CN114123249A - Load frequency control method of wind power interconnection-containing power system based on battery energy storage active response - Google Patents

Load frequency control method of wind power interconnection-containing power system based on battery energy storage active response Download PDF

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CN114123249A
CN114123249A CN202111515796.0A CN202111515796A CN114123249A CN 114123249 A CN114123249 A CN 114123249A CN 202111515796 A CN202111515796 A CN 202111515796A CN 114123249 A CN114123249 A CN 114123249A
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power
formula
bess
energy storage
wind
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CN114123249B (en
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任鑫
李邦兴
张晓辉
王海明
郑建飞
苏人奇
武青
吕亮
万抒策
朱珂言
王玮
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Huaneng Clean Energy Research Institute
North China Electric Power University
Clean Energy Branch of Huaneng Zhejiang Energy Development Co Ltd
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Huaneng Clean Energy Research Institute
North China Electric Power University
Clean Energy Branch of Huaneng Zhejiang Energy Development Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J2203/20—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
    • H02J2300/20—The dispersed energy generation being of renewable 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
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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]
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Control Of Eletrric Generators (AREA)
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Abstract

The invention discloses a load frequency control method of a wind power interconnection-containing power system with battery energy storage active response. The invention can minimize the system frequency difference when the load is disturbed, can well inhibit the frequency deviation from changing in a large range under the high wind power fluctuation, enables the frequency difference to be stabilized within the allowable deviation of 0.2Hz, and provides reliable frequency stability for a new energy power system.

Description

Load frequency control method of wind power interconnection-containing power system based on battery energy storage active response
Technical Field
The invention belongs to the field of new energy power generation load frequency control, relates to a fuzzy gain scheduling load frequency control method based on self-adaptive adjustment of a battery energy storage system, and particularly relates to a load frequency control method of a wind power-containing interconnected power system based on active response of battery energy storage.
Background
In recent years, environmental problems are increasingly severe, in order to realize a paris climate agreement, the global temperature rise is limited within 2 ℃, the decarbonization speed of a global energy system is accelerated, new energy represented by wind power is developed rapidly, however, wind power naturally has volatility and uncertainty, the large-scale grid connection of new energy represented by wind power enables the energy balance, the frequency stability and the like of the power system to face more challenges, the allowable frequency fluctuation range of the power system is limited, according to GB/T15945-2008 'electric energy quality power system frequency allowable deviation', the frequency deviation allowable value of the power system in China is 0.2Hz, and when the system capacity is large, the maximum deviation value is widened to 0.5 Hz. Therefore, the active response capability of excavating and improving flexible resources in the system is an important means for ensuring the safe and stable operation of the new energy power system.
The battery energy storage technology is an important support for improving the access capability of large-scale renewable power generation in the future, and by introducing the large-scale energy storage technology, the operation characteristics of an intermittent power supply can be effectively improved in real time, the robustness and controllability of a new energy power station are improved, the utilization level of a delivery section of a wind power collection area is improved, and the win-win situation that the economic benefit of the new energy station and the safety and stability of a power grid are improved is achieved. The large-capacity battery energy storage power station is a carrier form for realizing access to a power grid and participation in operation after a large-scale Battery Energy Storage System (BESS) is integrated. Active power fluctuation output by the wind power plant can meet grid-connected requirements by designing active response of the battery energy storage system, some large-scale battery energy storage systems can provide certain types of auxiliary services, have the remarkable advantages of quick and reliable response and distributed characteristics, and can respond to system signals such as voltage and frequency and participate in system regulation. However, in a new energy interconnected power grid system, how to adaptively control the load frequency in real time is an important problem that I face at present.
Object of the Invention
The invention aims to solve the problems in the prior art, and provides a load frequency control method of a new energy interconnection system based on battery energy storage active response.
Disclosure of Invention
The invention provides a load frequency control method of a wind power interconnection-containing power system based on battery energy storage active response, which comprises the following steps:
step 1: constructing a load frequency control model of the interconnected power system containing wind power;
step 2: the control of a speed regulator of a traditional generator is simulated by utilizing the primary frequency modulation reserve margin of an energy storage power station, and a droop coefficient R is setBESSAnd obtaining a response power reference value of a battery energy storage system BESS controlled by speed regulation and droop
Figure BDA0003406807410000021
And step 3: designing a first-order low-pass filter to restrain the fluctuation of active power in a power grid and determining the output delta P of the low-pass filterLPF；
And 4, step 4: outputting power Delta P of battery energy storage system BESSBESSDesigned as power reference value
Figure BDA0003406807410000022
And filter output Δ PLPFA difference of (d);
and 5: calculating the real-time state of charge (SOC) (t) of the energy storage power station, and designing a droop coefficient R based on the SOC (t) in real timeBESSThe self-adaptive frequency response of the BESS is realized by the dynamic adjustment of the BESS;
step 6: aiming at a multi-region interconnected load frequency control object, a fuzzy gain scheduling strategy FGS is designed, and an input scale factor adjustable parameter of the FGS and an output gain factor of the FGS are determined by adopting a particle swarm optimization algorithm.
Preferably, the load frequency control model of the interconnected power system with wind power, which is constructed in the step 1, includes a speed regulator model, a non-reheat turbine model, a generator and load model, and a mathematical model of a wind turbine generator:
wherein the governor model is expressed as shown in equations (1) to (2):
Figure BDA0003406807410000031
Figure BDA0003406807410000032
in the formula,. DELTA.PVFor governor input, Δ u is controller input command, Δ f is frequency deviation, Δ PGOutputting a command for the speed regulator;
the non-reheat turbine model is expressed as shown in equation (3):
Figure BDA0003406807410000033
in the formula,. DELTA.PTFor turbine output power variation, Δ PGOutputting a command for the speed regulator;
the generator and load model is expressed as shown in equation (4):
Figure BDA0003406807410000034
in the formula, Kps＝1/D，TpsD is a load frequency dependent parameter expressed as D-P ═ 2H/fDl/f；PlIs the rated load, H is the inertia constant, f is the rated frequency;
the mathematical model of the wind turbine generator is expressed as shown in formula (5):
Figure BDA0003406807410000035
in the formula,. DELTA.PwIs the output power of the wind turbine, A is the effective wind sweeping area of the fan, VwIs the wind speed, ρ is the density of air, CpIs the wind power conversion coefficient, lambda is the tip speed ratio, beta is the fan pitch angle;
the load frequency response of the interconnected power system of the interconnected region containing the wind power is expressed as shown in formula (6):
Δf＝GP(s)(ΔPT(s)+ΔPw(s)-ΔPD(s)) (6)，
in the formula,. DELTA.PDTo change into loadMelting;
the controlled quantity of the interconnected power system of the interconnected region containing the wind power is expressed as shown in a formula (7):
ACEi＝ΔPtie,i+BiΔfi (7)，
in the formula, ACEiIs the zone control deviation, Δ P, of zone itie,iIndicating area
Figure BDA0003406807410000041
Exchange power of the tie lines, BiDenotes the frequency deviation constant, Δ f, of the region iiIndicating the frequency deviation of the area i.
Preferably, the battery energy storage system BESS response power reference value controlled by the speed regulation droop in the step 2
Figure BDA0003406807410000042
Is represented as shown in formula (8);
Figure BDA0003406807410000043
in the formula RBESSThe droop coefficient of the battery energy storage system BESS, Δ f(s) is the grid frequency offset.
Preferably, the output Δ P of the first order low pass filter in step 3LPFExpressed as shown in formula (9):
Figure BDA0003406807410000044
in the formula, PLPF(s) is the output of the low pass filter; t isdelayIs the filter time constant;
preferably, the output power Δ P of the battery energy storage system BESS in step 4BESSIs designed as shown in formula (10):
Figure BDA0003406807410000045
in the formula,. DELTA.PBESSFor the output power of the battery energy storage system BESS,
Figure BDA0003406807410000046
as power reference value, Δ PLPFIs the filter output.
Preferably, the real-time state of charge soc (t) of the energy storage power station in step 5 is calculated as shown in formula (11):
Figure BDA0003406807410000047
in the formula, SOC0Is the initial state of charge of the battery energy storage system BESS, eta is the charging and discharging efficiency of the battery energy storage system BESS, EcapThe maximum storage capacity of the battery energy storage system BESS.
Further preferably, the droop coefficient R is performed in real time based on soc (t) in step 5BESSExpressed as shown in equation (12):
Figure BDA0003406807410000051
in the formula, SOCminAnd SOCmaxRespectively the minimum value and the maximum value, R, of the state of charge SOC of the battery energy storage system BESSmaxAnd RminRespectively the corresponding maximum and minimum droop coefficients.
Preferably, in step 6, the controlled quantities ACE and ACE of the interconnected power system containing the interconnected region of the wind power are set to be differential
Figure BDA0003406807410000052
As two inputs to the FGS controller; determining modified gain delta K of PID controller control coefficient by establishing fuzzy control rulep、ΔKi、ΔKd(ii) a The automatic adjustment of the parameters of the PID controller by the ACE-based FGS controller is expressed as shown in equations (13) - (15):
Kp＝Kp0+ΔKp (13)，
Ki＝Ki0+ΔKi (14)，
Kd＝Kd0+ΔKd (15)，
in the formula, Kp0、Ki0、Kd0Respectively, the initial control parameter, Δ K, of the PID controllerp、ΔKi、ΔKdModified gain of PID controller control coefficient for FGS output, Kp，Ki，KdIs a dynamic PID control coefficient.
Further preferably, the determining of the relationship of the input to the FGS controller to the control quantity is expressed as shown in equation (16):
Figure BDA0003406807410000053
in the formula, Kp、Ki、KdBeing dynamic PID control coefficients, ACEiIs the zone control deviation of zone i, uiIs the control quantity of the area i.
Further preferably, the step of performing particle swarm optimization on FGS in step 6 includes the following sub-steps:
substep S1: determining an optimization objective function, designing a performance standard of an integral ITAE of time multiplied by absolute error in load frequency control, and expressing the objective function of particle swarm optimization under a two-region interconnected power system as shown in formula (17):
Figure BDA0003406807410000061
wherein J is the optimization objective function, Δ f1And is Δ f2Deviation of system frequency, Δ PtieIs the incremental change in tie line power, tsimIs the time range of the simulation;
step S2: design KeAnd KecAs an input scale factor adjustable parameter for FGS, K1、K2、K3AsAn FGS output gain factor;
step S3: determining constraint conditions of particle swarm optimization problems, setting parameter ranges of an FGS controller, setting J as an optimization objective function, and meeting the constraint conditions of formulas (18) to (22) when minimizing the J:
Figure BDA0003406807410000062
Figure BDA0003406807410000063
K1min≤K1≤K1max (20)，
K2min≤K2≤K2max (21)，
K3min≤K3≤K3max (22)，
in which the indices min and max represent the minimum and maximum values, respectively, KeAnd KecInput scale factor adjustable parameter, K, being FGS respectively1、K2、K3The output gain factors are FGS respectively;
step S4: a population-based search algorithm PSO is adopted, wherein each individual is called a particle and represents a candidate solution; each particle in the PSO flies through a search space at an adaptive velocity that is dynamically altered according to its own flight experience and the flight experience of other particles, each particle improving itself by mimicking its characteristics of successful peer; each particle has a memory that can remember the best position in the search space it has accessed; setting the position corresponding to the optimal fitness as pbest and the overall optimal point in all the particles as gbest; the initial positions of pbest and gbest are different, using the current speed and from pbestj,gTo gbestgThe modified velocity and position of each particle are calculated as shown in equations (23) - (25):
Figure BDA0003406807410000071
Figure BDA0003406807410000072
Figure BDA0003406807410000073
wherein j is 1, 2.. times.n; 1,2,. m; n is the number of particles in the population, m is the vector vjAnd xjT is the number of iterations,
Figure BDA0003406807410000074
is the g-th component of the velocity of particle j at iteration t, w is the inertial weight factor, c1，c2Are respectively a cognitive acceleration factor and a social acceleration factor, r1，r2Are random numbers uniformly distributed in the range of (0, 1),
Figure BDA0003406807410000075
is the fourth component of the position of particle j at iteration t, pbestjIs the best fitness of the position of particle j, gbestgIs the global optimum among all particles in group g; parameter c1And c2To determine the relative pull of pbest and gbest, the parameter r1And r2For randomly changing the coefficients of the pulling forces, the superscript of each parameter represents the number of iterations; iteratively optimizing the optimal FGS adjustable parameter K of the input scale factor by using the PSO execution step of the particle swarm optimization algorithmeAnd KecAnd FGS output gain factor K1、K2、K3。
Drawings
Fig. 1 is a schematic method flow diagram of the overall concept of the present invention.
Fig. 2 shows the wind field raw output power in the two-region interconnected power system.
Fig. 3 shows droop coefficient adaptive adjustment characteristics based on real-time varying SOC.
Fig. 4 is a model of fuzzy gain scheduling load frequency control of a two-region power system in which the battery energy storage system participates according to the present invention.
Figure 5 is a schematic diagram of an FGS controller.
FIG. 6 is a simulation diagram of frequency comparison between the method of the present invention and the conventional method under wind power fluctuation.
FIG. 7 is a graph of frequency versus simulation of the method of the present invention versus a conventional method under load disturbances.
Detailed Description
The invention provides a self-adaptive fuzzy gain scheduling load frequency control design method based on a battery energy storage system, and the invention is further explained by combining the attached drawings and the specific embodiment.
Fig. 1 is a flowchart of a load frequency control method involving a battery energy storage system. The load frequency control method comprises a self-adaptive response mode of a battery energy storage system and a fuzzy gain scheduling control strategy of particle swarm optimization, and comprises the following specific implementation steps of:
1) the specific model is shown in fig. 4, and the specific model is determined to be an implementation object of the interconnected power system including the two wind power regions.
The two-zone governor model is:
Figure BDA0003406807410000081
Figure BDA0003406807410000082
in the formula,. DELTA.PVFor governor input, Δ u is controller input command, Δ f is frequency deviation, Δ PGAnd outputting a command for the speed regulator.
The non-reheat turbine model adopted in both areas is as follows:
Figure BDA0003406807410000083
in the formula,. DELTA.PTFor turbine output power variation, Δ PGAnd outputting a command for the speed regulator.
The generator and load model is:
Figure BDA0003406807410000084
in which K isps＝1/D，Tps2H/fD. D is a load frequency dependent parameter, D ═ Pl/f。PlIs the rated load, H is the inertia constant, and f is the rated frequency.
The mathematical model of the wind turbine generator is as follows:
Figure BDA0003406807410000085
in the formula: delta PwOutputting power for the wind turbine; a is the effective wind sweeping area of the fan; vwIs the wind speed; ρ is the density of air; cpConverting the coefficient into wind power; λ is tip speed ratio; beta is the pitch angle of the fan.
The load frequency response containing wind power is as follows:
Δf＝GP(s)(ΔPT(s)+ΔPw(s)-ΔPD(s))
in the formula,. DELTA.PDIs a load change.
The controlled quantities of the interconnected power systems are:
ACEi＝ΔPtie,i+BiΔfi
in the formula, ACEiIs the zone control deviation, Δ P, of zone itie,iIndicating area
Figure BDA0003406807410000091
Exchange power of the tie lines, BiDenotes the frequency deviation constant, Δ f, of the region iiIndicating the frequency deviation of the area i.
2) Determining a mathematical model of the battery energy storage system, and firstly designing a BESS response power parameter based on speed regulation droop controlExamination value
Figure BDA0003406807410000092
Figure BDA0003406807410000093
In the formula (I), the compound is shown in the specification,
Figure BDA0003406807410000094
a reference value for the BESS response power; rBESSΔ f(s) is the grid frequency offset, which is the droop coefficient of the BESS.
3) Designing a first-order low-pass filter according to the BESS response power reference value:
Figure BDA0003406807410000095
in the formula, PLPF(s) is the output of the low pass filter; t isdelayIs the filter time constant;
Figure BDA0003406807410000096
a reference value for the BESS response power;
4) output power of BESS Δ PBESSDesigned as the difference between the power reference and the filter output:
Figure BDA0003406807410000097
in the formula,. DELTA.PBESSIs the output power of the BESS and,
Figure BDA0003406807410000101
as power reference value, Δ PLPFIs the filter output.
5) Calculating the state of charge (SOC) (t) of the energy storage power station in real time as follows:
Figure BDA0003406807410000102
in the formula, SOC0Initial state of charge of BESS, h charge-discharge efficiency of BESS, EcapMaximum storage capacity for BESS; SOC (t) is the state of charge of the real-time energy storage power station;
6) in order to realize the self-adaptive frequency modulation response of the BESS, a droop coefficient R is designed based on SOC (t) in real time according to the system frequency modulation requirement and the actual measurement SOC (t) according to a linear interpolation method, as shown in figure 3BESSThe dynamic adjustment of (2) is as follows:
Figure BDA0003406807410000103
in the formula, RBESSIs the droop coefficient, SOC, of the BESSminAnd SOCmaxMinimum and maximum BESS state of charge SOC, RmaxAnd RminThe corresponding maximum and minimum droop coefficients.
7) Further, the adaptive load frequency response based on the battery energy storage system is as follows:
Δf＝GP(s)(ΔPT(s)+ΔPBESS(s)-ΔPw(s)-ΔPD(s))
in the formula,. DELTA.PDIs a load change; delta PBESSOutput power of BESS; delta PTIs a turbine output power change; delta PwThe reference is the wind power oscillation power shown in fig. 2.
8) Designing fuzzy gain scheduling strategy for designing multi-region interconnected load frequency control object to adjust optimal gain value of PID controller in real time, firstly setting differential of ACE and ACE
Figure BDA0003406807410000104
The FGS controller structure is shown in fig. 5 as two inputs to the FGS controller.
9) Determining the correction gain delta K of PID controller parameters by formulating fuzzy control rulesp，ΔKi，ΔKdThe ACE-based FGS controller is used for automatic adjustment of PID controller parameters as follows:
Kp＝Kp0+ΔKp
Ki＝Ki0+ΔKi
Kd＝Kd0+ΔKd
in the formula, Kp0，Ki0，Kd0For PID initial control parameter, Δ Kp，ΔKi，ΔKdCorrection gain of control coefficient for FGS output, Kp，Ki，KdIs a dynamic PID control parameter.
Further, the relationship between the input and the control quantity of the FGS controller is determined as follows:
Figure BDA0003406807410000111
in the formula, Kp，Ki，KdFor dynamic PID control parameters, ACEiIs the zone control deviation of zone i, uiIs the control quantity of the area i.
10) The particle swarm optimization steps adopted for FGS are as follows:
step 101: determining an optimization objective function, designing a performance standard of time-multiplied absolute error Integral (ITAE) in load frequency control, and optimizing the objective function of a particle swarm under a two-region interconnected power system as follows:
Figure BDA0003406807410000112
wherein J is the optimization objective function, Δ f1And is Δ f2Deviation of system frequency. Delta PtieIs an incremental change in tie line power; t is tsimIs the time range of the simulation.
Step 102: design KeAnd KecAs an input scale factor adjustable parameter for FGS. K1，K2，K3As the output gain factor of FGS.
Step 103: determining the constraint of the particle swarm optimization problem, and setting the parameter range of the FGS controller; the design problem is expressed as an optimization problem as follows:
Minimize J
Figure BDA0003406807410000113
Figure BDA0003406807410000114
K1min≤K1≤K1max
K2min≤K2≤K2max
K3min≤K3≤K3max
where J is the optimization objective function, the subscripts min and max represent the minimum and maximum values, respectively, and KeAnd KecInput scale factor adjustable parameter, K, for FGS1，K2，K3Is the output gain factor of FGS.
Step 104: for particle swarm algorithm PSO based on population search, each individual is called a particle, representing a candidate solution, and each particle has a memory capable of remembering the best position in the search space it has visited. The position corresponding to the best fitness is set to pbest and the global best point among all particles in the population is set to gbest. The initial positions of pbest and gbest are different, but with different directions of best and optimal, all agents will gradually approach global optimality.
Using current speed and from pbestj,gTo gbestgThe modified velocity and position of each particle is calculated as follows:
Figure BDA0003406807410000121
Figure BDA0003406807410000122
wherein j is 1, 2.. times.n; 1,2,. m; n is the number of particles in the population, m is the vector vjAnd xjT is the number of iterations,
Figure BDA0003406807410000123
is the g-th component of the velocity of particle j at iteration t, w is the inertial weight factor, c1，c2Are respectively a cognitive acceleration factor and a social acceleration factor, r1，r2Are random numbers uniformly distributed in the range of (0, 1),
Figure BDA0003406807410000124
is the fourth component of the position of particle j at iteration t, pbestjIs the best fitness of the position of particle j, gbestgIs the global optimum among all particles in group g. Parameter c1And c2Determining the relative tension of pbest and gbest, parameter r1And r2Which helps to randomly vary these pulling forces. In the above equation, the superscript represents the number of iterations.
Step 105: due to the constant c1And c2The weights of the random acceleration terms that pull each particle to the optimal position and the optimal position are related. Lower values may cause the particles to roam away from the target area before being pulled back. On the other hand, higher values may result in sudden movements towards or beyond the target area. Therefore, the acceleration constant is set to 2.0. Choosing the appropriate inertial weights w provides a balance between global and local exploration, thus requiring fewer iterations of the above average to find a sufficiently optimal solution. The linear decrease in design w from 0.9 to 0.4 during operation is as follows:
Figure BDA0003406807410000131
in the formula, wmaxThe design is 0.9; w is aminThe design is 0.4; iter is the number of iterations.
Step 106: steps 81 to 85 are executed to iteratively optimize the optimal FGS input scale factor adjustable parameterKeAnd KecAnd FGS output gain factor K1，K2，K3。
11) The optimized engineering setting PID is used as a strategy 1, the PID added into the battery energy storage system is used as a strategy 2, the complete set of self-adaptive fuzzy gain scheduling control based on the battery energy storage system is used as a strategy 3, and the verification optimization control effect of the electric power system interconnected with the two regions shown in the figure 4 is shown in figures 6 and 7.
In conclusion, the droop coefficient which is adaptively adjusted based on the real-time SOC is designed, the adaptive response mode of the battery energy storage system is established, the fuzzy gain control strategy of particle swarm optimization is used for controlling the load frequency of the multi-region interconnected power system, the requirement of stabilizing wind power fluctuation is met, and the control quality of the interconnected system containing wind power is improved. The invention realizes real-time change of the control parameters of the controller according to the change of the regional deviation of the power system, has simple design and convenient engineering realization, and aiming at relatively independent battery energy storage response, the particle swarm optimized FGS controller can fully excite the self-adaptive mode of the battery energy storage system.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
The invention has the beneficial effects that:
the flexibly schedulable battery energy storage resources in the power system are excavated, the multi-energy complementation is realized by matching with an advanced regulation and control method, and the designed fuzzy gain scheduling strategy based on the battery energy storage system self-adaption can be used for compensating the defects of a classic controller in the new energy power system, so that the random fluctuation of the new energy power is stabilized, and the power grid acceptance capacity is improved. The method has a better effect on reducing the frequency drop amplitude, greatly shortens the frequency recovery time, effectively controls the fluctuation of the power of the tie line, improves the load frequency control quality of the wind power interconnection system, and provides reliable frequency stability for the new energy power system.

Claims (10)

1. A load frequency control method of a wind power interconnection-containing power system based on battery energy storage active response is characterized by comprising the following steps:
step 1: constructing a load frequency control model of the interconnected power system containing wind power;
step 2: the control of a speed regulator of a traditional generator is simulated by utilizing the primary frequency modulation reserve margin of an energy storage power station, and a droop coefficient R is setBESSAnd obtaining a response power reference value of a battery energy storage system BESS controlled by speed regulation and droop
Figure FDA0003406807400000011
And step 3: designing a first-order low-pass filter to restrain the fluctuation of active power in a power grid and determining the output delta P of the low-pass filterLPF；
And 4, step 4: outputting power Delta P of battery energy storage system BESSBESSDesigned as power reference value
Figure FDA0003406807400000012
And filter output Δ PLPFA difference of (d);
and 5: calculating the real-time state of charge (SOC) (t) of the energy storage power station, and designing a droop coefficient R based on the SOC (t) in real timeBESSThe self-adaptive frequency response of the BESS is realized by the dynamic adjustment of the BESS;
step 6: aiming at a multi-region interconnected load frequency control object, a fuzzy gain scheduling strategy FGS is designed, and an input scale factor adjustable parameter of the FGS and an output gain factor of the FGS are determined by adopting a particle swarm optimization algorithm.
2. The load frequency control method of the wind power-containing interconnected power system according to claim 1, wherein the load frequency control model of the wind power-containing interconnected power system constructed in the step 1 comprises a speed regulator model, a non-reheat turbine model, a generator and load model, and a mathematical model of a wind turbine unit:
wherein the governor model is expressed as shown in equations (1) to (2):
Figure FDA0003406807400000021
Figure FDA0003406807400000022
in the formula,. DELTA.PVFor governor input, Δ u is controller input command, Δ f is frequency deviation, Δ PGOutputting a command for the speed regulator;
the non-reheat turbine model is expressed as shown in equation (3):
Figure FDA0003406807400000023
in the formula,. DELTA.PTFor turbine output power variation, Δ PGOutputting a command for the speed regulator;
the generator and load model is expressed as shown in equation (4):
Figure FDA0003406807400000024
in the formula, Kps＝1/D，TpsD is a load frequency dependent parameter expressed as D-P ═ 2H/fDl/f；PlIs the rated load, H is the inertia constant, f is the rated frequency;
the mathematical model of the wind turbine generator is expressed as shown in formula (5):
Figure FDA0003406807400000025
in the formula,. DELTA.PwOf wind-power plantsOutput power, A is the effective wind sweeping area of the fan, VwIs the wind speed, ρ is the density of air, CpIs the wind power conversion coefficient, lambda is the tip speed ratio, beta is the fan pitch angle;
the load frequency response of the interconnected power system containing the wind power is expressed as shown in a formula (6):
Δf＝GP(s)(ΔPT(s)+ΔPw(s)-ΔPD(s)) (6)，
in the formula,. DELTA.PDIs the load variation;
the controlled quantity of the interconnected power system containing wind power is expressed as shown in a formula (7):
ACEi＝ΔPtie,i+BiΔfi (7)，
in the formula, ACEiIs the zone control deviation, Δ P, of zone itie,iIndicating area
Figure FDA0003406807400000031
Exchange power of the tie lines, BiDenotes the frequency deviation constant, Δ f, of the region iiIndicating the frequency deviation of the area i.
3. The method for controlling the load frequency of the wind-power interconnected electric power system as claimed in claim 1, wherein the battery energy storage system BESS response power reference value controlled by the speed-adjusting droop in the step 2
Figure FDA0003406807400000032
Is represented as shown in formula (8);
Figure FDA0003406807400000033
in the formula RBESSThe droop coefficient of the battery energy storage system BESS, Δ f(s) is the grid frequency offset.
4. The load frequency control method of wind-powered interconnected power system according to claim 1, wherein the method comprisesCharacterised in that the output Δ P of the first order low pass filter in step 3LPFExpressed as shown in formula (9):
Figure FDA0003406807400000034
in the formula, PLPF(s) is the output of the low pass filter; t isdelayIs the filter time constant.
5. The method as claimed in claim 1, wherein the step 4 is performed by using BESS output power Δ P of the battery energy storage systemBESSIs designed as shown in formula (10):
Figure FDA0003406807400000035
in the formula,. DELTA.PBESSFor the output power of the battery energy storage system BESS,
Figure FDA0003406807400000036
as power reference value, Δ PLPFIs the filter output.
6. The method for controlling the load frequency of the wind-power-containing interconnected power system according to claim 1, wherein the real-time state of charge (SOC) (t) of the energy storage power station in the step 5 is calculated as shown in the formula (11):
Figure FDA0003406807400000041
in the formula, SOC0Is the initial state of charge of the battery energy storage system BESS, eta is the charging and discharging efficiency of the battery energy storage system BESS, EcapThe maximum storage capacity of the battery energy storage system BESS.
7. According to claimThe load frequency control method for the interconnected power system with wind power generation system according to claim 6, wherein droop coefficient R is performed in real time based on SOC (t) in step 5BESSExpressed as shown in equation (12):
Figure FDA0003406807400000042
in the formula, SOCminAnd SOCmaxRespectively the minimum value and the maximum value, R, of the state of charge SOC of the battery energy storage system BESSmaxAnd RminRespectively the corresponding maximum and minimum droop coefficients.
8. The load frequency control method of the interconnected wind and power system according to claim 1, wherein in step 6, the controlled quantities ACE of the interconnected wind and power system and the differential of ACE are set
Figure FDA0003406807400000043
As two inputs to the FGS controller; determining modified gain delta K of PID controller control coefficient by establishing fuzzy control rulep、ΔKi、ΔKd(ii) a The automatic adjustment of the parameters of the PID controller by the ACE-based FGS controller is expressed as shown in equations (13) - (15):
Kp＝Kp0+ΔKp (13)，
Ki＝Ki0+ΔKi (14)，
Kd＝Kd0+ΔKd (15)，
in the formula, Kp0、Ki0、Kd0Respectively, the initial control parameter, Δ K, of the PID controllerp、ΔKi、ΔKdModified gain of PID controller control coefficient for FGS output, Kp，Ki，KdIs a dynamic PID control coefficient.
9. The load frequency control method for a wind-powered interconnected power system according to claim 8, wherein the relationship between the input and the control quantity of the FGS controller is determined as shown in equation (16):
Figure FDA0003406807400000051
in the formula, Kp、Ki、KdBeing dynamic PID control coefficients, ACEiIs the zone control deviation of zone i, uiIs the control quantity of the area i.
10. The load frequency control method of the interconnected wind power system according to claim 9, wherein the step of performing particle swarm optimization on FGS in step 6 comprises the following substeps:
substep S1: determining an optimization objective function, designing a performance standard of an integral ITAE of time multiplied by absolute error in load frequency control, and expressing the objective function of particle swarm optimization under a two-region interconnected power system as shown in formula (17):
Figure FDA0003406807400000052
wherein J is the optimization objective function, Δ f1And is Δ f2Deviation of system frequency, Δ PtieIs the incremental change in tie line power, tsimIs the time range of the simulation;
step S2: design KeAnd KecAs an input scale factor adjustable parameter for FGS, K1、K2、K3As the output gain factor of FGS;
step S3: determining constraint conditions of particle swarm optimization problems, setting parameter ranges of an FGS controller, setting J as an optimization objective function, and meeting the constraint conditions of formulas (18) to (22) when minimizing the J:
Figure FDA0003406807400000061
Figure FDA0003406807400000062
K1min≤K1≤K1max (20)，
K2min≤K2≤K2max (21)，
K3min≤K3≤K3max (22)，
in which the indices min and max represent the minimum and maximum values, respectively, KeAnd KecInput scale factor adjustable parameter, K, being FGS respectively1、K2、K3The output gain factors are FGS respectively;
step S4: a population-based search algorithm PSO is adopted, wherein each individual is called a particle and represents a candidate solution; each particle in the PSO flies through a search space at an adaptive velocity that is dynamically altered according to its own flight experience and the flight experience of other particles, each particle improving itself by mimicking its characteristics of successful peer; each particle has a memory that can remember the best position in the search space it has accessed; setting the position corresponding to the optimal fitness as pbest and the overall optimal point in all the particles as gbest; the initial positions of pbest and gbest are different, using the current speed and from pbestj,gTo gbestgThe modified velocity and position of each particle are calculated as shown in equations (23) - (25):
Figure FDA0003406807400000063
Figure FDA0003406807400000064
Figure FDA0003406807400000065
wherein j is 1, 2.. times.n; 1,2,. m; n is the number of particles in the population, m is the vector vjAnd xjT is the number of iterations,
Figure FDA0003406807400000071
is the g-th component of the velocity of particle j at iteration t, w is the inertial weight factor, c1、c2Are respectively a cognitive acceleration factor and a social acceleration factor, r1，r2Are random numbers uniformly distributed in the range of (0, 1),
Figure FDA0003406807400000072
is the fourth component of the position of particle j at iteration t, pbestjIs the best fitness of the position of particle j, gbestgIs the global optimum among all particles in group g; parameter c1And c2To determine the relative pull of pbest and gbest, the parameter r1And r2For randomly changing the coefficients of the pulling forces, the superscript of each parameter represents the number of iterations; iteratively optimizing the optimal FGS adjustable parameter K of the input scale factor by using the PSO execution step of the particle swarm optimization algorithmeAnd KecAnd FGS output gain factor K1、K2、K3。
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