Power system load frequency tube distributed model predictive control

Abstract

The present application relates to the technical field of automatic control of power system, and particularly relates to a kind of power system load frequency tube distributed model predictive control, comprising: establishing several regional interconnected power system load frequency control simulation model;Set up the nominal model of power system load frequency control that describes the dynamic characteristics of interconnected power system under the condition of ignoring uncertain disturbance and interregional coupling action;According to the nominal model of power system load frequency control, determine the objective function and constraint condition;Establish a distributed feedback control law to compensate for the trajectory difference between the nominal model of power system and the simulation model due to disturbance;The feedback control quantity and the nominal control quantity are added to obtain the final control quantity and are applied to the actual system. It can be used for the wind speed disturbance problem caused by wind power grid connection, and a power system distributed model predictive control method with stronger robustness can effectively handle uncertain disturbances, ensuring the safe and stable operation of the power system.

Description

Technical Field

[0001] This invention relates to the field of power system automatic control technology, and in particular to a distributed model predictive control of power system load frequency tube. Background Technology

[0002] Load frequency control (LFC) is an important research area in power system control. The main control objective of LFC is to enable the power system's generation to automatically track load changes in real time, maintain a balance between generation and load power, and thus keep the power system frequency and tie-line exchange power at rated values.

[0003] With increasing human demand for electricity, the electricity market has developed rapidly, and the scale of power systems has grown increasingly large. A modern power grid is a large-scale, geographically dispersed interconnected system composed of multiple distributed generation units. With the rapid development of renewable energy, especially wind power, the power system's low-power fuel cell (LFC) task faces more challenges. On the one hand, the randomness, intermittency, and uncontrollability of wind power variations in nature give wind power generation limitations such as high volatility and unpredictability. On the other hand, wind power systems typically operate in maximum power point tracking (MPPT) mode, lacking power reserves and the ability to respond to power system frequency fluctuations. When large-scale wind power is integrated into the grid, it becomes impossible to maintain a balance between the power supplied by the grid and the power demanded by the load, directly jeopardizing the power quality and economic operation of the power system and jeopardizing its safe and stable operation.

[0004] Distributed model predictive control (DMC) fully utilizes the network communication capabilities of industrial processes, allowing information exchange between subsystems. It assigns a global control optimization problem to each subsystem, reducing computational load while avoiding the performance degradation caused by decentralized control. It has been widely applied in LFC problems.

[0005] Chinese Patent Publication No. CN115549187A discloses a stability assessment method for wind power grid connection, which includes the following steps: Step S1: Constructing a planning risk assessment system for a power generation and transmission system including wind power; Step S2: Establishing a small-disturbance probabilistic stability model for a power system with random fluctuations caused by wind power grid connection; Step S3: Constructing an operational risk assessment system for a power generation and transmission system including wind power; Step S4: Establishing a model of the impact of wind power grid connection on voltage stability; Step S5: Establishing a static voltage stability probabilistic assessment model for a wind power grid-connected system; Step S6: Establishing an optimal power flow model for risk assessment of the power generation and transmission system based on DC power flow, and accurately assessing the risk of the power generation and transmission system including wind power grid connection. It is evident that the described technical solution does not consider the problem of unsafe and unstable operation of the power system caused by wind speed disturbances resulting from wind power grid connection. Summary of the Invention

[0006] Therefore, the present invention provides a distributed model predictive control of power system load frequency tube to overcome the problem of unsafe and unstable operation of the power system caused by wind speed disturbances brought about by wind power grid connection in the prior art.

[0007] To achieve the above objectives, the present invention provides a distributed model predictive control for power system load frequency tube, characterized in that it includes:

[0008] Step S1: Establish simulation models for load frequency control of several regional interconnected power systems, including thermal power units, wind power units, and model predictive controllers;

[0009] Step S2: Set up a nominal model for power system load frequency control that describes the dynamic characteristics of the interconnected power system under the condition of neglecting uncertain disturbances and inter-regional coupling effects;

[0010] Step S3: Determine the objective function and constraints based on the nominal model of power system load frequency control;

[0011] Step S4: Based on the power system load frequency control simulation model and the power system load frequency control nominal model, establish a distributed feedback control law to compensate for the trajectory difference between the power system nominal model and the simulation model caused by disturbances.

[0012] Step S5: Add the feedback control quantity to the nominal control quantity to obtain the final control quantity and apply it to the actual system.

[0013] Furthermore, in step S1, the mechanism model of the wind turbine is as follows:

[0014]

[0015] Where, x WT =[ω g ω r θ] T u WT =[β T g ] T ω g ω is the generator speed. r Where θ is the rotor speed of the wind turbine, β is the shaft torsion angle, and T is the pitch angle. g T represents the generator torque. r For aerodynamic torque, v r Effective wind speed.

[0016] Furthermore, regarding the effective wind speed v r (t), set v r (t)=v m ([t / T])+v d(t), where v m V represents the wind speed measured at a specific location and within a sampling time T. d This indicates the error in wind speed measurement.

[0017] Furthermore, regarding the wind speed measurement error v d It is an unknown bounded disturbance, defined as ||v d (t)||2≤ε.

[0018] Furthermore, this invention employs virtual inertial control technology to compensate for the low inertia of the fan, setting...

[0019]

[0020] Wherein, ΔP ESS K is the output of the virtual inertial controller. vi For virtual inertial control gain, D vi Let Δf be the virtual damping constant. i This represents the frequency deviation.

[0021] Furthermore, after linearization, for the power system load frequency control simulation model x i (k+1), set

[0022]

[0023] Among them, w i (k) represents the disturbance quantity that includes uncertain wind speed interference. This refers to the coupling terms between power generation areas.

[0024] Furthermore, in step 2, the power system load frequency control simulation model x is used. i Based on (k+1), a nominal model x′ for power system load frequency control is constructed while neglecting uncertain disturbances and inter-regional coupling. i (k+1), set Wherein, ΔP di (k) represents the power system load change command.

[0025] Further, in step 3, based on the nominal model of power system load frequency control, the objective function and constraints are determined, and the objective function is set as follows:

[0026]

[0027] The constraints are:

[0028]

[0029]

[0030] Where, N p For predicting the time domain, N c To control the time domain, Q i ,R i Q N U is the weight matrix. i and X i These are the sets of system control and state constraints defined based on physical characteristics, Ω. i For robust control invariant sets, Θ represents addition to the Minkowski set, and Θ represents subtraction of the set.

[0031] Furthermore, in step 4, for the distributed feedback control law u i (k), setting

[0032]

[0033] Among them, e i (k)=x i (k)-x i ′(k) represents the deviation of the state trajectory, K ii ,K ij For feedback control gain.

[0034] Furthermore, regarding the control gain K i and K ij ,set up

[0035] (A ii +B ii K i ) T P i (A ii +B ii K i )-P i <0, P i >0

[0036]

[0037] Where L represents all neighboring regions of region i.

[0038] Compared with the prior art, the beneficial effect of the present invention is that it establishes a power system model containing interconnected wind turbine units and takes into account the impact of wind speed disturbance on the system load frequency control.

[0039] A distributed model predictive control strategy based on the tube method was established. Compared with centralized model predictive control, it significantly reduces the computational burden while maintaining control performance and improving the system's disturbance rejection performance. This improves the dynamic performance of load frequency control in interconnected power systems and ensures the safe and stable operation of the power system. Attached Figure Description

[0040] Figure 1 This is a flowchart of the distributed model predictive control of power system load frequency tube described in this invention;

[0041] Figure 2 This is a structural diagram of the simulation model of the thermal power area load frequency control system according to an embodiment of the present invention;

[0042] Figure 3 This is a structural diagram of the simulation model of the wind power area load frequency control system according to an embodiment of the present invention;

[0043] Figure 4 This is a block diagram of the power system load frequency tube distributed model predictive control strategy described in this invention.

[0044] Where, Δf i For frequency deviation, ΔP gi The generator output power deviation, ΔX gi For governor valve position deviation, ΔP tie,i For the active power deviation of the tie line, ΔP di For load disturbance, ΔP ci For control signals, ΔP ri K represents the reheater output power deviation. ri For reheater gain, T ri K is the reheater time constant. pi For the power system gain, T pi T is the time constant of the power system. Gi T is the time constant of the speed controller. Ti K is the turbine time constant. Sij For inter-region interconnection gain, R i Δβ is the droop coefficient, ΔT is the pitch angle change, and ΔT is the propeller angle change. g ΔP represents the change in generator torque. e Let ΔP be the change in output power of the wind farm. ESS K is the output of the virtual inertial controller. vi For virtual inertial control gain, D vi This is the virtual damping constant. Detailed Implementation

[0045] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0046] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0048] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0049] Please see Figures 1 to 4 As shown, where, Figure 1 This is a flowchart of the distributed model predictive control of power system load frequency tube described in this invention. Figure 2 This is a structural diagram of the simulation model of the thermal power area load frequency control system according to an embodiment of the present invention. Figure 3 This is a structural diagram of the simulation model of the wind power area load frequency control system according to an embodiment of the present invention. Figure 4 This is a block diagram of the distributed model predictive control strategy for power system load frequency tube according to the present invention. The present invention provides a distributed model predictive control for power system load frequency tube, characterized in that it includes:

[0050] Step S1: Establish simulation models for load frequency control of several regional interconnected power systems, including thermal power units, wind power units, and model predictive controllers;

[0051] Step S2: Set up a nominal model for power system load frequency control that describes the dynamic characteristics of the interconnected power system under the condition of neglecting uncertain disturbances and inter-regional coupling effects;

[0052] Step S3: Determine the objective function and constraints based on the nominal model of power system load frequency control;

[0053] Step S4: Based on the power system load frequency control simulation model and the power system load frequency control nominal model, establish a distributed feedback control law to compensate for the trajectory difference between the power system nominal model and the simulation model caused by disturbances.

[0054] Step S5: Add the feedback control quantity to the nominal control quantity to obtain the final control quantity and apply it to the actual system.

[0055] Specifically, in step S1, the mechanism model of the wind turbine is as follows:

[0056]

[0057] Where, x WT =[ω g ω r θ] T u WT =[β T g ] T ω g ω is the generator speed. r Where θ is the rotor speed of the wind turbine, β is the shaft torsion angle, and T is the pitch angle. g T represents the generator torque. r For aerodynamic torque, v r Effective wind speed.

[0058] Specifically, for the effective wind speed v r (t), set v r (t)=v m ([t / T])+v d (t), where v m V represents the wind speed measured at a specific location and within a sampling time T. d This indicates the error in wind speed measurement.

[0059] Specifically, regarding the wind speed measurement error v d It is an unknown bounded disturbance, defined as ||v d (t)||2≤ε.

[0060] Specifically, this invention employs virtual inertial control technology to compensate for the low inertia of the wind turbine, and sets...

[0061]

[0062] Wherein, ΔP ESS K is the output of the virtual inertial controller. viFor virtual inertial control gain, D vi Let Δf be the virtual damping constant. i This represents the frequency deviation.

[0063] Specifically, after linearization, for the power system load frequency control simulation model x i (k+1), set

[0064]

[0065] Among them, w i (k) represents the disturbance quantity that includes uncertain wind speed interference. This refers to the coupling terms between power generation areas.

[0066] Specifically, in step 2, the power system load frequency control simulation model x is used. i Based on (k+1), a nominal model x′ for power system load frequency control is constructed while neglecting uncertain disturbances and inter-regional coupling. i (k+1), set Wherein, ΔP di (k) represents the power system load change command.

[0067] Specifically, in step 3, the objective function and constraints are determined based on the nominal model of power system load frequency control, and the objective function is set as follows:

[0068]

[0069] The constraints are:

[0070]

[0071]

[0072] Where, N p For predicting the time domain, N c To control the time domain, Q i ,R i Q N U is the weight matrix. i and X i These are the sets of system control and state constraints defined based on physical characteristics, Ω. i For robust control invariant sets, Θ represents addition to the Minkowski set, and Θ represents subtraction of the set.

[0073] Specifically, in step 4, for the distributed feedback control law u i (k), setting

[0074]

[0075] Among them, e i (k)=x i (k)-x′ i (k) represents the state trajectory deviation, K ii ,K ij For feedback control gain.

[0076] Specifically, for the control gain K i and K ij ,set up

[0077] (A ii +B ii K i ) T P i (A ii +B ii K i )-P i <0, P i >0

[0078]

[0079] Where L represents all neighboring regions of region i.

[0080] Specifically, the robust control invariant set is computed so that the dynamic trajectory of the actual system can be kept within a neighborhood centered on the nominal system trajectory.

[0081] Specifically, the control quantity optimized by the scheme of the present invention at each sampling time is applied to the control system.

[0082] Example 1

[0083] The experiment was set with a discrete sampling time of 0.01s and a simulation time of 50s. A square wave sequence with a mean wind speed of 15m / s and an amplitude of 0.5m / s was given to verify the anti-uncertainty interference capability of the tube distributed model predictive control method.

[0084] Example 2

[0085] The discrete sampling time and simulation time are the same as in Example 1. A turbulent wind speed sequence with a mean of 15 m / s is given. A load step disturbance of -0.005 pu is added to region 2 at 15 s, and a load step disturbance of 0.01 pu is added to region 3 at 25 s. The program for the tube distributed model predictive control method is run in MATLAB, and compared with the tube centralized model predictive method to verify its effectiveness in reducing computational burden and eliminating local disturbances.

[0086] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed model predictive control method for load frequency tube in a power system, characterized in that, include: Step S1: Establish simulation models for load frequency control of several regional interconnected power systems, including thermal power units, wind power units, and model predictive controllers; Step S2: Set up a nominal model for power system load frequency control that describes the dynamic characteristics of the interconnected power system under the condition of neglecting uncertain disturbances and inter-regional coupling effects; Step S3: Determine the objective function and constraints based on the nominal model of power system load frequency control; Step S4: Based on the power system load frequency control simulation model and the power system load frequency control nominal model, establish a distributed feedback control law to compensate for the trajectory difference between the power system nominal model and the simulation model caused by disturbances. Step S5: Add the feedback control quantity to the nominal control quantity to obtain the final control quantity and apply it to the actual system.

2. The power system load frequency tube distributed model predictive control method according to claim 1, characterized in that, In step S1, the mechanism model of the wind turbine is as follows: ； in, , For generator speed, The fan rotor speed, For the axis of twist angle, The pitch angle is the propeller angle. For generator torque, For aerodynamic torque, Effective wind speed.

3. The power system load frequency tube distributed model predictive control method according to claim 2, characterized in that, For the effective wind speed ,set up in, Indicates at a specific location and sampling time The wind speed measured internally, This indicates the error in wind speed measurement.

4. The power system load frequency tube distributed model predictive control method according to claim 3, characterized in that, Regarding the wind speed measurement error It is an unknown bounded disturbance, set as follows: .

5. The power system load frequency tube distributed model predictive control method according to claim 4, characterized in that, Virtual inertial control technology is used to compensate for the low inertia of the wind turbine, and the settings are as follows: ; in, This is the output of the virtual inertial controller. For virtual inertial control gain, The virtual damping constant is... This represents the frequency deviation.

6. The power system load frequency tube distributed model predictive control method according to claim 5, characterized in that, After linearization, for the power system load frequency control simulation model ,set up ； in, This is the disturbance quantity that includes uncertainties in wind speed. This refers to the coupling terms between power generation areas.

7. The power system load frequency tube distributed model predictive control method according to claim 1, characterized in that, In step 2, the power system load frequency control simulation model is used. Based on this, a nominal model for power system load frequency control is constructed while neglecting uncertain disturbances and inter-regional coupling effects. ,in, This is a command for changes in power system load.

8. The power system load frequency tube distributed model predictive control method according to claim 1, characterized in that, In step 3, based on the nominal model of power system load frequency control, the objective function and constraints are determined, and the objective function is set as follows: ； The constraints are: ; in, To predict the time domain, To control the time domain, This is the weight matrix. and These are the sets of system control and state constraints defined based on physical characteristics. For robust control invariant sets, This indicates the addition of the Minkowski set. This indicates a reduction in the set.

9. The power system load frequency tube distributed model predictive control method according to claim 1, characterized in that, In step 4, for the distributed feedback control law ,set up ; in, For state trajectory deviation, For feedback control gain.

10. The power system load frequency tube distributed model predictive control method according to claim 1, characterized in that, For the control gain , set up ; in, For the region All adjacent areas.

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