A variable range control method for heat and power coordination in off-grid integrated energy systems

Abstract

The present invention discloses a variable interval control method for heat and electricity coordination of an off-grid integrated energy system, relates to the field of integrated energy systems and automatic control technology, solves the technical problems that the system electric side tracking control under the traditional tracking control scheme is not flexible and fast enough, and the system thermal load deviates from the set value for a long time under the traditional interval control scheme. The key points of the technical solution are that the flexibility and rapidity of the electric side tracking control can be effectively improved by appropriately relaxing the thermal side control requirements during the dynamic adjustment process; by constructing a dynamic interval width change mechanism, the active conversion of the thermal side optimization space to the electric side is realized, and the excessive degradation of the thermal side control performance is avoided while realizing the rapid and stable tracking of the user's electric load demand.

Description

Technical Field

[0001] The present application relates to the field of integrated energy systems and automatic control technologies, specifically to the coordinated control technology of integrated energy systems, and in particular to a variable interval control method for thermal power coordination in off-grid integrated energy systems. Background Art

[0002] With the proposal of my country's "carbon peak and carbon neutrality" goals, building a high-proportion renewable integrated energy system to achieve efficient energy utilization and local consumption of renewable resources has become one of the important ways to transform the energy system into a low-carbon one. However, there is strong coupling and large differences in dynamic characteristics between the energy subsystems in the integrated energy system, and there is also strong uncertainty on both the source and load sides. The above characteristics make the operation and control of the system extremely challenging. Therefore, it is urgent to study advanced operation and control strategies suitable for high-proportion renewable integrated energy systems to ensure stable, economical and flexible energy supply of the system and demonstrate the advantages of the integrated energy system.

[0003] At present, the research on the operation optimization of cogeneration integrated energy systems is mainly focused on the system scheduling level, aiming to provide optimized set value instructions for the operation of the integrated energy system in the long term. However, the rapid and smooth tracking of the optimal scheduling instructions is still highly dependent on the design of the underlying control strategy and algorithm. Therefore, some scholars have studied the coordinated control methods of cogeneration integrated energy systems from the perspective of real-time control, aiming to simultaneously and quickly meet the real-time supply and demand balance of electricity and heat loads. However, due to the strong coupling and response speed differences between the electrical and thermal processes in the integrated energy system, these methods are prone to produce poor control effects or impose a heavy burden on the controller's calculations, especially for off-grid systems with limited energy storage, where the control effect on the electrical side of the system is even more difficult to guarantee.

[0004] At the same time, in integrated energy systems, the response speed of electrical processes is often much faster than that of thermal processes and has higher requirements for supply and demand balance; due to the insensitivity of the human body to temperature, the supply and demand balance on the heat side can be relaxed within a certain range. Interval model predictive control provides a possible implementation solution for the relaxation of the supply and demand balance on the heat side during system operation control by expanding the set value tracking index to an interval tracking index. However, traditional interval control is developed based on the characteristics of industrial processes (such as the control of pH value in chemical processes), and its interval width is usually a preset fixed value. This design method easily leads to long-term deviation of heat load from the set value in the integrated energy system, thereby reducing the long-term energy supply quality. In addition, the current regional control research in the field of energy systems mainly focuses on single equipment, such as coal-fired power plants, batteries, and steam compression processes, and its application in complex multi-energy systems has not yet been developed. Summary of the invention

[0005] The present application provides a variable range control method for thermal-electric coordination in an off-grid integrated energy system. Its technical purpose is to appropriately relax the thermal side control requirements during the dynamic adjustment process, thereby improving the flexibility and rapidity of the system's electrical side tracking control; to achieve active conversion of the thermal side optimization space to the electrical side, and to avoid excessive degradation of the system's thermal side control performance.

[0006] The above technical objectives of this application are achieved through the following technical solutions:

[0007] A variable range control method for heat and power coordination of an off-grid integrated energy system, comprising:

[0008] Step 1: Perform an open-loop step experiment on the mechanism model of the off-grid integrated energy system to obtain a dynamic characteristic experimental curve; wherein the off-grid integrated energy system includes a thermal electron system, and the thermal electron system includes a micro-turbine and a heat pump, and the controlled quantity includes the micro-turbine fuel mass flow rate and the heat pump compressor speed, and the controlled quantity includes the residual electric power and the water temperature at the outlet of the water supply header;

[0009] Step 2: According to the data corresponding to the dynamic characteristic experimental curve, a discrete state space model characterizing the dynamic characteristics of the thermal electron system is identified by a subspace identification method, the discrete state space model is expanded into an incremental state space model, and then the incremental state space model is derived to obtain a prediction model;

[0010] Step 3: Construct an interval control objective function and a dynamic interval width change mechanism, and design a thermal-electric coordination controller corresponding to the thermal electronic system in combination with the prediction model. The thermal-electric coordination controller is used to implement the control modes of the system's electric-side tracking control and the thermal-side variable-interval control, thereby improving the flexibility of thermal-electric coordination in off-grid integrated energy systems, greatly improving the system's electric-side control performance and avoiding excessive degradation of the thermal-side control performance.

[0011] The beneficial effects of this application are:

[0012] (1) The variable interval control method for heat and power coordination described in this application can effectively improve the flexibility and rapidity of the power side tracking control by appropriately relaxing the heat side control requirements during the dynamic adjustment process.

[0013] (2) The dynamic interval width change mechanism described in the present application realizes the active conversion of the thermal side optimization space to the electric side, and avoids excessive degradation of the thermal side control performance while achieving rapid and smooth tracking of the user's electric load demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A system schematic diagram of a specific implementation method of this application;

[0015] Figure 2 This is a control system structure diagram of a specific implementation method of this application;

[0016] Figure 3 A control algorithm flow chart for a specific implementation of this application;

[0017] Figure 4 It is a schematic diagram of a system open-loop step response curve for a specific implementation mode of the present application;

[0018] Figure 5 A schematic diagram of simulation experiment conditions for a specific implementation method of the present application;

[0019] Figure 6 This is a schematic diagram of a controlled quantity result curve of a specific implementation method of this application;

[0020] Figure 7 This is a schematic diagram of the control quantity result curve of a specific implementation method of this application. DETAILED DESCRIPTION

[0021] The technical solution of the present application will be described in detail below with reference to the accompanying drawings.

[0022] The corresponding off-grid integrated energy system of heat and power generation in this embodiment is as follows: Figure 1 As shown. The main equipment of the system includes micro gas turbine (hereinafter referred to as "micro gas turbine"), air source heat pump (hereinafter referred to as "heat pump"), photovoltaic power generation equipment, lithium battery, busbar; other auxiliary equipment includes heat exchanger, pipeline, valve, circulating water pump, controller and related electrical equipment. Among them, lithium battery is only used to balance the bus voltage and does not participate in power balance regulation; micro gas turbine is a micro gas turbine with waste heat utilization function, and its high-temperature exhaust is used to produce hot water through heat exchanger and sent to water supply manifold; the hot water produced by the heat pump is mixed with the hot water produced by the waste heat of the micro gas turbine in the water supply manifold, and the water supply valve controls the mass flow of hot water flowing to the heating user, thereby adjusting the user's indoor temperature, and the water supply valve is controlled by a local PID controller; the power consumption of each power-consuming equipment in the system is met by the system power generation equipment, and there is no external power grid.

[0023] Therefore, in order to achieve the control goal of fast and stable tracking of user heat and electricity demand, this application proposes a variable interval control method for heat and power coordination of off-grid integrated energy system. More specifically, the control structure of the integrated energy system described in this embodiment consists of a heat and power coordination controller and a local PID controller, such as Figure 2 As shown. The physical object controlled by the thermoelectric coordination controller is the thermoelectric system of the integrated energy system, including the micro-turbines and heat pumps. The thermoelectric coordination controller is designed by the variable interval predictive control algorithm, which adjusts the residual electric power of the thermoelectric system and the outlet water temperature of the water supply header by adjusting the fuel mass flow of the micro-turbines and the compressor speed of the heat pump.

[0024] The above-mentioned variable range control method for heat and power coordination of off-grid integrated energy system specifically includes the following steps:

[0025] Step 1: Conduct an open-loop step experiment on the mechanism model of the off-grid integrated energy system to obtain a dynamic characteristic experimental curve; wherein the off-grid integrated energy system includes a thermal electron system, and the thermal electron system includes a micro-turbine and a heat pump, and the controlled quantity includes the micro-turbine fuel mass flow rate and the heat pump compressor speed, and the controlled quantity includes the residual electric power and the water temperature at the water supply manifold outlet.

[0026] Step 2: According to the data corresponding to the dynamic characteristic experimental curve, a discrete state space model characterizing the dynamic characteristics of the thermal electron system is identified through a subspace identification method, the discrete state space model is expanded into an incremental state space model, and then the incremental state space model is derived to obtain a prediction model.

[0027] Step 3: Construct an interval control objective function and a dynamic interval width change mechanism, and design a thermoelectric coordination controller corresponding to the thermoelectric system in combination with the prediction model. The thermoelectric coordination controller realizes the control modes of the system's electric side tracking control and the thermal side variable interval control, thereby improving the flexibility of thermoelectric coordination in the off-grid integrated energy system, greatly improving the system's electric side control performance and avoiding excessive degradation of the thermal side control performance, and ultimately realizing thermoelectric coordination control of the off-grid integrated energy system.

[0028] Specifically, in step 1, the calculation formula of the remaining electric power is as follows:

[0029] N s =N MGT +N PV -N HP -N user ; (1)

[0030] Among them, N s Indicates the remaining electrical power; N MGT Indicates the power generated by the micro-turbine; N PV Represents photovoltaic power generation; N HP Indicates the power consumption of the heat pump; N user Indicates the power consumption of the user.

[0031] In step 2, the discrete state space model is expressed as:

[0032]

[0033] Where u(k)=[m f (k) r c (k)] T , y(k)=[N s(k) T feed (k)] T ; u(k) represents the control quantity, m f (k) represents the mass flow rate of the micro-turbine at time k, r c (k) represents the speed of the heat pump compressor at time k; y(k) represents the controlled quantity, N s (k) represents the remaining electric power at time k, T feed (k) represents the outlet water temperature of the water supply manifold at time k; A d , B d , C d is the corresponding system matrix obtained by the subspace identification method; x d (k) represents the system state vector at time k, x d The value of (k) is estimated by the Kalman filter in each control cycle.

[0034] The discrete state space model is transformed to obtain the incremental state space model, and the incremental state space model is expressed as:

[0035]

[0036] Where x(k)=[Δx d T (k) y T (k)] T , Δx d (k) = x d (k)-x d (k-1); Δu(k)=u(k)-u(k-1); C=[OI]; O represents the zero matrix; I represents the identity matrix.

[0037] The prediction model is derived from the incremental state space model, and the prediction model is expressed as:

[0038] Y(k)=S x ·x(k)+S u ΔU(k); (4)

[0039]

[0040] Where Y(k)=[y(k+1|k) T ,y(k+2|k) T ,...,y(k+P|k) T ] T Indicates the change trajectory of the controlled quantity predicted in the future P time domain; ΔU(k)=[u(k|k) T ,u(k+1|k) T ,...,u(k+M-1|k)T ] T ; M represents the control time domain, P represents the prediction time domain, and M≤P.

[0041] In step 3, the interval control objective function is expressed as:

[0042]

[0043]

[0044]

[0045]

[0046] D H =|Y2(k)-Y 2H (k)|; (10)

[0047] D L =|Y 2L (k)-Y2(k)|; (11)

[0048] Z=|Y 2H (k)-Y 2L (k)|; (12)

[0049] Us min ≤U≤U max ,ΔU min ≤ΔU≤ΔU max ;

[0050] Among them, φ E Indicates the tracking control target of the system electrical side; φ T Indicates the system hot side interval control target; φ ΔU represents the system control incremental penalty term; Y1(k)=[N S (k+1|k) T ,N S (k+2|k) T ,...,N S (k+P|k) T ] T Represents the changing trajectory of the remaining electric power in the future prediction time domain; Y 1set (k) represents the reference trajectory of the remaining electric power in the future prediction time domain; Y2(k) = [T feed (k+1|k) T ,T feed (k+2|k) T ,...,T feed (k+P|k) T ] TIt represents the changing trajectory of the outlet water temperature of the water supply manifold in the future prediction time domain; Indicates the upper boundary of the set interval corresponding to the outlet water temperature of the water supply manifold; represents the lower boundary of the set interval corresponding to the outlet water temperature of the water supply manifold; ΔU(k) ​​represents the control increment sequence of the system in the future control time domain; Q1=diag(q1,q1,...,q1) P×P Represents the output weight matrix corresponding to the residual electric power, Q2 = diag(q2,q2,...,q2) P×P Represents the output weight matrix corresponding to the outlet water temperature of the water supply manifold; R = diag(r,r,...,r) M×M represents the control weight matrix, where r = diag (r1, r2), r1 represents the control weight coefficient corresponding to the micro-turbine fuel mass flow rate, and r2 represents the control weight coefficient corresponding to the heat pump compressor speed; D H It represents the distance from the predicted value of the outlet water temperature of the water supply manifold to the upper boundary of the interval in the future prediction time domain; D L It represents the distance from the predicted value of the outlet water temperature of the water supply manifold in the future prediction time domain to the lower boundary of the interval; Z represents the changing trajectory of the interval width in the future prediction time domain.

[0051] It is worth noting that when the upper and lower boundaries of the setting interval corresponding to the outlet water temperature of the water supply manifold are equal, that is, the width of the corresponding setting interval is zero, the hot side interval control target φ T The electric side tracking control target φ will be degenerated to be the same as the electric side E At this time, both the thermal and electrical sides of the system are in tracking control mode.

[0052] In step 3, the dynamic interval width change mechanism is implemented through a dynamic interval change function, whose input is the actual value of the remaining electric power at the current moment, and the output is the set interval width corresponding to the water temperature at the outlet of the water supply manifold at the current moment; in each control cycle, the set interval width corresponding to the water temperature at the outlet of the water supply manifold is dynamically determined by the real-time remaining electric power.

[0053] The dynamic interval change function is expressed as:

[0054]

[0055] Among them, α and β are constant coefficients; tanh(·) represents the hyperbolic tangent function; λ∈[0,1] represents the loss coefficient; z k Indicates the width of the set interval corresponding to the outlet water temperature of the water supply manifold at time k.

[0056] The construction process of the variable interval predictive control algorithm is as follows: Figure 3As shown, it specifically includes: according to the data corresponding to the dynamic characteristic experimental curve, a discrete state space model of the off-grid integrated energy system is established by a subspace identification method; the discrete state space model is transformed to obtain an incremental state space model; the incremental state space model is derived to obtain a prediction model; in each control cycle, a Kalman filter is used to estimate the state quantity of the thermal electron system, and then the change trajectory of the controlled quantity in the future prediction time domain is obtained in combination with the prediction model; at the same time, according to the real-time residual electric power signal, the set interval width corresponding to the outlet water temperature of the water supply header is obtained by the dynamic interval change function; the change trajectory and reference trajectory of the controlled quantity in the future prediction time domain are sent to the interval control optimizer; wherein, the reference trajectory of the outlet water temperature of the water supply header is the upper and lower boundaries of the set interval; the optimization problem corresponding to the interval control objective function is solved to obtain the optimal control quantity of the current control cycle, that is, the optimal micro-turbine fuel mass flow rate and heat pump compressor speed, and the residual electric power and the outlet water temperature of the water supply header of the off-grid integrated energy system are adjusted by the optimal control quantity.

[0057] The following calculation is based on the actual system as an example. The specific process is as follows:

[0058] Firstly, based on the established mechanism model of off-grid integrated energy system, an open-loop step experiment is carried out to obtain the corresponding dynamic characteristic experimental curve, such as Figure 4 shown.

[0059] Then, taking 1s as the sampling period, according to the corresponding dynamic characteristics experimental data, the subspace identification method is used to obtain the discrete state space model, which is expressed as shown in formula (14):

[0060]

[0061] After appropriate transformation, equation (2) obtains the incremental state space model, and the expression is shown in equation (3). Subsequently, the prediction model is further derived, and the expression is shown in equation (4). Finally, according to equations (6) to (12), the optimization problem of thermal power coordinated control can be constructed, and the optimization solution can be performed to control the off-grid integrated energy system, wherein the set interval width corresponding to the outlet water temperature of the water supply manifold can be calculated by equation (14).

[0062] In order to verify the superiority of the variable interval predictive control algorithm (ZMPC-VZW) proposed in this embodiment, it is compared with the traditional tracking predictive control algorithm (TMPC) and the traditional interval predictive control algorithm (CZMPC). To ensure fairness, except for the set interval width corresponding to the outlet water temperature of the water supply manifold, the other parameters of the heat and power coordination controller corresponding to the three control algorithms are consistent, as shown in Table 1.

[0063] Table 1- Basic parameters of three thermal power coordination controllers

[0064]

[0065] The simulation results are as follows Figures 5 to 7 As shown, Figure 5 Schematic diagram of simulation experimental conditions. Figure 6 It is a schematic diagram of the result curve of the controlled quantity of the simulation experiment. Figure 7 It is a schematic diagram of the simulation experiment control quantity result curve.

[0066] like Figure 6 As shown in the figure, the absolute integral error (IAE) index values ​​of the three controllers are calculated to evaluate the control effects of the power side and the heat side respectively. The results show that when there is high-frequency interference on the power side, the IAE of the ZMPC-VZW strategy is much smaller than that of the TMPC strategy, and even close to that of the CZMPC strategy, which means that the proposed ZMPC-VZW strategy can help the integrated energy system obtain a power supply quality similar to that of the CZMPC strategy, and is much better than that of the TMPC strategy. At the same time, the fluctuation of the outlet temperature of the water supply manifold under the ZMPC-VZW strategy is much smaller than that of the CZMPC strategy. After these power-side interferences disappear, the outlet temperature of the water supply manifold under the ZMPC-VZW strategy can still track its set value, while the outlet temperature of the water supply manifold under the CZMPC strategy may deviate from its set value. This shows that the proposed ZMPC-VZW strategy can avoid excessive degradation of the heat-side control performance while achieving rapid and stable tracking of the user's power load demand, and effectively ensure that the system obtains a stable heat-side control effect over a long period of time.

[0067] Figure 7 It also clearly demonstrates the effectiveness of the dynamic interval width change mechanism under the ZMPC-VZW strategy. Specifically: when the remaining electric power is large, the ZMPC-VZW strategy will choose a wider interval width to further relax the control requirements on the hot side, thereby improving the power supply quality. At this time, the control quantity trajectory of this strategy is closer to the CZMPC strategy; when the remaining electric power becomes smaller, the corresponding interval width on the hot side in the ZMPC-VZW strategy will gradually turn to zero to achieve more accurate thermal energy management. At the same time, the control quantity trajectory of this strategy will gradually approach the TMPC strategy. In addition, the change trajectory of the water supply valve opening under the ZMPC-VZW strategy is also smoother than that of the CZMPC strategy.

[0068] In summary, the off-grid integrated energy system thermal power coordinated control algorithm based on variable interval predictive control proposed in this application has better overall control performance.

[0069] The above are exemplary embodiments of the present application, and the protection scope of the present application is defined by the claims and their equivalents.

Claims

1. A variable range control method for heat and power coordination in off-grid integrated energy systems, It is characterized in that include: Step 1: Perform an open-loop step experiment on the mechanism model of the off-grid integrated energy system to obtain a dynamic characteristic experimental curve; wherein the off-grid integrated energy system includes a thermal electron system, and the thermal electron system includes a micro-turbine and a heat pump, and the controlled quantity includes the micro-turbine fuel mass flow rate and the heat pump compressor speed, and the controlled quantity includes the residual electric power and the water temperature at the outlet of the water supply header; Step 2: According to the data corresponding to the dynamic characteristic experimental curve, a discrete state space model characterizing the dynamic characteristics of the thermal electron system is identified by a subspace identification method, the discrete state space model is expanded into an incremental state space model, and then the incremental state space model is derived to obtain a prediction model; Step 3: construct an interval control objective function and a dynamic interval width change mechanism, and design a thermoelectric coordination controller corresponding to the thermoelectric system in combination with the prediction model, and realize the control mode of system electric side tracking control and thermal side variable interval control through the thermoelectric coordination controller, thereby realizing thermoelectric coordination control of the off-grid integrated energy system; Wherein, in step 3, the interval control objective function is expressed as: D H =|Y 2 (k)-Y 2H (k)|; D L =|Y 2L (k)-Y 2 (k)|; Z=|Y 2H (k)-Y 2L (k)|; stU min ≤U≤U max ,ΔU min ≤ΔU≤ΔU max ; Among them, φ E Indicates the tracking control target of the system electrical side; φ T Indicates the system hot side interval control target; φ ΔU represents the incremental penalty term of system control; Y 1 (k) = [N S (k+1|k) T ,N S (k+2|k) T ,...,N S (k+P|k) T ] T Represents the changing trajectory of the remaining electric power in the future prediction time domain; Y 1set (k) represents the reference trajectory of the remaining electric power in the future prediction time domain; Y 2 (k) = [T feed (k+1|k) T ,T feed (k+2|k) T ,...,T feed (k+P|k) T ] T It represents the changing trajectory of the outlet water temperature of the water supply manifold in the future prediction time domain; Indicates the upper boundary of the set interval corresponding to the outlet water temperature of the water supply manifold; represents the lower boundary of the set interval corresponding to the outlet water temperature of the water supply manifold; ΔU(k) ​​represents the control increment sequence of the system in the future control time domain; Q 1 =diag(q 1 ,q 1 ,...,q 1 ) P×P represents the output weight matrix corresponding to the residual electric power, Q 2 =diag(q 2 ,q 2 ,...,q 2 ) P×P Represents the output weight matrix corresponding to the outlet water temperature of the water supply manifold; R = diag(r,r,...,r) M×M represents the control weight matrix, where r = diag(r 1 ,r 2 ), r 1 represents the control weight coefficient corresponding to the micro-turbine fuel mass flow rate, r 2 Indicates the control weight coefficient corresponding to the speed of the heat pump compressor; D H It represents the distance from the predicted value of the outlet water temperature of the water supply manifold to the upper boundary of the interval in the future prediction time domain; D L It represents the distance from the predicted value of the outlet water temperature of the water supply manifold in the future prediction time domain to the lower boundary of the interval; Z represents the change trajectory of the interval width in the future prediction time domain; In step 3, the dynamic interval width change mechanism is implemented by a dynamic interval change function, and the dynamic interval change function is expressed as: Among them, α and β are constant coefficients; tanh(·) represents the hyperbolic tangent function; λ∈[0,1] represents the loss coefficient; z k Indicates the width of the set interval corresponding to the outlet water temperature of the water supply manifold at time k.

2. The variable range control method according to claim 1, It is characterized in that In step 1, the calculation formula of the remaining electric power is as follows: N s =N MGT +N PV -N HP -N user ; Among them, N s Indicates the remaining electrical power; N MGT Indicates the power generated by the micro-turbine; N PV Represents photovoltaic power generation; N HP Indicates the power consumption of the heat pump; N user Indicates the power consumption of the user.

3. The variable range control method according to claim 2, It is characterized in that In step 2, the discrete state space model is expressed as: Where u(k)=[m f (k)r c (k)] T , y(k)=[N s (k)T feed (k)] T ; u(k) represents the control quantity, m f (k) represents the mass flow rate of the micro-turbine at time k, r c (k) represents the speed of the heat pump compressor at time k; y(k) represents the controlled quantity, N s (k) represents the remaining electric power at time k, T feed (k) represents the outlet water temperature of the water supply manifold at time k; A d , B d , C d is the corresponding system matrix obtained by the subspace identification method; x d (k) represents the system state vector at time k, x d The value of (k) is estimated by the Kalman filter in each control cycle; The incremental state space model is obtained by transforming the discrete state space model, and the incremental state space model is expressed as: x(k+1)=Ax(k)+BΔu(k); y(k)=Cx(k); Among them, Δx d (k)=x d (k)-x d (k-1);Δu(k)=u(k)-u(k-1); C = [OI]; O represents the zero matrix; I represents the identity matrix; The incremental state space model is derived to obtain a prediction model, and the prediction model is expressed as: Y(k)=S x x(k)+S u ΔU(k); Where Y(k)=[y(k+1k) T ,y(k+2k) T ,...,y(k+Pk) T ] T Indicates the change trajectory of the controlled quantity predicted in the future P time domain; ΔU(k)=[u(kk) T ,u(k+1k) T ,...,u(k+M-1k) T ] T ; M represents the control time domain, P represents the prediction time domain, and M≤P.

4. The variable range control method according to any one of claims 1 to 3, It is characterized in that The off-grid integrated energy system also includes photovoltaic and lithium batteries connected to the bus, and the lithium batteries are only used to balance the bus voltage; the control structure of the off-grid integrated energy system includes a thermal power coordination controller and a local PID controller; The micro-turbine is a micro gas turbine with waste heat utilization function, and its high-temperature exhaust gas is used to produce hot water through a heat exchanger and sent to the water supply manifold; the heat pump is an air source heat pump, and the hot water produced by it is mixed with the hot water produced by the micro-turbine in the water supply manifold, and the mass flow rate of the hot water flowing to the heating user is controlled by the water supply valve, and the water supply valve is controlled by the local PID controller.

5. The variable range control method according to any one of claims 1 to 3, It is characterized in that The controlled physical object of the thermoelectric coordination controller is the thermoelectric system, and the thermoelectric coordination controller is designed by a variable interval predictive control algorithm; the thermoelectric coordination controller adjusts the residual electric power of the thermoelectric system and the water temperature at the outlet of the water supply manifold by adjusting the fuel mass flow of the micro-turbine and the compressor speed of the heat pump.

6. The variable range control method according to claim 5, It is characterized in that The construction process of the variable interval predictive control algorithm includes: According to the data corresponding to the dynamic characteristic experimental curve, a discrete state space model of the off-grid integrated energy system is established by a subspace identification method; Transforming the discrete state space model to obtain an incremental state space model; Derivation of the incremental state space model to obtain a prediction model; In each control cycle, the Kalman filter is used to estimate the state quantity of the thermal electron system, and then the change trajectory of the controlled quantity in the future prediction time domain is obtained by combining the prediction model; at the same time, according to the real-time residual power signal, the set interval width corresponding to the outlet water temperature of the water supply header is obtained by the dynamic interval change function; The change trajectory and reference trajectory of the controlled quantity in the future prediction time domain are sent to the interval control optimizer; wherein the reference trajectory of the water temperature at the outlet of the water supply manifold is the upper and lower boundaries of the set interval; The optimization problem corresponding to the interval control objective function is solved to obtain the optimal control quantity of the current control cycle, that is, the optimal micro-turbine fuel mass flow rate and heat pump compressor speed. The residual electric power and the water temperature at the water supply manifold outlet of the off-grid integrated energy system are adjusted through the optimal control quantity.

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