CN115395576A - Energy storage adaptive damping-inertia control method facing wind power maximum power tracking - Google Patents
Energy storage adaptive damping-inertia control method facing wind power maximum power tracking Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/40—Synchronising a generator for connection to a network or to another generator
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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Abstract
The invention discloses an energy storage self-adaptive damping-inertia control method for maximum power tracking of wind power, which is characterized in that self-adaptive compensation items of inertia control parameters and damping control parameters are designed and substituted into a typical virtual synchronous control motion equation to obtain a self-adaptive virtual synchronous control expression, and a parameter self-adaptive adjustment rule is designed and a control parameter selection range is set, so that the external characteristic that a grid connection point interface presents virtual synchronization is realized while the maximum output of a wind power plant is realized, and the frequency stability of a grid connection point is improved.
Description
Technical Field
The invention relates to the technical field of new energy power system stability control, in particular to an energy storage adaptive damping-inertia control method for wind power maximum power tracking.
Background
Due to the low inertia characteristic of the wind power plant, the frequency stability of a power grid is reduced along with the continuous improvement of the wind power permeability, and the safe and stable operation of the power grid is seriously influenced. The electrochemical energy storage response is quick, the adjustment is flexible, and the key points for solving the power balance problem and the system stability risk caused by the grid connection of a large amount of new energy are achieved. The wind power station is matched with energy storage, the frequency response characteristic of the wind power station can be improved, and the influence of intermittence, randomness and fluctuation of new energy is relieved. At present, most provinces of new wind power plants in China are required to be matched with energy storage with 5% -20% rated output.
The virtual synchronization control strategy can realize virtual synchronization of the grid-connected interface of the power electronic device by adjusting the control strategy of the power electronic device. Compared with the traditional power supply, the virtual synchronous control strategy has active/reactive decoupling control and four-quadrant control capabilities, and the parameters of the rotor motion equation are virtual quantities, so that the dynamic regulation capability is more flexible than that of a thermal power generating unit. If the wind Power plant runs in the virtual synchronous control mode, although the frequency stability of a grid-connected Point can be improved, the control mode cannot realize Maximum Power Point Tracking (MPPT) of the wind Power plant, and the generated Power of the wind Power plant is remarkably reduced. In addition, in the virtual synchronous control strategy of the energy storage matched with the existing wind power plant, the virtual inertia and the damping are generally fixed and cannot be adjusted in a self-adaptive manner according to the running state, so that the oscillation damping and inertia supporting effect of the energy storage cannot be optimal, and the frequency stability of a wind storage system cannot be maximized.
Disclosure of Invention
In order to solve the technical problem that the existing virtual synchronous control strategy for the matched energy storage of the wind power plant cannot be adjusted in a self-adaptive mode according to the running state, the invention provides an energy storage self-adaptive damping-inertia control method for wind power maximum power tracking, which can realize the frequency stability of a grid-connected point of the wind power plant through matched energy storage while realizing the maximum output of the wind power plant.
In order to achieve the technical purpose, the technical scheme of the invention is that,
an energy storage adaptive damping-inertia control method facing wind power maximum power tracking is characterized by comprising the following steps:
for a wind power plant operating in a Maximum Power Point Tracking (MPPT) control mode and an energy storage system matched with the wind power plant, a self-adaptive damping-inertia controller is adopted to control the energy storage system, wherein the self-adaptive damping-inertia controller is realized by the following processes:
respectively setting self-adaptive compensation items for inertia control parameters and damping control parameters in virtual synchronous control, and substituting the self-adaptive compensation items into a typical virtual synchronous control motion equation to obtain a self-adaptive damping-inertia control expression;
setting an adjustment rule of a self-adaptive compensation item according to the relationship between the frequency deviation and the frequency deviation change rate in the transient process and the inertia control parameter and the damping control parameter in the virtual synchronous control;
and step three, establishing a small signal model analysis operation boundary, and sequentially deducing and selecting the value ranges of the damping control parameters and the inertia control parameters through small signal analysis so as to obtain the energy storage self-adaptive damping-inertia controller.
In the method, in the first step, the adaptive virtual synchronous control expression is:
wherein d omega/dt is a differential term of a difference value between a virtual rotor angular speed of a grid-connected point of a wind storage system including a wind power plant and an energy storage system matched with the wind power plant and a grid angular speed, J 0 、D 0 Respectively, the initial values of the inertia control parameter and the damping control parameter of the energy storage system, K J And K D And the compensation coefficients are respectively an inertia control parameter and a damping control parameter of the energy storage system, and the delta P is active power sent by the energy storage system.
In the second step, the adjustment rule of the adaptive compensation term is as follows:
Δω·(dω/dt)>at 0 time, J comp Rising, D comp Descending; Δ ω · (d ω/dt)<At 0 time, J comp Descent, D comp Rising;
wherein, delta omega is the difference value between the virtual rotor angular speed and the grid angular speed of the grid-connected point, J comp And D comp Respectively are compensation items of inertia control parameters and damping control parameters of the energy storage system,
K J and K D Compensation coefficients of inertia control parameter and damping control parameter respectively, and J = J 0 +J comp ，D＝D 0 +D comp J and D are an inertia control parameter and a damping control parameter D in the virtual synchronization control, respectively.
The method, the third step includes:
establishing a system small signal model containing wind power plant grid connection, solving a characteristic root of a state space equation in the small signal model, obtaining a motion track of the characteristic root changing along with a control parameter by adjusting an inertia control parameter J and a damping control parameter D so as to obtain an operation boundary, and selecting a value range of the damping control parameter and an inertia control parameter by combining the operation boundary under the condition that oscillation mode damping DR corresponding to the characteristic root is more than 0.1.
An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the aforementioned methods.
A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the aforementioned method.
The method has the technical effects that the method can avoid real-time acquisition and calculation of the frequency deviation and the frequency deviation change rate of the system, and can realize smooth self-adaptive energy storage damping-inertia control of the energy storage converter by transforming a typical virtual synchronous control active loop. The invention has strong compatibility and expansibility, when K J And K D When the energy storage self-adaptive damping and inertia control strategy is not equal to 0, the control strategy is energy storage self-adaptive damping-inertia control; when K is J ≠0，K D When the sum is not less than 0, the control strategy is self-adaptive virtual inertia control, and when the sum is K J ＝0，K D When the value is not equal to 0, the control strategy is self-adaptive virtual damping control; when K is J And K D When all are equal to 0, the control strategy is typical fixed-parameter virtual synchronous control.
Drawings
FIG. 1 is a topological structure diagram of a wind farm and its associated energy storage system in an embodiment of the present invention;
FIG. 2 is a flow chart of the control method of the present invention.
Fig. 3 is a diagram of analyzing a power angle response curve of the VSG corresponding to step S1 of the method of the present invention;
FIG. 4 is a block diagram of a control scheme for an energy storage system according to the inventive method steps, in accordance with an embodiment of the present invention;
fig. 5 is a diagram illustrating a variation of the characteristic root locus obtained in step 3 of the method according to the control parameter in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be further clearly and completely described below with reference to the accompanying drawings. It should be noted that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention can be applied to a wind Power plant containing matched energy storage to realize Maximum Power Point Tracking (MPPT) of the wind Power plant and frequency stability improvement of a grid-connected Point interface of a wind storage system. The method comprises the following steps:
s1: adaptive virtual synchronization control expression derivation. And (3) deriving an adaptive virtual synchronous control expression, designing adaptive compensation terms of inertia control parameters and damping control parameters, and substituting the adaptive compensation terms into a typical virtual synchronous control motion equation.
S2: and (5) controlling parameters to adaptively adjust rule design. And analyzing the relationship between the frequency deviation and the frequency deviation change rate in the transient process and the inertia control parameter and the damping control parameter in the virtual synchronous control, and setting a self-adaptive compensation mode.
S3: and setting a control parameter selection range. And establishing a small signal model analysis operation boundary, and sequentially deducing and selecting the value ranges of the damping control parameters and the inertia control parameters through small signal analysis so as to complete the design of the energy storage self-adaptive damping-inertia controller.
S4: and the wind storage systems operate in a combined mode. The wind power plant controller operates in the MPPT control mode, the wind power plant is matched with the energy storage to operate in the virtual synchronization control mode, so that the grid-connected point interface can present virtual synchronization external characteristics while the wind power plant maximizes output, and the grid-connected point frequency stability is improved.
The steps are as follows:
in S1, adding self-adaptive compensation terms as shown in formulas (1) and (2) for inertia control parameters J and damping control parameters D in virtual synchronous control, wherein J is 0 And D 0 Dividing into inertia control parameters and damping control parameters fixed without adding compensation terms in virtual synchronous control, J comp And D comp Adaptive compensation terms, K, for the inertia control parameter and the damping control parameter, respectively J And K D The compensation coefficients are respectively inertia control parameters and damping control parameters, delta omega is the difference value between the virtual rotor angular speed of the grid-connected point and the angular speed of the power grid, and d omega/dt is the differential term between the virtual rotor angular speed of the grid-connected point and the angular speed of the power grid of a wind storage system including a wind power plant and an energy storage system matched with the wind power plant.
Substituting inertia control parameters J and damping control parameters D added with self-adaptive compensation terms into a typical second-order synchronous generator motion equation
The obtained virtual synchronous control equation containing inertia and damping compensation is shown as a formula (3).
The formula (3) is arranged into a one-dimensional quadratic equation of d ω/dt to obtain:
according to the Werdan theorem, two roots of one course and two courses can be solved.
Cases of Δ ω · (d ω/dt) >0 and Δ ω · (d ω/dt) <0 both exist during transients, thus leaving unreasonable negative roots.
The numerator denominator is obtained through physicochemical calculation, and the expression of energy storage self-adaptive damping-inertia control is obtained as shown in the formula (6).
That is, according to the above equation, the derivative term d ω/dt is obtained by two control parameters, and the final rotation speed control amount is obtained.
In S2, when Δ ω · (d ω/dt)>At 0, the system inertia needs to be enhanced to enhance the anti-interference capability of the system, but a certain response speed is sacrificed, so that the damping needs to be reduced at the same time. When Δ ω · (d ω/dt)<At 0, inertia needs to be reduced to make the response speed of the system fast, but the anti-interference capability of the system is reduced, so the damping should be increased while the inertia is reduced. Thus, Δ ω · (d ω/dt)>At 0, J comp Elevation, D comp Descending; thus, Δ ω · (d ω/dt)<At 0 time, J comp Descent, D comp And (4) rising.
In S3, further selecting reasonable initial control parameters for energy storage adaptive damping-inertia control, and selecting J 0 And D 0 Are suitable parameter values. And establishing a small signal model of the system including the wind power plant grid connection, and performing characteristic root solving on a state space equation in the small signal model. By adjusting the inertia control parameter J and the damping control parameter D, the motion trail of the characteristic root changing along with the control parameters can be obtained. Selecting control parameters to meet the oscillation mode damping DR corresponding to the characteristic root>0.1。
The following embodiments are described with reference to specific wind farm cases:
the wind farm and the associated energy storage topology thereof in the embodiment are shown in fig. 1. The wind power plant has 23 doubly-fed wind generating sets with single machine capacity of 2.2MW, and the rated capacity of 50.6MW; the matching energy storage of the wind power plant is configured according to 10% of rated capacity of the wind power plant, and the energy storage capacity is 5MW/10MWh. The parameters of the wind turbine generator are shown in table 1, and the parameters of the lithium battery energy storage are shown in table 2.
An energy storage self-adaptive damping-inertia control method for maximizing wind power plant tracking planned power generation is shown in a specific control parameter design figure 2. And obtaining an expression of energy storage self-adaptive damping-inertia control shown in the formula (6) through the steps S1-S2. A control block diagram of an energy storage system employing virtual synchronous control is shown in fig. 3.
TABLE 1 doubly-fed wind turbine related parameters
TABLE 2 lithium cell energy storage related parameters
Reasonable simulation parameters are selected for the adaptive virtual synchronous control in the step S3, and after the wind power plant is accessed into an IEEE3 machine 9 node system, the change track of the characteristic root of the wind power plant along with the control parameters is analyzed and is shown in FIG. 4. Selecting reasonable parameters for the energy storage self-adaptive virtual synchronous control through the root track change corresponding to the parameter change, and finally determining J 0 The value is 27,D 0 The value is 90 J The value is 0.45, K D The value was-0.65. The inertia control parameter J and the damping control parameter D are compensated through self-adaption comp And D comp And carrying out dynamic adjustment.
And operating the wind power plant in an MPPT control mode, and operating the stored energy in the self-adaptive virtual synchronous control provided by the invention. And setting a three-phase short-circuit fault on a grid-connected bus of the wind power plant, clearing the fault after 0.15s, and comparing the dynamic response of the system under different control strategies of the wind power plant matched energy storage. The method is energy storage self-adaptive damping-inertia control (AVSG control), and the comparison method comprises the steps of (a) no matched energy storage of the wind power station, (b) the matched energy storage of the wind power station is PQ control, and (c) the matched energy storage of the wind power station is traditional virtual synchronous control (VSG control). The time domain simulation result of the wind power plant on the large disturbance transient response of the wind power plant grid-connected point under different control strategies is shown in fig. 5, and the correlation frequency index pair is shown in table 3.
As can be seen from the simulation of fig. 5 and the comparison of the frequency indexes in table 3, the control strategy proposed by the present invention has a better suppression effect on the frequency oscillation; compared with typical fixed parameter VSG control, the self-adaptive damping-inertia control provided by the invention enables the frequency fluctuation range to be reduced by 20.59%, the adjusting time to be shortened by 57%, and the transient performance of the system to be greatly improved; and the wind turbine generator system operates in the MPPT mode, so that better output effect is achieved.
TABLE 3 dynamic response of wind storage system under transient short-circuit fault
The invention also provides an electronic device and a computer readable medium according to the embodiment of the invention.
Wherein electronic equipment includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the aforementioned method.
In specific use, a user can interact with a server which is also used as a terminal device through an electronic device which is used as the terminal device and based on a network, and functions of receiving or sending messages and the like are realized. The terminal device is generally a variety of electronic devices provided with a display device and used based on a human-computer interface, including but not limited to a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like. Various specific application software can be installed on the terminal device according to needs, including but not limited to web browser software, instant messaging software, social platform software, shopping software and the like. The server is a network server for providing various services.
Similarly, the computer readable medium of the invention, on which a computer program is stored, which when executed by a processor, implements the method of an embodiment of the invention.
The above-mentioned embodiments only express one or several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (6)
1. An energy storage adaptive damping-inertia control method facing wind power maximum power tracking is characterized by comprising the following steps:
for a wind power plant operating in a Maximum Power Point Tracking (MPPT) control mode and an energy storage system matched with the wind power plant, a self-adaptive damping-inertia controller is adopted to control the energy storage system, wherein the self-adaptive damping-inertia controller is realized by the following processes:
respectively setting self-adaptive compensation items for inertia control parameters and damping control parameters in virtual synchronous control, and substituting the self-adaptive compensation items into a typical virtual synchronous control motion equation to obtain a self-adaptive damping-inertia control expression;
setting an adjustment rule of a self-adaptive compensation item according to the relationship between the frequency deviation and the frequency deviation change rate in the transient process and the inertia control parameter and the damping control parameter in the virtual synchronous control;
and step three, establishing a small signal model analysis operation boundary, and sequentially deducing and selecting the value ranges of the damping control parameters and the inertia control parameters through small signal analysis so as to obtain the energy storage self-adaptive damping-inertia controller.
2. The method according to claim 1, wherein in step one, the adaptive virtual synchronous control expression is:
wherein d omega/dt is a differential term of a difference value between a virtual rotor angular speed of a grid-connected point and a grid angular speed of a wind storage system including a wind power plant and an energy storage system matched with the wind power plant, J 0 、D 0 Initial values, K, of the inertia control parameter and the damping control parameter of the energy storage system, respectively J And K D The compensation coefficients are respectively inertia control parameters and damping control parameters of the energy storage system, and the delta P is active power sent by the energy storage system.
3. The method according to claim 2, wherein in the second step, the adjustment rule of the adaptive compensation term is:
Δω·(dω/dt)>at 0, J comp Rising, D comp Descending; Δ ω · (d ω/dt)<At 0, J comp Descent, D comp Rising;
wherein, delta omega is the difference value between the virtual rotor angular speed of the grid-connected point and the grid angular speed, J comp And D comp Respectively are compensation items of inertia control parameters and damping control parameters of the energy storage system,
K J and K D Compensation coefficients of inertia control parameter and damping control parameter respectively, and J = J 0 +J comp ，D＝D 0 +D comp J and D are an inertia control parameter and a damping control parameter D in the virtual synchronization control, respectively.
4. The method of claim 2, wherein said step three comprises:
establishing a system small signal model containing wind power plant grid connection, solving a characteristic root of a state space equation in the small signal model, obtaining a motion track of the characteristic root changing along with a control parameter by adjusting an inertia control parameter J and a damping control parameter D so as to obtain an operation boundary, and selecting a value range of the damping control parameter and an inertia control parameter by combining the operation boundary under the condition that oscillation mode damping DR corresponding to the characteristic root is more than 0.1.
5. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-4.
6. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116454910A (en) * | 2023-01-17 | 2023-07-18 | 国网江苏省电力有限公司 | Virtual synchronous machine inertia and primary frequency modulation cooperative self-adaptive control method and system |
| CN116632866A (en) * | 2023-07-25 | 2023-08-22 | 西安热工研究院有限公司 | Hybrid energy storage self-adaptive inertia VSG control method for liquid flow super-capacity lithium battery |
| CN117200260A (en) * | 2023-11-07 | 2023-12-08 | 国网江西省电力有限公司电力科学研究院 | Method and system for inhibiting low-frequency oscillation of power system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116454910A (en) * | 2023-01-17 | 2023-07-18 | 国网江苏省电力有限公司 | Virtual synchronous machine inertia and primary frequency modulation cooperative self-adaptive control method and system |
| CN116454910B (en) * | 2023-01-17 | 2024-03-01 | 国网江苏省电力有限公司 | Virtual synchronous machine inertia and primary frequency modulation cooperative self-adaptive control method and system |
| CN116632866A (en) * | 2023-07-25 | 2023-08-22 | 西安热工研究院有限公司 | Hybrid energy storage self-adaptive inertia VSG control method for liquid flow super-capacity lithium battery |
| CN116632866B (en) * | 2023-07-25 | 2023-12-01 | 西安热工研究院有限公司 | Hybrid energy storage self-adaptive inertia VSG control method for liquid flow super-capacity lithium battery |
| CN117200260A (en) * | 2023-11-07 | 2023-12-08 | 国网江西省电力有限公司电力科学研究院 | Method and system for inhibiting low-frequency oscillation of power system |
| CN117200260B (en) * | 2023-11-07 | 2024-03-12 | 国网江西省电力有限公司电力科学研究院 | Method and system for inhibiting low-frequency oscillation of power system |
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