Boiler and steam turbine coordinated control optimization device and method based on heating load fluctuation

Abstract

The present invention discloses a device and method for optimizing coordinated control of boilers and turbines based on heating load fluctuations. The device comprises: a data acquisition and processing module for collecting steam usage data from all heat users, including at least boiler and turbine operating data, and performing preprocessing; an optimization control solution module for determining the optimal operating state of the unit, analyzing the current boiler and turbine operating states, predicting the next stage of operating mode and state, and issuing pre-operation instructions; a unit control module for receiving pre-operation instructions from the optimization control solution module; an operation execution module for completing the operation instructions of the unit control module; and a signal processing and feedback module for feeding back the execution status of the operation execution module to the unit control module and tracking and providing feedback on its real-time operating parameters and execution results. This device can solve problems such as difficulty in coordinated control of units, poor operating stability, delayed dynamic response, and energy loss caused by large heating load fluctuations.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to a device and method for optimizing coordinated control of boilers and steam turbines based on heating load fluctuations. Background Art

[0002] Currently, the main-pipe heating system in a cogeneration plant primarily adheres to the principle of "heat determines electricity," generating as much power as possible while prioritizing downstream heat users. This entire process relies heavily on years of operator experience to achieve conversion and control between heat and electricity. This process is subject to randomness and subjectivity, and fails to consider unit operating efficiency and economics. This can lead to steam supply exceeding demand and insufficient steam supply in the steam network, resulting in operational fluctuations and energy loss. The main-pipe heating system in a cogeneration plant is a complex dynamic system characterized by nonlinearity, large time lags, high inertia, time-varying behavior, and uncertainty. This results in a highly nonlinear heating load. Especially with the implementation of metered heating, users can freely adjust terminal heating supply, further increasing the randomness and nonlinearity of the heating load, making it difficult to develop a control model and method for heating load fluctuations. The heating load is the most important regulating parameter in the heating system. The size, characteristics and changing patterns of the heating load are very important for the operation management, energy conservation and consumption reduction of the heating system. In the dynamic regulation process of the heating load, the change of the heat load must be able to track the changes in the heat consumption of heat users. Therefore, how to establish an optimal heat load fluctuation control method is of great significance for the stable operation of thermal power plants, the optimization of the control process and energy conservation and consumption reduction.

[0003] In cogeneration plants, the scheduling and operation of steam networks involves the coordinated control of boilers and steam turbines, as well as the balance between steam production and consumption. With the development of information technology, today's cogeneration plants generally have the ability to collect data on boilers, steam turbines, and steam production and consumption. However, due to the complex process flows and the coordination and cooperation between various equipment involved in cogeneration plants, when the steam consumption of downstream heating users in the steam network fluctuates frequently, the boilers and steam turbines cannot dynamically respond and implement relevant control measures in a timely manner. This leads to significant lag in the entire steam network system, resulting in unstable unit operation and frequent operations and adjustments. Typically, unit operators communicate in the control room through monitoring panels and manual information transmission. This requires operators to constantly operate and adjust to maintain stable unit operation, which is labor-intensive and inefficient, especially due to the lag and distortion in the information transmission process. Therefore, it is necessary to develop a boiler and steam turbine coordinated control optimization method and device to solve these problems, such as large fluctuations in steam network heating load, difficult coordinated control of boilers and turbines, delayed dynamic response of units, untimely information transmission, and serious energy loss and waste, to provide cogeneration enterprises with optimized control methods and devices for units, and to provide strong technical support for the enterprise's automated operation, management, energy conservation, consumption reduction and cost reduction. Summary of the Invention

[0004] In order to overcome the deficiencies of the above technologies, the present invention provides a device and method for optimizing the coordinated control of boilers and steam turbines based on heating load fluctuations.

[0005] The present invention addresses the problems of large fluctuations in heating load, long dynamic response time, difficulty in coordinated control of boilers and turbines, and poor unit operation stability in cogeneration enterprises. It proposes a boiler and steam turbine coordinated control optimization device based on heating load fluctuations and a boiler and steam turbine coordinated control optimization method based on heating load fluctuations. The device and method provided by the present invention can specifically address the problems currently existing in cogeneration enterprises, such as large fluctuations in heating load causing difficulty in coordinated control of units, poor unit operation stability, delayed dynamic response, and energy loss and waste. Based on the fluctuations in the thermal load of heat users, the control optimization method is used to provide advance feedback messages to the operating status and control parameters of the boiler and steam turbine in advance, so that the units have ample reaction and adjustment time to execute relevant actions to reduce the fluctuation of unit operation and improve the stability and agility of unit operation. This can not only improve the operation of the centralized heating network supervision system, but also improve the operating efficiency, reliability, and economy of the heating network system, which is of great significance to the efficient operation, intelligent management, and energy conservation and consumption reduction of cogeneration enterprises.

[0006] Explanation of terms:

[0007] 1. APC: advanced Process Control.

[0008] 2. DEH: Digital Electric Hydraulic Control System, steam turbine digital electro-hydraulic control system.

[0009] The technical solution adopted by the present invention to overcome the technical problems is:

[0010] A boiler and steam turbine coordinated control optimization device based on heating load fluctuations, comprising at least:

[0011] The data acquisition and processing module is used to collect steam usage data of all heat users, at least the operating data of boilers and steam turbines, and pre-process all the collected data;

[0012] The optimization control solution module, whose input end is connected to the output end of the data acquisition and processing module, is used to perform an optimal solution with total economic benefits as the objective function. Based on the solution results, the optimal operating state of the unit is determined, and the current operating state of the boiler and steam turbine is analyzed. The operating mode and state of the next stage are predicted, and then pre-operation instructions are given;

[0013] The unit control module, whose input end is connected to the output end of the optimization control solution module, includes a boiler APC control module and a steam turbine DEH control module, and is used to receive pre-operation instructions from the optimization control solution module and distribute the received pre-operation instructions to the boiler APC control module and the steam turbine DEH control module. The operation information of the boiler APC control module and the steam turbine DEH control module are interconnected;

[0014] An operation execution module, whose input terminal is connected to the output terminal of the unit control module, is used to complete the operation instructions of the unit control module through the actuator;

[0015] The signal processing feedback module has its input end connected to the output end of the operation execution module and its output end connected to the input end of the unit control module. It is used to feed back the execution status of the operation execution module to the unit control module and track and provide feedback on the real-time operating parameters of the unit control module and the execution results of the operation execution module.

[0016] Furthermore, in the data acquisition and processing module, the steam consumption data of all heat users include at least the flow, pressure and temperature data of m large users, the steam supply master meter and each steam consumption sub-meter, where m ≥ 1. Large users are users whose instantaneous or continuous steam consumption accounts for 8% to 10% of the total steam supply outside the pipeline network; the operating data of boilers and steam turbines include at least the steam production flow, steam production pressure, and temperature of the boilers, and the steam inlet flow, exhaust flow, pressure, and temperature data of the steam turbines.

[0017] Furthermore, in the data acquisition and processing module, preprocessing of all collected data includes:

[0018] Calculate the average value of the pressure, flow rate and pipe loss rate of each large user;

[0019] Based on the average values ​​of pressure, flow and pipe loss rate of each large user, bad points and abnormal data are eliminated to obtain an accurate data pool.

[0020] Furthermore, the pressure, flow rate and pipe loss rate of each large user are averaged, specifically including:

[0021] 1) For a large user j, j = 1, 2, ..., m, obtain n pressure values ​​according to a preset time interval Δt, where n ≥ 2. The average pressure value of the large user j is:

[0022]

[0023] In the above formula, P jav represents the average pressure of large user j in the time interval Δt, P j1 、P j2 、P jn They represent the first, second, and nth pressure values ​​of the large user j obtained within the time interval Δt, respectively, i = 1, 2, ..., n;

[0024] 2) For large user j, calculate the average traffic volume of large user j according to the preset time interval Δt:

[0025]

[0026] In the above formula, F jav is the average traffic of large user j in the time interval Δt, f jt represents the real-time traffic value of large user j at time t;

[0027] 3) According to the preset time interval Δt, obtain the real-time values ​​of n tube loss rates. The average value of the tube loss rate is:

[0028]

[0029] In the above formula, δ1, δ2, δ x Respectively represent the real-time values ​​of the 1st, 2nd, and xth pipe loss rates.

[0030] Furthermore, in the optimization control solution module, when the total economic benefit is used as the objective function for optimal solution, the objective function is:

[0031]

[0032] In the above formula, minc represents the total economic benefit, N represents the number of devices, and Y sIndicates the start / stop status of the device is 0 or 1, E fy is the equipment depreciation expense, c fuel represents the fuel price, F fuel represents fuel consumption, c s represents the price of purchased steam, F s Indicates the amount of purchased steam, c power represents the price of purchased electricity, and Q represents the amount of purchased electricity.

[0033] Furthermore, in the optimization control solution module, the operation mode and state of the next stage are predicted by calculating the change rate of the traffic of all large users in the future time interval Δt. The change rate of the traffic of large user j in the future time interval Δt is calculated as follows:

[0034] According to the preset time interval Δt, the values ​​of the future time interval Δt are collected. The future traffic change rate of large user j based on the current time t is:

[0035]

[0036] In the above formula, It represents the traffic change rate of large user j in the future interval Δt based on the current time t, tF tj+Δt represents the real-time traffic of large user j after a period of time Δt in the future based on the current time t, F tj represents the real-time traffic of large user j at the current time t.

[0037] The present invention also discloses a method for optimizing coordinated control of boilers and steam turbines based on heating load fluctuations, comprising the steps of:

[0038] Step 1: Preset parameters: Set the interval of the flow rate F of the sub-meter to [a z ,b z ], boiler load is F b The interval of the flow rate F in the sub-meter corresponds to the exhaust pressure P of the steam turbine. b The interval is [c k ,d k ], the flow and pressure of large user j are F j and P j , the minimum steam supply pressure of large user j is P j0 , the pipe loss rate GSL interval is [e,f], where j = 1, 2, ..., m, m is the number of large users, m ≥ 1;

[0039] Step 2: Collect sub-meter flow F and boiler load F b , exhaust steam pressure P of steam turbine b , traffic F of large user j j and pressure Pj , pipe loss rate GSL;

[0040] Step 3: Collect the current turbine operation mode signal and determine whether it is a TCP signal or a power supply signal: If the current turbine operation mode is a TCP signal, proceed to the next step; otherwise, proceed to step 10;

[0041] Step 4: At this time, the turbine is in TCP operation mode, and then the range of the current exhaust pressure of the turbine is judged: If c k ≤P bu <d k , then proceed to the next step, otherwise proceed to step eight;

[0042] Step 5: Determine the steam supply pressure of large user j: If any P j <P j0 , then execute the next step, otherwise execute the load increase sub-process in TCP mode;

[0043] Step 6: Calculate the average value of the pipe loss rate GSL within 2 to 5 minutes av1 , and GSL av1 The interval range is judged: if e≤GSL av1 <f, the unit maintains the current operation, otherwise the load reduction sub-process in TCP mode is executed;

[0044] Step 7: Determine the pipe loss rate GSL at this time av1 Size: If GSL av1 <e, then execute the load increase sub-process in TCP mode, otherwise execute the load reduction sub-process in TCP mode;

[0045] Step 8. Determine the exhaust pressure of the steam turbine at this time: If P bu <c k , then execute the load increase sub-process in TCP mode, otherwise execute the load reduction sub-process in TCP mode;

[0046] Step 9: Input the result of the load increase sub-process in the TCP mode and the result of the load reduction sub-process in the TCP mode into the signal processor, and then return to step 4;

[0047] Step 10: At this time, the turbine is in power operation mode, and then the range of the current exhaust pressure of the turbine is determined: If c k ≤P bu <d k , then proceed to the next step, otherwise proceed to step 12;

[0048] Step 11: Determine the steam supply pressure of large user j: If any P j <Pj0 , then execute the next step, otherwise execute the load increase sub-process in power mode;

[0049] Step 12: Calculate the average value of the pipe loss rate GSL within 2 to 5 minutes. av2 , and GSL av2 The interval range is judged: if e≤GSL av2 <f, the unit maintains the current operation, otherwise proceed to the next step;

[0050] Step 13: Determine the pipe loss rate GSL at this time av2 Size: If GSL av2 <e, then execute the load increase sub-process in the power mode, otherwise execute the load reduction sub-process in the power mode;

[0051] Step 14: Determine the exhaust pressure of the steam turbine at this time: If P bu <c k , then execute the load increase sub-process in the power mode, otherwise execute the load reduction sub-process in the power mode;

[0052] Step 15: Input the result of the load increase sub-process in the power mode and the result of the load reduction sub-process in the power mode into the signal processor, and then return to step 10.

[0053] Furthermore, in step 1, according to the set flow rate F of the submeter and the exhaust pressure range of the steam turbine corresponding to the range of flow rate F of the submeter, the current corresponding flow range [a z ,b z ] and pressure range [c k ,d k ],as follows:

[0054]

[0055]

[0056] Among them, F u The unit is t / h, P bu The unit is MPa.

[0057] Furthermore, the load increase sub-process in TCP mode includes:

[0058] Step 11) Define the turbine inlet steam pressure P a , the interval is [P a1 , P a2 ];

[0059] Step 12) Execute the boiler load increase instruction, the boiler load increase F b*M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result to the steam turbine DEH control module. The steam turbine DEH control module executes step 13 after receiving the signal feedback.

[0060] Step 13) Reduce the turbine inlet steam pressure setting value (P a2 -P a1 )*M, where the value range of M is [2%, 3%];

[0061] Step 14) Determine the exhaust pressure of the steam turbine at this time: If P a <P a1 , then execute step 15), otherwise execute step 16);

[0062] Step 15) Switch the turbine operation mode to the power operation mode, and make the power operation signal true;

[0063] Step 16) End the load reduction sub-process and execute step 9.

[0064] Furthermore, the load reduction sub-process in TCP mode includes:

[0065] Step 21) Define the turbine inlet steam pressure P a , the interval is [P a1 , P a2 ];

[0066] Step 22) Execute the boiler load reduction instruction, the boiler load reduction F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result to the steam turbine DEH control module. The steam turbine DEH control module executes 23 after receiving the signal feedback.

[0067] Step 23) Increase the turbine inlet steam pressure setting value (P a2 -P a1 )*M, where the value range of M is [2%, 3%];

[0068] Step 24) Determine the exhaust steam pressure of the steam turbine at this time: If P a >P a2 , then execute step 25), otherwise execute step 26);

[0069] Step 25) Switch the turbine operation mode to the power operation mode, and make the power operation signal true;

[0070] Step 26) End the load reduction sub-process and execute step 9.

[0071] Furthermore, the load increase sub-process in the power mode includes:

[0072] Step 31) Define the rated power of the steam turbine as P e ;

[0073] Step 32) Execute the boiler load increase instruction, the boiler load increase F b *M, M value range is [2%, 3%], the boiler APC control module will feed back the execution result information to the steam turbine DEH control module;

[0074] Step 33) Determine whether the temperature reduction and pressure reduction device is in operation in the boiler power mode: if it is in operation, execute step 34); otherwise, execute step 35);

[0075] Step 34) increasing the opening of the pressure regulating valve of the temperature and pressure reduction device by 2% to 3% to adjust the flow rate of the temperature-reducing water;

[0076] Step 35) Increase the turbine power N*P e , where the value range of N is [0.3%~0.4%].

[0077] Furthermore, the load reduction sub-process in the power mode includes:

[0078] Step 41) Define the rated power of the steam turbine as P e ;

[0079] Step 42) Execute the boiler load reduction instruction, the boiler load reduction F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result information to the steam turbine DEH control module;

[0080] Step 43) In the boiler power mode, determine whether the temperature reduction and pressure reduction device is in operation: if it is in operation, execute step 44); otherwise, execute step 45);

[0081] Step 44) reducing the opening of the pressure regulating valve of the temperature reduction and pressure reduction device by 2% to 3% to adjust the flow rate of the temperature reduction water;

[0082] Step 45) Reduce the turbine power N*P e , where the value range of N is [0.3%~0.4%].

[0083] The beneficial effects of the present invention are:

[0084] 1. Intelligent decision-making: To address the problems of boiler and steam turbine coordinated control lag caused by heating load fluctuations in the steam pipe network heating system of cogeneration enterprises, the present invention optimizes and solves the current steam pipe network and unit operation status with total economic benefits as the objective function, provides a control plan for unit operation based on the solution results, automatically generates relevant execution instructions for the unit, automatically executes the instructions through the operation execution module and feeds back the execution results to the unit. The entire process realizes intelligent control and smart decision-making, which can effectively solve the current problems of difficult coordinated control of units, delayed dynamic response, and untimely and inaccurate decision-making and operation.

[0085] 2. Control Automation: This technology enables automated coordinated control of equipment such as boilers and steam turbines. This technology automates everything from data acquisition, optimization, signal transmission, operation execution, to information processing and feedback. The entire process requires virtually no human intervention, significantly improving the efficiency and level of existing coordinated control of boilers and turbines and significantly reducing operator workload. This technology provides strong support for automated management, energy conservation, consumption reduction, and cost reduction in enterprises.

[0086] 3. Economical operation: Existing cogeneration companies suffer from a large amount of energy waste and loss due to reasons such as the inability to achieve intelligent decision-making and control, low level of control and operation automation, and untimely information transmission and communication. The present invention can greatly improve the level of automated operation, realize intelligent control and smart decision-making, and optimize the solution with total economic benefit as the optimization objective function. The operating status of the unit is continuously adjusted and optimized to achieve the optimal operating mode, greatly improving the efficiency of enterprise operation and energy utilization, and providing strong technical support and implementation path for enterprises to save energy, reduce consumption and save costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 This is a schematic diagram of the principle of a boiler and steam turbine coordinated control optimization device based on heating load fluctuation according to an embodiment of the present invention.

[0088] Figure 2 This is a flow chart of a method for optimizing the coordinated control of boilers and steam turbines based on heating load fluctuations according to an embodiment of the present invention.

[0089] Figure 3 This is a schematic diagram of the load increase sub-process in the steam turbine TCP mode according to an embodiment of the present invention.

[0090] Figure 4 This is a schematic diagram of the load reduction sub-process of the steam turbine TCP mode according to an embodiment of the present invention.

[0091] Figure 5 This is a schematic diagram of the load increase sub-process in the steam turbine power mode according to an embodiment of the present invention.

[0092] Figure 6This is a schematic diagram of the load reduction sub-process of the steam turbine power mode according to an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to facilitate those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the scope of protection of the present invention.

[0094] Example 1:

[0095] like Figure 1 As shown, the boiler-turbine coordinated control optimization device based on heating load fluctuations described in this embodiment includes at least a data acquisition and processing module 1, an optimization control solution module 2, a unit control module 3, an operation execution module 4, and a signal processing and feedback module 5. These five modules complement each other and cover the overall content of steam network scheduling and boiler-turbine coordinated control in a cogeneration enterprise. Their specific functions are described below.

[0096] In this embodiment, the data acquisition and processing module 1 is used to collect steam usage data of all heat users, at least collect operating data of boilers and steam turbines, and pre-process all collected data.

[0097] Specifically, the steam consumption data of all heat users include at least the flow, pressure and temperature data of m large users, the steam supply master meter and each steam consumption sub-meter, that is, the flow, pressure and temperature data of each large user, the flow, pressure and temperature data of the steam supply master meter, and the flow, pressure and temperature data of each steam consumption sub-meter are collected, where m≥1. The large users mentioned here are users whose instantaneous or continuous steam consumption is greater than or equal to 10t / h or whose steam consumption accounts for 8% or more of the total steam supply outside the pipeline network (the larger of the two values); in addition to boilers and steam turbines, it also includes the operating parameters of some other auxiliary equipment, such as water pumps, fans, etc., and the boiler coal supply data involved in the coal feeding system, where the operating data of boilers and steam turbines include at least the steam production flow, steam production pressure, and temperature of the boiler and the steam inlet flow, exhaust flow, pressure, and temperature data of the steam turbine.

[0098] Specifically, preprocessing of all collected data includes:

[0099] 1. Calculate the average value of the pressure, flow rate and pipe loss rate for each large user.

[0100] In this embodiment, two large users (set as large user 1 and large user 2) are used as an example for illustration. Calculating the average value of the pressure, flow rate, and pipe loss rate of the two large users specifically includes:

[0101] 1) For large user 1 and large user 2, obtain n pressure values ​​for each at a preset time interval Δt, where n ≥ 2. The more values ​​of n, the more accurate the average value. For example, obtain a value every 5 seconds.

[0102] The average pressure of large user 1 is:

[0103]

[0104] The average pressure of heavy user 2 is:

[0105]

[0106] In the above formula, P 1av and P 2av They represent the average pressure of large user 1 and large user 2 in the time interval Δt, P 11 、P 12 、P 1n They represent the first pressure value, the second pressure value, and the nth pressure value of the large user 1 obtained within the time interval Δt, respectively. 21 、P 22 、P 2n They represent the first pressure value, the second pressure value, and the nth pressure value of the heavy user 2 obtained within the time interval Δt, respectively, i=1, 2, ..., n, for example, n here can be 5.

[0107] 2) For large users 1 and 2, calculate the average traffic volume of each of the large users according to the preset time interval Δt:

[0108] The average traffic volume of large user 1 is:

[0109]

[0110] The average traffic volume of large user 1 is:

[0111]

[0112] In the above formula, F 1av and F 2av are the mean traffic values ​​of large user 1 and large user 2 in the time interval Δt, respectively, and f 1t and f 2t They represent the real-time traffic values ​​of large user 1 and large user 2 at time t respectively.

[0113] 3) According to the preset time interval Δt, such as Δt = 30s, obtain the real-time values ​​of n tube loss rates. The average value of the tube loss rate is:

[0114]

[0115] In the above formula, δ1, δ2, δx They represent the real-time values ​​of the 1st, 2nd, and xth pipe loss rates respectively. For example, x can be 6 here.

[0116] 2. Based on the obtained average values ​​of pressure, flow and pipe loss rate of each large user, bad points and abnormal data are eliminated to obtain an accurate data pool, which is then transmitted to the optimization control solution module 2.

[0117] In this embodiment, the input end of the optimization control solution module 2 is connected to the output end of the data acquisition and processing module 1, and is used to perform optimal solution with total economic benefits as the objective function, determine the optimal operating state of the unit based on the solution results, analyze the current operating state of the boiler and steam turbine, predict the operating mode and state of the next stage, and then give pre-operation instructions.

[0118] Specifically, when the total economic benefit is used as the objective function for optimal solution, the objective function is:

[0119]

[0120] In the above formula, minc represents the total economic benefit, N represents the number of devices, and Y s Indicates the start / stop status of the device is 0 or 1, E fy is the equipment depreciation expense, c fuel represents the fuel price, F fuel represents fuel consumption, c s represents the price of purchased steam, F s Indicates the amount of purchased steam, c power represents the price of purchased electricity, and Q represents the amount of purchased electricity.

[0121] In addition, the optimization control solution module 2 needs to meet constraints such as mass conservation, energy conservation, equipment capacity restrictions, and energy and electricity demand.

[0122] The mass conservation constraint means that the mass of the steam entering and leaving each turbine and the water entering and leaving each boiler are equal. The expression is as follows:

[0123]

[0124] Where, ρ in 、F in Respectively represent the density and flow rate of water / steam flowing into each steam turbine or boiler, ρ out 、F out They represent the density and flow rate of water / steam flowing out of each steam turbine or boiler respectively.

[0125] The energy balance constraint means that the energy carried by the medium entering each steam turbine and boiler is equal to the sum of the output energy of each device, the energy carried by the medium output by each device, and the heat dissipation loss of the device. The expression is as follows:

[0126]

[0127] Where, F in 、H in Respectively represent the flow rate and enthalpy of water / steam flowing into each steam turbine or boiler, F out 、H out Respectively represent the flow rate and enthalpy of water / steam flowing out of each steam turbine or boiler, W n Indicates the energy output of each device, Q n Represents the sum of heat dissipation losses of each device.

[0128] Specifically, in the optimization control solution module 2, the traffic change rate of all large users in the future time interval Δt is calculated to predict the operation mode and state of the next stage. The traffic change rate of large user 1 and large user 2 in the future time interval Δt is calculated as follows:

[0129] According to the preset time interval Δt, collect the value of the interval Δt in the future, such as Δt = 5s, with the current time t as the benchmark;

[0130] The future traffic change rate of large user 1 is:

[0131]

[0132] The future traffic change rate of large user 1 is:

[0133]

[0134] In the above formula, and They represent the traffic change rate of large user 1 and large user 2 in the future time interval Δt based on the current time t, respectively. t1+Δt and F t2+Δt They represent the real-time traffic of large user 1 and large user 2 after a period of time Δt in the future based on the current time t, respectively. t1 and F t2 They represent the real-time traffic of large user 1 and large user 2 at the current time t respectively.

[0135] The rate of change of flow of all steam users within the future time interval Δt is calculated as the basis for adjusting the overall load scheduling of the steam network. This data, combined with the time lag of the steam network, can be used to predict the load of the steam network and adjust the load of boilers and steam turbines accordingly to achieve stable operation of the turbine-boiler network.

[0136] In this embodiment, the input end of the unit control module 3 is connected to the output end of the optimization control solution module 2. The unit control module 3 includes a boiler APC control module and a steam turbine DEH control module, which is used to receive the pre-operation instructions of the optimization control solution module 2 and distribute the received pre-operation instructions to the boiler APC control module and the steam turbine DEH control module. Among them, the operating information of the boiler APC control module and the steam turbine DEH control module are interconnected, and the relevant parameters are mutually constrained. They coordinate and cooperate with each other to maintain the stable operation of the unit.

[0137] In this embodiment, the input end of the operation execution module 4 is connected to the output end of the unit control module 3, and is used to complete the operation instructions of the unit control module 3 through the actuator, wherein the actuator includes a valve, an electric pump, a hydraulic drive, etc., which is specifically determined by the configuration of the unit pipeline network.

[0138] In this embodiment, the input end of the signal processing feedback module 5 is connected to the output end of the operation execution module 4, and the output end is connected to the input end of the unit control module 3, so as to feed back the execution status of the operation execution module 4 to the unit control module 3 and track and feedback the real-time operating parameters of the unit control module and the execution results of the operation execution module 4, thereby completing the switching of the unit operation mode and the optimization of the operation status.

[0139] Example 2:

[0140] This embodiment discloses a method for optimizing the coordinated control of boilers and steam turbines based on heating load fluctuations. Figure 2 As shown, the steps include:

[0141] Step 1: Preset parameters: Set the interval of the flow rate F of the sub-meter to [a z ,b z ], the interval of the flow rate F corresponds to the exhaust pressure P of the steam turbine b The interval is [c k ,d k ], the flow and pressure of large user j are F j and P j , the minimum steam supply pressure of large user j is P j0 The pipe loss rate GSL interval is [e,f], where j = 1, 2, ..., m, m is the number of large users, m ≥ 1.

[0142] In this embodiment, there are two large users, namely large user 1 and large user 2. The flow rate and pressure of large user 1 are F1 and P1 respectively, the flow rate and pressure of large user 2 are F2 and P2 respectively, and the minimum steam supply pressure of large user 1 is P 10 , the minimum steam supply pressure of large user 2 is P 20 .

[0143] According to the set flow rate F of the submeter and the exhaust pressure range of the steam turbine corresponding to the flow rate F of the submeter, the current corresponding flow range [a z ,b z ] and pressure range [c k ,d k ],as follows:

[0144]

[0145] Among them, F u The unit is t / h, P bu The unit is MPa.

[0146] Step 2: Collect sub-meter flow F and boiler load F b , exhaust steam pressure P of steam turbine b , traffic F of large user j j and pressure P j Specifically, the flow rate and pressure of large user 1 are F1 and P1 respectively, and the flow rate and pressure of large user 2 are F2 and P2 respectively.

[0147] Step 3: Collect the current turbine operation mode signal and determine whether it is a TCP signal or a power supply signal: If the current turbine operation mode is a TCP signal, proceed to the next step; otherwise, proceed to step 10.

[0148] Step 4: At this time, the turbine is in TCP operation mode, and then the range of the current exhaust pressure of the turbine is judged: If c k ≤P bu <d k , then proceed to the next step, otherwise proceed to step eight.

[0149] Step 5: Determine the steam supply pressure of large user j: If any P j <P j0 , then execute the next step, otherwise execute the load increase sub-process in TCP mode.

[0150] Specifically, the steam supply pressure of large user 1 and large user 2 is judged: if P1 < P 10 Or P2<P 20 , then execute the next step, otherwise execute the load increase sub-process in TCP mode.

[0151] Step 6: Calculate the average value of the pipe loss rate GSL within 2 to 5 minutes av1 , and GSL av1 The interval range is judged: if e≤GSL av1 <f, the unit maintains the current operation, otherwise proceed to the next step;

[0152] Step 7: Determine the pipe loss rate GSL at this time av1 Size: If GSL av1 <e, then execute the load increase sub-process in TCP mode, otherwise execute the load reduction sub-process in TCP mode;

[0153] Step 8. Determine the exhaust pressure of the steam turbine at this time: If P bu <c k , then execute the load increase sub-process in TCP mode, otherwise execute the load reduction sub-process in TCP mode.

[0154] Step 9: Input the result of the load increase sub-process under the TCP mode and the result of the load reduction sub-process under the TCP mode into the signal processor, and then return to execute step 4.

[0155] Step 10: At this time, the turbine is in power operation mode, and then the range of the current exhaust pressure of the turbine is determined: If c k ≤P bu <d k , then proceed to the next step, otherwise proceed to step fourteen;

[0156] Step 11: Determine the steam supply pressure of large user j: If any P j <P j0 , then execute the next step, otherwise execute the load increase sub-process in power mode.

[0157] Specifically, the steam supply pressure of large user 1 and large user 2 is judged: if P1 < P 10 Or P2<P 20 , then execute the next step, otherwise execute the load increase sub-process in power mode.

[0158] Step 12: Calculate the average value of the pipe loss rate GSL within 2 to 5 minutes. av2 , and GSL av2 The interval range is judged: if e≤GSL av2 <f, the unit maintains the current operation, otherwise proceed to the next step.

[0159] Step 13: Determine the pipe loss rate GSL at this time av2 Size: If GSL av2 <e, then execute the load increase sub-process in the power mode, otherwise execute the load reduction sub-process in the power mode;

[0160] Step 14: Determine the exhaust pressure of the steam turbine at this time: If P bu <c k , then execute the load increase sub-process in the power mode, otherwise execute the load reduction sub-process in the power mode.

[0161] Step 15: Input the result of the load increase sub-process in the power mode and the result of the load reduction sub-process in the power mode into the signal processor, and then return to step 10.

[0162] In this embodiment, Figure 3 As shown, the load increase sub-process in TCP mode includes:

[0163] Step 11) Define the turbine inlet steam pressure P a , the interval is [P a1 , P a2 ];

[0164] Step 12) Execute the boiler load increase instruction, and the boiler increases the load F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result to the steam turbine DEH control module. The steam turbine DEH control module executes step 13 after receiving the signal feedback.

[0165] Step 13) Reduce the turbine inlet steam pressure setting value (P a2 -P a1 )*M, where the value range of M is [2%, 3%];

[0166] Step 14) Determine the exhaust pressure of the steam turbine at this time: If P a <P a1 , then execute step 15), otherwise execute step 16);

[0167] Step 15) Switch the turbine operation mode to the power operation mode, and make the power operation signal true;

[0168] Step 16) End the load reduction sub-process and execute step 9.

[0169] In this embodiment, Figure 4 As shown, the load reduction sub-process in TCP mode includes:

[0170] Step 21) Define the turbine inlet steam pressure P a , the interval is [P a1 , P a2 ];

[0171] Step 22) Execute the boiler load reduction instruction, and the boiler reduces the load F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result to the steam turbine DEH control module. The steam turbine DEH control module executes 23 after receiving the signal feedback.

[0172] Step 23) Increase the turbine inlet steam pressure setting value (P a2 -Pa1 )*M, where the value range of M is [2%, 3%];

[0173] Step 24) Determine the exhaust steam pressure of the steam turbine at this time: If P a >P a2 , then execute step 25), otherwise execute step 26);

[0174] Step 25) Switch the turbine operation mode to the power operation mode, and make the power operation signal true;

[0175] Step 26) End the load reduction sub-process and execute step 9.

[0176] In this embodiment, Figure 5 As shown, the load increase sub-process in power mode includes:

[0177] Step 31) Define the rated power of the steam turbine as P e ;

[0178] Step 32) Execute the boiler load increase instruction, and the boiler increases the load F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result information to the steam turbine DEH control module;

[0179] Step 33) Determine whether the temperature reduction and pressure reduction device is in operation in the boiler power mode: if it is in operation, execute step 34); otherwise, execute step 35);

[0180] Step 34) increasing the opening of the pressure regulating valve of the temperature and pressure reduction device by 2% to 3% to adjust the flow rate of the temperature-reducing water;

[0181] Step 35) Increase the turbine power N*P e , where the value range of N is [0.3%~0.4%].

[0182] In this embodiment, Figure 6 As shown in FIG, the load reduction sub-process in power mode includes:

[0183] Step 41) Define the rated power of the steam turbine as P e ;

[0184] Step 42) Execute the boiler load reduction instruction, and the boiler reduces the load F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result information to the steam turbine DEH control module;

[0185] Step 43) In the boiler power mode, determine whether the temperature reduction and pressure reduction device is in operation: if it is in operation, execute step 44); otherwise, execute step 45);

[0186] Step 44) reducing the opening of the pressure regulating valve of the temperature and pressure reduction device by 2% to 3% to adjust the flow rate of the temperature-reducing water;

[0187] Step 45) Reduce the turbine power N*P e , where the value range of N is [0.3%~0.4%].

[0188] In the above steps, the signal acquisition and judgment involved are all completed by the data acquisition and processing module 1 provided in this embodiment 1. The switching between the steam turbine TCP operating mode and the power operating mode involved is solved by the optimization control solution module 2, and an execution instruction is issued to complete the corresponding mode switching. Both modes can be switched smoothly and without interference. Specifically, the switching between the two steam turbine operating modes is completed by the optimization control solution module 2 based on the steam network heating load and the fluctuation of the steam consumption of heat users in real time to complete the selection of the corresponding mode. The steam turbine inlet pressure set point, the double reduction valve opening value, the increase and decrease steam turbine power value, and the increase and decrease boiler load value are also calculated and provided by the optimization control solution module 2.

[0189] The above only describes the basic principles and preferred embodiments of the present invention. Those skilled in the art may make many changes and improvements based on the above description, and these changes and improvements should fall within the scope of protection of the present invention.

Claims

1. A boiler and steam turbine coordinated control optimization device based on heating load fluctuation, characterized in that: At least: A data acquisition and processing module (1) is used to collect steam usage data of all heat users, at least the operating data of boilers and steam turbines, and pre-process all the collected data; The optimization control solution module (2) has an input end connected to the output end of the data acquisition and processing module (1) and is used to perform an optimal solution with the total economic benefit as the objective function, determine the optimal operating state of the unit based on the solution result, analyze the current operating state of the boiler and steam turbine, prejudge the operating mode and state of the next stage, and then give pre-operation instructions; The unit control module (3) has an input end connected to the output end of the optimization control solution module (2). The unit control module (3) includes a boiler APC control module and a steam turbine DEH control module, and is used to receive the pre-operation instruction of the optimization control solution module (2) and distribute the received pre-operation instruction to the boiler APC control module and the steam turbine DEH control module, wherein the operation information of the boiler APC control module and the steam turbine DEH control module are interconnected; An operation execution module (4), the input end of which is connected to the output end of the unit control module (3), and is used to complete the operation instructions of the unit control module (3) through an actuator; a signal processing feedback module (5), whose input end is connected to the output end of the operation execution module (4) and whose output end is connected to the input end of the unit control module (3), and is used to feed back the execution status of the operation execution module (4) to the unit control module (3) and to track and provide feedback on the real-time operating parameters of the unit control module and the execution results of the operation execution module (4); In the data acquisition and processing module (1), the steam consumption data of all heat users include at least the flow, pressure and temperature data of m large users, the steam supply master meter and each steam consumption sub-meter, where m ≥ 1. Large users are users whose instantaneous or continuous steam consumption accounts for 8% to 10% of the total steam supply outside the pipeline network; the operating data of boilers and steam turbines include at least the steam production flow, steam production pressure and temperature of boilers and the steam inlet flow, exhaust flow, pressure and temperature data of steam turbines; In the data acquisition and processing module (1), all collected data are pre-processed including: Calculate the average value of the pressure, flow rate and pipe loss rate of each large user; Based on the average values ​​of pressure, flow, and pipe loss rate for each major user, we eliminate bad points and abnormal data to obtain an accurate data pool. The average values ​​of the pressure, flow rate and pipe loss rate of each large user are as follows: 1) For large user j, j = 1, 2, ..., m, obtain n pressure values ​​according to the preset time interval Δt, n ≥ 2, and the average pressure value of large user j is: In the above formula, P jav represents the average pressure of large user j in the time interval Δt, P j1 、P j2 、P jn They represent the first, second, and nth pressure values ​​of large user j obtained within the time interval Δt, respectively, i = 1, 2, ..., n; 2) For large user j, calculate the average traffic volume of large user j according to the preset time interval Δt: In the above formula, F jav is the average traffic of large user j in the time interval Δt, f jt represents the real-time traffic value of large user j at time t; 3) According to the preset time interval Δt, obtain the real-time values ​​of n tube loss rates. The average value of the tube loss rate is: In the above formula, δ1, δ2, δ x Respectively represent the real-time values ​​of the 1st, 2nd, and xth pipe loss rates; In the optimization control solution module (2), when the total economic benefit is used as the objective function for optimal solution, the objective function is: In the above formula, minc represents the total economic benefit, N represents the number of devices, and Y s Indicates the start / stop status of the device is 0 or 1, E fy is the equipment depreciation expense, c fuel represents the fuel price, F fuel represents fuel consumption, c s represents the price of purchased steam, F s Indicates the amount of purchased steam, c power represents the price of purchased electricity, and Q represents the amount of purchased electricity.

2. The boiler-turbine coordinated control optimization device based on heating load fluctuation according to claim 1 is characterized in that: In the optimization control solution module (2), the operation mode and state of the next stage are predicted by calculating the change rate of the traffic of all large users in the future time interval Δt. The change rate of the traffic of large user j in the future time interval Δt is calculated as follows: According to the preset time interval Δt, the values ​​of the future time interval Δt are collected. The future traffic change rate of large user j based on the current time t is: In the above formula, It represents the traffic change rate of large user j in the future time interval Δt based on the current time t, represents the real-time traffic of large user j after a period of time Δt in the future based on the current time t, represents the real-time traffic of large user j at the current time t.

3. A method for optimizing coordinated control of boilers and steam turbines based on heating load fluctuations, characterized in that: Including steps: Step 1: Preset parameters: Set the interval of the flow rate F of the sub-meter to [a z ,b z ], boiler load is F b The interval of the flow rate F in the sub-meter corresponds to the exhaust pressure P of the steam turbine. b The interval is [c k ,d k ], the flow and pressure of large user j are F j and P j , the minimum steam supply pressure of large user j is P j0 , the pipe loss rate GSL interval is [e,f], where j = 1, 2, ..., m, m is the number of large users, m ≥ 1; Step 2: Collect sub-meter flow F and boiler load F b , exhaust steam pressure P of steam turbine b , traffic F of large user j j and pressure P j , pipe loss rate GSL; Step 3: Collect the current turbine operation mode signal and determine whether it is a TCP signal or a power supply signal: If the current turbine operation mode is a TCP signal, proceed to the next step; otherwise, proceed to step 10; Step 4: At this time, the turbine is in TCP operation mode, and then the range of the current exhaust pressure of the turbine is judged: If c k ≤P bu <d k , then proceed to the next step, otherwise proceed to step eight; Step 5: Determine the steam supply pressure of large user j: If any P j <P j0 , then execute the next step, otherwise execute the load increase sub-process in TCP mode; Step 6: Calculate the average value of the pipe loss rate GSL within 2 to 5 minutes av1 , and GSL av1 The interval range is judged: if e≤GSL av1 <f, the unit maintains the current operation, otherwise the load reduction sub-process in TCP mode is executed; Step 7: Determine the pipe loss rate GSL at this time av1 Size: If GSL av1 <e, then execute the load increase sub-process in TCP mode, otherwise execute the load reduction sub-process in TCP mode; Step 8. Determine the exhaust pressure of the steam turbine at this time: If P bu <c k , then execute the load increase sub-process in TCP mode, otherwise execute the load reduction sub-process in TCP mode; Step 9: Input the result of the load increase sub-process in the TCP mode and the result of the load reduction sub-process in the TCP mode into the signal processor, and then return to step 4; Step 10: At this time, the turbine is in power operation mode, and then the range of the current exhaust pressure of the turbine is determined: If c k ≤P bu <d k , then proceed to the next step, otherwise proceed to step 12; Step 11: Determine the steam supply pressure of large user j: If any P j <P j0 , then execute the next step, otherwise execute the load increase sub-process in power mode; Step 12: Calculate the average value of the pipe loss rate GSL within 2 to 5 minutes. av2 , and GSL av2 The interval range is judged: if e≤GSL av2 <f, the unit maintains the current operation, otherwise proceed to the next step; Step 13: Determine the pipe loss rate GSL at this time av2 Size: If GSL av2 <e, then execute the load increase sub-process in the power mode, otherwise execute the load reduction sub-process in the power mode; Step 14: Determine the exhaust pressure of the steam turbine at this time: If P bu <c k , then execute the load increase sub-process in the power mode, otherwise execute the load reduction sub-process in the power mode; Step 15: Input the result of the load increase sub-process in the power mode and the result of the load reduction sub-process in the power mode into the signal processor, and then return to step 10.

4. The method for optimizing boiler and steam turbine coordinated control based on heating load fluctuation according to claim 3 is characterized in that: In step 1, according to the set flow rate F of the submeter and the exhaust pressure range of the steam turbine corresponding to the flow rate F of the submeter, the current corresponding flow range [a z ,b z ] and pressure range [c k ,d k ],as follows: Working conditions ua z ≤F u <b z , c k ≤P bu <d k Working condition 1 a1≤1<b1,c1≤P b1 <d1 Working condition 2 a2≤F2<b2, c2≤P b2 <d2 Engineering 3 a3≤F3<b3,c3≤P b3 <d3 …… ……，…… Among them, F u The unit is t / h, P bu The unit is MPa.

5. The method for optimizing boiler and steam turbine coordinated control based on heating load fluctuation according to claim 3 is characterized in that: The load increase sub-process in TCP mode includes: Step 11) Define the turbine inlet steam pressure P a , the interval is [P a1 , P a2 ]; Step 12) Execute the boiler load increase instruction, the boiler load increase F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result to the steam turbine DEH control module. The steam turbine DEH control module executes step 13 after receiving the signal feedback); Step 13) Reduce the turbine inlet steam pressure setting value (P a2 -P a1 )*M, where the value range of M is [2%, 3%]; Step 14) Determine the turbine exhaust pressure at this time: If P a <P a1 , then go to step 15), otherwise go to step 16); Step 15) Switch the turbine operation mode to the power operation mode, and make the power operation signal true; Step 16) End the load reduction sub-process and proceed to step 9.

6. The method for optimizing boiler and steam turbine coordinated control based on heating load fluctuation according to claim 3, characterized in that: The load reduction sub-process in TCP mode includes: Step 21) Define the turbine inlet steam pressure P a , the interval is [P a1 , P a2 ]; Step 22) Execute the boiler load reduction instruction, the boiler load reduction F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result to the steam turbine DEH control module. The steam turbine DEH control module executes 23 after receiving the signal feedback); Step 23) Increase the turbine inlet steam pressure setting value (P a2 -P a1 )*M, where the value range of M is [2%, 3%]; Step 24) Determine the turbine exhaust pressure at this time: If P a >P a2 , then go to step 25), otherwise go to step 26); Step 25) Switch the turbine operation mode to the power operation mode, and make the power operation signal true; Step 26) End the load reduction sub-process and proceed to step 9.

7. The method for optimizing boiler and steam turbine coordinated control based on heating load fluctuation according to claim 3, characterized in that: The load increase sub-process in power mode includes: Step 31) Define the rated power of the steam turbine as P e ; Step 32) Execute the boiler load increase instruction, the boiler load increase F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result information to the steam turbine DEH control module; Step 33) Under the boiler power mode, determine whether the temperature and pressure reduction device is in operation: if it is in operation, execute step 34), otherwise execute step 35); Step 34) Increase the opening of the pressure regulating valve of the temperature and pressure reduction device by 2% to 3% to adjust the flow rate of the temperature-reducing water; Step 35) Increase turbine power N*P e , where the value range of N is [0.3%~0.4%].

8. The method for optimizing boiler and steam turbine coordinated control based on heating load fluctuation according to claim 3 is characterized in that: The load reduction sub-process in power mode includes: Step 41) Define the rated power of the turbine as P e ; Step 42) Execute the boiler load reduction instruction, the boiler load reduction F b *M, the value range of M is [2%, 3%]. The boiler APC control module feeds back the execution result information to the steam turbine DEH control module; Step 43) Under the boiler power mode, determine whether the temperature and pressure reduction device is in operation: if it is in operation, execute step 44), otherwise execute step 45); Step 44) Reduce the opening of the pressure regulating valve of the desuperheating and pressure reducing device by 2% to 3% to adjust the flow rate of the desuperheating water; Step 45) Reduce the turbine power N*P e , where the value range of N is [0.3%~0.4%].

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