Primary frequency modulation method and device for new energy station containing energy storage

Abstract

The invention discloses a primary frequency modulation method and device for a new energy station containing energy storage, and the method comprises the steps: building a self-adaptive control model of the virtual inertia and damping coefficient of the new energy station based on the rotational inertia of a synchronous generator and the control direction of the damping coefficient through the angular frequency variation and the angular frequency change rate; taking the minimum angular frequency cumulative variation in the sampling time period as a target function of the adaptive control model; based on the objective function, an improved particle swarm algorithm is adopted to iteratively solve the adaptive control model, and optimal solutions of the virtual inertia and the damping coefficient are obtained; constructing an inertia response stage frequency adjustment coefficient according to the optimal solution of the virtual inertia and the damping coefficient; constructing a variable coefficient droop control model according to the inertia response stage frequency adjustment coefficient and the droop control stage frequency adjustment coefficient; and performing primary frequency modulation control on the new energy station containing energy storage based on the variable coefficient droop control model. According to the invention, VSG control and droop control combining new energy power generation and energy storage are realized, and comprehensive primary frequency modulation control is carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system frequency regulation, and in particular, relates to a primary frequency regulation method and device for a new energy station including energy storage. Background Art

[0002] In recent years, "new energy + energy storage" will become the mainstream mode of new energy development. In the existing technology, the planning of independent power grids containing new energy mainly focuses on the optimal configuration of power capacity of multiple power supply combinations under the isolated operation mode. However, in some remote areas, due to the difficulty and high cost of fuel transportation, power supply is completely dependent on the construction of interconnection lines with the main grid or the use of local renewable energy generation. When line construction is difficult or fails, planning to use energy storage systems to cooperate with renewable energy generation for local power supply is an effective way to solve the problem. At present, there is a lack of energy storage capacity optimization configuration methods for such independent power grids that are completely powered by renewable energy. The operation and scheduling methods of traditional power grids are based on the predictable, controllable and dispatchable characteristics of power generation. For independent power grids with new energy access or as the only power source, the traditional operation and scheduling methods are no longer applicable because renewable energy generation has the characteristics of unpredictable, uncontrollable and undispatched. The existing operation methods of independent power grids containing new energy do not take into account the uncertainty of renewable energy, and the control methods of independent power grids containing new energy are based on the configuration of sufficient controllable auxiliary power generation units. They cannot be applied to independent power grids built with new energy generation as the power source, which limits the improvement of the penetration rate of new energy generation. Moreover, the independent power grid is compatible with the load management and adjustment on the power generation side and the demand side. On the one hand, it needs to interact effectively with the large power grid to improve the operation efficiency. On the other hand, it needs to realize its own coordinated and reliable operation when operating independently. The flexible operation mode of the independent power grid is also an important feature that distinguishes it from the interconnected operation of the traditional power grid. Therefore, the operation control method of the independent power grid containing new energy cannot directly use the existing traditional power grid operation and dispatching method. It is necessary to comprehensively consider the uncertainty characteristics of renewable energy generation and the flexible operation mode of the independent power grid to carry out research. In addition, in the prior art, the parameter calculation process of the traditional method is relatively cumbersome, which makes the overall control effect of the virtual synchronous generator (VSG) control method poor. Summary of the invention

[0003] In order to solve the deficiencies in the prior art, the present invention provides a primary frequency regulation method and device for a new energy station with energy storage, which simultaneously realizes VSG control and droop control combining new energy power generation and energy storage, and performs comprehensive control on the primary frequency regulation of the system.

[0004] The present invention adopts the following technical solution.

[0005] The present invention proposes a primary frequency modulation method for a new energy station with energy storage, comprising:

[0006] Obtaining the initial angular frequency and the real-time angular frequency to determine the angular frequency change amount and the angular frequency change rate;

[0007] Based on the control direction of the moment of inertia and damping coefficient of the synchronous generator, the adaptive control model of the virtual inertia and damping coefficient of the new energy station is constructed by using the angular frequency change amount and angular frequency change rate;

[0008] The objective function of the adaptive control model is to minimize the cumulative change of the angular frequency within the sampling period. Based on the objective function, the adaptive control model is iteratively solved using an improved particle swarm algorithm to obtain the optimal solution of the virtual inertia and damping coefficient.

[0009] The frequency adjustment coefficient of the inertial response stage is constructed with the optimal solution of the virtual inertia and the damping coefficient. The variable coefficient droop control model is jointly constructed with the frequency adjustment coefficient of the inertial response stage and the frequency adjustment coefficient of the droop control stage. Based on the variable coefficient droop control model, a frequency adjustment control is performed on the new energy station with energy storage.

[0010] Preferably, the factors affecting the output frequency are analyzed based on the rotor motion equation, and combined with the analysis of the synchronous generator power angle and frequency oscillation curve, the control direction of the synchronous generator moment of inertia J and damping coefficient D in different intervals is obtained, including:

[0011] When the angular frequency change rate dωdt is greater than 0 and the angular frequency change amount Δω is greater than 0, the moment of inertia J increases and the damping coefficient D increases;

[0012] When the angular frequency change rate dωdt is less than 0 and the angular frequency change Δω is greater than 0, the moment of inertia J decreases and the damping coefficient D increases;

[0013] When the angular frequency change rate dωdt is less than 0 and the angular frequency change amount Δω is less than 0, the moment of inertia J increases and the damping coefficient D increases;

[0014] When the angular frequency change rate dωdt is greater than 0 and the angular frequency change amount Δω is less than 0, the moment of inertia J decreases and the damping coefficient D increases.

[0015] Preferably, the adaptive control model of the virtual inertia and damping coefficient of the new energy station satisfies the following relationship:

[0016]

[0017] In the formula,

[0018] J new and D new are the virtual inertia and damping coefficient of the new energy station,

[0019] J0 and D0 are the initial values ​​of virtual inertia and damping coefficient when the new energy station is not disturbed, respectively.

[0020] J min , J max They are the lower and upper limits of the virtual inertia when the new energy station is stable.

[0021] D min , D max are the lower and upper limits of the damping coefficient when the new energy station is stable,

[0022] The symbol for extracting (dω / dt)·Δω is

[0023] Logistic() is a function ranging from 0 to 1 on the vertical axis.

[0024] m is the set threshold of the adaptive control. When Δω>m, the adaptive control is started, and when Δω<m, the adaptive control is not started.

[0025] Preferably, the minimum cumulative change of the angular frequency within the sampling time period is used as the objective function of the adaptive control model, satisfying the following relationship:

[0026]

[0027] Where ITAE is the objective function of the adaptive control model, Δω (t) is the frequency deviation of the sampling period t, where t is the sampling period.

[0028] Preferably, the particle swarm algorithm is improved, and based on the objective function, the improved particle swarm algorithm is used to iteratively solve the adaptive control model to obtain the optimal solution of the initial value of the virtual inertia and the initial value of the damping coefficient;

[0029] Among them, the improvements of particle swarm algorithm include:

[0030] 1) Use the adaptive inertia weight w as the particle inertia, and set the minimum and maximum values ​​of the adaptive inertia weight respectively;

[0031] 2) Establish an adaptive inertia weight model to satisfy the following relationship:

[0032]

[0033] In the formula,

[0034] is the adaptive inertia weight of the ith particle at the dth iteration,

[0035] w min 、w max are the minimum and maximum values ​​of the adaptive inertia weight,

[0036] is the average fitness of all particles at the dth iteration,

[0037] is the i-th particle x at the d-th iteration i d The fitness of

[0038] is the minimum fitness value of all particles at the dth iteration.

[0039] Preferably, based on the adaptive control model, the optimal values ​​of the virtual inertia and the damping coefficient are determined by using the optimal solutions of the initial values ​​of the virtual inertia and the damping coefficient.

[0040] Preferably, the frequency adjustment coefficient of the inertia response stage is constructed with the optimal solution of the virtual inertia and the damping coefficient; the variable coefficient droop control model is constructed with the frequency adjustment coefficient of the inertia response stage and the frequency adjustment coefficient of the droop control stage, satisfying the following relationship:

[0041]

[0042] In the formula, ΔP is the power regulation amount, Δf is the system frequency change, K f is the frequency adjustment coefficient in the droop control stage, M f It is the frequency adjustment coefficient in the variable coefficient droop control stage.

[0043] The present invention also proposes a primary frequency regulation device for a new energy station with energy storage, comprising:

[0044] Acquisition module, adaptive control model building module, virtual inertia and damping coefficient solution module, primary frequency modulation control module;

[0045] An acquisition module is used to obtain the initial angular frequency and the real-time angular frequency to determine the angular frequency change amount and the angular frequency change rate;

[0046] The adaptive control model building module is used to build an adaptive control model of the virtual inertia and damping coefficient of the new energy station based on the control direction of the rotational inertia and damping coefficient of the synchronous generator and using the angular frequency change amount and angular frequency change rate;

[0047] The virtual inertia and damping coefficient solution module is used to take the minimum cumulative change of angular frequency within the sampling time period as the objective function of the adaptive control model; based on the objective function, the improved particle swarm algorithm is used to iteratively solve the adaptive control model to obtain the optimal solution of the virtual inertia and damping coefficient;

[0048] The primary frequency regulation control module is used to construct the frequency regulation coefficient of the inertial response stage with the optimal solution of the virtual inertia and the damping coefficient; to jointly construct a variable coefficient droop control model with the frequency regulation coefficient of the inertial response stage and the frequency regulation coefficient of the droop control stage; and to perform primary frequency regulation control on the new energy station with energy storage based on the variable coefficient droop control model.

[0049] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0050] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0051] The beneficial effects of the present invention are that, compared with the prior art, at least the following are included: the primary frequency regulation method of a new energy station with energy storage proposed in the present invention can simultaneously realize VSG control and droop control, is more suitable for new energy stations with energy storage, and adaptively reduces the adverse effects of the volatility of new energy power generation itself on the frequency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a primary frequency modulation method of a new energy station with energy storage proposed by the present invention;

[0053] Figure 2 is a power angle curve and a frequency oscillation curve of a synchronous generator in an embodiment of the present invention;

[0054] Figure 3 It is a comparison diagram between the frequency difference calculated by the method proposed in the embodiment of the present invention and the frequency difference calculated by the traditional method. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0056] The present invention proposes a primary frequency regulation method for a new energy station with energy storage, which is more suitable for new energy stations with energy storage than traditional frequency regulation methods. It takes into account factors such as the fluctuation characteristics of the new energy power generation access system, the system frequency characteristics, and the real-time charge state of the energy storage. By adopting coordinated control of virtual inertia and damping coefficient, the primary frequency regulation effect of the traditional synchronous generator is achieved, and the adverse impact of the volatility of new energy power generation itself on the frequency of the power system is adaptively reduced.

[0057] like Figure 1 As shown, the primary frequency modulation method includes:

[0058] Step 1, obtaining the initial angular frequency and the real-time angular frequency to determine the angular frequency change amount and the angular frequency change rate;

[0059] Specifically, within the sampling time period, the angular frequency corresponding to the starting time τ0 of the sampling time period t is used as the initial angular frequency ω0, the angular frequency corresponding to any time τ within the sampling time period t is used as the real-time angular frequency ω, the difference between the initial angular frequency and the real-time angular frequency is used as the angular frequency change Δω, and the ratio of the angular frequency change to the time difference τ-τ0 is used as the angular frequency change rate dωdt.

[0060] Among them, the angular frequency change Δω has a positive change (Δω>0) and a negative change (Δω<0), and the angular frequency change rate dωdt has a positive change rate (dωdt>0) and a negative change rate (dωdt<0).

[0061] Step 2: Based on the control method of the moment of inertia and damping coefficient of the synchronous generator, an adaptive control model of the virtual inertia and damping coefficient of the new energy station is constructed using the angular frequency change amount and angular frequency change rate.

[0062] Specifically, step 2 includes:

[0063] Step 2.1, analyze the factors affecting the output frequency based on the rotor motion equation, and then combine the synchronous generator power angle and frequency oscillation curve analysis to obtain the control direction of the synchronous generator's moment of inertia J and damping coefficient D in different intervals.

[0064] The power angle curve and frequency oscillation curve of the synchronous generator are as follows: Figure 2 As shown, Figure 2 The waveform on the left is the power angle curve. Figure 2 The waveform on the right is the frequency oscillation curve. When the active power command value of the synchronous generator increases from P1 to P2, the system stable operation point moves from point A to point B. During this period, the changes in power and frequency undergo an attenuated oscillation process. A typical attenuated oscillation process includes 4 intervals, as shown in Table 1:

[0065] Table 1 Control direction of moment of inertia and damping coefficient in different intervals

[0066]

[0067]

[0068] Figure 2 The parameters in Table 1 are described as follows:

[0069] t1, t2, t3, t4 and t5 are sampling moments on the frequency oscillation curve respectively; ω0, ω, Δω, dωdt are initial angular frequency, real-time angular frequency, angular frequency change and angular frequency change rate respectively.

[0070] In interval (2), when the angular frequency change is a positive value, the angular frequency change rate is a negative value, indicating that the frequency is in the frequency rising stage; in interval (4), when the angular frequency change is a negative value, the angular frequency change rate is a positive value, indicating that the frequency recovers from not meeting the rated frequency requirement to the rated frequency.

[0071] It can be seen from Table 1 that when the angular frequency change amount and the angular frequency change rate are in the same direction (both positive or negative), the control direction of the moment of inertia J is to increase; conversely, when the angular frequency change amount and the angular frequency change rate are in different directions, the control direction of the moment of inertia J is to decrease. In addition, when the moment of inertia changes and the absolute value of the angular frequency change amount is large (exceeding the set threshold), the control direction of the damping coefficient D is to increase; conversely, when the moment of inertia changes but the absolute value of the angular frequency change amount is small (not exceeding the set threshold), the damping coefficient D remains unchanged.

[0072] Step 2.2, based on the hyperbolic tangent function and according to the control direction of the moment of inertia and damping coefficient of the synchronous generator, an adaptive control model of the virtual inertia and damping coefficient of the new energy station is constructed.

[0073] The adaptive control model proposed in the present invention is similar to the control direction of the moment of inertia and damping coefficient of the synchronous generator, and can provide a large virtual inertia when the frequency changes quickly and a small virtual inertia when the frequency changes slowly.

[0074] Specifically, the adaptive control model of the virtual inertia and damping coefficient of the new energy station satisfies the following relationship:

[0075]

[0076]

[0077] In the formula,

[0078] J new and D new are the virtual inertia and damping coefficient of the new energy station,

[0079] J0 and D0 are the initial values ​​of virtual inertia and damping coefficient when the new energy station is not disturbed, respectively.

[0080] J min , J max are the lower limit and upper limit of the virtual inertia when the new energy station is stable. In the embodiment, J min The value is 0.05, Jmax The value is 0.5.

[0081] D min , D max are the lower and upper limits of the damping coefficient when the new energy station is stable. In the embodiment, D min The value is 10.51, D max The value is 25.33.

[0082] The symbol for extracting (dω / dt)·Δω is used to introduce the control method of the moment of inertia and damping coefficient of the synchronous generator into the adaptive control of the virtual inertia and damping coefficient of the new energy station.

[0083] Logistic() is a function that ranges from 0 to 1 on the vertical axis.

[0084] m is the set threshold of adaptive control, and the preferred value of m is 0.1. When Δω>m, it means that the virtual inertia and damping coefficient of the new energy station are insufficient to support the system frequency, and adaptive control needs to be started. When Δω<m, adaptive control is not started.

[0085] In the adaptive control model, the selection of the initial value of the virtual inertia and the initial value of the damping coefficient when the new energy station is undisturbed is very important. Since the new energy station containing energy storage cannot be equated with the traditional synchronous generator, the initial value of the virtual inertia and the initial value of the damping coefficient when the new energy station is undisturbed cannot be set by referring to the physical quantities corresponding to the traditional synchronous generator. Moreover, the initial value of the virtual inertia and the initial value of the damping coefficient have an impact on the virtual inertia and the damping coefficient under adaptive control. Therefore, it is necessary to reasonably select the initial value of the virtual inertia and the initial value of the damping coefficient.

[0086] Step 3, taking the minimum cumulative change of angular frequency within the sampling time period as the objective function of the adaptive control model; based on the objective function, using the improved particle swarm algorithm to iteratively solve the adaptive control model to obtain the optimal solution of virtual inertia and damping coefficient.

[0087] Specifically, step 3 includes:

[0088] Step 3.1, taking the minimum cumulative change of angular frequency within the sampling time period as the objective function of the adaptive control model, satisfies the following relationship:

[0089]

[0090] Where ITAE is the objective function of the adaptive control model, Δω (t) is the frequency deviation of the sampling period t, where t is the sampling period.

[0091] tΔω (t)|Indicates the product of frequency deviation and time, and is an evaluation indicator.

[0092] In step 3.2, the particle swarm algorithm is improved. Based on the objective function, the improved particle swarm algorithm is used to iteratively solve the adaptive control model to obtain the optimal solution for the initial value of the virtual inertia and the initial value of the damping coefficient.

[0093] Specifically, the improvements of particle swarm optimization include:

[0094] 1) Use the adaptive inertia weight w as the particle inertia, and set the minimum and maximum values ​​of the adaptive inertia weight respectively;

[0095] Among them, the setting of the minimum and maximum values ​​of the inertia weight has an impact on the accuracy and convergence speed of the particle swarm algorithm. In the actual optimization process, in the early stage, it is hoped that the particles have better global search ability, that is, the value of the inertia weight in the early stage should be larger; in the later stage, it has better local search ability, so the weight value should be reduced at this time. Compared with the fixed inertia weight, the adaptive inertia weight can give full play to the advantages of the particle swarm algorithm. Therefore, it is proposed to use the adaptive inertia weight as an improvement method of particle inertia.

[0096] Moreover, the adaptive control model is based on the moment of inertia and damping coefficient of the traditional synchronous generator. In order to obtain the optimal solution of the virtual inertia and damping coefficient of the new energy station that can realize VSG control, the reasonable selection of the value range of the adaptive inertia weight can further improve the search efficiency. In order to avoid the reduction of search ability caused by the inappropriate value range of the adaptive inertia weight, the moment of inertia and damping coefficient of the traditional synchronous generator in the typical scenario are used as the optimal solution of the adaptive control model, and the particle swarm algorithm is applied in reverse to derive the minimum and maximum inertia weights corresponding to the moment of inertia and damping coefficient in the typical scenario as the minimum and maximum values ​​of the adaptive inertia weight in the improved particle swarm algorithm.

[0097] 2) Establish an adaptive inertia weight model to satisfy the following relationship:

[0098]

[0099] In the formula,

[0100] is the adaptive inertia weight of the ith particle at the dth iteration,

[0101] w min 、w max are the minimum and maximum values ​​of the adaptive inertia weight respectively. In the embodiment, w is determined by calculation. min The value is 0.4, w max The value is 0.9.

[0102] is the average fitness of all particles at the dth iteration,

[0103] is the i-th particle x at the d-th iteration i d The fitness of

[0104] is the minimum fitness value of all particles at the dth iteration.

[0105] Step 3.3, based on the adaptive control model, the optimal solutions of the initial values ​​of the virtual inertia and the damping coefficient are used to determine the optimal values ​​of the virtual inertia and the damping coefficient.

[0106] The present invention takes the minimum cumulative change of angular frequency within a sampling time period as the objective function of the adaptive control model. Based on the objective function, when the improved particle swarm algorithm is used to iteratively solve the adaptive control model, it actually searches for the virtual inertia and damping coefficient that can achieve the minimum change of angular frequency from the global to the local, so as to obtain the optimal solution of the initial value of the virtual inertia and the initial value of the damping coefficient.

[0107] The optimal solutions of the obtained initial values ​​of virtual inertia and damping coefficient are actually physical quantities that can achieve the minimum cumulative change in angular frequency. However, considering that the volatility of wind, light and other new energy power generation connected to the new energy station with energy storage will have a real-time impact on the angular frequency, it is necessary to judge again whether Δω>m is established based on the adaptive control model. Obviously, when Δω>m, it means that under the influence of the volatility of new energy power generation, the optimal solutions of the initial values ​​of virtual inertia and damping coefficient of the new energy station are not sufficient to support the stability of the system frequency, and adaptive control needs to be started. Therefore, the optimal values ​​of virtual inertia and damping coefficient are further determined according to (dω / dt)·Δω, and the adaptive control is not started when it is judged that Δω<m. Therefore, the adaptive control model proposed in the present invention can not only realize VSG control, but also adaptively reduce the adverse effects of the volatility of new energy power generation itself on the frequency of the power system, and is more suitable for frequency stability control of new energy stations with energy storage.

[0108] Step 4: construct the frequency adjustment coefficient of the inertial response stage with the optimal solution of the virtual inertia and the damping coefficient; construct a variable coefficient droop control model with the frequency adjustment coefficient of the inertial response stage and the frequency adjustment coefficient of the droop control stage; and perform a frequency adjustment control on the new energy station with energy storage based on the variable coefficient droop control model.

[0109] Specifically, the traditional droop control model satisfies the following relationship:

[0110] ΔP e =K f Δf

[0111] Where ΔP e is the change in system rated power, Δf is the change in system frequency, K f It is the frequency adjustment coefficient in the droop control stage.

[0112] Specifically, the variable coefficient droop control model satisfies the following relationship:

[0113]

[0114] Where ΔP is the power regulation amount, M f It is the frequency adjustment coefficient in the variable coefficient droop control stage.

[0115] Based on the variable coefficient droop control model, a frequency regulation control is performed on the new energy station containing energy storage, and VSG control and droop control are realized at the same time, which can not only alleviate the frequency change but also allow the energy storage to have enough time to perform a frequency regulation.

[0116] The present invention also proposes a primary frequency regulation device for a new energy station with energy storage, comprising:

[0117] Acquisition module, adaptive control model building module, virtual inertia and damping coefficient solution module, primary frequency modulation control module;

[0118] An acquisition module is used to obtain the initial angular frequency and the real-time angular frequency to determine the angular frequency change amount and the angular frequency change rate;

[0119] The adaptive control model building module is used to build an adaptive control model of the virtual inertia and damping coefficient of the new energy station based on the control direction of the rotational inertia and damping coefficient of the synchronous generator and using the angular frequency change amount and angular frequency change rate;

[0120] The virtual inertia and damping coefficient solution module is used to take the minimum cumulative change of angular frequency within the sampling time period as the objective function of the adaptive control model; based on the objective function, the improved particle swarm algorithm is used to iteratively solve the adaptive control model to obtain the optimal solution of the virtual inertia and damping coefficient;

[0121] The primary frequency regulation control module is used to construct the frequency regulation coefficient of the inertial response stage with the optimal solution of the virtual inertia and the damping coefficient; to jointly construct a variable coefficient droop control model with the frequency regulation coefficient of the inertial response stage and the frequency regulation coefficient of the droop control stage; and to perform primary frequency regulation control on the new energy station with energy storage based on the variable coefficient droop control model.

[0122] In the method and system proposed in the present invention, the energy storage frequency regulation response unit simulates the mode of primary frequency regulation of traditional units, and adjusts the frequency according to the deviation Δf of the grid frequency and the frequency regulation coefficient K. fAdjust its own output to participate in the rapid regulation of the grid frequency. The energy storage frequency response unit is put into operation when the frequency disturbance occurs, and adjusts the coefficient M according to the change rate of the grid frequency deviation dfdt and the frequency deviation change rate. f Adjust its own output until the frequency deviation reaches the maximum, at which time the frequency change rate is zero. This stage is the virtual inertia control stage of the energy storage frequency regulation response; then switch to the droop control mode, and adjust the frequency according to the deviation Δf of the grid frequency and the frequency regulation coefficient K. f Adjust its own output. This stage is called the droop control stage of the energy storage frequency regulation response. Fixed droop control always uses frequency deviation as the energy storage output decision variable and controls it with a constant frequency regulation coefficient: while improved droop control uses the frequency deviation change rate of zero as the demarcation point, and controls the energy storage battery output in stages according to the frequency deviation change rate and frequency deviation, so as to achieve the purpose of assisting wind power in participating in system frequency regulation. Among them, VSG control comprehensively considers the parameter selection of virtual inertia and damping coefficient when the frequency changes, and adaptively reduces the adverse effects of the volatility of wind power generation itself on the frequency of the power system; auxiliary frequency regulation control imitates the droop characteristics and inertia links of conventional units to improve the primary frequency regulation capability of wind power generation.

[0123] This section conducts simulation verification of the power system. The example conditions are t = 1s, the wind speed of the permanent magnet wind turbine suddenly changes from 6m / s to 7m / s; t = 2s, the light intensity of the photovoltaic power generation changes from 1200W / m 2 Sudden change to 1000W / m 2 ; t = 3s, the load power suddenly changes from 5kW to 11kW. The calculation results are as follows Figure 3 As shown, among them, the frequency difference calculation result without energy storage has the largest variation range, and the frequency difference calculated by the method proposed in the present invention is very close to the frequency difference calculated by the variable K droop control, but it is obvious that the frequency difference calculated by the method proposed in the present invention is better than the frequency difference calculated by the variable K droop control; Figure 3 It can be seen that compared with the traditional frequency modulation method, the frequency modulation strategy of the present invention comprehensively considers the fluctuation requirements of the wind power access system, the system frequency, the real-time charge state of the battery energy storage and other factors to adjust the filter time constant, and adaptively reduces the adverse effects of the volatility of wind power generation itself on the power system frequency. Through simulation analysis and comparison with other control strategies, it is found that the primary frequency modulation method and device of the new energy station with energy storage proposed by the present invention are superior and effective in primary frequency modulation.

[0124] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0125] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0126] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0127] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

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

Claims

1. A primary frequency modulation method for a new energy station with energy storage, characterized in that: The method comprises: Obtaining the initial angular frequency and the real-time angular frequency to determine the angular frequency change amount and the angular frequency change rate; Based on the control direction of the moment of inertia and damping coefficient of the synchronous generator, the adaptive control model of the virtual inertia and damping coefficient of the new energy station is constructed by using the angular frequency change amount and angular frequency change rate; The objective function of the adaptive control model is to minimize the cumulative change of the angular frequency within the sampling period. Based on the objective function, the adaptive control model is iteratively solved using an improved particle swarm algorithm to obtain the optimal solution of the virtual inertia and damping coefficient. The frequency adjustment coefficient of the inertial response stage is constructed with the optimal solution of the virtual inertia and the damping coefficient. The variable coefficient droop control model is jointly constructed with the frequency adjustment coefficient of the inertial response stage and the frequency adjustment coefficient of the droop control stage. Based on the variable coefficient droop control model, a frequency adjustment control is performed on the new energy station with energy storage.

2. The primary frequency modulation method of a new energy station with energy storage according to claim 1, characterized in that: Based on the rotor motion equation, the factors affecting the output frequency are analyzed. Combined with the analysis of the synchronous generator power angle and frequency oscillation curve, the control direction of the synchronous generator's moment of inertia J and damping coefficient D in different intervals is obtained, including: When the angular frequency change rate dωdt is greater than 0 and the angular frequency change amount Δω is greater than 0, the moment of inertia J increases and the damping coefficient D increases; When the angular frequency change rate dωdt is less than 0 and the angular frequency change Δω is greater than 0, the moment of inertia J decreases and the damping coefficient D increases; When the angular frequency change rate dωdt is less than 0 and the angular frequency change amount Δω is less than 0, the moment of inertia J increases and the damping coefficient D increases; When the angular frequency change rate dωdt is greater than 0 and the angular frequency change amount Δω is less than 0, the moment of inertia J decreases and the damping coefficient D increases.

3. The primary frequency modulation method of a new energy station with energy storage according to claim 2, characterized in that: The adaptive control model of virtual inertia and damping coefficient of new energy stations satisfies the following relationship: In the formula, J new and D new are the virtual inertia and damping coefficient of the new energy station, J0 and D0 are the initial values ​​of virtual inertia and damping coefficient when the new energy station is not disturbed, respectively. J min , J max They are the lower and upper limits of the virtual inertia when the new energy station is stable. D min , D max are the lower and upper limits of the damping coefficient when the new energy station is stable, The symbol for extracting (dω / dt)·Δω is Logistic() is a function ranging from 0 to 1 on the vertical axis. m is the set threshold of the adaptive control. When Δω>m, the adaptive control is started, and when Δω<m, the adaptive control is not started.

4. The primary frequency modulation method of a new energy station with energy storage according to claim 2, characterized in that: The minimum cumulative change of angular frequency within the sampling time period is used as the objective function of the adaptive control model, satisfying the following relationship: Where ITAE is the objective function of the adaptive control model, Δω (t) is the frequency deviation of the sampling period t, where t is the sampling period.

5. The primary frequency modulation method of a new energy station with energy storage according to claim 4, characterized in that: The particle swarm algorithm is improved. Based on the objective function, the improved particle swarm algorithm is used to iteratively solve the adaptive control model to obtain the optimal solution of the initial value of the virtual inertia and the initial value of the damping coefficient. Among them, the improvements of particle swarm algorithm include: 1) Use the adaptive inertia weight w as the particle inertia, and set the minimum and maximum values ​​of the adaptive inertia weight respectively; 2) Establish an adaptive inertia weight model to satisfy the following relationship: In the formula, is the adaptive inertia weight of the ith particle at the dth iteration, w min 、w max are the minimum and maximum values ​​of the adaptive inertia weight, is the average fitness of all particles at the dth iteration, is the i-th particle at the d-th iteration The fitness of is the minimum fitness value of all particles at the dth iteration.

6. The primary frequency modulation method of a new energy station with energy storage according to claim 5, characterized in that: Based on the adaptive control model, the optimal values ​​of the virtual inertia and the damping coefficient are determined by using the optimal solutions of the initial values ​​of the virtual inertia and the damping coefficient.

7. The primary frequency modulation method of a new energy station with energy storage according to claim 1, characterized in that: The frequency adjustment coefficient of the inertia response stage is constructed with the optimal solution of the virtual inertia and the damping coefficient; the variable coefficient droop control model is constructed with the frequency adjustment coefficient of the inertia response stage and the frequency adjustment coefficient of the droop control stage, satisfying the following relationship: In the formula, ΔP is the power regulation amount, Δf is the system frequency change, K f is the frequency adjustment coefficient in the droop control stage, M f It is the frequency adjustment coefficient in the variable coefficient droop control stage.

8. A primary frequency modulation device for a new energy station with energy storage, characterized in that: The system comprises: Acquisition module, adaptive control model building module, virtual inertia and damping coefficient solution module, primary frequency modulation control module; An acquisition module is used to obtain the initial angular frequency and the real-time angular frequency to determine the angular frequency change amount and the angular frequency change rate; The adaptive control model building module is used to build an adaptive control model of the virtual inertia and damping coefficient of the new energy station based on the control direction of the rotational inertia and damping coefficient of the synchronous generator and using the angular frequency change amount and angular frequency change rate; The virtual inertia and damping coefficient solution module is used to take the minimum cumulative change of angular frequency within the sampling time period as the objective function of the adaptive control model; based on the objective function, the improved particle swarm algorithm is used to iteratively solve the adaptive control model to obtain the optimal solution of the virtual inertia and damping coefficient; The primary frequency regulation control module is used to construct the frequency regulation coefficient of the inertial response stage with the optimal solution of the virtual inertia and the damping coefficient; to jointly construct a variable coefficient droop control model with the frequency regulation coefficient of the inertial response stage and the frequency regulation coefficient of the droop control stage; and to perform primary frequency regulation control on the new energy station with energy storage based on the variable coefficient droop control model.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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