CN112736917A - Wind-solar-fire bundling and delivery system STATCOM-POD coordinated optimization design method - Google Patents

Wind-solar-fire bundling and delivery system STATCOM-POD coordinated optimization design method Download PDF

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CN112736917A
CN112736917A CN202011602020.8A CN202011602020A CN112736917A CN 112736917 A CN112736917 A CN 112736917A CN 202011602020 A CN202011602020 A CN 202011602020A CN 112736917 A CN112736917 A CN 112736917A
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wind
solar
fire bundling
pod
statcom
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CN112736917B (en
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和萍
李凯章
李从善
方祺元
和艳萍
武小鹏
赵艺芳
陶玉昆
杨海晶
李钊
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Zhengzhou University of Light Industry
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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
    • H02J3/002—Flicker reduction, e.g. compensation of flicker introduced by non-linear load
    • 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
    • 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
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • 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
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
    • 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
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
    • 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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • 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
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]

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Abstract

The invention provides a STATCOM-POD coordinated optimization design method of a wind-solar-fire bundling delivery system, which is used for solving the technical problem of low-frequency oscillation of the existing wind-solar-fire bundling delivery system. The method comprises the following steps: firstly, constructing a wind-solar-fire bundling and delivering system, and connecting the STATCOM and the POD into the wind-solar-fire bundling and delivering system; then constructing a simulation model of the wind-solar-fire bundling and delivering system based on MATLAB/Simulink; secondly, solving the characteristic value of the simulation model, and calculating the damping ratio of the simulation model according to the characteristic value; then, constructing a target function based on the damping ratio by taking the system control parameters of the simulation model as constraint conditions; and finally, optimizing the objective function by using a genetic algorithm, and outputting system control parameters corresponding to the optimal damping ratio. The invention can effectively improve the integral damping characteristic and the dynamic stability of the system through the coordinated optimization of the system control parameters, and meets the basic requirements of the delivery of large-scale traditional energy and renewable energy.

Description

Wind-solar-fire bundling and delivery system STATCOM-POD coordinated optimization design method
Technical Field
The invention relates to the technical field of power systems, in particular to a STATCOM-POD coordinated optimization design method for a wind-solar-fire bundling delivery system.
Background
In recent years, the energy demand of China is rapidly increased, and energy production and consumption face transformation. New energy power generation, particularly wind and light, is more and more concerned because the new energy power generation can relieve energy crisis, is clean and low-carbon and has rich resources, and particularly large-scale wind power centralized grid connection in the three north area is realized. The wind-solar-fire bundled delivery form can weaken adverse effects caused by wind energy and solar energy fluctuation, the wind-solar-fire bundled delivery is smoother and more controllable than the wind power delivery or photovoltaic power delivery alone, coal power is matched, energy storage is matched, the stability of long-distance delivery is improved, and the extra-high voltage matched wind-solar-fire bundled delivery of a large new energy base is trended to have great significance for realizing 2030 carbon emission peak reaching in China and realizing carbon neutralization in 2060 years.
Around the problem of low-frequency oscillation caused by bundling and outward sending of wind-solar fire, a large amount of research is carried out by scholars at home and abroad, and certain results are obtained. The research of large-scale high-efficiency safe delivery of large-scale energy-based earth wind, light and thermal power is realized by extra-high voltage direct current [ J ] China Motor engineering report, 2014, 34 (16): 2513 and 2522 the system researches the feasibility problem of realizing the bundling and outward delivery of wind, light and fire in large energy bases. The result shows that on the basis of ensuring safe and stable operation of the power system, the installed capacities of wind power and photovoltaic are optimized, and the proportion of abandoned wind and abandoned light can be reduced to a great extent. Literature [ wu nu, chen hao, zhao soldier, etc.. wind-solar-fire bundling alternating current-direct current parallel-serial delivery system interaction influence and stability research [ J ] power grid technology, 2016, 40 (07): 1934 & lt 1942 & gt, a wind-light-fire bundling alternating-current and direct-current hybrid-series delivery system model is established, and based on short-circuit capacity of wind-power and photovoltaic grid-connected points, an energy conservation law and unbalanced power redistribution characteristics, the influence mechanism of matched fire and electricity on direct-current, wind-power, photovoltaic and networking alternating-current systems is researched. Document [ tension, YuanXiajiang, Bayong, etc.. wind-solar-fire bundled delivery power supply planning with consideration of new energy penetration power and risk [ J ] power system automation, 2018, 42 (19): 71-76, 132 a multi-objective optimization configuration model considering the penetration power limit and the output reverse peak regulation of new energy and large fluctuation risk is built, the result shows that the large-scale new energy access can cause negative influence on an interconnected power system, and the wind turbine generator, the photovoltaic power station and the synchronous generator interact with each other to change the power flow of the system and influence the damping characteristic of the system, so that the power system can be easily influenced by low-frequency oscillation due to damping reduction when encountering interference. Random fluctuation of wind power generation and photovoltaic power generation can also obviously increase the probability of unbalanced load flow of a power system and induce more oscillation problems. Therefore, it is important to improve the damping characteristics of the wind-solar-fire bundled external power generation system.
Flexible AC Transmission Systems (FACTS) devices provide a new means for suppressing inter-area oscillation due to flexibility of installation locations and good dynamic performance, and more researchers apply FACTS devices to new energy power Systems. Document [ and nun, gunns culvert, yao-eln, etc.. UPFC improves damping characteristics analysis of wind-containing power systems [ J ]. power automation equipment, 2017, 37 (8): 208-. Literature [ guogonge, wanglingmi, korean west, etc.. wind fire bundling transmission system transient stability studies based on PSS and SSSC [ J ]. power system protection and control, 2012, 40 (19): 61-65, 71 ] research shows that the damping characteristic of the wind-fire bundling Power transmission System is weak, and the System can effectively increase the System damping and inhibit the inter-area low-frequency oscillation by installing a Power System Stabilizer (PSS) and a static series synchronous compensation wide-area damping controller in the System. Literature [ WANG L, CHANG C H, PROKHOROV AV.Stablility improvement of a two-area power system connected with an integrated onshore and offset with farm using a STATCOM [ C ]//2016IEEE Industry Applications Society annular meeting.Portland, USA: IEEE, 2016: 1-9 ] based on a modal control theory phase compensation method, a Static Synchronous Compensator (STATCOM) power oscillation damping controller containing a wind power system is designed, so that the STATCOM can absorb oscillation energy in the wind power system, and the system damping is improved. Document [ caokuchun, kontao, yan 32730, et al STATCOM research to suppress sub-synchronous resonance of doubly-fed wind farms [ J ]. grid technology, 2019, 43 (3): 895-902-analyzes the mechanism of STATCOM parallel equivalent resistance for inhibiting the sub-synchronous resonance of the doubly-fed wind power plant, and provides an additional control strategy for inhibiting the sub-synchronous oscillation. Document [ Liu approach, Liu Hui, Chen Hua, etc.. STATCOM inhibits low frequency oscillations and stabilizes the application of voltage in wind farms research [ J ]. power capacitors and reactive compensation, 2020, 41 (02): 18-25 ] a STATCOM is additionally arranged in a fan system, a control method based on active disturbance rejection is provided, parameters of a controller are optimized through a particle swarm optimization algorithm, and low-frequency oscillation of the system is effectively inhibited while voltage stabilization is guaranteed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a STATCOM-POD coordinated optimization design method for a wind-solar-fire bundling and delivering system, which is used for bundling and delivering wind power and photovoltaic power and nearby thermal power, can meet the basic requirements of large-scale traditional energy and renewable energy delivery, and solves the technical problem of low-frequency oscillation of the existing wind-solar-fire bundling and delivering system.
The technical scheme of the invention is realized as follows:
a wind-solar-fire bundling and delivery system STATCOM-POD coordinated optimization design method comprises the following steps:
the method comprises the following steps: constructing a wind-light-fire bundling delivery system, and connecting a static synchronous compensator containing a wind power system and an additional power oscillation damper into the wind-light-fire bundling delivery system;
step two: constructing a simulation model of the wind-solar-fire bundling and delivering system based on MATLAB/Simulink;
step three: solving a characteristic value of a simulation model of the wind-solar-fire bundling delivery system, and calculating a damping ratio of the simulation model of the wind-solar-fire bundling delivery system according to the characteristic value;
step four: constructing a target function based on a damping ratio by using system control parameters of a simulation model of the wind-solar-fire bundling delivery system and positive and negative characteristic values of the system as constraint conditions;
step five: and optimizing the objective function in the fourth step by using a genetic algorithm, and outputting a system control parameter corresponding to the optimal damping ratio.
The static synchronous compensator comprising the wind power system is equivalent to a time constant regulator, and the injected reactive power Q and the injected current i of the node of the static synchronous compensator areSHRespectively expressed as:
Figure BDA0002871777160000031
Q＝iSHVi
wherein the content of the first and second substances,
Figure BDA0002871777160000032
representing STATCOM injection current iSHFirst order differential of (V)refIs a reference voltage, ViThe voltage is measured for the node(s),
Figure BDA0002871777160000033
for POD output signal, KrVoltage regulator gain, T, for a static synchronous compensatorrIs the time constant of the static synchronous compensator.
The transfer function g(s) of the additional power oscillation damper is:
Figure BDA0002871777160000034
wherein, KPODFor amplification of additional power oscillation dampers, TWTime constant of the blocking element, T1、T2、T3And T4Are all lead-lag time constants, and s is a complex variable.
The damping ratio of the simulation model of the wind-solar-fire bundling delivery system is as follows:
Figure BDA0002871777160000035
where ξ is the damping ratio, σ is the real part of the eigenvalue, and ω is the imaginary part of the eigenvalue.
The target function and the constraint condition based on the damping ratio are respectively as follows:
min f
Figure BDA0002871777160000036
wherein the content of the first and second substances,
Figure BDA0002871777160000037
i is the set of all low-frequency oscillation modes of the wind-light-fire bundling delivery system, D is the iteration number, TnIs the n-th POD lead-lag time constant, n is 1,2,3,4,
Figure BDA0002871777160000038
for the minimum value of the set POD magnification,
Figure BDA0002871777160000039
for the maximum value of the set POD magnification,
Figure BDA00028717771600000310
to set the minimum value of the STATCOM voltage regulator gain,
Figure BDA00028717771600000311
for the maximum value of the set STATCOM voltage regulator gain,
Figure BDA00028717771600000312
for the minimum value of the set POD dc-blocking element time constant,
Figure BDA00028717771600000313
to set the maximum value of the POD dc-blocking element time constant,
Figure BDA00028717771600000314
to set the minimum value of the POD lead-lag time constant,
Figure BDA00028717771600000315
to set the maximum value of the POD lead-lag time constant,
Figure BDA00028717771600000316
to set the minimum value of the STATCOM time constant,
Figure BDA00028717771600000317
is the maximum value of the STATCOM time constant set.
The method for optimizing the objective function by using the genetic algorithm and outputting the system control parameters corresponding to the optimal damping ratio comprises the following steps:
s51, setting the number of the population to be M, setting the iteration number D to be 0, and setting the maximum iteration number to be Dmax；
S52, inputting original data of the wind-solar-fire bundling system, initial values of a static synchronous compensator and an additional power oscillation damper into a simulation model, and determining system control parameters to be optimized;
s53, randomly coding the system control parameters to be optimized by using a genetic algorithm to generate an initial population;
s54, decoding the initial population to obtain a population of the D-th iteration;
s55, inputting system control parameters corresponding to the D-th iteration population into a simulation model of the wind-solar-fire bundling system, and calculating all characteristic values of the wind-solar-fire bundling system;
s56, respectively calculating damping ratios according to all the characteristic values in the step S55, and screening out the minimum damping ratio;
s57, calculating the value of the target function according to the minimum damping ratio, judging whether the value of the target function reaches an expected value, if so, outputting a system control parameter corresponding to the minimum damping ratio, otherwise, executing a step S58;
s58, judging whether the iteration number D is less than the maximum iteration number D or not when the iteration number D is equal to D +1maxIf so, selecting, crossing and mutating the population to obtain an updated population, and returning to the step S55, otherwise, outputting the system control parameter corresponding to the minimum damping ratio.
The method for randomly coding the system control parameters to be optimized comprises the following steps:
converting each variable to be optimized in system control parameters into binary system, and aiming at any variable x to be optimizedjVariable x to be optimizedjHas an interval of [ aj,bj]The number of binary string bits is mjThen the variable x is to be optimizedjThe encoded values satisfy the following formula:
Figure BDA0002871777160000041
where J is 1,2, …, J indicates the number of variables to be optimized.
The method for decoding the initial population comprises the following steps:
Figure BDA0002871777160000042
among them, decimal (substracting)j) Represents the jth to be optimizedA ten-carry value of the variable.
The method for inputting the system control parameters corresponding to the D-th iteration population into the simulation model of the wind-solar-fire bundling system and calculating all characteristic values of the wind-solar-fire bundling system comprises the following steps:
inputting system control parameters corresponding to the D-th iteration population into the wind-solar-fire bundling system, and calculating the power flow of the wind-solar-fire bundling system by using a Newton-Raphson method;
linearizing the wind-light-fire bundling system near a stable point by adopting a Lyapunov linearization method to obtain a linearization model of the wind-light-fire bundling system and solving a state matrix A of the wind-light-fire bundling system;
and solving the state matrix A by adopting a QR method to obtain all characteristic values of the wind-solar-fire bundling system.
The beneficial effect that this technical scheme can produce: the method adopts a current injection type STATCOM to design, adopts line active power as an input signal of an additional power oscillation damping controller (POD), adopts a QR method to obtain all characteristic values of the system based on the Lyapunov stability rule, constructs a target function considering the minimum damping ratio of all oscillation modes of the system, adopts a genetic algorithm to carry out global coordination optimization on the designed STATCOM-POD parameters, and finally adopts characteristic root analysis and time domain simulation to research the influence of the provided STATCOM-POD coordination optimization control strategy on the low-frequency oscillation characteristic and robustness of the power grid in a wind-light-fire bundling delivery system. Under different working conditions, the oscillation amplitude and the stabilization time of the system are also obviously shortened, and the optimized parameters have better adaptability to various operation modes.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a wind-solar-fire bundling and delivery system of the present invention.
Fig. 2 is a STATCOM equivalent circuit and control block diagram of the invention, wherein, (a) is the STATCOM equivalent circuit, and (b) is the STATCOM control block diagram.
Fig. 3 is a block diagram of STATCOM-POD control of the present invention.
Fig. 4 is a closed loop transfer function of the system of the present invention.
FIG. 5 is a distribution region diagram of expected characteristic values of the present invention
FIG. 6 is a flow chart of the coordinated optimization of the present invention.
FIG. 7 is a simulation model of the wind-solar-fire bundling and delivering system of the present invention.
FIG. 8 is a three-phase short-circuit response curve of the system of the present invention, wherein (a1) is a thermal generator G1Relative thermal power generator G3The relative power angle (a2) is a thermal power generator G2Relative thermal power generator G3The relative power angle (b1) is a thermal power generator G4The active power output curve (b2) is a thermal power generator G5Active power output curve of (1).
FIG. 9 is a response curve of different tie line powers of the present invention, wherein (a1) is the thermal generator G at 350MW for tie line power1Relative thermal power generator G3Relative power angle of (a2) is the thermal power generator G when the tie line power is 350MW5Relative thermal power generator G3Relative power angle of (b1) is thermal power generator G when the power of the tie line is 300MW1Relative thermal power generator G3Relative power angle of (b2) is thermal power generator G when the power of the tie line is 300MW5Relative thermal power generator G3Relative power angle of (c1) is the thermal power generator G when the power of the tie line is 250MW1Relative thermal power generator G3Relative power angle of (c2) is the thermal power generator G when the power of the tie line is 250MW5Relative thermal power generator G3Relative power angle of (d).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The embodiment of the invention provides a wind-solar-fire bundling and outward-sending system STATCOM-POD coordinated optimization design method, which is designed by adopting a current injection type STATCOM and adopts line active power as POD input signals. Based on the Lyapunov stability law, a QR method is adopted to obtain all characteristic values of the system, a target function considering the minimum damping ratio of all oscillation modes of the system is constructed, a genetic algorithm is adopted to carry out global coordination optimization on the designed STATCOM-POD parameters, and finally, in a wind-solar-fire bundling delivery system, the influence of the proposed STATCOM-POD coordination optimization control strategy on the low-frequency oscillation characteristic and robustness of the power grid is researched by adopting characteristic root analysis and time domain simulation. The method comprises the following specific steps:
the method comprises the following steps: constructing a wind-light-fire bundling delivery system, and connecting a static synchronous compensator containing a wind power system and an additional power oscillation damper into the wind-light-fire bundling delivery system;
as shown in fig. 1, a sending end system of the wind-solar-fire bundling and sending system is composed of a large-scale thermal power generating unit, a wind power generating unit and a photovoltaic power station, and is connected with a receiving end infinite alternating current system through an alternating current or direct current transmission line.
The STATCOM is a parallel reactive compensation FACTS device, the basic function of a control system of the STATCOM is to generate a pulse width modulation trigger pulse to control a switch device, and the STATCOM can adjust the injection current of a static synchronous compensator according to the requirement and compensation quantity of safe and stable operation of a power grid to obtain the expected operation parameters of the power grid, so that the safe and stable operation is ensured. The current of the STATCOM is always kept in quadrature with the bus voltage, only reactive power is converted between the power system and the STATCOM. According to the invention, a STATCOM current injection model is adopted, as shown in FIG. 2, a static synchronous compensator (STATCOM) containing a wind power system is equivalent to a time constant regulator and a static synchronous compensatorInjected reactive power Q and injected current i of the node ofSHRespectively expressed as:
Figure BDA0002871777160000061
Q＝iSHVi
wherein the content of the first and second substances,
Figure BDA0002871777160000062
representing the first differential, V, of the STATCOM injection currentrefIs a reference voltage, ViThe voltage is measured for the node(s),
Figure BDA0002871777160000063
for POD output signal, KrVoltage regulator gain, T, for a static synchronous compensatorrIs the time constant of the static synchronous compensator.
The additional power oscillation damping control can improve the damping characteristic of the system and improve the anti-interference capability of the system. The POD designed by the invention comprises the steps of amplification, blocking, phase compensation and amplitude limiting, and the transfer function G(s) of the additional power oscillation damper is as follows:
Figure BDA0002871777160000064
wherein, KPODFor amplification of additional power oscillation dampers, TWTime constant of the blocking element, T1、T2、T3And T4Are all lead-lag time constants, and s is a complex variable. By means of additional POD control, the STATCOM can output or absorb reactive power to the maximum extent, system damping is increased, low-frequency oscillation is effectively inhibited, and system operation characteristics are improved.
As shown in fig. 3, an additional control signal is added to an ac voltage control part of the STATCOM, and the additional control signal is first isolated by a dc-blocking link and then finally modulates an injection current i by a two-stage lead-lag linkSHIs transported by POD controlThe appropriate current is drawn to increase the system damping. V is the difference between the STATCOM node measurement voltage and the reference voltage; the POD input signal can select the active power, the reactive power, the current amplitude, the voltage and the like of the line according to the condition, and the invention selects the active power P of the lineLAs STATCOM-POD input signal. The amplitude limiting link is mainly used for limiting the adjusting amplitude of the additional control voltage generated by the damping control link so as to avoid overshoot.
Let V pass through STATCOM control box to obtain input signal PLFor an open-loop transfer function of G(s), with PLThe damping controller transfer function h(s) as a feedback variable, the closed loop transfer function of the system is shown in fig. 4.
Step two: constructing a simulation model of the wind-solar-fire bundling and delivering system based on MATLAB/Simulink;
step three: solving a characteristic value of a simulation model of the wind-solar-fire bundling delivery system, and calculating a damping ratio of the simulation model of the wind-solar-fire bundling delivery system according to the characteristic value;
when the low-frequency oscillation occurs in the power system due to the load fluctuation, the large-amplitude power oscillation may be caused, and the stability of the system is further affected. The magnitude of the system damping is closely related to the amplitude of the power oscillation, the amplitude of the power oscillation can be reduced by increasing the system damping, and the small interference stability of the system is enhanced. For a multi-machine system, a state matrix a of the system may be formed with complex eigenvalues λ ═ σ ± j ω, each pair of complex eigenvalues corresponding to one oscillation mode. The negative real part represents ringing; the positive real part represents the amplified oscillation. The real part of the characteristic value is referred to as the damping of the respective oscillation mode, while the imaginary part indicates the frequency of the oscillation, which is f ω/2 pi. The corresponding damping ratio is defined as:
Figure BDA0002871777160000071
where ξ is the damping ratio, σ is the real part of the eigenvalue, and ω is the imaginary part of the eigenvalue. The damping ratio determines the damping rate and damping characteristics of the oscillation amplitude.
As shown in fig. 5, one smallThe characteristic root of the power system with stable interference is located on the left half plane of the complex plane, and is set as lambdaC＝αC±jβCIs the characteristic root, lambda, of the low-frequency oscillation mode of the power system closest to the imaginary axisCIf a reasonable set of controller parameters is designed such that λ is the smallest in the low frequency oscillation mode of the power systemCDamping ratio xi ofCThe lambda can be made as large as possible while ensuring the stability of the systemCFar from the imaginary axis, the small disturbance stability of the power system can be sufficiently ensured. The objective function of the present invention can be expressed as:
Figure BDA0002871777160000072
wherein i is a set of low-frequency oscillation modes, D is an iteration number, and the low-frequency oscillation modes include five oscillation modes of the researched wind-solar-fire bundling delivery system, which are respectively: mode 1: thermal generator set G3、G4The oscillation in between; mode 2: thermal generator set G1、G2The oscillation in between; mode 3: thermal generator set G1、G2、G5The oscillation in between; mode 4: thermal generator set G3、G5The oscillation in between; mode 5: global oscillation mode among all the units of the whole system except photovoltaic. The aim of optimizing simulation is to make the damping ratio as large as possible, and simultaneously, because the initial population is randomly generated by a genetic algorithm, the condition that the system characteristic value is smaller than 0, namely the system is unstable, can occur, and the lambda needs to be ensuredi<0。
Step four: constructing a target function based on a damping ratio by taking system control parameters of a simulation model of the wind-solar-fire bundling delivery system as constraint conditions; the target function and the constraint condition based on the damping ratio are respectively as follows:
min f
Figure BDA0002871777160000081
wherein，
Figure BDA0002871777160000082
i is a set of low-frequency oscillation modes, D is iteration times, the low-frequency oscillation modes comprise all low-frequency oscillation modes of the wind-solar-fire bundling system, and T isnIs the n-th POD lead-lag time constant, n is 1,2,3,4,
Figure BDA0002871777160000083
for the minimum value of the set POD magnification,
Figure BDA0002871777160000084
for the maximum value of the set POD magnification,
Figure BDA0002871777160000085
to set the minimum value of the STATCOM voltage regulator gain,
Figure BDA0002871777160000086
for the maximum value of the set STATCOM voltage regulator gain,
Figure BDA0002871777160000087
to set the maximum value of the POD dc-blocking element time constant,
Figure BDA0002871777160000088
to set the maximum value of the POD dc-blocking element time constant,
Figure BDA0002871777160000089
to set the minimum value of the POD lead-lag time constant,
Figure BDA00028717771600000810
to set the maximum value of the POD lead-lag time constant,
Figure BDA00028717771600000811
to set the minimum value of the STATCOM time constant,
Figure BDA00028717771600000812
is the maximum value of the STATCOM time constant set. Wherein T iswHas a value range of [1,20 ]]，Tn、TrHas a value range of [0.01,1 ]]，KrValue range of [1,60 ]]，KPODHas a value range of [ -10,1 ]]The maximum value of the damping ratio is 1.
Step five: and optimizing the objective function in the fourth step by using a genetic algorithm, and outputting a system control parameter corresponding to the optimal damping ratio. The invention adopts a genetic algorithm to optimize the parameters of the damping controller, the iteration number of the algorithm is set as 800, and the population number is 200. The genetic algorithm selection function is set as a random uniform function, the cross probability is 0.8, and the variation function is a Gaussian function. A flow chart as shown in fig. 6 is given.
The basic flow of the genetic algorithm is as follows:
s51, setting the number of the population to be M, setting the iteration number D to be 0, and setting the maximum iteration number to be Dmax；
S52, inputting original data of the wind-solar-fire bundling system, initial values of a static synchronous compensator and an additional power oscillation damper into a simulation model, and determining system control parameters to be optimized; and inputting simulation data of the wind-solar-fire bundling and transmitting system, importing the STATCOM and POD initial values into an algorithm, and determining parameters to be optimized.
S53, randomly coding the system control parameters to be optimized by using a genetic algorithm to generate an initial population; since the precision selected by the invention is four digits after decimal point, each variable at least requires to be divided into (b)j-aj)×104Part for converting each variable to be optimized in system control parameters into binary system, aiming at any variable x to be optimizedjVariable x to be optimizedjHas an interval of [ aj,bj]The number of binary string bits is mjThen the variable x is to be optimizedjThe encoded values satisfy the following formula:
Figure BDA0002871777160000091
where J is 1,2, …, J indicates the number of variables to be optimized.
S54, decoding the initial population to obtain a population of the D-th iteration; the initial population, randomly generated in binary, is decoded, returning the actual value from the binary, as follows:
Figure BDA0002871777160000092
among them, decimal (substracting)j) Representing the decimal value of the jth variable to be optimized.
S55, inputting system control parameters corresponding to the D-th iteration population into a simulation model of the wind-solar-fire bundling system, and calculating all characteristic values of the wind-solar-fire bundling system;
the method for calculating all characteristic values of the wind-solar-fire bundling system comprises the following steps:
a) inputting system control parameters corresponding to the D-th iteration population into the wind-solar-fire bundling system, and calculating the power flow of the wind-solar-fire bundling system by using a Newton-Raphson method;
b) linearizing the wind-light-fire bundling system near a stable point by adopting a Lyapunov linearization method to obtain a linearization model of the wind-light-fire bundling system and solving a state matrix A of the wind-light-fire bundling system;
c) and solving the state matrix A by adopting a QR method to obtain all characteristic values of the wind-solar-fire bundling system.
S56, respectively calculating damping ratios according to all the characteristic values in the step S55, and screening out the minimum damping ratio; and screening a low-frequency oscillation mode. And judging roots strongly related to delta omega and delta variables by using the correlation ratio of the electromechanical circuit, and determining a low-frequency oscillation mode. Obtaining the damping ratio of the low-frequency oscillation mode, and selecting the minimum damping ratio xic D。
S57, calculating the value of the target function according to the minimum damping ratio, judging whether the value of the target function reaches an expected value, if so, outputting a system control parameter corresponding to the minimum damping ratio, otherwise, executing a step S58;
s58, iteration number D ═ D +1Judging whether the iteration number D is less than the maximum iteration number DmaxIf so, selecting, crossing and mutating the population to obtain an updated population, and returning to the step S55, otherwise, outputting the system control parameter corresponding to the minimum damping ratio.
Simulation of experiment
The simulation model of the wind-solar-fire bundling and delivery system shown in figure 7 is constructed under MATLAB/Simulink. The system comprises a sending end system and a receiving end system which are connected through an alternating current transmission line. The sending end system is a wind-solar-fire bundling and outward sending system and comprises a thermal power generating unit, a wind power generating unit and a photovoltaic power station. For convenience of analysis, the wind power plant is replaced by a fan single-machine model, and the whole photovoltaic array is replaced by a photovoltaic single-machine model. The receiving end system is an IEEE four-machine two-area system, which comprises 2 area subsystems connected through 2 connecting lines, and each area is provided with two tightly coupled generator sets. The reference capacity of the generator set is set to be 100MVA, the frequency is 50Hz, and the transmission power of the tie line is 400 MW. Because the four-machine two-area system is a weak damping system, a II-type speed Regulator (TG) and a III-type Automatic Voltage Regulator (AVR) are added in an initial system to carry out primary frequency modulation and Voltage regulation on a synchronous machine set.
For convenience of analysis, the wind turbine and photovoltaic single-machine model is adopted to replace a lumped model of a wind power plant and a photovoltaic power plant. A wind power plant, a thermal power plant and a photovoltaic power plant are bundled and connected into a bus 06, the output of a fan is 30MW, the photovoltaic power plant is connected into a system through a bus 10, the output of the photovoltaic power plant is 40MW, the output of a new fire power generating set is 300MW, a STATCOM is connected into the system at a bus 08, and meanwhile, a power signal on a connecting line is taken as a feedback signal. Table 1 shows the controller parameters before and after optimization.
In order to illustrate the functions of the designed STATCOM additional POD control and the method, characteristic value analysis and dynamic time domain simulation are adopted for the initial system and the systems before and after the optimization controller.
TABLE 1 System controller parameters
Figure BDA0002871777160000101
Table 1 shows the characteristic values of the wind-solar fire bundling system under the corresponding optimized conditions when the power of the tie line is 400 MW. There are 5 oscillation modes: mode 1 is intra-zone oscillation of zone 2; modes 3 and 4 are interval oscillations between zone 1 and zone 2, including generator G in the wind-solar-fire bundling module5(ii) a Mode 5 is global oscillation between all the banks of the entire system except the photovoltaic. Without STATCOM-POD control, the damping ratio of mode 4 is the smallest among the 5 low frequency oscillation modes. With the addition and optimization of the controller, the minimum damping ratio of the low-frequency oscillation mode of the system is continuously increased, the minimum damping ratio of the optimized system is improved by about 28% compared with that before optimization, and is improved by about 57% compared with that when the controller is not accessed.
TABLE 2 Low-frequency oscillation mode of wind-solar-fire bundling system
Figure BDA0002871777160000102
Figure BDA0002871777160000111
Assuming that a three-phase short-circuit fault occurs in 1 loop of the double- loop links 07 and 08 when t is 1.0s, the fault clearing time is 0.2s, and the line is put into operation again at 1.2 s. The simulation time was 15 s. Fig. 8 shows the relevant response curves of the thermal generator set when a three-phase short-circuit fault occurs, where 3 curves are:
1: not accessing the controller system;
2: accessing a controller without optimizing a parameter system;
3: and optimizing the controller parameter system.
As can be seen from fig. 8, the three-phase short circuit curve of the system is improved when the system is connected to the STATCOM-POD control, which also proves that the designed scheme can provide enough additional damping for the system. Through genetic algorithm optimization, the oscillation of the power angle and the active power of the generator can be further reduced, and the stability of the system is effectively improved.
The output of the generator or the change of the load power can change the transmission power of the tie line, and even change the power transmission direction of the tie line. According to the invention, under the operating conditions that a STATCOM-POD controller is additionally arranged on a system, the output of a wind power plant is 30MW, the output of photovoltaic power is 40MW, the electrical distance between wind, light and fire is 50km, and the transmission distance between wind, light and fire is 50km, the influence of different transmission power of a connecting line on inter-regional oscillation modes is researched by changing the output of a synchronous generator.
The region 1 transmits power to the region 2, the wind-solar-fire bundling system is arranged on the power transmission side, the transmission power of the connecting line is adjusted by changing the output of the unit in the region 1, the damping change rule of the oscillation mode between the regions is considered, and the calculation result is shown in table 3. It can be seen that as the transmission power on the interconnection line is gradually reduced from 400MW to 250MW, both the oscillation frequency and the damping ratio of the low-frequency oscillation mode of the system are changed, but by comparing the system before optimization with the system after optimization, it is found that the minimum damping ratio of the system after optimization is larger than that of the system before optimization, the genetic algorithm can optimize the parameters of the system controller to increase the minimum damping ratio of the system, and enhance the stability of the system.
TABLE 3 Change of Low-frequency oscillation mode of Link Transmission Power System
Figure BDA0002871777160000112
Figure BDA0002871777160000121
The same three-phase short-circuit fault is set for the system, as shown in fig. 9, generator G1 and G are given under different tie line power5The three curves represent specific meanings as follows:
1: not accessing the controller system;
2: accessing a controller without optimizing a parameter system;
3: and optimizing the controller parameter system.
Prior to parameter optimization, the original system always takes longer to cancel the oscillation as the transmission power changes. By parameter optimization, generator G1Relative power angle sum G5The amplitude of the relative power angle is obviously inhibited, and the time for recovering the stability is shorter. This also proves that the method of the present invention can provide enough additional damping for the system, and effectively improve the stability of the system. When the power of the tie line changes, the parameters of the controller optimized by the genetic algorithm can still improve the relevant curve of the unit.
The invention constructs a frame of a wind-solar-fire bundling delivery system, establishes a simulation model of wind-solar-fire bundling delivered to a four-machine two-area system through alternating current based on MATLAB/Simulink, provides a control strategy of STATCOM additional damping control, optimizes controller parameters by taking a damping ratio as a target function based on a genetic algorithm, and coordinates POD and STATCOM to carry out global optimization. In order to evaluate the effectiveness of the proposed control scheme, a large number of examples are considered, and small-disturbance eigenvalue analysis and large-disturbance dynamic time-domain simulation are performed. Simulation results show that the system stability is effectively improved through parameter coordination and optimization. Under different working conditions, the oscillation amplitude and the stabilization time of the system are also obviously shortened. By coordinating the controller parameters simultaneously, the overall damping characteristics and dynamic stability of the system can be effectively improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (9)

1. A wind-solar-fire bundling and outward-sending system STATCOM-POD coordinated optimization design method is characterized by comprising the following steps:
the method comprises the following steps: constructing a wind-light-fire bundling delivery system, and connecting a static synchronous compensator containing a wind power system and an additional power oscillation damper into the wind-light-fire bundling delivery system;
step two: constructing a simulation model of the wind-solar-fire bundling and delivering system based on MATLAB/Simulink;
step three: solving a characteristic value of a simulation model of the wind-solar-fire bundling delivery system, and calculating a damping ratio of the simulation model of the wind-solar-fire bundling delivery system according to the characteristic value;
step four: constructing a target function based on a damping ratio by using system control parameters of a simulation model of the wind-solar-fire bundling delivery system and positive and negative characteristic values of the system as constraint conditions;
step five: and optimizing the objective function in the fourth step by using a genetic algorithm, and outputting a system control parameter corresponding to the optimal damping ratio.
2. The wind-solar-fire bundling and delivering system STATCOM-POD coordinated optimization design method according to claim 1, wherein a static synchronous compensator comprising a wind power system is equivalent to a time constant regulator, and the reactive power Q and the injection current i of the node of the static synchronous compensator are injectedSHRespectively expressed as:
Figure FDA0002871777150000011
Q＝iSHVi
wherein the content of the first and second substances,
Figure FDA0002871777150000012
representing STATCOM injection current iSHFirst order differential of (V)refIs a reference voltage, ViThe voltage is measured for the node(s),
Figure FDA0002871777150000013
for POD output signal, KrVoltage regulator gain, T, for a static synchronous compensatorrIs the time constant of the static synchronous compensator.
3. The wind-solar-fire bundling and outgoing system STATCOM-POD coordinated optimization design method according to claim 2, wherein the transfer function G(s) of the additional power oscillation damper is:
Figure FDA0002871777150000014
wherein, KPODFor amplification of additional power oscillation dampers, TWTime constant of the blocking element, T1、T2、T3And T4Are all lead-lag time constants, and s is a complex variable.
4. The STATCOM-POD coordinated optimization design method of the wind-solar-fire bundling and delivery system according to claim 3, wherein the damping ratio of the simulation model of the wind-solar-fire bundling and delivery system is:
Figure FDA0002871777150000015
where ξ is the damping ratio, σ is the real part of the eigenvalue, and ω is the imaginary part of the eigenvalue.
5. The wind-solar-fire bundling and delivery system STATCOM-POD coordinated optimization design method according to claim 4, wherein the damping ratio-based objective function and constraint condition are respectively:
min f
Figure FDA0002871777150000021
wherein the content of the first and second substances,
Figure FDA0002871777150000022
i is the set of all low-frequency oscillation modes of the wind-light-fire bundling delivery system, D is the iteration number, TnIs the n-th POD lead-lag time constant, n is 1,2,3,4,
Figure FDA0002871777150000023
for the minimum value of the set POD magnification,
Figure FDA0002871777150000024
for the maximum value of the set POD magnification,
Figure FDA0002871777150000025
to set the minimum value of the STATCOM voltage regulator gain,
Figure FDA0002871777150000026
for the maximum value of the set STATCOM voltage regulator gain,
Figure FDA0002871777150000027
for the minimum value of the set POD dc-blocking element time constant,
Figure FDA0002871777150000028
to set the maximum value of the POD dc-blocking element time constant,
Figure FDA0002871777150000029
to set the minimum value of the POD lead-lag time constant,
Figure FDA00028717771500000210
to set the maximum value of the POD lead-lag time constant,
Figure FDA00028717771500000211
to set the minimum value of the STATCOM time constant,
Figure FDA00028717771500000212
is the maximum value of the STATCOM time constant set.
6. The wind-solar-fire bundling and delivering system STATCOM-POD coordination optimization design method according to claim 5, wherein the method for optimizing the objective function by using the genetic algorithm and outputting the system control parameters corresponding to the optimal damping ratio comprises the following steps:
s51, setting the number of the population to be M, setting the iteration number D to be 0, and setting the maximum iteration number to be Dmax；
S52, inputting original data of the wind-solar-fire bundling system, initial values of a static synchronous compensator and an additional power oscillation damper into a simulation model, and determining system control parameters to be optimized;
s53, randomly coding the system control parameters to be optimized by using a genetic algorithm to generate an initial population;
s54, decoding the initial population to obtain a population of the D-th iteration;
s55, inputting system control parameters corresponding to the D-th iteration population into a simulation model of the wind-solar-fire bundling system, and calculating all characteristic values of the wind-solar-fire bundling system;
s56, respectively calculating damping ratios according to all the characteristic values in the step S55, and screening out the minimum damping ratio;
s57, calculating the value of the target function according to the minimum damping ratio, judging whether the value of the target function reaches an expected value, if so, outputting a system control parameter corresponding to the minimum damping ratio, otherwise, executing a step S58;
s58, judging whether the iteration number D is less than the maximum iteration number D or not when the iteration number D is equal to D +1maxIf so, selecting, crossing and mutating the population to obtain an updated population, and returning to the step S55, otherwise, outputting the system control parameter corresponding to the minimum damping ratio.
7. The wind-solar-fire bundling and delivery system STATCOM-POD coordination optimization design method according to claim 6, wherein the method for randomly encoding the system control parameters to be optimized is as follows:
converting each variable to be optimized in system control parameters into binary system, and aiming at any variable x to be optimizedjVariable x to be optimizedjHas an interval of [ aj,bj]The number of binary string bits is mjThen the variable x is to be optimizedjThe encoded values satisfy the following formula:
Figure FDA0002871777150000031
where J is 1,2, …, J indicates the number of variables to be optimized.
8. The wind-solar-fire bundling and delivery system STATCOM-POD coordinated optimization design method according to claim 7, wherein the method for decoding the initial population is as follows:
Figure FDA0002871777150000032
among them, decimal (substracting)j) Representing the decimal value of the jth variable to be optimized.
9. The wind-solar-fire bundling and outward-sending system STATCOM-POD coordination optimization design method according to claim 6, wherein the method for inputting the system control parameters corresponding to the D-th iteration population into the simulation model of the wind-solar-fire bundling system and calculating all the characteristic values of the wind-solar-fire bundling system comprises the following steps:
inputting system control parameters corresponding to the D-th iteration population into the wind-solar-fire bundling system, and calculating the power flow of the wind-solar-fire bundling system by using a Newton-Raphson method;
linearizing the wind-light-fire bundling system near a stable point by adopting a Lyapunov linearization method to obtain a linearization model of the wind-light-fire bundling system and solving a state matrix A of the wind-light-fire bundling system;
and solving the state matrix A by adopting a QR method to obtain all characteristic values of the wind-solar-fire bundling system.
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