A control method, device, medium and product for a dual-time-scale micro-turbine cogeneration system

Abstract

The present invention discloses a control method, device, medium and product of a dual-time-scale micro-turbine cogeneration system, and relates to the field of cogeneration optimization control. The present invention analyzes the dynamic characteristics of different components of the micro-turbine cogeneration system, and reduces the order of the complex nonlinear solution model based on different time scales, constructs a steady-state solution model of the first time scale and a steady-state solution model of the second time scale, and solves them respectively, thereby improving the control accuracy of the micro-turbine cogeneration system and reducing the complexity of problem solving in the optimization control process of the micro-turbine cogeneration system.

Description

Technical Field

[0001] The present invention relates to the field of heat and power cogeneration optimization control, and in particular to a control method, equipment, medium and product for a dual-time-scale micro-turbine heat and power cogeneration system. Background Art

[0002] Distributed energy systems are widely used because they can fully absorb renewable energy and improve the stability of energy supply. Among them, the micro-turbine cogeneration system uses the micro-turbine as the prime mover, uses the high-temperature flue gas generated by the combustion of natural gas to drive the rotor to rotate and do work, converts mechanical energy into electrical energy, and uses the waste heat of the flue gas after work to meet the heat load, thereby realizing the cascade utilization of energy. Compared with large power stations, the total energy utilization efficiency of the system is as high as 80%-90%, which has become a more efficient way of energy utilization.

[0003] However, the cogeneration system is usually composed of multiple modular components with different dynamic characteristics, showing large inertia, strong coupling, nonlinearity and time scale diversity. In addition, the user side is easily affected by environmental interference such as ambient temperature and solar radiation, resulting in complex and changeable load-side demand. This not only affects the control accuracy of the control system, but also greatly increases the computational complexity of solving the optimization problem. Summary of the invention

[0004] The purpose of the present invention is to provide a dual-time scale micro-turbine cogeneration system control method, equipment, medium and product, which can improve the control accuracy of the micro-turbine cogeneration system and reduce the complexity of problem solving in the optimization control process of the micro-turbine cogeneration system.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A control method for a dual-time-scale micro-turbine cogeneration system, the method comprising:

[0007] Analyze the state variables in the micro-turbine cogeneration system, determine the state variables of the first time scale to form the state quantity of the first time scale, and determine the state variables of the second time scale to form the state quantity of the second time scale, wherein the first time scale is greater than the second time scale;

[0008] According to the state quantity of the first time scale and the state quantity of the second time scale, a steady-state solution model of the first time scale and a steady-state solution model of the second time scale of the micro-turbine cogeneration system are constructed;

[0009] With the goal of reducing energy supply costs and improving user comfort, the online rolling optimization method is used to solve the steady-state solution model of the first time scale, and the optimal sequence of input quantities of the first time scale and the reference sequence of input quantities and state quantities of the second time scale are obtained;

[0010] The input reference sequence and state reference sequence of the second time scale are used as reference signals for solving the steady-state solution model of the second time scale. The steady-state solution model of the second time scale is solved by an online rolling optimization method to obtain the optimal input sequence of the second time scale.

[0011] Optionally, constructing a first time scale steady-state solution model and a second time scale steady-state solution model of the micro-turbine cogeneration system according to the state quantity of the first time scale and the state quantity of the second time scale, specifically includes:

[0012] The overall steady-state solution model of the micro-turbine cogeneration system is constructed; the overall steady-state solution model is:

[0013]

[0014]

[0015] y s (τ) = h s (x s );

[0016] y f (τ) = h f (x f );

[0017] Among them, x s is the state quantity of the first time scale, x f is the state quantity of the second time scale, the state quantity of the first time scale is composed of the state variables of the first time scale, and the state quantity of the second time scale is composed of the state variables of the second time scale; u s is the input quantity of the first time scale, u f is the input quantity of the second time scale, f s () and h s () are the state quantity change mapping function and output quantity mapping function of the first time scale, respectively, f () and h f () is the state quantity change mapping function and output quantity mapping function of the second time scale, d is the interference quantity, ε is a dimensionless parameter, ε=τ f / τ s , τ s is the first time scale, τ f is the second time scale, τ is the time variable; y s(τ) and y f (τ) are the outputs of the first time scale and the second time scale, respectively;

[0018] According to the overall steady-state solution model, let ε→0 and construct the steady-state solution model of the first time scale as follows:

[0019]

[0020] y s (τ) = h s (x s );

[0021] y f (τ) = h f (u f ,u s );

[0022] According to the overall steady-state solution model, the transient model of the second time scale is reconstructed as:

[0023]

[0024]

[0025] y f (τ) = h f (x f );

[0026] According to the transient model of the second time scale, let ε→0 and construct the steady-state solution model of the second time scale as follows:

[0027]

[0028]

[0029] y f (τ) = h f (x f ).

[0030] Optionally, with the goal of reducing energy supply costs and improving user comfort, the expression of the steady-state solution model of the first time scale is solved by an online rolling optimization method:

[0031]

[0032]

[0033]

[0034] Among them, u s is the input quantity of the first time scale, u fis the input quantity of the second time scale, y sp,1 ,y sp,2 , They represent the set value of electric power, the set value of heating temperature and the target range of indoor temperature respectively. The superscript γ represents the relaxation variable. is the number of time steps solved for the first time scale, and are the weights of the electric power optimization target, heating temperature optimization target, indoor temperature optimization target and fuel consumption when solving the first time scale, respectively. The superscript s represents the first time scale. y1(k+i+1), y2(k+i+1) and y3(k+i+1) are the electric power, heating temperature and indoor temperature of the i+1th time step after the sampling time k obtained by solving the solution, respectively. G f is the fuel consumption of the micro-turbine cogeneration system, y sp,1 (k+i+1),y sp,2 (k+i+1) and are the set value of electric power, the set value of heating temperature and the target range of indoor temperature at the i+1th time step after sampling time k,

[0035] x s (k+i+1) and x f (k+i+1) are the state quantities of the first time scale and the second time scale of the i+1th time step after the sampling time k, respectively. s (k+i) and x f (k+i) are the state quantities of the first time scale and the second time scale of the i-th time step after the sampling time k, u s (k+i) is the input of the first time scale at the i-th time step after sampling time k, d(k+i) is the interference at the i-th time step after sampling time k, y s (k+i+1) and y f (k+i+1) are the outputs of the first time scale and the second time scale of the i+1th time step after sampling time k, respectively. s () and h s () are the state quantity change mapping function and output quantity mapping function of the first time scale, respectively, h f () is the output mapping function of the second time scale;

[0036] u j min and u j max are the upper and lower limits of the input quantity constraint at time scale j, respectively, and u j (k+i) is the input quantity of time scale j at the i-th time step after sampling time k, xj min and x j max are the lower and upper limits of the state quantity constraints of time scale j, respectively, j (k+i+1) is the state quantity of time scale j at the i+1th time step after sampling time k, Δu j min and Δu j max are the lower and upper limits of the input change rate constraint at time scale j, Δu j (k+i) is the rate of change of the input quantity at time scale j at the i-th time step after sampling time k; y L (k+i) and y H (k+i) are the upper and lower limits of the target range of the indoor temperature at the i-th time step after sampling time k, is the target interval of the indoor temperature in the i-th time step after the sampling time k, j=s or f, when j=s, the time scale j is the first time scale, when j=f, the time scale j is the second time scale.

[0037] Optionally, the input quantity reference sequence and the state quantity reference sequence of the second time scale are used as reference signals for solving the steady-state solution model of the second time scale, and the expression for solving the steady-state solution model of the second time scale by using the online rolling optimization method is:

[0038]

[0039]

[0040] Among them, u f is the input quantity of the second time scale, The number of time steps solved for the second time scale, and are the weights of the electric power optimization target, heating temperature optimization target, indoor temperature optimization target and fuel consumption when solving the second time scale, y1(k+i+1) is the electric power of the i+1th time step after the sampling time k, G f is the fuel consumption of the micro-turbine cogeneration system, y sp,1 (k+i+1) is the set value of the electric power at the i+1th time step after sampling time k, x f (k+i+1) is the state quantity of the second time scale at the i+1th time step after sampling time k, u f (k+i+1) is the input quantity of the second time scale at the i+1th time step after sampling time k, is the state quantity reference value of the second time scale at the i+1th time step after the sampling time k, is the input reference value of the second time scale at the i+1th time step after the sampling time k;

[0041] x f (k+i) is the state quantity of the second time scale at the i-th time step after sampling time k, u s (k+i) and u f (k+i) are the input quantities of the first time scale and the second time scale of the i-th time step after sampling time k, respectively. f (k+i+1) is the output of the second time scale at the i+1th time step after sampling time k, f f () and h f () is the state quantity change mapping function and output quantity mapping function of the second time scale;

[0042] u f min and u f max are the lower and upper limits of the input quantity constraints of the second time scale, respectively, x f min and x f max are the lower and upper limits of the state quantity constraints of the second time scale, Δu f min and Δu f max are the lower and upper limits of the input change rate constraint of the second time scale, Δu f (k+i) is the rate of change of the input quantity on the second time scale at the i-th time step after sampling time k.

[0043] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above method.

[0044] A computer-readable storage medium stores a computer program, which implements the above method when executed by a processor.

[0045] A computer program product comprises a computer program, and the computer program implements the above method when executed by a processor.

[0046] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0047] The embodiment of the present invention provides a control method, equipment, medium and product for a dual-time-scale micro-turbine cogeneration system, the method comprising: analyzing state variables in the micro-turbine cogeneration system, determining state variables of a first time scale to form state quantities of the first time scale, and determining state variables of a second time scale to form state quantities of the second time scale, constructing a steady-state solution model of the first time scale and a steady-state solution model of the second time scale for the micro-turbine cogeneration system; with the goal of reducing energy supply costs and improving user comfort, using an online rolling optimization method to solve the steady-state solution model of the first time scale, obtaining an optimal sequence of input quantities of the first time scale and a reference sequence of input quantities and a reference sequence of state quantities of the second time scale; using the reference sequence of input quantities and the reference sequence of state quantities of the second time scale as reference signals for solving the steady-state solution model of the second time scale, using an online rolling optimization method to solve the steady-state solution model of the second time scale, and obtaining an optimal sequence of input quantities of the second time scale. The embodiment of the present invention analyzes the dynamic characteristics of different components of the micro-turbine cogeneration system, and reduces the order of the complex nonlinear solution model based on different time scales, constructs a steady-state solution model of the first time scale and a steady-state solution model of the second time scale, and solves the steady-state solution model of the first time scale and the steady-state solution model of the second time scale respectively, thereby improving the control accuracy of the micro-turbine cogeneration system and reducing the complexity of problem solving in the optimization control process of the micro-turbine cogeneration system.

[0048] In addition, considering the thermal comfort of users, a relaxation variable is introduced into the set value of the indoor temperature to form a target interval, that is, interval tracking is introduced to further expand the economic benefits of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A schematic flow chart of a control method for a dual-time-scale micro-turbine cogeneration system provided by an embodiment of the present invention;

[0051] Figure 2 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] The purpose of the present invention is to provide a dual-time scale control method, equipment, medium and product for a micro-turbine cogeneration system, aiming to improve the control accuracy of the micro-turbine cogeneration system and reduce the complexity of problem solving in the optimization control process of the micro-turbine cogeneration system.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] The complex cogeneration system features multiple variables, large delays, and strong coupling, which require more advanced algorithms for efficient control. At the same time, due to economic considerations, the consumption of natural gas needs to be taken into account during system operation.

[0056] Therefore, it is necessary to conduct an in-depth analysis of the dynamic characteristics of different components of the system, reduce the order of complex nonlinear models based on different time scales, and design an interval tracking economic model predictive controller (EMPC) accordingly to solve the above problems. In addition, considering the thermal comfort of customers, a relaxation variable is introduced into the set value of the indoor temperature to form a target interval, that is, interval tracking is introduced to further expand the economic benefits of system operation.

[0057] Example 1

[0058] like Figure 1 As shown, Embodiment 1 of the present invention provides a control method for a dual-time-scale micro-turbine cogeneration system, the method comprising:

[0059] Step 101, analyzing the state variables in the micro-turbine cogeneration system, determining the state variables of the first time scale to form the state quantity of the first time scale, and determining the state variables of the second time scale to form the state quantity of the second time scale, wherein the first time scale is greater than the second time scale, specifically includes:

[0060] Step 1: Establish a nonlinear model of the micro-turbine cogeneration system (hereinafter referred to as the system or cogeneration system), that is, an overall dynamic mathematical model. The overall dynamic mathematical model of the system is expressed as follows:

[0061]

[0062] y(t)=h(x(t),u(t),d(t));

[0063] in: is a differential, x = [P c2 ,P b2 ,G t ,T ra2 ,T rj ,T rg2 ,w,T hg2 ,T hw2 ,T1,T2,T3,T4,T5,T f ,T r ] T , u=[G f ,G cw ,G hs ] T , d=[T out ,S r ] T and y = [N e ,T h ,T r ] T are the state quantity, input quantity, interference quantity and output quantity of the model respectively, and f and h are mapping functions respectively; specifically, P c2 and P b2 are the compressor outlet pressure and the combustion chamber outlet pressure respectively; G t , G f , G cw and G hs Indicates the flue gas flow at the turbine outlet, the amount of fuel, the cold water flow at the heat exchanger inlet, and the heating hot water flow; w, N e and S r represents the rotor angular velocity, generator power and solar irradiance; T is the temperature parameter, correspondingly, T ra2 , T rj , and T rg2 is the temperature of the regenerator outlet air, metal wall and outlet flue gas, T hg2 , T hw2 are the temperatures of flue gas and water at the heat exchanger outlet respectively; T1~T5 represent the temperatures of each layer of the heat storage tank; T h , T f , T r and T out They represent the heating temperature, floor temperature, room temperature and outdoor environment temperature respectively.

[0064] Step 2: Based on the overall dynamic mathematical model of the cogeneration system established in step 1, the time scale analysis of its state variables is performed, and all time constants corresponding to the two time scales are divided into two categories, including the fast time scale (i.e., the second time scale τf ) and the slow time scale (i.e. the first time scale τ s ).

[0065] Step 102, based on the state quantity of the first time scale and the state quantity of the second time scale, construct a steady-state solution model of the first time scale (hereinafter referred to as the slow sub-model) and a steady-state solution model of the second time scale (hereinafter referred to as the fast sub-model) of the micro-turbine cogeneration system, specifically including:

[0066] Step 3: Based on the decomposition in step 2, apply singular perturbation theory to calculate the dimensionless parameter: ε = τ f / τ s .

[0067] Step 4: Based on the dimensionless parameters calculated in step 3, ignoring the fast-changing process, obtain the steady-state solution of the slow-changing process that plays a major role in the system.

[0068] The overall system model can be written in the following form:

[0069]

[0070]

[0071] y s (τ) = h s (x s )

[0072] y f (τ) = h f (x f )

[0073] Where: x s and x f are the state quantities of the first time scale and the second time scale, respectively, representing the grouping of state variables with slow and fast dynamic characteristics, u s Also affects x s and x f ,u f Impact f ;y s By x s Decide, and y f By x f Decide;

[0074] ε→0, we can get 0=f f (x f ,u f ,u s ), then x f Can be done by u s and u f So the slow submodel can be expressed as:

[0075]

[0076] Thus, the steady-state solution of the slow-varying process can be quickly obtained;

[0077] Step 5: Reconstruct the fast transient process based on the original quasi-steady-state model in step 4:

[0078] y f (τ) = h f (x f ).

[0079] ε→0, we get Then the fast sub-model can be expressed as:

[0080] y f (τ) = h f (x f ).

[0081] In this way, the solution of the fast transient process can be obtained. Combined with the steady-state solution of the slow-varying process in step 4, the complex solution problem of the nonlinear system model is decomposed into the solution problem of two reduced-order sub-models.

[0082] Step 103, with the goal of reducing energy supply costs and improving user comfort, an online rolling optimization method is used to solve the steady-state solution model of the first time scale, and the optimal sequence of input quantities of the first time scale and the reference sequence of input quantities and state quantity reference sequences of the second time scale are obtained.

[0083] Step 104, using the input quantity reference sequence and the state quantity reference sequence of the second time scale as reference signals for solving the steady-state solution model of the second time scale, using an online rolling optimization method to solve the steady-state solution model of the second time scale, and obtaining the optimal sequence of the input quantity of the second time scale.

[0084] Step 103 and step 104 specifically include:

[0085] Step 6: Based on the fast and slow sub-models obtained by the reduced-order decomposition in step 5, a predictive control algorithm for the interval tracking economic model based on dual time scales is proposed.

[0086] Step 7: According to the design theory of the economic model predictive control method in step 6, different prediction time domains and sampling periods are selected for the fast and slow subsystems, and the corresponding EMPC controller is designed.

[0087] Step 8, calculate the predicted value of the model output of the slow subsystem in the slow sampling period (i.e., the slow submodel), substitute it into the economic indicator function that considers both the system energy supply cost and the user comfort, and use the online rolling optimization method to solve it. The slow input quantity, slow state quantity, fast input quantity and state quantity obtained by the slow EMPC optimization are transmitted to the fast EMPC as reference signals.

[0088] Considering both the system energy cost and user comfort, the optimization solution of the slow EMPC can be expressed as follows:

[0089]

[0090]

[0091]

[0092] Where min means minimizing the objective function, i = 0, 1, ..., N s p -1, and are the weights of the electric power optimization target, heating temperature optimization target, indoor temperature optimization target and fuel consumption when solving the first time scale, G f is the fuel consumption of the micro-turbine cogeneration system; sp,1 ,y sp,2 , They represent the set value of electric power, the set value of heating temperature and the target range of indoor temperature respectively, the superscript γ represents the relaxation variable; j = s, f is the superscript of the relevant time scale, s represents the first time scale, i.e. the slow time scale, and f represents the second time scale, i.e. the fast time scale; y L (k+i) and y H (k+i) are the upper and lower limits of the target interval of the indoor temperature at the i-th time step after the sampling time k, that is, the upper and lower limits of the corresponding target interval; u j min 、u j max 、x j min 、x j max , Δu j min and Δu j max The corresponding input quantity, state quantity and input quantity change rate constraints, specifically, x j min and x j max are the lower and upper limits of the state quantity constraints of time scale j, respectively, and uj min and u j max are the upper and lower limits of the input quantity constraint for time scale j, Δu j min and Δu j max are the lower and upper limits of the input change rate constraint of time scale j respectively; further, at each sampling time k, the slow EMPC solves the optimization problem and obtains the optimal sequence of the input quantity of the first time scale: {u * s (k),u * s (k+1),…,u * s (k+N s p -1)}, the reference sequence of the input quantity of the second time scale is: {u * f (k),u * f (k+1),…,u * f (k+N s p -1), and {y ε sp,3 (k),y ε sp,3 (k+1),…,y ε sp,3 (k+N s p -1)} is the variable sequence related to the corresponding interval tracking; then, the optimal sequence of input quantities of the first time scale is passed to the system, and the reference sequence of input quantities and the reference sequence of state quantities of the second time scale are sent to the fast EMPC as reference signals. At the next sampling time point k+1, the above process is repeated to calculate a new optimal variable sequence. In addition, the reference signal of the fast EMPC is defined, that is, u * f (k) = u f s , x * f (k+1)=x f s .

[0093] Step 9, calculating the model output prediction value of the fast subsystem in the fast sampling period, and solving it using the online rolling optimization method;

[0094] Construct the optimized solution expression of fast EMPC:

[0095]

[0096] stx f (k+i+1)=f f (x f (k+i),u f (k+i),u s (k+i))

[0097] y f (k+i+1)=h f (x f (k+i+1))

[0098] u f min ≤u(k+i)≤u f max

[0099] x f min ≤x(k+i+1)≤x f max

[0100] Δu f min ≤Δu(k+i)≤Δu f max

[0101] Where i = 0, 1, ..., Nf p -1; α f 1,2,3,4 is the weight coefficient of fast EMPC, and are the weights of the electric power optimization target, heating temperature optimization target, indoor temperature optimization target and fuel consumption when solving the second time scale, G f is the fuel consumption of the micro-turbine cogeneration system; sp,1 is the set value of the electric power; f is the superscript of the second time scale; u f min ,u f max , x f min , x f max , Δu f min and Δu f max Corresponding to the constraints of fast time scale input quantity, state quantity and input quantity change rate, specifically, u f min and u f maxare the lower and upper limits of the input quantity constraints of the second time scale, respectively, x f min and x f max are the lower and upper limits of the state quantity constraints of the second time scale, Δu f min and Δu f max are the lower and upper limits of the input change rate constraint of the second time scale respectively; x * f ,u * f is the reference value signal from the slow EMPC; specifically, at each sampling time k, the fast EMPC solves the optimization problem to obtain the optimal sequence of input quantities at the second time scale: {u * f (k),u * f (k+1),…,u * f (k+N s p -1)}, used to control the fast subsystem; at the next sampling time point k+1, the above process is repeated to calculate a new optimal variable sequence.

[0102] Example 2

[0103] Embodiment 1 of the present invention provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method in Embodiment 1.

[0104] The computer device may be a database, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the method in Example 1 is implemented.

[0105] Example 3

[0106] Embodiment 1 of the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method in Embodiment 1 is implemented.

[0107] Example 4

[0108] Embodiment 1 of the present invention provides a computer program product, including a computer program, which implements the method in Embodiment 1 when executed by a processor.

[0109] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0111] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A dual-time-scale micro-turbine cogeneration system control method, characterized in that: The method comprises: Analyze the state variables in the micro-turbine cogeneration system, determine the state variables of the first time scale to form the state quantity of the first time scale, and determine the state variables of the second time scale to form the state quantity of the second time scale, wherein the first time scale is greater than the second time scale; According to the state quantity of the first time scale and the state quantity of the second time scale, a steady-state solution model of the first time scale and a steady-state solution model of the second time scale of the micro-turbine cogeneration system are constructed; With the goal of reducing energy supply costs and improving user comfort, the online rolling optimization method is used to solve the steady-state solution model of the first time scale, and the optimal sequence of input quantities of the first time scale and the reference sequence of input quantities and state quantities of the second time scale are obtained; The input quantity reference sequence and the state quantity reference sequence of the second time scale are used as reference signals for solving the steady-state solution model of the second time scale, and the steady-state solution model of the second time scale is solved by an online rolling optimization method to obtain the optimal sequence of the input quantity of the second time scale; According to the state quantity of the first time scale and the state quantity of the second time scale, a steady-state solution model of the first time scale and a steady-state solution model of the second time scale of the micro-turbine cogeneration system are constructed, which specifically includes: The overall steady-state solution model of the micro-turbine cogeneration system is constructed; the overall steady-state solution model is: y s (τ)=h s (x s ); y f (τ)=h f (x f ); Among them, x s is the state quantity of the first time scale, x f is the state quantity of the second time scale, the state quantity of the first time scale is composed of the state variables of the first time scale, and the state quantity of the second time scale is composed of the state variables of the second time scale; u s is the input quantity of the first time scale, u f is the input quantity of the second time scale, f s () and h s () are the state quantity change mapping function and output quantity mapping function of the first time scale, respectively, f () and h f () is the state quantity change mapping function and output quantity mapping function of the second time scale, d is the interference quantity, ε is a dimensionless parameter, ε=τ f / τ s , τ s is the first time scale, τ f is the second time scale, τ is the time variable; y s (τ) and y f (τ) are the outputs of the first time scale and the second time scale, respectively; According to the overall steady-state solution model, let ε→0 and construct the steady-state solution model of the first time scale as follows: y s (τ)=h s (x s ); y f (τ)=h f (u f ,u s ); According to the overall steady-state solution model, the transient model of the second time scale is reconstructed as: y f (τ)=h f (x f ); According to the transient model of the second time scale, let ε→0 and construct the steady-state solution model of the second time scale as follows: y f (τ)=h f (x f )。 2. The control method of the dual-time-scale micro-turbine cogeneration system according to claim 1 is characterized in that: With the goal of reducing energy supply costs and improving user comfort, the online rolling optimization method is used to solve the steady-state solution model of the first time scale: Among them, u s is the input quantity of the first time scale, u f is the input quantity of the second time scale, y sp,1 ,y sp,2 , They represent the set value of electric power, the set value of heating temperature and the target range of indoor temperature respectively. The superscript γ represents the relaxation variable. is the number of time steps solved for the first time scale, and are the weights of the electric power optimization target, heating temperature optimization target, indoor temperature optimization target and fuel consumption when solving the first time scale, respectively. The superscript s represents the first time scale. y1(k+i+1), y2(k+i+1) and y3(k+i+1) are the electric power, heating temperature and indoor temperature of the i+1th time step after the sampling time k obtained by solving the solution, respectively. G f is the fuel consumption of the micro-turbine cogeneration system, y sp,1 (k+i+1),y sp,2 (k+i+1) and are the set value of electric power, the set value of heating temperature and the target range of indoor temperature at the i+1th time step after sampling time k, x s (k+i+1) and x f (k+i+1) are the state quantities of the first time scale and the second time scale of the i+1th time step after sampling time k, respectively. s (k+i) and x f (k+i) are the state quantities of the first time scale and the second time scale of the i-th time step after the sampling time k, u s (k+i) is the input of the first time scale at the i-th time step after sampling time k, d(k+i) is the interference at the i-th time step after sampling time k, y s (k+i+1) and y f (k+i+1) are the outputs of the first time scale and the second time scale of the i+1th time step after sampling time k, respectively. s () and h s () are the state quantity change mapping function and output quantity mapping function of the first time scale, respectively, h f () is the output mapping function of the second time scale; u j min and u j max are the upper and lower limits of the input quantity constraint at time scale j, respectively, and u j (k+i) is the input quantity of time scale j at the i-th time step after sampling time k, x j min and x j max are the lower and upper limits of the state quantity constraints of time scale j, respectively, j (k+i+1) is the state quantity of time scale j at the i+1th time step after sampling time k, Δu j min and Δu j max are the lower and upper limits of the input change rate constraint at time scale j, Δu j (k+i) is the rate of change of the input quantity at time scale j at the i-th time step after sampling time k; y L (k+i) and y H (k+i) are the upper and lower limits of the target range of the indoor temperature at the i-th time step after sampling time k, is the target interval of the indoor temperature in the i-th time step after the sampling time k, j=s or f, when j=s, the time scale j is the first time scale, when j=f, the time scale j is the second time scale.

3. The control method of the dual-time-scale micro-turbine cogeneration system according to claim 1 is characterized in that: The input reference sequence and state reference sequence of the second time scale are used as reference signals for solving the steady-state solution model of the second time scale. The expression for solving the steady-state solution model of the second time scale using the online rolling optimization method is: Among them, u f is the input quantity of the second time scale, The number of time steps solved for the second time scale, and are the weights of the electric power optimization target, heating temperature optimization target, indoor temperature optimization target and fuel consumption when solving the second time scale, y1(k+i+1) is the electric power of the i+1th time step after the sampling time k, G f is the fuel consumption of the micro-turbine cogeneration system, y sp,1 (k+i+1) is the set value of the electric power at the i+1th time step after sampling time k, x f (k+i+1) is the state quantity of the second time scale at the i+1th time step after sampling time k, u f (k+i+1) is the input quantity of the second time scale at the i+1th time step after sampling time k, is the state quantity reference value of the second time scale at the i+1th time step after the sampling time k, is the input reference value of the second time scale at the i+1th time step after the sampling time k; x f (k+i) is the state quantity of the second time scale at the i-th time step after sampling time k, u s (k+i) and u f (k+i) are the input quantities of the first time scale and the second time scale of the i-th time step after sampling time k, respectively. f (k+i+1) is the output of the second time scale at the i+1th time step after sampling time k, f f () and h f () is the state quantity change mapping function and output quantity mapping function of the second time scale; u f min and u f max are the lower and upper limits of the input quantity constraints of the second time scale, respectively, x f min and x f max are the lower and upper limits of the state quantity constraints of the second time scale, Δu f min and Δu f max are the lower and upper limits of the input change rate constraint of the second time scale, Δu f (k+i) is the rate of change of the input quantity on the second time scale at the i-th time step after sampling time k.

4. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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