Whole-process reactive voltage optimization control method and system for power distribution network

Abstract

The invention provides a whole-process reactive voltage optimization control method for a power distribution network. The method comprises the following steps: S1, optimizing a slowly-changing discrete control variable in the day; s2, reactive power output of a wind and light grid-connected unit is optimized on the time scale of 15 minutes in the day, and the voltage amplitude of a grid-connected point of the wind and light grid-connected unit is calculated through power flow; and S3, in a real-time control stage, according to a reactive voltage sensitivity criterion, taking a constant grid-connected point voltage amplitude as a target, and finely adjusting reactive power output of the wind and light grid-connected unit in real time. According to the method, the voltage stability and the voltage quality of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of reactive voltage optimization control of distribution networks, and relates to a method for optimizing reactive voltage of distribution networks over the entire process based on multiple time scales, in particular to a method for controlling reactive voltage of distribution networks containing a high proportion of renewable distributed power sources and having a certain reactive power regulation capability. Background Art

[0002] With the rapid development of the economy, the development of new energy in my country has entered a fast track. In particular, the development of distributed photovoltaic and wind power has become an important source of energy in the distribution network. This has not only changed the energy structure of the power grid and solved the problem of fossil energy shortage, but also brought unprecedented challenges to the safe and stable operation of the power grid due to the uncertainty and randomness of new energy power generation. Especially in the distribution network, due to the large-scale access of distributed new energy power generation, not only the peak-to-valley difference of the net load has increased, but also the accuracy of net load prediction has been greatly reduced. Due to economic reasons, there are currently few continuously adjustable reactive compensation devices such as phase regulators and SVG in low-voltage distribution networks. The above situations have brought great difficulties to the reactive optimization and voltage control of the distribution network.

[0003] How to provide a whole process reactive power voltage optimization control method is a problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to integrate the advantages of the existing technology and seek to design a full-process distribution network reactive voltage optimization control method suitable for grid-connected power generation with a high proportion of renewable distributed energy. The method can comprehensively consider various reactive voltage control resources in the power grid, perform full-process collaborative optimization in three links: day-ahead, intra-day minute level and real-time control, and comprehensively consider the system economy and voltage stability, and provide a full-process optimal control decision-making plan.

[0005] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0006] In order to achieve the above object, the present invention proposes a multi-time scale full-process reactive voltage optimization control method.

[0007] A method for optimizing reactive voltage control in the whole process of a distribution network comprises the following steps:

[0008] Step S1, optimizing the slowly changing discrete control variables on the day before;

[0009] Step S2, optimizing the reactive output of wind and solar grid-connected units on a 15-minute time scale within a day and calculating the voltage amplitude of their grid-connected points through power flow;

[0010] Step S3, in the real-time control stage, based on the reactive voltage sensitivity criterion and with the goal of keeping the voltage amplitude at the grid connection point constant, the reactive output of the wind and solar grid-connected units is fine-tuned in real time.

[0011] Optionally, step S1 specifically includes: optimizing discrete slow-changing control variables such as the position of transformer taps and the number of capacitor switching groups based on the predicted probability distribution of load power and renewable energy power generation output and taking the minimum expected system active network loss as the objective function on the premise of satisfying various system constraints.

[0012] Optionally, in step S1, in the process of optimizing the day-ahead slowly varying control variables, the randomness of the predicted load power and the active output of renewable energy power generation is represented by normal distribution, and the probabilistic power flow is solved by the three-point method.

[0013] Optionally, step S2 specifically includes: taking 15 minutes as the time scale within the day, minimizing the sum of the absolute values ​​of the voltage amplitude deviations of each node within this period as the objective function, optimizing the reactive output of the new energy equipment at the grid-connected node, and calculating the voltage amplitude of its grid-connected point.

[0014] Optionally, an improved particle swarm algorithm based on biological simulation is used as an optimization method, and discrete control variables and continuous control variables are optimized in step S1 and step S2 respectively.

[0015] Optionally, step S3 specifically includes: within each 15-minute time window, according to the reactive voltage sensitivity relationship, fine-tuning continuous control variables such as wind power and photovoltaic reactive output with the goal of maintaining a constant voltage amplitude at the renewable energy grid connection point, so as to further limit voltage fluctuations at each node of the system.

[0016] The present invention also proposes a whole-process reactive power voltage optimization control system for a distribution network, comprising:

[0017] The day-ahead optimization module optimizes the slowly changing discrete control variables on the day-ahead;

[0018] The intraday optimization module optimizes the reactive power output of wind and solar grid-connected units on a 15-minute time scale and calculates the voltage amplitude of their grid-connected points through power flow;

[0019] The real-time fine-tuning module, in the real-time control stage, fine-tunes the reactive output of wind and solar grid-connected units in real time based on the reactive voltage sensitivity criterion and with the goal of keeping the voltage amplitude at the grid-connected point constant.

[0020] Optionally, the day-ahead optimization module specifically includes: on the day-ahead, based on the predicted probability distribution of load power and renewable energy power generation output, and on the premise of satisfying various system constraints, taking the minimum expected system active network loss as the objective function, optimizing discrete slow-changing control variables such as the position of transformer taps and the number of capacitor switching groups.

[0021] Optionally, the intraday optimization module specifically includes: taking 15 minutes as the time scale within the day, minimizing the sum of the absolute values ​​of the voltage amplitude deviations of each node within this period as the objective function, optimizing the reactive output of the grid-connected node new energy equipment, and calculating the voltage amplitude of its grid-connected point.

[0022] Optionally, the real-time fine-tuning module specifically includes: within each 15-minute time window, according to the reactive voltage sensitivity relationship, fine-tuning continuous control variables such as wind power and photovoltaic reactive output with the goal of maintaining a constant voltage amplitude at the renewable energy grid-connected point, thereby further limiting voltage fluctuations at each node of the system.

[0023] Compared with the prior art, the present invention can comprehensively utilize various reactive voltage control means in the distribution network to achieve full-process reactive voltage control covering multiple time scales, taking into account the economy and voltage stability of the system, and more fully utilizing the reactive regulation potential of distributed new energy to increase its grid-connected absorption ratio.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a flow chart of the method for optimizing reactive power voltage during the whole process of distribution network of the present invention;

[0027] Figure 2 It is a schematic diagram of the improved IEEE33 node system topology;

[0028] Figure 3 This is the prediction curve of the net load in each period of a typical day in spring for the IEEE30-bus system;

[0029] Figure 4 It is the optimization result curve of reactive power output;

[0030] Figure 5This is a schematic diagram comparing the voltage amplitude of nodes before and after the reactive power output of new energy participates in voltage adjustment;

[0031] Figure 6 A structural diagram of a computer device is shown. DETAILED DESCRIPTION

[0032] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0033] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0034] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0035] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0036] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0037] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0038] like Figure 1 As shown, the whole-process reactive voltage optimization control method of the distribution network containing a high proportion of renewable distributed generation of the present invention comprises the following steps: step S1, optimizing the slow-changing discrete control variables on the day before; step S2, optimizing the reactive output of the wind and photovoltaic grid-connected units on a 15-minute time scale within the day and calculating the voltage amplitude of the grid-connected point through the power flow; step S3, in the real-time control stage, according to the reactive voltage sensitivity criterion, with the goal of constant voltage amplitude at the grid-connected point, real-time fine-tuning the reactive output of the wind and photovoltaic grid-connected units, so as to further suppress the voltage fluctuation of each node of the system.

[0039] Optionally, the above step S1 specifically includes: on the day before, according to the predicted probability distribution of load power and renewable energy power generation output, under the premise of satisfying various system constraints, taking the expected minimum system active network loss as the objective function, optimizing discrete slow-changing control variables such as the position of transformer taps and the number of capacitor switching groups.

[0040] Optionally, in the above step S1, in the process of optimizing the day-ahead slowly changing control variables, the normal distribution is used to represent the randomness of the predicted load power and the active output of renewable energy power generation, and the three-point method is used to solve the probabilistic power flow.

[0041] Optionally, the above step S2 specifically includes: taking 15 minutes as the time scale within the day, taking the minimum sum of the absolute values ​​of the voltage amplitude deviations of each node within this period as the objective function, optimizing the reactive output of the grid-connected node new energy equipment, and calculating the voltage amplitude of its grid-connected point.

[0042] Optionally, an improved particle swarm algorithm based on biological simulation is used as an optimization method to optimize discrete control variables and continuous control variables in step S1 and step S2 respectively.

[0043] Optionally, the above step S3 specifically includes: within each 15-minute time window, according to the reactive voltage sensitivity relationship, fine-tuning continuous control variables such as wind power and photovoltaic reactive output with the goal of maintaining a constant voltage amplitude at the renewable energy grid connection point, so as to further limit the voltage fluctuations of each node in the system.

[0044] The following is a verification of the reactive voltage optimization control method for a distribution network containing a high proportion of distributed wind and solar power generation in combination with a specific embodiment. An improved IEEE33-node system is analyzed on a typical day in spring as an example. The results show the effectiveness and rationality of the method.

[0045] The improved IEEE 33 node system topology of this embodiment is as follows: Figure 2 shown.

[0046] Step S1, day-ahead optimization.

[0047] The objective function is:

[0048]

[0049] Among them, X is a random variable, including the size of the load and the output of renewable energy. In order to maximize the adjustment range of renewable energy reactive output during reactive voltage control within the day, the power factor of renewable energy output is generally planned as 1 in the day-ahead planning. D is the control variable, including the number of capacitor groups switched and the tap position of the on-load tap-changing transformer; u is the state variable, including the node voltage amplitude and phase angle. V i 、V j represents the voltage amplitude of nodes i and j; θ ij is the phase difference between the voltages at nodes i and j; G ij , is the conductance of the tie line between nodes i and j, N is the number of nodes. E represents the mathematical expectation.

[0050] The equality constraints are:

[0051]

[0052] P is , Q is represents the active power and reactive power injected into node i; B ij is the susceptance of the tie line between nodes i and j.

[0053] The inequality constraints are:

[0054] V i,min ≤V i ≤V i,max

[0055] T kmin ≤T k ≤T kmax

[0056] Q cmin ≤Q c ≤Q cmax

[0057]

[0058] V i,max and V i,min Represents the upper and lower limits of the voltage amplitude at node i. k is the transformation ratio of the kth transformer, T kmax , T kmin It is the upper and lower limits of the transformation ratio. c is the compensation capacity of the Cth reactive power compensation device, Q cmax , Q cmin It is the upper and lower limits of the compensation capacity. kmax 、N cmax Indicates the maximum number of operations of the transformer and compensation device in one day.

[0059] Figure 3 The prediction curve of net load in each period of a typical day in spring for IEEE30-bus system is given.

[0060] The reactive power planning results of the day are shown in Table 2. The on-load tap-changing transformer changes gears at 5, 9, 16, 22, and 24, respectively; capacitor bank 1 is switched at 5, 9, 15, 22, and 24, and capacitor bank 2 is switched at 4, 10, 14, 21, and 24, all of which meet the constraint requirement of a maximum number of operations of 5.

[0061] Table 2

[0062]

[0063] Step S2: Optimization on the intraday 15-minute time scale.

[0064] In the 15-minute time scale, the randomness of load and renewable energy generation output is not considered, and the predicted values ​​are taken as their given values. The objective function is the voltage amplitude V i With the rated value V ie The sum of the absolute values ​​of the deviations is minimal:

[0065]

[0066] The constraints that need to be met are the voltage amplitude constraint and the upper and lower limits of the reactive output of wind and solar generators. Figure 4 The optimization results of reactive power output are given.

[0067] As can be seen from the figure, on adjacent 15-minute time scales, the reactive output of the renewable energy power generation system changes little. These changes are mainly used to suppress changes in the predicted active output of renewable energy in different time periods, changes in capacitor switching gears, and disturbances caused by load increases and decreases. Figure 5 The voltage amplitude of node 18 before and after the reactive output of new energy sources participated in voltage regulation at 96 moments was compared.

[0068] It can be seen from the figure that although the voltage amplitude of node 18 is still within the constraint range when there is no new energy power generation equipment involved in voltage regulation, the voltage amplitude fluctuates greatly in a short period of time compared with the voltage curve with the participation of new energy reactive output. This shows that the participation of new energy reactive output in voltage regulation can largely suppress the voltage fluctuation caused by the randomness of new energy output and the gear change of voltage regulation equipment, and improve the stability of system voltage.

[0069] Step S3: Intraday real-time reactive power and voltage control stage.

[0070] According to the reactive voltage sensitivity relationship, when the voltage fluctuation at the renewable energy grid connection point is ΔV i In order to keep the voltage amplitude of the node constant, the increase in the reactive power output of the new energy at the node is:

[0071]

[0072] in:

[0073]

[0074] In each 15-minute time window, the disturbances in the distribution network usually come mainly from fluctuations in wind and power generation output. This method controls the conservation of voltage amplitude at wind and solar grid connection points by adjusting the reactive output of wind and solar power sources in real time, that is, it can control the voltage of certain nodes in the radial distribution network, thus further controlling the voltage volatility of other nodes in the system. For example, at node 18, in all 15-minute time windows on this typical day, the maximum fluctuation range of the voltage amplitude is less than 0.2% of the rated voltage of the system. Without the installation of other voltage regulating equipment, this method improves the voltage stability and voltage quality of the system.

[0075] In one embodiment, a distribution network full-process reactive voltage optimization control system is also disclosed, including: a day-ahead optimization module, which optimizes slowly changing discrete control variables on the day-ahead; an intraday optimization module, which optimizes the reactive output of wind and solar grid-connected units on a 15-minute time scale within the day and calculates the voltage amplitude of their grid connection points through power flow; a real-time fine-tuning module, which, in the real-time control stage, fine-tunes the reactive output of wind and solar grid-connected units in real time based on the reactive voltage sensitivity criterion and with the goal of keeping the voltage amplitude of the grid connection point constant.

[0076] Optionally, the day-ahead optimization module specifically includes: on the day-ahead, based on the predicted probability distribution of load power and renewable energy power generation output, and on the premise of satisfying various system constraints, taking the minimum expected system active network loss as the objective function, optimizing discrete slow-changing control variables such as the position of transformer taps and the number of capacitor switching groups.

[0077] Optionally, the intraday optimization module specifically includes: taking 15 minutes as the time scale within the day, minimizing the sum of the absolute values ​​of the voltage amplitude deviations of each node within this period as the objective function, optimizing the reactive output of the grid-connected node new energy equipment, and calculating the voltage amplitude of its grid-connected point.

[0078] Optionally, the real-time fine-tuning module specifically includes: within each 15-minute time window, according to the reactive voltage sensitivity relationship, fine-tuning continuous control variables such as wind power and photovoltaic reactive output with the goal of maintaining a constant voltage amplitude at the renewable energy grid-connected point, thereby further limiting voltage fluctuations at each node of the system.

[0079] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0080] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0081] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0082] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0084] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing reactive power voltage during the whole process of a distribution network, characterized in that: The following steps are involved: Step S1, optimizing the slowly changing discrete control variables on the day before; Step S2, optimizing the reactive output of wind and solar grid-connected units on a 15-minute time scale within a day and calculating the voltage amplitude of their grid-connected points through power flow; Step S3, in the real-time control stage, based on the reactive voltage sensitivity criterion and with the goal of keeping the voltage amplitude at the grid connection point constant, the reactive output of the wind and solar grid-connected units is fine-tuned in real time.

2. A method for optimizing reactive power voltage during the whole process of a distribution network as claimed in claim 1, characterized in that: The step S1 specifically includes: optimizing the position of transformer taps, the number of capacitor switching groups and other discrete slow-changing control variables based on the predicted probability distribution of load power and renewable energy power generation output and taking the minimum expected system active network loss as the objective function on the premise of satisfying various system constraints.

3. A method for optimizing reactive power voltage during the whole process of a distribution network as claimed in claim 2, characterized in that: In step S1, in the process of optimizing the day-ahead slowly changing control variables, the normal distribution is used to represent the randomness of the predicted load power and the active output of renewable energy power generation, and the three-point method is used to solve the probabilistic power flow.

4. A method for optimizing reactive power and voltage in the whole process of a distribution network as claimed in claim 1, characterized in that: The step S2 specifically includes: taking 15 minutes as the time scale within a day, minimizing the sum of the absolute values ​​of the voltage amplitude deviations of each node within this period as the objective function, optimizing the reactive output of the new energy equipment at the grid-connected node, and calculating the voltage amplitude of its grid-connected point.

5. A method for optimizing reactive power voltage during the whole process of a distribution network as claimed in claim 1, characterized in that: An improved particle swarm algorithm based on biological simulation is adopted as an optimization method, and discrete control variables and continuous control variables are optimized in step S1 and step S2 respectively.

6. A method for optimizing reactive power and voltage in the whole process of a distribution network as claimed in claim 1, characterized in that: The step S3 specifically includes: within each 15-minute time window, according to the reactive voltage sensitivity relationship, fine-tuning continuous control variables such as wind power and photovoltaic reactive output with the goal of maintaining a constant voltage amplitude at the renewable energy grid connection point, and further limiting voltage fluctuations at each node of the system.

7. A reactive power voltage optimization control system for the entire distribution network, characterized in that: include: The day-ahead optimization module optimizes the slowly changing discrete control variables on the day-ahead; The intraday optimization module optimizes the reactive power output of wind and solar grid-connected units on a 15-minute time scale and calculates the voltage amplitude of their grid-connected points through power flow; The real-time fine-tuning module, in the real-time control stage, fine-tunes the reactive output of wind and solar grid-connected units in real time based on the reactive voltage sensitivity criterion and with the goal of keeping the voltage amplitude at the grid-connected point constant.

8. A distribution network whole process reactive power voltage optimization control system as claimed in claim 7, characterized in that: The day-ahead optimization module specifically includes: on the day-ahead, according to the predicted probability distribution of load power and renewable energy power generation output, and on the premise of satisfying various system constraints, taking the minimum expected system active network loss as the objective function, optimizing discrete slow-changing control variables such as the position of transformer taps and the number of capacitor switching groups.

9. A distribution network whole process reactive power voltage optimization control system as claimed in claim 7, characterized in that: The intraday optimization module specifically includes: taking 15 minutes as the time scale within the day, minimizing the sum of the absolute values ​​of the voltage amplitude deviations of each node within this period as the objective function, optimizing the reactive output of the new energy equipment of the grid-connected node, and calculating the voltage amplitude of its grid-connected point.

10. A distribution network whole process reactive power voltage optimization control system as claimed in claim 7, characterized in that: The real-time fine-tuning module specifically includes: within each 15-minute time window, according to the reactive voltage sensitivity relationship, fine-tuning continuous control variables such as wind power and photovoltaic reactive output with the goal of maintaining a constant voltage amplitude at the renewable energy grid connection point, further limiting the voltage fluctuations at each node of the system.

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