Method for testing and adjusting electric load of cogeneration unit in heat supply period

Abstract

The invention discloses a cogeneration unit heat supply period electric load test and adjustment method comprising the following steps: collecting operation data of a cogeneration unit in a heat supply period, and constructing a digital twinborn model; simulating unit operation states under different electric loads in the digital twin model, and determining an optimal operation interval; formulating an electrical load adjustment strategy based on an electrical load test result in combination with a heat supply demand and a power grid dispatching instruction; monitoring the electrical load and the thermal load of the unit in real time, and performing dynamic adjustment according to an adjustment strategy; through the fault diagnosis function of the digital twinborn model, the operation state of the unit is monitored in real time, early warning is performed in advance, corresponding measures are taken, and the method is suitable for the 350MW supercritical cogeneration unit. The invention belongs to the field of thermoelectric unit monitoring, and particularly relates to an electric load testing and adjusting method for a cogeneration unit in a heat supply period.

Description

Technical Field

[0001] The present invention belongs to the field of monitoring of thermal power units, and specifically refers to a method for testing and adjusting the electric load during the heating period of a cogeneration unit. Background Art

[0002] When a cogeneration unit operates during the heating period, it needs to meet both the heating demand and ensure the stability of power output. Due to the coupling relationship between heating and power generation, traditional methods for adjusting thermal and electric loads often struggle to balance the requirements of both, resulting in low unit operating efficiency and even affecting the stability of the power grid. Most existing methods for optimizing thermal and electric loads are based on single-objective optimization, lacking comprehensive consideration of the dual demands of heating and power generation, and it is difficult to achieve the optimal operation of the unit during the heating period.

[0003] With the development of digital twin technology and intelligent control technology, the operation control of cogeneration units is gradually moving towards the direction of intelligence and refinement. However, there is still a lack of a solution in the existing technology that can effectively combine the heating and power generation demands and achieve dynamic adjustment of thermal and electric loads. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to improve the operating efficiency and economic benefits of the unit during the heating period.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows: The method for testing and adjusting the electric load during the heating period of a cogeneration unit proposed by the present invention includes the following steps:

[0006] Collect the operation data of the cogeneration unit during the heating period and construct a digital twin model;

[0007] Simulate the operating state of the unit under different electric loads in the digital twin model to determine the optimal operating range;

[0008] Based on the results of the electric load test, combined with the heating demand and the power grid dispatching instructions, formulate an electric load adjustment strategy;

[0009] Real-time monitor the electric load and thermal load of the unit and perform dynamic adjustment according to the adjustment strategy;

[0010] Through the fault diagnosis function of the digital twin model, real-time monitor the operating state of the unit, give early warnings and take corresponding measures.

[0011] Further, the construction of the digital twin model includes the following steps:

[0012] Collect the operation data of the unit, such as electric load, thermal load, boiler parameters, steam turbine parameters, and ambient temperature;

[0013] Based on the collected data, construct a digital twin model of the unit and simulate the operating state of the unit under different working conditions.

[0014] Furthermore, the electrical load test includes the following steps:

[0015] Simulate the operating state of the unit under different electrical loads in the digital twin model;

[0016] Record key parameters such as thermal load, boiler efficiency, and steam turbine efficiency under each electrical load;

[0017] Determine the optimal operating range of the unit under different electrical loads through simulation tests.

[0018] Furthermore, the formulation of the electrical load adjustment strategy includes the following steps:

[0019] Based on the results of the electrical load test, combined with the heating demand and the power grid dispatching instructions, formulate an electrical load adjustment strategy;

[0020] Adopt a predictive control algorithm to predict the electrical load demand in the future period of time and adjust the operating parameters of the unit in advance.

[0021] Furthermore, the real-time adjustment and optimization include the following steps:

[0022] Real-time monitor the electrical load and thermal load of the unit, and perform dynamic adjustment according to the preset adjustment strategy;

[0023] Through the real-time feedback of the digital twin model, optimize the electrical load distribution to ensure the efficient operation of the unit during the heating period.

[0024] Furthermore, the fault diagnosis and early warning include the following steps:

[0025] During the electrical load adjustment process, real-time monitor the operating state of the unit and identify potential fault risks;

[0026] Through the fault diagnosis function of the digital twin model, give early warnings and take corresponding measures to avoid abnormal operation of the unit.

[0027] Furthermore, the method is applicable to 350MW supercritical thermoelectric co-generation units.

[0028] Furthermore, the digital twin model also includes the following functions:

[0029] Real-time update the operating data of the unit to ensure the accuracy of the model;

[0030] Provide a historical data playback function to facilitate the analysis of the operating state of the unit.

[0031] Furthermore, the electrical load adjustment strategy also includes the following steps:

[0032] Dynamically adjust the electrical load output of the unit according to the real-time dispatching instructions of the power grid;

[0033] Through an optimization algorithm, ensure that the unit maximizes the power output efficiency while meeting the heating demand.

[0034] Furthermore, the fault diagnosis and early warning further includes the following steps:

[0035] Analyze the operation data of the unit through a machine learning algorithm to identify potential fault patterns;

[0036] Provide fault handling suggestions to help operators quickly respond to and handle faults.

[0037] The beneficial effects obtained by the present invention using the above method are as follows:

[0038] 1. The method for testing and adjusting the electrical load of the cogeneration unit during the heating period proposed in this solution realizes the dynamic optimization adjustment of the electrical load through a digital twin model and an intelligent control algorithm, improving the operation efficiency of the unit during the heating period.

[0039] 2. The method for testing and adjusting the electrical load of the cogeneration unit during the heating period proposed in this solution comprehensively considers the dual demands of heating and power generation, ensuring the efficient operation of the unit during the heating period.

[0040] 3. The method for testing and adjusting the electrical load of the cogeneration unit during the heating period proposed in this solution reduces the energy consumption of the unit and the operating cost by optimizing the electrical load distribution, and ensures the stability of the electrical output of the unit and improves the stability of the power grid by adjusting the electrical load in real time. Description of the Drawings

[0041] Figure 1 It is a schematic diagram of the overall method steps of the present invention.

[0042] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] As Figure 1As shown in the figure, the method for testing and adjusting the electric load during the heating period of the cogeneration unit proposed by the present invention includes the following steps: collecting the operation data of the cogeneration unit during the heating period and constructing a digital twin model; simulating the operation status of the unit under different electric loads in the digital twin model to determine the optimal operation range; formulating an electric load adjustment strategy based on the electric load test results, combined with the heating demand and the power grid dispatching instructions; real-time monitoring the electric load and heat load of the unit and dynamically adjusting according to the adjustment strategy; through the fault diagnosis function of the digital twin model, real-time monitoring the operation status of the unit, giving early warnings and taking corresponding measures. The method is applicable to 350MW supercritical cogeneration units.

[0045] The construction of the digital twin model includes the following steps: collecting operation data such as the electric load, heat load, boiler parameters, steam turbine parameters, and environmental temperature of the unit, and based on the collected data, constructing a digital twin model of the unit to simulate the operation status of the unit under different working conditions. The digital twin model also includes the following functions: real-time updating the operation data of the unit to ensure the accuracy of the model; providing a historical data playback function for facilitating the analysis of the operation status of the unit. The electric load adjustment strategy also includes the following steps: dynamically adjusting the electric load output of the unit according to the real-time dispatching instructions of the power grid; through an optimization algorithm, ensuring that the unit maximizes the power output efficiency while meeting the heating demand.

[0046] The electric load test includes the following steps: simulating the operation status of the unit under different electric loads in the digital twin model; recording key parameters such as the heat load, boiler efficiency, and steam turbine efficiency under each electric load; through simulation tests, determining the optimal operation range of the unit under different electric loads.

[0047] The formulation of the electric load adjustment strategy includes the following steps: formulating an electric load adjustment strategy based on the electric load test results, combined with the heating demand and the power grid dispatching instructions; adopting a predictive control algorithm to predict the electric load demand in a future period of time and adjusting the operation parameters of the unit in advance.

[0048] Real-time adjustment and optimization include the following steps: real-time monitoring the electric load and heat load of the unit and dynamically adjusting according to the preset adjustment strategy; through the real-time feedback of the digital twin model, optimizing the electric load distribution to ensure the efficient operation of the unit during the heating period.

[0049] Fault diagnosis and early warning include the following steps: during the electric load adjustment process, real-time monitoring the operation status of the unit to identify potential fault risks; through the fault diagnosis function of the digital twin model, giving early warnings and taking corresponding measures to avoid abnormal operation of the unit. Analyzing the operation data of the unit through machine learning algorithms to identify potential fault patterns; providing fault handling suggestions to help operators respond and handle faults quickly.

[0050] When specifically used, it includes the following content:

[0051] Data collection and modeling:

[0052] Collect the operation data of the cogeneration unit during the heating period, including electric load, heat load, boiler parameters, steam turbine parameters, ambient temperature, etc.;

[0053] Based on the collected data, build a digital twin model of the cogeneration unit to simulate the operation status of the unit under different working conditions.

[0054] Electric load test:

[0055] In the digital twin model, simulate the operation status of the unit under different electric loads, and record the key parameters such as heat load, boiler efficiency, steam turbine efficiency, etc. under each electric load;

[0056] Through simulation tests, determine the optimal operation range of the unit under different electric loads.

[0057] Formulation of electric load adjustment strategy:

[0058] Based on the results of the electric load test, combined with the heating demand and the power grid dispatching instructions, formulate an electric load adjustment strategy;

[0059] Adopt a predictive control algorithm to predict the electric load demand in the next period of time and adjust the operation parameters of the unit in advance.

[0060] Real-time adjustment and optimization:

[0061] During actual operation, monitor the electric load and heat load of the unit in real time, and make dynamic adjustments according to the preset adjustment strategy;

[0062] Through the real-time feedback of the digital twin model, optimize the electric load distribution to ensure the efficient operation of the unit during the heating period.

[0063] Fault diagnosis and early warning:

[0064] During the electric load adjustment process, monitor the operation status of the unit in real time and identify potential fault risks;

[0065] Through the fault diagnosis function of the digital twin model, give early warnings and take corresponding measures to avoid abnormal operation of the unit.

[0066] The specific steps include:

[0067] S1: Collect the operation data of the cogeneration unit during the heating period and build a digital twin model;

[0068] S2: Simulate the operation status under different electric loads for testing and determine the optimal range;

[0069] S3: Based on the electrical load test results, adjust the operating parameters of the unit in advance;

[0070] S4: Monitor the electrical load and the real-time feedback of the model in real time, and adjust the electrical load distribution;

[0071] S5: Monitor the operating status of the unit in real time and give early warnings through the digital twin model.

[0072] The above describes the present invention and its implementation manners. Such description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual steps are not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments to this technical solution without creative efforts without departing from the purpose of the present invention creation, they shall fall within the protection scope of the present invention.

Claims

1. A method for testing and adjusting the electric load of a cogeneration unit during the heating period, characterized in that, It includes the following steps: Collect the operation data of the cogeneration unit during the heating period and construct a digital twin model; Simulate the operation status of the unit under different electrical loads in the digital twin model to determine the optimal operation range; Based on the electrical load test results, combine the heating demand and the power grid dispatching instructions to formulate an electrical load adjustment strategy; Real-time monitor the electrical load and heat load of the unit and perform dynamic adjustment according to the adjustment strategy; Through the fault diagnosis function of the digital twin model, real-time monitor the operation status of the unit, give early warnings and take corresponding measures.

2. The method for testing and adjusting the electric load during the heating period of the cogeneration unit according to claim 1, wherein: The construction of the digital twin model includes the following steps: Collect the operation data such as the electrical load, heat load, boiler parameters, steam turbine parameters, and ambient temperature of the unit; Based on the collected data, construct a digital twin model of the unit to simulate the operation status of the unit under different working conditions.

3. The method for testing and adjusting the electric load during the heating period of the combined heat and power unit according to claim 1, wherein: The electrical load test includes the following steps: Simulate the operation status of the unit under different electrical loads in the digital twin model; Record the key parameters such as heat load, boiler efficiency, and steam turbine efficiency under each electrical load; Through simulation tests, determine the optimal operation range of the unit under different electrical loads.

4. The method for testing and adjusting the electric load during the heating period of the combined heat and power unit according to claim 1, wherein: The formulation of the electrical load adjustment strategy includes the following steps: Based on the electrical load test results, combine the heating demand and the power grid dispatching instructions to formulate an electrical load adjustment strategy; Adopt a predictive control algorithm to predict the electrical load demand in the next period of time and adjust the operation parameters of the unit in advance.

5. The method for testing and adjusting the electric load during the heating period of the combined heat and power unit according to claim 1, characterized in that: The real-time adjustment and optimization include the following steps: Real-time monitor the electrical load and heat load of the unit and perform dynamic adjustment according to the preset adjustment strategy; Through the real-time feedback of the digital twin model, optimize the electrical load distribution to ensure the efficient operation of the unit during the heating period.

6. The method for testing and adjusting the electric load during the heating period of a combined heat and power unit according to claim 1, characterized in that: The fault diagnosis and early warning include the following steps: During the electrical load adjustment process, real-time monitor the operation status of the unit and identify potential fault risks; Through the fault diagnosis function of the digital twin model, give early warnings and take corresponding measures to avoid abnormal operation of the unit.

7. The method for testing and adjusting the electric load during the heating period of the combined heat and power unit according to any one of claims 1-6, characterized in that: The method is applicable to 350MW supercritical cogeneration units.

8. The method for testing and adjusting the electric load during the heating period of the combined heat and power unit according to claim 1, wherein: The digital twin model also includes the following functions: Real-time update the operation data of the unit to ensure the accuracy of the model; Provide a historical data playback function to facilitate the analysis of the operation status of the unit.

9. The method for testing and adjusting the electric load during the heating period of a combined heat and power unit according to claim 1, wherein: The electrical load adjustment strategy also includes the following steps: Dynamically adjust the electrical load output of the unit according to the real-time dispatching instructions of the power grid; Through an optimization algorithm, ensure that the unit maximizes the power output efficiency while meeting the heating demand.

10. The method for testing and adjusting the electric load during the heating period of the combined heat and power unit according to claim 1, wherein: The fault diagnosis and early warning also include the following steps: Through machine learning algorithms, analyze the operation data of the unit to identify potential fault modes; Provide fault handling suggestions to help operators quickly respond to and handle faults.

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