Efficient deep peak regulation decision-making system and method for coal-fired thermal power generating unit

Abstract

The invention discloses a coal-fired thermal power generating unit efficient deep peak regulation decision system and method, and relates to the technical field of energy, when a coal-fired unit participates in peak regulation or deep peak regulation operation, the peak regulation decision system sends out a target instruction, each subsystem participates in cooperative operation, and the target instruction is sent out to the coal-fired thermal power generating unit; the economic level of the unit under a certain peak regulation capability is comprehensively given for economic systems such as a fuel management system, an energy consumption online monitoring system, an auxiliary service management system and an equipment operation cost management system, then the reliability operation condition of the current peak regulation unit is given through a risk assessment system, and meanwhile, the management system is cooperatively and flexibly transformed. According to the method, the flexibility transformation potential and the peak regulation target of the peak regulation unit are judged, accurate judgment of the peak regulation capacity of the peak regulation unit is achieved through multi-dimensional systematization, a thermal power enterprise is guided to effectively participate in deep peak regulation, and the comprehensive operation capacity and level of the peak regulation unit under the current power market condition are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a high-efficiency deep peak regulation decision system and method for coal-fired thermal power units. Background Art

[0002] With the continuous advancement of the "dual carbon" goals, the rapid development of new energy power generation in recent years, and the influence of factors such as overcapacity of coal-fired power generation, low-load operation of coal-fired units will become the norm for a considerable period of time in the future. In order to adapt to market demand, various thermal power generation companies strive to adopt flexibility transformation and other means to deeply tap the peak-shaving potential of units and strive to expand the peak-shaving range of units. This puts higher requirements on the safety and stability of the long-term operation of power generation equipment of power generation companies. Summary of the invention

[0003] In order to solve the technical problems existing in the background technology, the present invention proposes an efficient deep peak-shaving decision system and method for coal-fired power units. Through the coordinated operation of each subsystem in the system, an in-depth and comprehensive assessment of the economic and safety issues faced by coal-fired power units participating in deep peak-shaving is achieved, and comprehensive real-time peak-shaving data is given through data calculation, ultimately achieving the optimal deep peak-shaving level of the unit, ensuring the safety and reliability of the unit when operating at low load under current power market conditions, while maintaining a high economic operation level of the peak-shaving unit.

[0004] The technical solution adopted by the present invention to solve the above problems is: a high-efficiency deep peak-shaving decision-making system for coal-fired thermal power units, including a fuel management system, an online energy consumption monitoring system, an auxiliary service management system, an equipment operation cost management system, a risk assessment system, a flexibility transformation management system and an information compensation system. The subsystems share data and coordinate operations to achieve the optimal judgment of the peak-shaving capacity of the peak-shaving unit and provide the optimal decision-making basis for the peak-shaving unit. The fuel management system is based on the production management system of the coal-fired thermal power unit, and mainly includes a plurality of fuel characteristic attribute sets composed of fuel parameters of different dimensions. The online energy consumption monitoring system is based on the energy-saving diagnosis management platform of the coal-fired thermal power unit, and gives the coal consumption level of the unit under different working conditions in real time. The auxiliary service management system is based on the auxiliary service database of the coal-fired thermal power unit, and gives the real-time data of the auxiliary service operation of the thermal power unit participating in the power market in real time. The equipment operation cost management system is based on the actual statistical data of the production of the coal-fired thermal power unit, including a set of comprehensive cost accounting data involved in the operation of the deep peak-shaving unit equipment. The risk assessment system makes a real-time risk assessment based on the big data of the peak-shaving unit, which is mainly used to assess the health risk level of the deep peak-shaving unit equipment operation. The flexibility transformation management system is based on multiple potential directions derived from data judgment, including the flexibility transformation potential direction determined according to the economic and safety levels of the peak-shaving units. The information compensation system is based on explicit and implicit statistical data existing in the operation of the peak-shaving units, including a collection of implicit and explicit influencing factors in the production and operation of thermal power units, and is used to track and compensate the deep peak-shaving decision system in real time.

[0005] When the coal-fired units participate in peak-shaving or deep peak-shaving operation, the peak-shaving decision system issues target instructions, and each subsystem participates in collaborative calculations respectively. The economic systems such as the fuel management system, online energy consumption monitoring system, auxiliary service management system, and equipment operating cost management system comprehensively give the economic level of the unit under a certain peak-shaving capacity. Then, the risk assessment system is used to give the reliability operation status of the current peak-shaving unit. At the same time, the flexibility transformation management system is coordinated to judge the flexibility transformation potential and peak-shaving targets of the peak-shaving units. The peak-shaving capacity of the peak-shaving units is accurately judged in a systematic and multi-dimensional manner, guiding thermal power companies on how to effectively participate in deep peak-shaving, and improving the comprehensive operating capabilities and levels of peak-shaving units under current power market conditions. This opens up new technical ideas and development directions for the effective carbon reduction of coal-fired thermal power units of power generation companies, and helps the country achieve its dual carbon goals.

[0006] Compared with the prior art, the present invention has the following advantages and effects:

[0007] 1. It can solve the problem of typical coal-fired power units efficiently participating in deep peak regulation according to the real-time operating status of the units under the dual-carbon background, and provide the optimal peak regulation capacity.

[0008] 2. It solves the problem of only focusing on individual obvious problems on the surface of the unit when the unit participates in deep peak regulation, rather than comprehensively and systematically considering the hidden problems involving various factors affecting safety and economy.

[0009] 3. The information compensation system can timely enrich and improve the peak-shaving unit database and achieve self-repair until the optimal peak-shaving decision is made.

[0010] 4. It has universal adaptability and is applicable to coal-fired power units of different types and capacities, and plays a positive role in guiding the long-term safe and reliable operation of the units at low loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a structural schematic diagram of the efficient deep peak regulation decision system for coal-fired thermal power units in the present invention.

[0012] Figure 2 It is a structural schematic diagram of the energy consumption online monitoring system in the present invention. DETAILED DESCRIPTION

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and by way of examples. The following examples are intended to explain the present invention but the present invention is not limited to the following examples.

[0014] Example.

[0015] See also Figure 1 to Figure 2 In this embodiment, a high-efficiency deep peak-shaving decision-making system for coal-fired thermal power units is used to give the optimal peak-shaving capacity of coal-fired thermal power units participating in peak-shaving under different operating conditions, so as to improve the peak-shaving capacity and level of the units. The system includes a fuel management system A, an online energy consumption monitoring system B, an auxiliary service management system C, an equipment operation cost management system D, a risk assessment system E, a flexibility transformation management system F and an information compensation system G.

[0016] First, based on the database of deep peak-shaving units participating in auxiliary services, the risk influencing factors of peak-shaving units’ operation reliability are analyzed and determined, and flexibility transformation is carried out in a targeted manner according to the different risk levels of the units. Then, through the coordinated calculation of the fuel management system A, the online energy consumption monitoring system B, and the equipment operation cost management system D, the economic efficiency level of the peak-shaving units in the current state is obtained, and the optimal peak-shaving decision is determined by comprehensively considering the economy and safety of the peak-shaving units. At the same time, the peak-shaving decision is corrected in real time through the information compensation system G.

[0017] In this embodiment, the fuel management system A is used to manage and optimize the various fuel parameter attributes of the peak load unit, including fuel type L, fuel weight M, fuel characteristic attribute R and optimal mixing method P, wherein the fuel characteristic attribute R mainly includes fuel moisture M ar 、Fuel ash A ar、Fuel volatile matter V daf 、Fuel calorific value Q net.ar , fuel grindability index HGI, fuel ash melting point T. The functions realized by the fuel management system A can be expressed as:

[0018] P oij =f(L i , R j )

[0019] Where: P oij The optimal fuel blending method under any peak load condition

[0020] L i (i=1, 2, ..., m) is the set of different fuel types under actual conditions

[0021] R j (j=1, 2, ..., n) is the set of different fuel characteristic attributes corresponding to different fuel categories

[0022] In this embodiment, the energy consumption online monitoring system B uses the energy-saving diagnosis and management platform of the thermal power unit to monitor the real-time operation data, boiler efficiency η gl , Steam turbine heat rate HR t , Unit power consumption rate Coal consumption for power supply of unit b gt Real-time testing and analysis of key energy consumption indicators such as energy consumption and energy efficiency.

[0023] Among them, the boiler efficiency is calculated by testing, analyzing and calculating the raw coal element analysis, industrial analysis and calorific value analyzer①, armored thermocouple②, flue gas analyzer③, thermometer and hygrometer④, empty box barometer⑤, ash and slag combustible real-time analyzer⑥ and other equipment. The heat consumption rate of the steam turbine is obtained by the relevant test instruments such as steam turbine pressure, temperature, flow rate, etc.⑦. The power consumption rate of the unit is obtained by the power test instrument⑧. Finally, the power supply coal consumption of the peak-shaving unit under the actual working conditions is obtained, and the calculation formula is as follows:

[0024]

[0025] η gl =100-(q2+q3+q4+q5+q6+q oth -q ex )

[0026]

[0027] in:

[0028] b gt ——Unit power supply coal consumption b ft ——Coal consumption of power generation unit ——Unit power consumption rate η gl ——Boiler efficiency

[0029] η gd Pipeline efficiency

[0030] HR t ——Turbine heat rate q2——Exhaust heat loss

[0031] q3——Heat loss from incomplete combustion of gas q4——Heat loss from incomplete combustion of solid q5——Heat loss from boiler heat dissipation q6——Physical heat loss from ash q oth ——Other heat losses q ex ——Percentage of external heat and low calorific value F thr ——Superheated steam flow F ffw ——Water flow

[0032] F crh ——Cold section reheat steam flow F shs ——Superheater desuperheating water volume F rhs ——Reheater desuperheating water volume H thr ——Superheated steam enthalpy H ffw ——Feed water enthalpy

[0033] H hrh ——Hot section reheat steam enthalpy H crh ——Cold section reheat steam enthalpy

[0034] H shs ——Enthalpy of superheated water

[0035] H rhs ——Reheat desuperheating water enthalpy

[0036] W GC ——Power of high voltage plant transformer

[0037] W LC ——Excitation transformer power

[0038] W CG ——Generator output power

[0039] In this embodiment, the auxiliary service management system C uses the auxiliary service database of the thermal power unit to monitor and calculate the compensation income M obtained by participating in the auxiliary service in real time. bc and trend judgment.

[0040] The compensation benefit can be expressed as:

[0041] M bc =f(X i , Y j )

[0042] Where: M bc The compensation income obtained by the generator set under any peak load condition

[0043] X i (i=1, 2, ..., m) is the peak load capacity of the generator set.

[0044] Y j (j=1, 2, ..., n) is the compensation value corresponding to any peak load regulation capacity of the generator set

[0045] In this embodiment, the equipment operation cost management system D is based on the fuel cost C rl 、Basic electricity price C dj , Equipment maintenance cost C wh Flexibility transformation cost C gz Other potential costs C qt The real-time operating cost C of the peak load unit is obtained sj , calculated as follows:

[0046] C sj =C mh +C wh +C gz +C qt

[0047] Where: C sj ——Real-time operating cost of any generator set

[0048] C mh ——Actual coal consumption cost of any generating unit, expressed as C mh =f(C rl , b gt , C dj )

[0049] In this embodiment, the risk assessment system E is used to assess and determine the safety risk level S of the deep peak load unit, including high risk S hr , Medium Risk mr and low risk S lr The level is determined by:

[0050] S=f(S hr , S mr , S lr )

[0051] Where: S——actual safety risk level of any peak load-shaving unit

[0052] S hr ——The risk level of the peak load generating units that are judged to have major safety risk factors

[0053] S mr ——The risk level of the peak load generating units that are judged to have only a relatively large safety risk factor

[0054] S lr——It is determined that there is only a peak load unit with no major safety risk factors at the risk level

[0055] In this embodiment, the flexibility transformation management system F is used to implement the flexibility transformation direction according to the real-time risk level of the peak-shaving unit.

[0056] In this embodiment, the information compensation system G is used to correct the peak-shaving decision in real time according to the implicit and explicit influencing factors existing in the operation of the peak-shaving units.

[0057] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0058] Although the present invention has been disclosed as above by way of embodiments, it is not intended to limit the protection scope of the present invention. Any changes and modifications made by any technician familiar with the technology without departing from the concept and scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A high-efficiency deep peak-shaving decision-making system for coal-fired thermal power units, characterized in that: It includes a fuel management system, an online energy consumption monitoring system, an auxiliary service management system, an equipment operation cost management system, a risk assessment system, a flexibility transformation management system and an information compensation system. The subsystems share data and collaborate to achieve the optimal judgment of the peak-shaving capacity of the peak-shaving unit and provide the optimal decision-making basis for the peak-shaving unit. When the coal-fired unit participates in peak-shaving or deep peak-shaving operation, the peak-shaving decision system issues a target instruction, and each subsystem participates in the collaborative calculation respectively. The fuel management system, the online energy consumption monitoring system, the auxiliary service management system and the equipment operation cost management system comprehensively give the economic level of the unit under a certain peak-shaving capacity, and then give the reliability operation status of the current peak-shaving unit through the risk assessment system. At the same time, the flexibility transformation management system is coordinated to judge the flexibility transformation potential and peak-shaving targets of the peak-shaving unit. The peak-shaving capacity of the peak-shaving unit is systematically and accurately judged in a multi-dimensional manner, guiding thermal power companies on how to effectively participate in deep peak-shaving, and improving the comprehensive operation capacity and level of peak-shaving units under current power market conditions. It opens up new technical ideas and development directions for the effective carbon reduction of coal-fired thermal power units of power generation companies, and helps achieve the dual carbon goals.

2. A decision method for the high-efficiency deep peak load regulation decision system for coal-fired thermal power units according to claim 1, Its characteristics are as follows: first, based on the database of deep peak-shaving units participating in auxiliary services, the risk influencing factors of peak-shaving units’ operation reliability are analyzed and determined, and flexibility transformation is carried out in a targeted manner according to the different risk levels of the units. Then, through the coordinated calculation of the fuel management system, the online energy consumption monitoring system, and the equipment operation cost management system, the economic level of the peak-shaving units’ operation in the current state is obtained, and the optimal peak-shaving decision is determined by comprehensively considering the economic and safety of the peak-shaving units. At the same time, the peak-shaving decision is corrected in real time through the information compensation system. The fuel management system manages and optimizes the various fuel parameter attributes of the peak load units, including fuel type L, fuel weight M, fuel property attribute R and optimal mixing method P, among which the fuel property attribute R includes fuel moisture M ar 、Fuel ash A ar 、Fuel volatile matter V daf 、Fuel calorific value Q net.ar , fuel grindability index HGI and fuel ash melting point T, the function realized by the fuel management system is expressed as: P oij =f(L i ,R j ) Where: P oij The optimal fuel blending method under any peak load condition L i (i=1, 2, ..., m) is the set of different fuel types under actual conditions R j (j=1, 2, ..., n) is the set of different fuel characteristic attributes corresponding to different fuel categories The energy consumption online monitoring system uses the energy-saving diagnosis and management platform of thermal power units to monitor real-time operation data, boiler efficiency η gl , Steam turbine heat rate HR t , Unit power consumption rate Coal consumption for power supply of unit b gt Real-time testing and analysis of key energy consumption indicators; Among them, the boiler efficiency is calculated by testing and analyzing the raw coal element analysis, industrial analysis and calorific value analyzer, armored thermocouple, flue gas analyzer, thermometer and hygrometer, empty box barometer, and ash combustible real-time analyzer; the turbine heat rate is obtained by the relevant test instruments such as turbine pressure, temperature, and flow; the unit power consumption rate is obtained by the power test instrument; finally, the power supply coal consumption of the peak load unit under the actual working conditions is obtained, and the calculation formula is as follows: η gl =100-(q2+q3+q4+q5+q6+q oth -q ex ) in: bgt——unit power supply coal consumption b ft ——Coal consumption of power generation unit ——Power consumption rate of the unit ηgl——Boiler efficiency ηgd——Pipeline efficiency HR t ——Heat rate of steam turbine q2——Exhaust heat loss q3——Heat loss due to incomplete combustion of gas q4——Heat loss from incomplete combustion of solids q5——Boiler heat loss q6——Physical heat loss of ash q oth ——Other heat losses q ex ——Percentage of external heat and low calorific value F thr ——Superheated steam flow F ffw ——Water flow F crh ——Cold section reheat steam flow F shs ——Superheater desuperheating water volume F rhs ——Reheater desuperheating water volume H thr ——Superheated steam enthalpy H ffw ——Feed water enthalpy H hrh ——Hot section reheat steam enthalpy H crh ——Cold section reheat steam enthalpy H shs ——Enthalpy of superheated water H rhs ——Reheat desuperheating water enthalpy W GC ——Power of high voltage plant transformer W LC ——Excitation transformer power W CG ——Generator output power The auxiliary service management system uses the auxiliary service database of thermal power units to monitor and calculate the compensation income M obtained from participating in auxiliary services in real time. bc and trend judgment; The compensation benefit is expressed as: M bc =f(X i ,Y j ) Where: M bc The compensation income obtained by the generator set under any peak load condition X i (i=1, 2, ..., m) is the peak load capacity of the generator set. Y j (j=1, 2, ..., n) is the compensation value corresponding to any peak load regulation capacity of the generator set Equipment operation cost management system based on fuel cost C rl 、Basic electricity price C dj , Equipment maintenance cost C wh Flexibility transformation cost C gz Other potential costs C qt The real-time operating cost C of the peak load unit is obtained sj , calculated as follows: C sj =C mh +C wh +C gz +C qt Where: C sj ——Real-time operating cost of any generator set C mh ——Actual coal consumption cost of any generating unit, expressed as C mh =f(C rl , b gt , C dj ) The risk assessment system evaluates and determines the safety risk level S of the deep peak load units, including high risk S hr , Medium Risk S mr and low risk S lr The level is determined by: S=f(S hr ,S mr ,S lr ) Where: S——actual safety risk level of any peak load-shaving unit S hr ——The risk level of the peak load generating units that are judged to have major safety risk factors S mr ——The risk level of the peak load generating units that are judged to have only a relatively large safety risk factor S lr ——It is determined that there is only a peak load unit with no major safety risk factors at the risk level The flexibility transformation management system implements the flexibility transformation direction according to the real-time risk level of the peak-shaving units; The information compensation system makes real-time corrections to the peak-shaving decisions based on the implicit and explicit influencing factors in the operation of the peak-shaving units.

Images