operation control optimization model into a linear optimization model.

15. The device according to claim 14, characterized in that The linear optimization model is as follows: In the above formula, t j is the auxiliary variable of the jth node.

16. The device according to claim 12, characterized in that The method of adjusting the reactive power of the smart photovoltaic inverter on the grid node based on the linear control coefficient of the smart photovoltaic inverter on the grid node and the variation of the injected photovoltaic active power of the grid node includes: Adjust the reactive power of the smart photovoltaic inverter on the grid node as follows: In the above formula, is the reactive power regulation value of the kth smart photovoltaic inverter containing distributed photovoltaic nodes.

17. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the reactive power control method for power distribution network for suppressing photovoltaic uncertainty as claimed in any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the reactive power control method for a distribution network for suppressing photovoltaic uncertainty as claimed in any one of claims 1 to 8 is implemented.

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