Double-time-scale double-layer self-discipline optimization method for wind power cluster access power grid

Abstract

The invention provides a double-time-scale double-layer self-discipline optimization method for accessing a wind power cluster to a power grid, and relates to the technical field of power grid planning, and the method comprises the steps: obtaining the minimum active power loss and voltage deviation of a system, so as to build an upper-layer reactive power optimization mathematical model; taking original data as input, solving the upper-layer reactive power optimization mathematical model by adopting a particle swarm algorithm, and obtaining an optimal solution to obtain a reactive power output value; obtaining the minimum total active power loss of the internal line of the wind power cluster to establish a lower-layer reactive power optimization model; taking the line original parameters, the wind power active power output and the reactive power output value obtained in the step S2 as input, and adopting a particle swarm algorithm to solve the lower reactive power optimization model to obtain a reactive power output scheme of each group of fan units in the wind power cluster; according to the invention, a series of problems of voltage out-of-limit and the like caused by access of the wind power cluster to the power grid are solved, reactive power output of the wind power cluster is coordinated, and active power loss is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a dual-time-scale dual-layer self-discipline optimization method for accessing a wind power cluster to a power grid. Background Art

[0002] With the rapid development of society, the process of economic globalization and industrialization has accelerated, and energy demand has increased significantly. Traditional energy sources such as fossil energy will gradually be unable to support the sustainable development of human society. Energy low-carbon transformation is an inevitable trend in global energy development. New energy is an important prerequisite for leading the green and low-carbon development and transformation of the power system, and will occupy a dominant position in the future power system. With the increase in the proportion of centralized new energy and various distributed new energy in the distribution network, the volatility and intermittency of new energy have increased the impact on the distribution network, resulting in frequent fluctuations in the voltage of the distribution network, which brings new challenges to reactive support and voltage safety. To this end, domestic and foreign scholars have conducted a lot of research on reactive optimization and self-discipline optimization of power systems. Reactive optimization of power systems is a necessary means to improve the voltage level of the power grid and improve the economic efficiency of system operation. The optimal value of each variable operation is obtained through algorithm optimization, and the optimal reactive power flow distribution is realized, so as to achieve economic, safe and efficient operation of the power grid. The core idea of ​​self-discipline optimization is to use the fast adjustable resources within the region to optimize the state quantity changes caused by the injection quantity fluctuations in the sub-region within a certain target value or a given range.

[0003] At present, there are few studies on the coordinated cooperation of reactive power optimization and self-discipline optimization to improve the voltage level of the power grid and the economic efficiency of system operation. The access of wind power clusters to the power grid will cause a series of problems such as voltage over-limit, which will lead to a decrease in the operation efficiency of the power grid.

[0004] Based on this, this application proposes a dual-time-scale dual-layer autonomous optimization method for wind power cluster access to the power grid to solve the above problems. Summary of the invention

[0005] The object of the present invention is to provide a solution for wind power cluster access to solve the problem of reduced grid operation efficiency caused by voltage exceeding the limit when wind power cluster is connected to the grid.

[0006] The technical solution of the present invention is:

[0007] In a first aspect, the present application provides a dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to a power grid, which comprises the following steps:

[0008] S1, obtaining the minimum active power loss and voltage deviation of the system to establish an upper-level reactive power optimization mathematical model;

[0009] S2. Using the original data as input, the particle swarm algorithm is used to solve the upper reactive power optimization mathematical model, and the optimal solution is obtained to obtain the reactive power output value;

[0010] S3, obtaining the minimum total active power loss of the internal lines of the wind power cluster to establish a lower-layer reactive power optimization model;

[0011] S4, taking the original line parameters, wind power active output, and reactive output value obtained by S2 as input, the particle swarm algorithm is used to solve the lower reactive power optimization model to obtain the reactive power output scheme of each wind turbine unit in the wind power cluster.

[0012] Furthermore, in step S1, the calculation process of establishing the upper layer reactive power optimization mathematical model includes:

[0013] Minimum active power loss of the system:

[0014]

[0015] Where P loss is the total network loss within the wind power cluster, U i is the voltage value of node i in the system, U j is the voltage value of node j in the system, G ij is the conductance between nodes i and j, θ ij is the voltage phase difference between nodes i and j, N c is the total number of reactive power compensation devices in the system;

[0016] System voltage deviation:

[0017]

[0018] ΔU imax =U imax -U imin

[0019] Where ΔU is the system voltage deviation, N is the set of system nodes, is the voltage reference value of node i in the system, ΔU imax is the maximum voltage deviation allowed at node i in the system, U imax is the maximum voltage allowed at node i in the system, U imin is the minimum voltage allowed at node i in the system;

[0020] Power balance constraints:

[0021]

[0022]

[0023] Where P Giis the active power of node i in the system, P li is the active load of node i in the system, Q Gi is the reactive power of node i in the system, G ij is the conductance between nodes i and j, B ij is the susceptance between nodes i and j, Q li is the reactive load of node i in the system, Q Ci The reactive power provided by the reactive compensation device at node i in the system;

[0024] Constraints on safe operation of power grid:

[0025] U gimin ≤U gi ≤U gimax i∈N g

[0026] P gi,min ≤P gi ≤P gi,max i∈N g

[0027] Q gi,min ≤Q gi ≤Q gi,max i∈N g

[0028] Q ci,min ≤Q ci ≤Q ci,max i∈N c

[0029] T kmin ≤T k ≤T kmax i∈N t

[0030] Where U gimax The upper limit of the terminal voltage of the i-th generator, U gi is the terminal voltage of the ith generator, U gimin is the lower limit of the terminal voltage of the i-th generator, P gimax The upper limit of the active output of the i-th generator, P gi is the active output of the i-th generator, P gimin is the lower limit of the active output of the i-th generator, Q gimax is the upper limit of reactive power output of the i-th generator, Q gi is the reactive power output of the i-th generator, Q gimin is the lower limit of reactive power output of the i-th generator, Q cimax The upper limit of the switching capacity of the i-th reactive compensation device, Q ci The switching capacity of the i-th reactive power compensation device, Qcimin is the lower limit of the switching capacity of the i-th reactive power compensation device, T kimax The upper limit of the ratio of the i-th voltage regulating transformer, T ki is the transformation ratio of the i-th voltage regulating transformer, T kimin is the lower limit of the transformation ratio of the i-th voltage regulating transformer, N g is the total number of generators in the system, N c is the total number of reactive power compensation devices in the system, N t is the total number of voltage regulating transformers in the system;

[0031] OLTC-related constraints:

[0032] u i,t =k ij,t u j,t

[0033] k ij,t =k ij,0 +K ij,t Δk ij

[0034]

[0035]

[0036] In the formula, u i,t is the voltage of node i at time t, k ij,t is the transformation ratio of OLTC at time t, u j,t is the voltage of node j at time t, k ij,0 is the transformation ratio of OLTC at the initial moment, Δk ij,t is the change in ratio, K ij,t is the gear position at time t, is the maximum number of tap change steps for OLTC, is the highest gear, and Z is the number of adjustable gears of OLTC;

[0037] CB related constraints:

[0038]

[0039]

[0040]

[0041] In the formula, is the reactive power output of the CB of node i at time t, is the number of CBs of node i at time t, is the reactive power output of each CB at node i, is the maximum number of CB switching changes, It is the maximum number of switching groups of CB.

[0042] Furthermore, in step S2, the above-mentioned original data includes network component parameters, active and reactive output values ​​of conventional power sources, conventional loads and their power factors.

[0043] Furthermore, in step S3, the calculation process of establishing the lower layer reactive power optimization model includes:

[0044]

[0045] Where P loss is the total network loss within the wind power cluster, N′ is the total number of lines, ΔP i，loss is the active power loss of each line;

[0046] Power flow constraints:

[0047]

[0048] Q WT_min (P WTi )≤Q WTi ≤Q WT_max (P WTi )

[0049] U i_min ≤U i ≤U i_max

[0050] Where P WTi is the active power output of the i-th wind turbine generator set, Q WTi is the reactive power output of the i-th wind turbine generator set, Q WTi_max is the upper limit of reactive power output of the i-th wind turbine generator set, Q WTi_min is the lower limit of reactive power output of the i-th wind turbine generator set, U i_max is the upper voltage limit of the access point of the i-th wind turbine group, U i is the voltage value of node i in the system, U i_min are the lower limits of voltage at the access points of the i-th group of wind turbines respectively.

[0051] In a second aspect, the present application provides an electronic device, including:

[0052] A memory for storing one or more programs;

[0053] processor;

[0054] When the one or more programs are executed by the processor, a dual-time-scale dual-layer autonomous optimization method for access of wind power clusters to a power grid as described in any one of the first aspects is implemented.

[0055] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a dual-time-scale, two-layer autonomous optimization method for wind power cluster access to a power grid as described in any one of the first aspects above.

[0056] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0057] The present invention discloses a dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to a power grid. Considering the dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to a power grid, an upper-layer multi-objective reactive power optimization model for minimizing voltage deviation and network loss is proposed, and the reactive output value of the wind power cluster is obtained by optimizing the number of capacitor groups switched on and off, the output of a static reactive power compensation device, and the transformation ratio of a voltage regulating transformer. On this basis, a lower-layer self-discipline optimization model is established with the goal of minimizing the active power loss between each wind turbine group, and the reactive output of each wind turbine unit is taken as the quantity to be optimized. Compared with the self-discipline optimization of each wind turbine in the wind power cluster not being considered, the reactive output of the wind power cluster is coordinated, and the active power loss is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A step diagram of a dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to a power grid according to the present invention;

[0060] Figure 2 A network topology diagram of the present invention on a modified IEEE39 node system;

[0061] Figure 3 To optimize the topology of wind power cluster structure in the lower layer system;

[0062] Figure 4 This is a comparison chart of system network loss before and after upper layer optimization;

[0063] Figure 5 This is a comparison chart of the voltage amplitude at each moment before and after the upper layer optimization;

[0064] Figure 6 This is a comparison chart of active power loss before and after optimization of the lower layer;

[0065] Figure 7 Comparison of active power loss at each moment after optimization for three scenarios;

[0066] Figure 8 The figure is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0067] Icon: 101, memory; 102, processor; 103, communication interface. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0070] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0071] It should be noted that, in this article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0072] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0073] Example 1

[0074] See also Figure 1 , Figure 1 The figure shows a step diagram of a dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to the power grid provided by an embodiment of the present application.

[0075] In a first aspect, the present application provides a dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to a power grid, which comprises the following steps:

[0076] S1, obtaining the minimum active power loss and voltage deviation of the system to establish an upper-level reactive power optimization mathematical model;

[0077] S2. Using the original data as input, the particle swarm algorithm is used to solve the upper reactive power optimization mathematical model, and the optimal solution is obtained to obtain the reactive power output value;

[0078] S3, obtaining the minimum total active power loss of the internal lines of the wind power cluster to establish a lower-layer reactive power optimization model;

[0079] S4, taking the original line parameters, wind power active output, and reactive output value obtained by S2 as input, the particle swarm algorithm is used to solve the lower reactive power optimization model to obtain the reactive power output scheme of each wind turbine unit in the wind power cluster.

[0080] Among them, in step S2, after the original data of the upper model is input, the multi-objective particle swarm algorithm is used to solve the upper model, and a set of Pareto optimal solutions are obtained. The optimal compromise solution is selected, that is, the reactive output values ​​of each wind power cluster and multiple reactive sources within the optimization time period, and it is used as the input of the lower optimization model.

[0081] As a preferred implementation, in step S1, the calculation process of establishing the upper layer reactive power optimization mathematical model includes:

[0082] Minimum active power loss of the system:

[0083]

[0084] Where P loss is the total network loss within the wind power cluster, U i is the voltage value of node i in the system, U j is the voltage value of node j in the system, G ij is the conductance between nodes i and j, θ ij is the voltage phase difference between nodes i and j, N c is the total number of reactive power compensation devices in the system;

[0085] System voltage deviation:

[0086]

[0087] ΔU imax =U imax -U imin

[0088] Where ΔU is the system voltage deviation, N is the set of system nodes, is the voltage reference value of node i in the system, ΔU imax is the maximum voltage deviation allowed at node i in the system, U imax is the maximum voltage allowed at node i in the system, U imin is the minimum voltage allowed at node i in the system;

[0089] Power balance constraints:

[0090]

[0091]

[0092] Where P Gi is the active power of node i in the system, P li is the active load of node i in the system, Q Gi is the reactive power of node i in the system, G ij is the conductance between nodes i and j, B ij is the susceptance between nodes i and j, Q li is the reactive load of node i in the system, Q Ci The reactive power provided by the reactive compensation device at node i in the system;

[0093] Constraints on safe operation of power grid:

[0094] U gimin ≤U gi ≤U gimax i∈N g

[0095] P gi,min ≤P gi ≤P gi,max i∈N g

[0096] Q gi,min ≤Q gi ≤Q gi,max i∈N g

[0097] Q ci,min ≤Q ci ≤Q ci,max i∈N c

[0098] T kmin ≤T k ≤T kmax i∈N t

[0099] Where U gimax The upper limit of the terminal voltage of the i-th generator, U giis the terminal voltage of the ith generator, U gimin is the lower limit of the terminal voltage of the i-th generator, P gimax The upper limit of the active output of the i-th generator, P gi is the active output of the i-th generator, P gimin is the lower limit of the active output of the i-th generator, Q gimax is the upper limit of reactive power output of the i-th generator, Q gi is the reactive power output of the i-th generator, Q gimin is the lower limit of reactive power output of the i-th generator, Q cimax The upper limit of the switching capacity of the i-th reactive compensation device, Q ci The switching capacity of the i-th reactive power compensation device, Q cimin is the lower limit of the switching capacity of the i-th reactive power compensation device, T kimax The upper limit of the ratio of the i-th voltage regulating transformer, T ki is the transformation ratio of the i-th voltage regulating transformer, T kimin is the lower limit of the transformation ratio of the i-th voltage regulating transformer, N g is the total number of generators in the system, N c is the total number of reactive power compensation devices in the system, N t is the total number of voltage regulating transformers in the system;

[0100] OLTC-related constraints:

[0101] u i,t =k ij,t u j,t

[0102] k ij,t =k ij,0 +K ij,t Δk ij

[0103]

[0104]

[0105] In the formula, u i,t is the voltage of node i at time t, k ij,t is the transformation ratio of OLTC at time t, u j,t is the voltage of node j at time t, k ij,0 is the transformation ratio of OLTC at the initial moment, Δk ij,t is the change in ratio, K ij,t is the gear position at time t, is the maximum number of tap change steps for OLTC, is the highest gear, and Z is the number of adjustable gears of OLTC;

[0106] CB related constraints:

[0107]

[0108]

[0109]

[0110] In the formula, is the reactive power output of the CB of node i at time t, is the number of CBs of node i at time t, is the reactive power output of each CB at node i, is the maximum number of CB switching changes, It is the maximum number of switching groups of CB.

[0111] It should be noted that in the reactive power optimization model of large-scale wind power clusters connected to the power grid, usually only the total active network loss of the system operation is considered, but the impact of the optimized voltage deviation on the network and the important impact of reactive power regulation on the system network loss and voltage are ignored. Therefore, in the upper model, an hourly optimization model is established, with the minimum system active network loss and voltage deviation as the objective function.

[0112] As a preferred implementation, in step S2, the original data includes network component parameters, active and reactive output values ​​of conventional power sources, conventional loads and their power factors.

[0113] As a preferred implementation, in step S3, the calculation process of establishing the lower layer reactive power optimization model includes:

[0114]

[0115] Where P loss is the total network loss within the wind power cluster, N′ is the total number of lines, ΔP i，loss is the active power loss of each line;

[0116] Power flow constraints:

[0117]

[0118] Q WT_min (P WTi )≤Q WTi ≤Q WT_max (P WTi )

[0119] U i_min ≤U i ≤U i_max

[0120] Where P WTi is the active power output of the i-th wind turbine generator set, QWTi is the reactive power output of the i-th wind turbine generator set, Q WTi_max is the upper limit of reactive power output of the i-th wind turbine generator set, Q WTi_min is the lower limit of reactive power output of the i-th wind turbine generator set, U i_max is the upper voltage limit of the access point of the i-th wind turbine group, U i is the voltage value of node i in the system, U i_min are the lower limits of voltage at the access points of the i-th group of wind turbines respectively.

[0121] It should be noted that the lower layer establishes a 15-minute rolling optimization model for the wind farm cluster with the minimum total active power loss of the internal lines of the wind farm cluster as the objective function. The active output value of a group of wind turbines is input every 15 minutes, and the reactive output Qset of the upper layer at 24 moments is used as the initial parameter of the lower layer optimization.

[0122] Example 2

[0123] The present invention performs simulation calculation on the modified IEEE 39-node system. The system structure is as follows: Figure 2 As shown in the figure, the base power of the system is 100MVA. In the system, nodes 7, 26, 20, and 29 are connected to wind power clusters WT1, WT2, WT3, and WT4, each with an installed capacity of 600MW; on-load tap-changing transformers are configured in the four lines 2-30, 11-12, 22-35, and 29-38, with a total of 8 gears and an adjustment step of 0.025pu; switchable capacitor groups (CB) are connected to nodes 8 and 27, with a maximum number of switchable groups of 5 and a single group capacity of 20Mvar; static VAR compensation devices (SVC) with a capacity of 100Mvar are connected to nodes 4, 12, and 21, respectively.

[0124] The topological structure of each wind power cluster in the lower optimization model is as follows: Figure 3 The wind power cluster includes 10 wind turbines with a rated capacity of 60MW, which are connected to the grid through high-voltage transmission lines after voltage boosting.

[0125] Figure 4 This is a comparison chart of the system network loss before and after the upper-level optimization. It can be seen that the active network loss of the optimized system at each moment has decreased significantly. Before the optimization, the average network loss power of the system in 24 hours a day was 386.13MW, and the average voltage deviation was 19.31pu. After reactive power optimization, the average network loss power of the system was 337.44MW, the average voltage deviation was 10.25pu, and the loss reduction rate reached 12.84%.

[0126] Figure 5The following is a comparison of the voltage amplitude of the system at each time before and after the upper layer optimization. It can be seen that before the optimization, the voltage of some nodes has exceeded the safety range of the system. However, after the optimization, the voltage level in each time period is lower than the level before the optimization. By comparison, it can be concluded that the voltage amplitude after optimization has improved overall and is still within a reasonable range. This optimization measure is conducive to reducing system network losses and maintaining voltage stability.

[0127] The lower optimization model takes the reactive power Qset of the wind farm cluster after the upper optimization as input, and optimizes it with the minimum network loss between each wind turbine in each wind farm cluster as the objective function to obtain the reactive output of each wind turbine. By comparing the internal network loss of the lower wind farm cluster before and after optimization, the results are as follows: Figure 6 It can be seen that compared with the case where the self-discipline optimization of each wind turbine is not considered, the active network loss at each moment has been significantly reduced after optimization, which proves the effectiveness of the proposed method.

[0128] The optimization scenarios are set. Scenario 1 does not consider the impact of reactive power compensation and the transformer ratio adjustment system, and the impact of the self-discipline optimization of each wind turbine in the wind power cluster on the active power loss. Scenario 2 does not consider the impact of reactive power compensation and the transformer ratio adjustment system, and only considers the impact of the self-discipline optimization of each wind turbine in the wind power cluster on the active power loss. Scenario 3 considers the impact of reactive power compensation and the transformer ratio adjustment system, and the impact of the self-discipline optimization of each wind turbine in the wind power cluster on the active power loss.

[0129] By solving the optimization model, the optimization results of the three scenarios are as follows Figure 7 As shown in the figure, it can be easily found that the active network loss in optimization scenario 1 is 20.61MW. The active network loss in optimization scenario 2 is 4.58MW. The active loss in scenario 3 is 0.41MW. Considering the impact of reactive power compensation and transformer ratio adjustment on the system, the self-discipline optimization of each wind turbine in the wind power cluster on the active power loss has a better loss reduction result.

[0130] Example 3

[0131] See also Figure 8 , Figure 8 A schematic structural block diagram of an electronic device provided in an embodiment of the present application.

[0132] An electronic device includes a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used to communicate signaling or data with other node devices.

[0133] Among them, the memory 101 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc.

[0134] The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0135] It is understood that the structure shown in the figure is only for illustration, and a dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to the power grid may also include more or fewer components than those shown in the figure, or have a different configuration than those shown in the figure. Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0136] In the embodiments provided in the present application, it should be understood that the disclosed method can also be implemented in other ways. The embodiments described above are merely schematic, for example, the flowchart or block diagram in the accompanying drawings shows the possible implementation architecture, functions and operations of the method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0138] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0139] In summary, the embodiment of the present application provides a dual-time-scale, two-layer autonomous optimization method for wind power clusters accessing the power grid. By considering the dual-time-scale, two-layer autonomous optimization method for wind power clusters accessing the power grid, an upper-layer multi-objective reactive optimization model that minimizes voltage deviation and network loss is proposed, and the reactive output value of the wind power cluster is obtained by optimizing the number of capacitor groups switched on and off, the output of the static reactive compensation device, and the transformation ratio of the voltage regulating transformer; on this basis, the lower layer establishes an autonomous optimization model with the goal of minimizing the active power loss between each wind turbine unit, and takes the reactive output of each wind turbine unit as the quantity to be optimized. Compared with not considering the autonomous optimization of each wind turbine within the wind power cluster, the reactive output of the wind power cluster is coordinated, and the active power loss is effectively reduced.

[0140] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0141] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to the power grid, characterized in that: The following steps are involved: S1, obtaining the minimum active power loss and voltage deviation of the system to establish an upper-level reactive power optimization mathematical model; S2. Using the original data as input, the particle swarm algorithm is used to solve the upper reactive power optimization mathematical model, and the optimal solution is obtained to obtain the reactive power output value; S3, obtaining the minimum total active power loss of the internal lines of the wind power cluster to establish a lower-layer reactive power optimization model; S4, taking the original line parameters, wind power active output, and reactive output value obtained by S2 as input, the particle swarm algorithm is used to solve the lower reactive power optimization model to obtain the reactive power output scheme of each wind turbine unit in the wind power cluster.

2. The dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to the power grid as claimed in claim 1, characterized in that: In step S1, the calculation process of establishing the upper layer reactive power optimization mathematical model includes: Minimum active power loss of the system: Where P loss is the total network loss within the wind power cluster, U i is the voltage value of node i in the system, U j is the voltage value of node j in the system, G ij is the conductance between nodes i and j, θ ij is the voltage phase difference between nodes i and j, N c is the total number of reactive power compensation devices in the system; System voltage deviation: ΔU imax =U imax -U imin Where ΔU is the system voltage deviation, N is the set of system nodes, is the voltage reference value of node i in the system, ΔU imax is the maximum voltage deviation allowed at node i in the system, U imax is the maximum voltage allowed at node i in the system, U imin is the minimum voltage allowed at node i in the system; Power balance constraints: Where P Gi is the active power of node i in the system, P li is the active load of node i in the system, Q Gi is the reactive power of node i in the system, G ij is the conductance between nodes i and j, B ij is the susceptance between nodes i and j, Q li is the reactive load of node i in the system, Q Ci The reactive power provided by the reactive compensation device at node i in the system; Constraints on safe operation of power grid: IN gimin ≤U gi ≤U gimax i∈N g P gi,min ≤P gi ≤P gi,max i∈N g Q gi,min ≤Q gi ≤Q gi,max i∈N g Q ci,min ≤Q ci ≤Q ci,max i∈N c T kmin ≤T k ≤T kmax i∈N t Where U gimax The upper limit of the terminal voltage of the i-th generator, U gi is the terminal voltage of the ith generator, U gimin is the lower limit of the terminal voltage of the i-th generator, P gimax The upper limit of the active output of the i-th generator, P gi is the active output of the i-th generator, P gimin is the lower limit of the active output of the i-th generator, Q gimax is the upper limit of reactive power output of the i-th generator, Q gi is the reactive power output of the i-th generator, Q gimin is the lower limit of reactive power output of the i-th generator, Q cimax The upper limit of the switching capacity of the i-th reactive compensation device, Q ci The switching capacity of the i-th reactive compensation device, Q cimin is the lower limit of the switching capacity of the i-th reactive power compensation device, T kimax The upper limit of the ratio of the i-th voltage regulating transformer, T ki is the transformation ratio of the i-th voltage regulating transformer, T kimin is the lower limit of the transformation ratio of the i-th voltage regulating transformer, N g is the total number of generators in the system, N c is the total number of reactive power compensation devices in the system, N t is the total number of voltage regulating transformers in the system; OLTC-related constraints: in i,t =k ij,t in j,t k ij,t =k ij,0 +K ij,t Δk ij In the formula, u i,t is the voltage of node i at time t, k ij,t is the transformation ratio of OLTC at time t, u j,t is the voltage of node j at time t, k ij,0 is the transformation ratio of OLTC at the initial moment, Δk ij,t is the change in ratio, K ij,t is the gear position at time t, is the maximum number of tap change steps for OLTC, is the highest gear, and Z is the number of adjustable gears of OLTC; CB related constraints: In the formula, is the reactive power output of the CB of node i at time t, is the number of CBs of node i at time t, is the reactive power output of each CB at node i, is the maximum number of CB switching changes, It is the maximum number of switching groups of CB.

3. The dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to the power grid as claimed in claim 1, characterized in that: In step S2, the original data includes network component parameters, active and reactive output values ​​of conventional power sources, conventional loads and their power factors.

4. The dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to the power grid as claimed in claim 1, characterized in that: In step S3, the calculation process of establishing the lower layer reactive power optimization model includes: Where P loss is the total network loss within the wind power cluster, N′ is the total number of lines, ΔP i，loss is the active power loss of each line; Power flow constraints: Q WT_min (P WTi )≤Q WTi ≤Q WT_max (P WTi ) IN i_min ≤U i ≤U i_max Where P WTi is the active power output of the i-th wind turbine generator set, Q WTi is the reactive power output of the i-th wind turbine generator set, Q WTi_max is the upper limit of reactive power output of the i-th wind turbine generator set, Q WTi_min is the lower limit of reactive power output of the i-th wind turbine generator set, U i_max is the upper voltage limit of the access point of the i-th wind turbine group, U i is the voltage value of node i in the system, U i_min are the lower limits of voltage at the access points of the i-th group of wind turbines respectively.

5. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the dual-time-scale dual-layer autonomous optimization method for wind power cluster access to a power grid as described in any one of claims 1 to 4 is implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the dual-time-scale dual-layer self-discipline optimization method for wind power cluster access to a power grid as described in any one of claims 1 to 4 is implemented.

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