Optimal scheduling method for parallel heating operation of extraction condensing units and high back pressure heating units

Abstract

The present invention discloses an optimized scheduling method for parallel heating operation of an extraction condensing unit and a high back-pressure heating unit, and relates to the technical field of energy-saving of coal-fired units. This patent has made a fundamental improvement to the operation scheduling mode of a cogeneration system, and by combining the optimized operation scheduling of extraction condensing units and high back-pressure boilers, it has achieved the goal of improving energy utilization efficiency and system economy while meeting the heat load demand. Compared with traditional separately adjusted heating equipment, this method has comprehensively optimized at the system level, reduced energy consumption and costs, and reduced environmental emissions. The optimized system can flexibly adapt to market and environmental changes, adjust the weights of operating costs and emission costs in real time, and maintain the best balance between economic benefits and environmental protection. The introduction of a graphical user interface and a real-time monitoring module has improved operational efficiency and decision support, promoted the efficient operation of the heating system, extended the life of the equipment, and created greater economic value for operators.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation of coal-fired units, and in particular to an optimization scheduling method for parallel heating operation of an extraction condensing unit and a high back pressure heating unit. Background Art

[0002] Cogeneration is an efficient way of utilizing energy to produce both electricity and heat. By comprehensively utilizing the thermal energy of fuel, the cogeneration system can significantly improve energy efficiency, reduce pollutant emissions, and has significant economic and environmental benefits. The main heat source equipment commonly used in cogeneration systems are extraction condensing steam turbine units and high back pressure heating boilers. The extraction condensing unit mainly plays the role of power generation, and can also extract some steam for heating. The high back pressure boiler directly converts the thermal energy of the fuel into high temperature and high pressure steam for regional heating purposes. Due to differences in design concepts and structural parameters, the two types of equipment have significant differences in operating characteristics.

[0003] In order to meet the actual heat load demand, the cogeneration system usually needs to enable the condensing unit and the high back pressure boiler to operate in parallel for heating. However, due to the different operating mechanisms and parameters of the two equipment, how to effectively coordinate their output combination and load distribution to achieve the best economy and energy efficiency of the entire system is a key issue that needs to be solved urgently. The traditional scheduling method of separately adjusting the condensing unit and the high back pressure boiler is often used in the prior art. This method only considers the single operating characteristics of each equipment, and cannot give full play to the complementary advantages of different models. The overall heating efficiency and economy are difficult to reach the optimal level. At the same time, due to the lack of comprehensive modeling and comprehensive optimization of the entire thermal system, the energy consumption and operating costs of the system are difficult to control at the lowest level.

[0004] Therefore, there is an urgent need for a new optimization scheduling method that can organically combine the operating characteristics of the condensing unit and the high back-pressure boiler, and comprehensively model and optimize the entire cogeneration system in multiple objectives while meeting the heat load demand, so as to maximize energy utilization efficiency and minimize operating costs, thereby completely solving the above-mentioned technical problems. Summary of the invention

[0005] The object of the present invention is to provide an optimized scheduling method for the parallel heating operation of a condensing unit and a high back pressure heating unit to solve the above-mentioned problems.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for optimizing the scheduling of parallel heating operation of an extraction condensing unit and a high back pressure heating unit, comprising the following steps: The load forecasting module uses forecasting algorithms such as time series analysis, combined with influencing factors such as meteorology and user energy consumption patterns, to make rolling forecasts of hourly and daily heat load curves in the future; The unit modeling module establishes models reflecting the power generation and heating performance of the extraction condensing unit and the heating performance of the high back-pressure boiler, taking into account the influencing factors such as their structural parameters and operating parameters; The heat network modeling module uses the heat network partitioning method and loop solving algorithm to establish a distributed parameter model of the heat pipe network of the entire heating system, and combines the operating condition models of various equipment; The optimization module takes the above load forecast as the constraint target, and based on the above unit model and heat network model, establishes a multi-objective optimization problem, and obtains the optimal output of the extraction condensing unit and the high back pressure boiler and the optimal operating parameters of other equipment in the system through the optimization algorithm; The coordination control module coordinates the calculation results of the optimization module, formulates the unit start and stop strategy, parameter control curve and pipeline network control strategy, and realizes the smooth and efficient execution of the optimized scheduling plan.

[0007] Based on the above technical features, the working principle of the present invention is based on the effective parallel operation of the condensing unit and the high back pressure heating unit. Through precise load prediction, unit modeling, system optimization and coordinated control, the operation of the cogeneration system can achieve unprecedented efficiency and economy. The preliminary work is to perform time series analysis through the load prediction module. This module combines meteorological conditions and user energy consumption patterns to accurately predict the heat load demand in the future and formulate corresponding heating plans. The unit modeling module constructs mathematical models of the condensing unit and the high back pressure boiler, describes their respective power generation and heating behaviors in detail, and includes their performance parameters under different working conditions. The heat network modeling module adopts the heat network partitioning method and loop solving algorithm to create a distributed parameter model of the thermal network, which provides a basis for predicting key operating parameters such as flow, pressure and temperature. In addition, the optimization module is the core of the entire method. It sets multi-objective optimization problems based on the aforementioned models and load forecasts, and uses efficient algorithms such as genetic algorithms and particle swarm algorithms to solve them in order to find the optimal output combination and operating parameters. Finally, the coordination control module is responsible for converting the optimization results into specific operating instructions to ensure that the system can still operate smoothly and efficiently under dynamically changing conditions.

[0008] Preferably, in the above-mentioned optimal scheduling method for parallel heating operation of the condensing unit and the high back-pressure heating unit, the load forecasting module adopts a grey prediction model, a neural network or other time series prediction algorithm, combined with meteorological data, user energy consumption patterns and other influencing factors, to perform rolling forecasts on the hourly and daily heat load curves in the future.

[0009] Based on the above technical features, the load forecasting module plays a vital role in this solution. It not only provides a dynamic system that can reflect future demand changes, but also provides accurate data connections between the various modules of the cogeneration system (such as the optimization module and the coordination control module) to ensure the coordinated and efficient operation of the entire system, thus establishing the innovation and advancement of this invention in the field of energy management.

[0010] Preferably, in the above-mentioned method for optimizing the scheduling of parallel heating operation of the condensing unit and the high back-pressure heating unit, the heat network modeling module establishes a basic control equation group including conservation of mass, conservation of momentum, and conservation of energy, and uses algorithms such as the loop solving method to solve the distributed parameter model of the heating system to predict operating parameters such as flow, pressure, and temperature.

[0011] Based on the above technical features, the heat network modeling module plays a fundamental role in this solution. It not only ensures the stability and safety of the system operation, but also provides the necessary information and tools to achieve economy and efficiency. By utilizing the prediction and simulation capabilities of this module, the performance of the heating system can be highly optimized, the potential capacity of the pipeline network can be fully realized, and the effectiveness of the overall system operation can be improved.

[0012] Preferably, in the above-mentioned optimization scheduling method for parallel heating operation of the condensing unit and the high back-pressure heating unit, the optimization module includes a multi-objective optimization process, which uses genetic algorithms, particle swarm algorithms or other suitable mathematical algorithms to establish objective functions for factors such as power generation, heating, fuel costs, emission costs, etc., and solves thermal load balance constraints, unit operating parameter constraints, pipeline network operating parameter constraints and other related constraints.

[0013] Based on the above technical features, the optimization module in this solution greatly enhances the overall performance of the cogeneration system in terms of economy, environmental protection, technology and safety by executing a multi-objective optimization process and effectively meeting various constraints, reflecting the advancement and practicality of the invention in solving system-level optimization problems.

[0014] Preferably, in the above-mentioned optimization scheduling method for parallel heating operation of the condensing unit and the high back-pressure heating unit, the coordination control module is responsible for converting the calculation results of the optimization module into feasible operating parameters and operating curves, ensuring that the startup, shutdown and operation switching of the unit and the pipeline network are completed smoothly and stably according to the optimization results, while taking into account the dynamic response time of each component of the system to ensure the balance and safety of the scheduling process.

[0015] Based on the above technical features, the coordination control module in this scheme plays a bridging role in the specific execution of the calculation results of the optimization module, ensuring that the optimization algorithm can be effectively implemented in the real world. This module ensures that the results of the optimization process can be correctly and smoothly applied to the cogeneration system in the physical world, thereby ensuring the stability, efficiency and safety of the entire system.

[0016] Preferably, in the above-mentioned optimization scheduling method for parallel heating operation of the condensing unit and the high back pressure heating unit, the optimization module establishes a multi-objective optimization problem, and the objective function is to minimize the weighted superposition of factors such as power generation, heating, fuel cost, operating cost, and emission cost; The constraints include heat load balance constraints, unit operation parameter constraints, pipe network operation parameter constraints, etc. The solution is obtained by using intelligent optimization algorithms such as genetic algorithm and particle swarm algorithm.

[0017] Based on the above technical features, the multi-objective optimization process implemented by the optimization module in this solution combines operational efficiency, cost-effectiveness and environmental impact, ensuring high-performance operation of the energy supply system. Through the solution of intelligent algorithms, this module supports real-time dynamic decision-making under complex system conditions, and plays a core supporting role in the optimal scheduling of the entire cogeneration system.

[0018] Preferably, in the above-mentioned method for optimizing the scheduling of the parallel heating operation of the condensing unit and the high back-pressure heating unit, during the optimization process, the optimization objective function may consider the real-time market price signal as a weight factor, and dynamically adjust the proportion of operating cost and emission cost to adapt to market and environmental changes.

[0019] Based on the above technical features, in the above description, the dynamic adjustment mechanism of the optimization objective function including real-time market price signals greatly enhances the strategic flexibility and adaptability of the cogeneration system. Such a function is the key to achieving the robust operation of the system in a complex market environment, and is crucial to the dual goals of economic benefits and environmental responsibility.

[0020] Preferably, in the above-mentioned method for optimizing the scheduling of the parallel heating operation of the condensing unit and the high back-pressure heating unit, the method further includes a real-time monitoring module for collecting the operating data of the condensing unit, the high back-pressure boiler and the thermal network in real time, and providing real-time feedback data for modules such as load prediction and optimization calculation.

[0021] Based on the above technical features, the role of the real-time monitoring module in this solution can provide a data basis for the optimized operation of the entire cogeneration system. It ensures the real-time and accuracy of the optimization calculation, and is a key component for improving the system's responsiveness, operating efficiency and safety. Through real-time monitoring of the system, operators can ensure that the data used by the optimization module is timely and accurate, thus playing a vital role in ensuring system stability and efficiency optimization.

[0022] Preferably, in the above-mentioned optimization scheduling method for parallel heating operation of the condensing unit and the high back-pressure heating unit, the objective function of the multi-objective optimization problem takes into account not only the power and heat output of the system, but also the environmental indicators of the system operation, such as carbon dioxide emissions and emissions of other pollutants, so as to achieve an environmentally friendly operation strategy.

[0023] Based on the above technical features, in the above description, the setting of the multi-objective optimization problem including environmental indicators emphasizes that in addition to traditional economic benefits, environmental protection is also an indispensable part of the optimal design and operation of the cogeneration system. This shows that this solution not only focuses on current benefits, but also foresees the importance of green energy and sustainability in the future, and promotes the energy system to move forward more responsibly.

[0024] Preferably, in the above-mentioned method for optimizing the scheduling of the parallel heating operation of the condensing unit and the high back-pressure heating unit, a graphical human-machine interface is used to display the optimized scheduling plan and provide auxiliary decision support, and the operator can make appropriate adjustments to the plan according to actual conditions.

[0025] Based on the above technical features, in the above description, the graphical human-computer interface acts as a bridge of communication between the user and the optimization system. It not only makes it easier to understand the optimization scheduling plan, but also provides convenience for real-time adjustment and optimization decision-making. Such a design significantly improves the availability and flexibility of the system and enhances the actual application effect of the optimization scheduling decision.

[0026] Compared with the prior art, the present invention has the following beneficial effects: 1. This patented technology has made a fundamental improvement to the existing cogeneration system operation and scheduling method. By combining the operation optimization scheduling of the extraction condensing unit and the high back pressure boiler, this method can not only meet the heat load demand in different time periods, but also significantly improve the energy utilization efficiency and system economy without sacrificing the quality of heating and power generation. Compared with the traditional practice of adjusting each heating equipment separately, this patent has achieved comprehensive optimization at the system level, effectively reducing the energy consumption and cost during the system operation, while reducing environmental emissions; 2. The optimized system can dynamically adapt to market and environmental changes. The flexible setting of the optimization objective function enables the weights of operating costs and emission costs to be adjusted instantly under changing market price signals, thereby ensuring the best balance between economic benefits and environmental protection. In addition, the introduced graphical user interface and real-time monitoring module provide operators with an intuitive operating platform and decision-making support, greatly improving the response efficiency and practicality of the scheduling plan. These improvements not only make the operation of heat source equipment more stable, but also promote the efficient operation of the entire heating system, improve equipment life, and create greater economic value for operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 This is a step diagram of the optimization scheduling method of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 The method for optimizing the parallel heating operation of the condensing unit and the high back pressure heating unit comprises the following steps: The load forecasting module uses forecasting algorithms such as time series analysis, combined with influencing factors such as meteorology and user energy consumption patterns, to make rolling forecasts of hourly and daily heat load curves in the future; The unit modeling module establishes models reflecting the power generation and heating performance of the extraction condensing unit and the heating performance of the high back-pressure boiler, taking into account the influencing factors such as their structural parameters and operating parameters; The heat network modeling module uses the heat network partitioning method and loop solving algorithm to establish a distributed parameter model of the heat pipe network of the entire heating system, and combines the operating condition models of various equipment; The optimization module takes the above load forecast as the constraint target, and based on the above unit model and heat network model, establishes a multi-objective optimization problem, and obtains the optimal output of the extraction condensing unit and the high back pressure boiler and the optimal operating parameters of other equipment in the system through the optimization algorithm; The coordination control module coordinates the calculation results of the optimization module, formulates the unit start and stop strategy, parameter control curve and pipeline network control strategy, and realizes the smooth and efficient execution of the optimized scheduling plan.

[0031] The load forecasting module adopts a grey forecasting model, a neural network or other time series forecasting algorithms, combined with meteorological data, user energy consumption patterns and other influencing factors, to make rolling forecasts of hourly and daily thermal load curves in the future. The load forecasting module plays a vital role in this solution. It not only provides a dynamic system that can reflect future demand changes, but also provides accurate data connections between the various modules of the cogeneration system (such as the optimization module and the coordination control module) to ensure the coordinated and efficient operation of the entire system, thus establishing the innovation and advancement of this invention in the field of energy management.

[0032] The heat network modeling module establishes a set of basic control equations including conservation of mass, conservation of momentum, and conservation of energy, and uses algorithms such as loop solving to solve the distributed parameter model of the heating system and predict operating parameters such as flow, pressure, and temperature. The heat network modeling module plays a fundamental role in this solution. It not only ensures the stability and safety of system operation, but also provides the necessary information and tools to achieve economy and efficiency. By using the prediction and simulation capabilities of this module, the performance of the heating system can be highly optimized, the potential capacity of the pipeline network can be fully realized, and the effectiveness of the overall system operation can be improved.

[0033] The optimization module includes a multi-objective optimization process, which uses genetic algorithms, particle swarm algorithms or other appropriate mathematical algorithms to establish objective functions for factors such as power generation, heat supply, fuel cost, emission cost, etc., and solves thermal load balance constraints, unit operating parameter constraints, pipeline operating parameter constraints and other related constraints. The optimization module in this solution greatly enhances the overall performance of the cogeneration system in terms of economy, environmental protection, technology and safety performance by executing a multi-objective optimization process and effectively meeting various constraints, reflecting the advancement and practicality of the invention in solving system-level optimization problems.

[0034] The coordination control module is responsible for converting the calculation results of the optimization module into feasible operating parameters and operating curves, ensuring that the startup, shutdown and operation switching of the unit and pipeline network are completed smoothly and stably according to the optimization results, while considering the dynamic response time of each component of the system to ensure the balance and safety of the scheduling process. In this scheme, the coordination control module plays a bridging role in the specific implementation of the calculation results of the optimization module, ensuring that the optimization algorithm can be effectively implemented in the real world. This module ensures that the results of the optimization process can be correctly and smoothly applied to the cogeneration system in the physical world, thereby ensuring the stability, efficiency and safety of the entire system.

[0035] The optimization module establishes a multi-objective optimization problem. The objective function is to minimize the weighted superposition of factors such as power generation, heat supply, fuel cost, operating cost, and emission cost. The constraints include heat load balance constraints, unit operating parameter constraints, and pipeline network operating parameter constraints. Intelligent optimization algorithms such as genetic algorithms and particle swarm algorithms are used to solve the problem. The multi-objective optimization process implemented by the optimization module in this solution combines operating efficiency, cost-effectiveness, and environmental impact to ensure high-performance operation of the energy supply system. Through the solution of intelligent algorithms, the module supports real-time dynamic decision-making under complex system conditions, and plays a core supporting role in the optimal scheduling of the entire cogeneration system.

[0036] During the optimization process, the optimization objective function can consider the real-time market price signal as a weight factor, and dynamically adjust the proportion of operating costs and emission costs to adapt to market and environmental changes. In the above description, the dynamic adjustment mechanism of the optimization objective function including the real-time market price signal greatly enhances the strategic flexibility and adaptability of the cogeneration system. Such a function is the key to achieving the robust operation of the system in a complex market environment, and is crucial to the dual goals of economic benefits and environmental responsibility.

[0037] The method further includes a real-time monitoring module for real-time collection of operating data of the condensing unit, high back pressure boiler and thermal pipeline network, and providing real-time feedback data for load forecasting, optimization calculation and other modules. The role of the real-time monitoring module in this scheme can provide a data basis for the optimized operation of the entire cogeneration system. It ensures the real-time and accuracy of the optimization calculation, and is a key component for improving the system's responsiveness, operating efficiency and safety. Through real-time monitoring of the system, the operator can ensure that the data used by the optimization module is timely and accurate, thereby playing a vital role in ensuring system stability and optimal efficiency.

[0038] The objective function of the multi-objective optimization problem considers not only the power and heat output of the system, but also the environmental indicators of the system operation, such as carbon dioxide emissions and emissions of other pollutants, in order to achieve an environmentally friendly operation strategy. In the above description, the setting of the multi-objective optimization problem including environmental indicators emphasizes that in addition to traditional economic benefits, environmental protection is also an indispensable part of the optimal design and operation of the cogeneration system. This shows that this solution not only focuses on current benefits, but also foresees the importance of green energy and sustainability in the future, and promotes the energy system to move forward more responsibly.

[0039] A graphical human-machine interface is used to display the optimization scheduling plan and provide auxiliary decision support. The operator can make appropriate adjustments to the plan according to the actual situation. In the above description, the graphical human-machine interface acts as a bridge of communication between the user and the optimization system. It not only makes it easier to understand the optimization scheduling plan, but also facilitates real-time adjustment and optimization decisions. This design significantly improves the availability and flexibility of the system and strengthens the actual application effect of the optimization scheduling decision.

[0040] Working principle: The working principle of the present invention is based on the effective parallel operation of the condensing unit and the high back pressure heating unit. Through precise load forecasting, unit modeling, system optimization and coordinated control, the operation of the cogeneration system can achieve unprecedented efficiency and economy. The preliminary work is to perform time series analysis through the load forecasting module. This module combines meteorological conditions and user energy consumption patterns to accurately predict the heat load demand in the future and formulate corresponding heating plans. The unit modeling module constructs mathematical models of the condensing unit and the high back pressure boiler, describes their respective power generation and heating behaviors in detail, and includes their performance parameters under different working conditions. The heat network modeling module adopts the heat network partitioning method and loop solving algorithm to create a distributed parameter model of the thermal network, which provides a basis for predicting key operating parameters such as flow, pressure and temperature. In addition, the optimization module is the core of the entire method. It sets multi-objective optimization problems based on the aforementioned models and load forecasts, and uses efficient algorithms such as genetic algorithms and particle swarm algorithms to solve them in order to find the optimal output combination and operating parameters. Finally, the coordination control module is responsible for converting the optimization results into specific operating instructions to ensure that the system can still operate smoothly and efficiently under dynamically changing conditions.

[0041] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0042] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing the scheduling of parallel heating operation of an extraction condensing unit and a high back pressure heating unit, characterized in that: The following steps are involved: The load forecasting module uses forecasting algorithms such as time series analysis, combined with influencing factors such as meteorology and user energy consumption patterns, to make rolling forecasts of hourly and daily heat load curves in the future; The unit modeling module establishes models reflecting the power generation and heating performance of the extraction condensing unit and the heating performance of the high back-pressure boiler, taking into account the influencing factors such as their structural parameters and operating parameters; The heat network modeling module uses the heat network partitioning method and loop solving algorithm to establish a distributed parameter model of the heat pipe network of the entire heating system, and combines the operating condition models of various equipment; The optimization module takes the above load forecast as the constraint target, and based on the above unit model and heat network model, establishes a multi-objective optimization problem, and obtains the optimal output of the extraction condensing unit and the high back pressure boiler and the optimal operating parameters of other equipment in the system through the optimization algorithm; The coordination control module coordinates the calculation results of the optimization module, formulates the unit start and stop strategy, parameter control curve and pipeline network control strategy, and realizes the smooth and efficient execution of the optimized scheduling plan.

2. The method for optimizing the parallel operation of the condensing unit and the high back pressure heating unit according to claim 1 is characterized in that: The load forecasting module adopts a grey forecasting model, a neural network or other time series forecasting algorithms, combined with influencing factors such as meteorological data and user energy consumption patterns, to perform rolling forecasts on hourly and daily heat load curves in the future.

3. The method for optimizing the parallel operation of the condensing unit and the high back pressure heating unit according to claim 2 is characterized in that: The heat network modeling module establishes a basic control equation group including conservation of mass, conservation of momentum, and conservation of energy, and uses algorithms such as loop solving method to solve the distributed parameter model of the heating system to predict operating parameters such as flow, pressure and temperature.

4. The method for optimizing the parallel operation of heat supply by the extraction condensing unit and the high back pressure heating unit according to claim 3 is characterized in that: The optimization module includes a multi-objective optimization process, which uses genetic algorithms, particle swarm algorithms or other suitable mathematical algorithms to establish objective functions for factors such as power generation, heat supply, fuel cost, emission cost, etc., and solves thermal load balance constraints, unit operating parameter constraints, pipeline network operating parameter constraints and other related constraints.

5. The method for optimizing the parallel operation of heat supply by the extraction condensing unit and the high back pressure heating unit according to claim 4, characterized in that: The coordination control module is responsible for converting the calculation results of the optimization module into feasible operating parameters and operating curves, ensuring that the startup, shutdown and operation switching of the unit and pipeline network are completed smoothly and stably according to the optimization results, while taking into account the dynamic response time of each component of the system to ensure the balance and safety of the scheduling process.

6. The method for optimizing the scheduling of parallel heating operation of the extraction condensing unit and the high back pressure heating unit according to claim 5, characterized in that: The optimization module establishes a multi-objective optimization problem, and the objective function is to minimize the weighted superposition of factors such as power generation, heat supply, fuel cost, operating cost, and emission cost; The constraints include heat load balance constraints, unit operation parameter constraints, pipe network operation parameter constraints, etc. The solution is obtained by using intelligent optimization algorithms such as genetic algorithm and particle swarm algorithm.

7. The method for optimizing the scheduling of parallel heating operation of an extraction condensing unit and a high back pressure heating unit according to claim 6, characterized in that: During the optimization process, the optimization objective function may consider real-time market price signals as weight factors, and dynamically adjust the proportions of operating costs and emission costs to adapt to market and environmental changes.

8. The method for optimizing the scheduling of parallel heating operation of an extraction condensing unit and a high back pressure heating unit according to claim 7, characterized in that: The method further includes a real-time monitoring module for collecting real-time operating data of the condensing unit, high back pressure boiler and thermal network, and providing real-time feedback data for load prediction, optimization calculation and other modules.

9. The method for optimizing the scheduling of parallel heating operation of the condensing unit and the high back pressure heating unit according to claim 8, characterized in that: The objective function of the multi-objective optimization problem considers not only the power and heat output of the system, but also the environmental indicators of the system operation, such as carbon dioxide emissions and emissions of other pollutants, to achieve an environmentally friendly operation strategy.

10. The method for optimizing the scheduling of parallel heating operation of an extraction condensing unit and a high back pressure heating unit according to claim 9, characterized in that: The graphical human-machine interface is used to display the optimized scheduling plan and provide auxiliary decision support, and the operator can make appropriate adjustments to the plan according to actual conditions.

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