Locating and sizing method for distributed phase modifier under fan multi-output scene

Abstract

The invention discloses a locating and sizing method for a distributed phase modifier in a fan multi-output scene, and belongs to the field of power systems. According to the method, Latin hypercube sampling and a backward reduction method are adopted to generate a typical wind power output scene, steady-state performance and economic cost optimization are taken as optimization objectives, an optimal configuration model of the distributed phase modifier in a fan multi-output scene is constructed, and a particle swarm optimization algorithm is adopted to solve the optimal model to obtain a corresponding optimal configuration scheme. Therefore, the randomness and volatility of fan output can be considered, a typical fan output scene is formed, and the active power network loss of the system is effectively reduced and the voltage safety margin of the system is improved while economic indexes such as phase modifier purchase, power consumption and operation and maintenance cost are considered.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for site selection and capacity determination of distributed phase regulators in a wind turbine multi-output scenario. Background Art

[0002] At present, the reactive power compensation devices of the power system often use power electronic devices such as parallel capacitor banks, static VAR compensators (SVC) and static VAR generators (SVG). However, such devices are affected by the system and there is a certain delay in reactive power response. The phase regulators in the power grid are divided into large-capacity centralized and small-capacity distributed. Large-capacity centralized phase regulators are mostly concentrated at the AC bus of the new energy transmission converter station; while small-capacity distributed phase regulators are mostly dispersedly configured on the low-voltage side of the new energy collection station or the grid connection point of the station. Relevant studies have shown that installing distributed phase regulators on the low-voltage side of the new energy station / collection station is more conducive to suppressing transient overvoltage at the machine end and preventing new energy from being disconnected from the grid due to overvoltage. Different installation locations and capacity configurations have different dynamic support capabilities for the system.

[0003] The optimization configuration of distributed phase-shifting devices is divided into two aspects: site selection and capacity determination. For the site selection problem of reactive power compensation devices, existing studies mainly screen and sort nodes based on various indicators, and basically adopt the method of site selection first and then capacity determination. Few studies consider the site selection and capacity determination of distributed phase-shifting devices at the same time, and most studies select sites and determine capacity based on a single scenario, without considering the randomness and volatility of wind power output. Summary of the invention

[0004] The present invention provides a method for site selection and sizing of distributed phase regulators in a wind turbine multi-output scenario, so as to solve the problem that the site selection and sizing of distributed phase regulators in a wind farm are not considered simultaneously when the site selection and sizing of distributed phase regulators are selected and sizing are determined based on a single scenario, and the randomness and volatility of wind power output are not considered.

[0005] An embodiment of the present invention provides a method for site selection and capacity determination of a distributed phase regulator in a wind turbine multi-output scenario, comprising the following steps:

[0006] S1, the wind speed samples are sampled using the Latin hypercube sampling method, and the sampled wind speed samples are reduced based on the backward reduction method of Kantorovich distance to obtain a typical wind farm output scenario;

[0007] S2, build a site selection and sizing optimization configuration model for distributed phase regulators in wind turbine multi-output scenarios with the optimization goal of minimizing the weighted system steady-state performance and total economic efficiency indicators and the input and output of each node in each typical wind farm output scenario satisfying the power flow equation as a constraint;

[0008] S3, using a particle swarm algorithm to solve the site selection and capacity optimization configuration model to obtain an optimal configuration solution for the distributed phase regulator.

[0009] Optionally, in one embodiment of the present invention, step S1 specifically includes:

[0010] The Weibull distribution function is used to describe the probability density of wind speed in a certain area:

[0011]

[0012] In the formula, C and K are the scale parameter and shape parameter of Weibull distribution respectively, V is the wind speed, and the scale parameter C and shape parameter K of Weibull distribution are approximately calculated by the mean and standard deviation of wind speed statistical data;

[0013] The cumulative probability density distribution function of the Weibull distribution of wind speed is:

[0014]

[0015] The relationship between the active output and wind speed of a doubly fed wind turbine is:

[0016]

[0017] In the formula, P and P N is the fan output active power and rated power, ρ is the air density, A r =πR 2 is the swept area of ​​the wind turbine blade, R is the blade radius, V in 、V N 、V out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively, C p is the wind energy utilization coefficient of the wind turbine, λ=ωR / V is the tip speed ratio, ω is the tip rotation angular velocity, and θ is the blade pitch angle;

[0018] The Latin hypercube sampling method is used to sample the cumulative probability density distribution function of the Weibull distribution of wind speed, and the sampled wind speed samples are reduced based on the backward reduction method of Kantorovich distance. According to the relationship between the active output and wind speed of the doubly fed wind turbine, the typical wind farm output scenario is obtained.

[0019] Optionally, in one embodiment of the present invention, step S2 specifically includes:

[0020] The system steady-state performance is divided into two parts: system active network loss and system voltage deviation. The site selection and sizing optimization configuration model of distributed phase regulators in the scenario of multi-output wind turbines takes the weighted minimum of system steady-state performance and total economic index as the optimization goal, and the optimization objective function is:

[0021] minF=min(λ1FW +λ2F C )

[0022] F W =ω1f loss +ω2f V

[0023] In the formula, F W , F C are the steady-state performance index and economic index of the system, respectively, loss and f V are the total active network loss and total voltage deviation of the system respectively, λ and ω are the weight coefficients of each index;

[0024] The total active network loss of the system is:

[0025]

[0026] Where N C is the number of scenes, Sc(1,2,…,N c ) is a typical wind power output scenario, W Sc is the weight of the typical wind power output scenario Sc, N L is the number of system branches, G ij and θ ij is the conductance and node voltage phase angle between node i and node j, U i and U j are the voltage amplitudes at nodes i and j respectively;

[0027] The total voltage deviation of the system is:

[0028]

[0029] Where W bi For node b i The weight of b i (1,2,…,N) are system nodes;

[0030] The economic performance index is composed of the purchase cost, power consumption cost and operation and maintenance cost of the condenser:

[0031] F C =C1+C2+C3

[0032] C1=N SC ×c1

[0033] C2=N SC ×c2×Q unit ×N year

[0034] C3=N SC ×c3×α i ×Nyear

[0035] Where C1, C2, and C3 are purchase cost, power consumption cost, and operation and maintenance cost, respectively. SC is the number of condenser installations, c1 is the configuration cost of a single condenser, c2 is the annual unit capacity power consumption of the condenser, Q unit is the phase regulator installation capacity, N year is the planned life, c3 is the operation and maintenance cost of a single phase regulator, α i is the operation and maintenance coefficient of the phase regulator at node i;

[0036] The constraints of the site selection and sizing optimization configuration model of distributed phase regulators in the scenario of multiple wind turbine outputs are divided into equality constraints and inequality constraints:

[0037] (1) The input and output of each node under each typical wind farm output scenario satisfies the equality constraints of the power flow equation:

[0038]

[0039] Where P gi,c , P di,c and P i,c are the active power injection, active power load and active power output of node i under scenario Sc, Q gi,c , Q di,c and Q i,c are the reactive injection, reactive load and reactive output of node i under scenario Sc, δ k,c and V k,c are the phase angle and voltage amplitude of node k in scenario Sc, δ i,c and V i,c For scene S c The phase angle and voltage amplitude of the lower node i, G ik , B ik are the conductance and susceptance between node i and node k in the admittance matrix, N and N C are the number of nodes and the number of scenes, respectively;

[0040] (2) Inequality constraints under various typical wind farm output scenarios:

[0041] V i,cmin ≤V i,c ≤V i,cmax i=1,2…,N

[0042] S Gj,cmin ≤S Gj,c ≤S Gj,cmax j=1,2…,N G

[0043] P Wn,cmin ≤PWn,c ≤P Wn,cmax n=1,2…,N W

[0044] Q SCm,cmin ≤Q SCm,c ≤Q SCm,cmax m=1,2…,N SC

[0045] Where V i,cmin , V i,cmax and V i,c are the minimum voltage, maximum voltage and voltage amplitude of node i in scenario Sc, respectively. Gj,cmin , S Gj,cmax and S Gj,c are the minimum apparent power, maximum apparent power and apparent power of generator j in scenario Sc, respectively. Wn,cmin , P Wn,cmax and P Wn,c are the minimum active output, maximum active output and active output of wind turbine n in scenario Sc, respectively. SCm,cmin , Q SCm,cmax and Q SCm,c are the minimum reactive output, maximum reactive output and reactive output of condenser m in scenario Sc, respectively. G , N W and N SC They are the number of generators, the number of fans and the number of phase regulators respectively.

[0046] Optionally, in one embodiment of the present invention, in step S3, solving the site selection and capacity optimization configuration model using a particle swarm algorithm specifically includes:

[0047] Step 1: Generate N groups of wind farm output scenarios using the Latin hypercube sampling method and generate N wind farm output scenarios based on the backward reduction method. C Typical wind power output scenarios;

[0048] Step 2: Take the scenario Sc and the number of iterations gen as 1, initialize the particle swarm, including the installation nodes and installation capacity;

[0049] Step 3: Calculate the total active network loss f of each indicator system under scenario Sc loss , the total voltage deviation of the system f V and economic index F C ;

[0050] Step 4: Determine whether the number of scenarios Sc is greater than or equal to the total number of typical wind power output scenarios N CIf yes, execute Step 5, otherwise increase the number of scenes Sc by one and return to Step 3;

[0051] Step 5: Calculate the optimization objective function, update the individual optimal position and the group optimal position, and update the particle speed and position;

[0052] Step 6: Determine whether the maximum number of iterations has been reached. If so, end the iteration and output the optimal configuration plan for the distributed phase regulator. Otherwise, return to Step 3.

[0053] The method for site selection and sizing of distributed phase-shifters in wind turbine multi-output scenarios of the embodiment of the present invention uses Latin hypercube sampling and backward reduction method to generate typical wind power output scenarios, takes steady-state performance and economic cost optimization as optimization goals, constructs an optimization configuration model of distributed phase-shifters in wind turbine multi-output scenarios, and uses particle swarm algorithm to solve the optimization model to obtain the corresponding optimization configuration scheme. Therefore, the present invention can take into account the randomness and volatility of wind turbine output, form a typical wind turbine output scenario, and effectively reduce system active network losses and improve system voltage safety margin while considering economic indicators such as phase-shifter purchase, power consumption and operation and maintenance costs.

[0054] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0056] Figure 1 It is a flow chart of a method for site selection and capacity determination of a distributed phase regulator in a wind turbine multi-output scenario provided according to an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of Latin hypercube sampling according to an embodiment of the present invention;

[0058] Figure 3 This is an overall structural diagram of a wind farm access IEEE39 node system according to an embodiment of the present invention;

[0059] Figure 4 A wind farm structure diagram of a wind farm connected to an IEEE39 node system according to an embodiment of the present invention;

[0060] Figure 5 A comparison between a typical wind speed scenario of an embodiment of the present invention and the original cumulative probability curve;

[0061] Figure 6 The voltage safety margin analysis before and after considering scenario factors in the embodiment of the present invention;

[0062] Figure 7 Analysis of active network loss before and after considering scenario factors in an embodiment of the present invention;

[0063] Figure 8 The system performance analysis before and after the configuration of scenario 1 of the embodiment of the present invention is shown in (a) for the comparison of voltage safety margins and (b) for the comparison of the accumulated values ​​of active network loss consumption;

[0064] Fig. 9 This is the system performance analysis before and after the configuration of scenario 5 of the embodiment of the present invention, (a) is the comparison of voltage safety margin, and (b) is the comparison of the accumulated values ​​of active network loss consumption. DETAILED DESCRIPTION

[0065] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0066] Figure 1 The present invention is a flowchart of a method for site selection and capacity determination of a distributed phase regulator in a wind turbine multi-output scenario provided in accordance with an embodiment of the present invention.

[0067] like Figure 1 As shown, the method for site selection and capacity determination of distributed phase regulators in the wind turbine multi-output scenario includes the following steps:

[0068] S1, the wind speed samples are sampled using the Latin hypercube sampling method, and the sampled wind speed samples are reduced based on the backward reduction method of Kantorovich distance to obtain a typical wind farm output scenario.

[0069] Wind speed has strong volatility and randomness. Studies have shown that wind speed usually follows a positively skewed distribution. The Weibull distribution function can be used to describe the probability density of wind speed in a certain area:

[0070]

[0071] In the formula, C and K are the scale parameter and shape parameter of Weibull distribution respectively, V is the wind speed, and the scale parameter C and shape parameter K of Weibull distribution are approximately calculated by the mean and standard deviation of wind speed statistical data;

[0072] The cumulative probability density distribution function of the Weibull distribution of wind speed is:

[0073]

[0074] For a doubly-fed wind turbine, its active output and wind speed can be expressed by the following formula:

[0075]

[0076] Where: P and P N is the fan output active power and rated power; ρ is the air density (kg / m 3 );A r =πR 2 is the swept area of ​​the wind turbine blade, R is the blade radius (m); V in 、V N 、V out are the cut-in, rated and cut-out wind speeds of the wind turbine respectively; C p is the wind energy utilization coefficient of the wind turbine; λ=ωR / V is the tip speed ratio, where ω is the tip rotation angular velocity; θ is the blade pitch angle.

[0077] The approximate calculation formula for wind energy utilization coefficient is as follows:

[0078]

[0079] in:

[0080]

[0081] According to Betz theory, C p The maximum value is 0.593, which is related to the tip speed ratio λ and the pitch angle θ of the wind turbine.

[0082] The Latin hypercube sampling method is used to sample the cumulative probability density distribution function of the Weibull distribution of wind speed, and the sampled wind speed samples are reduced based on the backward reduction method of Kantorovich distance. According to the relationship between the active output and wind speed of the doubly fed wind turbine, the typical wind farm output scenario is obtained.

[0083] Specifically, in order to accurately reflect the probability distribution of wind speed, Latin hypercube can be used to sample wind speed samples. Latin hypercube sampling is as follows: Figure 2 As shown. Divide the cumulative probability density distribution function of the Weibull distribution into N equal parts, and randomly select samples F from each interval. k , use the inverse function to calculate the sampling value V k In order to reduce the number of samples and improve the calculation efficiency, the present invention adopts the backward reduction method based on Kantorovich distance to reduce the wind speed samples, so as to obtain the typical wind farm output scenario.

[0084] S2, constructs a site selection and sizing optimization configuration model for distributed phase-converter in wind turbine multi-output scenarios with the optimization objective of minimizing the weighted system steady-state performance and total economic indicators and the input and output of each node in each typical wind farm output scenario satisfying the power flow equation as a constraint.

[0085] Aiming at the problem of optimal configuration of distributed phase regulators in wind power power system, a 50MVar phase regulator is installed on the 35kV bus of the wind farm to compensate the reactive power of the system. The installation location and capacity of the phase regulator are optimized with the weighted minimum of the system steady-state performance and total economic indicators as the optimization goal. The steady-state performance is divided into two parts: system active network loss and system voltage deviation. The optimization objective function is as shown in (6) and (7):

[0086] minF=min(λ1F W +λ2F C )(6)

[0087] F W =ω1f loss +ω2f V (7)

[0088] In the formula, F W , F C are the steady-state performance index and economic index of the system, respectively, loss and f V are the total active network loss and total voltage deviation of the system respectively, λ and ω are the weight coefficients of each index;

[0089] The total active network loss of the system can be expressed as:

[0090]

[0091] Where N C is the number of scenes, Sc(1,2,…,N c ) is a typical wind power output scenario, W Sc is the weight of the typical wind power output scenario Sc, N L is the number of system branches, G ij and θ ij is the conductance and node voltage phase angle between node i and node j, U i and U j are the voltage amplitudes at nodes i and j respectively;

[0092] The total system voltage deviation can be expressed as:

[0093]

[0094] Where W bi For node b i The weight of b i (1,2,…,N) are system nodes;

[0095] The economic performance index is composed of the purchase cost, power consumption cost and operation and maintenance cost of the condenser:

[0096]

[0097] Where C1, C2, and C3 are purchase cost, power consumption cost, and operation and maintenance cost respectively; N SC is the number of installed condensers; c1 is the configuration cost of a single condenser (10,000 yuan / unit); c2 is the annual unit capacity power consumption of the condenser (10,000 yuan / WM); Q unit is the installed capacity of the phase regulator (MW); N year is the planned life; c3 is the operation and maintenance cost of a single phase regulator (10,000 yuan / unit); α i is the phase regulator operation and maintenance coefficient at node i, and α is taken i =-0.05N i +1.05, the operation and maintenance coefficient will decrease as the number of installed units at the node increases.

[0098] The constraints of the site selection and sizing optimization configuration model of distributed phase regulators in the scenario of multiple wind turbine outputs are divided into equality constraints and inequality constraints:

[0099] (1) The input and output of each node under each typical wind farm output scenario satisfies the equality constraints of the power flow equation:

[0100]

[0101] Where P gi,c , P di,c and P i,c are the active power injection, active power load and active power output of node i under scenario Sc, Q gi,c , Q di,c and Q i,c are the reactive injection, reactive load and reactive output of node i under scenario Sc, δ k,c and V k,c are the phase angle and voltage amplitude of node k in scenario Sc, δ i,c and V i,c For scene S c The phase angle and voltage amplitude of the lower node i, G ik , B ik are the conductance and susceptance between node i and node k in the admittance matrix, N and N C are the number of nodes and the number of scenes, respectively;

[0102] (2) Inequality constraints under various typical wind farm output scenarios:

[0103]

[0104] Where V i,cmin , V i,cmax and V i,care the minimum voltage, maximum voltage and voltage amplitude of node i in scenario Sc, respectively. Gj,cmin , S Gj,cmax and S Gj,c are the minimum apparent power, maximum apparent power and apparent power of generator j in scenario Sc, respectively. Wn,cmin , P Wn,cmax and P Wn,c are the minimum active output, maximum active output and active output of wind turbine n in scenario Sc, respectively. SCm,cmin , Q SCm,cmax and Q SCm,c are the minimum reactive output, maximum reactive output and reactive output of condenser m in scenario Sc, respectively. G , N W and N SC They are the number of generators, the number of fans and the number of phase regulators respectively.

[0105] S3, using the particle swarm algorithm to solve the site selection and capacity optimization configuration model, and obtain the optimal configuration plan of the distributed phase regulator.

[0106] The site selection and capacity optimization configuration model describes a multivariate nonlinear optimization problem. In order to solve this optimization problem, the particle swarm optimization (PSO) algorithm is further introduced to solve the site selection and capacity optimization configuration model. The specific implementation steps are as follows:

[0107] Step 1: Use the Latin hypercube method to sample and generate N groups of wind farm output scenarios and generate N wind farm output scenarios based on the backward reduction method. C Typical wind power output scenarios;

[0108] Step 2: Take the scenario Sc and the number of iterations gen as 1, initialize the particle swarm, including the installation nodes and installation capacity;

[0109] Step 3: Calculate the total active network loss f of various indicators under scenario Sc according to formulas (8)-(10) loss , the total voltage deviation of the system f V and economic index F C ;

[0110] Step 4: Determine whether the number of scenarios Sc is greater than or equal to the total number of typical wind power output scenarios N C If yes, execute Step 5, otherwise increase the number of scenes Sc by one and return to Step 3;

[0111] Step 5: Calculate the optimization objective function according to formulas (6)-(7), update the individual optimal position and the group optimal position, and update the particle speed and position;

[0112] Step 6: Determine whether the maximum number of iterations has been reached. If so, end the iteration and output the optimal configuration plan for the distributed phase regulator. Otherwise, return to Step 3.

[0113] The following is a detailed description of the method for site selection and capacity determination of a distributed phase regulator in a wind turbine multi-output scenario of the present invention through a specific embodiment.

[0114] In the specific implementation process, the embodiment is Figure 3 and Figure 4 The wind farm structure diagram shown in the figure takes the IEEE 39-node system as an example. Three wind farms replace the 30, 32 and 35-node generators and are connected to the grid through a 35kV / 220kV step-up transformer. Each wind farm contains two feeders with different lengths. The feeders are spaced at the same distance and connected to a wind turbine with a rated capacity of 20MVA through a 0.69kV / 35kV step-up transformer. Each wind turbine contains 12 wind turbines. The wind turbines all adopt the double-fed induction asynchronous wind turbine generator model (DFIG) with a rated active power P n =1.5MW, rated apparent power S n =1.5 / 0.9MVA, the initial wind turbine active output is 15MW, no reactive power is generated. Cut-in wind speed V in =3m / s, rated wind speed V N =11.8m / s, cut-out wind speed V out =25m / s, blade radius R=35.2m, air density ρ=1.39kg / m 3 .

[0115] Taking the Weibull shape parameter K = 2 and the scale parameter C = 8.5, 3000 wind speed scenarios were obtained through Latin hypercube sampling, and the scenarios were reduced to obtain 10 typical scenarios. The wind speed, wind turbine output and probability under the 10 typical scenarios are summarized in Table 1. The cumulative probability density distribution function of the wind speed in the typical scenario is shown in Figure 5 shown.

[0116] Table 1 Typical scenarios of wind turbine output

[0117]

[0118]

[0119] The purchase cost of a 50MVar distributed phase condenser is 30 million yuan / unit; the planned service life is 30 years; the annual unit capacity power consumption is 662,256 yuan / MW; the operation and maintenance cost of a single phase condenser is 900,000 yuan / MVar. At this time, the economic performance indicators can be expressed as:

[0120]

[0121] According to the requirements of "Technical Regulations for Wind Farm Access to Power System Part 1: Onshore Wind Power" (GB / T 19963.1-2021), the grid connection point voltage should be controlled within the normal range, and the grid connection point W of three wind farms in the area should be assigned. bi =10, and the weight coefficients of the remaining nodes are W bi Take it as 1. The weight coefficient W of each scene Sc Take the corresponding probability value.

[0122] Phase regulators are mostly configured at the grid connection points of wind power collection systems and new energy stations, and the installation nodes are selected in three areas. When multiple scenarios are not considered, taking scenario 5 with a higher probability as an example, when one phase regulator is configured, the initial reactive output of 22.6965 MVar is obtained by installing a phase regulator at node 41 (35kV grid connection side of W1 feeder 2). When multiple scenarios are considered, the initial reactive output of 25.7122 MVar is obtained when the phase regulator is configured at node 41. When two phase regulators are configured, the initial reactive output of 13.5006 and -7.1766 MVar is obtained when phase regulators are configured at nodes 50 (DFIG2_1 high-voltage side of W1) and 55 (DFIG1_2 high-voltage side of W2) in scenario 5. In multiple scenarios, phase regulators are configured at nodes 50 and 56 (DFIG1_3 high-voltage side of W2) respectively, with initial reactive outputs of 16.2844 and -5.3285 MVar. Figure 6 and Figure 7 It reflects the configuration of different camera sets, considering only a single scene 5 and considering multiple scenes for optimal configuration. V The difference and f loss Difference.

[0123] from Figure 6 and Figure 7 It can be found that the difference in active network loss is always positive, so the optimization solution obtained by considering multiple scenarios always has less system network loss than when only a single scenario is considered. Taking the configuration of two phase regulators as an example, the system network loss in scenario 8 is reduced by 88.47kMar compared with the case when no scenario factor is considered. After considering multiple scenarios, f V The differences are mostly positive, indicating that the optimization results obtained after considering scenario factors can more comprehensively reflect the randomness of wind power output and obtain a comprehensive configuration plan.

[0124] Based on the above analysis, the optimal configuration solution of distributed phase regulators under multiple wind power output scenarios is considered. The number of installed units is taken as 1-4, and the weight coefficient is taken as 1 to form schemes 1-4. The performance indicators of the system are compared when different numbers of units are installed. The results are summarized in Table 2. Figure 8 and Fig. 9It reflects the changes in system voltage safety margin and active network loss before and after configuring the phase regulator in scenarios 1 and 5.

[0125] Table 2 Optimization results of condenser location and capacity

[0126]

[0127] It can be found from Table 2 that: as the number of phase regulators increases, the system network loss gradually decreases and the voltage safety margin gradually increases. When the number of phase regulators increases to 2, compared with 1, the system comprehensive network loss decreases by 1.22MW and the voltage margin increases by 8.12%; when the number increases to 3, compared with 2, the network loss decreases by 1.43MW and the voltage deviation decreases by 17.72%; however, when the number of phase regulators increases to 4, the changes in various indicators are not obvious. Compared with the optimization scheme 3, the economic cost increases, the performance improvement is not obvious, and the cost performance is not high.

[0128] According to the site selection and sizing method of distributed phase-shifting in wind turbine multi-output scenarios proposed in the embodiment of the present invention, Latin hypercube sampling and backward reduction method are used to generate typical wind power output scenarios, and the optimization objectives are the optimal steady-state performance and economic cost. The optimization configuration model of distributed phase-shifting in wind turbine multi-output scenarios is constructed, and the optimization model is solved by particle swarm algorithm to obtain the corresponding optimization configuration scheme. The present invention can take into account the randomness and volatility of wind turbine output, form a typical wind turbine output scenario, and effectively reduce the system active network loss and improve the system voltage safety margin while considering economic indicators such as phase-shifting purchase, power consumption and operation and maintenance costs.

[0129] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0130] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0131] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

Claims

1. A method for site selection and capacity determination of distributed phase regulators in a wind turbine multi-output scenario, characterized in that: The following steps are involved: S1, the wind speed samples are sampled using the Latin hypercube sampling method, and the sampled wind speed samples are reduced based on the backward reduction method of Kantorovich distance to obtain a typical wind farm output scenario; S2, build a site selection and sizing optimization configuration model for distributed phase regulators in wind turbine multi-output scenarios with the optimization goal of minimizing the weighted system steady-state performance and total economic efficiency indicators and the input and output of each node in each typical wind farm output scenario satisfying the power flow equation as a constraint; S3, using a particle swarm algorithm to solve the site selection and capacity optimization configuration model to obtain an optimal configuration solution for the distributed phase regulator.

2. The method according to claim 1, characterized in that Step S1 specifically includes: The Weibull distribution function is used to describe the probability density of wind speed in a certain area: In the formula, C and K are the scale parameter and shape parameter of Weibull distribution respectively, V is the wind speed, and the scale parameter C and shape parameter K of Weibull distribution are approximately calculated by the mean and standard deviation of wind speed statistical data; The cumulative probability density distribution function of the Weibull distribution of wind speed is: The relationship between the active output and wind speed of a doubly fed wind turbine is: In the formula, P and P N is the fan output active power and rated power, ρ is the air density, A r =πR 2 is the swept area of ​​the wind turbine blade, R is the blade radius, V in 、V N 、V out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively, C p is the wind energy utilization coefficient of the wind turbine, λ=ωR / V is the tip speed ratio, ω is the tip rotation angular velocity, and θ is the blade pitch angle; The Latin hypercube sampling method is used to sample the cumulative probability density distribution function of the Weibull distribution of wind speed, and the sampled wind speed samples are reduced based on the backward reduction method of Kantorovich distance. According to the relationship between the active output and wind speed of the doubly fed wind turbine, the typical wind farm output scenario is obtained.

3. The method according to claim 1, characterized in that Step S2 specifically includes: The system steady-state performance is divided into two parts: system active network loss and system voltage deviation. The site selection and sizing optimization configuration model of distributed phase regulators in the scenario of multi-output wind turbines takes the weighted minimum of system steady-state performance and total economic index as the optimization goal, and the optimization objective function is: minF=min(λ1F W +λ2F C ) F W =ω1f loss +ω2f V In the formula, F W , F C are the steady-state performance index and economic index of the system, respectively, loss and f V are the total active network loss and total voltage deviation of the system respectively, λ and ω are the weight coefficients of each index; The total active network loss of the system is: Where N C is the number of scenes, Sc(1,2,…,N c ) is a typical wind power output scenario, W Sc is the weight of the typical wind power output scenario Sc, N L is the number of system branches, G ij and θ ij is the conductance and node voltage phase angle between node i and node j, U i and U j are the voltage amplitudes at nodes i and j respectively; The total voltage deviation of the system is: Where W bi For node b i The weight of b i (1,2,…,N) are system nodes; The economic performance index is composed of the purchase cost, power consumption cost and operation and maintenance cost of the condenser: F C =C1+C2+C3 C1=N SC ×c1 C2=N SC ×c2×Q unit ×N year C3=N SC ×c3×α i ×N year Where C1, C2, and C3 are purchase cost, power consumption cost, and operation and maintenance cost, respectively. SC is the number of condenser installations, c1 is the configuration cost of a single condenser, c2 is the annual unit capacity power consumption of the condenser, Q unit is the phase regulator installation capacity, N year is the planned life, c3 is the operation and maintenance cost of a single phase regulator, α i is the operation and maintenance coefficient of the phase regulator at node i; The constraints of the site selection and sizing optimization configuration model of distributed phase regulators in the scenario of multiple wind turbine outputs are divided into equality constraints and inequality constraints: (1) The input and output of each node under each typical wind farm output scenario satisfies the equality constraints of the power flow equation: i=1,2,…,N c=1,2,…,N C Where P gi,c , P di,c and P i,c are the active power injection, active power load and active power output of node i under scenario Sc, Q gi,c , Q di,c and Q i,c are the reactive injection, reactive load and reactive output of node i under scenario Sc, δ k,c and V k,c are the phase angle and voltage amplitude of node k in scenario Sc, δ i,c and V i,c is the phase angle and voltage amplitude of node i in scenario Sc, G ik , B ik are the conductance and susceptance between node i and node k in the admittance matrix, N and N C are the number of nodes and the number of scenes, respectively; (2) Inequality constraints under various typical wind farm output scenarios: V i,cmin ≤V i,c ≤V i,cmax i=1,2…,N S Gj,cmin ≤S Gj,c ≤S Gj,cmax j=1,2…,N G P Wn,cmin ≤P Wn,c ≤P Wn,cmax n=1,2…,N W Q SCm,cmin ≤Q SCm,c ≤Q SCm,cmax m=1,2…,N SC Where V i,cmin , V i,cmax and V i,c are the minimum voltage, maximum voltage and voltage amplitude of node i in scenario Sc, respectively. Gj,cmin , S Gj,cmax and S Gj,c are the minimum apparent power, maximum apparent power and apparent power of generator j in scenario Sc, respectively. Wn,cmin , P Wn,cmax and P Wn,c are the minimum active output, maximum active output and active output of wind turbine n in scenario Sc, respectively. SCm,cmin , Q SCm,cmax and Q SCm,c are the minimum reactive output, maximum reactive output and reactive output of condenser m under scenario Sc, respectively. G , N W and N SC They are the number of generators, the number of fans and the number of phase regulators respectively.

4. The method according to claim 3, characterized in that In step S3, using the particle swarm algorithm to solve the site selection and capacity optimization configuration model specifically includes: Step 1: Generate N groups of wind farm output scenarios using the Latin hypercube sampling method and generate N wind farm output scenarios based on the backward reduction method. C Typical wind power output scenarios; Step 2: Take the scenario Sc and the number of iterations gen as 1, initialize the particle swarm, including the installation nodes and installation capacity; Step 3: Calculate the total active network loss f of each indicator system under scenario Sc loss , the total voltage deviation of the system f V and economic index F C ; Step 4: Determine whether the number of scenarios Sc is greater than or equal to the total number of typical wind power output scenarios N C If yes, execute Step 5, otherwise increase the number of scenes Sc by one and return to Step 3; Step 5: Calculate the optimization objective function, update the individual optimal position and the group optimal position, and update the particle speed and position; Step 6: Determine whether the maximum number of iterations has been reached. If so, end the iteration and output the optimal configuration plan for the distributed phase regulator. Otherwise, return to Step 3.

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