Wind power plant reactive power coordination optimization method and device

Abstract

The invention discloses a wind power plant reactive power coordination optimization method and device, and the method comprises the steps: carrying out the reactive power optimization calculation through employing a particle swarm algorithm based on the reactive power regulation capability of an on-load voltage regulation transformer and a capacitor bank of a wind power plant according to a day-ahead reactive power optimization model, obtaining the optimal switching value of the on-load voltage regulating transformer and the optimal switching value of the capacitor bank; and performing reactive power optimization calculation by adopting a particle swarm algorithm according to an intra-day reactive power optimization model on the basis of the reactive power regulation capability of the wind power plant fan and the network construction type energy storage system, the optimal switching value of the on-load tap changing transformer and the optimal switching value of the capacitor bank, so as to obtain the optimal switching value of the wind power plant fan and the network construction type energy storage system. Through the day-ahead-day combined multi-time-scale reactive power optimization control strategy of the wind power plant, accurate adjustment of reactive power resources of the wind power plant based on network construction type energy storage and dynamic balance of the system are achieved, and the voltage adjustment requirement of the wind power plant is met.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farms, and in particular to a method and device for reactive power coordination optimization of wind farms. Background Art

[0002] At present, the wind power system of wind farms has the characteristics of output uncertainty, resistance and weak support, and the voltage stability margin and dynamic voltage support capacity have been greatly reduced. The converters of wind turbines and energy storage devices in a large number of existing wind farms almost all adopt grid-following control. However, grid-following converters only show good voltage regulation performance under strong grid conditions and lack active support capabilities. Summary of the invention

[0003] In view of the problems in the prior art, an embodiment of the present invention provides a method and device for reactive power coordination optimization of a wind farm, which can meet the voltage regulation requirements of a wind farm.

[0004] In order to achieve the above object, an embodiment of the present invention provides a wind farm reactive power coordination optimization method, comprising:

[0005] Based on the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the wind farm, and according to the reactive power optimization model of the day before, the particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank;

[0006] Based on the reactive power regulation capability of the wind turbines and grid-type energy storage system of the wind farm, the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, and according to the intra-day reactive power optimization model, a particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal switching value of the wind turbines and grid-type energy storage of the wind farm.

[0007] In one embodiment, the reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank based on the wind farm is calculated by using a particle swarm algorithm according to a reactive power optimization model on the day before, and the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank are obtained, including:

[0008] Based on the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the wind farm, and according to the reactive power optimization model of the day before, the particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank;

[0009] The maximum number of operations of the on-load tap-changing transformer and the maximum number of operations of the capacitor bank are set respectively, and the optimal operation value of the on-load tap-changing transformer and the optimal operation value of the capacitor bank are processed by the optimal segmentation method to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank.

[0010] In one embodiment, the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank are processed by the optimal segmentation method to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, including:

[0011] Firstly, the optimal switching value of the on-load tap-changing transformer is determined, the optimal switching value of the on-load tap-changing transformer is set as a constant, and then the reactive power optimization calculation is continued to obtain the optimal switching value of the capacitor bank.

[0012] In one embodiment, the reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank is obtained by the following steps:

[0013] Based on the gear adjustment specification of the on-load tap-changing transformer connected to the wind farm and the maximum capacity of the capacitor bank, the reactive power adjustment capacity of the on-load tap-changing transformer and the capacitor bank is obtained.

[0014] In one embodiment, the reactive power regulation capability of the wind turbines and grid-connected energy storage system of the wind farm optimized during the day is obtained by the following steps:

[0015] Based on the daily predicted output curve of the wind farm and the charging and discharging predicted output curve of the grid-connected energy storage, the particle swarm algorithm is used to calculate the reactive power regulation capacity of the wind turbines in the wind farm and the grid-connected energy storage system.

[0016] In one embodiment, the objective function of the day-ahead reactive power optimization model is:

[0017]

[0018] Where n is the total number of network nodes; U i,t , U j,t are the voltage values ​​of nodes i and j at time t respectively; G i,j , B i,j ,q i,j are the branch conductance, branch susceptance and phase angle difference between nodes i and j respectively; U k,t , U k,t,ref are the actual voltage and reference voltage of node k at time t respectively; U k,t,max , U k,t,min are the upper and lower limits of the voltage deviation allowed at node k respectively; l is the voltage over-limit penalty coefficient.

[0019] In one embodiment, the equality constraint of the day-ahead reactive power optimization model is:

[0020]

[0021] Where P i,t , Q i,t are the injected active power and reactive power of node i at time t respectively.

[0022] In one embodiment, the inequality constraint of the day-ahead reactive power optimization model is:

[0023]

[0024] Where h is the reactive power compensation point in this dispatching stage; Q h,t is the amount of capacitor bank input at node h at time t; Q h,min , Q h,max are the lower and upper limits of the reactive capacity of the grid-connected energy storage at node h; T POI,t is the gear position of the main transformer at time t, T POI,min , T POI,max They are the lower and upper limits of the main transformer adjustment gear respectively; symbol Represents XOR operation, N h is the daily action upper limit of the capacitor bank at node h, N POI The upper limit of the number of daily operations of the main variable; U i,t is the voltage amplitude of node i at time t, U i,min , U i.max are the lower and upper limits of the voltage amplitude at node i.

[0025] In one embodiment, the objective function of the intraday reactive power optimization model is:

[0026] min F2=·U pcc -1·+x(Q max -Q);

[0027] Where U pcc is the voltage amplitude at the grid connection point; Q max is the upper limit of the reactive capacity of the grid-type energy storage; x is a Boolean variable for judging whether the grid-type energy storage needs to invest reactive capacity. When the reactive demand of the wind farm is greater than the maximum reactive capacity of the doubly-fed asynchronous wind turbine, x=1, otherwise, x=0.

[0028] In one embodiment, the equality constraint of the intra-day reactive power optimization model is:

[0029]

[0030] Where P i , Q i are the injected active power and reactive power of node i respectively.

[0031] In one embodiment, the inequality constraint of the intra-day reactive power optimization model is:

[0032]

[0033] In the formula, Qk,min , Q k,max are the maximum capacitive reactive power and the maximum inductive reactive power that can be adjusted by the kth wind turbine group; U i is the voltage amplitude at node i, U i,min , U i.max Soc is the lower and upper limits of the voltage amplitude at node i; t Soc is the state of charge of the grid-type energy storage at time t; max , Soc min are the upper and lower limits of the state of charge of the grid-building energy storage, respectively; P and Q are the active power and reactive power output of the grid-building energy storage, respectively. N is the rated active power of the grid-type energy storage converter; S max is the maximum available capacity of grid-type energy storage; U PCC is the voltage value of the common connection point of the grid-type energy storage; U PCS.max is the maximum voltage limit of the common connection point of the grid-connected energy storage; X is the grid-connected equivalent reactance of the grid-connected energy storage.

[0034] In one embodiment, the present invention further provides a wind farm reactive power coordination optimization device, comprising:

[0035] The day-ahead optimization module is used to optimize the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank based on the wind farm. According to the day-ahead reactive power optimization model, the particle swarm algorithm is used to perform reactive power optimization calculations to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank.

[0036] The intraday optimization module is used to perform reactive optimization calculations based on the reactive regulation capabilities of the wind turbines and grid-type energy storage system of the wind farm, the optimal switching value of the on-load tap-changing transformer, and the optimal switching value of the capacitor bank according to the intraday reactive optimization model, using a particle swarm algorithm to obtain the optimal switching values ​​of the wind turbines and grid-type energy storage of the wind farm.

[0037] In one embodiment, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0038] In one embodiment, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0039] In one embodiment, the present invention further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0040] The embodiment of the present invention provides a method and device for coordinated reactive power optimization of a wind farm. Based on the existing multi-period reactive power optimization framework, the present invention combines the reactive power regulation potential of grid-type energy storage, and proposes a multi-time scale reactive power optimization control strategy for wind farms that combines day-ahead and intraday operations in order to meet the safety and economical operation requirements of wind farms. This strategy achieves precise regulation of reactive resources and dynamic balance of the system through coordinated optimization control of multiple reactive sources, thus meeting the voltage regulation requirements of wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 creative work. In the drawings:

[0042] Figure 1 A schematic diagram of a grid-connected energy storage control structure in an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a GFMS reactive power-voltage control loop structure in an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of a process flow of a wind farm reactive power coordination optimization method according to an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of a wind farm topology structure in an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of a wind farm day-ahead output curve according to an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of an intraday ultra-short-term prediction curve of a wind farm in an embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram of voltages of each node at 18:00 and 1:00 obtained by simulation in an embodiment of the present invention;

[0049] Figure 8 A comparison diagram of active network loss of a system before and after 24h optimization in an embodiment of the present invention;

[0050] Fig. 9 It is a schematic diagram of a daily energy storage SoC variation curve obtained by combining GFMS power variation curve simulation in an embodiment of the present invention;

[0051] Fig.10It is a schematic diagram of a reactive power regulation capability curve of a full-field double-fed asynchronous generator in an embodiment of the present invention;

[0052] Fig.11 It is a schematic diagram of a grid connection point voltage change curve before day-ahead optimization, after day-ahead optimization and after intraday optimization in an embodiment of the present invention;

[0053] Fig.12 This is a schematic diagram of the reactive power regulation of a wind turbine and GFMS in a wind farm throughout the day in an embodiment of the present invention;

[0054] Fig.13 This is a schematic diagram of the structure of a wind farm reactive power coordination optimization device in an embodiment of the present invention;

[0055] Fig.14 The figure is a schematic diagram of the physical structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0057] In order to facilitate understanding of the technical solution provided by the present invention, relevant contents of the technical solution of the present invention are first described below.

[0058] Compared with the single-converter grid-connected energy storage battery, the structure and control of the back-to-back converter system used by the wind turbine are more complex, and good coordinated control is required to obtain stable DC voltage and output power. Therefore, grid-connected energy storage is the preferred solution for wind power systems. And because grid-connected control places higher requirements on the heat dissipation of power electronic devices and equipment, and in order to achieve source-grid-load-storage interaction and local rapid response, its monitoring system mostly adopts the "distributed collection, centralized monitoring" mode, which has high standards for communication capabilities. How to give full play to the role of grid-connected energy storage power stations in the reactive voltage regulation of wind farms, and effectively coordinate and optimize the control of multiple reactive sources in wind farms containing grid-connected energy storage, is a problem worthy of study. In new energy stations, it is difficult to meet voltage regulation and dynamic reactive power requirements by relying solely on conventional reactive power sources for reactive power optimization; wind and solar power stations often undertake important power generation tasks and have limited reactive power regulation capabilities. At the same time, new energy stations often configure energy storage systems to absorb new energy, and their utilization rates are usually not high. In order to improve the utilization rate of energy storage and effectively address system voltage stability issues, the voltage regulation capabilities of energy storage power stations can be explored.

[0059] like Figure 1The figure shows a schematic diagram of a grid-forming energy storage grid-connected control structure in an embodiment of the present invention. The present invention studies the reactive coordination optimization of wind farms based on GFMS (Grid-Forming Energy Storage). GFMS is an energy storage system that can build and maintain output voltage and frequency, operate with voltage source characteristics, and actively provide grid support. It realizes the bidirectional conversion of electric energy by controlling the charging and discharging process of the energy storage battery pack, and plays a role in stabilizing the voltage and frequency in the grid. The PCS in GFMS is a grid-forming energy storage converter (Power Control System for Grid-Forming Energy Storage, also referred to as GF-PCS). This converter can build and maintain output voltage and frequency, operate with voltage source characteristics, and actively provide grid support. It is the core control part of GFMS networking, and its control body is mainly composed of power control outer loops such as the active-frequency control loop of the output virtual power angle q and the reactive-voltage control loop of the output virtual internal potential amplitude E, which is a voltage and current double closed loop control inner loop. The power calculation link receives the sampling value of the grid connection point and calculates the actual active power and reactive power and sends it to the power outer loop control. The active-frequency control loop and reactive-voltage control loop calculate and output q and E respectively through the actual power and power command value received, and then send it to the inner loop control and output the modulated wave to generate a drive signal through PWM to realize the control of PCS. PCC is the grid connection point, that is, the high-voltage side bus of the main transformer. GFMS realizes flexible adjustment of output power by controlling the phase and amplitude of the internal potential. The expressions of its output active power P and reactive power Q are as follows.

[0060]

[0061] Where E and U are the virtual internal potential and external grid voltage of GFMS, respectively; φ is the phase angle difference between the grid-type energy storage and the external grid; Z and θ are the amplitude and angle of the connection impedance between GFMS and the synchronous generator, respectively.

[0062] like Figure 2 The figure shows a schematic diagram of the structure of a GFMS reactive-voltage control loop in an embodiment of the present invention. For the reactive-voltage control link, the present invention uses constant reactive power control and constant voltage control to adjust the voltage. During constant reactive power control, the GFMS output voltage follows the grid voltage change and does not have the ability to actively support the voltage, showing a grid-following characteristic, while the constant voltage control has the ability to actively support the voltage, showing a grid-building characteristic. In the figure, K is the control mode switching flag. When K=0, constant reactive power control is used, and vice versa, constant voltage control is used. Based on this, the virtual internal potential amplitude E output by the reactive-voltage control loop can be expressed as follows.

[0063]

[0064] In the formula, E set is the internal potential reference value, usually the initial value is 1p.u.; U, U ref are the measured value and command value of the energy storage PCC voltage respectively; Q, Q ref are the measured value and command value of GFMS output reactive power respectively; E max 、E min are the upper and lower limits of the internal potential respectively; T a is the internal potential integration constant; K u , K q They are the voltage regulation proportional coefficient and reactive power control coefficient respectively.

[0065] Obviously, by regulating Q ref and U ref , the amplitude E of the virtual internal potential can be adjusted, thereby adjusting the reactive output power of GFMS to meet the needs of grid-connected power generation or grid construction.

[0066] The following is a detailed introduction to the GFMS power regulation range and operation model. Since only the grid-following characteristics of GFMS are considered, the following only analyzes its voltage regulation range in steady state.

[0067] (1) Considering the reserved reactive power regulation capacity of GFMS and the service life of its energy storage battery, the overcurrent capacity of PCS is usually not considered during stable operation. Therefore, P should satisfy the following formula.

[0068] P￡P N ;

[0069] Where P N is the rated active power of PCS.

[0070] (2) At the same time, fully utilize the maximum available capacity S of PCS max and its adjustment capability, the maximum available capacity P and Q should satisfy the following formula.

[0071]

[0072] Based on the above principles, the GFMS operation model of reactive power coordinated optimization is proposed as follows.

[0073] (1) From the formula P￡P N Japanese style The reactive charging and discharging power constraints are obtained as follows.

[0074]

[0075] (2) The PCS voltage constraint can be expressed as follows.

[0076]

[0077] Where U PCC is the PCC voltage of GFMS; U PCS.max is the PCS voltage limit; X is the GFMS grid-connected equivalent reactance, which is mainly composed of the reactance of the grid-connected filter and transformer.

[0078] (3) The state of charge constraints are as follows.

[0079] Soc min £Soc t £Soc max ;

[0080] In the formula, Soc t Soc is the charge state of GFMS at time t; max , Soc min are the upper and lower limits of GFMS state of charge respectively.

[0081] The present invention connects grid-type energy storage to the wind farm, and proposes a reactive power coordination optimization strategy for the wind farm on the basis of the multi-time scale reactive power optimization strategy, so as to meet the voltage regulation requirements of the wind farm. The reactive power coordination optimization strategy for the day before and the day before, the reactive power optimization model for the day before, the reactive power optimization model for the day before and the solution method thereof are introduced below.

[0082] like Figure 3 FIG. 1 is a flow chart of a method for reactive power coordination optimization of a wind farm in an embodiment of the present invention. The method for reactive power coordination optimization of a wind farm in an embodiment of the present invention includes:

[0083] S301, based on the reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank of the wind farm, according to the reactive power optimization model of the day before, a particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank;

[0084] S302. Based on the reactive power regulation capability of the wind turbines and the grid-type energy storage system of the wind farm, the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, a particle swarm algorithm is used to perform reactive power optimization calculation according to an intra-day reactive power optimization model to obtain the optimal switching value of the wind turbines and the grid-type energy storage of the wind farm.

[0085] Specifically, the day-ahead-intraday reactive power coordination optimization strategy of the present invention includes a day-ahead optimization stage and an intraday optimization stage, wherein the day-ahead optimization stage includes reactive power regulation capability based on the on-load tap-changing transformer and capacitor bank of the wind farm, and reactive power optimization calculation is performed using a particle swarm algorithm according to the day-ahead reactive power optimization model to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank; the intraday optimization stage includes reactive power regulation capability based on the wind farm's wind turbine and grid-type energy storage system, the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, and reactive power optimization calculation is performed using a particle swarm algorithm according to the intraday reactive power optimization model to obtain the optimal switching value of the wind farm's wind turbine and grid-type energy storage. Based on the multi-period reactive power optimization framework, the present invention combines the reactive power regulation potential of grid-type energy storage, and proposes a multi-time scale reactive power optimization control strategy for wind farms that combines day-ahead-intraday power. This strategy achieves precise regulation of reactive resources and dynamic balance of the system through coordinated optimization control of multiple reactive sources, thus meeting the voltage regulation requirements of wind farms.

[0086] The present invention designs and constructs a reactive power optimization model for the day-ahead and a reactive power optimization model for the day, and uses a particle swarm algorithm to perform reactive power optimization calculations to obtain the optimal switching values ​​of wind turbines and grid-type energy storage in the wind farm. The particle swarm algorithm used in the present invention has the advantages of being simple and easy to implement, having strong global search capabilities, fast convergence speed, fewer parameters, and strong parallel processing capabilities. The present invention not only uses the PSO algorithm to solve in the intraday optimization stage, but also uses the PSO calculation of the number of discrete reactive equipment input groups as a continuous quantity in the day-ahead optimization stage, and uses the optimal segmentation method for regularization. In one embodiment, the particle swarm algorithm optimization parameter settings used in the present invention are shown in Table 1.

[0087] Table 1

[0088] Optimization parameters Population Maximum number of iterations Inertia weight coefficient Acceleration constant Parameter Value 50 100 0.95 1.4

[0089] In one embodiment, the reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank based on the wind farm is calculated by using a particle swarm algorithm according to a reactive power optimization model on the day before, and the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank are obtained, including:

[0090] Based on the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the wind farm, and according to the reactive power optimization model of the day before, the particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank;

[0091] The maximum number of operations of the on-load tap-changing transformer and the maximum number of operations of the capacitor bank are set respectively, and the optimal operation value of the on-load tap-changing transformer and the optimal operation value of the capacitor bank are processed by the optimal segmentation method to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank.

[0092] Specifically, the on-load tap-changing transformer is a part of the high-voltage transformer, which can set the voltage according to the grid load without cutting off the service. The present invention first uses the particle swarm algorithm to perform reactive optimization calculation based on the reactive regulation capability of the on-load tap-changing transformer and the capacitor bank of the wind farm according to the reactive optimization model of the day before, and obtains the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank; then sets the maximum action times of the on-load tap-changing transformer and the maximum action times of the capacitor bank respectively, and uses the optimal segmentation method to process the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank. Since the on-load tap-changing transformer (OLTC) and the capacitor bank are discrete reactive devices, it is necessary to limit the maximum number of actions. For example, the maximum number of actions of the on-load tap-changing transformer and the capacitor bank is set to 6 times, and the optimal segmentation method is used to regularize and limit the number of actions to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank.

[0093] In one embodiment, the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank are processed by the optimal segmentation method to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, including:

[0094] Firstly, the optimal switching value of the on-load tap-changing transformer is determined, the optimal switching value of the on-load tap-changing transformer is set as a constant, and then the reactive power optimization calculation is continued to obtain the optimal switching value of the capacitor bank.

[0095] Specifically, since the adjustment of the on-load tap-changing transformer will affect the voltage level of the entire line, and thus affect the action of reactive equipment such as capacitor banks, in the day-ahead optimization, the action plan of the on-load tap-changing transformer should be determined first, and then the action plan of each capacitor bank should be optimized. That is, the action time and action value of the on-load tap-changing transformer are first determined to obtain the day-ahead switching plan (optimal switching value) of the on-load tap-changing transformer. The day-ahead plan of the on-load tap-changing transformer is set as a constant, and reactive power optimization is performed again to obtain the optimal switching value of the capacitor bank. Similarly, the optimal segmentation method is used for regularization to obtain the action time and action value of the capacitor bank. At this point, the day-ahead switching plan of the on-load tap-changing transformer and the capacitor bank that meets the maximum number of action limits is calculated.

[0096] like Figure 4The figure shows a schematic diagram of a wind farm topology structure in an embodiment of the present invention. To verify the above-mentioned strategy, a 60MW wind farm is used as an example for calculation. The wind farm has a total of 4 collector lines, each of which is connected in parallel with 10 groups of doubly fed asynchronous generators (DFIGs). The capacity of each doubly fed asynchronous generator is 1.5MW, and it is stepped up to 10kV through a 0.69 / 10kV box transformer to connect to the collector line. The box transformer has a capacity of 1.5MVA, and each group of box transformers has the same parameters. The distance between two adjacent groups of wind turbines is 560m. The main transformer has a capacity of 60MVA, a transformation ratio of 10 / 110±8×1.25% kV, and a long-distance transmission line of 50km. On the low-voltage side of the main transformer (81 nodes), three groups of capacitors with a single unit capacity of 2MW and a grid-type energy storage power station with a capacity of 6MW / 24MWh are connected. The invention defines the allowable fluctuation range of the grid connection point as 0.97-1.07, and the allowable fluctuation range of other voltage nodes as 0.9-1.1. The wake effect and terrain influence of the wind farm are not considered, and the wind speed at each point in the wind farm is assumed to be equal, and the active power output by each double-fed asynchronous generator is equal. The specific line and transformer parameters are shown in Table 2.

[0097] Table 2

[0098] type <![CDATA[r / W×km -1 ]]> <![CDATA[x / W×km -1 ]]> <![CDATA[b / S×km -1 ]]> type <![CDATA[P0 / KW]]> <![CDATA[P K / KW]]> <![CDATA[U d %]]> <![CDATA[I0%]]> Collector line 0.16 0.1225 Box type transformer 1.155 10.3 4.5 0.5 Transmission lines 0.17 0.402 <![CDATA[2.78×10 -6 ]]> Main transformer 29.44 148.2 10.5 0.3

[0099] In one embodiment, the reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank is obtained by the following steps:

[0100] Based on the gear adjustment specification of the on-load tap-changing transformer connected to the wind farm and the maximum capacity of the capacitor bank, the reactive power adjustment capacity of the on-load tap-changing transformer and the capacitor bank is obtained.

[0101] Specifically, the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the present invention belongs to the mechanical characteristics themselves, that is, the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank can be obtained based on the gear adjustment specification of the on-load tap-changing transformer connected to the wind farm and the maximum capacity of the capacitor bank.

[0102] It is worth noting that in the entire simulation process of the present invention, it is necessary to first obtain the wind farm's day-ahead predicted output curve (the wind farm's day-ahead predicted output curve indicates how much power the wind farm can generate at each moment, that is, the wind farm's active output), and perform reactive coordination optimization based on the wind farm's active output represented by the wind farm's day-ahead predicted output curve.

[0103] like Figure 5The figure shows a schematic diagram of a wind farm day-ahead predicted output curve according to an embodiment of the present invention. In the day-ahead optimization stage of the present invention, optimization is performed based on the day-ahead predicted output curve of the wind farm, the reactive power compensation amount on a time scale of every 1 hour is calculated, and the action time and action value of the discrete reactive equipment in the system on the next day are determined. And it is assumed that the maximum number of day-ahead actions of the on-load tap-changing transformer and capacitor group of the discrete reactive equipment is 6 times. For the wind farm station under study, the discrete reactive equipment can often meet the day-ahead optimization and regulation requirements. In order to maximize the use of wind resources, the wind turbines are set not to participate in reactive power regulation in the day-ahead stage, and the GFMS adopts a constant reactive power control mode at this stage, and does not provide voltage regulation auxiliary services.

[0104] In one embodiment, the reactive power regulation capability of the wind turbines and grid-connected energy storage system of the wind farm optimized during the day is obtained by the following steps:

[0105] Based on the daily predicted output curve of the wind farm and the charging and discharging predicted output curve of the grid-connected energy storage, the particle swarm algorithm is used to calculate the reactive power regulation capacity of the wind turbines in the wind farm and the grid-connected energy storage system.

[0106] Specifically, after obtaining the intraday forecast output curve of the wind farm and the charge and discharge forecast output curve of the grid-type energy storage, the present invention uses a particle swarm algorithm to calculate the reactive power regulation capability of the wind turbines and the grid-type energy storage system of the wind farm based on the intraday forecast output curve of the wind farm and the charge and discharge forecast output curve of the grid-type energy storage. The reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the present invention belongs to mechanical characteristics and can also be calculated based on the hardware parameters of the equipment. The intraday forecast output curve of the wind farm of the present invention represents the active power output of the wind turbine. When a power of 0.95 is used to perform the particle swarm algorithm calculation, the reactive power output of the wind turbine can be calculated.

[0107] like Figure 6 The figure shows a schematic diagram of an intraday ultra-short-term prediction curve of a wind farm according to an embodiment of the present invention. In the intraday optimization stage of the present invention, in order to achieve fine regulation of reactive voltage, the time interval of the intraday stage optimization is shortened from 1 hour to 15 minutes. On the basis of the scheduling plan of the intraday optimization stage, the day-ahead optimization decision plan is revised according to the ultra-short-term prediction. If the reactive capacity of the doubly-fed asynchronous generators in the entire field is insufficient to regulate the reactive demand of the system, the shortfall is compensated by GFMS. At this stage, GFMS still adopts the constant reactive power control mode.

[0108] In one embodiment, the objective of optimizing the system network loss and taking into account the voltage over-limit penalty is that the objective function of the day-ahead reactive power optimization model is:

[0109]

[0110] Where n is the total number of network nodes; U i,t , Uj,t are the voltage values ​​of nodes i and j at time t respectively; G i,j , B i,j ,q i,j are the branch conductance, branch susceptance and phase angle difference between nodes i and j respectively; U k,t , U k,t,ref are the actual voltage and reference voltage of node k at time t respectively; U k,t,max , U k,t,min are the upper and lower limits of the voltage deviation allowed at node k, respectively; l is the voltage over-limit penalty coefficient, and its value will affect the optimization result. When the l value decreases, the objective function will focus more on optimizing the reduction of network losses. On the contrary, if the l value increases, the consideration of voltage offset in the optimization objective will increase, that is, its weight will increase. In order to achieve reactive power optimization while maintaining economy, this paper sets the value of λ to 1 based on empirical values.

[0111] In one embodiment, the equality constraint of the day-ahead reactive power optimization model is:

[0112]

[0113] Where P i,t , Q i,t are the injected active power and reactive power of node i at time t respectively.

[0114] In one embodiment, the inequality constraint of the day-ahead reactive power optimization model is:

[0115]

[0116] Where h is the reactive power compensation point in this dispatching stage; Q h,t is the amount of capacitor bank input at node h at time t; Q h,min , Q h,max are the lower and upper limits of the reactive capacity of the grid-connected energy storage at node h; T POI,t is the gear position of the main transformer at time t, T POI,min , T POI,max They are the lower and upper limits of the main transformer adjustment gear respectively; symbol Represents XOR operation, N h is the daily action upper limit of the capacitor bank at node h, N POI The upper limit of the number of daily operations of the main variable; U i,t is the voltage amplitude of node i at time t, U i,min , U i.max are the lower and upper limits of the voltage amplitude at node i.

[0117] Specifically, according to the reactive power optimization model of the day-ahead, under the premise of satisfying the equality and inequality constraints, the particle swarm optimization (PSO) algorithm is used to perform reactive power optimization calculations on the wind farm based on the objective function, and the optimal action values ​​of the on-load tap-changing transformer and the capacitor bank in 24 hours are obtained. Since the adjustment of the on-load tap-changing transformer will affect the voltage level of the entire line, and then affect the action of reactive equipment such as the capacitor bank, the action plan of the on-load tap-changing transformer should be determined first in the day-ahead optimization, and then the action plan of each capacitor bank should be optimized. For example, the 24h action value of the on-load tap-changing transformer is shown in Table 3.

[0118] Table 3

[0119]

[0120] The optimal segmentation method is used to determine the action time and action value of the on-load tap-changing transformer, as shown in Table 4.

[0121] Table 4

[0122]

[0123] The optimal switching value of the on-load tap-changing transformer obtained above is regarded as a constant, and the day-ahead reactive power optimization is performed again to obtain the optimal action value of the capacitor bank, as shown in Table 5.

[0124] Table 5

[0125]

[0126] Similarly, the action time and action value of the capacitor bank are obtained by the optimal segmentation method, as shown in Table 6.

[0127] Table 6

[0128]

[0129] The action values ​​of the on-load tap-changing transformer and capacitor bank obtained by the optimal partitioning method are regarded as constants, and the PSO algorithm is used again to perform the day-ahead reactive power optimization calculation to complete the rough adjustment in the station.

[0130] like Figure 7Shown is a schematic diagram of the voltage of each node (except the balancing node) at 18:00 and 1:00 obtained by a simulation in an embodiment of the present invention. The active output fluctuation of a wind farm is closely related to the voltage stability within the station, that is, when the output fluctuation increases, the voltage fluctuation also intensifies. If the wind farm can still keep the voltage within the station stable within a preset safety interval when it reaches its maximum and minimum active output, then it can be reasonably inferred that the voltage level can also be maintained within a reasonable range at any active output level between these two extreme output values. Therefore, by analyzing the voltage conditions of each node in the station at the two time points when the wind power output reaches the maximum and minimum, the voltage stability of the wind farm throughout the entire period can be effectively evaluated and confirmed. From Figure 7 It can be seen that most of the nodes in the initial system are close to the lower voltage limit, which has a very adverse effect on the safe operation of the system. After recent optimization, the voltage of each node has increased to a stable operating range, improving the safety of the system.

[0131] like Figure 8 The figure shows a comparison of active network loss of a system before and after 24h optimization in an embodiment of the present invention. The losses before and after optimization are 46.5781MW and 40.6453MW respectively. The network loss after optimization is reduced by 5.9328, with a reduction rate of 12.74%. The reactive power optimization strategy of the present invention improves the economy of the system.

[0132] In one embodiment, with the goal of minimizing the grid connection point voltage deviation and maximizing the GFMS reactive power reserve capacity, the objective function of the intraday reactive power optimization model is:

[0133] min F2=|U pcc -1|+x(Q max -Q);

[0134] Where U pcc is the voltage amplitude at the grid connection point; Q max is the upper limit of the reactive capacity of the grid-type energy storage; x is a Boolean variable for judging whether the grid-type energy storage needs to invest reactive capacity. When the reactive demand of the wind farm is greater than the maximum reactive capacity of the doubly-fed asynchronous wind turbine, x=1, otherwise, x=0.

[0135] The doubly fed asynchronous wind turbine generator (DFIG) consists of a wound-type asynchronous generator whose stator winding is directly connected to a fixed-frequency three-phase power grid and a bidirectional back-to-back IGBT voltage source converter installed on the rotor winding.

[0136] In one embodiment, the equality constraint of the intra-day reactive power optimization model is:

[0137]

[0138] Where P i , Q i are the injected active power and reactive power of node i respectively.

[0139] In particular, during the intraday optimization phase, the energy state constraints of GFMS, reactive charging and discharging power constraints, and PCS voltage constraints need to be considered.

[0140] In one embodiment, the inequality constraint of the intra-day reactive power optimization model is:

[0141]

[0142] In the formula, Q k,min , Q k,max are the maximum capacitive reactive power and the maximum inductive reactive power that can be adjusted by the kth wind turbine group; U i is the voltage amplitude at node i, U i,min , U i.max Soc is the lower and upper limits of the voltage amplitude at node i; t Soc is the state of charge of the grid-type energy storage at time t; max , Soc min are the upper and lower limits of the state of charge of the grid-building energy storage, respectively; P and Q are the active power and reactive power output of the grid-building energy storage, respectively. N is the rated active power of the grid-type energy storage converter; S max is the maximum available capacity of grid-type energy storage; U PCC is the voltage value of the common connection point of the grid-type energy storage; U PCS.max is the maximum voltage limit of the common connection point of the grid-connected energy storage; X is the grid-connected equivalent reactance of the grid-connected energy storage, which is mainly composed of the reactance of the grid-connected filter and transformer.

[0143] like Fig. 9 The figure shows a schematic diagram of a daily energy storage SoC variation curve obtained by simulating a power variation curve in combination with GFMS in an embodiment of the present invention; assuming that the initial state of charge Soc0 of GFMS is 0.7, and the upper and lower limits of SoC are 0.1 to 0.9, then Fig. 9 It can be seen that the SoC of the energy storage power station has never reached a full or fully discharged state within 24 hours. Therefore, the energy storage power station has a certain voltage regulation margin. It can be used as a continuous reactive device for reactive coordination optimization during the day, and can also be used as a dynamic reactive reserve capacity in the future.

[0144] like Fig.10 The figure shows a reactive power regulation capability curve of a full-field double-fed asynchronous generator in an embodiment of the present invention. Taking into account the reactive power regulation capability of DFIG in the wind farm, the present invention calculates the inductive and capacitive reactive power output of the full-field DFIG when the power factor is ±0.95.

[0145] like Fig.11 The figure shows a schematic diagram of the voltage change curve of the grid connection point before day-ahead optimization, after day-ahead optimization and after intraday optimization in an embodiment of the present invention. The day-ahead optimization obtains the pre-switching plan of the main transformer and capacitor, and the intraday short-term optimization is performed on this basis. The grid connection point voltage is a key parameter for the intersection of the wind farm and the power grid system, which is crucial for the effective access and stable transmission of wind power energy.

[0146] The present invention first successfully increases the voltage of the grid connection point to within the specified range by adjusting the gear position of the OLTC and optimizing the switching strategy of the capacitor. However, the increased voltage value is close to the lower limit edge, which is not conducive to the stable operation of the system. Secondly, in the intraday scheduling, by coordinating the reactive output between each wind turbine and the GMFS, the voltage of the grid connection point is accurately controlled, making it close to the ideal voltage level 1, thereby significantly enhancing the stability and operation efficiency of the system. As shown in Table 7, the average voltage value and standard deviation after intraday optimization are smaller than those of other schemes, making the voltage volatility of the wind farm grid connection point smaller, verifying the above statement.

[0147] Table 7

[0148] plan Average voltage throughout the day Standard Deviation Before optimization 0.961 0.0196 After recent optimization 0.9834 0.0079 After intraday optimization 0.9967 0.0028

[0149] like Fig.12 The figure shows a schematic diagram of the reactive power regulation of a wind turbine and GFMS in a wind farm throughout the day in an embodiment of the present invention. The present invention coordinates and optimizes the reactive output of each wind turbine and GFMS so that the grid connection point approaches 1. As the output fluctuation of the wind farm gradually increases throughout the day, the voltage fluctuation also gradually increases, and the voltage value becomes smaller and smaller. By coordinating the output of each wind turbine and GMFS in the wind farm, the effect required by the objective function is achieved. It can be seen from the intra-day optimization results that the reactive power regulation capability of the wind farm almost meets the reactive power demand of the wind farm, and the reactive power regulation of GFMS is used only at 3 moments, ensuring that the reactive power reserve capacity of GFMS is maximized.

[0150] The present invention first obtains the reactive regulation capacity of the on-load tap-changing transformer and the capacitor group based on the gear adjustment specification of the on-load tap-changing transformer connected to the wind farm and the maximum capacity of the capacitor group. Then, according to the reactive optimization model of the day-ahead, under the premise of satisfying the equality constraint and the inequality constraint, the particle swarm optimization (PSO) algorithm is used to perform reactive optimization calculation on the wind farm based on the objective function to obtain the optimal action value of the on-load tap-changing transformer and the capacitor group in 24 hours. Since the on-load tap-changing transformer and the capacitor group are discrete reactive devices, the maximum number of actions is limited. The present invention sets the maximum number of actions to 6 times, and uses the optimal segmentation method to regularize and limit the number of actions. Since the adjustment of the on-load tap-changing transformer will affect the voltage level of the entire line, and then affect the action of reactive devices such as capacitor groups, the action plan of the on-load tap-changing transformer should be determined first in the day-ahead optimization, and then the action plan of each capacitor group should be optimized. According to the optimal action value of the on-load tap-changing transformer and the aforementioned regularization method and sequence, the action time and action value of the on-load tap-changing transformer are first determined, and the day-ahead switching plan of the on-load tap-changing transformer is obtained. The day-ahead plan of the on-load tap-changing transformer is set as a constant, and reactive power optimization is performed again to obtain the optimal switching value of the capacitor bank. Similarly, the optimal segmentation method is used to regularize and obtain the action time and action value of the capacitor bank. So far, the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank that meet the maximum number of action limits are calculated. Finally, the intraday forecast output curve of the wind farm and the charging and discharging forecast output curve of the grid-type energy storage are obtained, and the reactive power regulation capacity of the wind farm and the grid-type energy storage system optimized intraday is calculated by the PSO algorithm. The optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank are regarded as constants and substituted into the intraday optimization stage. On this basis, according to the intraday reactive power optimization model, under the premise of satisfying the equality constraints and inequality constraints, the PSO algorithm is used to perform reactive power optimization calculation on the wind farm based on its objective function, and the optimal switching value of the wind turbine and grid-type energy storage of the wind farm is obtained, that is, the intraday switching plan is obtained. Based on the multi-period reactive power optimization framework, the present invention combines the reactive power regulation potential of grid-type energy storage, and proposes a multi-time scale reactive power optimization control strategy for wind farms that combines day-ahead and intraday operations in response to the safety and economical operation requirements of wind farms. This strategy achieves precise regulation of reactive resources and dynamic balance of the system through coordinated optimization control of multiple reactive sources.

[0151] like Fig.13 The figure shows a schematic structural diagram of a wind farm reactive power coordination optimization device in an embodiment of the present invention; in one embodiment, the present invention further provides a wind farm reactive power coordination optimization device 013, and the wind farm reactive power coordination optimization device 013 includes a day-ahead optimization module 131 and an intraday optimization module 132.

[0152] The day-ahead optimization module 131 is used to calculate the reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank based on the wind farm. According to the day-ahead reactive power optimization model, the particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank.

[0153] The intraday optimization module 132 is used to perform reactive optimization calculations based on the reactive regulation capabilities of the wind turbines and grid-type energy storage system of the wind farm, the optimal switching value of the on-load tap-changing transformer, and the optimal switching value of the capacitor bank according to the intraday reactive optimization model, using a particle swarm algorithm to obtain the optimal switching values ​​of the wind turbines and grid-type energy storage of the wind farm.

[0154] Since the principle of solving the problem by the wind farm reactive power coordination optimization device of the present invention is similar to the aforementioned wind farm reactive power coordination optimization method, the implementation of the wind farm reactive power coordination optimization device can refer to the implementation of the aforementioned wind farm reactive power coordination optimization method, and the repeated parts will not be repeated.

[0155] like Fig.14 The figure shows a schematic diagram of a physical structure of a computer device in an embodiment of the present invention. In one embodiment, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0156] In one embodiment, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0157] In one embodiment, the present invention further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0158] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0162] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for reactive power coordination optimization of a wind farm, characterized in that: include: Based on the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the wind farm, and according to the reactive power optimization model of the day before, the particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank; Based on the reactive power regulation capability of the wind turbines and grid-type energy storage system of the wind farm, the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, and according to the intra-day reactive power optimization model, a particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal switching value of the wind turbines and grid-type energy storage of the wind farm.

2. The method according to claim 1, characterized in that: The reactive power regulation capability of the on-load tap-changing transformer and capacitor bank based on the wind farm is calculated by using the particle swarm algorithm according to the reactive power optimization model of the day before, and the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank are obtained, including: Based on the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank of the wind farm, and according to the reactive power optimization model of the day before, the particle swarm algorithm is used to perform reactive power optimization calculation to obtain the optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank; The maximum number of operations of the on-load tap-changing transformer and the maximum number of operations of the capacitor bank are set respectively, and the optimal operation value of the on-load tap-changing transformer and the optimal operation value of the capacitor bank are processed by the optimal segmentation method to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank.

3. The method according to claim 2, characterized in that: The optimal action value of the on-load tap-changing transformer and the optimal action value of the capacitor bank are processed by the optimal segmentation method to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank, including: Firstly, the optimal switching value of the on-load tap-changing transformer is determined, the optimal switching value of the on-load tap-changing transformer is set as a constant, and then the reactive power optimization calculation is continued to obtain the optimal switching value of the capacitor bank.

4. The method according to claim 1, characterized in that: The reactive power regulation capability of the on-load tap-changing transformer and the capacitor bank is obtained by the following steps: Based on the gear adjustment specification of the on-load tap-changing transformer connected to the wind farm and the maximum capacity of the capacitor bank, the reactive power adjustment capacity of the on-load tap-changing transformer and the capacitor bank is obtained.

5. The method according to claim 1, characterized in that: The reactive power regulation capability of the wind turbines of the wind farm and the grid-connected energy storage system optimized within the day is obtained by the following steps: Based on the daily predicted output curve of the wind farm and the charging and discharging predicted output curve of the grid-connected energy storage, the particle swarm algorithm is used to calculate the reactive power regulation capacity of the wind turbines in the wind farm and the grid-connected energy storage system.

6. The method according to claim 1, characterized in that: The objective function of the day-ahead reactive power optimization model is: Where n is the total number of network nodes; U i,t , U j,t are the voltage values ​​of nodes i and j at time t respectively; G i,j , B i,j ,θ i,j are the branch conductance, branch susceptance and phase angle difference between nodes i and j respectively; U k,t , U k,t,ref are the actual voltage and reference voltage of node k at time t respectively; U k,t,max , U k,t,min are the upper and lower limits of the voltage deviation allowed at node k respectively; λ is the voltage over-limit penalty coefficient.

7. The method according to claim 1, characterized in that: The equality constraint of the day-ahead reactive power optimization model is: Where P i,t , Q i,t are the injected active power and reactive power of node i at time t respectively.

8. The method according to claim 1, characterized in that: The inequality constraints of the day-ahead reactive power optimization model are: Where h is the reactive power compensation point in this dispatching stage; Q h,t is the amount of capacitor bank input at node h at time t; Q h,min , Q h,max are the lower and upper limits of the reactive capacity of the grid-connected energy storage at node h respectively; T POI,t is the gear position of the main transformer at time t, T POI,min , T POI,max They are the lower and upper limits of the main transformer adjustment gear respectively; symbol Represents XOR operation, N h is the daily action upper limit of the capacitor bank at node h, N POI The upper limit of the number of daily operations of the main variable; U i,t is the voltage amplitude of node i at time t, U i,min , U i.max are the lower and upper limits of the voltage amplitude at node i.

9. The method according to any one of claims 1 to 8, characterized in that: The objective function of the intraday reactive power optimization model is: minF2=|U pcc -1|+ξ(Q max -Q); In the formula, U pcc is the voltage amplitude at the grid connection point; Q max is the upper limit of the reactive capacity of the grid-type energy storage; ξ is a Boolean variable for judging whether the grid-type energy storage needs to invest reactive capacity. When the reactive demand of the wind farm is greater than the maximum reactive capacity of the doubly-fed asynchronous wind turbine, ξ=1, otherwise, ξ=0.

10. The method according to claim 9, characterized in that The equality constraint condition of the intraday reactive power optimization model is: Where P i , Q i are the injected active power and reactive power of node i respectively.

11. The method according to claim 9, characterized in that: The inequality constraints of the intraday reactive power optimization model are: In the formula, Q k,min , Q k,max are the maximum capacitive reactive power and the maximum inductive reactive power that can be adjusted by the kth wind turbine group; U i is the voltage amplitude at node i, U i,min , U i.max Soc is the lower and upper limits of the voltage amplitude at node i; t Soc is the state of charge of the grid-type energy storage at time t; max , Soc min are the upper and lower limits of the state of charge of the grid-building energy storage, respectively; P and Q are the active power and reactive power output of the grid-building energy storage, respectively. N is the rated active power of the grid-type energy storage converter; S max is the maximum available capacity of grid-type energy storage; U PCC is the voltage value of the common connection point of the grid-type energy storage; U PCS.max is the maximum voltage limit of the common connection point of the grid-connected energy storage; X is the grid-connected equivalent reactance of the grid-connected energy storage.

12. A wind farm reactive power coordination optimization device, characterized in that: include: The day-ahead optimization module is used to optimize the reactive power regulation capability of the on-load tap-changing transformer and capacitor bank based on the wind farm. According to the day-ahead reactive power optimization model, the particle swarm algorithm is used to perform reactive power optimization calculations to obtain the optimal switching value of the on-load tap-changing transformer and the optimal switching value of the capacitor bank. The intraday optimization module is used to perform reactive optimization calculations based on the reactive regulation capabilities of the wind turbines and grid-type energy storage system of the wind farm, the optimal switching value of the on-load tap-changing transformer, and the optimal switching value of the capacitor bank according to the intraday reactive optimization model, using a particle swarm algorithm to obtain the optimal switching values ​​of the wind turbines and grid-type energy storage of the wind farm.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

15. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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