Control method and system for heat pump-micro gas turbine combined heat and power system

Abstract

The invention discloses a heat pump-micro gas turbine combined heat and power system control method and system, and relates to the technical field of thermal control, and the method comprises the steps: obtaining step response data, identifying input and output data, and taking the input and output data as a system state space model; the method comprises the following steps: constructing an augmented state space model based on disturbance amplification to obtain a state space model predicted value; and correcting the state quantity according to the deviation between the actual output of the controlled object and the predicted value. According to the method, through the improved heat pump-micro gas turbine combined heat and power system control method, the randomness and intermittency of renewable energy sources are effectively dealt with, and the stability of power supply is ensured, especially when the load changes or the output of the renewable energy sources fluctuates; by means of the control method, waste heat of the micro gas turbine can be more effectively utilized, heat supply of the heat pump is combined, the overall heat supply efficiency is improved, and different heat supply requirements of users are met.

Description

Technical Field

[0001] The present invention relates to the field of thermal control technology, and in particular to a heat pump-micro-turbine combined heat and power system control method and system. Background Art

[0002] Off-grid integrated energy systems have attracted worldwide attention for their remarkable features such as high efficiency, good user adaptability and strong system independence. It can easily provide energy to remote or rural areas where public power grids are inaccessible. Its strong system independence also gives off-grid integrated energy systems the ability to absorb local renewable energy to alleviate the impact of intermittent and random fluctuations of renewable energy on the public power grid. However, without the support of the public power grid, the integrated energy system is easily disturbed by multiple energy sources from both the supply and demand sides. Although equipping with a larger capacity energy storage system is a feasible solution, the high investment cost and potential safety hazards hinder the large-scale deployment of energy storage systems. Therefore, formulating a reasonable operation control strategy to coordinate the operation of multiple energy devices is crucial to promote the efficient utilization of energy systems.

[0003] Among various energy demands, power supply and heat supply are the two most important ones. In this context, the cogeneration integrated energy system has attracted great attention. Unlike the traditional power supply system, the cogeneration integrated energy system can meet a wider range of energy supply needs, which puts higher requirements on the control objectives and control requirements of the overall energy system. The patent of this invention selects a typical off-grid cogeneration integrated energy system as the research object to supply energy for a teaching building of a university. Its energy supply equipment mainly includes an 80kW micro gas turbine, a 45kW air source heat pump, a 100kW photovoltaic power generation equipment and a 60Ah lithium battery. The microgrid bus type is 380V DC and is a single bus form. The waste heat of the micro gas turbine generates domestic hot water through the heat exchanger, thereby realizing the cogeneration of heat and power of the micro gas turbine. At the same time, the micro gas turbine waste heat heating and heat pump heating are mixed in the same water supply manifold to jointly meet the heating needs of users.

[0004] In order to cope with the randomness, intermittency and volatility of renewable energy generation, it is particularly important to achieve coordinated control of two key adjustable devices, air source heat pump and micro gas turbine. Therefore, the control core of this system lies in the design of the heat pump-micro gas turbine combined heat and power system controller, which aims to maintain the electric load demand deviation at zero and achieve different water temperatures at the outlet of the water supply manifold by changing the micro gas turbine fuel flow and the air source heat pump compressor speed. However, the user side has extremely high requirements for power supply and a certain insensitivity to heating demand. The classical model predictive control method has limited ability to cope with control requirements of different scales. Therefore, how to improve the power supply and heat capacity of the heat pump-micro gas turbine combined heat and power system puts forward higher requirements on the control system design. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing control method of the cogeneration integrated energy system has the problems of insufficient power supply stability, low heating efficiency, limited model predictive control capability, and how to achieve coordinated control of the heat pump and the micro gas turbine to improve the system's power supply and heating capacity.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a heat pump-micro-turbine combined heat and power system control method, comprising acquiring step response data, identifying input and output data and using the input and output data as a system state space model; obtaining a state space model prediction value by constructing an augmented form state space model based on disturbance amplification; correcting the state quantity according to the deviation between the actual output of the controlled object and the predicted value; and obtaining the control quantity at the current moment by constructing the objective function of the model predictive control optimization problem.

[0008] As a preferred solution of the heat pump-micro gas turbine cogeneration system control method described in the present invention, the step response data obtained includes adding two control quantities, namely the micro gas turbine fuel quantity u1 and the air source heat pump compressor speed u2 under steady-state conditions, as step excitation signal data as input, and obtaining two controlled quantities, namely the electric load demand deviation y1 and the water temperature y2 at the water supply manifold outlet, as output step response data.

[0009] As a preferred solution of the heat pump-micro-turbine combined heat and power system control method described in the present invention, the identification of input and output data and using the input and output data as the system state space model includes identifying two control quantities of the micro-turbine fuel quantity u1 and the air source heat pump compressor speed u2 and two controlled quantity data of the electric load demand deviation y1 and the water temperature y2 at the outlet of the water supply manifold, and using the data as the system state space model; assuming that the dynamic characteristics of the system are described by a discrete state space model, it is expressed as:

[0010] x d (k+1)=A d x d (k)+B d u(k)

[0011] y(k)=C d x d (k)

[0012] in, They represent the state, input, and output of the system at time k, respectively. x 、n u 、n y Respectively represent the number of state quantities, input quantities, and output quantities of the system, Ad , B d , C d The coefficient matrices represent the state quantity, input quantity, and output quantity respectively.

[0013] As a preferred solution of the control method of the heat pump-micro-turbine combined heat and power system of the present invention, the construction of the augmented form state space model based on disturbance amplification includes adding an integral action to the controller to solve the problem that the predictive control algorithm based on the discrete state space model cannot guarantee the zero-static error control effect; the integral action is to introduce the disturbance model Δx d (k) = x d (k)-x d (k-1) and define Δu(k)=u(k)-u(k-1); where Δu(k) represents the change in the control amount of k at the current moment, u(k) represents the control amount of k at the current moment, and u(k-1) represents the control amount of k-1 at the previous moment; the following formula is obtained, which is expressed as:

[0014] Δx d (k+1)=A d Δx d (k)+B d Δu(k)

[0015] The output variable y(k) and Δx d (k) is connected and expressed as:

[0016] Δy(k+1)=y(k+1)-y(k)=C d Δx d (k)+C d B d Δu(k)

[0017] Define the augmented state variable vector The augmented discrete state space model is obtained, which is expressed as:

[0018]

[0019]

[0020] Where O represents n y ×n x dimensional zero matrix, Represents the identity matrix, dimension is n y ×n y , which represents the direct transfer relationship of the output variable, and the augmented discrete state space model is abbreviated as:

[0021]

[0022] Among them, A, B u, C represent the corresponding augmentation coefficient matrices respectively.

[0023] As a preferred solution of the control method of the heat pump-micro-turbine combined heat and power system of the present invention, the state space model prediction value is obtained by assuming that the prediction time domain is P, the control time domain is M, and M≤P; taking the current time k as the sampling starting point, the state variables at each future time are expressed in sequence as follows:

[0024]

[0025] Then the output variables at each future moment are expressed as follows:

[0026]

[0027] Define the output vector in the future prediction time domain P and the control increment in the future control time domain M, expressed as:

[0028]

[0029] Therefore, the prediction model is obtained, which is expressed as:

[0030]

[0031] in,

[0032] Among them, Y(k) represents the system output vector in the prediction time domain P starting from time k, ΔU(k) ​​represents the control quantity increment vector in the control time domain M starting from time k, φ x represents the state influence matrix, which represents the impact of the initial state on the future output, φ u Denotes the control impact matrix, which represents the impact of control increments on future outputs.

[0033] As a preferred solution of the control method of the heat pump-micro-turbine combined heat and power system of the present invention, the correction of the state quantity according to the deviation between the actual output and the predicted value of the controlled object includes estimating the amplified state x by using the Kalman filter method based on the fact that the state quantity x0 has no physical meaning and cannot be directly measured, and correcting the state quantity according to the deviation between the actual output and the predicted value to achieve feedback correction; the Kalman filter state estimation is expressed as:

[0034] x(k+1|k)=Ax(k|k)+B u Δu(k)

[0035] P(k+1|k)=AP(k|k)A T +Q n

[0036] K(k)=P(k+1|k)C T (CP(k+1|k)C T +V n ) -1

[0037] According to the measured value of the output variable y m , the covariance matrix P' and the augmented state x are updated, expressed as:

[0038] P'(k+1|k+1)=[IK(k)C]P'(k+1|k)

[0039] x(k+1|k+1)=x(k+1|k)+K(k)(y m (k)-y(k))

[0040] Among them, P' represents the posterior estimated covariance matrix, Q n 、V n They represent the covariance matrix of the state quantity and the measurement value noise respectively, and K represents the Kalman gain.

[0041] As a preferred solution of the heat pump-micro-turbine cogeneration system control method described in the present invention, wherein: the objective function of constructing the model predictive control optimization problem includes constructing the objective function of the classic model predictive control algorithm facing the electric load demand deviation y1, and constructing the objective function of the interval model predictive control algorithm facing the outlet water temperature y2 of the water supply manifold; constructing the objective function of the classic model predictive control algorithm facing the electric load demand deviation y1 includes achieving the set value tracking control target and ensuring that the controller action is not too large. The objective function of the finite time domain optimization problem in the classic MPC algorithm is expressed as:

[0042]

[0043] Stu min ≤u≤u max ,Δu min ≤Δu≤Δu max

[0044] Among them, matrix Q and matrix R represent the error weight coefficient matrix and control weight coefficient matrix respectively, y set (i) represents the set value of the controlled quantity, u min Represents the minimum value of the constraint control input, u max Expressed as the maximum value of the constraint control input, Δu min Expressed as the minimum increment of the constraint control increment, Δu maxIt is expressed as the maximum increment of the constraint control increment; constructing the interval model predictive control algorithm objective function facing the outlet water temperature y2 of the water supply manifold includes constructing the interval tracking deviation term by using the equation relationship between the predicted output and the upper and lower boundaries of the interval, and constructing the interval control objective function based on the objective function of the classic model predictive control algorithm; the relationship between the model predicted output and the interval boundary is divided into three cases, including higher than the upper boundary of the interval, within the interval range, and lower than the lower boundary of the interval; giving the distance d from the predicted output to the expected interval k , expressed as:

[0045]

[0046] Among them, d k represents a piecewise function, y k represents the predicted output value at the current time k, y H Represents the upper bound of the expected interval of the output, y L Indicates the lower bound of the expected interval of the output; because d k The performance index constructed by the piecewise function cannot be solved, so we use y k ,y H ,y L The relationship between k Perform further transformation processing;

[0047] The predicted output is greater than the upper limit of the interval, that is, y k >y H , and deduced, expressed as:

[0048]

[0049] Similarly, when y k >y H ,y L <y k <y H When , the distance from the predicted output to the expected interval is derived as above; the objective function of the interval model predictive control algorithm facing the outlet water temperature y2 of the water supply header is transformed into the interval control objective function, which is expressed as:

[0050]

[0051] Stu min ≤u≤u max ,Δu min ≤Δu≤Δu max

[0052] Note that when the upper and lower bounds of the interval are equal, that is, y L =y HWhen , the objective function of the interval model predictive control algorithm is equivalent to the objective function of the classical model predictive control algorithm, that is, the interval control objective function degenerates into the tracking control objective function; by constructing the boundary of the set value interval, the control objectives of tracking the electric load demand deviation y1 and the interval control of the water temperature y2 at the outlet of the water supply manifold are realized; after converting the constraint into the constraint on ΔU, the optimal control quantity increment ΔU at P moments is obtained, and then the instantaneous control quantity increment is extracted and added to the control quantity at the previous moment to obtain the control quantity at the current moment; the control quantity at the current moment is expressed as:

[0053] u(k)=u(k-1)+Δu

[0054] Finally, the control quantity at the current moment is applied to the controlled object.

[0055] Another object of the present invention is to provide a heat pump-micro-turbine cogeneration system control system, which can realize the tracking of electric load demand deviation y1 and the interval control of water temperature y2 at the outlet of the water supply manifold by constructing the objective function of the model predictive control optimization problem, thereby improving the ability of the classical model predictive control method to quickly track the set value in the multi-objective control optimization problem, and solving the problem that the current classical model predictive control method has limited ability in responding to control requirements of different scales.

[0056] As a preferred solution of the control system of the heat pump-micro-turbine cogeneration system described in the present invention, it includes: an identification module, a prediction module, a correction module, and a control module; the identification module is used to obtain step response data, identify input and output data, and use the input and output data as the system state space model; the prediction module is used to obtain the state space model prediction value by constructing an augmented form state space model based on disturbance amplification; the correction module is used to correct the state quantity according to the deviation between the actual output of the controlled object and the predicted value; the control module is used to obtain the control quantity at the current moment by constructing the objective function of the model prediction control optimization problem.

[0057] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a control method for a heat pump-micro-turbine combined heat and power generation system.

[0058] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a control method for a heat pump-micro-turbine combined heat and power generation system.

[0059] Beneficial effects of the present invention: The heat pump-micro gas turbine cogeneration system control method provided by the present invention effectively copes with the randomness and intermittency of renewable energy through an improved heat pump-micro gas turbine cogeneration system control method, and ensures the stability of power supply, especially when the load changes or the output of renewable energy fluctuates; the control method of the present invention can more effectively utilize the waste heat of the micro gas turbine, and combine it with the heat pump heating, thereby improving the overall heating efficiency and meeting the different heating needs of users; by introducing interval model predictive control, the limitations of classical model predictive control in dealing with control requirements of different scales are solved, and it performs well in simultaneously meeting high power supply requirements and relatively loose heating requirements. Through the control strategy, compared with the traditional method of expanding the scale of the energy storage system, the existing equipment can be more effectively utilized, and the investment cost and operating cost of the system can be reduced; through the improved coordinated control strategy, the safety hazards caused by fluctuations in energy supply and demand are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0061] Figure 1 An overall flow chart of a heat pump-micro-turbine combined heat and power system control method provided for the first embodiment of the present invention.

[0062] Figure 2 A schematic block diagram of the principle of a heat pump-micro-turbine combined heat and power system control method provided for the first embodiment of the present invention.

[0063] Figure 3 A schematic diagram of the operation of a heat pump-micro gas turbine combined heat and power system according to a control method of a heat pump-micro gas turbine combined heat and power system provided in accordance with a second embodiment of the present invention.

[0064] Figure 4 This is an output step response experimental diagram of a heat pump-micro-turbine combined heat and power system control method provided in the second embodiment of the present invention.

[0065] Figure 5 A schematic diagram of the output variable control effect of a heat pump-micro-turbine combined heat and power system control method provided in the second embodiment of the present invention.

[0066] Figure 6 A schematic diagram of input variable changes of a heat pump-micro-turbine combined heat and power system control method provided for the second embodiment of the present invention.

[0067] Figure 7An overall flow chart of a control system of a heat pump-micro-turbine combined heat and power system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0069] Example 1, reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides a heat pump-micro-turbine combined heat and power system control method, comprising:

[0070] S1: Obtain step response data, identify input and output data and use the input and output data as the system state space model.

[0071] Furthermore, the step response data is obtained by adding two control quantities, namely the micro gas turbine fuel quantity u1 under steady-state conditions and the air source heat pump compressor speed u2, as input step excitation signal data, and obtaining two controlled quantities, namely the electric load demand deviation y1 and the water supply manifold outlet water temperature y2, as output step response data.

[0072] It should be noted that identifying the input and output data and using the input and output data as the system state space model includes identifying the two control quantities of the micro gas turbine fuel quantity u1 and the air source heat pump compressor speed u2 and the two controlled quantity data of the electric load demand deviation y1 and the water supply header outlet water temperature y2, and using the data as the system state space model; assuming that the dynamic characteristics of the system are described by a discrete state space model, it is expressed as:

[0073] x d (k+1)=A d x d (k)+B d u(k)

[0074] y(k)=C d x d (k)

[0075] in, They represent the state, input, and output of the system at time k, respectively. x 、n u 、n y Respectively represent the number of state quantities, input quantities, and output quantities of the system, A d , B d , Cd The coefficient matrices represent the state quantity, input quantity, and output quantity respectively.

[0076] It should also be noted that the open-loop transfer function model of the system is identified and expressed as:

[0077]

[0078] The discrete state space model of the heat pump-micro turbine combined heat and power system is obtained by converting the transfer function into the state space equation.

[0079] S2: By constructing an augmented form state-space model based on disturbance amplification, the predicted value of the state-space model is obtained.

[0080] Furthermore, the construction of an augmented form state space model based on disturbance amplification includes adding an integral action to the controller to solve the problem that the predictive control algorithm based on the discrete state space model cannot guarantee the zero-static error control effect; the integral action is to introduce the disturbance model Δx d (k) = x d (k)-x d (k-1) and define Δu(k)=u(k)-u(k-1); where Δu(k) represents the change in the control amount of k at the current moment, u(k) represents the control amount of k at the current moment, and u(k-1) represents the control amount of k-1 at the previous moment; the following formula is obtained, which is expressed as:

[0081] Δx d (k+1)=A d Δx d (k)+B d Δu(k)

[0082] The output variable y(k) and Δx d (k) is connected and expressed as:

[0083] Δy(k+1)=y(k+1)-y(k)=C d Δx d (k)+C d B d Δu(k)

[0084] Define the augmented state variable vector The augmented discrete state space model is obtained, which is expressed as:

[0085]

[0086] Where O represents n y ×n x dimensional zero matrix, Represents the identity matrix, dimension is n y ×ny , which represents the direct transfer relationship of the output variable, and the augmented discrete state space model is abbreviated as:

[0087]

[0088] Among them, A, B u , C represent the corresponding augmentation coefficient matrices respectively.

[0089] It should be noted that obtaining the predicted value of the state space model includes assuming that the prediction time domain is P, the control time domain is M, and M≤P; taking the current time k as the sampling starting point, the state variables at each future time are expressed in sequence as follows:

[0090]

[0091] Then the output variables at each future moment are expressed as follows:

[0092]

[0093] Define the output vector in the future prediction time domain P and the control increment in the future control time domain M, expressed as:

[0094]

[0095] Therefore, the prediction model is obtained, which is expressed as:

[0096]

[0097] in,

[0098] Among them, Y(k) represents the system output vector in the prediction time domain P starting from time k, ΔU(k) ​​represents the control quantity increment vector in the control time domain M starting from time k, φ x represents the state influence matrix, which represents the impact of the initial state on the future output, φ u Denotes the control impact matrix, which represents the impact of control increments on future outputs.

[0099] S3: Correct the state quantity according to the deviation between the actual output of the controlled object and the predicted value.

[0100] Furthermore, the state quantity is corrected according to the deviation between the actual output and the predicted value of the controlled object. The state quantity x0 has no physical meaning and cannot be directly measured. The augmented state x is estimated by using the Kalman filter method, and the state quantity is corrected according to the deviation between the actual output and the predicted value to achieve feedback correction. The Kalman filter state estimation is expressed as:

[0101] x(k+1|k)=Ax(k|k)+Bu Δu(k)

[0102] P(k+1|k)=AP(k|k)A T +Q n

[0103] K(k)=P(k+1|k)C T (CP(k+1|k)C T +V n ) -1

[0104] According to the measured value of the output variable y m , the covariance matrix P' and the augmented state x are updated, expressed as:

[0105] P'(k+1|k+1)=[IK(k)C]P'(k+1|k)

[0106] x(k+1|k+1)=x(k+1|k)+K(k)(y m (k)-y(k))

[0107] Among them, P' represents the posterior estimated covariance matrix, Q n 、V n They represent the covariance matrix of the state quantity and the measurement value noise respectively, and K represents the Kalman gain.

[0108] S4: By constructing the objective function of the model predictive control optimization problem, the control quantity at the current moment is obtained.

[0109] Furthermore, the objective function of constructing the model predictive control optimization problem includes constructing the objective function of the classic model predictive control algorithm facing the electric load demand deviation y1 and constructing the objective function of the interval model predictive control algorithm facing the outlet water temperature y2 of the water supply manifold; constructing the objective function of the classic model predictive control algorithm facing the electric load demand deviation y1 includes achieving the set value tracking control target and ensuring that the controller action is not too large. The objective function of the finite time domain optimization problem in the classic MPC algorithm is expressed as:

[0110]

[0111] Stu min ≤u≤u max ,Δu min ≤Δu≤Δu max

[0112] Among them, matrix Q and matrix R represent the error weight coefficient matrix and control weight coefficient matrix respectively, y set (i) represents the set value of the controlled quantity, u min Represents the minimum value of the constraint control input, umax Expressed as the maximum value of the constraint control input, Δu min Expressed as the minimum increment of the constraint control increment, Δu max It is expressed as the maximum increment of the constraint control increment; constructing the interval model predictive control algorithm objective function facing the outlet water temperature y2 of the water supply manifold includes constructing the interval tracking deviation term by using the equation relationship between the predicted output and the upper and lower boundaries of the interval, and constructing the interval control objective function based on the objective function of the classic model predictive control algorithm; the relationship between the model predicted output and the interval boundary is divided into three cases, including higher than the upper boundary of the interval, within the interval range, and lower than the lower boundary of the interval; giving the distance d from the predicted output to the expected interval k , expressed as:

[0113]

[0114] Among them, d k represents a piecewise function, y k represents the predicted output value at the current time k, y H Represents the upper bound of the expected interval of the output, y L Indicates the lower bound of the expected interval of the output; because d k The performance index constructed by the piecewise function cannot be solved, so we use y k ,y H ,y L The relationship between k Further transformation processing is performed to predict the output to be greater than the upper limit of the interval, that is, y k >y H , and deduced, expressed as:

[0115]

[0116] Similarly, when y k >y H ,y L <y k <y H When , the distance from the predicted output to the expected interval is derived as above; the objective function of the interval model predictive control algorithm facing the outlet water temperature y2 of the water supply header is transformed into the interval control objective function, which is expressed as:

[0117]

[0118] Stu min ≤u≤u max ,Δu min ≤Δu≤Δu max

[0119] Note that when the upper and lower bounds of the interval are equal, that is, y L =y HWhen , the objective function of the interval model predictive control algorithm is equivalent to the objective function of the classical model predictive control algorithm, that is, the interval control objective function degenerates into the tracking control objective function; by constructing the boundary of the set value interval, the control objectives of tracking the electric load demand deviation y1 and the interval control of the water temperature y2 at the outlet of the water supply manifold are realized; after converting the constraint into the constraint on ΔU, the optimal control quantity increment ΔU at P moments is obtained, and then the instantaneous control quantity increment is extracted and added to the control quantity at the previous moment to obtain the control quantity at the current moment; the control quantity at the current moment is expressed as:

[0120] u(k)=u(k-1)+Δu

[0121] Finally, the control quantity at the current moment is applied to the controlled object.

[0122] It should be noted that at each sampling moment, the output prediction is substituted into the objective function to obtain the optimal control sequence ΔU, and the control amount u(k)=u(k-1)+Δu at the current moment is input into the system.

[0123] Example 2, reference Figure 3-Figure 6 , which is an embodiment of the present invention, provides a heat pump-micro-turbine combined heat and power system control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0124] like Figure 3 As shown, the micro gas turbine operates in the cogeneration mode, and the electricity generated is used to meet the demand on the power supply side; the waste heat of the exhaust is recovered through the heat exchanger to heat the feed water, and the hot water generated by the air source heat pump compressor and the hot water heated by the micro gas turbine waste heat are mixed in the water supply manifold to meet the demand on the heating side.

[0125] like Figure 4 As shown in the figure, when the amount of fuel in the micro gas turbine increases, the power generation of the micro gas turbine increases rapidly, and the rise time is about 10s. At this time, the speed of the air source heat pump compressor remains unchanged, that is, the power consumption of the system remains unchanged. Therefore, the system load demand deviation increases with the increase of the power generation of the micro gas turbine. At the same time, as the amount of fuel increases, the exhaust temperature and flow rate of the micro gas turbine increase accordingly, resulting in a corresponding increase in the waste heat heating capacity of the micro gas turbine, and then the water temperature at the outlet of the water supply manifold changes in the same direction. Due to the nature of the heat exchange process, the change of the water temperature at the outlet of the water supply manifold has a certain inertia, among which the rise time of the water temperature at the outlet of the water supply manifold is about 350s.

[0126] The data of a typical day in April at 12 noon in a certain area of ​​Jiangsu Province were selected. The power load on the power supply side increased from 90kW to 110kW at 700s, and from 110kW to 120kW at 1600s. The power load demand deviation must always be kept at 0kW; the outlet water temperature of the heating and water supply manifold is always maintained in the range of 75±2℃. Observe the actual changes of the power load demand deviation, the outlet water temperature of the heating manifold, the fuel amount of the micro-turbine, and the speed of the heat pump compressor under this control method.

[0127] like Figure 5 As shown, on the power supply side, when the control method of the present invention and the control method based on the classical model predictive control method are used, the fuel quantity of the micro-turbine and the speed of the heat pump compressor can be timely coordinated, the electric load can be met, and the electric load demand deviation can quickly return to 0kW. From the local method result diagram of the electric load demand deviation, it can be seen that compared with the classical model predictive control strategy, when the interval model predictive control strategy of the present invention is used, the overshoot of the system electric load demand deviation is greatly reduced or basically no overshoot is achieved.

[0128] like Figure 6 As shown, on the heating side, when the control method of the present invention and the classical model predictive control method are used, the outlet water temperature of the heating manifold can be stabilized within the set control area or set value when external disturbances occur, and the user's heat load demand can be met. Analysis shows that after the outlet water temperature of the water supply manifold adopts the interval control of the present invention, when the outlet water temperature of the water supply manifold is within the set interval, the interval model predictive controller no longer needs to consider its control, which releases the control space and realizes more powerful electric load tracking. At the same time, compared with the classical model predictive control strategy, when the control strategy of the present invention is adopted, the change amplitude and change frequency of the micro-turbine fuel amount and the heat pump compressor speed are greatly reduced.

[0129] It can be seen from this that the present invention is capable of utilizing the characteristics of slower dynamic response and lower demand accuracy on the heating side to release a wider dynamic optimization space on the power supply side, and further improves the tracking performance of the power supply side of the overall system while meeting the needs of the heating side.

[0130] Example 3, reference Figure 7 , which is an embodiment of the present invention, provides a heat pump-micro-turbine combined heat and power system control system, including an identification module, a prediction module, a correction module, and a control module.

[0131] Among them, the identification module is used to obtain step response data, identify input and output data and use the input and output data as the system state space model; the prediction module is used to obtain the state space model prediction value by constructing an augmented form state space model based on disturbance amplification; the correction module is used to correct the state quantity according to the deviation between the actual output of the controlled object and the predicted value; the control module is used to obtain the control quantity at the current moment by constructing the objective function of the model predictive control optimization problem.

[0132] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A heat pump-micro-turbine combined heat and power system control method, characterized in that: include: Obtain step response data, identify input and output data and use the input and output data as a system state space model; By constructing an augmented form state space model based on disturbance amplification, the predicted value of the state space model is obtained; Correct the state quantity according to the deviation between the actual output of the controlled object and the predicted value; By constructing the objective function of the model predictive control optimization problem, the control quantity at the current moment is obtained.

2. The heat pump-micro-turbine combined heat and power system control method according to claim 1, characterized in that: The step response data obtained includes adding two control quantities, namely the micro gas turbine fuel quantity u1 under steady-state conditions and the air source heat pump compressor speed u2, as input step excitation signal data, and obtaining two controlled quantities, namely the electric load demand deviation y1 and the water supply manifold outlet water temperature y2, as output step response data.

3. The heat pump-micro-turbine combined heat and power system control method according to claim 2, characterized in that: The identifying of input and output data and using the input and output data as a system state space model includes identifying two control quantities, namely, the micro gas turbine fuel quantity u1 and the air source heat pump compressor speed u2, and two controlled quantity data, namely, the electric load demand deviation y1 and the water supply header outlet water temperature y2, and using the data as a system state space model; Assuming that the dynamic characteristics of the system are described by a discrete state space model, it can be expressed as: x d (k+1)=A d x d (k)+B d u(k) y(k)=C d x d (k) in, They represent the state, input, and output of the system at time k, respectively. x 、n u 、n y Respectively represent the number of state quantities, input quantities, and output quantities of the system, A d , B d , C d The coefficient matrices represent the state quantity, input quantity, and output quantity respectively.

4. The heat pump-micro-turbine combined heat and power system control method according to claim 3, characterized in that: The construction of the augmented form state space model based on disturbance amplification includes adding an integral action to the controller to solve the problem that the predictive control algorithm based on the discrete state space model cannot ensure the zero-static error control effect; The role of integration is to introduce the disturbance model Δx d (k) = x d (k)-x d (k-1) and define Δu(k)=u(k)-u(k-1); Among them, Δu(k) represents the change of the control amount of k at the current moment, u(k) represents the control amount of k at the current moment, and u(k-1) represents the control amount of k-1 at the previous moment; Then we get the following formula, expressed as: Δx d (k+1)=A d Δx d (k)+B d Δu(k) The output variable y(k) and Δx d (k) is connected and expressed as: Δy(k+1)=y(k+1)-y(k)=C d Δx d (k)+C d B d Δu(k) Define the augmented state variable vector The augmented discrete state space model is obtained, which is expressed as: Where O represents n y ×n x dimensional zero matrix, Represents the identity matrix, dimension is n y ×n y , which represents the direct transfer relationship of the output variable, and the augmented discrete state space model is abbreviated as: Among them, A, B u , C represent the corresponding augmentation coefficient matrices respectively.

5. The heat pump-micro-turbine combined heat and power system control method according to claim 4, characterized in that: The state space model prediction value is obtained by assuming that the prediction time domain is P, the control time domain is M, and M≤P; Taking the current time k as the sampling starting point, the state variables at each future time are expressed as follows: Then the output variables at each future moment are expressed as follows: Define the output vector in the future prediction time domain P and the control increment in the future control time domain M, expressed as: Therefore, the prediction model is obtained, which is expressed as: in, Among them, Y(k) represents the system output vector in the prediction time domain P starting from time k, ΔU(k) ​​represents the control quantity increment vector in the control time domain M starting from time k, φ x represents the state influence matrix, which represents the impact of the initial state on the future output, φ u Denotes the control impact matrix, which represents the impact of control increments on future outputs.

6. The heat pump-micro-turbine combined heat and power system control method according to claim 5, characterized in that: The correction of the state quantity according to the deviation between the actual output and the predicted value of the controlled object includes estimating the amplified state x by using the Kalman filter method based on the fact that the state quantity x0 has no physical meaning and cannot be directly measured, and correcting the state quantity according to the deviation between the actual output and the predicted value to achieve feedback correction; Kalman filter state estimation, expressed as: x(k+1|k)=Ax(k|k)+B u Δu(k) P(k+1|k)=AP(k|k)A T +Q n K(k)=P(k+1|k)C T (CP(k+1|k)C T +V n ) -1 According to the measured value of the output variable y m , the covariance matrix P' and the augmented state x are updated, expressed as: P'(k+1|k+1)=[IK(k)C]P'(k+1|k) x(k+1|k+1)=x(k+1|k)+K(k)(y m (k)-y(k)) Among them, P' represents the posterior estimated covariance matrix, Q n 、V n They represent the covariance matrix of the state quantity and the measurement value noise respectively, and K represents the Kalman gain.

7. The heat pump-micro-turbine combined heat and power system control method according to claim 6, characterized in that: The objective function of constructing the model predictive control optimization problem includes constructing the objective function of the classic model predictive control algorithm facing the electric load demand deviation y1 and constructing the objective function of the interval model predictive control algorithm facing the outlet water temperature y2 of the water supply header; The objective function of the classical model predictive control algorithm for the load demand deviation y1 is constructed to achieve the set value tracking control target and ensure that the controller action is not too large. The objective function of the finite time domain optimization problem in the classical MPC algorithm is expressed as: stud min ≤u≤u max ,Δu min ≤Δu≤Δu max Among them, matrix Q and matrix R represent the error weight coefficient matrix and control weight coefficient matrix respectively, y set (i) represents the set value of the controlled quantity, u min Represents the minimum value of the constraint control input, u max Expressed as the maximum value of the constraint control input, Δu min Expressed as the minimum increment of the constraint control increment, Δu max It is expressed as the maximum increment of the constraint control increment; Constructing the interval model predictive control algorithm objective function facing the outlet water temperature y2 of the water supply manifold includes constructing the interval tracking deviation term by using the equation relationship between the predicted output and the upper and lower boundaries of the interval, and constructing the interval control objective function based on the objective function of the classic model predictive control algorithm; There are three situations in which the relationship between the model prediction output and the interval boundary includes above the upper limit of the interval, within the interval range, and below the lower limit of the interval; Give the distance d from the predicted output to the expected interval k , expressed as: Among them, d k represents a piecewise function, y k represents the predicted output value at the current time k, y H Represents the upper bound of the expected interval of the output, y L Represents the lower bound of the expected interval of the output; Because k The performance index constructed by the piecewise function cannot be solved, so we use y k ,y H ,y L The relationship between k Perform further transformation processing; The predicted output is greater than the upper limit of the interval, that is, y k >y H , and deduced, expressed as: Similarly, when y k >y H ,y L <y k <y H When , the above derivation is performed on the distance from the predicted output to the expected interval; The objective function of the interval model predictive control algorithm for the outlet water temperature y2 of the water supply header is transformed into the interval control objective function, which is expressed as: stud min ≤u≤u max ,Δu min ≤Δu≤Δu max Note that when the upper and lower bounds of the interval are equal, that is, y L =y H When , the objective function of the interval model predictive control algorithm is equivalent to the objective function of the classic model predictive control algorithm, that is, the interval control objective function degenerates into the tracking control objective function; By constructing the set value interval boundary, the control objectives of tracking the electric load demand deviation y1 and controlling the water temperature y2 interval at the outlet of the water supply header are achieved; After the constraint is converted into a constraint on ΔU, the optimal control quantity increment ΔU at P moments is obtained, and then the instantaneous control quantity increment is extracted and added to the control quantity at the previous moment to obtain the control quantity at the current moment; The control quantity at the current moment is expressed as: u(k)=u(k-1)+Δu Finally, the control quantity at the current moment is applied to the controlled object.

8. A system using the heat pump-micro-turbine combined heat and power system control method according to any one of claims 1 to 7, characterized in that: It includes identification module, prediction module, correction module and control module; The identification module is used to obtain step response data, identify input and output data and use the input and output data as a system state space model; The prediction module is used to obtain a state space model prediction value by constructing an augmented form state space model based on disturbance amplification; The correction module is used to correct the state quantity according to the deviation between the actual output of the controlled object and the predicted value; The control module is used to obtain the control quantity at the current moment by constructing the objective function of the model predictive control optimization problem.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the heat pump-micro-turbine combined heat and power system control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the heat pump-micro-turbine combined heat and power system control method according to any one of claims 1 to 7 are implemented.

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