Thermal power plant industrial load deep peak regulation optimization control platform

Abstract

The invention relates to the technical field of power system automation, and discloses a thermal power plant industrial load deep peak regulation optimization control platform, which comprises a thermal power generating unit state evaluation module, a peak regulation prediction module and a debugging module, and is characterized in that the thermal power generating unit state evaluation module is connected with the peak regulation prediction module through a network; the peak regulation prediction module is connected with the debugging module through a network, firstly, the thermal power generating unit state evaluation module is used for calculating the abnormal comprehensive degree of the thermal power generating unit, whether the thermal power generating unit needs to be optimized or not is judged, and then the load of the unit is predicted and adjusted. By accurately, sequentially and orderly forming a closed loop to adjust the critical operation parameters such as the fuel quantity, the water supply quantity, the smoke exhaust temperature and the steam flow, it is ensured that the unit can maintain the stable dynamic balance state. When the unit is faced with load change, the optimization strategy remarkably accelerates the response speed of the unit, and the adjustment accuracy is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and particularly to an optimization control platform for deep peak shaving of industrial loads in thermal power plants. Background Art

[0002] A thermal power unit is a device that converts the thermal energy generated by the combustion of solid and liquid fuels such as coal, petroleum, and natural gas into kinetic energy to produce electric energy. A thermal power unit mainly consists of a boiler and its auxiliary equipment, a steam turbine and its auxiliary equipment, a generator and an exciter, a main transformer, etc. The basic production process of a thermal power unit is as follows: heating water to generate steam when burning fuel, converting the chemical energy of the fuel into thermal energy; then using the pressure of the steam to drive the steam turbine to rotate, converting the thermal energy into mechanical energy; finally, driving the generator to rotate by the steam turbine, converting the mechanical energy into electric energy. As a traditional power generation method, thermal power generation occupies an important position in China's power industry. With the progress of technology and the improvement of environmental protection requirements, the thermal power industry is also constantly developing. In recent years, China's installed thermal power capacity has continued to grow, and at the same time, it has been continuously promoting the optimization and upgrading of the industrial structure, shutting down a large number of small thermal power units with low energy efficiency and heavy pollution, and accelerating the replacement of domestic thermal power equipment.

[0003] With the rapid development of new energy, thermal power units need to have higher operation flexibility and peak shaving ability. Traditional thermal power units have problems of high energy consumption during operation, and at the same time, the operation conditions of thermal power units cannot be evaluated, resulting in an impact on the operation of the power grid. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an optimization control platform for deep peak shaving of industrial loads in thermal power plants, which is capable of evaluating the abnormal comprehensiveness of thermal power units, discovering potential faults in advance, avoiding high maintenance costs and shutdown losses caused by sudden faults, and ensuring the normal operation of the power grid; by precisely and sequentially forming a closed loop to adjust crucial operation parameters such as fuel quantity, water supply quantity, flue gas temperature, and steam flow, it ensures that the unit can maintain a stable dynamic balance state. When the unit faces load changes, this optimization strategy significantly speeds up its response speed and greatly improves the accuracy of regulation.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: an optimization control platform for deep peak shaving of industrial loads in thermal power plants, including a thermal power unit state evaluation module, a peak shaving prediction module, and a commissioning module. The thermal power unit state evaluation module is connected to the peak shaving prediction module through a network, and the peak shaving prediction module is connected to the commissioning module through a network;

[0008] The thermal power unit status evaluation module includes a thermal power unit status analysis unit, a thermal power unit status evaluation unit, and an optimization unit. The thermal power unit status analysis unit is connected to the thermal power unit status evaluation unit through a network. The thermal power unit status evaluation unit is connected to the optimization unit through a network. The thermal power unit status analysis unit calculates the abnormal value of the thermal power unit operation status, the influence value of the thermal power unit operation environment, and the comprehensive abnormality degree of the thermal power unit through the current operation data of the thermal power unit. The thermal power unit status evaluation unit evaluates the calculation results to determine whether optimization is required. In the case where optimization is required, an optimization signal is sent to the optimization unit, and the optimization unit sends an optimization signal to the relevant staff;

[0009] The peak shaving prediction module includes a data acquisition unit and a load prediction unit. The data acquisition unit is connected to the load prediction unit through a network. The data acquisition unit collects the boiler operation parameters and the steam turbine operation parameters, and the load prediction unit predicts the unit load based on the collected data;

[0010] The debugging module adjusts the power of the thermal power unit according to the unit load.

[0011] Preferably, the thermal power unit operation data includes operation status data and operation environment impact data. The operation status data includes temperature data, pressure data, number of faults, and response time. The operation environment impact data includes emissions and emission concentrations.

[0012] Preferably, the calculation formula for the abnormal value of the thermal power unit operation status is:

[0013]

[0014] In the above calculation formula, HDyy represents the abnormal value of the thermal power unit operation status, wdsj s represents the actual temperature data, wdsj b represents the standard temperature data, ylsj s represents the actual pressure data, ylsj b represents the standard pressure data, gzcs s represents the actual number of faults, gzcs b represents the standard number of faults, xysj s represents the actual response time, xysj b represents the standard response time;

[0015] represents the relative deviation between the actual temperature data and the standard temperature data, θ 1 is the weight, represents the relative deviation between the actual pressure data and the standard pressure data, θ2 is the weight, used to measure the gap between the actual number of faults and the standard number of faults, θ 3 is the weight, used to evaluate the difference between the actual response time and the standard response time of a thermal power unit, θ 4 is the weight.

[0016] Preferably, the calculation formula for the influence value of the operating environment of the thermal power unit is:

[0017]

[0018] In the above calculation formula, HJyx represents the influence value of the operating environment of the thermal power unit, pfl represents the emission amount, pfnd represents the emission concentration, α represents the weight, and hjbz represents the influence value of the environmental standard.

[0019] Preferably, the calculation formula for the abnormal comprehensiveness of the thermal power unit is:

[0020] ZHyx = Fjyy·HJyx

[0021] In the above calculation formula, ZHyx represents the abnormal comprehensiveness of the thermal power unit.

[0022] Preferably, the method for judging whether optimization is needed is:

[0023] When the value of the abnormal comprehensiveness of the thermal power unit is less than the standard threshold value of the abnormal comprehensiveness of the thermal power unit, it means that optimization is needed.

[0024] Preferably, the expression of the historical load data is: {LSfh1 1 , LSfh2 2 , LSfh3 3 , ···, LSfh n}, where LSfh 1 represents the first historical load data, LSfh n represents the last historical load data, and n represents that there are n historical load data in total.

[0025] Preferably, the expression of the boiler operation parameters is: {FSsj, GZqd, WDys, SDys}, where FSsj represents the furnace temperature, GZqd represents the pressure, WDys represents the flue gas temperature, and SDsj represents the steam flow rate.

[0026] Preferably, the calculation formula for the unit load is:

[0027]

[0028] In the above calculation formula, YDxq represents the electricity demand, represents the average value of historical load data, FSsj·GZqd·WDys·SDys represents the boiler operation parameters, Bzyd represents the maximum rated load of the boiler unit, and ρ represents the weight.

[0029] Preferably, when the unit load is higher than the rated load, the power of the thermal power unit is reduced

[0030] Compared with the prior art, the present invention provides an optimization control platform for deep peak shaving of industrial loads in thermal power plants, having the following beneficial effects:

[0031] 1. The present invention evaluates the abnormal value of the operating state of the thermal power unit, the influence value of the operating environment of the thermal power unit, and the comprehensive degree of abnormality of the thermal power unit, and then determines whether it is necessary to optimize the thermal power unit according to the calculation result, timely discovers and adjusts the situation where the thermal power unit operates under non-optimal conditions, thereby improving the power generation efficiency of the thermal power unit, helping to discover potential faults and performance degradation in advance, avoiding high maintenance costs and downtime losses caused by sudden faults, and reducing the unstable situation of the power grid caused by thermal power unit faults, ensuring the normal operation of the power grid.

[0032] 2. The present invention accurately adjusts crucial operating parameters such as fuel quantity, water supply quantity, flue gas discharge temperature, and steam flow in sequence and in an orderly manner to form a closed loop, ensuring that the unit can maintain a stable dynamic balance state. When the unit faces load changes, this optimization strategy significantly speeds up its response speed and greatly improves the accuracy of regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structural system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying 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 of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Please refer to Figure 1 , an optimization control platform for deep peak shaving of industrial loads in a thermal power plant, including a thermal power unit state evaluation module, a peak shaving prediction module, and a debugging module. The thermal power unit state evaluation module is connected to the peak shaving prediction module through a network, and the peak shaving prediction module is connected to the debugging module through a network;

[0036] The thermal power unit status evaluation module includes a thermal power unit status analysis unit, a thermal power unit status evaluation unit, and an optimization unit. The thermal power unit status analysis unit is connected to the thermal power unit status evaluation unit through a network. The thermal power unit status evaluation unit is connected to the optimization unit through a network. The thermal power unit status analysis unit calculates the abnormal value of the thermal power unit operation status, the influence value of the thermal power unit operation environment, and the comprehensive abnormality degree of the thermal power unit based on the current operation data of the thermal power unit. The thermal power unit status evaluation unit evaluates the calculation results to determine whether optimization is required. In the case where optimization is required, an optimization signal is sent to the optimization unit, and the optimization unit sends the optimization signal to the relevant staff;

[0037] By evaluating the abnormal value of the thermal power unit operation status, the influence value of the thermal power unit operation environment, and the comprehensive abnormality degree of the thermal power unit, and then judging whether the thermal power unit needs to be optimized according to the calculation results, it can timely detect and adjust the situation of the thermal power unit operating under non-optimal conditions, thereby improving the power generation efficiency of the thermal power unit, helping to detect potential faults and performance degradation in advance, avoiding high maintenance costs and downtime losses caused by sudden faults, and reducing the instability of the power grid caused by thermal power unit faults, ensuring the normal operation of the power grid;

[0038] The operation data of the thermal power unit includes operation status data and operation environment influence data. The operation status data includes temperature data, pressure data, the number of faults, and response time. The operation environment influence data includes emissions and emission concentrations;

[0039] The calculation formula for the abnormal value of the thermal power unit operation status is:

[0040]

[0041] In the above calculation formula, HDyy represents the abnormal value of the thermal power unit operation status, wdsj s represents the actual temperature data, wdsj b represents the standard temperature data, ylsj s represents the actual pressure data, ylsj b represents the standard pressure data, gzcs s represents the actual number of faults, gzcs b represents the standard number of faults, xysj s represents the actual response time, xysj b represents the standard response time;

[0042] represents the relative deviation between the actual temperature data and the standard temperature data. When the actual temperature data is equal to the standard temperature data, the result is 0, indicating no deviation; when the actual temperature data is greater than the standard temperature data, the result is a positive number, indicating exceeding expectations; when the actual temperature data is less than the standard temperature data, the result is a negative number, indicating not meeting expectations, θ 1 is the weight;

[0043] represents the relative deviation between the actual pressure data and the standard pressure data, used to evaluate the difference between the actual pressure data and the standard pressure data. When the actual pressure data is equal to the standard pressure data, the result is 0, indicating no deviation; when the actual pressure data is greater than the standard pressure data, the result is a positive number, indicating exceeding expectations; when the actual pressure data is less than the standard pressure data, the result is a negative number, indicating not meeting expectations, θ 2 is the weight;

[0044] is used to measure the gap between the actual number of faults occurred and the standard number of faults. When the actual number of faults is equal to the standard number of faults, the result is 0, indicating no faults; when the actual number of faults is more than the standard number of faults, the result is a positive number, indicating frequent faults; when the actual number of faults is less than the standard number of faults, the result is a negative number, indicating fewer faults, θ 3 is the weight;

[0045] is used to evaluate the difference between the actual response time and the standard response time of the thermal power unit. When the actual response time is equal to the standard response time, the result is 0, indicating no deviation; when the actual response time is longer than the standard response time, the result is a positive number, indicating slower response; when the actual response time is shorter than the standard response time, the result is a negative number, indicating faster response, θ 4 is the weight;

[0046] The calculation formula for the operation environment impact value of the thermal power unit is:

[0047]

[0048] In the above calculation formula, HJyx represents the operation environment impact value of the thermal power unit, pfl represents the emission amount, pfnd represents the emission concentration, α represents the weight, and hjbz represents the environmental standard impact value;

[0049] The calculation formula for the abnormal comprehensiveness of the thermal power unit is:

[0050] ZHyx = Fjyy·HJyx

[0051] In the above calculation formula, ZHyx represents the abnormal comprehensiveness of the thermal power unit;

[0052] When the value of the abnormal comprehensive degree of the thermal power unit is less than the standard threshold of the abnormal comprehensive degree of the thermal power unit, it means that optimization is required;

[0053] Calculate the abnormal value of the operation status of the thermal power unit through temperature data, pressure data, number of faults, and response time, and calculate the environmental impact value of the operation of the thermal power unit through emissions and emission concentrations. Then, combine the abnormal value of the operation status of the thermal power unit with the environmental impact value of the operation of the thermal power unit to calculate the abnormal comprehensive degree of the thermal power unit, which can timely detect and handle the abnormal status of the thermal power unit, reduce downtime, improve the operation efficiency of the power plant, reduce operation and maintenance costs, and can more comprehensively evaluate the operation status of the thermal power unit and improve the prediction accuracy of the future performance of the thermal power unit; the data acquisition unit collects boiler operation parameters and steam turbine operation parameters, and the load prediction unit performs unit load prediction based on the collected data;

[0054] The peak shaving prediction module includes a data acquisition unit and a load prediction unit. The data acquisition unit is connected to the load prediction unit through a network. The data acquisition unit collects boiler operation parameters and steam turbine operation parameters, and the load prediction unit performs unit load prediction based on the collected data;

[0055] Analyzing historical load data is crucial for predicting future electricity demand and is essential for the operation planning of the power system. It can help power companies ensure that there is sufficient power supply to meet demand at any given time and make predictive plans in advance;

[0056] The expression of historical load data is: {LSfh 1 、LSfh 2 、LSfh 3 、···、LSfh n}, where LSfh 1 represents the first historical load data, LSfh n represents the last historical load data, and n represents that there are n historical load data in total;

[0057] The expression of the boiler operation parameters is: {FSsj, GZqd, WDys, SDys}, where FSsj represents the furnace temperature, GZqd represents the pressure, WDys represents the flue gas temperature, and SDsj represents the steam flow.

[0058] The calculation formula for the unit load is:

[0059]

[0060] In the above calculation formula, YDxq represents the electricity demand, represents the average value of historical load data, FSsj·GZqd·WDys·SDys represents the boiler operation parameters, Bzyd represents the maximum rated load of the boiler unit, and ρ represents the weight.

[0061] The debugging module adjusts the power of the thermal power unit according to the unit load. When the unit load is higher than the rated load, the power of the thermal power unit is reduced. The present invention precisely adjusts crucial operation parameters such as fuel quantity, water supply quantity, flue gas temperature, and steam flow in sequence and orderly to form a closed loop, ensuring that the unit can maintain a stable dynamic balance state. When the unit faces load changes, this optimization strategy significantly speeds up its response speed and greatly improves the accuracy of regulation.

[0062] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A deep peak load optimization control platform for thermal power plants, characterized by: It includes a thermal power unit state assessment module, a peak load prediction module and a debugging module, wherein the thermal power unit state assessment module is connected to the peak load prediction module via a network, and the peak load prediction module is connected to the debugging module via a network; The thermal power unit state evaluation module includes a thermal power unit state analysis unit, a thermal power unit state evaluation unit and an optimization unit. The thermal power unit state analysis unit is connected to the thermal power unit state evaluation unit through a network. The thermal power unit state evaluation unit is connected to the optimization unit through a network. The thermal power unit state analysis unit calculates the abnormal value of the thermal power unit operation state, the impact value of the thermal power unit operation environment and the abnormal comprehensiveness of the thermal power unit through the current thermal power unit operation data. The thermal power unit state evaluation unit evaluates the calculation results to determine whether optimization is required. If optimization is required, an optimization signal is sent to the optimization unit. The optimization unit sends the optimization signal to relevant staff. The peak load prediction module includes a data acquisition unit and a load prediction unit. The data acquisition unit and the load prediction unit are connected through a network. The data acquisition unit collects boiler operating parameters and turbine operating parameters. The load prediction unit predicts the unit load according to the collected data. The debugging module adjusts the power of the thermal power unit according to the unit load.

2. According to claim 1, a thermal power plant industrial load deep peak load optimization control platform is characterized by: The thermal power unit operation data includes operation status data and operation environment impact data. The operation status data includes temperature data, pressure data, number of failures, and response time. The operation environment impact data includes emission volume and emission concentration.

3. A thermal power plant industrial load deep peak load optimization control platform according to claim 2, characterized in that: The calculation formula for the abnormal value of the operating state of the thermal power unit is: In the above calculation formula, HDyy represents the abnormal value of the operating status of the thermal power unit, wdsj s Represents actual temperature data, wdsj b Represents standard temperature data, ylsj s Represents actual pressure data, ylsj b Represents standard pressure data, gzcs s Represents the actual number of failures, gzcs b Represents the standard failure number, xsysj s Represents the actual response time, xysj b represents the standard response time; Represents the relative deviation between the actual temperature data and the standard temperature data, θ 1 is the weight, Represents the relative deviation between the actual pressure data and the standard pressure data, θ 2 is the weight, It is used to measure the difference between the actual number of failures and the standard number of failures, θ 3 is the weight, Used to evaluate the difference between the actual response time and the standard response time of thermal power units, θ 4 is the weight.

4. According to claim 3, a thermal power plant industrial load deep peak load optimization control platform is characterized by: The calculation formula for the operating environment impact value of the thermal power unit is: In the above calculation formula, HJyx represents the environmental impact value of the thermal power unit operation, pfl represents the emission amount, pfnd represents the emission concentration, α represents the weight, and hjbz represents the environmental standard impact value.

5. According to claim 4, a thermal power plant industrial load deep peak load optimization control platform is characterized by: The calculation formula of the abnormal comprehensive degree of the thermal power unit is: ZHyx=Fjyy·HJyx In the above calculation formula, ZHyx represents the comprehensive degree of abnormality of thermal power units.

6. A thermal power plant industrial load deep peak load optimization control platform according to claim 5, characterized in that: The method for determining whether optimization is needed is: When the value of the thermal power unit abnormality comprehensiveness is less than the thermal power unit abnormality comprehensiveness standard threshold value, it means that optimization is required.

7. A thermal power plant industrial load deep peak load optimization control platform according to claim 6, characterized in that: The expression of the historical load data is: {LSfh1, LSfh2, LSfh3, . . . , LSfh n }, where LSfh1 represents the first historical load data, LSfh n Represents the last historical load data, and n represents that there are n historical load data in total.

8. The deep peak load optimization control platform for thermal power plants according to claim 7 is characterized by: The expression of the boiler operating parameters is: {FSsj, GZqd, WDys, SDys}, wherein FSsj represents the furnace temperature, GZqd represents the pressure, WDys represents the exhaust gas temperature, and SDsj represents the steam flow rate.

9. The deep peak load optimization control platform for thermal power plants according to claim 8 is characterized by: The calculation formula of the unit load is: In the above calculation formula, YDxq represents the electricity demand. represents the average value of historical load data, FSsj·GZqd·WDys·SDys represents boiler operating parameters, Bzyd represents the maximum rated load of the boiler unit, and ρ represents the weight.

10. A thermal power plant industrial load deep peak load optimization control platform according to claim 9, characterized in that: When the load of the unit is higher than the rated load, the power of the thermal power unit is reduced.

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