CN110932320A - Design method of distributed model predictive controller of automatic power generation control system - Google Patents

Design method of distributed model predictive controller of automatic power generation control system Download PDF

Info

Publication number
CN110932320A
CN110932320A CN201911248952.4A CN201911248952A CN110932320A CN 110932320 A CN110932320 A CN 110932320A CN 201911248952 A CN201911248952 A CN 201911248952A CN 110932320 A CN110932320 A CN 110932320A
Authority
CN
China
Prior art keywords
power generation
wind
control system
power
generation control
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.)
Pending
Application number
CN201911248952.4A
Other languages
Chinese (zh)
Inventor
孔小兵
刘向杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
North China Electric Power University
Original Assignee
North China Electric Power University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by North China Electric Power University filed Critical North China Electric Power University
Priority to CN201911248952.4A priority Critical patent/CN110932320A/en
Publication of CN110932320A publication Critical patent/CN110932320A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Landscapes

  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a design method of a distributed model predictive controller of an automatic power generation control system, which comprises the following steps: establishing a simulation model of an automatic power generation control system of a regional interconnected power system, wherein the simulation model comprises: a wind power generation model and a photovoltaic power generation model; designing a distributed model predictive control algorithm according to the simulation model of the automatic power generation control system, and establishing and optimizing a target function; determining constraint conditions aiming at the actual operation condition of the automatic power generation control system, and processing the constraint conditions; and optimizing by adopting a firework guiding algorithm according to the target function and the constraint condition to obtain controller output. The invention effectively improves the dynamic performance of the automatic power generation control system of the interconnected power grid and ensures the safe and stable operation of the power system.

Description

Design method of distributed model predictive controller of automatic power generation control system
Technical Field
The invention belongs to the technical field of automatic control of power systems, relates to a design method of a controller of an automatic power generation control system, and particularly relates to a design method of a distributed model predictive controller of an automatic power generation control system of a power grid containing wind power generation and photovoltaic power generation.
Background
Automatic Generation Control (AGC) is a key link to maintain stable operation of power systems. By ensuring a balance between generated power and load demand, the system frequency is kept constant and the tie line exchange power is maintained at a rated value.
In the past decades, electricity mainly comes from the combustion of traditional fossil energy such as coal, oil, natural gas, etc., resulting in serious resource shortage and environmental pollution problems. This has prompted the development of clean and renewable energy sources, such as wind and solar. At present, in areas with abundant wind energy and solar energy resources in China, large-scale wind energy and solar energy power generation systems are integrated into an interconnected power system so as to supplement the output of traditional energy sources. Due to the intermittency and uncertainty of the new energy, the grid connection of the new energy brings new challenges to the control of the power system. It is therefore necessary to design a controller for an automatic power generation control system incorporating wind and solar power generation.
Disclosure of Invention
The invention aims to provide a design method of a distributed model predictive controller of an automatic power generation control system, which aims to improve the dynamic response of the automatic power generation control system containing new energy power generation such as wind power generation, photovoltaic power generation and the like.
The invention provides a design method of a distributed model predictive controller of an automatic power generation control system, which comprises the following steps:
establishing a simulation model of an automatic power generation control system of a regional interconnected power system, wherein the simulation model comprises: a wind power generation model and a photovoltaic power generation model;
designing a distributed model predictive control algorithm according to the simulation model of the automatic power generation control system, and establishing and optimizing a target function;
determining constraint conditions aiming at the actual operation condition of the automatic power generation control system, and processing the constraint conditions;
and optimizing by adopting a firework guiding algorithm according to the target function and the constraint condition to obtain controller output.
Further, in the design method of the distributed model predictive controller of the automatic power generation control system, the automatic power generation control system of the power system is composed of M control areas, a part of the areas comprise a thermal power generating unit, a wind power generating unit and a controller, and a part of the areas comprise the thermal power generating unit, a photovoltaic power generating unit and the controller.
Further, in the above method for designing a distributed model predictive controller of an automatic power generation control system, the process of establishing the wind power generation model is as follows:
when the wind speed is less than the rated wind speedThe wind turbine generator carries out maximum power capture; mechanical power P of wind turbinewCan be expressed as:
Figure BDA0002308470000000021
in the formula: rho (1.205 kg/m)2) R (23.5m) is the radius of the wind wheel; cpFor power coefficient, β is pitch angle, value is 0, λ is tip speed ratio, v is wind speed;
Cpcan be expressed as β, a function of λ:
Cp＝(0.44-0.0167β)sin[π(λ-3)15-0.3β]-0.0184(λ-3)β
in the formula:
Figure BDA0002308470000000031
as tip speed ratio, wrIs the wind wheel angular velocity; by pair CpObtaining an optimal tip speed ratio by derivation, and capturing the maximum power of the wind turbine at the moment;
ignoring the non-linear term, the wind power system transfer function can be expressed as:
Figure BDA0002308470000000032
in the formula: delta PWTSFor wind turbines outputting electric power deviation, Δ PwFor mechanical power deviation of the wind wheel, KWTSFor the gain factor, T, of the wind turbineWTSIs a time constant of the wind turbine generator;
when the wind speed is higher than the rated wind speed, the output power of the wind turbine generator is always the rated value.
Further, in the above method for designing a distributed model predictive controller of an automatic power generation control system, the photovoltaic power generation model is established as follows:
the output power of the photovoltaic power generation system is as follows:
PPV＝ηSΦ{1-0.005(Ta+25)}
wherein η E (0.09,0.12) is the conversion efficiency of the photovoltaic array and S is the measurement of the photovoltaic arrayVolume area, phi is the amount of solar radiation, TaIs the ambient temperature; in the present invention, take TaMaintained at 25 ℃ PPVIs linearly related to phi;
neglecting the nonlinear terms, the transfer function of the photovoltaic power generation system can be written as a first-order inertia link:
Figure BDA0002308470000000033
in the formula: delta PPVFor the output power deviation of the photovoltaic system, delta phi is the change of the solar radiation quantity, KPVIs the system gain, TPVIs the system time constant.
Further, in the above method for designing a distributed model predictive controller of an automatic power generation control system, the distributed model of the system is as follows:
Figure BDA0002308470000000041
wherein A isii、Bii、Fii、CiiRespectively system matrix, input matrix, interference matrix and output matrix, x, of the systemi、ui、wi、yiRespectively is a state variable, a control quantity, a load interference variable and an output variable of the system;
using the zero-order keeper discretization method, the discretization state space model of the subsystem i is derived as follows:
Figure BDA0002308470000000042
further, in the above method for designing a distributed model predictive controller of an automatic power generation control system, the process of establishing and optimizing the objective function is as follows:
setting the control time domain to NcThe prediction time domain is NpAnd recursion is carried out according to the discrete state space model to obtain the predicted value of the state variable at the future moment:
Figure BDA0002308470000000043
wherein,
Figure BDA0002308470000000044
Figure BDA0002308470000000045
Figure BDA0002308470000000046
Figure BDA0002308470000000047
Figure BDA0002308470000000051
Figure BDA0002308470000000052
Figure BDA0002308470000000053
merging the whole system and distributing the system into M subsystem state space models, wherein the state space model of the subsystem i is as follows:
Figure BDA0002308470000000054
wherein,
Figure BDA0002308470000000055
Figure BDA0002308470000000056
E＝Λ-1μ,F＝Λ-1v,G＝Λ-1ξ
defining an objective function for subsystem i:
Figure BDA0002308470000000057
further, in the method for designing a distributed model predictive controller of an automatic power generation control system, the constraint condition is determined as follows:
the thermal power generating unit has generator change rate constraint (GRC):
Figure BDA0002308470000000061
the constraints can be written as:
Figure BDA0002308470000000062
t is a sampling period;
definition of
Figure BDA0002308470000000063
Due to delta Pgi＝xi3And then:
Figure BDA0002308470000000064
wherein,
Figure BDA0002308470000000065
Jii＝[0 0 1 0]
definition of
Figure BDA0002308470000000066
Obtain information about Δ Pgi(k) The constraint of (2) is written as:
Figure BDA0002308470000000067
Figure BDA0002308470000000068
further, in the method for designing the distributed model predictive controller of the automatic power generation control system, the specific steps of the controller outputting the result are as follows:
generating a plurality of groups of initial values output by the controller;
performing iterative optimization on each group of control quantity by guiding a firework algorithm and combining the objective function value and inequality constraint to obtain the output of the controller;
and (4) acting the control quantity of the current moment on the system, and continuing to carry out iterative optimization at the next moment to obtain a new control quantity until the simulation time is reached.
The invention has the beneficial effects that:
an automatic power generation control model of an interconnected power system containing wind power generation and photovoltaic power generation is established, and the influence of new energy volatility on system control is considered;
designing a distributed model predictive controller, which is suitable for a modern large-scale power system;
the method adopts the firework guiding algorithm for optimization, takes heuristic information in the optimization solving process into consideration, and has strong optimization problem solving capability. Compared with the traditional PI control method, the designed controller obtains a more optimized control effect, so that the dynamic performance of the automatic power generation control system of the interconnected power grid is improved, and the safe and stable operation of the power system is ensured.
Drawings
FIG. 1 is a flow chart of a method for designing an automatic generation control distributed model predictive controller provided by the present invention;
FIG. 2 is a schematic structural diagram of an automatic power generation control system constructed according to the present invention;
FIG. 3 is a model of the power system transfer function for control zone i;
FIG. 4 is a flow chart of the basic steps of the guided fireworks algorithm;
FIG. 5 is a graph of the response of a region-frequency deviation;
FIG. 6 is a graph of the response of the region two frequency deviations;
FIG. 7 is a graph of the response of a zone-to-zone crossline power deviation.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The technical scheme adopted by the invention is as follows: a design method of a distributed model predictive controller of an automatic power generation control system is used for improving the dynamic response of the automatic power generation control system containing new energy power generation such as wind power generation, photovoltaic power generation and the like.
Referring to fig. 1, a method for designing a distributed model predictive controller of an automatic power generation control system according to the present invention includes:
establishing a simulation model of an automatic power generation control system of a regional interconnected power system, wherein the simulation model comprises: a wind power generation model and a photovoltaic power generation model;
designing a distributed model predictive control algorithm according to the simulation model of the automatic power generation control system, and establishing and optimizing a target function;
determining constraint conditions aiming at the actual operation condition of the automatic power generation control system, and processing the constraint conditions;
and optimizing by adopting a firework guiding algorithm according to the target function and the constraint condition to obtain controller output.
The automatic power generation control system of the power system is composed of M control areas, a partial area comprises a thermal power generating unit, a wind power generating unit and a controller, and the partial area comprises the thermal power generating unit, a photovoltaic power generation unit and the controller.
The specific process is as follows:
1. establishing a simulation model of an automatic power generation control system of an interconnected power system, as shown in fig. 2:
1) establishing a wind power generation model:
and when the wind speed is less than the rated wind speed, the wind turbine generator captures the maximum power. Wind turbine machinePower PwCan be expressed as:
Figure BDA0002308470000000091
wherein rho (1.205 kg/m)2) R (23.5m) is the radius of the wind wheel; cpFor power coefficient, β is the pitch angle, value 0, λ is the tip speed ratio, v is the wind speed.
CpCan be expressed as β, a function of λ:
Cp＝(0.44-0.0167β)sin[π(λ-3)15-0.3β]-0.0184(λ-3)β
Figure BDA0002308470000000092
as tip speed ratio, wrIs the wind wheel angular velocity. By pair CpAnd solving a partial derivative to obtain an optimal tip speed ratio, and capturing the maximum power of the wind turbine at the moment.
Ignoring the non-linear term, the wind power system transfer function can be expressed as:
Figure BDA0002308470000000093
in the formula,. DELTA.PWTSIs the wind turbine generator output power deviation, delta PwFor mechanical power deviation of the wind wheel, KWTSFor the gain factor, T, of the wind turbineWTSAnd the time constant of the wind turbine generator is shown.
When the wind speed is higher than the rated wind speed, the output power of the wind turbine generator is constant at the rated power.
2) Establishing a mathematical model of photovoltaic power generation:
the output power of the photovoltaic power generation system is as follows:
PPV＝ηSΦ{1-0.005(Ta+25)}
wherein η E (0.09,0.12) is the conversion efficiency of the photovoltaic array, S is the area of the photovoltaic array, phi is the solar radiation amount, TaIs ambient temperature. In the present invention, take TaMaintained at 25 ℃ PPVLinearly related to Φ.
Neglecting the nonlinear terms, the transfer function of the photovoltaic power generation system can be written as a first-order inertia link:
Figure BDA0002308470000000101
wherein, Δ PPVFor the output power deviation of the photovoltaic power generation system, delta phi is the change of solar radiation, KPVFor system gain, TPVIs the system time constant.
2. The state space equation model of the system is obtained according to the figure 3:
designing a distributed model predictive control algorithm according to the simulation model of the automatic power generation control system to obtain a distributed model of the system:
Figure BDA0002308470000000102
wherein A isii、Bii、Fii、CiiRespectively system matrix, input matrix, interference matrix and output matrix, x, of the systemi、ui、wi、yiRespectively, a state variable, a control variable, a load disturbance variable and an output variable of the system.
Using the zero-order keeper discretization method, the discretization state space model of the subsystem i is derived as follows:
Figure BDA0002308470000000103
3. and establishing and optimizing an objective function:
setting the control time domain to NcThe prediction time domain is NpAnd recursion is carried out according to the discrete state space model to obtain the predicted value of the state variable at the future moment:
Figure BDA0002308470000000104
wherein,
Figure BDA0002308470000000105
Figure BDA0002308470000000106
Figure BDA0002308470000000107
Figure BDA0002308470000000111
Figure BDA0002308470000000112
Figure BDA0002308470000000113
Figure BDA0002308470000000114
in the formula,. DELTA.fiIs the frequency deviation, Δ P, of the region itieiIs the link power deviation, Δ P, of region igiIs the unit output deviation in area i, Δ XgiSteam turbine valve bias in zone i; kBiFrequency deviation coefficient, ACE, for region iiControlling the deviation for the zone of zone i; delta PdiFor load disturbances in region i, Δ PWTSFor wind turbines outputting electric power deviation, Δ PPVOutputting power deviation for photovoltaic power generation; kPiFor region i power system gain factor, TPiIs the time constant of the power system of zone i, KSijFor the interconnect gain between regions i and j, TGiIs the zone i governor time constant, TTiIs the turbine time constant in region i, RiAnd the difference adjustment coefficient is the thermal power generating unit.
Merging the whole system and distributing the system into M subsystem state space models, wherein the state space model of the subsystem i is as follows:
Figure BDA0002308470000000121
wherein,
Figure BDA0002308470000000122
Figure BDA0002308470000000123
E＝Λ-1μ,F＝Λ-1v,G＝Λ-1ξ
defining an objective function for subsystem i:
Figure BDA0002308470000000124
4. determining a constraint condition:
determining constraint conditions aiming at the actual operation condition of the automatic power generation control system, and processing the constraint conditions:
the thermal power generating unit has generator change rate constraint (GRC):
Figure BDA0002308470000000125
the constraints can be written as:
Figure BDA0002308470000000126
t is a sampling period;
definition of
Figure BDA0002308470000000127
Due to delta Pgi＝xi3And then:
Figure BDA0002308470000000128
wherein,
Figure BDA0002308470000000129
Jii＝[0 0 1 0]；
definition of
Figure BDA0002308470000000131
Obtain information about Δ Pgi(k) The constraint of (2) is written as:
Figure BDA0002308470000000132
Figure BDA0002308470000000133
5. the controller outputs the result:
the specific steps of obtaining the output result of the controller through a guided fireworks algorithm (GFWA) according to the objective function and the constraint condition of the system comprise:
and step 1, generating a plurality of groups of initial values output by the controller.
And 2, performing iterative optimization on each group of control quantity by guiding a firework algorithm and combining the objective function value and inequality constraint to obtain controller output.
The basic flow of the firework guiding algorithm is shown in fig. 4, and the basic steps include:
1) initializing related parameters and t fireworks with dimension NCSetting the iteration number I to be 0;
2) calculate each Firework XiFitness f (X)i) The current fireworks are used as the performance index value obtained when the controller controls the time domain output;
3) for each firework XiNumber of sparks generated lambdaiOptimum fitness individual XCFAmplitude of explosion A ofCFAnd the explosion amplitude A of other fireworksiThe calculation formula of (a) is as follows:
Figure BDA0002308470000000134
Figure BDA0002308470000000135
Figure BDA0002308470000000141
wherein, Ca、CrRespectively constants for controlling the number of sparks and the amplitude of the explosion, Ca>1,Cr<1。
4) Generating sparks according to the calculated value of the step 3).
5) All spark fitness values are arranged in ascending order, and σ λ is selectediThe individual with the best fitness and the worst fitness, each firework generates a Guide Vector (GV) Delta according to the following formulaiAnd guiding the spark Gi。
Figure BDA0002308470000000142
Gi＝Xi+Δi
6) Calculating the fitness of all the fireworks and the sparks, selecting the optimal individuals as the fireworks of the next generation, and randomly selecting t-1 individuals from the rest individuals as the fireworks of the next generation.
7) Adding 1 to the iteration frequency I to judge whether the maximum iteration frequency I is reachedmaxIf not, returning to execute the step 3); otherwise, ending the optimizing process and outputting an optimizing result, namely the value of the controlled variable in the control time domain.
And 3, acting the control quantity at the current moment on the system, and continuously adopting the GFWA to carry out iterative optimization at the next moment to obtain a new control quantity and act on the system until the simulation time is reached.
Example (b):
taking a two-region automatic power generation control system as an example, one region comprises a thermal power generating unit and a wind power generating unit, and the other region comprises the thermal power generating unit and photovoltaic power generation.
The prediction control parameter values are: n is a radical ofC＝5,NP＝15,T＝0.1,
Figure BDA0002308470000000143
qi＝diag(10 0 1)，
Figure BDA0002308470000000144
qj＝diag(0.1 0 0 0.1)，
Figure BDA0002308470000000151
The values of the initial parameters of the GFWA are as follows:
t＝2,λ＝150,A＝0.2,Ca＝1.2,Cr0.9, σ 0.2, and the maximum number of iterations is set to 80.
The parameters of the two-region automatic power generation control system are as follows:
TGi＝0.08s,TTi＝0.3s，TPi＝20s，KPi＝120Hz/p.u.,KS12＝-KS21＝0.5p.u./MW，Ri＝2.4Hz/p.u.，KBi＝0.425；KWTS＝1,TWTS＝1.5s；KPV＝1,TPV＝1.8s。
the capacities of wind power generation and photovoltaic power generation are both 150MW, and the capacities of the thermal power generating units in the two regions are both 1000 MW. Assuming that the first region is added with load disturbance 0.01p.u.MW at 0s, the wind speed is reduced from 9m/s to 7m/s at 10s, and the solar radiation quantity is reduced from 500MW/m2Increasing the temperature to 700MW/m2. The scheme of the distributed model predictive controller provided by the embodiment of the invention is respectively adopted by the traditional PI controller to control the interconnected power system.
The frequency deviation of the first region is shown in fig. 5, the frequency deviation of the second region is shown in fig. 6, and the tie line power deviation of the first region flowing to the second region is shown in fig. 7. In the figure, PI represents a traditional control method, and DMPC represents a control method designed by the invention.
As shown in fig. 5-7, compared with the conventional control scheme, the controller designed by the present invention can make the frequency deviation and the tie line power deviation of the interconnected power grid automatic power generation control system have obvious advantages in the aspects of adjusting time, peak value, etc., greatly improve the control effect on the automatic power generation control system, and ensure the safety and stability of electric power operation when the new energy output fluctuates due to load change and environmental change.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (8)

1. A design method for a distributed model predictive controller of an automatic power generation control system is characterized by comprising the following steps:
establishing a simulation model of an automatic power generation control system of a regional interconnected power system, wherein the simulation model comprises: a wind power generation model and a photovoltaic power generation model;
designing a distributed model predictive control algorithm according to the simulation model of the automatic power generation control system, and establishing and optimizing a target function;
determining constraint conditions aiming at the actual operation condition of the automatic power generation control system, and processing the constraint conditions;
and optimizing by adopting a firework guiding algorithm according to the target function and the constraint condition to obtain controller output.
2. The method for designing the distributed model predictive controller for an automated power generation control system according to claim 1, wherein: the automatic power generation control system of the power system is composed of M control areas, a partial area comprises a thermal power generating unit, a wind power generating unit and a controller, and the partial area comprises the thermal power generating unit, a photovoltaic power generation unit and the controller.
3. The method of claim 1, wherein the wind power generation model is established as follows:
when the wind speed is less than the rated wind speed, the wind turbine generator is startedCapturing the maximum power of the line; mechanical power P of wind turbinewCan be expressed as:
Figure FDA0002308469990000011
in the formula: rho (1.205 kg/m)2) R (23.5m) is the radius of the wind wheel; cpFor power coefficient, β is pitch angle, value is 0, λ is tip speed ratio, v is wind speed;
Cpcan be expressed as β, a function of λ:
Cp＝(0.44-0.0167β)sin[π(λ-3)15-0.3β]-0.0184(λ-3)β
in the formula:
Figure FDA0002308469990000021
as tip speed ratio, wrIs the wind wheel angular velocity; by pair CpObtaining an optimal tip speed ratio by derivation, and capturing the maximum power of the wind turbine at the moment;
ignoring the non-linear term, the wind power system transfer function can be expressed as:
Figure FDA0002308469990000022
in the formula: delta PWTSFor wind turbines outputting electric power deviation, Δ PwFor mechanical power deviation of the wind wheel, KWTSFor the gain factor, T, of the wind turbineWTSIs a time constant of the wind turbine generator;
when the wind speed is higher than the rated wind speed, the output power of the wind turbine generator is always the rated value.
4. The design method of the distributed model predictive controller of the automatic power generation control system according to claim 1, wherein the photovoltaic power generation model is established as follows:
the output power of the photovoltaic power generation system is as follows:
PPV＝ηSΦ{1-0.005(Ta+25)}
wherein η E (0.09,0.12) is the conversion efficiency of the photovoltaic array, S is the measured area of the photovoltaic array, phi is the solar radiation amount, T isaIs the ambient temperature; in the present invention, take TaMaintained at 25 ℃ PPVIs linearly related to phi;
neglecting the nonlinear terms, the transfer function of the photovoltaic power generation system can be written as a first-order inertia link:
Figure FDA0002308469990000023
in the formula: delta PPVFor the output power deviation of the photovoltaic system, delta phi is the change of the solar radiation quantity, KPVIs the system gain, TPVIs the system time constant.
5. The method of claim 1, wherein the distributed model of the system is as follows:
Figure FDA0002308469990000031
wherein A isii、Bii、Fii、CiiRespectively system matrix, input matrix, interference matrix and output matrix, x, of the systemi、ui、wi、yiRespectively is a state variable, a control quantity, a load interference variable and an output variable of the system;
using the zero-order keeper discretization method, the discretization state space model of the subsystem i is derived as follows:
Figure FDA0002308469990000032
6. the method of claim 1, wherein the objective function is established and optimized as follows:
setting the control time domain to NcThe prediction time domain is NpAnd recursion is carried out according to the discrete state space model to obtain the predicted value of the state variable at the future moment:
Figure FDA0002308469990000033
wherein,
Figure FDA0002308469990000034
Figure FDA0002308469990000035
Figure FDA0002308469990000036
Figure FDA0002308469990000037
Figure FDA0002308469990000041
Figure FDA0002308469990000042
Figure FDA0002308469990000043
merging the whole system and distributing the system into M subsystem state space models, wherein the state space model of the subsystem i is as follows:
Figure FDA0002308469990000044
wherein,
Figure FDA0002308469990000045
Figure FDA0002308469990000046
E＝Λ-1μ,F＝Λ-1v,G＝Λ-1ξ
defining an objective function for subsystem i:
Figure FDA0002308469990000047
。
7. the method of claim 1, wherein the constraints are determined by: the thermal power generating unit has generator change rate constraint (GRC):
Figure FDA0002308469990000051
the constraints can be written as:
Figure FDA0002308469990000052
t is a sampling period;
definition of
Figure FDA0002308469990000053
Due to delta Pgi＝xi3And then:
Figure FDA0002308469990000054
wherein,
Figure FDA0002308469990000055
Jii＝[0 0 1 0]
definition of
Figure FDA0002308469990000056
Obtain information about Δ Pgi(k) The constraint of (2) is written as:
Figure FDA0002308469990000057
Figure FDA0002308469990000058
8. the method for designing the distributed model predictive controller of the automatic power generation control system according to claim 1, wherein the specific steps of the controller outputting the result are as follows:
generating a plurality of groups of initial values output by the controller;
performing iterative optimization on each group of control quantity by guiding a firework algorithm and combining the objective function value and inequality constraint to obtain the output of the controller;
and (4) acting the control quantity of the current moment on the system, and continuing to carry out iterative optimization at the next moment to obtain a new control quantity until the simulation time is reached.
CN201911248952.4A 2019-12-09 2019-12-09 Design method of distributed model predictive controller of automatic power generation control system Pending CN110932320A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911248952.4A CN110932320A (en) 2019-12-09 2019-12-09 Design method of distributed model predictive controller of automatic power generation control system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911248952.4A CN110932320A (en) 2019-12-09 2019-12-09 Design method of distributed model predictive controller of automatic power generation control system

Publications (1)

Publication Number Publication Date
CN110932320A true CN110932320A (en) 2020-03-27

Family

ID=69857562

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911248952.4A Pending CN110932320A (en) 2019-12-09 2019-12-09 Design method of distributed model predictive controller of automatic power generation control system

Country Status (1)

Country Link
CN (1) CN110932320A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111934320A (en) * 2020-08-20 2020-11-13 三峡大学 Active power distribution network rapid reconstruction method based on linear power flow equation and improved firework algorithm
CN112531737A (en) * 2020-11-24 2021-03-19 绍兴市上虞区舜兴电力有限公司 Wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization
CN112583040A (en) * 2020-12-21 2021-03-30 中国科学院电工研究所 Active management and control method for distributed energy and user alternating current-direct current power distribution system
CN114200840A (en) * 2021-12-10 2022-03-18 广东工业大学 Traditional Chinese medicine pharmacy process operation optimization method based on distributed model predictive control

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105337415A (en) * 2015-11-18 2016-02-17 深圳合纵能源技术有限公司 Regional power grid dispatching system and method based on prediction control
CN109085755A (en) * 2018-08-14 2018-12-25 中国电力科学研究院有限公司 The advanced control method for frequency of wind-powered electricity generation group system and system based on distributed model predictive control

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105337415A (en) * 2015-11-18 2016-02-17 深圳合纵能源技术有限公司 Regional power grid dispatching system and method based on prediction control
CN109085755A (en) * 2018-08-14 2018-12-25 中国电力科学研究院有限公司 The advanced control method for frequency of wind-powered electricity generation group system and system based on distributed model predictive control

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
JUNZHI LI,SHAOQIU ZHENG，ET AL.: "The Effect of Information Utilization: Introducing a Novel Guiding Spark in the Fireworks Algorithm", 《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》 *
NONG HUIYUN,LIU XIANGJIE: "Distributed MPC for Interconnected Power System", 《PROCEEDINGS OF THE 32ND CHINESE CONTROL CONFERENCE》 *
YUBIN JIA: "Distributed MPC with Application to AGC of Power System withWind-Photovoltaic Hybrid Generation", 《PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE》 *
侯国莲 等: "基于灰狼优化算法的多源互联电力系统负荷频率控制", 《郑州大学学报（工学版）》 *
张怡: "分布式模型预测控制在新能源电力系统负荷频率控制中的应用", 《CNKI优秀博士学位论文全文库》 *
黄玉洁 等: "基于风光储的微电网离网稳定运行分析", 《2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111934320A (en) * 2020-08-20 2020-11-13 三峡大学 Active power distribution network rapid reconstruction method based on linear power flow equation and improved firework algorithm
CN111934320B (en) * 2020-08-20 2023-08-01 三峡大学 Active power distribution network rapid reconstruction method based on linear tide equation and improved firework algorithm
CN112531737A (en) * 2020-11-24 2021-03-19 绍兴市上虞区舜兴电力有限公司 Wind-light-fuel cell micro-grid frequency control method based on robust firefly-particle swarm hybrid optimization
CN112583040A (en) * 2020-12-21 2021-03-30 中国科学院电工研究所 Active management and control method for distributed energy and user alternating current-direct current power distribution system
CN114200840A (en) * 2021-12-10 2022-03-18 广东工业大学 Traditional Chinese medicine pharmacy process operation optimization method based on distributed model predictive control
CN114200840B (en) * 2021-12-10 2023-05-23 广东工业大学 Traditional Chinese medicine pharmaceutical process operation optimization method based on distributed model predictive control

Similar Documents

Publication Publication Date Title
CN111144668B (en) Method for establishing comprehensive energy system random optimization model considering scene simulation
CN110932320A (en) Design method of distributed model predictive controller of automatic power generation control system
CN106786677B (en) A kind of interconnected electric power system distributed dynamic matrix frequency control method
CN109103929B (en) Power distribution network economic optimization scheduling method based on improved dynamic kriging model
Qiu et al. Stochastic online generation control of cascaded run-of-the-river hydropower for mitigating solar power volatility
CN104408534A (en) Simultaneous equation model-based running optimization method for tower type solar thermoelectric generation system
KR20130099479A (en) Method of sensorless mppt neural control for wind energy conversion systems
CN113408962A (en) Power grid multi-time scale and multi-target energy optimal scheduling method
CN105162141A (en) Power grid reactive power optimization method with wind power uncertainty and voltage stability being taken into consideration
CN109669355B (en) Micro gas turbine combined cooling and power supply control system and control method based on generalized predictive control
CN109787227B (en) Multi-time scale optimization scheduling method for multi-energy complementary system
Gulzar et al. An efficient design of adaptive model predictive controller for load frequency control in hybrid power system
CN110912185B (en) Design method of PID controller of automatic power generation control system of power grid containing wind power generation
Ba et al. Optimization of the P&O-MPPT controller by the adaptive method (Ad-P&O) for stand-alone PV systems
CN102661243B (en) Forecast correction pitch variation control method for doubly-fed induction wind power generator set
Ma et al. Offshore wind power generation system control using robust economic MPC scheme
Dallmer-Zerbe et al. Assessment of capacity factor and dispatch flexibility of concentrated solar power units
CN113836819B (en) Bed temperature prediction method based on time sequence attention
CN116306242A (en) Real-time calculation method and system for low-level heat value of coal fed into thermal power plant
Katkout et al. Nonlinear power control strategies for variable-speed wind turbines
KR20160059335A (en) Real time MPPHT method for hybrid power generation system
CN115271244A (en) Two-stage distribution robust optimization-based short-term peak regulation model of cascade hydropower station
CN113031440A (en) Wind turbine variable pitch control method based on feedback linearization and prediction control
CN114079293A (en) New energy power fluctuation smoothing method for electric-gas interconnected system considering temperature control load and micro gas turbine
Simani et al. Self--Tuning Control Techniques for Wind Turbine and Hydroelectric Plant Systems

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200327