Coordinated control method of deep peak regulation and efficient heating of thermal power units

Abstract

The present application provides a method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units, including the following steps: 1. Thermodynamic modeling and performance analysis; 2. Multi-stage extraction optimization configuration; 3. Heat exchange and heat storage device design; 4. Dynamic control strategy development; 5. System performance verification. This method constructs a thermodynamic simulation model of the thermal power unit, collects operating parameters and performs thermal calculations, analyzes the thermal performance and heating capacity of the unit under different load conditions, optimizes steam distribution by adjusting the steam extraction ratio of high- and medium-pressure cylinders and low-pressure cylinders, and designs and integrates heat exchange devices and heat storage devices to balance the heating capacity under low-load and high-load conditions. The dynamic control strategy further realizes real-time optimization and regulation of combustion and heat supply to ensure stable operation of the system under deep peak regulation conditions. Through simulation modeling and dynamic control strategies, the present application effectively improves the thermal energy utilization efficiency and unit stability, while reducing coal consumption and adapting to the needs of deep peak regulation.

Description

Technical Field

[0001] The present application relates to the technical field of thermal power unit operation optimization, and in particular to a method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units. Background Art

[0002] With the rapid increase in the proportion of renewable energy generation in the power system, traditional thermal power units are gradually shifting from baseload units to regulating units. These units not only need to meet the grid's demands for rapid response and deep peak regulation, but also fulfill the heating needs of the heating network during the heating season. Improving the operating efficiency of thermal power units under complex load fluctuations while achieving coordinated optimization of power generation and heating has become a key research direction in this field.

[0003] In existing technologies, optimizing power generation and heating for thermal power units typically aims to improve operational efficiency and reduce energy consumption. Common methods include adjusting the steam pressure or flow rate at the turbine extraction point to meet varying heat network load requirements; improving the heat exchange efficiency between steam and circulating water by designing heat exchangers to meet heating temperature requirements; and, in some solutions, employing auxiliary heat storage or peak-shaving technologies to balance heating capacity. However, most of these methods optimize a single operating scenario or specific operating condition, lacking a comprehensive technical solution that comprehensively considers the coordinated optimization of deep peak-shaving and efficient heating.

[0004] With existing technology, when the load of thermal power units drops below 30% of rated load, it becomes difficult to balance boiler combustion stability with the heating network's heating capacity. Increased heating demand can lead to decreased combustion efficiency, while unstable combustion can affect system operating efficiency. Therefore, balancing combustion stability and heating capacity during deep peak-shaving conditions has become a critical issue that needs to be addressed. Summary of the Invention

[0005] The present application provides a method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units, which is used to solve the problem that it is difficult to balance the boiler combustion stability and heating capacity of thermal power units under deep peak regulation conditions.

[0006] The present application is implemented as follows: a method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units, comprising the following steps:

[0007] Step 1: Thermodynamic modeling and performance analysis:

[0008] Build a thermodynamic simulation model of the thermal power unit and collect the unit's operating parameters, including steam flow, enthalpy, pressure, and temperature. Use thermal calculation methods to simulate the unit's thermal performance and heating capacity under different load conditions. Analyze the heating characteristics of high- and medium-pressure cylinder extraction steam and low-pressure cylinder exhaust steam, providing an operating parameter basis for subsequent extraction steam optimization.

[0009] Step 2: Multi-stage steam extraction optimization configuration:

[0010] Optimize the multi-stage steam extraction configuration of the steam turbine. Based on the principle of cascaded thermal energy utilization and the load requirements of the heating network, by adjusting the steam extraction ratio between the high- and low-pressure cylinders, the optimal allocation of high- and low-pressure extraction steam and low-pressure exhaust steam is calculated, forming a steam extraction strategy that adapts to different heating network load conditions.

[0011] Step 3: Design of heat exchange and heat storage device:

[0012] Integrate heat exchangers and heat storage devices into the heating system of thermal power units. The heat exchangers improve heat transfer efficiency by optimizing the steam flow path and the contact area with the circulating water. The heat storage devices store excess heat during low-load operation and release heat during high-load demand to balance heating capacity and power generation load.

[0013] Step 4: Dynamic control strategy development:

[0014] Develop a dynamic control strategy for combustion and heating of thermal power units. By adjusting the boiler fuel injection rate and air ratio in real time, and dynamically adjusting the extraction ratio and steam flow of high and medium pressure cylinders, this ensures stable operation of thermal power units at loads below 30% of the rated load while meeting the heating network's heating needs.

[0015] Step 5: System performance verification:

[0016] Under actual operating conditions, the coal consumption, heating efficiency and heat network load response capabilities of the units at different peak-shaving depths are tested to verify the synergistic effect of deep peak-shaving and efficient heating.

[0017] In an optional embodiment, in step 1, the process of constructing a thermodynamic simulation model of a thermal power plant includes:

[0018] a. Operation data collection and initialization:

[0019] Collect the operating parameters of the thermal power unit, including steam flow, enthalpy, pressure and temperature, and build a thermodynamic operation database of the thermal power unit based on the collected data to provide initial parameters for the simulation model;

[0020] b. Model construction and parameterization:

[0021] Based on the thermal system structure of the thermal power unit, a thermodynamic simulation model including the boiler module, turbine module and heat network cycle module is constructed; by entering initial parameters and setting boundary conditions, the model is initialized and parameterized.

[0022] In an optional embodiment, the boundary conditions set include:

[0023] (1) Steam parameter boundary conditions:

[0024] The steam pressure entering the boiler and steam turbine is set to range from 8MPa to 25MPa, and the steam temperature range is set from 450℃ to 600℃ to reflect the thermodynamic state under different operating loads;

[0025] (2) Boundary conditions of heating network load:

[0026] The supply and return water temperature range of the heating network is set to 40℃ to 130℃, and the heating flow rate varies between 200t / h and 1200t / h according to the demand of the heating network;

[0027] (3) Operation load boundary conditions:

[0028] Set the unit's load variation range to 30% to 100% of rated load, and define stable combustion parameters at the lowest load and thermal limit parameters at the highest load;

[0029] (4) Environmental condition boundaries:

[0030] Set the ambient temperature range to -20℃ to 40℃, and consider the impact of air humidity on heat exchanger efficiency and boiler exhaust characteristics.

[0031] In an optional embodiment, in step 1, the process of simulating the thermal performance and heating capacity of the unit under different load conditions by a thermal calculation method includes the following steps:

[0032] (1) Load condition setting:

[0033] According to the operating range of the thermal power unit, multiple load conditions are set, including 30%, 50%, 75% and 100% rated load;

[0034] (2) Calculation of thermal parameters:

[0035] Under each load condition, the key parameters of the unit, including steam flow, pressure, temperature and extraction steam enthalpy, are calculated through thermodynamic formulas to obtain the unit's thermal performance;

[0036] (3) Heating capacity analysis:

[0037] Based on the calculation results, the heating capacity of the high and medium pressure cylinder extraction steam and the low pressure cylinder exhaust steam is analyzed, and combined with the heat network load demand, the heating capacity of the unit under different loads is evaluated.

[0038] In an optional embodiment, in step 2, the specific implementation process of the multi-stage steam extraction optimization configuration includes the following steps:

[0039] Step 2.1, heat network load demand analysis:

[0040] According to the real-time heat network load demand, calculate and set the steam flow and heat supply required under different operating load conditions, and analyze the matching relationship between the steam extraction volume of high-pressure and low-pressure cylinders and the heat network load;

[0041] Step 2.2: Application of the principle of cascade utilization of thermal energy:

[0042] Based on the principle of cascaded utilization of thermal energy, the enthalpy values ​​of the extracted steam at each level are analyzed to ensure that high-temperature and high-pressure steam is preferentially used in the high-efficiency heating system, and low-temperature and low-pressure steam is preferentially used in the low-temperature and low-pressure steam heating system, so as to maximize the utilization of thermal energy;

[0043] Step 2.3: Preliminary setting of extraction ratio:

[0044] Based on the analysis of the heat network load and thermal energy cascade utilization, the steam extraction ratio of the high- and medium-pressure cylinders and the low-pressure cylinders is preliminarily set to ensure that the steam flow distribution can adapt to the heating demand under different loads;

[0045] Step 2.4, optimization calculation and adjustment:

[0046] Using the particle swarm optimization algorithm, the optimal allocation plan for high- and medium-pressure extraction steam and low-pressure exhaust steam is calculated based on the set heat network load demand and heat energy cascade utilization goals to ensure maximum heating efficiency;

[0047] Step 2.5: Dynamically adjust the steam extraction ratio:

[0048] According to real-time load changes, the control system dynamically adjusts the steam extraction ratio between the high-pressure and low-pressure cylinders to ensure that the heating network's heating demand is always met when the load fluctuates, while ensuring stable operation of the unit;

[0049] Step 2.6: Form an adaptation strategy:

[0050] Based on the optimization results, a steam extraction strategy adapted to different heating network load conditions is formed.

[0051] In an optional embodiment, in step 3, the implementation process of integrating the heat exchange device and the heat storage device into the heating system of the thermal power unit includes:

[0052] (1) Integration of heat exchange device:

[0053] A shell and tube heat exchanger is selected as the core heat exchange device in the thermal power unit heating system. It is connected to the boiler outlet through a steam pipe so that the steam can fully contact the circulating water when flowing in the heat exchanger, achieving efficient heat exchange.

[0054] (2) Integration of heat storage device:

[0055] Design the type and capacity of the heat storage device based on the load fluctuation characteristics of the unit, and use a hot water storage tank to store excess heat during low-load operation;

[0056] The heat storage device is connected to the heat exchange device through a pipeline, so that the heat storage device receives excess heat from the heat exchange device when the load is low, and releases the stored heat to the heat network through the control system when the load is high, thereby maintaining a stable heat supply to the heat network;

[0057] (3) Integrated control system:

[0058] The control system enables the coordinated operation of the heat exchanger and heat storage device, and monitors the heat network load, boiler steam flow, and heat storage status of the heat storage device in real time;

[0059] When the load fluctuates, the control system automatically adjusts the heat output of the heat exchanger according to the demand of the heating network, and promptly activates the heat storage device to release the stored heat to ensure the balance between the heating network load and the power generation load.

[0060] In an optional embodiment, in step 4, the process of developing a dynamic control strategy is as follows:

[0061] Step 4.1, combustion control strategy:

[0062] By real-time monitoring of the temperature, pressure, and oxygen content in the boiler, the fuel injection amount and air ratio of the boiler are adjusted to achieve optimal combustion efficiency, avoid incomplete combustion, and reduce coal consumption;

[0063] Step 4.2, steam extraction ratio and flow adjustment:

[0064] According to the heat network load demand and unit load status, the high and medium pressure cylinder extraction steam ratio and steam flow are adjusted through real-time feedback control strategy, so that the unit can operate stably under different load conditions while meeting the heat network heating demand;

[0065] Step 4.3, feedback regulation mechanism:

[0066] Based on real-time data feedback and load changes, the combustion and extraction parameters are dynamically fine-tuned through the feedback regulation mechanism to cope with load fluctuations and system changes, allowing the unit to operate stably under different load conditions and maintain a balance between the heating network's heating capacity and power generation load.

[0067] In an optional implementation, in step 5, the specific process of system performance verification includes:

[0068] Step 5.1, coal consumption test:

[0069] Under actual operating conditions, the coal consumption of thermal power units at different peak-shaving depths is tested. By measuring the relationship between the actual fuel consumption of the units and the peak-shaving depth, the fuel utilization efficiency of the system is evaluated.

[0070] Step 5.2, heating efficiency test:

[0071] Based on the unit operating data under different load conditions, the heating efficiency is tested, including the ratio of heat input to heat output, to verify the system's heating capacity at different peak load depths;

[0072] Step 5.3: Test the heat network load response capability:

[0073] Test the unit's response capability to heat network load fluctuations. By simulating load fluctuations and measuring the system's response time and stability to load changes, verify the system's regulation capability under different operating conditions.

[0074] Compared with the prior art, this application has the following beneficial effects:

[0075] 1. This application is implemented through the process of thermodynamic modeling and performance analysis and multi-stage steam extraction optimization configuration, which can achieve efficient utilization of thermal energy of thermal power units under different load conditions. By constructing a thermodynamic simulation model, collecting key operating parameters and performing thermal calculations, the precise distribution of thermal energy under different loads is ensured. Through the principle of cascade utilization of thermal energy, high-temperature and high-pressure steam is preferentially used in high-efficiency heating systems, while low-temperature and low-pressure steam is preferentially used in low-temperature and low-pressure steam heating systems, thereby greatly improving the utilization efficiency of thermal energy. This optimization not only improves the heating capacity of the unit, but also reduces unnecessary energy waste, thereby improving the overall thermal efficiency of the thermal power unit.

[0076] 2. This application, through the design of heat exchange and heat storage devices and the development of dynamic control strategies, can enhance the adaptability and stability of thermal power units under load fluctuations. By integrating heat exchange and heat storage devices, not only can heat exchange efficiency be improved, but excess heat can also be stored during low loads and released during high loads, thereby balancing heating capacity and power generation load. Furthermore, the development of dynamic control strategies enables thermal power units to more flexibly respond to load fluctuations, further improving peak-shaving capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0078] Figure 1A flow chart of a method for coordinated regulation of deep peak regulation and efficient heat supply for a thermal power unit provided in one embodiment of the present application. DETAILED DESCRIPTION

[0079] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application are clearly and completely described below. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of this application.

[0080] The embodiment of the present application provides a method for coordinated control of deep peak regulation and efficient heat supply of thermal power units, including five processes: thermodynamic modeling and performance analysis, multi-stage steam extraction optimization configuration, heat exchange and heat storage device design, dynamic control strategy development, and system performance verification. Figure 1 As shown in FIG, the specific process of the method for coordinated regulation of deep peak regulation and efficient heating of thermal power units is as follows:

[0081] Step S1: Thermodynamic modeling and performance analysis:

[0082] Build a thermodynamic simulation model of the thermal power unit and collect the unit's operating parameters, including steam flow, enthalpy, pressure, and temperature. Use thermal calculation methods to simulate the unit's thermal performance and heating capacity under different load conditions. Analyze the heating characteristics of high- and medium-pressure cylinder extraction steam and low-pressure cylinder exhaust steam, providing an operating parameter basis for subsequent extraction steam optimization.

[0083] Step S2: Multi-stage steam extraction optimization configuration:

[0084] Optimize the multi-stage steam extraction configuration of the steam turbine. Based on the principle of cascaded thermal energy utilization and the load requirements of the heating network, by adjusting the steam extraction ratio between the high- and low-pressure cylinders, the optimal allocation of high- and low-pressure extraction steam and low-pressure exhaust steam is calculated, forming a steam extraction strategy that adapts to different heating network load conditions.

[0085] Step S3: Design of heat exchange and heat storage device:

[0086] Integrate heat exchangers and heat storage devices into the heating system of thermal power units. The heat exchangers improve heat transfer efficiency by optimizing the steam flow path and the contact area with the circulating water. The heat storage devices store excess heat during low-load operation and release heat during high-load demand to balance heating capacity and power generation load.

[0087] Step S4: Dynamic control strategy development:

[0088] Develop a dynamic control strategy for combustion and heating of thermal power units. By adjusting the boiler fuel injection rate and air ratio in real time, and dynamically adjusting the extraction ratio and steam flow of high and medium pressure cylinders, this ensures stable operation of thermal power units at loads below 30% of the rated load while meeting the heating network's heating needs.

[0089] Step S5: System performance verification:

[0090] Under actual operating conditions, the coal consumption, heating efficiency and heat network load response capabilities of the units at different peak-shaving depths are tested to verify the synergistic effect of deep peak-shaving and efficient heating.

[0091] This step involves system performance verification to ensure reliable operation in actual operation. Efficient operation of thermal power units is not only reflected in reduced coal consumption and improved heating efficiency, but also requires ensuring a balance between heating and power generation under varying load conditions. System performance verification ensures the stability and economic efficiency of the units in long-term operation.

[0092] The method for coordinated control of deep peak regulation and efficient heat supply of thermal power units provided in the embodiment of the present application can achieve efficient utilization of thermal energy of thermal power units under different load conditions through the process implementation of thermodynamic modeling and performance analysis and multi-stage steam extraction optimization configuration. By constructing a thermodynamic simulation model, collecting key operating parameters and performing thermal calculations, the accurate distribution of thermal energy under different loads is ensured. Through the principle of cascade utilization of thermal energy, high-temperature and high-pressure steam is preferentially used in high-efficiency heating systems, while low-temperature and low-pressure steam is preferentially used in low-temperature and low-pressure steam heating systems, thereby greatly improving the utilization efficiency of thermal energy. This optimization not only improves the heating capacity of the unit, but also reduces unnecessary energy waste, thereby improving the overall thermal efficiency of the thermal power unit.

[0093] Furthermore, through the implementation of the heat exchange and heat storage device design and dynamic control strategy development process, the embodiments of this application can enhance the adaptability and stability of thermal power units under load fluctuations. By integrating the heat exchange and heat storage devices, not only can heat exchange efficiency be improved, but also excess heat can be stored during low loads and released during high loads, thereby balancing heating capacity and power generation load. Furthermore, the development of dynamic control strategies enables thermal power units to more flexibly respond to load fluctuations, further improving peak-shaving capabilities.

[0094] Furthermore, in step S1, the process of constructing a thermal power unit thermodynamic simulation model includes:

[0095] a. Operation data collection and initialization:

[0096] Collect the operating parameters of the thermal power unit, including steam flow, enthalpy, pressure and temperature, and build a thermodynamic operation database of the thermal power unit based on the collected data to provide initial parameters for the simulation model;

[0097] b. Model construction and parameterization:

[0098] Based on the thermal system structure of the thermal power unit, a thermodynamic simulation model including the boiler module, turbine module and heat network cycle module is constructed; by entering initial parameters and setting boundary conditions, the model is initialized and parameterized.

[0099] During the process of operating data collection and initialization, this embodiment can provide initial data support for the simulation model by real-time collection of key operating parameters of the thermal power unit (steam flow, enthalpy value, pressure and temperature). The thermodynamic operation database of the thermal power unit is established through the collected data. These data can provide a reliable basis for subsequent simulation and optimization, making the modeling process more in line with actual operating conditions, thereby helping to accurately simulate the performance of the unit under different load conditions and avoiding the limitations of traditional empirical models.

[0100] Furthermore, this embodiment, based on the thermal system structure of a thermal power unit, constructs a thermodynamic simulation model consisting of a boiler module, a steam turbine module, and a heat network circulation module. By inputting initial parameters and setting reasonable boundary conditions, the simulation model can better reflect the actual operating status of the unit and provide support for subsequent optimization calculations and control strategies. Compared with traditional single-module modeling methods, this multi-module approach improves the model's reliability and applicability, providing more accurate analysis and recommendations for different load and operating conditions.

[0101] In addition, the embodiment of the present application constructs a thermodynamic simulation model of the thermal power unit, which not only helps in the performance analysis of the unit, but also provides a key numerical basis for the subsequent multi-stage steam extraction optimization configuration and dynamic control strategy, so as to help achieve more precise control and more efficient coordinated operation of heating and power generation.

[0102] Furthermore, the boundary conditions set include:

[0103] (1) Steam parameter boundary conditions:

[0104] The steam pressure entering the boiler and steam turbine is set to range from 8MPa to 25MPa, and the steam temperature range is set from 450℃ to 600℃ to reflect the thermodynamic state under different operating loads;

[0105] (2) Boundary conditions of heating network load:

[0106] The supply and return water temperature range of the heating network is set to 40℃ to 130℃, and the heating flow rate varies between 200t / h and 1200t / h according to the demand of the heating network;

[0107] (3) Operation load boundary conditions:

[0108] Set the unit's load variation range to 30% to 100% of rated load, and define stable combustion parameters at the lowest load and thermal limit parameters at the highest load;

[0109] (4) Environmental condition boundaries:

[0110] Set the ambient temperature range to -20℃ to 40℃, and consider the impact of air humidity on heat exchanger efficiency and boiler exhaust characteristics.

[0111] In this embodiment, the input range of the simulation model is clarified by setting the steam parameters, heating network load, operating load, and environmental condition boundaries. These boundary conditions provide multi-dimensional constraints and guidance for the simulation model. For example, the steam parameter boundary conditions (pressure range 8MPa to 25MPa, temperature range 450℃ to 600℃) can cover the main load conditions of the unit operation and reflect the thermodynamic performance of the thermal system under different states. Setting boundary conditions in this way can avoid model deviations caused by missing parameters or unreasonable ranges, and improve the credibility and accuracy of the simulation results.

[0112] In addition, by setting the heat network load boundary conditions and operating load boundary conditions, the simulation model can cover the full range of operating conditions from low load (30% of rated load) to full load (100% of rated load), while meeting the dynamic needs of the heat network (supply and return water temperature 40°C to 130°C, heating flow rate 200t / h to 1200t / h). These settings make the simulation model applicable under both deep peak regulation and efficient heating requirements. Especially at the lowest load, clear combustion stability parameters can help the simulation model accurately reflect the low-load operating characteristics of the boiler and turbine, supporting subsequent control optimization.

[0113] Furthermore, the inclusion of environmental boundary conditions (ranging ambient temperature from -20°C to 40°C, and accounting for the impact of humidity on heat exchanger efficiency) further enhances the simulation model's applicability under diverse climate conditions. These boundary conditions help the model fully account for the impact of external environmental factors on thermal system operation, such as the effects of low temperatures on boiler combustion and exhaust characteristics, and the limitations of high temperatures on heat exchange efficiency. These settings enable the simulation model to more accurately predict system performance under diverse operating environments, enhancing the model's robustness.

[0114] Furthermore, in step S1, the process of simulating the thermal performance and heating capacity of the unit under different load conditions by using a thermal calculation method includes the following steps:

[0115] (1) Load condition setting:

[0116] According to the operating range of the thermal power unit, multiple load conditions are set, including 30%, 50%, 75% and 100% of the rated load.

[0117] By setting load conditions, the operating range of the thermal power unit is clearly defined under multiple typical operating conditions: 30%, 50%, 75%, and 100% of rated load. This setting not only covers the unit's main operating states, from deep peak regulation to full load, but also provides a diverse operating condition reference for subsequent performance calculations. By covering multiple load conditions, a more comprehensive analysis of the unit's thermal performance and heating capacity under different loads can be achieved, providing data support for subsequent optimization. This hierarchical setting method avoids the limitations of single-condition analysis and makes the simulation results more universal and applicable.

[0118] (2) Calculation of thermal parameters:

[0119] Under each load condition, the key parameters of the unit, including steam flow, pressure, temperature and extraction steam enthalpy, are calculated through thermodynamic formulas to obtain the unit's thermal performance.

[0120] Steam flow, pressure, temperature, and extraction steam enthalpy reflect the unit's thermal performance under varying loads, providing accurate data support for performance analysis. The calculation of extraction steam enthalpy, in particular, is directly related to the assessment of the unit's heating efficiency. This detailed thermal parameter calculation captures the unit's operating characteristics under dynamic load conditions, providing a more reliable foundation for heating capacity analysis and optimization decisions.

[0121] (3) Heating capacity analysis:

[0122] Based on the calculation results, the heating capacity of the high and medium pressure cylinder extraction steam and the low pressure cylinder exhaust steam is analyzed, and combined with the heat network load demand, the heating capacity of the unit under different loads is evaluated.

[0123] During the heating capacity analysis phase, the heating capacities of high- and medium-pressure cylinder extraction steam and low-pressure cylinder exhaust steam are evaluated based on thermal calculation results. This analysis, combined with the heat network load requirements, allows for a matching analysis. This analysis clearly identifies the unit's heating potential and load response capabilities under varying loads, providing clear guidance for subsequent extraction steam optimization and dynamic control strategies. By matching heating capacity with heat network requirements, extraction strategies can be designed more precisely, improving heat network operational stability and heating efficiency.

[0124] Furthermore, in step S2, the specific implementation process of the multi-stage steam extraction optimization configuration includes the following steps:

[0125] Step S21: Analysis of heat network load demand:

[0126] According to the real-time heat network load demand, the steam flow and heat supply required under different operating load conditions are calculated and set, and the matching relationship between the extraction volume of high-pressure and low-pressure cylinders and the heat network load is analyzed.

[0127] This step calculates the required steam flow and heat supply through heat network load demand analysis combined with real-time heat network load changes. It also analyzes the relationship between the steam extraction volume of high- and medium-pressure cylinders and low-pressure cylinders and the heat network load. This process can help accurately understand the needs of the heat network under different operating conditions and achieve a precise match between the steam extraction configuration and the heat network needs.

[0128] Step S22: Application of the thermal energy cascade utilization principle:

[0129] Based on the principle of cascaded utilization of thermal energy, the enthalpy values ​​of the extracted steam at each level are analyzed to ensure that high-temperature and high-pressure steam is preferentially used in the high-efficiency heating system, and low-temperature and low-pressure steam is preferentially used in the low-temperature and low-pressure steam heating system, so as to maximize the utilization of thermal energy;

[0130] Step S23: Preliminary setting of steam extraction ratio:

[0131] Based on the analysis of the heat network load and thermal energy cascade utilization, the steam extraction ratio of the high and medium pressure cylinders and the low pressure cylinders is preliminarily set to ensure that the steam flow distribution can adapt to the heating demand under different loads.

[0132] Step S24: Optimization calculation and adjustment:

[0133] Using the particle swarm optimization algorithm, the optimal allocation plan for high- and medium-pressure extraction steam and low-pressure exhaust steam is calculated based on the set heating network load demand and thermal energy cascade utilization goals to ensure maximum heating efficiency.

[0134] Particle swarm optimization (PSO) is a commonly used intelligent optimization algorithm suitable for solving multi-objective and nonlinear optimization problems. It excels in multi-objective optimization, excelling in this area. Its advantages lie in its ability to quickly search for the global optimal solution and its ability to handle optimization problems in highly nonlinear and complex systems. For the optimal allocation of high- and medium-pressure extraction steam and low-pressure exhaust steam, this method can find the extraction steam allocation scheme with the highest heating efficiency, based on the set heating network load requirements and the thermal energy cascade utilization objectives.

[0135] To more clearly illustrate the application process of the particle swarm optimization algorithm in this solution, the specific implementation steps are as follows:

[0136] Step S241: Problem modeling

[0137] Define the high and medium pressure extraction steam ratio x1 and the low pressure exhaust steam ratio x2, and the constraint condition is x1+x2=1.

[0138] Maximize heating efficiency objective function:

[0139] f(x1, x2) = αQ h (x1)+βQ l (x2)

[0140] Among them, Q h (x1) and Q l (x2) is the heating supply provided by high and medium pressure extraction steam and low pressure exhaust steam respectively. α and β are the thermal energy weight coefficients, corresponding to the heating efficiency weights of high and medium pressure extraction steam and low pressure exhaust steam respectively.

[0141] The constraint condition is that the heating capacity meets the load demand of the heating network:

[0142] Q h (x1)+Q l( x2)≥Q demand

[0143] Among them, Q demand Represents the actual load demand of the heating network (i.e. the amount of heat required by the heating network).

[0144] Step S242: Particle initialization

[0145] Several particles are randomly generated in the search space. The position of each particle is represented by x1 and x2, and the constraint x1+x2=1 is satisfied.

[0146] Initialize the particle velocities v1 and v2 to small values ​​to ensure that the initial adjustment amplitude is reasonable.

[0147] Step S243: Calculation of fitness function

[0148] Calculate the fitness value of each particle, that is, the heating efficiency function value f(x1, x2).

[0149] If the particle does not meet the heat network load demand constraint, a penalty function is introduced to correct the fitness:

[0150] f′(x1,x2)=f(x1,x2)-λ·max(0,Q demand -Q h (x1)-Q l (x2))

[0151] Among them, λ is the penalty function weight coefficient, which is used to reduce the fitness value of particles that do not meet the constraints; f(x1, x2) is the original fitness function (objective function value), which is the optimization objective function of heating efficiency, that is, αQ h (x1)+βQ l (x2).

[0152] Step S244: Speed ​​and position update

[0153] According to the standard formula of particle swarm optimization algorithm, the speed and position of the particles are updated:

[0154] v i，j (t+1)=w·v i，j (t)+c1·r1·(p best，j -x i，j (t))+c2·r2·(g best，j -x i，j (t))

[0155] x i，j (t+1)=x i，j (t)+v i，j (t+1)

[0156] Among them, w is the inertia weight; c1, c2 are acceleration coefficients, usually c1 and c2 are in the range of [0, 2]; r1, r2 are random numbers, with a range of [0, 1], which are used to introduce randomness to avoid falling into local optimality. best，j and g best，j are the individual optimal position of the particle and the global optimal position, respectively.

[0157] Ensure that the updated particle positions still meet the constraint x1+x2=1, and normalize particles that are out of the constraint range.

[0158] Step S245: Iteration and optimization

[0159] Through multiple iterations, the speed and position of the particles are continuously updated, gradually approaching the global optimal solution.

[0160] When the fitness value does not change significantly after several iterations, or when the maximum number of iterations is reached, the optimization process is terminated, and the optimal allocation scheme for the high and medium pressure extraction steam ratio x1 and the low pressure exhaust steam ratio x2 is output.

[0161] Through the above process, the optimal distribution ratio of high- and medium-pressure extraction steam and low-pressure exhaust steam can be quickly obtained according to the load demand of the heating network and the target of thermal energy cascade utilization, thereby ensuring the maximization of heating efficiency and achieving efficient operation and energy-saving optimization of the heating system.

[0162] This embodiment applies the principle of cascaded thermal energy utilization in step S22 and implements a preliminary setting of the extraction ratio in step S2.3. Based on this principle, the steam enthalpy is analyzed, prioritizing high-temperature, high-pressure steam for high-efficiency heating systems while allocating low-temperature, low-pressure steam to low-temperature heating needs. This enthalpy-based allocation method fully utilizes the thermal energy characteristics of each stage of extraction, maximizing thermal energy utilization. Furthermore, the preliminary setting of the extraction ratio between the high- and low-pressure cylinders helps to rationally distribute steam flow under different load conditions, thereby better ensuring that the heating system can flexibly respond to load fluctuations and maintain operational efficiency.

[0163] Step S25: Dynamically adjust the steam extraction ratio:

[0164] Based on real-time load changes, the control system dynamically adjusts the steam extraction ratio between the high- and low-pressure cylinders to ensure that the heating network's heating needs are consistently met during load fluctuations, while also ensuring stable unit operation. This dynamic adjustment allows the system to quickly respond to load fluctuations and avoids issues such as reduced heating efficiency and unstable operation caused by improper steam extraction configuration.

[0165] Step S26: Form an adaptation strategy:

[0166] Based on the optimization results, a steam extraction strategy adapted to different heating network load conditions is formed.

[0167] Furthermore, in step S3, the implementation process of integrating the heat exchange device and the heat storage device into the heating system of the thermal power unit includes:

[0168] (1) Integration of heat exchange device:

[0169] The shell and tube heat exchanger is selected as the core heat exchange device in the heating system of the thermal power unit. It is connected to the boiler outlet through a steam pipe so that the steam can fully contact the circulating water when flowing in the heat exchanger, realizing efficient heat exchange.

[0170] The heat exchanger uses a shell-and-tube heat exchanger as its core element. This heat exchanger is connected to the boiler outlet via a steam pipe, ensuring full contact between the steam and the circulating water. This design improves heat transfer efficiency between the steam and the circulating water, effectively reducing heat loss. Furthermore, the shell-and-tube heat exchanger's simple structure and ease of maintenance make it suitable for the long-term operation of thermal power units.

[0171] (2) Integration of heat storage device:

[0172] The type and capacity of the heat storage device are designed according to the load fluctuation characteristics of the unit, and a hot water storage tank is used to store excess heat during low-load operation.

[0173] This process connects the heat storage device to the heat exchanger via pipes, making heat transfer and utilization more efficient. During low-load periods, the heat storage device receives excess heat from the heat exchanger. During high-load periods, the control system releases the stored heat to the heating network, maintaining a stable heating supply. This design allows the heat storage device to flexibly adjust the system's heat supply during load fluctuations, thereby maintaining the network's stable heating capacity.

[0174] (3) Integrated control system:

[0175] The control system enables the coordinated operation of the heat exchanger and heat storage device, and monitors the heat network load, boiler steam flow, and heat storage status of the heat storage device in real time;

[0176] When the load fluctuates, the control system automatically adjusts the heat output of the heat exchanger according to the demand of the heating network, and promptly activates the heat storage device to release the stored heat to ensure the balance between the heating network load and the power generation load.

[0177] Furthermore, in step S4, the process of developing the dynamic control strategy is as follows:

[0178] Step S41, combustion control strategy:

[0179] By real-time monitoring of boiler temperature, pressure, and oxygen content, the fuel injection rate and air ratio are adjusted to optimize combustion efficiency, avoid incomplete combustion, and reduce coal consumption. This combustion control method optimizes combustion efficiency, reduces incomplete combustion, and thus reduces coal consumption. Compared with traditional empirical control methods, real-time monitoring and dynamic adjustment not only improve fuel utilization efficiency but also reduce energy losses caused by excess air, providing important support for the efficient operation of the entire heating and power generation system.

[0180] Step S42: Adjust the steam extraction ratio and flow rate:

[0181] According to the load demand of the heating network and the load status of the unit, the extraction ratio and steam flow of the high and medium pressure cylinders are adjusted through real-time feedback control strategy, so that the unit can operate stably under different load conditions and meet the heating demand of the heating network at the same time.

[0182] Step S43: Feedback adjustment mechanism:

[0183] Based on real-time data feedback and load changes, the combustion and extraction parameters are dynamically fine-tuned through the feedback regulation mechanism to cope with load fluctuations and system changes, allowing the unit to operate stably under different load conditions and maintain a balance between the heating network's heating capacity and power generation load.

[0184] In this process, the feedback control mechanism in step S43 dynamically fine-tunes combustion and extraction parameters based on real-time data feedback and load changes. Compared to traditional static control methods, this mechanism can more quickly respond to load fluctuations and operating parameter changes during system operation. For example, when the load suddenly increases, the feedback control mechanism can immediately adjust the combustion intensity and extraction ratio to avoid system overload or insufficient heating. Through this dynamic fine-tuning, the entire unit can maintain stable operation under complex operating conditions, while balancing the heating network's heating capacity and power generation load, further improving system reliability.

[0185] Furthermore, in step S5, the specific process of system performance verification includes:

[0186] Step S51: Coal consumption test:

[0187] Under actual operating conditions, the coal consumption of thermal power units at different peak-shaving depths is tested, and the fuel utilization efficiency of the system is evaluated by measuring the relationship between the actual fuel consumption of the units and the peak-shaving depth.

[0188] This testing method can clearly reflect the fuel consumption characteristics of the unit under deep peak load regulation conditions, providing a data basis for optimizing combustion efficiency and reducing coal consumption. It can also help identify inefficient operating conditions, thereby further improving the operating economy of thermal power units.

[0189] Step S52: Heating efficiency test:

[0190] Based on the unit operating data under different load conditions, the heating efficiency is tested, including the ratio of heat energy input to heat energy output, to verify the system's heating capacity at different peak regulation depths.

[0191] Step S53: Testing the heat network load response capability:

[0192] Testing the unit's responsiveness to heat network load fluctuations. By simulating load fluctuations and measuring the system's response time and stability to these changes, the system's regulation capabilities under various operating conditions are verified. This testing method verifies the system's regulation capabilities under diverse operating conditions, particularly its dynamic adaptability and stability under sudden load changes. Furthermore, these test results can provide guidance for optimizing the system's dynamic regulation strategies, improving system reliability under complex operating conditions and mitigating heating or power generation instability caused by load fluctuations.

[0193] In order to verify the effectiveness of the coordinated control method of deep peak regulation and efficient heating of thermal power units in the embodiment of the present application, it was applied to the heating system of a 350MW supercritical indirect air-cooled condensing coal-fired power generation unit for actual testing.

[0194] 1. Basic information of thermal power units

[0195] This thermal power unit utilizes a supercritical design with steam extraction capabilities for both high- and low-pressure cylinders. Its designed heating capacity is 600 t / h, with a heat network supply water temperature range of 70°C to 130°C and a return water temperature of 40°C to 70°C. Steam parameters at rated load are 25 MPa and 600°C, and the minimum stable operating load at low load is 30% of the rated load. The air cooling system ensures exhaust steam cooling capacity under low-load conditions, making it suitable for deep peak-shaving operations.

[0196] 2. Implementation process

[0197] The method for coordinated control of deep peak shaving and efficient heat supply for thermal power units, as provided in the aforementioned embodiments of this application, collects unit operating parameters, constructs a thermodynamic simulation model, optimizes the steam extraction ratios between high- and low-pressure cylinders, and designs and integrates a shell-and-tube heat exchanger with a hot water storage tank. A dynamic control strategy is employed to adjust boiler combustion intensity and steam extraction parameters in real time, ensuring stable unit operation under varying load conditions. System performance verification testing is also conducted.

[0198] 3. Test results

[0199] (1) Coal consumption and heating efficiency:

[0200] At loads of 30%, 50%, 75%, and 100%, heating efficiency increased by 10%, 8%, 6%, and 4%, respectively. Coal consumption was reduced by 4% to 7%, with significant energy savings particularly under low-load, deep peak-shaving conditions.

[0201] (2) Load adaptability:

[0202] In the event of a sudden increase or decrease in the heat network load, the system responds within 2 minutes through a dynamic control strategy, ensuring the stable operation of the heat network while avoiding overheating or insufficient heat supply.

[0203] (3) Operational economy:

[0204] After one month of operation test, the unit saved an average of about 4-6 tons of coal consumption per hour.

[0205] At the same time, the annual coal saving potential is predicted based on the following formula and actual operating data:

[0206] Basic formula:

[0207] Annual coal saving (tons) = coal saving per unit per hour (tons) × annual operating hours

[0208] The hourly coal savings, based on test results, are 4-6 tons / hour. Annual operating hours are typically calculated based on the full year's operating hours of thermal power units (assuming 4,000-5,000 hours, depending on the region and power dispatch conditions). The operating hour range is determined by the power plant's load demand, downtime for maintenance, and peak-shaving operating conditions.

[0209] Assuming that the coal saving per hour is 5 tons (taking the median value) and the annual operating time is 4500 hours, then:

[0210] Annual coal saving = 5 tons / hour × 4500 hours = 22500 tons

[0211] Taking the upper and lower limits of hourly coal saving as 4 tons and 6 tons, we can get:

[0212] Annual coal saving range = 4 tons / hour × 4500 hours = 18,000 tons Annual coal saving range = 6 tons / hour × 4500 hours = 27,000 tons

[0213] Taking into account possible changes in operating conditions (such as a higher proportion of time under low load), the comprehensive adjustment annual coal saving range is 15,000-25,000 tons, slightly lower than the ideal calculation result, in order to maintain the objectivity of the estimate.

[0214] From the above calculations and predictions, it can be concluded that the annual coal saving potential is expected to reach 15,000-25,000 tons, which significantly improves the operating economy.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units, characterized in that: The following steps are involved: Step 1: Thermodynamic modeling and performance analysis: Build a thermodynamic simulation model of the thermal power unit and collect the unit's operating parameters, including steam flow, enthalpy, pressure, and temperature. Use thermal calculation methods to simulate the unit's thermal performance and heating capacity under different load conditions. Analyze the heating characteristics of high- and medium-pressure cylinder extraction steam and low-pressure cylinder exhaust steam, providing an operating parameter basis for subsequent extraction steam optimization. Step 2: Multi-stage steam extraction optimization configuration: Optimize the multi-stage steam extraction configuration of the steam turbine. Based on the principle of cascaded thermal energy utilization and the load requirements of the heating network, by adjusting the steam extraction ratio between the high- and low-pressure cylinders, the optimal allocation of high- and low-pressure extraction steam and low-pressure exhaust steam is calculated, forming a steam extraction strategy that adapts to different heating network load conditions. Step 3: Design of heat exchange and heat storage device: Integrate heat exchangers and heat storage devices into the heating system of thermal power units. The heat exchangers improve heat transfer efficiency by optimizing the steam flow path and the contact area with the circulating water. The heat storage devices store excess heat during low-load operation and release heat during high-load demand to balance heating capacity and power generation load. Step 4: Dynamic control strategy development: Develop a dynamic control strategy for combustion and heating of thermal power units. By adjusting the boiler fuel injection rate and air ratio in real time, and dynamically adjusting the extraction ratio and steam flow of high and medium pressure cylinders, this ensures stable operation of the thermal power units at loads below 30% of the rated load while meeting the heating network's heating needs. Step 5: System performance verification: Under actual operating conditions, the coal consumption, heating efficiency, and heat network load response capabilities of the units at different peak-shaving depths were tested to verify the synergistic effect of deep peak-shaving and efficient heating. In step 2, the specific implementation process of the multi-stage extraction steam optimization configuration includes the following steps: Step 2.1: Analysis of heat network load demand: According to the real-time heat network load demand, calculate and set the steam flow and heat supply required under different operating load conditions, and analyze the matching relationship between the steam extraction volume of high-pressure and low-pressure cylinders and the heat network load; Step 2.2: Application of the principle of cascade utilization of thermal energy: Based on the principle of cascaded utilization of thermal energy, the enthalpy values ​​of the extracted steam at each level are analyzed to ensure that high-temperature and high-pressure steam is preferentially used in the high-efficiency heating system, and low-temperature and low-pressure steam is preferentially used in the low-temperature and low-pressure steam heating system, so as to maximize the utilization of thermal energy; Step 2.3: Preliminary setting of extraction ratio: Based on the analysis of the heat network load and thermal energy cascade utilization, the steam extraction ratio of the high- and medium-pressure cylinders and the low-pressure cylinders is preliminarily set to ensure that the steam flow distribution can adapt to the heating demand under different loads; Step 2.4, optimization calculation and adjustment: Using the particle swarm optimization algorithm, the optimal allocation plan for high- and medium-pressure extraction steam and low-pressure exhaust steam is calculated based on the set heat network load demand and heat energy cascade utilization goals to ensure maximum heating efficiency; Step 2.5: Dynamically adjust the steam extraction ratio: According to real-time load changes, the control system dynamically adjusts the steam extraction ratio between the high-pressure and low-pressure cylinders to ensure that the heating network's heating demand is always met when the load fluctuates, while ensuring stable operation of the unit; Step 2.6: Form an adaptation strategy: Based on the optimization results, a steam extraction strategy adapted to different heating network load conditions is formed.

2. The method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units according to claim 1, characterized in that: In step 1, the process of constructing a thermodynamic simulation model of a thermal power plant includes: a. Operation data collection and initialization: Collect the operating parameters of the thermal power unit, including steam flow, enthalpy, pressure and temperature, and build a thermodynamic operation database of the thermal power unit based on the collected data to provide initial parameters for the simulation model; b. Model construction and parameterization: Based on the thermal system structure of the thermal power unit, a thermodynamic simulation model including the boiler module, turbine module and heat network cycle module is constructed; by entering initial parameters and setting boundary conditions, the model is initialized and parameterized.

3. The method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units according to claim 2, characterized in that: The boundary conditions set include: (1) Steam parameter boundary conditions: The steam pressure entering the boiler and steam turbine is set to range from 8MPa to 25MPa, and the steam temperature range is set from 450℃ to 600℃ to reflect the thermodynamic state under different operating loads; (2) Boundary conditions of heating network load: The supply and return water temperature range of the heating network is set to 40℃ to 130℃, and the heating flow rate varies between 200t / h and 1200t / h according to the demand of the heating network; (3) Operation load boundary conditions: Set the load variation range of the unit to 30% to 100% of the rated load, and define the stable combustion parameters at the lowest load and the thermal limit parameters at the highest load; (4) Environmental condition boundaries: Set the ambient temperature range to -20℃ to 40℃, and consider the impact of air humidity on heat exchanger efficiency and boiler exhaust characteristics.

4. The method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units according to claim 1, characterized in that: In step 1, the process of simulating the thermal performance and heating capacity of the unit under different load conditions by thermal calculation methods includes the following steps: (1) Load condition setting: According to the operating range of the thermal power unit, multiple load conditions are set, including 30%, 50%, 75% and 100% rated load; (2) Calculation of thermal parameters: Under each load condition, the key parameters of the unit, including steam flow, pressure, temperature and extraction steam enthalpy, are calculated through thermodynamic formulas to obtain the unit's thermal performance; (3) Analysis of heating capacity: Based on the calculation results, the heating capacity of the high and medium pressure cylinder extraction steam and the low pressure cylinder exhaust steam is analyzed, and combined with the heat network load demand, the heating capacity of the unit under different loads is evaluated.

5. The method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units according to claim 1, characterized in that: In step 3, the implementation process of integrating the heat exchange device and the heat storage device into the heating system of the thermal power unit includes: (1) Integration of heat exchange devices: A shell and tube heat exchanger is selected as the core heat exchange device in the thermal power unit heating system. It is connected to the boiler outlet through a steam pipe so that the steam can fully contact the circulating water when flowing in the heat exchanger, achieving efficient heat exchange. (2) Integration of heat storage devices: Design the type and capacity of the heat storage device based on the load fluctuation characteristics of the unit, and use a hot water storage tank to store excess heat during low-load operation; The heat storage device is connected to the heat exchange device through a pipeline, so that the heat storage device receives excess heat from the heat exchange device when the load is low, and releases the stored heat to the heat network through the control system when the load is high, thereby maintaining a stable heat supply to the heat network; (3) Integrated control system: The control system enables the coordinated operation of the heat exchanger and heat storage device, and monitors the heat network load, boiler steam flow, and heat storage status of the heat storage device in real time; When the load fluctuates, the control system automatically adjusts the heat output of the heat exchanger according to the demand of the heating network, and promptly activates the heat storage device to release the stored heat to ensure the balance between the heating network load and the power generation load.

6. The method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units according to claim 1, characterized in that: In step 4, the process of developing a dynamic control strategy is as follows: Step 4.1, combustion control strategy: By real-time monitoring of the temperature, pressure, and oxygen content in the boiler, the fuel injection amount and air ratio of the boiler are adjusted to achieve optimal combustion efficiency, avoid incomplete combustion, and reduce coal consumption; Step 4.2, steam extraction ratio and flow adjustment: According to the heat network load demand and unit load status, the high and medium pressure cylinder extraction steam ratio and steam flow are adjusted through real-time feedback control strategy, so that the unit can operate stably under different load conditions and meet the heat network heating demand at the same time; Step 4.3, feedback regulation mechanism: Based on real-time data feedback and load changes, the combustion and extraction parameters are dynamically fine-tuned through the feedback regulation mechanism to cope with load fluctuations and system changes, allowing the unit to operate stably under different load conditions and maintain a balance between the heating network's heating capacity and power generation load.

7. The method for coordinated regulation of deep peak regulation and efficient heat supply of thermal power units according to claim 1, characterized in that: In step 5, the specific process of system performance verification includes: Step 5.1, coal consumption test: Under actual operating conditions, the coal consumption of thermal power units at different peak-shaving depths is tested. By measuring the relationship between the actual fuel consumption of the units and the peak-shaving depth, the fuel utilization efficiency of the system is evaluated. Step 5.2, heating efficiency test: Based on the unit operating data under different load conditions, the heating efficiency is tested, including the ratio of heat input to heat output, to verify the system's heating capacity at different peak load depths; Step 5.3: Test the heat network load response capability: Test the unit's response capability to heat network load fluctuations. By simulating load fluctuations and measuring the system's response time and stability to load changes, verify the system's regulation capability under different operating conditions.

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