CN116093917B - Multi-control parameter optimization design method for energy storage power station - Google Patents

Multi-control parameter optimization design method for energy storage power station Download PDF

Info

Publication number
CN116093917B
CN116093917B CN202310296521.5A CN202310296521A CN116093917B CN 116093917 B CN116093917 B CN 116093917B CN 202310296521 A CN202310296521 A CN 202310296521A CN 116093917 B CN116093917 B CN 116093917B
Authority
CN
China
Prior art keywords
energy storage
coefficient
storage unit
stability margin
virtual
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202310296521.5A
Other languages
Chinese (zh)
Other versions
CN116093917A (en
Inventor
刘文迪
黄泽杭
杨苓
陈思哲
罗坚强
章云
陈璟华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Technology
Original Assignee
Guangdong University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology filed Critical Guangdong University of Technology
Priority to CN202310296521.5A priority Critical patent/CN116093917B/en
Publication of CN116093917A publication Critical patent/CN116093917A/en
Application granted granted Critical
Publication of CN116093917B publication Critical patent/CN116093917B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • H02J1/00—Circuit arrangements for dc mains or dc distribution networks
    • H02J1/14—Balancing the load in a network
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses an energy storage power station multi-control parameter optimization design method, which comprises the following steps: s1: determining multiple control parameters to be optimally designed, and establishing a small signal model of the energy storage power station; s2: calculating a characteristic value of the system according to a small signal model under the change of the sagging coefficient, ensuring the stability of the system and maintaining the voltage deviation of the direct current bus, and searching the sagging coefficient combination with the maximum stability margin of the system; s3: after determining the value of the sagging coefficient combination, calculating the characteristic value of the system according to the small signal model under the change of the virtual inertial coefficient, ensuring the stability of the system, and searching the virtual inertial coefficient combination with the maximum inertial support capacity of the system; the invention establishes a combined design flow of multiple control parameters, determines the value ranges and the adjusting directions of different types of control parameters of the system, improves the stability of the system and improves the inertia capacity of coping with fluctuation.

Description

Multi-control parameter optimization design method for energy storage power station
Technical Field
The invention relates to the field of new energy distributed power generation, in particular to a multi-control parameter optimization design method for an energy storage power station.
Background
With the improvement of the performance and the reduction of the cost of the energy storage battery and the strong support of the auxiliary policy, the role of the energy storage power station in the power system is gradually developed. At present, the power level of battery energy storage is required to rise gradually, so that more than one energy storage unit is often used in an energy storage power station, and because the energy storage units directly complete charge and discharge through a power electronic converter, the energy storage units have cross coupling action, and various stability problems are easily derived. In addition, when the voltage of the direct current bus fluctuates, the energy storage unit serving as the standby capacity of the system can provide virtual inertial power according to the virtual inertial control coefficient and the voltage variation, and the defects of fluctuation, intermittence and the like of new energy are effectively overcome.
The prior researches mainly focus on the influence of different types of control parameters of a single energy storage unit or the same type of control parameters of a plurality of energy storage units on the stability of a system, and the current analysis conclusion is difficult to directly apply if the cross coupling effect among the multiple control parameters of the energy storage power station is considered. Because few scholars at present relate to research of analyzing the interaction coupling effect among different types of parameters of an energy storage power station by using a characteristic value method, the value range and the adjusting direction of the parameters of the energy storage power station are difficult to determine, and therefore, the invention provides a multi-control parameter optimization design method of the energy storage power station.
Disclosure of Invention
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
an energy storage power station multi-control parameter optimization design method comprises the following steps:
s1: determining multiple control parameters to be optimally designed, and establishing a small signal model of the energy storage power station;
s2: calculating a characteristic value of the system according to a small signal model under the change of the sagging coefficient, ensuring the stability of the system and maintaining the voltage deviation of the direct current bus, and searching the sagging coefficient combination with the maximum stability margin of the system;
s3: after determining the value of the sagging coefficient combination, calculating the characteristic value of the system according to the small signal model under the change of the virtual inertial coefficient, ensuring the stability of the system, and searching the virtual inertial coefficient combination with the maximum inertial support capacity of the system;
further, in the step S1, the specific steps of establishing the small signal model of the energy storage power station are as follows:
the system small signal model is as followsWherein->Is a system state variable x sys Is a derivative of x sys ＝[x bn ,x d ,x L ] T ，x bn For n state variables with energy storage units connected in parallel, x d Is the state variable of the direct current transmission line, x L A is a state variable of constant power load sys Is a system state matrix;
further, in the step S2, the specific steps for searching the droop coefficient combination with the largest system stability margin are as follows:
s2-1: determining the total power P of the load L Initializing control parameters of a system:
setting sagging coefficient k of energy storage unit droop1 ,k droop2 ,…,k droopn Wherein n is the total number of the energy storage units;
s2-2: and (3) obtaining the result of eigenvalue analysis under the change of the sagging coefficient:
according to the system state matrix A sys (k droop1 ,k droop2 ,…,k droopn ) Solving for eigenvalues lambda of the system 1 ,λ 2 ,…,λ m 1 to m represent oscillation eigenvalues with non-zero real part, and represent the stability margin of the system in terms of the distance of the oscillation eigenvalue with the largest real part from the imaginary axis, i.e., the stability margin of the system is ζ= -max (Re (λ) 1 ,λ 2 ,…,λ m ) Determine the system stability margin ζ>0 and bus voltage deviationWhether or not to meet, where u oN And u o For rated voltage and outlet voltage after conversion of the energy storage unit, if satisfied, the droop coefficient k is stored droop1 ,k droop2 ,…,k droopn And the data of the system stability margin xi, and adjust the sagging coefficient according to the calculated step length, repeat the step S2-2; otherwise, step S2-3 is carried out;
s2-3: determining an optimal droop coefficient combination:
output droop coefficient k droop1 ,k droop2 ,…,k droopn And a data set of the system stability margin xi, searching the value of the sagging coefficient with the maximum system stability margin;
further, in the step S3, the specific steps for searching the virtual inertia coefficient combination with the maximum inertial support capacity of the system are as follows:
s3-1: determining the value of a droop coefficient combination, and initializing a virtual inertia coefficient of a system:
determining the magnitude of a sagging coefficient according to the maximum system stability margin and keeping the sagging coefficient unchanged, and setting a virtual inertia coefficient C of an energy storage unit virb1 ,C virb2 ,…,C virbn Wherein n is the total number of the energy storage units;
s3-2: and (3) obtaining a result of eigenvalue analysis under the change of the virtual inertia coefficient:
according to the system state matrix A sys (C virb1 ,C virb2 ,…,C virbn ) Solving for eigenvalues lambda of the system 1 ,λ 2 ,…,λ m 1 to m represent oscillation characteristic values with a real part other than zero, and the stability margin of the system is ζ= -max (Re (λ) 1 ,λ 2 ,…,λ m ) Determine the system stability margin ζ>0, if so, storing the virtual inertia coefficient C at the moment virb1 ,C virb2 ,…,C virbn According to the calculated step length, the virtual inertia coefficient is adjusted, and the step S3-2 is repeated; otherwise, step S3-3 is carried out;
s3-3: determining an optimal virtual inertia coefficient combination:
virtual coefficient of inertia C virb1 ,C virb2 ,…,C virbn Searching the value of the energy storage virtual inertia coefficient when the sum of the energy storage virtual inertia coefficients is maximum.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
according to the scheme, firstly, multiple control parameters to be optimally designed are determined according to a system state space model, then, a small signal model of an energy storage power station is established, a characteristic value of a system is obtained according to the small signal model under the change of a sagging coefficient, a sagging coefficient combination with the largest stability margin of the system is found, after the value of the sagging coefficient combination is determined, the characteristic value of the system is obtained according to the small signal model under the change of a virtual inertia coefficient, a virtual inertia coefficient combination with the largest inertial support capacity of the system is found, the maximum stability margin of the system is achieved, and the parallel energy storage units have the capacity of providing larger inertia.
The scheme provides an energy storage power station multi-control parameter optimization design method, sets a multi-control parameter combination design flow, aims to increase system damping to improve stability, enables multiple sources to provide larger inertia to cope with system fluctuation, and solves the problem that the value range and the adjustment direction of different types of control parameters of the energy storage power station are difficult to determine.
Drawings
FIG. 1 is a flow chart of a multi-control parameter optimization design method in an embodiment of the invention;
FIG. 2 is a topology and control block diagram of an energy storage power station in an embodiment of the present invention;
fig. 3 is a graph showing the waveform change of the dc bus voltage and the bus power before and after the multi-control parameter optimization design in the embodiment of the invention.
Detailed Description
The invention is further illustrated by the following examples:
fig. 1 is a flowchart of an energy storage power station multi-control parameter optimization design method, fig. 2 is a topological structure and a control block diagram of an energy storage power station, and the energy storage power station multi-control parameter optimization design method in this embodiment includes the following steps:
s1: determining multiple control parameters to be optimally designed, and establishing a small signal model of the energy storage power station; the specific process is as follows:
the energy storage units adopt a bidirectional DC/DC converter, the control mode is virtual inertia control and droop control, wherein the virtual inertia control aims at improving the inertia of the system and improving the dynamic performance of the direct current bus voltage, the droop control realizes the output power distribution among a plurality of energy storage units, and the state space model of the energy storage units is as follows:
the small signal model of the energy storage unit isWherein->For the state variable x of the energy storage unit b Is a derivative of x b ＝[Δi b ,ΔS u ,ΔS i ] T ，u b And i b Before converting the voltage and current for the energy storage unit S u Rated voltage u after conversion for energy storage unit oN And an outlet voltage u o The square difference of (2) is output to a variable after passing through a first-order inertia link, S i Current reference value i before conversion for energy storage unit bref And current i b The difference is output by the integrating link, i o For the outlet current after conversion of the energy storage unit, A b Is a coefficient matrix of the energy storage unit, B b Voltage u after conversion for energy storage unit o Coefficient matrix of state variables of (a), T is time constant, s is Law's transformation complex variable operator, k droop C is the sag factor virb Is virtual inertia coefficient, d is duty ratio of bidirectional DC/DC converter in energy storage unit, R b 、L b And C s Is parasitic resistance, filter inductance and supporting capacitance of the energy storage unit, k p And k i The variable band delta is the corresponding state variable of the proportional and integral coefficient of the PI current controller of the energy storage unit; the system small signal model is as followsWherein->Is a system state variable x sys Is a derivative of x sys ＝[x b3 ,x d ,x L ] T ，x b3 For 3 state variables, x, of energy storage units connected in parallel d Is the state variable of the direct current transmission line, x L A is a state variable of constant power load sys Is a system state matrix;
step S2, calculating a characteristic value of the system according to a small signal model under the change of the sagging coefficient, ensuring the stability of the system, maintaining the voltage deviation of the direct current bus, and searching the sagging coefficient combination with the largest stability margin of the system; the specific process is as follows:
s2-1: determining the total power P of the load L Initializing control parameters of a system:
setting sagging coefficient k of energy storage unit droop1 ,k droop2 ,k droop3 The value range and the calculation step length of the (a);
s2-2: and (3) obtaining the result of eigenvalue analysis under the change of the sagging coefficient:
according to the system state matrix A sys (k droop1 ,k droop2 ,k droop3 ) Solving for eigenvalues lambda of the system 1 ,λ 2 ,…,λ m 1 to m represent oscillation eigenvalues with non-zero real part, and represent the stability margin of the system in terms of the distance of the oscillation eigenvalue with the largest real part from the imaginary axis, i.e., the stability margin of the system is ζ= -max (Re (λ) 1 ,λ 2 ,…,λ m ) Determine the system stability margin ζ>0 and bus voltage deviationIf so, store the droop coefficient k at this time droop1 ,k droop2 ,k droop3 And the data of the system stability margin xi, and adjust the sagging coefficient according to the calculated step length, repeat the step S2-2; otherwise, step S2-3 is carried out;
s2-3: determining an optimal droop coefficient combination:
output droop coefficient k droop1 ,k droop2 ,k droop3 And a data set of the system stability margin xi, searching the value of the sagging coefficient with the maximum system stability margin;
step S3, after determining the value of the sagging coefficient combination, calculating the characteristic value of the system according to the small signal model under the change of the virtual inertia coefficient, ensuring the stability of the system, and searching the virtual inertia coefficient combination with the maximum inertial support capacity of the system; the specific process is as follows:
s3-1: determining the value of a droop coefficient combination, and initializing a virtual inertia coefficient of a system:
determining the magnitude of a sagging coefficient according to the maximum system stability margin and keeping the sagging coefficient unchanged, and setting a virtual inertia coefficient C of an energy storage unit virb1 ,C virb2 ,C virb3 The value range and the calculation step length of the (a);
s3-2: and (3) obtaining a result of eigenvalue analysis under the change of the virtual inertia coefficient:
according to the system state matrix A sys (C virb1 ,C virb2 ,C virb3 ) Solving for eigenvalues lambda of the system 1 ,λ 2 ,…,λ m 1 to m represent oscillation characteristic values with a real part other than zero, and the stability margin of the system is ζ= -max (Re (λ) 1 ,λ 2 ,…,λ m ) Determine the system stability margin ζ>0, if so, storing the virtual inertia coefficient C at the moment virb1 ,C virb2 ,C virb3 According to the calculated step length, the virtual inertia coefficient is adjusted, and the step S3-2 is repeated; otherwise, step S3-3 is carried out;
s3-3: determining an optimal virtual inertia coefficient combination:
virtual coefficient of inertia C virb1 ,C virb2 ,C virb3 Searching the value of the energy storage virtual inertia coefficient when the sum of the energy storage virtual inertia coefficients is maximum.
In order to verify the effectiveness of the multi-control parameter optimization design method, an energy storage power station model of the figure 2 is built on an experimental platform of the RT-LAB. According to the above steps, the optimal design of the multiple control parameters of the system is carried out, and under the condition of the total power of the load of 51kW, as can be known from the step S2, the sagging coefficients of the first, second and third energy storage units are respectively 0.6, 0.55 and 0.55. As can be seen from step S3, the sum of the virtual inertia coefficients of the first, second and third energy storage units is 0.23 at maximum, and the virtual inertia coefficients of the corresponding first, second and third energy storage units are 0.08. 0.05 and 0.1. The waveform change chart of the DC bus voltage and bus power before and after the multi-control parameter optimization design is shown in FIG. 3, wherein u DC Represents the voltage of a direct current bus, P DC The direct current bus voltage is shown, and after time t=2 seconds, the stability and the dynamic performance of the working condition before design are poor, because the multiple control parameters are not optimized at the moment, and the damping and inertial support capacity of the system are small.
In a steady state stage after the sudden increase of load power, the oscillation amplitude of the working condition after design is reduced from 0.9V to 0.35V near 1000Hz compared with the working condition before design, which indicates that the bus voltage and the power waveform are relatively more stable under the working condition after design, because the sagging coefficient corresponding to the working condition after design is small, the system damping is large, and the system stability is better. In the dynamic process after the sudden increase of the load power, the abrupt change time of the buffer busbar voltage of the working condition before design and the working condition after design is 0.8 second and 1.25 seconds respectively, which indicates that the working condition after design has larger inertial capability, because the sum of virtual inertial coefficients corresponding to the working condition after design is larger, the energy storage unit provides more abrupt changes of the buffer busbar voltage of inertia.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, so variations in shape and principles of the present invention should be covered.

Claims (1)

1. The multi-control parameter optimization design method for the energy storage power station is characterized by comprising the following steps of:
s1: determining multiple control parameters to be optimally designed, and establishing a small signal model of the energy storage power station;
s2: calculating a characteristic value of the system according to a small signal model under the change of the sagging coefficient, ensuring the stability of the system and maintaining the voltage deviation of the direct current bus, and searching the sagging coefficient combination with the maximum stability margin of the system;
s3: after determining the value of the sagging coefficient combination, calculating the characteristic value of the system according to the small signal model under the change of the virtual inertial coefficient, ensuring the stability of the system, and searching the virtual inertial coefficient combination with the maximum inertial support capacity of the system;
in the step S1, the specific steps of establishing a small signal model of the energy storage power station are as follows:
the energy storage units adopt a bidirectional DC/DC converter, the control mode is virtual inertia control and droop control, wherein the virtual inertia control aims at improving the inertia of the system and improving the dynamic performance of the direct current bus voltage, the droop control realizes the output power distribution among a plurality of energy storage units, and the state space model of the energy storage units is as follows:
the small signal model of the energy storage unit isWherein->For the state variable x of the energy storage unit b Is a derivative of x b ＝[Δi b ,ΔS u ,ΔS i ] T ，u b And i b Before converting the voltage and current for the energy storage unit S u Rated voltage u after conversion for energy storage unit oN And an outlet voltage u o The square difference of (2) is output to a variable after passing through a first-order inertia link, S i Current reference value i before conversion for energy storage unit bref And current i b The difference is output by the integrating link, i o For the outlet current after conversion of the energy storage unit, A b Is a coefficient matrix of the energy storage unit, B b Voltage u after conversion for energy storage unit o Coefficient matrix of state variables of (a), T is time constant, s is Law's transformation complex variable operator, k droop C is the sag factor virb Is virtual inertia coefficient, d is duty ratio of bidirectional DC/DC converter in energy storage unit, R b 、L b And C s Is parasitic resistance, filter inductance and supporting capacitance of the energy storage unit, k p And k i The variable band delta is the corresponding state variable of the proportional and integral coefficient of the PI current controller of the energy storage unit; the system small signal model is as followsWherein->Is a system state variable x sys Is a derivative of x sys ＝[x bn ,x d ,x L ] T ，x bn For n state variables with energy storage units connected in parallel, x d Is the state variable of the direct current transmission line, x L A is a state variable of constant power load sys Is a system state matrix;
in the step S2, the specific steps for searching the droop coefficient combination with the largest system stability margin are as follows:
s2-1: determining the total power P of the load L Initializing control parameters of a system:
setting sagging coefficient k of energy storage unit droop1 ,k droop2 ,…,k droopn Wherein n is the total number of the energy storage units;
s2-2: and (3) obtaining the result of eigenvalue analysis under the change of the sagging coefficient:
according to the system state matrix A sys (k droop1 ,k droop2 ,…,k droopn ) Solving for eigenvalues lambda of the system 1 ,λ 2 ,…,λ m 1 to m represent oscillation eigenvalues with non-zero real part, and represent the stability margin of the system in terms of the distance of the oscillation eigenvalue with the largest real part from the imaginary axis, i.e., the stability margin of the system is ζ= -max (Re (λ) 1 ,λ 2 ,…,λ m ) Determine the system stability margin ζ>0 and bus voltage deviationWhether or not to meet, where u oN And u o Rated voltage and outlet voltage after conversion for energy storage unit, if satisfied, storingThe sagging coefficient k at this time droop1 ,k droop2 ,…,k droopn And the data of the system stability margin xi, and adjust the sagging coefficient according to the calculated step length, repeat the step S2-2; otherwise, step S2-3 is carried out;
s2-3: determining an optimal droop coefficient combination:
output droop coefficient k droop1 ,k droop2 ,…,k droopn And a data set of the system stability margin xi, searching the value of the sagging coefficient with the maximum system stability margin;
in the step S3, the specific steps for searching the virtual inertia coefficient combination with the maximum inertial support capacity of the system are as follows:
s3-1: determining the value of a droop coefficient combination, and initializing a virtual inertia coefficient of a system:
determining the magnitude of a sagging coefficient according to the maximum system stability margin and keeping the sagging coefficient unchanged, and setting a virtual inertia coefficient C of an energy storage unit virb1 ,C virb2 ,…,C virbn Wherein n is the total number of the energy storage units;
s3-2: and (3) obtaining a result of eigenvalue analysis under the change of the virtual inertia coefficient:
according to the system state matrix A sys (C virb1 ,C virb2 ,…,C virbn ) Solving for eigenvalues lambda of the system 1 ,λ 2 ,…,λ m 1 to m represent oscillation characteristic values with a real part other than zero, and the stability margin of the system is ζ= -max (Re (λ) 1 ,λ 2 ,…,λ m ) Determine the system stability margin ζ>0, if so, storing the virtual inertia coefficient C at the moment virb1 ,C virb2 ,…,C virbn According to the calculated step length, the virtual inertia coefficient is adjusted, and the step S3-2 is repeated; otherwise, step S3-3 is carried out;
s3-3: determining an optimal virtual inertia coefficient combination:
virtual coefficient of inertia C virb1 ,C virb2 ,…,C virbn Searching the value of the energy storage virtual inertia coefficient when the sum of the energy storage virtual inertia coefficients is maximum.
CN202310296521.5A 2023-03-22 2023-03-22 Multi-control parameter optimization design method for energy storage power station Active CN116093917B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310296521.5A CN116093917B (en) 2023-03-22 2023-03-22 Multi-control parameter optimization design method for energy storage power station

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310296521.5A CN116093917B (en) 2023-03-22 2023-03-22 Multi-control parameter optimization design method for energy storage power station

Publications (2)

Publication Number Publication Date
CN116093917A CN116093917A (en) 2023-05-09
CN116093917B true CN116093917B (en) 2023-08-04

Family

ID=86187145

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310296521.5A Active CN116093917B (en) 2023-03-22 2023-03-22 Multi-control parameter optimization design method for energy storage power station

Country Status (1)

Country Link
CN (1) CN116093917B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112186767A (en) * 2020-10-21 2021-01-05 国网福建省电力有限公司 Optimization control method for frequency stability of island microgrid containing high-proportion renewable energy
CN113419588A (en) * 2021-07-01 2021-09-21 湘潭大学 Two-stage cascade converter system stability analysis method based on impedance characteristics
WO2022036787A1 (en) * 2020-08-21 2022-02-24 西安热工研究院有限公司 Method for improving wind power grid-connected primary frequency modulation performance by utilizing adaptive virtual parameters
CN114445239A (en) * 2022-01-30 2022-05-06 北方工业大学 New energy interconnection system stability analysis and parameter optimization method
CN114784784A (en) * 2022-03-31 2022-07-22 广东工业大学 Direct-current micro-grid stability optimization control method based on longitudinal and transverse intersection algorithm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022036787A1 (en) * 2020-08-21 2022-02-24 西安热工研究院有限公司 Method for improving wind power grid-connected primary frequency modulation performance by utilizing adaptive virtual parameters
CN112186767A (en) * 2020-10-21 2021-01-05 国网福建省电力有限公司 Optimization control method for frequency stability of island microgrid containing high-proportion renewable energy
CN113419588A (en) * 2021-07-01 2021-09-21 湘潭大学 Two-stage cascade converter system stability analysis method based on impedance characteristics
CN114445239A (en) * 2022-01-30 2022-05-06 北方工业大学 New energy interconnection system stability analysis and parameter optimization method
CN114784784A (en) * 2022-03-31 2022-07-22 广东工业大学 Direct-current micro-grid stability optimization control method based on longitudinal and transverse intersection algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A Virtual Inertia Control Strategy for DC Microgrids Analogized With Virtual Synchronous Machines;Wenhua Wu等;《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》;第64卷(第07期);6005-6016 *
直流微电网的稳定性分析及有源阻尼控制研究;朱晓荣等;《高电压技术》;第46卷(第05期);1675-1686 *

Also Published As

Publication number Publication date
CN116093917A (en) 2023-05-09

Similar Documents

Publication Publication Date Title
CN108808682B (en) Single-three-phase hybrid multi-microgrid voltage control method based on composite robust control
CN109921504B (en) Vehicle-mounted hybrid energy storage system and nonlinear robust self-adaptive power control method thereof
CN113263960A (en) Self-adaptive energy management method for hydrogen fuel cell automobile
CN112670975B (en) Taylor expansion-based direct current power distribution and utilization system state feedback control method
WO2022105068A1 (en) Control method and device for grid-connected inverter
CN114094600B (en) Collaborative operation control method and system for multi-optical storage VSG system
CN115714435A (en) Photovoltaic hybrid energy storage system power distribution and virtual inertia control method based on virtual synchronous generator
CN111162546B (en) Adaptive adjustment power smoothing control method applied to energy storage
CN112952862A (en) Hybrid energy storage frequency division coordination controller for stabilizing wind power fluctuation and implementation method
CN113419418A (en) Reduced-order modeling method suitable for multi-converter direct-current system
CN104868773A (en) Single-phase grid-connected inverter control device based on Lyapunov state function
CN103501018A (en) Hybrid energy storage system based on fuzzy algorithm and DSP (Digital Signal Processor) and power smoothing method
CN116093917B (en) Multi-control parameter optimization design method for energy storage power station
CN111740440B (en) Inter-station coupling discrimination and internal model decoupling control method for flexible direct current transmission system
CN117458534A (en) Novel liquid flow energy storage peak regulation and frequency modulation method and device
CN106374452B (en) A kind of feedback Passivity based control method of direct-current grid current transformer
CN115549504B (en) Control method of three-level energy storage converter
CN114710055B (en) Two-parallel power converter model prediction control method based on finite set single vector
CN116054171A (en) Wind-solar-storage direct-current micro-grid voltage self-adaptive control method and device
WO2022218270A1 (en) Method and apparatus for controlling output power of power generator, and power generator system
CN112421976B (en) Three-level inverter power supply reduced-order modeling method based on hybrid system theory
CN106227041A (en) A kind of H ∞ controller and the control method of motor-car networking commutator
CN113346561A (en) Stability analysis method for energy storage droop compensation module
CN117914172B (en) Method, equipment and medium for designing control parameters of voltage loop of grid-formed inverter
Yang et al. Modelling and design of a droop-based cascaded controller for llc resonant converter

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant