Thermoelectric unit peak regulation control method and device based on digital twinborn model

Abstract

The invention provides a thermoelectric unit peak regulation control method and device based on a digital twinborn model, and belongs to the field of combined heat and power generation control. The method comprises the steps that according to a predicted air temperature curve, a return water temperature curve of a target thermoelectric unit adopting the bypass heat supply technology and a predicted output curve of renewable energy sources, the predicted peak regulation demand quantity of the target thermoelectric unit in the future time period is determined; calculating the distance between the predicted peak regulation demand quantity and the critical peak regulation interval of the target thermoelectric unit; when the distance is smaller than a set threshold value, the predicted peak regulation demand quantity is input into a digital twinborn model of the target thermoelectric unit, and the simulation adjustment quantity and the simulation operation state of the target thermoelectric unit are obtained; calculating a fluctuation value of the current running state and the simulated running state of the target thermoelectric unit; and determining an actual adjustment amount according to the actual peak regulation demand, the predicted peak regulation demand, the fluctuation value and the simulated adjustment amount of the target thermoelectric unit in the future time period. According to the invention, the stability and safety of the system can be enhanced while the peak regulation requirement is met.

Description

Technical Field

[0001] The present invention relates to the field of cogeneration control technology, and in particular to a peak-shaving control method and device for a thermal power unit based on a digital twin model. Background Art

[0002] Cogeneration (CHP) is a common yet effective technology for improving the efficiency of thermal power plants. CHP units achieve energy savings through cascaded energy utilization. However, power generation and heat supply are coupled, so the heating and power loads of CHP units cannot be adjusted independently. The adjustable electrical load range is limited by the thermal load, a phenomenon known as "heat-electric coupling." This coupling mechanism is the primary factor limiting the operational flexibility and deep peak-shaving capabilities of CHP units.

[0003] To improve the deep peak-shaving capabilities of cogeneration units, bypass heating technologies, such as low-pressure cylinder zero-output modification or high- and low-pressure two-stage bypass modification, can be used to achieve heat-electric decoupling. However, when using bypass heating for heat-electric decoupling, the cogeneration unit will frequently switch between extraction and condensing and bypass heating during deep peak-shaving, which in turn affects the unit's heating stability and safety during deep peak-shaving. Summary of the Invention

[0004] The embodiments of the present invention provide a peak-shaving control method and device for a thermal power unit based on a digital twin model, so as to take into account the issues of heating stability and safety while meeting the peak-shaving requirements.

[0005] In a first aspect, an embodiment of the present invention provides a peak-shaving control method for a thermal power plant based on a digital twin model, comprising:

[0006] Determine the predicted peak load demand of the target thermal power unit in the future period based on the predicted temperature curve, the return water temperature curve of the target thermal power unit using bypass heating technology, and the predicted output curve of renewable energy;

[0007] Calculating the distance between the predicted peak-shaving demand and a critical peak-shaving interval of the target thermal power unit, wherein the critical peak-shaving interval is the peak-shaving demand between the extraction and condensation states and the bypass heating state;

[0008] When the distance is less than a set threshold, the predicted peak-shaving demand is input into the digital twin model of the target thermal power unit to obtain a simulated adjustment amount and a simulated operating state of the target thermal power unit;

[0009] Calculating a fluctuation value between a current operating state of the target thermal power unit and the simulated operating state;

[0010] The actual adjustment amount of the target thermal power group is determined according to the actual peak-shaving demand of the target thermal power group in the future period, the predicted peak-shaving demand, the fluctuation value and the simulated adjustment amount.

[0011] In one possible implementation, the predicted peak load demand of the target thermal power unit in a future period is determined based on the predicted air temperature curve, the return water temperature curve of the target thermal power unit using the bypass heating technology, and the predicted output curve of renewable energy, including:

[0012] Determine the predicted heating demand of the target thermal power unit in the future period based on the predicted air temperature curve and the return water temperature curve of the target thermal power unit using bypass heating technology;

[0013] Determine the predicted power demand of the target thermal power unit in the future period based on the predicted output curve of renewable energy;

[0014] The predicted peak-shaving demand of the target thermal power unit in the future period is obtained according to the predicted heating demand and the predicted power demand.

[0015] In one possible implementation, calculating the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit includes:

[0016] Determining a critical peak-shaving interval of the power generation power of the target thermal power unit according to the predicted heat demand, which is recorded as the critical power generation peak-shaving interval;

[0017] The distance between the predicted power supply demand and the critical power generation peak-shaving interval is calculated as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit.

[0018] In one possible implementation, calculating the distance between the predicted power demand and the critical power generation peak-shaving interval as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit includes:

[0019] Calculating a first difference between the predicted power demand and a minimum value in the critical power generation peak shaving interval, and a second difference between the predicted power demand and a maximum value in the critical power generation peak shaving interval;

[0020] The smaller value between the first difference and the second difference is determined as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group.

[0021] In a possible implementation, calculating the fluctuation value between the current operating state of the target thermal power unit and the simulated operating state includes:

[0022] Calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state;

[0023] According to the mutual information, a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state is obtained.

[0024] In a possible implementation, calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state includes:

[0025] according to Calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state;

[0026] Among them, I(x,y) is the mutual information between the current operating state x of the target thermal power unit and the simulated operating state y, p(x,y) is the joint probability density function of the current operating state x and the simulated operating state y, p(x) is the marginal probability density function of the current operating state x, and p(y) is the marginal probability density function of the simulated operating state y.

[0027] In a possible implementation, obtaining, based on the mutual information, a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state includes:

[0028] Calculating an operating state difference between a current operating state of the target thermal power unit and the simulated operating state;

[0029] The operating state difference is weighted according to the mutual information to obtain a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state.

[0030] In one possible implementation, determining the actual adjustment amount of the target thermal power unit according to the actual peak-shaving demand of the target thermal power unit in the future period, the predicted peak-shaving demand, the fluctuation value, and the simulated adjustment amount includes:

[0031] Determining a predicted fluctuation value of the target thermal power unit under the actual peak-shaving demand according to the actual peak-shaving demand of the target thermal power unit in the future period, the predicted peak-shaving demand, and the fluctuation value;

[0032] The simulated adjustment amount is adjusted according to the predicted fluctuation value to determine the actual adjustment amount of the target thermal power unit.

[0033] In a possible implementation, after calculating the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit, the method further includes:

[0034] When the distance is greater than or equal to the set threshold, the target thermal power unit is switched between the extraction condensation and bypass heating states according to the actual peak load demand of the target thermal power unit in the future period.

[0035] In a second aspect, an embodiment of the present invention provides a peak-shaving control device for a thermal power unit based on a digital twin model, comprising:

[0036] The first processing module is used to determine the predicted peak-shaving demand of the target thermal power unit in the future period based on the predicted temperature curve, the return water temperature curve of the target thermal power unit using the bypass heating technology, and the predicted output curve of the renewable energy;

[0037] A second processing module is configured to calculate a distance between the predicted peak-shaving demand and a critical peak-shaving interval of the target thermal power unit, wherein the critical peak-shaving interval is the peak-shaving demand between the extraction and condensation states and the bypass heating state;

[0038] a simulation module, configured to input the predicted peak-shaving demand into the digital twin model of the target thermal power unit when the distance is less than a set threshold, to obtain a simulated adjustment amount and a simulated operating state of the target thermal power unit;

[0039] a third processing module, configured to calculate a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state;

[0040] The peak regulation control module is used to determine the actual adjustment amount of the target thermal power group according to the actual peak regulation demand of the target thermal power group in the future period, the predicted peak regulation demand, the fluctuation value and the simulated adjustment amount.

[0041] An embodiment of the present invention provides a peak-shaving control method and device for a thermal power unit based on a digital twin model. The method first determines the predicted peak-shaving demand of the target thermal power unit in a future period based on a predicted air temperature curve, a return water temperature curve of a target thermal power unit using bypass heating technology, and a predicted output curve of renewable energy. Then, the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit is calculated. When the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit is less than a set threshold, the predicted peak-shaving demand is input into the digital twin model of the target thermal power unit to obtain a simulated adjustment amount and a simulated operating state of the target thermal power unit. Then, the fluctuation value between the simulated operating state and the current operating state of the target thermal power unit under the predicted peak-shaving demand is calculated. The simulated adjustment amount is smoothed according to the actual peak-shaving demand, the predicted peak-shaving demand, and the fluctuation value of the target thermal power unit in the future period to determine the actual adjustment amount of the target thermal power unit, so that the control of the target thermal power unit is smoother and the system stability and safety are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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.

[0043] Figure 1 This is a flowchart of an implementation method for peak-shaving control of a thermal power unit based on a digital twin model provided by an embodiment of the present invention;

[0044] Figure 2 2. It is a schematic diagram of the safe operation feasible region of the low-pressure cylinder cutting modification provided by an embodiment of the present invention;

[0045] Figure 3 It is a structural schematic diagram of a thermal power unit peak-shaving control device based on a digital twin model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0048] Figure 1 The implementation flow chart of the peak-shaving control method for thermal power units based on the digital twin model provided in the embodiment of the present invention is detailed as follows:

[0049] Step 101 : Determine the predicted peak load demand of the target thermal power unit in the future period based on the predicted temperature curve, the return water temperature curve of the target thermal power unit using the bypass heating technology, and the predicted output curve of renewable energy.

[0050] In this embodiment, the predicted air temperature curve and the return water temperature curve of the target thermal power unit are used to extract the heating characteristics of the target thermal power unit. Considering the access of renewable energy to the target thermal power unit, the predicted output curve of renewable energy is used to extract the power supply characteristics, and then the predicted peak-shaving demand for heating and power supply of the target thermal power unit in the future period can be determined.

[0051] Optionally, determining the predicted peak load demand of the target thermal power unit in the future period based on the predicted air temperature curve, the return water temperature curve of the target thermal power unit using the bypass heating technology, and the predicted output curve of renewable energy may include:

[0052] Based on the predicted air temperature curve and the return water temperature curve of the target thermal power unit using bypass heating technology, the predicted heating demand of the target thermal power unit in the future period is determined. Based on the predicted output curve of renewable energy, the predicted power supply demand of the target thermal power unit in the future period is determined. Based on the predicted heating demand and the predicted power demand, the predicted peak-shaving demand of the target thermal power unit in the future period is obtained.

[0053] Among them, in order to achieve early prediction and facilitate subsequent overshoot smoothing control, when determining the predicted heating demand of the target thermal power unit in the future period based on the predicted air temperature curve and the return water temperature curve of the target thermal power unit, a basic heating demand can be first determined based on the return water temperature curve of the target thermal power unit, and then a heating demand deviation can be determined based on the predicted air temperature curve, and then the predicted heating demand can be determined based on the basic heating demand and the heating demand deviation.

[0054] For example, the change in heating demand can be fitted based on the air temperature curve and the return water temperature curve to obtain a fitting function of the change in heating demand with respect to the air temperature and the return water temperature, and then a change in heating demand can be determined based on the fitting function and the predicted air temperature value on the predicted air temperature curve and the return water temperature value on the return water temperature curve as the corresponding heating demand deviation.

[0055] Among them, in order to improve the absorption capacity of renewable energy, the predicted output curve of renewable energy is taken into consideration when determining the predicted power supply demand. If the predicted output curve based on renewable energy can meet the target power demand, the predicted power supply demand of the target thermal power unit can be zero, thereby giving priority to the use of renewable energy for power supply.

[0056] Step 102 : Calculate the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit.

[0057] Among them, the critical peak-shaving interval is the peak-shaving demand between the extraction and condensation and bypass heating states.

[0058] For example, Figure 2 As shown in the figure, when the bypass heating technology of low-pressure cylinder cutting transformation (i.e. low-pressure cylinder zero output transformation) is adopted, the minimum heat load of the target thermal power unit after the transformation is Q D1 , the minimum electrical load is P D1 , the maximum heat load of the target thermal power unit after transformation is Q B1 , the maximum electrical load is P B1The area between B1D1 and ABCDE can be identified as the critical peak-shaving interval. When the predicted peak-shaving demand falls within the critical peak-shaving interval, the target thermal power unit can be switched from the extraction-condensing state to the bypass heating state. When the predicted peak-shaving demand exceeds the critical peak-shaving interval, the target thermal power unit can be switched from the bypass heating state to the extraction-condensing state. To avoid frequent switching, the distance between the predicted peak-shaving demand and the critical peak-shaving interval can be calculated, and the decision to switch can be made based on the distance.

[0059] For example, when the distance between the predicted peak-shaving demand and the critical peak-shaving interval is greater than or equal to a set threshold, the target thermal power unit can be switched between extraction and condensation and bypass heating based on the target unit's actual peak-shaving demand in the future period. When the distance between the predicted peak-shaving demand and the critical peak-shaving interval is less than a set threshold, the state switch can be omitted and an overshoot can be performed based on the predicted peak-shaving demand, allowing for smoother control of the target thermal power unit and enhancing system stability and safety.

[0060] It should be noted that the threshold value is set as the margin when switching between the state of no extraction and condensation and the state of bypass heating, which can be adjusted according to actual needs and is not limited in this embodiment.

[0061] Optionally, calculating the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit may include: determining the critical peak-shaving interval of the power generation power of the target thermal power unit based on the predicted heating demand, recorded as the critical power generation peak-shaving interval, and calculating the distance between the predicted power supply demand and the critical power generation peak-shaving interval as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit.

[0062] In this embodiment, when determining the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit, due to the existence of the thermoelectric coupling mechanism during the extraction and condensation state operation, after obtaining the predicted heating demand, the critical peak-shaving interval of the target thermal power unit's power generation power, that is, the critical power generation peak-shaving interval, can be determined based on the predicted heating demand, and then the distance between the predicted power supply demand and the critical power generation peak-shaving interval is calculated as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit.

[0063] Optionally, calculating the distance between the predicted power demand and the critical power generation peak-shaving interval as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit may include:

[0064] A first difference between the predicted power demand and a minimum value in a critical power generation peak shaving interval, and a second difference between the predicted power demand and a maximum value in the critical power generation peak shaving interval are calculated.

[0065] The smaller value between the first difference and the second difference is determined as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit.

[0066] For example, since the critical power generation peak-shaving interval generally has an upper limit and a lower limit, that is, a maximum value and a minimum value, when calculating the distance between the predicted power supply demand and the critical power generation peak-shaving interval, the first difference between the predicted power supply demand and the minimum value in the critical power generation peak-shaving interval, and the second difference between the predicted power supply demand and the maximum value in the critical power generation peak-shaving interval can be calculated first, and then a smaller value is determined from the first difference and the second difference as the distance between the predicted power supply demand and the critical power generation peak-shaving interval, that is, the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit.

[0067] Step 103 : When the distance is less than the set threshold, the predicted peak-shaving demand is input into the digital twin model of the target thermal power unit to obtain the simulated adjustment amount and simulated operating status of the target thermal power unit.

[0068] In this embodiment, considering that the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit is small, that is, when the distance is less than the set threshold, priority is given to meeting the peak-shaving demand through overshoot smoothing control without switching between extraction and condensation and bypass heating states, so as to reduce the frequency of state switching and enhance the system stability and safety of the thermal power unit.

[0069] A digital twin is a virtual model of a real physical object built using digital technology. This model can reflect the various characteristics of the real physical object, sense the state of the physical object, reflect the entire life cycle of the physical equipment, and provide guidance for relevant decisions about the physical object.

[0070] To meet peak-shaving demands through overshoot smoothing control when the distance is less than a set threshold, a digital twin model of the target thermal power unit can be constructed. The predicted peak-shaving demand can be input into the digital twin model to obtain simulated adjustments and simulated operating states for the target thermal power unit. For example, depending on actual conditions, the simulated adjustments for the target thermal power unit may include steam inlet adjustment, speed adjustment, and extraction adjustment, while the simulated operating states for the target thermal power unit may include light load, heavy load, full load, first-level light load, second-level light load, first-level heavy load, and second-level heavy load, etc. The actual adjustments for the target thermal power unit are then determined based on the simulated adjustments and simulated operating states to achieve overshoot smoothing control.

[0071] Step 104 : Calculate the fluctuation value between the current operating state and the simulated operating state of the target thermal power unit.

[0072] In this embodiment, after obtaining the simulated adjustment amount and simulated operating state of the target thermal power unit, in order to achieve overshoot smoothing control, the fluctuation value between the current operating state and the simulated operating state of the target thermal power unit can be calculated first, and the overshoot amount can be determined based on the fluctuation value to reduce the fluctuation.

[0073] Optionally, calculating the fluctuation value between the current operating state and the simulated operating state of the target thermal power unit may include: calculating the mutual information between the current operating state and the simulated operating state of the target thermal power unit, and obtaining the fluctuation value between the current operating state and the simulated operating state of the target thermal power unit based on the mutual information.

[0074] Optionally, you can Calculate the mutual information between the current operating state and the simulated operating state of the target thermal power unit.

[0075] Where I(x,y) is the mutual information between the current operating state x and the simulated operating state y of the target thermal power unit, p(x,y) is the joint probability density function of the current operating state x and the simulated operating state y, p(x) is the marginal probability density function of the current operating state x, and p(y) is the marginal probability density function of the simulated operating state y.

[0076] Optionally, obtaining the fluctuation value between the current operating state and the simulated operating state of the target thermal power unit based on the mutual information may include: calculating the operating state difference between the current operating state and the simulated operating state of the target thermal power unit, weighting the operating state difference based on the mutual information, and obtaining the fluctuation value between the current operating state and the simulated operating state of the target thermal power unit.

[0077] In this embodiment, mutual information can be considered to represent the reduction in uncertainty of the original random variable given another random variable. Therefore, by calculating the mutual information between the current operating state and the simulated operating state, the credibility of the simulated operating state under the current operating state can be measured. Furthermore, based on the credibility of the simulated operating state under the current operating state, the fluctuation values ​​of the current operating state and the simulated operating state can be measured, making the obtained fluctuation value more meaningful for reference. For example, the operating state difference between the current operating state and the simulated operating state can be weighted by the mutual information to obtain the fluctuation value of the current operating state and the simulated operating state of the target thermal power unit.

[0078] Step 105 , determining the actual adjustment amount of the target thermal power unit according to the actual peak-shaving demand, the predicted peak-shaving demand, the fluctuation value and the simulated adjustment amount of the target thermal power unit in the future period.

[0079] In this embodiment, the actual peak-shaving demand of the target thermal power unit in the future period corresponds to the predicted peak-shaving demand of the target thermal power unit in the future period. After evaluating the simulated adjustment amount corresponding to the predicted peak-shaving demand and the fluctuation value of the operating status, the simulated adjustment amount can be adjusted by measuring the difference between the actual peak-shaving demand in the future period and the predicted peak-shaving demand to obtain the actual adjustment amount whose fluctuation meets the requirements of system stability and safety. For example, the simulated steam intake adjustment amount, speed adjustment amount, and extraction adjustment amount can be adjusted to obtain the actual required steam intake adjustment amount, speed adjustment amount, and extraction adjustment amount.

[0080] Optionally, determining the actual adjustment amount of the target thermal power unit based on the actual peak-shaving demand, predicted peak-shaving demand, fluctuation value, and simulated adjustment amount of the target thermal power unit in the future period may include:

[0081] According to the actual peak-shaving demand, predicted peak-shaving demand and fluctuation value of the target thermal power unit in the future period, the predicted fluctuation value of the target thermal power unit under the actual peak-shaving demand is determined.

[0082] The simulated adjustment amount is adjusted according to the predicted fluctuation value to determine the actual adjustment amount of the target thermal power unit.

[0083] In this embodiment, in order to obtain the actual adjustment amount so that the fluctuation meets the system stability and safety, the predicted fluctuation value of the target thermal power unit under the actual peak-shaving demand can be determined based on the actual peak-shaving demand, predicted peak-shaving demand and fluctuation value of the target thermal power unit in the future period, and then the simulated adjustment amount can be adjusted according to the predicted fluctuation value to obtain the actual adjustment amount so that the fluctuation meets the system stability and safety.

[0084] For example, a corresponding relationship between the peak-shaving demand and the fluctuation value can be fitted based on the predicted peak-shaving demand and the fluctuation value. The fluctuation value under the actual peak-shaving demand can then be determined based on this corresponding relationship, serving as the predicted fluctuation value for the target thermal power unit under the actual peak-shaving demand. Alternatively, the fluctuation value can be corrected and adjusted based on the difference between the actual peak-shaving demand and the predicted peak-shaving demand for the target thermal power unit in a future time period to obtain the predicted fluctuation value for the target thermal power unit under the actual peak-shaving demand. This embodiment does not limit this, and an appropriate method can be selected based on actual circumstances.

[0085] The embodiment of the present invention first determines the predicted peak-shaving demand of the target thermal power group in the future time period based on the predicted air temperature curve, the return water temperature curve of the target thermal power group using bypass heating technology, and the predicted output curve of renewable energy, and then calculates the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group. When the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group is less than a set threshold, the predicted peak-shaving demand is input into the digital twin model of the target thermal power group to obtain the simulated adjustment amount and simulated operating state of the target thermal power group, and then the fluctuation value of the simulated operating state and the current operating state of the target thermal power group under the predicted peak-shaving demand is calculated, so that the simulated adjustment amount is smoothed according to the actual peak-shaving demand, predicted peak-shaving demand and fluctuation value of the target thermal power group in the future time period to determine the actual adjustment amount of the target thermal power group, so that the control of the target thermal power group is smoother, and the system stability and safety are enhanced.

[0086] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0087] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0088] Figure 3 The following is a schematic diagram of the structure of a thermal power plant peak regulation control device based on a digital twin model provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0089] like Figure 3 As shown, the peak-shaving control device of a thermal power unit based on a digital twin model includes: a first processing module 31 , a second processing module 32 , a simulation module 33 , a third processing module 34 and a peak-shaving control module 35 .

[0090] The first processing module 31 is used to determine the predicted peak load demand of the target thermal power unit in the future period based on the predicted temperature curve, the return water temperature curve of the target thermal power unit using the bypass heating technology, and the predicted output curve of renewable energy;

[0091] The second processing module 32 is configured to calculate the distance between the predicted peak-shaving demand and a critical peak-shaving interval of the target thermal power unit, wherein the critical peak-shaving interval is the peak-shaving demand between the extraction and condensation states and the bypass heating state;

[0092] a simulation module 33 for inputting the predicted peak-shaving demand into the digital twin model of the target thermal power unit when the distance is less than a set threshold, to obtain a simulated adjustment amount and a simulated operating state of the target thermal power unit;

[0093] A third processing module 34 is configured to calculate a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state;

[0094] The peak-shaving control module 35 is configured to determine the actual adjustment amount of the target thermal power group according to the actual peak-shaving demand of the target thermal power group in the future period, the predicted peak-shaving demand, the fluctuation value and the simulated adjustment amount.

[0095] The embodiment of the present invention first determines the predicted peak-shaving demand of the target thermal power group in the future time period based on the predicted air temperature curve, the return water temperature curve of the target thermal power group using bypass heating technology, and the predicted output curve of renewable energy, and then calculates the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group. When the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group is less than a set threshold, the predicted peak-shaving demand is input into the digital twin model of the target thermal power group to obtain the simulated adjustment amount and simulated operating state of the target thermal power group, and then the fluctuation value of the simulated operating state and the current operating state of the target thermal power group under the predicted peak-shaving demand is calculated, so that the simulated adjustment amount is smoothed according to the actual current peak-shaving demand, predicted peak-shaving demand and fluctuation value of the target thermal power group in the future time period to determine the actual adjustment amount of the target thermal power group, so that the control of the target thermal power group is smoother and the system stability and safety are enhanced.

[0096] In one possible implementation, the first processing module 31 may be configured to determine a predicted heating demand of the target thermal power unit in a future period based on a predicted air temperature curve and a return water temperature curve of the target thermal power unit using the bypass heating technology;

[0097] Determine the predicted power demand of the target thermal power unit in the future period based on the predicted output curve of renewable energy;

[0098] The predicted peak-shaving demand of the target thermal power unit in the future period is obtained according to the predicted heating demand and the predicted power demand.

[0099] In a possible implementation, the second processing module 32 may be configured to determine a critical peak-shaving interval of the power generation of the target thermal power unit based on the predicted heat demand, which is recorded as the critical power generation peak-shaving interval;

[0100] The distance between the predicted power supply demand and the critical power generation peak-shaving interval is calculated as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power generation unit.

[0101] In one possible implementation, the second processing module 32 may be configured to calculate a first difference between the predicted power demand and a minimum value in the critical power generation peak shaving interval, and a second difference between the predicted power demand and a maximum value in the critical power generation peak shaving interval;

[0102] The smaller value between the first difference and the second difference is determined as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group.

[0103] In a possible implementation, the third processing module 34 may be configured to calculate the mutual information between the current operating state of the target thermal power unit and the simulated operating state;

[0104] According to the mutual information, a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state is obtained.

[0105] In a possible implementation, the third processing module 34 may be configured to: Calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state;

[0106] Among them, I(x,y) is the mutual information between the current operating state x of the target thermal power unit and the simulated operating state y, p(x,y) is the joint probability density function of the current operating state x and the simulated operating state y, p(x) is the marginal probability density function of the current operating state x, and p(y) is the marginal probability density function of the simulated operating state y.

[0107] In a possible implementation, the third processing module 34 may be configured to calculate an operating state difference between the current operating state of the target thermal power unit and the simulated operating state;

[0108] The operating state difference is weighted according to the mutual information to obtain a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state.

[0109] In a possible implementation, the peak-shaving control module 35 may be configured to determine the predicted fluctuation value of the target thermal power unit under the actual peak-shaving demand according to the actual peak-shaving demand of the target thermal power unit in the future period, the predicted peak-shaving demand, and the fluctuation value;

[0110] The simulated adjustment amount is adjusted according to the predicted fluctuation value to determine the actual adjustment amount of the target thermal power unit.

[0111] In one possible implementation, the peak-shaving control module 35 can also be used to switch the target thermal power unit between the extraction and condensing and bypass heating states according to the actual peak-shaving demand of the target thermal power unit in the future period when the distance is greater than or equal to the set threshold.

[0112] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0113] Those skilled in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0114] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the thermal power plant peak-shaving control method based on the digital twin model. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0115] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A peak-shaving control method for thermal power units based on a digital twin model, characterized in that: include: Determine the predicted peak load demand of the target thermal power unit in the future period based on the predicted temperature curve, the return water temperature curve of the target thermal power unit using bypass heating technology, and the predicted output curve of renewable energy; Calculating the distance between the predicted peak-shaving demand and a critical peak-shaving interval of the target thermal power unit, wherein the critical peak-shaving interval is the peak-shaving demand between the extraction and condensation states and the bypass heating state; When the distance is less than a set threshold, the predicted peak-shaving demand is input into the digital twin model of the target thermal power unit to obtain a simulated adjustment amount and a simulated operating state of the target thermal power unit; Calculating a fluctuation value between a current operating state of the target thermal power unit and the simulated operating state; The actual adjustment amount of the target thermal power group is determined according to the actual peak-shaving demand of the target thermal power group in the future period, the predicted peak-shaving demand, the fluctuation value and the simulated adjustment amount.

2. The peak-shaving control method for thermal power units based on a digital twin model according to claim 1 is characterized in that: The predicted peak load demand of the target thermal power unit in the future period is determined based on the predicted air temperature curve, the return water temperature curve of the target thermal power unit using bypass heating technology, and the predicted output curve of renewable energy, including: Determine the predicted heating demand of the target thermal power unit in the future period based on the predicted air temperature curve and the return water temperature curve of the target thermal power unit using bypass heating technology; Determine the predicted power demand of the target thermal power unit in the future period based on the predicted output curve of renewable energy; The predicted peak-shaving demand of the target thermal power unit in the future period is obtained according to the predicted heating demand and the predicted power demand.

3. The peak-shaving control method for thermal power units based on a digital twin model according to claim 2 is characterized in that: Calculating the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit includes: Determining a critical peak-shaving interval of the power generation power of the target thermal power unit according to the predicted heat demand, which is recorded as the critical power generation peak-shaving interval; The distance between the predicted power supply demand and the critical power generation peak-shaving interval is calculated as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power generation unit.

4. The peak-shaving control method for thermal power units based on a digital twin model according to claim 3 is characterized in that: Calculating the distance between the predicted power demand and the critical power generation peak-shaving interval as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit includes: Calculating a first difference between the predicted power demand and a minimum value in the critical power generation peak shaving interval, and a second difference between the predicted power demand and a maximum value in the critical power generation peak shaving interval; The smaller value between the first difference and the second difference is determined as the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power group.

5. The peak-shaving control method for thermal power units based on a digital twin model according to claim 1, characterized in that: Calculating a fluctuation value between a current operating state of the target thermal power unit and the simulated operating state includes: Calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state; According to the mutual information, a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state is obtained.

6. The peak-shaving control method for thermal power units based on a digital twin model according to claim 5 is characterized in that: Calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state includes: according to Calculating the mutual information between the current operating state of the target thermal power unit and the simulated operating state; Among them, I(x,y) is the mutual information between the current operating state x of the target thermal power unit and the simulated operating state y, p(x,y) is the joint probability density function of the current operating state x and the simulated operating state y, p(x) is the marginal probability density function of the current operating state x, and p(y) is the marginal probability density function of the simulated operating state y.

7. The peak-shaving control method for thermal power units based on a digital twin model according to claim 5, characterized in that: Obtaining fluctuation values ​​of the current operating state of the target thermal power unit and the simulated operating state according to the mutual information includes: Calculating an operating state difference between a current operating state of the target thermal power unit and the simulated operating state; The operating state difference is weighted according to the mutual information to obtain a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state.

8. The peak-shaving control method for thermal power units based on a digital twin model according to claim 1, characterized in that: Determining the actual adjustment amount of the target thermal power unit according to the actual peak-shaving demand of the target thermal power unit in the future period, the predicted peak-shaving demand, the fluctuation value, and the simulated adjustment amount includes: Determining a predicted fluctuation value of the target thermal power unit under the actual peak-shaving demand according to the actual peak-shaving demand of the target thermal power unit in the future period, the predicted peak-shaving demand, and the fluctuation value; The simulated adjustment amount is adjusted according to the predicted fluctuation value to determine the actual adjustment amount of the target thermal power unit.

9. The peak-shaving control method for thermal power units based on a digital twin model according to claim 1, characterized in that: After calculating the distance between the predicted peak-shaving demand and the critical peak-shaving interval of the target thermal power unit, the method further includes: When the distance is greater than or equal to the set threshold, the target thermal power unit is switched between the extraction condensation and bypass heating states according to the actual peak load demand of the target thermal power unit in the future period.

10. A thermal power plant peak regulation control device based on a digital twin model, characterized in that: include: The first processing module is used to determine the predicted peak-shaving demand of the target thermal power unit in the future period based on the predicted temperature curve, the return water temperature curve of the target thermal power unit using the bypass heating technology, and the predicted output curve of the renewable energy; A second processing module is configured to calculate a distance between the predicted peak-shaving demand and a critical peak-shaving interval of the target thermal power unit, wherein the critical peak-shaving interval is the peak-shaving demand between the extraction and condensation states and the bypass heating state; a simulation module, configured to input the predicted peak-shaving demand into the digital twin model of the target thermal power unit when the distance is less than a set threshold, to obtain a simulated adjustment amount and a simulated operating state of the target thermal power unit; a third processing module, configured to calculate a fluctuation value between the current operating state of the target thermal power unit and the simulated operating state; The peak regulation control module is used to determine the actual adjustment amount of the target thermal power group according to the actual peak regulation demand of the target thermal power group in the future period, the predicted peak regulation demand, the fluctuation value and the simulated adjustment amount.

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