CN114123249B - Wind power interconnection-containing power system load frequency control method based on active response of battery energy storage - Google Patents
Wind power interconnection-containing power system load frequency control method based on active response of battery energy storage Download PDFInfo
- Publication number
- CN114123249B CN114123249B CN202111515796.0A CN202111515796A CN114123249B CN 114123249 B CN114123249 B CN 114123249B CN 202111515796 A CN202111515796 A CN 202111515796A CN 114123249 B CN114123249 B CN 114123249B
- Authority
- CN
- China
- Prior art keywords
- power
- bess
- energy storage
- fgs
- battery energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Eletrric Generators (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a load frequency control method of a wind power interconnection-containing power system with active response of battery energy storage. The invention can minimize the system frequency difference during load disturbance, well inhibit the frequency deviation from changing in a large range under high wind power fluctuation, ensure that the frequency difference is stabilized within the allowable deviation of 0.2Hz, and provide reliable frequency stability for a new energy power system.
Description
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 regulation of a battery energy storage system, and particularly relates to a load frequency control method of a wind power interconnection-containing power system based on active response of battery energy storage.
Background
In recent years, environmental problems are increasingly serious, global temperature rise is limited within 2 ℃ to realize Paris climate protocol, the decarbonization speed of a global energy system is accelerated, new energy represented by wind power is rapidly developed, however, wind power naturally has volatility and uncertainty, large-scale grid connection of new energy power represented by wind power can lead the energy balance, frequency stability and the like of a power system to face more challenges, the range of allowable frequency fluctuation of the power system is limited, and the allowable frequency deviation of the power system in China is 0.2Hz according to GB/T15945-2008 electric energy quality power system frequency deviation, and when the system capacity is large, the maximum deviation value is relaxed to 0.5Hz. Therefore, the active response capability of flexible resources in the system is excavated and improved, and the active response capability is an important means for guaranteeing 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, the intermittent power supply operation characteristic can be effectively improved in real time through the introduction of the large-scale energy storage technology, the robustness and the controllability of a new energy power station are improved, the utilization level of a delivery section of a wind power collecting area is improved, and the win-win effect of improving the economic benefit of the new energy station and the safety and stability of a power grid is achieved. The large-capacity battery energy storage power station is in a carrier form of accessing a power grid and participating in operation after a large-scale Battery Energy Storage System (BESS) is integrated. Active power fluctuation output by a wind farm meets grid-connected requirements through active response of a battery energy storage system, and some large-scale battery energy storage systems can provide certain types of auxiliary services, have the obvious advantages of being rapid and reliable in response and distributed, can respond to system signals such as voltage and frequency, and participate in system adjustment. However, in a new energy interconnected grid system, how to adaptively perform real-time control of load frequency is an important difficulty facing the present day.
Object of the Invention
The invention aims to solve the problems in the prior art, and provides a new energy interconnection system load frequency control method based on active response of battery energy storage, which utilizes large-scale battery energy storage system integration, and accesses a power grid to improve the load frequency control quality of a wind power interconnection system so as to solve the problems of energy unbalance and frequency oscillation caused by large-scale grid connection of wind power, and can be used for stabilizing wind power random fluctuation and improving the acceptance capacity of the power grid.
Disclosure of Invention
The invention provides a load frequency control method of a wind-power-contained interconnected power system based on active response of battery energy storage, which comprises the following steps:
step 1: constructing a load frequency control model of the wind power interconnection-containing power system;
step 2: the primary frequency modulation reserve margin of the energy storage power station is utilized to simulate the control of a speed regulator of a traditional generator, and a sagging coefficient R is set BESS And obtaining a response power reference value of the battery energy storage system BESS controlled by speed regulation sagging
Step 3: designing a first order low pass filter to suppress fluctuations in active power in the grid and determining the output ΔP of the low pass filter LPF ；
Step 4: output power delta P of battery energy storage system BESS BESS Designed as a power reference value
And filter output DeltaP LPF Is a difference in (2);
step 5: calculating a 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 time BESS The self-adaptive frequency modulation response of the BESS is realized;
step 6: aiming at a multi-region interconnection 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, wherein the specific process is that differential of controlled quantities ACE and ACE of a wind power interconnection electric power system is set
Two inputs as FGS controller; determining the correction gain delta K of the control coefficient of the PID controller by formulating a fuzzy control rule p 、△K i 、△K d The method comprises the steps of carrying out a first treatment on the surface of the The ACE-based FGS controller for automatic adjustment of PID controller parameters is represented as shown in formulas (1) - (3):
K p ＝K p0 +△K p (1)，
K i ＝K i0 +△K i (2)，
K d ＝K d0 +△K d (3)，
wherein K is p0 、K i0 、K d0 Respectively the initial control parameters of the PID controller, delta K p 、△K i 、△K d Correction gain, K of control coefficient of PID controller for FGS output p ，K i ，K d Is a dynamic PID control coefficient;
the relationship between the input of the FGS controller and the control amount is determined as shown in the formula (4):
wherein K is p 、K i 、K d ACE is a dynamic PID control coefficient i The area control deviation of the area i, u i A control amount for the region i;
the step of optimizing the FGS by adopting the particle swarm comprises the following substeps:
substep S1: determining an optimization objective function, designing a performance standard of an integral ITAE multiplied by an absolute error in load frequency control, and expressing the objective function of particle swarm optimization under the power system with two interconnected areas as shown in a formula (5):
wherein J is an optimization objective function, Δf 1 And is Deltaf 2 Systematic frequency deviation, ΔP tie Is the incremental change value of the link power, t sim Is a simulated time horizon;
step S2: design K e And K ec K as an input scale factor adjustable parameter for FGS 1 、K 2 、K 3 As an output gain factor for FGS;
step S3: determining constraint conditions of particle swarm optimization problems, setting parameter ranges of the FGS controller, setting J as an optimization objective function, and minimizing the parameter ranges by satisfying the constraint conditions as shown in formulas (6) - (10):
K emin ≤K e ≤K emax (6)，
K ecmin ≤K ec ≤K ecmax (7)，
K 1min ≤K 1 ≤K 1max (8)，
K 2min ≤K 2 ≤K 2max (9)，
K 3min ≤K 3 ≤K 3max (10)，
wherein, subscripts min and max respectively represent minimum and maximum values, K e And K ec Input scale factor adjustable parameters, K, of FGS respectively 1 、K 2 、K 3 Output gain factors of FGS respectively;
step S4: employing a population-based search algorithm PSO, wherein each individual is referred to as a particle, representing a candidate solution; each particle in the PSO flies through the 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 enhancing itself by mimicking its characteristics of successful peers; each particle has a memory which can memorize the optimal position in the search space which is accessed by the particle; the position corresponding to the optimal fitness is set as pbest, and the overall optimal point among all particles is set as gbest; the initial positions of the pbest and the gbest are different, the current speed is used and the slave pbest is used j,g To gbest g The modified velocity and position of each particle are calculated as shown in equations (11) - (13):
where j=1, 2, n; g=1, 2,. -%, m; n is the number of particles in the population, m is the vector v j And x j Is the number of components, item is the number of iterations,
is the g-th component of the velocity of particle j at iteration t, w is the inertial weight factor, c 1 、c 2 Cognitive acceleration factor and social acceleration factor, r 1 ，r 2 Is a random number uniformly distributed in the (0, 1) range,/is>
Is the fourth component of the j position of the particle at iteration t, pbest j Is the best fitness of the j position of the particle, gbest g Is the overall best point in all particles in group g; parameter c 1 And c 2 To determine the relative tension of pbest and gbest, the parameter r 1 And r 2 To randomly change the coefficients of these tensions, the superscript of each parameter indicates the number of iterations; iterative optimization of the input scaling factor adjustable parameter K of the optimal FGS by using the particle swarm optimization algorithm PSO execution step e And K ec Output gain factor K of FGS 1 、K 2 、K 3 。
Preferably, the load frequency control model of the interconnected power system containing wind power 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 represented as shown in formulas (14) - (15):
wherein DeltaP V Is the input of the speed regulator, delta u is the output of the controllerInstructions are entered, Δf is the frequency deviation, ΔP G Outputting an instruction for the speed governor;
the non-reheat turbine model is represented as shown in formula (16):
in the formula DeltaP T For variation of turbine output power, ΔP G Outputting an instruction for the speed governor;
the generator and load model is represented as shown in formula (17):
wherein K is ps ＝1/D，T ps =2h/fD, D is a load frequency dependent parameter, denoted d=p l /f；P l Is the rated load, H is the inertia constant, and f is the rated frequency;
the mathematical model of the wind turbine generator is expressed as shown in a formula (18):
wherein DeltaP w The output power of the wind turbine generator is A is the effective wind sweeping area of the fan, V w Wind speed, ρ is the density of air, C p The wind power conversion coefficient is lambda is the tip speed ratio, and beta is the fan pitch angle;
the load frequency response of the wind power-containing interconnected power system is represented as shown in formula (19):
△f＝G P (s)(△P T (s)+ △P w (s)-△P D (s)) (19)，
wherein DeltaP D Is the load variation;
the controlled variable of the interconnected power system containing wind power is represented as shown in a formula (20):
ACE i ＝△P tie,i +B i △f i (20)，
in ACE i Is the area control deviation of the area, ΔP tie,i Representing the link switching power of region i, B i Frequency deviation constant of the representation area, Δf i Indicating the frequency deviation of the region.
Further preferably, the battery energy storage system BESS response power reference value of the speed regulation droop control in step 2
Represented by formula (21);
wherein R is BESS The sag coefficient of the battery energy storage system BESS, and Δf(s) is the power grid frequency offset.
4. The method for controlling the load frequency of a wind power-containing interconnected power system according to claim 1, wherein the output Δp of the first-order low-pass filter in step 3 LPF Represented by the following formula (22):
wherein DeltaP LPF (s) is the output of the low pass filter; t (T) delay Is the filter time constant;
further preferably, the battery energy storage system BESS output power DeltaP in step 4 BESS Designed as shown in formula (23):
wherein DeltaP BESS For the output power of the battery energy storage system BESS,
for the power reference value, ΔP LPF And(s) is the output of the low pass filter.
Further preferably, the calculation of the real-time state of charge SOC (t) of the energy storage power station in step 5 is as shown in formula (24):
in SOC 0 The initial charge state of the battery energy storage system BESS is eta, the charge and discharge efficiency of the battery energy storage system BESS is E cap Is the maximum storage capacity of the battery energy storage system BESS.
Further preferably, the droop coefficient R is performed in real time based on the SOC (t) in step 5 BESS Is expressed as shown in formula (25):
in SOC min And SOC (System on chip) max Respectively minimum value and maximum value of BESS charge state SOC of battery energy storage system, R max And R is min The corresponding maximum sag factor and minimum sag factor are respectively.
Drawings
Fig. 1 is a flow chart of a method of the overall idea of the present invention.
Fig. 2 shows the raw output power of a wind farm in a two-region interconnected power system.
Fig. 3 is a droop coefficient adaptive adjustment characteristic based on a real-time variation SOC.
Fig. 4 is a fuzzy gain scheduling load frequency control model of a two-region power system with participation of the battery energy storage system.
Fig. 5 is a schematic diagram of an FGS controller.
FIG. 6 is a frequency comparison simulation diagram of the method of the present invention and the conventional method under wind power fluctuation.
FIG. 7 is a frequency comparison simulation of the method of the present invention and a conventional method under load disturbance.
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 described below with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a method for controlling load frequency with which a battery energy storage system participates. The load frequency control method comprises two parts of 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:
1) And determining an electric power system with the interconnection of two areas containing wind power as an implementation object, wherein a specific model is shown in fig. 4.
The two-zone governor model is:
wherein DeltaP V For the input of the speed regulator, deltau is the input instruction of the controller, deltf is the frequency deviation, deltaP G Outputting an instruction for the speed governor;
the non-reheat turbine model used in both regions is:
in the formula DeltaP T For variation of turbine output power, ΔP G Outputting an instruction for the speed governor;
the generator and load model is:
wherein K is ps ＝1/D，T ps =2h/fD, D is a load frequency dependent parameter, denoted d=p l /f；P l Is 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:
wherein DeltaP w The output power of the wind turbine generator is A is the effective wind sweeping area of the fan, V w Wind speed, ρ is the density of air, C p And lambda is the tip speed ratio, and beta is the fan pitch angle.
The load frequency response of wind-containing electricity is:
△f＝G P (s)(△P T (s)+△P w (s)-△P D (s))
in the formula DeltaP D Is a load change.
The controlled quantities of the interconnected power systems are:
ACE i ＝△P tie,i +B i △f i
in ACE i Is the area control deviation of area i, ΔP tie,i Representing the link switching power of region i, B i Frequency deviation constant, Δf, representing region i i The frequency deviation of region i is indicated.
2) Determining a mathematical model of a battery energy storage system, firstly designing a BESS response power reference value based on speed regulation droop control
In the method, in the process of the invention,
a reference value for the BESS response power; r is R BESS The sag factor of the BESS, Δf(s), is the grid frequency offset.
3) Designing a first-order low-pass filter according to the BESS response power reference value:
wherein P is LPF (s) is the output of the low pass filter; t (T) delay Is the filter time constant;
a reference value for the BESS response power;
4) Output power DeltaP of BESS BESS Designed as the difference between the power reference and the filter output:
wherein DeltaP BESS For the output power of the BESS,
for the power reference value, ΔP LPF Is the filter output.
5) The state of charge SOC (t) of the energy storage power station is calculated in real time as follows:
in SOC 0 The initial charge state of the battery energy storage system BESS is eta, the charge and discharge efficiency of the battery energy storage system BESS is E cap Is the maximum storage capacity of the battery energy storage system BESS.
6) To realize the adaptive frequency modulation response of BESS, according to the system frequency modulation requirement, according to the measured SOC (t), a linear interpolation method is adopted, as shown in FIG. 3, the droop coefficient R is designed to be real-time based on the SOC (t) BESS The dynamic adjustment of (2) is as follows:
wherein R is BESS Sag coefficient, SOC, of BESS min And SOC (System on chip) max R is the minimum value and the maximum value of the BESS charge state SOC max And R is min Is the corresponding maximum sagging systemNumber and minimum sag factor.
7) Further, the load frequency response based on the battery energy storage system adaptation is:
△f＝G P (s)(△P T (s)+△P BESS (s)-△P w (s)-△P D (s))
in the formula DeltaP D Is a load change; deltaP BESS Output power for BESS; deltaP T The output power of the steam turbine changes; deltaP w The wind power oscillation power is shown in fig. 2.
8) Designing a fuzzy gain scheduling strategy for designing a multi-region interconnection load frequency control object to adjust an optimal gain value of a PID controller in real time, and firstly setting differentiation of ACE and ACE
(ACE) is used as both inputs to the FGS controller, the structure of which is shown in fig. 5.
9) Determining the correction gain DeltaK of PID controller parameters by formulating fuzzy control rules p ，△K i ，△K d An ACE-based FGS controller was used for automatic adjustment of PID controller parameters as follows:
K p ＝K p0 +△K p
K i ＝K i0 +△K i
K d ＝K d0 +△K d
k in the formula p0 、K i0 、K d0 Respectively the initial control parameters of the PID controller, delta K p 、△K i 、△K d Correction gain, K of control coefficient of PID controller for FGS output p ，K i ，K d Is a dynamic PID control coefficient.
Further, the relation between the input and the control quantity of the FGS controller is determined as follows:
wherein K is p 、K i 、K d ACE is a dynamic PID control coefficient i The area control deviation of the area i, u i Is the control amount of the region i.
10 Using particle swarm optimization for FGS as follows:
step 101: determining an optimization objective function, designing a performance standard of Integration (ITAE) of absolute error multiplied by time in load frequency control, and optimizing a particle swarm in a power system with two interconnected regions, wherein the objective function is as follows:
wherein J is an optimization objective function, Δf 1 And is Deltaf 2 Systematic frequency deviation. DeltaP tie Is the incremental change value of the link power, t sim Is the time frame of the simulation.
Step 102: design K e And K ec As an input scale factor adjustable parameter for FGS. K (K) 1 ，K 2 ，K 3 As the output gain factor of FGS.
Step 103: determining constraint of particle swarm optimization problem, and setting parameter range of FGS controller; the design problem is expressed as an optimization problem as follows:
Minimize J
K 1min ≤K 1 ≤K 1max
K 2min ≤K 2 ≤K 2max
K 3min ≤K 3 ≤K 3max
wherein J is an optimization objective function, subscripts min and max respectively represent a minimum value and a maximum value, K e And K ec Input ratio for FGSExample factor adjustable parameter, K 1 ，K 2 ，K 3 Is the output gain factor of FGS.
Step 104: for the particle swarm algorithm PSO based on population searching, each individual is called a particle, representing a candidate solution, and each particle has a memory that keeps track of the best location in the search space it has accessed. The position corresponding to the optimum fitness is set as pbest, and the overall optimum point among all particles in the population is set as gbest. The initial positions of the pbest and the gbest are different, but with the best and the different directions of the best, all agents gradually approach global best.
Using current speed and slave pbest j,g To gbest g The modified speed and position of each particle are calculated as follows:
where j=1, 2, n; g=1, 2,. -%, m; nn is the number of particles in the population, mm is the vector v j And x j Is the number of components, item is the number of iterations,
is the g-th component of the velocity of particle j at iteration t, w is the inertial weight factor, c 1 、c 2 Cognitive acceleration factor and social acceleration factor, r 1 ，r 2 Is a random number uniformly distributed in the (0, 1) range,/is>
Is the fourth component of the j position of the particle at iteration t, pbest j Is the best fitness of the j position of the particle, gbest g Is the overall best point in all particles in the g-group. Parameter c 1 And c 2 Determining the relative tension of the pbest and the gbest, and the parameter r 1 And r 2 Helping to randomly change these tensions. The above-mentionedIn the equation, the superscript indicates the number of iterations.
Step 105: due to constant c 1 And c 2 The weights of the random acceleration terms that pull each particle toward the optimal position and the optimal position are related. A lower value may cause the particle to roam away from the target area before being pulled back. On the other hand, a higher value may result in a sudden movement towards or beyond the target area. Therefore, the acceleration constant is set to 2.0. Selecting the appropriate inertial weights w provides a balance between global and local exploration, thus requiring fewer iterations on the upper average to find a sufficiently optimal solution. Design w decreases linearly from 0.9 to 0.4 during operation as follows:
wherein w is max Designed to be 0.9; w (w) min Designed to be 0.4; item is the number of iterations.
Step 106: performing steps 81 to 85 to iteratively optimize the optimal input scale factor adjustable parameter K of FGS e And K ec Output gain factor K of FGS 1 ，K 2 ，K 3 。
11 The PID of the battery energy storage system is added as a strategy 1, the PID of the battery energy storage system is added as a strategy 2, the self-adaptive fuzzy gain scheduling control based on the battery energy storage system is used as a strategy 3, and the verification and optimization control effects of the power system which is interconnected in two areas shown in fig. 4 are shown in fig. 6 and 7.
In summary, the droop coefficient 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 optimized by the particle swarm is used for carrying out the load frequency control of the multi-region interconnected power system, the requirement of stabilizing the wind power fluctuation is met, and the control quality of the wind power-containing interconnected system is improved. The FGS controller which is optimized for the relatively independent battery energy storage response and the particle swarm can fully excite the self-adaptive mode of the battery energy storage system.
The present invention is not limited to the preferred embodiments, and any changes or substitutions that would be apparent to one skilled in the art within the scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
The invention has the beneficial effects that:
the flexible and 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 self-adaption of the battery energy storage system can be used for compensating the defect of a classical controller in the new energy power system, so that the random fluctuation of the new energy power is stabilized, and the power grid acceptance is improved. The method has better effect on reducing the frequency drop amplitude, greatly shortens the frequency recovery time, effectively controls the fluctuation of the power of the connecting 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 (7)
1. The method for controlling the load frequency of the interconnected power system containing wind power based on the active response of the battery energy storage is characterized by comprising the following steps:
step 1: constructing a load frequency control model of the wind power interconnection-containing power system;
step 2: the primary frequency modulation reserve margin of the energy storage power station is utilized to simulate the control of a speed regulator of a traditional generator, and a sagging coefficient R is set BESS And obtaining a response power reference value of the battery energy storage system BESS controlled by speed regulation sagging
Step 3: designing a first order low pass filter to suppress fluctuations in active power in the grid and determining the output ΔP of the low pass filter LPF ；
Step 4: battery energy storage systemBESS output Power DeltaP BESS Designed as a power reference value
And filter output DeltaP LPF Is a difference in (2);
step 5: calculating a 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 time BESS The self-adaptive frequency modulation response of the BESS is realized;
step 6: aiming at a multi-region interconnection 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, wherein the specific process is that differential of controlled quantities ACE and ACE of a wind power interconnection electric power system is set
Two inputs as FGS controller; determining the correction gain delta K of the control coefficient of the PID controller by formulating a fuzzy control rule p 、△K i 、△K d The method comprises the steps of carrying out a first treatment on the surface of the The ACE-based FGS controller for automatic adjustment of PID controller parameters is represented as shown in formulas (1) - (3):
K p ＝K p0 +△K p (1)，
K i ＝K i0 +△K i (2)，
K d ＝K d0 +△K d (3)，
wherein K is p0 、K i0 、K d0 Respectively the initial control parameters of the PID controller, delta K p 、△K i 、△K d Correction gain, K of control coefficient of PID controller for FGS output p ，K i ，K d Is a dynamic PID control coefficient;
the relationship between the input of the FGS controller and the control amount is determined as shown in the formula (4):
wherein K is p 、K i 、K d ACE is a dynamic PID control coefficient i The area control deviation of the area i, u i A control amount for the region i;
the step of optimizing the FGS by adopting the particle swarm comprises the following substeps:
substep S1: determining an optimization objective function, designing a performance standard of an integral ITAE multiplied by an absolute error in load frequency control, and expressing the objective function of particle swarm optimization under the power system with two interconnected areas as shown in a formula (5):
wherein J is an optimization objective function, Δf 1 And is Deltaf 2 Systematic frequency deviation, ΔP tie Is the incremental change value of the link power, t sim Is a simulated time horizon;
step S2: design K e And K ec K as an input scale factor adjustable parameter for FGS 1 、K 2 、K 3 As an output gain factor for FGS;
step S3: determining constraint conditions of particle swarm optimization problems, setting parameter ranges of the FGS controller, setting J as an optimization objective function, and minimizing the parameter ranges by satisfying the constraint conditions as shown in formulas (6) - (10):
K 1min ≤K 1 ≤K 1max (8)，
K 2min ≤K 2 ≤K 2max (9)，
K 3min ≤K 3 ≤K 3max (10)，
wherein, subscripts min and max respectively represent minimum and maximum values, K e And K ec Input scale factor adjustable parameters, K, of FGS respectively 1 、K 2 、K 3 Output gain factors of FGS respectively;
step S4: employing a population-based search algorithm PSO, wherein each individual is referred to as a particle, representing a candidate solution; each particle in the PSO flies through the 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 enhancing itself by mimicking its characteristics of successful peers; each particle has a memory which can memorize the optimal position in the search space which is accessed by the particle; the position corresponding to the optimal fitness is set as pbest, and the overall optimal point among all particles is set as gbest; the initial positions of the pbest and the gbest are different, the current speed is used and the slave pbest is used j,g To gbest g The modified velocity and position of each particle are calculated as shown in equations (11) - (13):
where j=1, 2, n; g=1, 2,. -%, m; n is the number of particles in the population, m is the vector v j And x j Is the number of components, item is the number of iterations,
is the g-th component of the velocity of particle j at iteration t, w is the inertial weight factor, c 1 、c 2 Cognitive acceleration factors, respectivelyAnd social acceleration factor, r 1 ，r 2 Is a random number uniformly distributed in the (0, 1) range,/is>
Is the fourth component of the j position of the particle at iteration t, pbest j Is the best fitness of the j position of the particle, gbest g Is the overall best point in all particles in group g; parameter c 1 And c 2 To determine the relative tension of pbest and gbest, the parameter r 1 And r 2 To randomly change the coefficients of these tensions, the superscript of each parameter indicates the number of iterations; iterative optimization of the input scaling factor adjustable parameter K of the optimal FGS by using the particle swarm optimization algorithm PSO execution step e And K ec Output gain factor K of FGS 1 、K 2 、K 3 。
2. The method for controlling the load frequency of the interconnected power system containing wind power according to claim 1, wherein the load frequency control model of the interconnected power system containing wind power 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 represented as shown in formulas (14) - (15):
wherein DeltaP V For the input of the speed regulator, deltau is the input instruction of the controller, deltf is the frequency deviation, deltaP G Outputting an instruction for the speed governor;
the non-reheat turbine model is represented as shown in formula (16):
in the formula DeltaP T For variation of turbine output power, ΔP G Outputting an instruction for the speed governor;
the generator and load model is represented as shown in formula (17):
wherein K is ps ＝1/D，T ps =2h/fD, D is a load frequency dependent parameter, denoted d=p l /f；P l Is the rated load, H is the inertia constant, and f is the rated frequency;
the mathematical model of the wind turbine generator is expressed as shown in a formula (18):
wherein DeltaP w The output power of the wind turbine generator is A is the effective wind sweeping area of the fan, V w Wind speed, ρ is the density of air, C p The wind power conversion coefficient is lambda is the tip speed ratio, and beta is the fan pitch angle;
the load frequency response of the wind power-containing interconnected power system is represented as shown in formula (19):
△f＝G P (s)(△P T (s)+△P w (s)-△P D (s)) (19)，
wherein DeltaP D Is the load variation;
the controlled variable of the interconnected power system containing wind power is represented as shown in a formula (20):
ACE i ＝△P tie,i +B i △f i (20)，
in ACE i Is the zone control deviation of zone ii, ΔP tie,i Representing the link switching power of region i, B i Frequency deviation constant, Δf, representing region i i The frequency deviation of region i is indicated.
3. The method for controlling load frequency of interconnected power system with wind power according to claim 1, wherein the battery energy storage system BESS response power reference value for controlling speed governing droop in step 2
Represented by formula (21);
wherein R is BESS The sag coefficient of the battery energy storage system BESS, and Δf(s) is the power grid frequency offset.
4. The method for controlling the load frequency of a wind power-containing interconnected power system according to claim 1, wherein the output Δp of the first-order low-pass filter in step 3 LPF Represented by the following formula (22):
wherein DeltaP LPF (s) is the output of the low pass filter; t (T) delay Is the filter time constant.
5. The method for controlling the load frequency of a wind-power-containing interconnected power system according to claim 1, wherein the battery energy storage system BESS in step 4 outputs power Δp BESS Designed as shown in formula (23):
wherein DeltaP BESS For the output power of the battery energy storage system BESS,
for the power reference value, ΔP LPF And(s) is the output of the low pass filter.
6. The method for controlling the load frequency of the interconnected power system with wind power according to claim 1, wherein the calculation of the real-time state of charge SOC (t) of the energy storage power station in step 5 is shown in the following formula (24):
in SOC 0 The initial charge state of the battery energy storage system BESS is eta, the charge and discharge efficiency of the battery energy storage system BESS is E cap Is the maximum storage capacity of the battery energy storage system BESS.
7. The method for controlling the load frequency of a wind power interconnection-containing power system according to claim 6, wherein the droop coefficient R is performed in real time based on the SOC (t) in step 5 BESS Is expressed as shown in formula (25):
in SOC min And SOC (System on chip) max Respectively minimum value and maximum value of BESS charge state SOC of battery energy storage system, R max And R is min The corresponding maximum sag factor and minimum sag factor are respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111515796.0A CN114123249B (en) | 2021-12-13 | 2021-12-13 | Wind power interconnection-containing power system load frequency control method based on active response of battery energy storage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111515796.0A CN114123249B (en) | 2021-12-13 | 2021-12-13 | Wind power interconnection-containing power system load frequency control method based on active response of battery energy storage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114123249A CN114123249A (en) | 2022-03-01 |
| CN114123249B true CN114123249B (en) | 2023-07-14 |
Family
ID=80365099
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111515796.0A Active CN114123249B (en) | 2021-12-13 | 2021-12-13 | Wind power interconnection-containing power system load frequency control method based on active response of battery energy storage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114123249B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011114900A (en) * | 2009-11-25 | 2011-06-09 | Fuji Electric Systems Co Ltd | Apparatus and method for controlling supply and demand of micro-grid |
| CN109560573A (en) * | 2018-12-10 | 2019-04-02 | 国网青海省电力公司 | A kind of optimization method and device of variable-speed wind-power unit frequency controller parameter |
| CN110266039A (en) * | 2019-05-29 | 2019-09-20 | 南京理工大学 | Consider wind power fluctuation and cuts the governor parameter optimization method of machine-cut load disturbance frequency modulation next time |
| CN112865132A (en) * | 2020-12-31 | 2021-05-28 | 燕山大学 | Processing method of load frequency control parameters of double-region interconnected power system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10177574B2 (en) * | 2016-09-28 | 2019-01-08 | Nec Corporation | Dynamic frequency control scheme for microgrids using energy storage |
-
2021
- 2021-12-13 CN CN202111515796.0A patent/CN114123249B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011114900A (en) * | 2009-11-25 | 2011-06-09 | Fuji Electric Systems Co Ltd | Apparatus and method for controlling supply and demand of micro-grid |
| CN109560573A (en) * | 2018-12-10 | 2019-04-02 | 国网青海省电力公司 | A kind of optimization method and device of variable-speed wind-power unit frequency controller parameter |
| CN110266039A (en) * | 2019-05-29 | 2019-09-20 | 南京理工大学 | Consider wind power fluctuation and cuts the governor parameter optimization method of machine-cut load disturbance frequency modulation next time |
| CN112865132A (en) * | 2020-12-31 | 2021-05-28 | 燕山大学 | Processing method of load frequency control parameters of double-region interconnected power system |
Non-Patent Citations (4)
| Title |
|---|
| Coordinated_Control_Strategy_of_Wind_Power_Fluctuation_Suppression_and_Frequency_Modulation_Based_on_Hybrid_Energy_Storage_System;Hongwei Liu;《2021 IEEE 4th International Electrical and Energy Conference(CIEEC)》;第1-6页 * |
| Fuzzy_PID_with_Filtered_Derivative_Mode_Based_Load_Frequency_Control_of_Two-Area_Power_System;Mokhtar Shouran;《2021 56th International Universities Power Engineering Conference(UPEC)》;第1-6页 * |
| 多区域互联电网的分散式模糊PID负荷频率控制;项雷军;《华侨大学学报(自然科学版)》;第35卷(第2期);第121-126页 * |
| 风电高渗透率下中长期时间尺度系统频率波动仿真研究;于汀;《电网与清洁能源》;第30卷(第1期);第87-108页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114123249A (en) | 2022-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2024007465A1 (en) | Adaptive variable-parameter frequency modulation method and device for doubly fed variable-speed pumped-storage unit | |
| CN109861251A (en) | A kind of double-fed fan comprehensive control method for the temporary steady frequency optimization of microgrid | |
| CN108092323B (en) | AGC optimization control method for electric power system with DFIG | |
| CN111030141A (en) | Source-load cooperative distributed optimization regulation and control method based on consistency algorithm | |
| CN113098029A (en) | Wind power storage combined frequency modulation control method based on wind power short-term prediction | |
| CN109659961B (en) | Dynamic power system load frequency coordination method based on frequency division control | |
| CN116667389B (en) | Novel wind-storage combined prediction correction control method for inertia lifting of power system | |
| CN115313416B (en) | Multi-objective optimization control method suitable for auxiliary frequency modulation system of energy storage power station | |
| CN114123249B (en) | Wind power interconnection-containing power system load frequency control method based on active response of battery energy storage | |
| CN110912185B (en) | Design method of PID controller of automatic power generation control system of power grid containing wind power generation | |
| Zhu et al. | $\mu $-Synthesis Robust Control of Variable Speed Wind Turbine Generators for Participating in Microgrid Frequency Regulation | |
| CN115483715A (en) | Virtual synchronous generator self-adaptive control method and system for centralized photovoltaic power station | |
| CN113629728B (en) | Wind turbine generator set droop control method based on execution dependency heuristic dynamic programming | |
| CN111725819B (en) | Method and system for battery energy storage to participate in frequency recovery control | |
| CN115173410A (en) | Power grid rotational inertia threshold calculation method for improving GWO algorithm | |
| CN115102228A (en) | Multi-target coordination frequency optimization method and device for wind power plant containing flywheel energy storage | |
| CN114172202A (en) | Load frequency control method of wind power interconnection-containing power system based on active response of demand side resources | |
| Liang et al. | Reinforcement learning-based inertia and droop control for wind farm frequency regulation | |
| CN113410850A (en) | Light-heat wind-power combined frequency modulation model and frequency modulation strategy based on MPC | |
| Wang et al. | An optimal over-frequency droop control for DFIG-based wind farm under unreliable communication | |
| CN113839398B (en) | Variable droop coefficient control method for double-fed fans participating in primary frequency modulation of power grid | |
| CN111725820B (en) | Device for restraining low-frequency oscillation of power grid based on high-order momentum tracking control | |
| Gu et al. | Enhancing dynamic stability performance of hybrid ac/dc power grids using decentralised model predictive control | |
| CN113098030B (en) | Frequency control optimization method for wind power plant participating in primary frequency modulation | |
| Ali et al. | Frequency Stability Enhancement in Low-Inertia Power System Using an Optimal Control Scheme |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |