A coordinated control method and system for cogeneration units based on predictive control

Abstract

The present invention discloses a method and system for coordinated control of a cogeneration unit based on predictive control, which relates to the field of cogeneration unit control. The method includes constructing a predictive controller optimization target model; inputting a predicted value sequence and a reference set value sequence of the controlled variable in the prediction time period and the control value of the control variable at the current moment into the predictive controller optimization target model to obtain an optimal control sequence of the control variable in the prediction time period; coordinating control of the cogeneration unit model through the optimal control sequence to obtain a real-time value of the controlled variable in the prediction time period, and repeating the above steps until the real-time value of the controlled variable meets a preset condition. The present invention constructs a predictive controller optimization target model by using the relationship between the three controlled variables of fuel quantity, main steam throttle opening and heating extraction steam throttle opening and the three controlled variables of electric load, main steam pressure and heating extraction steam mass flow, and uses the solved optimal control sequence of the control variable to perform control, thereby improving the load change rate.

Description

Technical Field

[0001] The present invention relates to the field of cogeneration unit control, and in particular to a method and system for cogeneration unit coordinated control based on predictive control. Background Art

[0002] With the large-scale integration of renewable energy sources such as wind power, improving the load tracking capabilities of cogeneration units will enhance the grid's ability to accommodate renewable energy generation, which is crucial for the safe and stable operation of power systems. Cogeneration units typically utilize coordinated boiler-turbine control, which tracks load changes and maintains main steam pressure stability through fuel flow and main steam throttle valve opening. However, the main steam throttle valve opening is limited by the limited heat storage on the boiler side, making it difficult to significantly increase the unit's load change rate beyond 1.5% / min of rated load. Therefore, a predictive control-based coordinated control method for cogeneration units is needed to improve the load change rate. Summary of the Invention

[0003] The object of the present invention is to provide a method and system for coordinated control of a cogeneration unit based on predictive control, which can improve the load change rate of the cogeneration unit model.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for coordinated control of a cogeneration unit based on predictive control, the method comprising:

[0006] S1: Constructing a predictive controller optimization target model, wherein the predictive controller optimization target model represents the predictive control relationship between the control variable and the regulated variable in the cogeneration unit model; the control variable includes the fuel quantity, the main steam regulating valve opening, and the heating extraction steam regulating valve opening; the regulated variable includes the electrical load, the main steam pressure, and the heating extraction steam mass flow rate;

[0007] S2: Taking the predicted value sequence of each controlled variable in the prediction time period, the reference set value sequence of each controlled variable in the prediction time period, and the control value of each controlled variable at the current moment as input, solving the optimization target model of the predictive controller to obtain the optimal control sequence of each controlled variable in the prediction time period;

[0008] S3: Input the optimal control sequence of each of the control quantities in the forecast time period into the cogeneration unit model, perform coordinated control on the controlled quantities of the cogeneration unit model, obtain the real-time value of each of the controlled quantities in the forecast time period, and determine whether the real-time value of the controlled quantity after the forecast time period meets the preset conditions. If not, return to step S2 to perform optimization solution for the next forecast time period.

[0009] Optionally, the expression for the prediction controller to optimize the target model is:

[0010]

[0011] Where Y is the target model for the prediction controller, j = 1, 2, 3, i and k represent the time, p represents the prediction time period, a j is the preset weight of the jth controlled variable, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, c is the output error weight coefficient, y j (k+i|k) is the predicted value of the jth controlled variable at time k+i within the prediction time period, r j (k+i|k) is the reference set value of the jth controlled variable at time k+i within the prediction time period, is the proportional factor corresponding to the jth controlled variable, m is the control time period, d is the control increment weight coefficient, u j (k+i|k) is the control value of the j-th control variable at time k+i in the control time period, u j (k+i-1|k) is the control value of the j-th control variable at time k+i-1 within the control time period, is the proportional factor corresponding to the j-th control quantity.

[0012] Optionally, before S2, the step further includes: determining a dynamic weight of the regulated variable, and the determination process is as follows:

[0013] Calculating the deviation of each of the controlled quantities according to the real-time value of the controlled quantity and the set value of the controlled quantity at the current moment;

[0014] The dynamic weight of each of the regulated quantities is calculated according to the deviation, the maximum deviation value and the minimum dynamic weight setting value of each of the regulated quantities.

[0015] Optionally, the expression of the dynamic weight is:

[0016]

[0017] Among them, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, j = 1, 2, 3; e j (k) is the deviation of the jth controlled variable at time k, is the maximum deviation, b min The minimum setting value of dynamic weight.

[0018] Optionally, before S2, the step further includes: determining a reference set value of the heating extraction steam mass flow rate, and the determination process is as follows:

[0019] The reference set value of the heating extraction steam mass flow rate is calculated according to the set value of the heating extraction steam mass flow rate and the real-time value of the heating extraction steam mass flow rate.

[0020] Optionally, the expression for the reference set value of the heating extraction steam mass flow rate is:

[0021]

[0022] Among them, q mH(dsp) is the reference set value of the heating extraction steam mass flow rate, q mH(sp) is the set value of the heating extraction steam mass flow rate, t2 is the current moment, and the initial moment when the heating extraction steam mass flow rate deviates from its set value is t1<t2, q mH is the real-time value of the heating extraction steam mass flow rate, K Q It is the proportional factor for converting the integral formula to the set value.

[0023] The present invention also provides a coordinated control system for a cogeneration unit based on predictive control, the system comprising:

[0024] A predictive controller optimization target model construction module is used to construct a predictive controller optimization target model, wherein the predictive controller optimization target model represents the predictive control relationship between the control variable and the regulated variable in the cogeneration unit model; the control variable includes the fuel quantity, the main steam regulating valve opening, and the heating extraction steam regulating valve opening; the regulated variable includes the electrical load, the main steam pressure, and the heating extraction steam mass flow rate;

[0025] a model solving module, configured to take as input a predicted value sequence of each controlled variable in a predicted time period, a reference set value sequence of each controlled variable in the predicted time period, and a control value of each controlled variable at a current moment, and solve the optimization target model of the predictive controller to obtain an optimal control sequence of each controlled variable in the predicted time period;

[0026] The control module is used to input the optimal control sequence of each of the control quantities in the prediction time period into the cogeneration unit model, coordinate and control the controlled quantities of the cogeneration unit model, obtain the real-time value of each of the controlled quantities in the prediction time period, and determine whether the real-time value of the controlled quantity after the prediction time period meets the preset conditions. If not, return to the model solution module to perform optimization solution for the next prediction time period.

[0027] Optionally, the expression for the prediction controller to optimize the target model is:

[0028]

[0029] Where Y is the target model for the prediction controller, j = 1, 2, 3, i and k represent the time, p represents the prediction time period, a j is the preset weight of the jth controlled variable, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, c is the output error weight coefficient, y j (k+i|k) is the predicted value of the jth controlled variable at time k+i within the prediction time period, r j (k+i|k) is the reference set value of the jth controlled variable at time k+i within the prediction time period, is the proportional factor corresponding to the jth controlled variable, m is the control time period, d is the control increment weight coefficient, u j (k+i|k) is the control value of the j-th control variable at time k+i in the control time period, u j (k+i-1|k) is the control value of the j-th control variable at time k+i-1 within the control time period, is the proportional factor corresponding to the j-th control quantity.

[0030] Optionally, the system further includes a dynamic weight determination module for an adjusted quantity; the dynamic weight determination module for an adjusted quantity is configured to calculate the deviation of each adjusted quantity based on the real-time value of the adjusted quantity and the set value of the adjusted quantity at the current moment; and calculate the dynamic weight of each adjusted quantity based on the deviation, maximum deviation and minimum dynamic weight set value of each adjusted quantity;

[0031] The expression of the dynamic weight is:

[0032]

[0033] Among them, b j (k) is the dynamic weight of the jth controlled variable, j = 1, 2, 3; e j (k) is the deviation of the jth controlled variable at time k, is the maximum deviation, b min The minimum setting value of dynamic weight.

[0034] Optionally, a module for determining a reference set value of the heating extraction steam mass flow rate is further included; the module for determining a reference set value of the heating extraction steam mass flow rate is configured to calculate a reference set value of the heating extraction steam mass flow rate based on the set value of the heating extraction steam mass flow rate and the real-time value of the heating extraction steam mass flow rate; wherein the expression for the reference set value of the heating extraction steam mass flow rate is:

[0035]

[0036] Among them, q mH(dsp) is the reference set value of the heating extraction steam mass flow rate, qmH(sp) is the set value of the heating extraction steam mass flow rate, t2 is the current moment, and the initial moment when the heating extraction steam mass flow rate deviates from its set value is t1<t2, q mH is the real-time value of the heating extraction steam mass flow rate, K Q It is the proportional factor for converting the integral formula to the set value.

[0037] According to a specific embodiment provided by the present invention, the present invention discloses the following technical effects: the present invention provides a method and system for coordinated control of a cogeneration unit based on predictive control, the method comprising: constructing a predictive controller optimization target model, the predictive controller optimization target model representing the predictive control relationship between the control variable and the regulated variable in the cogeneration unit model; the control variables include the fuel quantity, the main steam throttle valve opening, and the heating extraction steam throttle valve opening; the regulated variables include the electrical load, the main steam pressure, and the heating extraction steam mass flow rate; taking as input a predicted value sequence of each regulated variable in a prediction time period, a reference set value sequence of each regulated variable in the prediction time period, and the control value of each control variable at the current moment, solving the predictive controller optimization target model to obtain an optimal control sequence for each control variable in the prediction time period; inputting the optimal control sequence for each control variable in the prediction time period into the cogeneration unit model, performing coordinated control on the regulated variables of the cogeneration unit model, obtaining a real-time value of each regulated variable in the prediction time period, and continuously repeating the above steps until the real-time value of the regulated variable after the prediction time period meets a preset condition, and then stopping control. The present invention adopts three control variables, namely fuel quantity, main steam regulating valve opening and heating extraction steam regulating valve opening, and three controlled variables, namely electric load, main steam pressure and heating extraction steam mass flow, to construct a predictive controller optimization target model through the predictive control relationship between the control variables and the controlled variables. The predictive controller optimization target model is solved to obtain the optimal control sequence of the control variables, so as to control the cogeneration unit model using the optimal control sequence, thereby improving the load change rate of the cogeneration unit model. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0039] Figure 1 A schematic flow chart of a method for coordinated control of a cogeneration unit based on predictive control provided in Example 1 of the present invention;

[0040] Figure 2 A schematic diagram showing the principle of a coordinated control method for a cogeneration unit based on predictive control provided in Example 1 of the present invention;

[0041] Figure 3 A comparison chart of the control effects of the coordinated control method of the present invention and the traditional coordinated control method when the load instruction changes from 235MW to 265MW based on the 300MW cogeneration unit model provided in Example 1 of the present invention;

[0042] Figure 4 A comparison chart of the control effects of setting different weighting levels for the controlled variable based on a 300MW cogeneration unit provided in Example 1 of the present invention;

[0043] Figure 5 This is a block diagram of a coordinated control system for a cogeneration unit based on predictive control provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0045] At present, the control of the heating extraction steam flow can only ensure the tracking of the set value after the fluctuation period, and cannot guarantee the balance of the total supply and demand of heat. In the long run, it will have a greater impact on heat users. The heating network contains a large amount of heat storage. Reasonable use will be conducive to the improvement of the load change rate. The control objectives of the cogeneration unit include fast tracking of the electrical load, maintaining the stability of the main steam pressure, and ensuring the heating effect. The improvement of any control objective will lead to a decrease in the other control effects. Therefore, it is necessary to coordinate these control objectives, appropriately emphasize certain objectives, or sacrifice the control effects of some objectives to achieve the improvement of the control effects of other objectives. At present, the setting of the degree of emphasis of these control objectives often relies on changing the numerous control parameters of the controller. The method is cumbersome and not intuitive enough.

[0046] Based on the above-mentioned shortcomings of the prior art, the present invention provides a method and system for coordinated control of a cogeneration unit based on predictive control, which adopts three control quantities, namely fuel quantity, main steam regulating valve opening and heating extraction steam regulating valve opening, and three controlled quantities, namely electric load, main steam pressure and heating extraction steam mass flow, to construct a predictive controller optimization target model through the predictive control relationship between the control quantities and the controlled quantities, and solves the predictive controller optimization target model to obtain the optimal control sequence of the control quantities, thereby using the optimal control sequence to control the cogeneration unit model, and through the three-input and three-output predictive control, the three control quantities are reasonably allocated to track the electric load instructions, thereby improving the load change rate of the cogeneration unit model. The present invention adopts a dynamic setting value of the heating extraction steam mass flow to ensure the heating effect, and ensures the balance of total heat supply and demand after the control fluctuation period ends. The present invention sets the weight coefficient of each controlled quantity through dynamic weight and preset weight (self-set weight), which can intuitively and conveniently realize the setting of the degree of emphasis of the control target.

[0047] 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.

[0048] Example 1

[0049] like Figure 1 As shown, the present invention provides a method for coordinated control of a cogeneration unit based on predictive control, the method comprising:

[0050] S1: Construct a predictive controller optimization target model, which represents the predictive control relationship between the control variable and the regulated variable in the cogeneration unit model; the control variable includes the fuel quantity, the main steam regulating valve opening and the heating extraction steam regulating valve opening; the regulated variable includes the electrical load, the main steam pressure and the heating extraction steam mass flow rate.

[0051] S2: Take the predicted value sequence of each controlled variable in the predicted time period, the reference set value sequence of each controlled variable in the predicted time period and the control value of each controlled variable at the current moment as input, solve the optimization target model of the predictive controller, and obtain the optimal control sequence of each controlled variable in the predicted time period.

[0052] S3: Input the optimal control sequence of each of the control quantities in the forecast time period into the cogeneration unit model, perform coordinated control on the controlled quantities of the cogeneration unit model, obtain the real-time value of each of the controlled quantities in the forecast time period, and determine whether the real-time value of the controlled quantity after the forecast time period meets the preset conditions. If not, return to step S2 to perform optimization solution for the next forecast time period.

[0053] The expression of the optimization target model of the above predictive controller is:

[0054]

[0055] Where Y is the target model for the prediction controller, j = 1, 2, 3, i and k represent the sampling time, p represents the prediction time period (prediction time domain), a j b j (k) is the weight coefficient of the jth controlled variable at the current time k, a j is the preset weight of the jth controlled variable, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, c is the output error weight coefficient, y j (k+i|k) is the predicted value of the jth controlled variable at the k+i moment within the prediction time period. The predicted values of each controlled variable at all moments within the prediction time period constitute the predicted value sequence of each controlled variable. j (k+i|k) is the reference set value of the jth controlled variable at time k+i within the prediction time period. The reference set values of each controlled variable at all times within the prediction time period constitute the reference set value sequence of each controlled variable. is the proportional factor corresponding to the jth controlled variable, m is the control time period (control time domain), d is the control increment weight coefficient, u j (k+i|k) is the control value of the j-th control variable at time k+i in the control time period. The control values of each control variable at all times in the prediction time period constitute the control value sequence of each control variable, among which the one with the smallest Y is the optimal control sequence of the control variable in the prediction time period, u j (k+i-1|k) is the control value of the j-th control quantity at time k+i-1 in the control time period. When i is equal to 1, u j (k+i-1|k) is the control value of the control quantity at the current moment, u j (k+i|k) is the control value of the control quantity at the next moment, is the proportional factor corresponding to the jth control quantity. y1, y2, y3 correspond to the electric load N e , main steam pressure P t , heating extraction steam mass flow q mH The future prediction value is calculated based on the unit model imported into the controller in advance using algorithms such as model predictive control and generalized predictive control. j is the reference tracking trajectory of the predictive controller, that is, r1 = N e(sp) , r2=P t(sp) , r3=q mH(dsp) u1, u2, and u3 correspond to the fuel quantities q mB , Main steam regulating valve opening u T , Heating extraction steam valve opening u HIt can be seen that the target model of the predictive controller is a control variable u j (k+i|k) is the quadratic programming function with independent variables.

[0056] The present invention controls three controlled variables: electrical load, main steam pressure, and heating extraction steam mass flow rate, using three controlled variables: fuel quantity, main steam throttle valve opening, and heating extraction steam butterfly valve opening. Based on the above closed-loop control structure, the present invention may further include determining a reference setpoint for the heating extraction steam mass flow rate. The determination process is as follows:

[0057] The reference set value of the heating extraction steam mass flow rate is calculated according to the set value of the heating extraction steam mass flow rate and the real-time value of the heating extraction steam mass flow rate.

[0058] The expression of Q is:

[0059] Q is the sum of historical deviations before the current time t2. The result of this formula = the integral result of the previous period + the deviation of the previous period * the sampling period.

[0060] In order to achieve the overall heat supply and demand balance during the entire control period, a supply prediction control is designed as the dynamic set value q of the heating extraction steam mass flow rate of the heating extraction steam reference tracking trajectory. mH(dsp) , whose expression is:

[0061]

[0062] The above two formulas can be used to obtain the reference set value of the heating extraction steam mass flow rate:

[0063]

[0064] Among them, q mH(dsp) is the reference set value of the heating extraction steam mass flow rate, q mH(sp) is the set value of the heating extraction steam mass flow rate, t2 is the current moment, and the initial moment when the heating extraction steam mass flow rate deviates from its set value is t1<t2, q mH is the real-time value of the heating extraction steam mass flow rate, Q is the real-time value of the heating extraction steam mass flow rate q mH and the set value of heating extraction steam mass flow rate q mH(sp) The deviation integral of Q is the difference between heat supply and demand over a period of time, which characterizes the quality of the heating effect. Q It is the proportional factor of Q to the heating extraction steam set value, which is set according to the expected control length of Q. The longer the expected control time of Q, the greater the K Q The smaller the value, the existence of this item is to avoid the accumulation of small deviations of heating extraction steam over time, resulting in a large heat load supply and demand difference. It is expected that the heating extraction steam mass flow rate will reach the overall balance faster, so K is set. QThe larger the value, the K value is set based on the experiment. Q Around 20. During the control process, q mH(dsp) Keep changing until q mH(dsp) Stable at q mH(sp) , and Q is stable at 0.

[0065] Before S2, it also includes: determining the dynamic weight b of the controlled quantity j , b j The determination process is as follows:

[0066] The deviation of each of the controlled quantities is calculated according to the real-time value of the controlled quantity and the set value of the controlled quantity at the current moment.

[0067] The dynamic weight of each of the regulated quantities is calculated according to the deviation, the maximum deviation value and the minimum dynamic weight setting value of each of the regulated quantities.

[0068] Among them, the above dynamic weight b j The expression of (k) is:

[0069]

[0070] Among them, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, j = 1, 2, 3; e j (k) is the deviation of the jth controlled variable at time k, is the maximum deviation, b min The minimum setting value of dynamic weight.

[0071] b j (k) represents the deviation e between each controlled variable and the set value at the current moment k j The dynamic weight of (k) is mainly to avoid a large error in a certain controlled variable during the control process. At the same time, in order to avoid the oscillation caused by the large change in the proportion between the three weight coefficients b(k) when approaching the reference trajectory (reference set value), the dynamic weight minimum set value b is set for it. min (Generally make b min =c). Where, A maximum allowable deviation can be set manually, or it can be the maximum value of all calculated deviations of the controlled variables.

[0072] First, set the three controlled variable set values: electric load command, main steam pressure set value, and heating extraction steam mass flow set value. It should be noted that the above three set values are determined by the user according to actual control needs. The specific settings of the reference tracking trajectory of the predictive controller (i.e., the reference set values of each controlled variable) are as follows: Electric load N e(sp) , main steam pressure P t(sp)The reference tracking trajectories of the two controlled variables correspond to the above-mentioned electric load instruction (electric load set value) and the main steam pressure set value. The above-mentioned set value (electric load instruction and main steam pressure set value) is a variable value with time as the independent variable. It can be fixed or change with time, and the user can set it according to needs. For example Figure 3 (a), where the electric load command is a step signal. The reference tracking trajectory of the heating extraction steam mass flow corresponds to the dynamic setting value of the heating extraction steam mass flow q mH(dsp) . Figure 2 Shown a j Represents the self-set weights for the three controlled variables’ errors, which are set by the unit operator. The minimum value among the three values (a1, a2, a3) is set to 1. The larger the other values are, the greater the weight of the controlled variables and the better the control effect. The total external weight (weight coefficient of the controlled variable) is obtained by multiplying the dynamic weight of the controlled variable deviation and the self-set weight, that is, the total external weight of each controlled variable is a j b j .

[0073] Then, based on the constraints of the control quantity, control increment and controlled quantity, the predicted value sequence of each controlled quantity in the prediction time period, the reference set value sequence of each controlled quantity in the prediction time period and the control value of each controlled quantity at the current moment are input into the prediction controller optimization target model. The prediction controller optimization target model is optimized by quadratic programming, and the optimal control sequence u of each controlled quantity that minimizes Y at the next upcoming moment (prediction time period) can be obtained. j (k+i|k), the length of the optimal control sequence is m. j (k+1) is input as the current control variable into the cogeneration unit model for coordinated control of the controlled variable. Each sampling period is a control cycle (control time period). The above steps are repeated once in each cycle, and the cycle continues until the controlled variable reaches the control target (set condition). Once the controlled variable reaches the control target and the cogeneration unit model reaches a stable state, the optimal control sequence of the control variable corresponding to the stable state is used to continue controlling the cogeneration unit model. The control target (i.e., the difference between the real-time value of the controlled variable after the prediction time period and the set value) is within the set threshold. This achieves control of the entire cogeneration model. The control variable, control increment, and controlled variable constraints are their respective limit ranges and can be set by the user based on experience.

[0074] Taking the 300MW cogeneration unit model as an example, under the rated heating condition, its power generation load is 235MW, the main steam pressure is 16.67MPa, and the heating extraction steam mass flow rate is 400t / h, which is used as the initial operating condition of the control system. Linearization is performed at this operating point to obtain the three-input and three-output control model of the cogeneration unit, which is used as the controlled system model inside the predictive controller (predictive controller optimization target model). According to the expected control length of the heating deviation integral Q, the proportional factor K for its conversion to the heating extraction steam mass flow rate is set. Q is 20. At 500s, a +30MW step is applied to the electric load instruction, and the weight coefficient is set to 20:1:1. The simulation results are as follows Figure 3 In addition, a +30MW step is applied to the electric load instruction at 500s, and different self-set weight coefficients are set. The simulation results are shown as follows: Figure 4 shown.

[0075] Taking the cogeneration unit model system tracking the increase of the electric load instruction as an example, the coordinated control process of the present invention is introduced in detail:

[0076] like Figure 3 As shown in the figure, when the system needs to increase the power generation, the predictive controller (predictive controller optimization target model) increases the three control variables at the same time, and the degree of increase of each control variable is determined by the total weight (a j b j ). Since the response time of the main steam regulating valve opening and the heating extraction steam butterfly valve opening to the power generation load of the cogeneration unit model is short, the two are jointly responsible for the climb of the electric load in the early stage of the fluctuation, and at the same time cause the main steam pressure and the heating extraction steam mass flow rate to decrease. As time goes on, the increase in the amount of fuel in the early stage of the fluctuation begins to increase the power generation load until the electric load basically climbs to the specified position. At this time, the predictive controller begins to reduce the main steam regulating valve opening and the heating extraction steam regulating valve opening because it still needs to track the set values of the main steam pressure and the heating extraction steam mass flow rate, so that the controlled quantities corresponding to these two control quantities increase until the requirements are met (that is, the difference between the real-time value of the controlled quantity and the set value is within the set threshold range).

[0077] in, Figure 3 (a) Figure 3 (b) Figure 3 (c) and Figure 3 (d) are comparison diagrams of the effects of the heat and power cogeneration unit coordinated control method based on predictive control provided by the present invention and the traditional coordinated control method on the electric load, main steam pressure, heating extraction steam mass flow rate and heating deviation integral, respectively. Figure 3As can be seen in the figure, the coordinated control method of the present invention significantly improves the cogeneration unit model's ability to track changes in the electrical load command, and the main steam pressure changes more smoothly. Furthermore, after the controller terminates, the heating deviation integral Q stabilizes near 0, ensuring a balanced overall heat supply and demand during this period and improving heating quality.

[0078] Conventional control, in order to increase the power generation load, increases the main steam valve opening in the initial stage of control, increasing the amount of steam entering the steam pipe, thereby interfering with the heating extraction steam flow rate and causing it to increase. However, the present invention utilizes the dual effects of increasing the main steam valve opening and reducing the heating extraction steam flow rate to rapidly increase the power generation load in the initial stage of fluctuation. As a result, the heating extraction steam flow rate initially exhibits a downward trend. Consequently, the heating extraction steam mass flow rates of the two control strategies exhibit opposite trends.

[0079] Figure 4 This is a comparison chart of the control effects of setting different weights for the control target based on the above 300MW cogeneration unit. The proportional formula in the legend represents the self-set weight a. j The setting value of Figure 4 (a) Figure 4 (b) Figure 4 (c) and Figure 4 (d) are comparison diagrams of the effects of the cogeneration unit coordinated control method based on predictive control provided by the present invention and the traditional coordinated control method on electric load, main steam pressure, heating extraction steam mass flow rate and heating deviation integral.

[0080] from Figure 4 It can be clearly seen that the method provided by the present invention is simple and intuitive. When emphasizing a particular target (controlled variable), simply setting a larger custom weight for that target can achieve a better control effect. In actual application, different custom weights can be set for each target according to actual needs to achieve the overall desired control effect.

[0081] Example 2

[0082] like Figure 5 As shown, this embodiment provides a coordinated control system for a cogeneration unit based on predictive control, the system comprising:

[0083] The predictive controller optimization target model construction module T1 is used to construct the predictive controller optimization target model, which represents the predictive control relationship between the control variable and the regulated variable in the cogeneration unit model; the control variable includes the fuel quantity, the main steam regulating valve opening and the heating extraction steam regulating valve opening; the regulated variable includes the electrical load, the main steam pressure and the heating extraction steam mass flow rate.

[0084] The model solving module T2 is used to take the predicted value sequence of each controlled variable in the prediction time period, the reference set value sequence of each controlled variable in the prediction time period and the control value of each controlled variable at the current moment as input, solve the optimization target model of the predictive controller, and obtain the optimal control sequence of each controlled variable in the prediction time period.

[0085] The control module T3 is used to input the optimal control sequence of each of the control variables in the prediction time period into the cogeneration unit model, coordinate and control the controlled variables of the cogeneration unit model, obtain the real-time value of each of the controlled variables in the prediction time period, and determine whether the real-time value of the controlled variable after the prediction time period meets the preset conditions. If not, return to the model solution module to perform optimization solution for the next prediction time period.

[0086] The expression of the prediction controller optimization target model is:

[0087]

[0088] Where Y is the target model for the prediction controller, j = 1, 2, 3, i and k represent the time, p represents the prediction time period, a j is the preset weight of the jth controlled variable, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, c is the output error weight coefficient, y j (k+ik) is the predicted value of the jth controlled variable at time k+i within the prediction time period, r j (k+i|k) is the reference set value of the jth controlled variable at time k+i within the prediction time period, is the proportional factor corresponding to the jth controlled variable, m is the control time period, d is the control increment weight coefficient, u j (k+i|k) is the control value of the j-th control variable at time k+i in the control time period, u j (k+i-1|k) is the control value of the j-th control variable at time k+i-1 within the control time period, is the proportional factor corresponding to the j-th control quantity.

[0089] In this embodiment, the system further includes a dynamic weight determination module for a controlled quantity; the dynamic weight determination module for a controlled quantity is configured to calculate a deviation of each controlled quantity based on a real-time value of the controlled quantity and a set value of the controlled quantity at a current moment; and calculate a dynamic weight of each controlled quantity based on the deviation, maximum deviation, and minimum dynamic weight set value of each controlled quantity;

[0090] The expression of the dynamic weight is:

[0091]

[0092] Among them, b j (k) is the dynamic weight of the jth controlled variable, j = 1, 2, 3; e j (k) is the deviation of the jth controlled variable at time k, is the maximum deviation, b min The minimum setting value of dynamic weight.

[0093] In this embodiment, the system further includes a module for determining a reference set value of the heating extraction steam mass flow rate; the module for determining a reference set value of the heating extraction steam mass flow rate is configured to calculate a reference set value of the heating extraction steam mass flow rate based on the set value of the heating extraction steam mass flow rate and a real-time value of the heating extraction steam mass flow rate; wherein the expression for the reference set value of the heating extraction steam mass flow rate is:

[0094]

[0095] Among them, q mH(dsp) is the reference set value of the heating extraction steam mass flow rate, q mH(sp) is the set value of the heating extraction steam mass flow rate, t2 is the current moment, and the initial moment when the heating extraction steam mass flow rate deviates from its set value is t1<t2, q mH is the real-time value of the heating extraction steam mass flow rate, K Q It is the proportional factor for converting the integral formula to the set value.

[0096] Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0097] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A coordinated control method for a cogeneration unit based on predictive control, characterized in that: The method comprises: S1: Construct a predictive controller optimization target model, which represents the predictive control relationship between the control variable and the regulated variable in the cogeneration unit model; the control variables include the fuel quantity, the main steam throttle valve opening, and the heating extraction steam throttle valve opening; the regulated variables include the electrical load, the main steam pressure, and the heating extraction steam mass flow rate; the expression of the predictive controller optimization target model is: Where Y is the target model for the prediction controller, j = 1, 2, 3, i and k represent the time, p represents the prediction time period, a j is the preset weight of the jth controlled variable, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, c is the output error weight coefficient, y j (k+i|k) is the predicted value of the jth controlled variable at time k+i within the prediction time period, r j (k+i|k) is the reference set value of the jth controlled variable at time k+i within the prediction time period, is the proportional factor corresponding to the jth controlled variable, m is the control time period, d is the control increment weight coefficient, u j (k+i|k) is the control value of the j-th control variable at time k+i in the control time period, u j (k+i-1|k) is the control value of the j-th control variable at time k+i-1 within the control time period, is the proportional factor corresponding to the j-th control quantity; The expression of dynamic weight is: Among them, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, j = 1, 2, 3; e j (k) is the deviation of the jth controlled variable at time k, is the maximum deviation, b min is the minimum setting value of dynamic weight; The expression for the reference set value of the heating extraction steam mass flow rate is: Among them, q mH(dsp) is the reference set value of the heating extraction steam mass flow rate, q mH(sp) is the set value of the heating extraction steam mass flow rate, t2 is the current moment, and the initial moment when the heating extraction steam mass flow rate deviates from its set value is t1<t2, q mH is the real-time value of the heating extraction steam mass flow rate, K Q It is the proportional factor for converting the integral formula to the set value; S2: Taking the predicted value sequence of each controlled variable in the prediction time period, the reference set value sequence of each controlled variable in the prediction time period, and the control value of each controlled variable at the current moment as input, solving the optimization target model of the predictive controller to obtain the optimal control sequence of each controlled variable in the prediction time period; S3: Input the optimal control sequence of each of the control quantities in the forecast time period into the cogeneration unit model, perform coordinated control on the controlled quantities of the cogeneration unit model, obtain the real-time value of each of the controlled quantities in the forecast time period, and determine whether the real-time value of the controlled quantity after the forecast time period meets the preset conditions. If not, return to step S2 to perform optimization solution for the next forecast time period.

2. The method for coordinated control of a cogeneration unit based on predictive control according to claim 1, characterized in that: Before S2, the following step is also included: determining the dynamic weight of the controlled variable. The determination process is as follows: Calculating the deviation of each of the controlled quantities according to the real-time value of the controlled quantity and the set value of the controlled quantity at the current moment; The dynamic weight of each of the regulated quantities is calculated according to the deviation, the maximum deviation value and the minimum dynamic weight setting value of each of the regulated quantities.

3. The method for coordinated control of a cogeneration unit based on predictive control according to claim 1, characterized in that: Before S2, the following step is also included: determining a reference set value of the heating extraction steam mass flow rate, and the determination process is as follows: The reference set value of the heating extraction steam mass flow rate is calculated according to the set value of the heating extraction steam mass flow rate and the real-time value of the heating extraction steam mass flow rate.

4. A coordinated control system for a combined heat and power unit based on predictive control, characterized in that: The system comprises: The prediction controller optimization target model construction module is used to construct the prediction controller optimization target model. The prediction controller optimization target model represents the prediction control relationship between the control variable and the regulated variable in the cogeneration unit model. The control variables include the fuel quantity, the main steam regulating valve opening, and the heating extraction steam regulating valve opening. The regulated variables include the electrical load, the main steam pressure, and the heating extraction steam mass flow rate. The expression of the prediction controller optimization target model is: Where Y is the target model for the prediction controller, j = 1, 2, 3, i and k represent the time, p represents the prediction time period, a j is the preset weight of the jth controlled variable, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, c is the output error weight coefficient, y j (k+i|k) is the predicted value of the jth controlled variable at time k+i within the prediction time period, r j (k+i|k) is the reference set value of the jth controlled variable at time k+i within the prediction time period, is the proportional factor corresponding to the jth controlled variable, m is the control time period, d is the control increment weight coefficient, u j (k+i|k) is the control value of the j-th control variable at time k+i in the control time period, u j (k+i-1|k) is the control value of the j-th control variable at time k+i-1 within the control time period, is the proportional factor corresponding to the j-th control quantity; The expression of dynamic weight is: Among them, b j (k) is the dynamic weight of the jth controlled variable calculated at the current time k, j = 1, 2, 3; e j (k) is the deviation of the jth controlled variable at time k, is the maximum deviation, b min is the minimum setting value of dynamic weight; The expression for the reference set value of the heating extraction steam mass flow rate is: Among them, q mH(dsp) is the reference set value of the heating extraction steam mass flow rate, q mH(sp) is the set value of the heating extraction steam mass flow rate, t2 is the current moment, and the initial moment when the heating extraction steam mass flow rate deviates from its set value is t1<t2, q mH is the real-time value of the heating extraction steam mass flow rate, K Q It is the proportional factor for converting the integral formula to the set value; a model solving module, configured to take as input a predicted value sequence of each controlled variable in a predicted time period, a reference set value sequence of each controlled variable in the predicted time period, and a control value of each controlled variable at a current moment, and solve the optimization target model of the predictive controller to obtain an optimal control sequence of each controlled variable in the predicted time period; The control module is used to input the optimal control sequence of each control variable in the prediction time period into the cogeneration unit model, coordinate and control the controlled variables of the cogeneration unit model, obtain the real-time value of each controlled variable in the prediction time period, and determine whether the real-time value of the controlled variable after the prediction time period meets the preset conditions. If not, return to the model solution module to perform optimization solution for the next prediction time period.

5. The coordinated control system for cogeneration units based on predictive control according to claim 4, characterized in that: It also includes a dynamic weight determination module for the controlled quantity; the dynamic weight determination module for the controlled quantity is used to calculate the deviation of each controlled quantity based on the real-time value of the controlled quantity and the set value of the controlled quantity at the current moment; and calculate the dynamic weight of each controlled quantity based on the deviation, maximum deviation and minimum dynamic weight setting value of each controlled quantity.

6. The coordinated control system for cogeneration units based on predictive control according to claim 4, characterized in that: It also includes a reference set value determination module for the heating extraction steam mass flow rate; the reference set value determination module for the heating extraction steam mass flow rate is used to calculate the reference set value of the heating extraction steam mass flow rate based on the set value of the heating extraction steam mass flow rate and the real-time value of the heating extraction steam mass flow rate.

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