CN110932320A - Design method of distributed model predictive controller of automatic power generation control system - Google Patents
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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
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:
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: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:
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:
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:
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:
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:
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:
E=Λ-1μ,F=Λ-1v,G=Λ-1ξ
defining an objective function for subsystem i:
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):
the constraints can be written as:
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:
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
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:
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:
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:
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:
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:
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:
E=Λ-1μ,F=Λ-1v,G=Λ-1ξ
defining an objective function for subsystem i:
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):
the constraints can be written as:
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:
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。
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,qi=diag(10 0 1),qj=diag(0.1 0 0 0.1),
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:
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: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:
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:
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:
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:
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:
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:
E=Λ-1μ,F=Λ-1v,G=Λ-1ξ
defining an objective function for subsystem i:
7. the method of claim 1, wherein the constraints are determined by: the thermal power generating unit has generator change rate constraint (GRC):
the constraints can be written as:
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.
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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 |
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