Thermal power generating unit deep peak regulation and efficient heat supply cooperative regulation and control method

Abstract

The invention provides a thermal power generating unit deep peak regulation and efficient heat supply cooperative regulation and control method. The method comprises the steps of 1, thermodynamic modeling and performance analysis; 2, multi-section steam extraction optimal configuration; 3, designing a heat exchange and storage device; 4, dynamic regulation and control strategy development; and 5, verifying the system performance. According to the method, thermal performance and heat supply capacity of the unit under different load conditions are analyzed by constructing a thermal power unit thermodynamic simulation model, collecting operation parameters and performing thermotechnical calculation, steam distribution is optimized by adjusting the steam extraction proportion of a high-medium pressure cylinder and a low pressure cylinder, and meanwhile a heat exchange device and a heat storage device are designed and integrated. The method is used for balancing the heat supply capacity under the low-load and high-load working conditions. The dynamic regulation and control strategy further realizes real-time optimization and adjustment of combustion and heat supply, and ensures that the system stably operates under the deep peak regulation condition. Through simulation modeling and a dynamic regulation and control strategy, the heat energy utilization efficiency and the unit stability are effectively improved, meanwhile, coal consumption is reduced, and the deep peak regulation requirement is met.

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 heating of thermal power units. Background Art

[0002] With the rapid increase in the proportion of renewable energy power generation in the power system, traditional thermal power units are gradually changing from base load units to regulating units. They not only need to meet the grid's demand for rapid response and deep peak regulation, but also need to undertake the heating task of the heating network during the heating season. How to improve the operating efficiency of thermal power units under complex load fluctuation conditions and achieve coordinated optimization of power generation and heating has become an important research direction in the current field.

[0003] In the prior art, the optimization of power generation and heating of thermal power units usually aims to improve operating efficiency and reduce energy consumption. Common methods include adjusting the steam pressure or flow rate at the steam turbine extraction point to meet the load requirements of different heat networks; improving the heat exchange efficiency of steam and circulating water by designing heat exchange devices to meet the heating temperature requirements; and using auxiliary heat storage or peak-shaving technology in some schemes to balance the heating capacity. However, most of these methods take a single operating scenario or specific operating condition as the optimization object, and lack an overall technical solution that comprehensively considers the coordinated optimization of deep peak-shaving and efficient heating.

[0004] In the existing technology, when the load of the thermal power unit drops below 30% of the rated load, it is difficult to balance the boiler combustion stability and the heating capacity of the heat network. The increase in heating demand may lead to a decrease in combustion efficiency, while unstable combustion will affect the system operation efficiency. Therefore, how to balance combustion stability and heating capacity under deep peak load conditions has become a key issue that needs to be solved urgently. Summary of the invention

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

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

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

[0008] Construct a thermodynamic simulation model of the thermal power unit, collect the operating parameters of the thermal power unit, including steam flow, enthalpy value, pressure and temperature, simulate the thermal performance and heating capacity of the unit under different load conditions through thermal calculation methods, analyze the heating characteristics of high and medium pressure cylinder extraction steam and low pressure cylinder exhaust steam, and provide the operating parameter basis for subsequent extraction steam optimization;

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

[0010] Optimize the multi-stage steam extraction configuration of the steam turbine. Based on the principle of cascade utilization of thermal energy and the load demand of the heat network, by adjusting the steam extraction ratio of the high-pressure and low-pressure cylinders, calculate the optimal allocation plan of high-pressure and low-pressure exhaust steam, and form a steam extraction strategy that adapts to different heat 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, where 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 are used to 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 amount and air ratio in real time, dynamically adjust the steam extraction ratio and steam flow of high and medium pressure cylinders, ensure the stable operation of thermal power units under the condition of 30% lower than the rated load, and meet the heating demand of the heating network;

[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 implementation, in step 1, the process of constructing a thermodynamic simulation model of a thermal power unit 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 establish 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 a boiler module, a steam turbine module and a heat network cycle module is constructed; by inputting initial parameters and setting boundary conditions, the model is initialized and parameterized.

[0022] In an optional implementation, 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 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 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;

[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 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 load demand of the heating network, 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 heating capacity 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 cascade utilization of thermal energy, the enthalpy values ​​of steam extraction at each level are analyzed to ensure that high-temperature and high-pressure steam is used first in the high-efficiency heating system, and low-temperature and low-pressure steam is used first 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-pressure 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 scheme of high and medium pressure extraction steam and low pressure exhaust steam is calculated according to the set heat network load demand and heat energy cascade utilization target to ensure the maximum heating efficiency;

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

[0048] According to the 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 demand of the heating network is always met when the load fluctuates, while ensuring the 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 implementation, 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 devices:

[0053] The shell and tube heat exchanger is selected as the core heat exchange device in the heating system of the thermal power unit, and it is connected to the boiler outlet through the steam pipe, so that the steam can fully contact with the circulating water when flowing in the heat exchanger to achieve efficient heat exchange;

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

[0055] Design the type and capacity of the heat storage device according to 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, so as to maintain stable heat supply of the heat network;

[0057] (3) Integrated control system:

[0058] The control system enables the coordinated work of the heat exchange device and the 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 implementation, 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 boiler fuel injection amount and air ratio are adjusted to achieve optimal combustion efficiency, avoid incomplete combustion and reduce coal consumption;

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

[0064] According to the heat network load demand and unit load status, the high and medium pressure cylinder steam extraction 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;

[0065] Step 4.3, feedback regulation mechanism:

[0066] Based on real-time data feedback and load changes, the combustion and steam extraction parameters are dynamically fine-tuned through the feedback adjustment mechanism to cope with load fluctuations and system changes, so that the unit can operate stably under different load conditions and maintain a balance between the heating capacity of the heat network and the 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 load depths is tested, and the fuel efficiency of the system is evaluated by measuring the relationship between the actual fuel consumption of the units and the peak load depth;

[0070] Step 5.2, heating efficiency test:

[0071] According to the unit operation data under different load conditions, test the heating efficiency, including the ratio of heat input to heat output, and verify the heating capacity of the system under different peak load depths;

[0072] Step 5.3, heat network load response capability test:

[0073] Test the unit's ability to respond 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 ability to adjust 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 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 can not only improve the heating capacity of the unit, but also reduce unnecessary energy waste, thereby improving the overall thermal efficiency of the thermal power unit.

[0076] 2. This application can enhance the adaptability and stability of thermal power units under load fluctuations through the design of heat exchange and heat storage devices and the development of dynamic control strategies. By integrating heat exchange devices and heat storage devices, it can not only improve the heat exchange efficiency, but also store excess heat at low loads and release this heat at high load demands, thereby balancing the heating capacity and power generation load. At the same time, the development process of dynamic control strategies enables thermal power units to respond to load fluctuations more flexibly and further improve peak load regulation 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 paying any creative work.

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

[0079] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application is clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present application.

[0080] The embodiment of the present application provides a method for coordinated regulation of deep peak regulation and efficient heat supply of a thermal power unit, including five processes: thermodynamic modeling and performance analysis, multi-stage steam extraction optimization configuration, heat exchange and heat storage device design, dynamic regulation strategy development, and system performance verification. Figure 1 As shown in FIG. 1 , 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] Construct a thermodynamic simulation model of the thermal power unit, collect the operating parameters of the thermal power unit, including steam flow, enthalpy value, pressure and temperature, simulate the thermal performance and heating capacity of the unit under different load conditions through thermal calculation methods, analyze the heating characteristics of high and medium pressure cylinder extraction steam and low pressure cylinder exhaust steam, and provide the 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 cascade utilization of thermal energy and the load demand of the heat network, by adjusting the steam extraction ratio of the high-pressure and low-pressure cylinders, calculate the optimal allocation plan of high-pressure and low-pressure exhaust steam, and form a steam extraction strategy that adapts to different heat 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, where 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 are used to 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 amount and air ratio in real time, dynamically adjust the steam extraction ratio and steam flow of high and medium pressure cylinders, ensure the stable operation of thermal power units under the condition of 30% lower than the rated load, and meet the heating demand of the heating network;

[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] The implementation of system performance verification in this step is to ensure reliable application in actual operation. Since the efficient operation of thermal power units is not only reflected in the reduction of coal consumption and the improvement of heating efficiency, it is also necessary to ensure that the heating network and power generation load can be balanced under different load conditions. System performance verification is carried out to ensure the stability and economy of the units in long-term operation.

[0092] The method for coordinated regulation of deep peak regulation and efficient heating of thermal power units provided in the embodiment of the present application can realize 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 efficient 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 can not only improve the heating capacity of the unit, but also reduce unnecessary energy waste, thereby improving the overall thermal efficiency of the thermal power unit.

[0093] In addition, the embodiment of the present application can enhance the adaptability and stability of thermal power units under load fluctuations through the implementation of the process of heat exchange and heat storage device design and dynamic control strategy development. By integrating heat exchange devices and heat storage devices, it can not only improve the heat exchange efficiency, but also store excess heat at low loads and release this heat at high load demands, thereby balancing the heating capacity and power generation load. At the same time, the development process of the dynamic control strategy enables thermal power units to respond to load fluctuations more flexibly and further improve the peak load regulation capacity.

[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 establish 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 a boiler module, a steam turbine module and a heat network cycle module is constructed; by inputting initial parameters and setting boundary conditions, the model is initialized and parameterized.

[0099] In 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 (steam flow, enthalpy value, pressure and temperature) of the thermal power unit, and establish a thermodynamic operation database of the thermal power unit 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] In addition, this embodiment is based on the thermal system structure of the thermal power unit, by constructing a thermodynamic simulation model including a boiler module, a turbine module and a heat network cycle module, and 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 the traditional single module modeling method, this multi-module combination method improves the reliability and applicability of the model, and can provide more accurate analysis and suggestions for different loads 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 the unit performance analysis, but also provides 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 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 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;

[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, by setting the steam parameters, heat network load, operating load and environmental condition boundaries, the input range of the simulation model is clarified, and 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 deviations caused by missing parameters or unreasonable ranges in the model, 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% rated load) to full load (100% rated load), while meeting the dynamic needs of the heat network (supply and return water temperature 40°C to 130°C, heating flow 200t / h to 1200t / h). These settings make the simulation model applicable to both deep peak regulation and efficient heating. Especially at the lowest load, clear combustion stability parameters can help the simulation model accurately reflect the low-load operation characteristics of boilers and turbines, and support subsequent regulation and optimization.

[0113] At the same time, the setting of environmental condition boundaries (ambient temperature -20℃ to 40℃, and considering the effect of air humidity on heat exchanger efficiency) further enhances the applicability of the simulation model under different climatic conditions. This boundary condition can help the model fully consider the impact of external environmental factors on the operation of the thermal system, such as the impact of low temperature environment on boiler combustion and smoke exhaust characteristics, and the limitation of high temperature environment on heat exchange efficiency. These settings enable the simulation model to more accurately predict system performance under diverse operating environments and enhance the robustness of the model.

[0114] Furthermore, in step S1, 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:

[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 thermal power units under multiple typical operating conditions of 30%, 50%, 75% and 100% rated load is clarified. This setting not only covers the main operating states of the unit from deep peak regulation to full load, but also provides a variety of operating condition references for subsequent performance calculations. By covering multiple load conditions, it can more comprehensively analyze the thermal performance and heating capacity of the unit under different loads, providing data support for subsequent optimization. This hierarchical setting method avoids the limitations of single operating 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 enthalpy, are calculated through thermodynamic formulas to obtain the unit's thermal performance.

[0120] Parameters such as steam flow, pressure, temperature and extraction enthalpy reflect the thermal performance of the unit under different loads and can provide accurate data support for performance analysis. In particular, the calculation of extraction enthalpy is directly related to the evaluation of the unit's heating efficiency. This detailed thermal parameter calculation can capture the operating characteristics of the unit under dynamic load conditions and provide a more reliable basis 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 load demand of the heating network, the heating capacity of the unit under different loads is evaluated.

[0123] In the heating capacity analysis stage, based on the thermal calculation results, the heating capacity of the high and medium pressure cylinder extraction steam and the low pressure cylinder exhaust steam is evaluated respectively, and a matching analysis is performed in combination with the heat network load demand. This analysis method can clarify the heating potential and load response capacity of the unit under different loads, thereby providing a clear direction for subsequent extraction optimization and dynamic control strategies. By matching the heating capacity with the heat network demand, the extraction strategy can be designed more accurately to improve the stability of the heat network operation and the 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, heat network load demand analysis:

[0126] According to the real-time heat network load demand, the steam flow and heating supply required under different operating load conditions are calculated and set, and the matching relationship between the steam 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 heating supply through the analysis of the heat network load demand and the real-time heat network load changes. It also analyzes the relationship between the steam extraction volume of the high-pressure and low-pressure cylinders and the heat network load. This process can help accurately understand the demand for the heat network under different operating conditions and achieve precise matching of the steam extraction configuration with the heat network demand.

[0128] Step S22, application of thermal energy cascade utilization principle:

[0129] Based on the principle of cascade utilization of thermal energy, the enthalpy values ​​of steam extraction at each level are analyzed to ensure that high-temperature and high-pressure steam is used first in the high-efficiency heating system, and low-temperature and low-pressure steam is used first 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-pressure and medium-pressure cylinders and the low-pressure cylinder 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 according to the set heat network load demand and thermal energy cascade utilization target to ensure maximum heating efficiency.

[0134] Particle swarm optimization (PSO) is a commonly used intelligent optimization algorithm, which is suitable for solving multi-objective and nonlinear optimization problems, especially in multi-objective optimization. Its advantages are that it can quickly search for the global optimal solution and can handle the optimization problems of 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 according to the set heat network load demand and thermal energy cascade utilization goals.

[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 the 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 the heating supply provided by low pressure exhaust steam, 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 satisfies the constraint x1+x2=1.

[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, update the velocity and position of the particle:

[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 [0, 2]; r1, r2 are random numbers, the range of values ​​is [0, 1], which are used to introduce randomness to avoid falling into the local optimum. 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 satisfy the constraint x1+x2=1, and normalize the 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 in several iterations, or reaches the maximum number of iterations, the optimization process is terminated, and the optimal allocation scheme of 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 realizing efficient operation and energy-saving optimization of the heating system.

[0162] This embodiment applies the principle of step-by-step utilization of thermal energy in step S22 and implements the preliminary setting of the steam extraction ratio in step S2.3. Based on the principle of step-by-step utilization of thermal energy, the steam enthalpy value is analyzed, and high-temperature and high-pressure steam is preferentially used for the high-efficiency heating system, while low-temperature and low-pressure steam is allocated to low-temperature heating needs. This allocation method based on enthalpy value can make full use of the thermal energy characteristics of each stage of steam extraction to maximize the utilization of thermal energy. At the same time, the preliminary setting of the steam extraction ratio between the high-pressure cylinder and the low-pressure cylinder helps to reasonably allocate the steam flow under different load conditions, thereby better ensuring that the heating system can flexibly respond to load fluctuations and maintain operating efficiency.

[0163] Step S25, dynamically adjust the steam extraction ratio:

[0164] According to the real-time load changes, the control system dynamically adjusts the steam extraction ratio of 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 the stable operation of the unit. Through this dynamic adjustment method, the system can quickly respond to load fluctuations and avoid problems such as reduced heating efficiency or unstable operation caused by unreasonable 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 devices:

[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 to achieve efficient heat exchange.

[0170] The heat exchange device uses a shell and tube heat exchanger as the core heat exchange equipment, and connects the heat exchanger to the boiler outlet through a steam pipe, so that the steam can fully contact the circulating water when it flows. This design can improve the heat transfer efficiency between steam and circulating water and effectively reduce heat loss. At the same time, the shell and tube heat exchanger has a simple structural design and is easy to maintain, which is suitable for the long-term operation requirements of thermal power units.

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

[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] In this process, the heat storage device is connected to the heat exchange device through a pipeline, and the heat transfer and utilization are more efficient. 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, so as to maintain the stable heat supply of the heat network. This design enables the heat storage device to flexibly adjust the heat supply of the system when the load fluctuates, thereby maintaining the stable heating capacity of the heat network.

[0174] (3) Integrated control system:

[0175] The control system enables the coordinated work of the heat exchange device and the 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 the temperature, pressure and oxygen content in the boiler, the boiler fuel injection amount and air ratio are adjusted to achieve optimal combustion efficiency, avoid incomplete combustion, and reduce coal consumption. This combustion control method can optimize combustion efficiency, reduce the occurrence of incomplete combustion, and thus reduce coal consumption. Compared with traditional empirical control methods, real-time monitoring and dynamic adjustment not only improve the utilization efficiency of fuel, but also reduce the energy loss caused by excess air, providing important support for the efficient operation of the overall heating and power generation system.

[0180] Step S42, steam extraction ratio and flow rate adjustment:

[0181] According to the load demand of the heating network and the load status of the unit, the extraction ratio and steam flow rate 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.

[0182] Step S43: Feedback adjustment mechanism:

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

[0184] In this process, the feedback adjustment mechanism of step S43 dynamically fine-tunes the combustion and steam extraction parameters through real-time data feedback and load changes. Compared with the traditional static control method, this mechanism can quickly respond to load fluctuations and operating parameter changes in system operation. For example, when the load suddenly increases, the feedback adjustment mechanism can immediately adjust the combustion intensity and steam 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 capacity of the heat network and the power generation load, further improving the reliability of system operation.

[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 test method can clearly reflect the fuel consumption characteristics of the unit under deep peak load conditions, providing data basis for optimizing combustion efficiency and reducing coal consumption. At the same time, this test 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 operation 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] Test the unit's ability to respond 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 ability to adjust under different operating conditions. This test method can verify the system's ability to adjust under different operating conditions, especially its dynamic adaptability and stability under load mutation conditions. Moreover, this test result can provide optimization direction for the system's dynamic control strategy, while improving the system's reliability under complex conditions and reducing the problem of unstable heating or power generation 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] The thermal power unit adopts supercritical parameter design, has high-pressure cylinder and low-pressure cylinder extraction capacity, designed heating capacity of 600t / h, heating network water supply temperature range of 70℃~130℃, return water temperature of 40℃~70℃. The steam parameters of the unit under rated load are pressure 25MPa and temperature 600℃, and the minimum stable operating load at low load is 30% of the rated load. The air cooling system design ensures the exhaust cooling capacity of the unit under low load conditions, which is suitable for deep peak load regulation operation conditions.

[0196] 2. Implementation process

[0197] The method for coordinated regulation of deep peak load regulation and efficient heating of thermal power units provided in the above-mentioned embodiment of the present application is adopted to collect the operating parameters of the thermal power units, build a thermodynamic simulation model, optimize the steam extraction ratio of high-pressure and medium-pressure cylinders and low-pressure cylinders, and design and integrate the shell and tube heat exchanger and hot water storage tank. A dynamic regulation strategy is adopted to adjust the boiler combustion intensity and steam extraction parameters in real time to ensure the stable operation of the unit under different load conditions, and to conduct system performance verification tests.

[0198] 3. Test results

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

[0200] At 30%, 50%, 75% and 100% load, the heating efficiency increased by 10%, 8%, 6% and 4% respectively. Coal consumption was reduced by 4% to 7%, especially under low-load deep peak-shaving conditions, the energy-saving effect was more significant.

[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 per hour.

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

[0206] Basic formula:

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

[0208] Among them, the hourly coal saving, according to the test results, is 4-6 tons / hour. The annual operating hours are usually based on the annual operating hours of thermal power units (assuming the operating hours are 4000-5000 hours, depending on the region and power dispatching conditions). The operating hours range is based on the load demand of the power plant, shutdown maintenance time and peak load operation conditions.

[0209] Assuming that the coal saving per hour is 5 tons (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 4 tons and 6 tons of hourly coal saving as follows:

[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 it. Although the present application has been described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate 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 heating of thermal power units, characterized in that: The following steps are involved: Step 1: Thermodynamic modeling and performance analysis: Construct a thermodynamic simulation model of the thermal power unit, collect the operating parameters of the thermal power unit, including steam flow, enthalpy value, pressure and temperature, simulate the thermal performance and heating capacity of the unit under different load conditions through thermal calculation methods, analyze the heating characteristics of high and medium pressure cylinder extraction steam and low pressure cylinder exhaust steam, and provide the 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 cascade utilization of thermal energy and the load demand of the heat network, by adjusting the steam extraction ratio of the high-pressure and low-pressure cylinders, calculate the optimal allocation plan of high-pressure and low-pressure exhaust steam, and form a steam extraction strategy that adapts to different heat 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, where 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 are used to 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 amount and air ratio in real time, dynamically adjust the steam extraction ratio and steam flow of high and medium pressure cylinders, ensure the stable operation of thermal power units under the condition of 30% lower than the rated load, and meet the heating demand of the heating network; 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 are tested to verify the synergistic effect of deep peak-shaving and efficient heating.

2. The method for coordinated regulation of deep peak load 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 unit includes: a. Operation data collection and initialization: Collect the operating parameters of the thermal power unit, including steam flow, enthalpy, pressure and temperature, and establish 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 a boiler module, a steam turbine module and a heat network cycle module is constructed; by inputting initial parameters and setting boundary conditions, the model is initialized and parameterized.

3. The method for coordinated regulation of deep peak load regulation and efficient heating of thermal power units according to claim 2 is 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 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 load regulation and efficient heating 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 method 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 enthalpy, are calculated through thermodynamic formulas to obtain the unit's thermal performance; (3) Heating capacity analysis: 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 load demand of the heating network, the heating capacity of the unit under different loads is evaluated.

5. The method for coordinated regulation of deep peak load regulation and efficient heat supply of thermal power units according to claim 1, characterized in that: In step 2, the specific implementation process of the multi-stage extraction steam optimization configuration includes the following steps: Step 2.1, heat network load demand analysis: According to the real-time heat network load demand, calculate and set the steam flow and heating capacity 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 cascade utilization of thermal energy, the enthalpy values ​​of steam extraction at each level are analyzed to ensure that high-temperature and high-pressure steam is used first in the high-efficiency heating system, and low-temperature and low-pressure steam is used first 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-pressure 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 scheme of high and medium pressure extraction steam and low pressure exhaust steam is calculated according to the set heat network load demand and heat energy cascade utilization target to ensure the maximum heating efficiency; Step 2.5: Dynamically adjust the steam extraction ratio: According to the 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 demand of the heating network is always met when the load fluctuates, while ensuring the 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.

6. The method for coordinated regulation of deep peak load 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: The shell and tube heat exchanger is selected as the core heat exchange device in the heating system of the thermal power unit, and it is connected to the boiler outlet through the steam pipe, so that the steam can fully contact with the circulating water when flowing in the heat exchanger to achieve efficient heat exchange; (2) Integration of heat storage devices: Design the type and capacity of the heat storage device according to 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, so as to maintain stable heat supply of the heat network; (3) Integrated control system: The control system enables the coordinated work of the heat exchange device and the 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.

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

8. The method for coordinated regulation of deep peak load 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 load depths is tested, and the fuel efficiency of the system is evaluated by measuring the relationship between the actual fuel consumption of the units and the peak load depth; Step 5.2, heating efficiency test: According to the unit operation data under different load conditions, test the heating efficiency, including the ratio of heat input to heat output, and verify the heating capacity of the system under different peak load depths; Step 5.3, heat network load response capability test: Test the unit's ability to respond 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 ability to adjust under different operating conditions.

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