CN114110716B - Operation energy efficiency monitoring control system and method of thermoelectric unit, computer equipment and storage medium - Google Patents
Operation energy efficiency monitoring control system and method of thermoelectric unit, computer equipment and storage medium Download PDFInfo
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Abstract
The invention belongs to the technical field of intelligent monitoring of thermoelectric units, and discloses an operation energy efficiency monitoring control system of a thermoelectric unit, which comprises a heating power pipe network, a big data management cloud platform, a data server, a primary thermoelectric management platform, a secondary thermoelectric management platform and a plurality of thermoelectric plants; the big data management cloud platform is used for acquiring energy efficiency data of the thermal power plant, the heating power pipe network and the heat and power supply area and analyzing and processing the acquired energy efficiency data; the data server is connected with the big data management cloud platform and is used for storing the data analyzed and processed by the big data management cloud platform; the first-level thermoelectric management platform is used for acquiring data analyzed and processed by the big data management cloud platform; the second-level thermoelectric management platform is connected with the thermal power plant through the first-level thermoelectric management platform to distribute and manage the generated energy and the heat of the thermal power plant. The invention also discloses a monitoring control method, which can realize intelligent monitoring and control of the operation energy efficiency of the thermoelectric unit, thereby improving the energy efficiency level of the thermoelectric unit.
Description
Technical Field
The invention belongs to the technical field of intelligent monitoring of thermoelectric units, and particularly relates to an operation energy efficiency monitoring control system and method of a thermoelectric unit, computer equipment and a storage medium.
Background
The thermal power plant is a thermal power plant which utilizes the extraction steam of the unit to supply heat while generating electricity. In the northern urban heating season of China, heat and electricity are used as the heat and electricity co-production operation mode commonly adopted by a thermal power plant.
Due to the rapid development of urban construction, the urban scale is obviously increased, the ultra-large cities are more and more, the urban heating requirements are changed every year, and the problem that how to acquire the accurate heat load requirements of the cities so as to avoid energy waste is needed to be solved.
At the same time, the heat loss effect of the thermodynamic line is also becoming increasingly non-negligible due to the greatly increased transport distance. The multiple thermal power plants in the city generally have different generated energy and generated heat, and the distances between the different thermal power plants and the heat supply sites are different. Therefore, how to distribute the heat and the power generation of each thermal power plant and obtain the optimal heat supply path, thereby improving the energy efficiency level of the whole thermal power plant in the city, and becoming a problem to be solved urgently.
Moreover, the thermoelectric unit in the thermoelectric power plant generally has a plurality of heat supply working modes, and the heat generation power, the power generation power and the energy efficiency coefficient of different modes are different, so that the output of the thermoelectric power plant can meet the social requirement and can obtain the maximum energy efficiency, and the thermoelectric power plant is also a problem to be solved.
In addition, the heating effect of the northern severe cold city is related to the production and living chiffons, once the thermoelectric unit fails, the influence is quite deep, and once the heat supply is stopped, the production energy efficiency of the whole area is also greatly reduced, so that the risk resistance of the thermoelectric unit to the failure is improved, the whole heat supply and power supply energy efficiency is not influenced, and the problem in the monitoring process of the thermoelectric unit is also solved.
Disclosure of Invention
The invention aims to provide an operation energy efficiency monitoring control system and method of a thermoelectric unit, which solve the problems that the heat and the electricity generation amount of each thermoelectric plant cannot be distributed, the optimal heat supply path cannot be obtained and the energy efficiency level of the whole thermoelectric plant in a city cannot be improved.
The invention is realized by the following technical scheme:
a method for monitoring and controlling the operation energy efficiency of a thermoelectric unit comprises the following steps:
step S1, updating and correcting the historical heat load demand of the city, and calculating the whole heat load demand of the current city;
s2, distributing heat loads of the thermal power plants contained in the current city according to the whole heat load demand of the current city calculated in the step S1, and calculating the heat generation quantity of each thermal power plant;
step S3, collecting and counting historical data of each thermoelectric unit, constructing an energy efficiency index model of each thermoelectric unit according to the heat generation amount of the current thermoelectric unit calculated in the step S2, searching an optimal working mode of an energy efficiency index, and planning the working mode and the working time of each thermoelectric unit;
s4, acquiring the operation parameters of each thermoelectric unit according to the working mode and the working time length of each thermoelectric unit obtained in the step S3; according to the operation parameters of the thermoelectric unit and the characteristics of the heat storage tank, an energy efficiency fusion model of the thermoelectric unit is established; collecting the extraction power, the generation power, the storage capacity of the heat storage tank and the heat release capacity of the heat storage tank, inputting the energy efficiency fusion model of the thermoelectric unit, and calculating to obtain the fuel quantity, the opening of the steam door and the opening of the steam extraction valve, and the opening of the inlet valve and the opening of the outlet valve of the heat storage tank.
Further, the calculation method in step S1 is as follows:
Ht＝H0+∑Ni；
wherein H0 is the historical heat load demand of the city, ni is the heat load demand correction value of the ith newly added heat supply area, and Ht is the whole heat load demand of the current city;
ni is calculated according to the following formula:
Ni＝Ni0*α s *|Tin-Tout|*β m * Sa; wherein Ni0 is the initial value of the heat load of the current area, alpha s For seasonal factors, tout is regional outdoor temperature, tin is the indoor temperature weighted average, β m For the user satisfaction coefficient, sa is the occupancy rate.
Further, in step S1, the historical heat load demand of the city is obtained according to the historical record data of the city, where the historical data includes heat supply amount, air temperature data, heat supply point distribution, thermal power plant distribution, heat distribution line data and heat supply period, and the heat supply period includes an initial cold period, a severe cold period and an end cold period.
Further, the step S2 specifically includes:
on the premise of meeting the current urban integral heat load demand, optimizing with the aim of highest integral coal-fired energy efficiency, comprehensively considering the maximum heat generation amount and energy efficiency coefficients of different thermal power plants, the conveying distance between the thermal power plants and a heat supply area, and the heat loss coefficients of heating power pipelines of different conveying routes, and distributing and optimizing the heat generation amount of each thermal power plant, thereby calculating the heat generation amount Hi of each thermal power plant according to the following formula;
Maxη＝(∑Hi+∑Hei)/(∑Qri)；
∑Hi+∑Gi≥Ht；
wherein η is an energy efficiency coefficient of the whole thermal power plant, hei is an electric power generation amount of the ith thermal power plant, qri is a fuel consumption amount of the ith thermal power plant;
gi is the i-th thermodynamic line thermal load correction value calculated according to the following equation:
Gi＝Gi0*α s *|Tin-Tout|*Lg*β s the method comprises the steps of carrying out a first treatment on the surface of the Wherein Gi0 is the initial value of the thermal load of the heating power pipeline, alpha s Is the seasonal coefficient, lg is the pipe network conveying distance, beta s For the delivery loss factor, tout is the regional outdoor temperature and Tin is the indoor temperature weighted average.
Further, in step S2, the initial thermal load Gi0 of the thermal pipeline is the initial thermal load value obtained by the big data management cloud platform through inductive analysis according to the historical data; and predicting the initial value Gi0 of the heat load of the heating power pipeline by constructing a deep learning prediction model.
Further, the step S3 specifically includes the following steps:
step S31, collecting and counting historical data of each thermoelectric unit, and recording average power Pi and working time Ti of each thermoelectric unit in n thermoelectric units in various heating modes respectively;
s32, constructing an energy efficiency index model of each thermoelectric unit;
e=a+ab; wherein E is an energy efficiency index parameter, A is the efficiency of the thermoelectric unit, B is a risk index parameter, and a is a risk correction coefficient;
B＝c*Q u +d.Hi, where c is the failure rate of the thermoelectric unit, Q u D is a heat supply correction coefficient, and Hi is the heat generation amount of the current thermal power plant calculated according to the step S2;
step S33, obtaining a thermoelectric unit mode combination with optimal energy efficiency indexes;
on the premise that the heat generation amount or the electricity generation amount of the thermal power plant meets the heat supply demand amount of the current thermal power plant, taking the power weighted average value of the energy efficiency indexes of n thermoelectric units in the thermal power plant as the maximum optimization target, and planning the working mode and the working time length of each thermoelectric unit;
that is, max et= Σ (e×qj)/Σqj;
∑Qj*Tj≥H
wherein Et is an energy efficiency index comprehensive parameter, H is a heat supply demand of the current thermal power plant, E is an energy efficiency index parameter of each thermoelectric unit calculated according to step S32, qj is an average heat generation power of each thermoelectric unit, and Tj is an operation duration of each thermoelectric unit.
Further, in step S3, the working modes include a dead steam heating mode, a dead steam pumping heating mode, an idle heating mode, a high back pressure heating mode for steam pumping, a high back pressure heating mode and a high side heating mode;
in step S4, the parameters of the operation energy efficiency change include the fuel amount, the steam door opening and the steam extraction valve opening of the thermoelectric unit, and the inlet valve opening and the outlet valve opening of the heat storage tank.
The invention also discloses an operation energy efficiency monitoring control system of the thermoelectric unit for realizing the operation energy efficiency monitoring control method, which comprises a heating power pipe network, a big data management cloud platform, a data server, a primary thermoelectric management platform, a secondary thermoelectric management platform and a plurality of thermoelectric plants;
each thermal power plant comprises a plurality of thermoelectric units, a heat storage tank and a thermoelectric unit energy efficiency control system;
the big data management cloud platform is used for acquiring energy efficiency data of the thermal power plant, the heating power pipe network and the heat and power supply area, analyzing and processing the acquired energy efficiency data to obtain urban historical heat load demands;
the data server is connected with the big data management cloud platform and is used for storing the urban historical heat load demand;
the primary thermoelectric management platform is in communication connection with the big data management cloud platform and is used for acquiring the historical heat load demand of the city, updating and correcting the historical heat load demand of the city and calculating the whole heat load demand of the current city; according to the whole heat load demand of the current city, carrying out heat load distribution on the heat power plants contained in the current city, and calculating the heat generation quantity of each heat power plant;
the second-level thermoelectric management platform is connected with the first-level thermoelectric management platform, and the first-level thermoelectric management platform is in communication connection with the thermal power plant and is used for distributing and managing the generated energy and the generated heat of the thermal power plant;
the thermoelectric unit energy efficiency control system is used for analyzing and controlling a plurality of thermoelectric units in any thermoelectric power plant, searching for a working mode with the best energy efficiency index, and planning out the working mode and the working time length of each thermoelectric unit; and the fuel quantity, the steam door opening and the steam extraction valve opening of the thermoelectric unit, and the inlet valve opening and the outlet valve opening of the heat storage tank are calculated through the thermoelectric unit energy efficiency fusion model according to the steam extraction power, the power generation power, the heat storage amount of the heat storage tank and the heat release amount of the heat storage tank.
The invention also discloses a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the operation energy efficiency monitoring control method of the thermoelectric unit when executing the computer program.
The invention also discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the operation energy efficiency monitoring control method of the thermoelectric unit when being executed by a processor.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention discloses an operation energy efficiency monitoring control method of a thermoelectric unit, which can correct the historical heat load demand of a city by updating a correction algorithm so as to accurately predict the whole heat load demand of the current city, thereby avoiding energy waste caused by excessive heat productivity of power generation; the invention can optimize with the highest overall coal-fired energy efficiency as the target to obtain reasonable heat generation quantity of each thermal power plant, and distributes the output quantity of each thermal power plant from the factors of the overall thermal power plant distribution, the distribution of power supply and heat supply areas and the transmission distance of the thermal power pipeline of the city, so as to meet the current overall thermal load requirement of the city as a constraint condition, and optimize with the highest overall coal-fired energy efficiency as the target to obtain reasonable heat generation quantity of each thermal power plant; the invention can distribute reasonable mode combination for a plurality of units in the thermal power plant, so that the thermoelectric unit obtains optimal energy efficiency coefficient, the thermal power plant is internally provided with a plurality of units generally, each unit has a plurality of heat supply working modes, the heat supply power, the power generation power and the unit efficiency of different modes are different, and the invention can lead the thermoelectric unit to obtain optimal energy efficiency coefficient; in order to improve the risk resistance, the flow rate of the heat storage tank needs to be analyzed and controlled, the characteristics of the thermoelectric unit and the heat storage tank are combined, and an energy efficiency combination model of the thermoelectric unit is built, so that the control quantity of the thermoelectric unit can be obtained through the model, the heat storage quantity can be regulated in a reasonable range, the fault risk resistance is improved, and the energy efficiency drop caused by the thermoelectric unit fault is avoided.
Further, various parameters such as historical data, seasonal variation, indoor and outdoor temperatures, user satisfaction, occupancy rate, heat dissipation influence of building materials and thickness of a heating area and the like are fully considered in correction, and the corrected data can accurately represent the whole heat load requirement of the current city.
Further, as the city scale is enlarged, the distance from the power supply and heat supply area to the main city area is also greatly increased, so that the transmission heat loss of the heating power pipeline is considered when calculating the heat generation amount of each thermal power plant.
Furthermore, the energy efficiency index model is improved, and the energy efficiency index model is obtained by optimizing the energy efficiency index according to the risk resistance of the thermoelectric unit in consideration of the fact that the northern city heating effect is related to the production and living chiffons and the unit is quite deeply influenced once the unit fails.
Drawings
FIG. 1 is a schematic block diagram of an operating energy efficiency monitoring control system for a thermoelectric unit in accordance with the present invention;
FIG. 2 is a schematic structural view of a thermal power plant;
FIG. 3 is a flow chart of a method for monitoring and controlling the operating energy efficiency of a thermoelectric unit according to the present invention.
Detailed Description
The invention will now be described in further detail with reference to specific examples, which are intended to illustrate, but not to limit, the invention.
As shown in fig. 1-2, the present invention discloses an operation energy efficiency monitoring control system of a thermoelectric unit, comprising: the system comprises a heating power pipe network, a big data management cloud platform, terminal equipment, a Web server, a data server, a primary thermoelectric management platform, a secondary thermoelectric management platform and a plurality of thermal power plants; each thermal power plant comprises a plurality of thermoelectric units, a heat storage tank and a thermoelectric unit energy efficiency control system;
the big data management cloud platform is used for acquiring energy efficiency data of the thermal power plant, the heating power pipe network and the heat and power supply area and analyzing and processing the acquired energy efficiency data;
the data server is used for storing the data processed by the big data management cloud platform analysis;
the terminal equipment is used for enabling operators to log in the Web server so as to view real-time data;
the second-level thermoelectric management platform is in communication connection with the big data management cloud platform through the first-level thermoelectric management platform, and the data analyzed and processed by the big data management cloud platform are obtained; the first-stage thermoelectric management platform is connected with the thermal power plant and is used for distributing and managing the generated energy and the generated heat of the thermal power plant.
The thermoelectric unit energy efficiency control system comprises: a thermoelectric unit control part, a thermoelectric unit execution part, a thermoelectric unit monitoring part, a heat storage tank control part, a heat storage tank execution part and a heat storage tank monitoring part. The thermoelectric unit executing part is used for adjusting the fuel quantity, the steam door opening and the steam extraction valve opening of the thermoelectric unit; the heat storage tank executing part is used for adjusting the opening degree of an inlet valve and the opening degree of an outlet valve of the heat storage tank.
Specifically, the primary thermoelectric management platform is a municipal thermoelectric management platform, and the secondary thermoelectric management platform is an provincial thermoelectric management platform.
As shown in fig. 3, the invention further provides a method for monitoring and controlling the operation energy efficiency of the thermoelectric unit, which comprises the following steps:
step S1, updating and correcting the historical heat load demand H0 of the city, and calculating the integral heat load demand Ht of the current city according to the following formula;
Ht＝H0+∑Ni；
wherein Ni is the heat load demand correction value of the ith newly added heat supply area, and the calculation is performed according to the following formula:
Ni＝Ni0*α s *|Tin-Tout|*β m * Sa; wherein Ni0 is the initial value of the heat load of the current area, alpha s For seasonal factors, tout is regional outdoor temperature, tin is the indoor temperature weighted average, β m For the user satisfaction coefficient, sa is the occupancy rate.
Step S2, carrying out heat load distribution on the thermal power plants contained in the current city according to the overall heat load demand Ht of the current city calculated in the step S1, and calculating the heat generation quantity Hi of each thermal power plant;
on the premise of meeting the current urban integral heat load demand, optimizing with the aim of highest integral coal-fired energy efficiency, comprehensively considering the maximum heat generation amount and energy efficiency coefficients of different thermal power plants, the conveying distance between the thermal power plants and a heat supply area, and the heat loss coefficients of heating power pipelines of different conveying routes, and distributing and optimizing the heat generation amount of each thermal power plant, thereby calculating the heat generation amount Hi of each thermal power plant according to the following formula;
Maxη＝(∑Hi+∑Hei)/(∑Qri)；
∑Hi+∑Gi≥Ht；
wherein η is an energy efficiency coefficient of the whole thermal power plant, hei is an electric power generation amount of the ith thermal power plant, qri is a fuel consumption amount of the ith thermal power plant;
gi is the i-th thermodynamic line thermal load correction value calculated according to the following equation:
Gi＝Gi0*α s *|Tin-Tout|*Lg*β s the method comprises the steps of carrying out a first treatment on the surface of the Wherein Gi0 is the initial value of the thermal load of the heating power pipeline, alpha s Is the seasonal coefficient, lg is the pipe network conveying distance, beta s Is the transport loss coefficient.
Step S3, analyzing and controlling a plurality of thermoelectric units in any thermal power plant, and searching for a working mode with the best energy efficiency index, wherein the method specifically comprises the following steps:
step S31, collecting statistical historical data, and recording average power Pi and working time Ti of each thermoelectric unit in the n thermoelectric units in a plurality of heating modes respectively;
s32, constructing an energy efficiency index model of each unit;
E＝A+aB；
wherein E is an energy efficiency index parameter, A is the efficiency of the thermoelectric unit, B is a risk index parameter, and a is a risk correction coefficient;
B＝c*Q u +d.Hi, where c is the failure rate of the thermoelectric unit, Q u D is a heat supply correction coefficient, and Hi is the heat generation amount of the current thermal power plant calculated according to the step S2;
step S33, obtaining a thermoelectric unit mode combination with optimal energy efficiency indexes;
on the premise that the heat production capacity or the electricity generation capacity of a power plant meets the heat supply demand capacity of the current thermal power plant, taking the power weighted average value of the energy efficiency indexes of n thermoelectric units in the thermal power plant as the maximum optimization target, and planning the working mode and the working time length of each unit;
that is, max et= Σ (ej×qj)/Σqj;
∑Qj*Tj≥H
wherein Et is an energy efficiency index comprehensive parameter, ej is an energy efficiency index parameter calculated according to step S32, qj is an average heat generation power of each unit, and Tj is an operation duration.
Step S4, setting according to the mode combination and the working time calculated in the step S33, and obtaining the operation parameters of each unit, wherein the operation parameters are generally steam temperature, pressure, power generation efficiency and power supply coal consumption; the operation parameters of the thermoelectric unit and the characteristics of the heat storage tank are synthesized, and an energy efficiency fusion model of the thermoelectric unit is established; according to the extraction power, the generation power, the heat storage capacity and the heat release quantity of the heat storage tank, the fuel quantity, the steam door opening and the steam extraction valve opening of the thermoelectric unit energy efficiency fusion model are used for calculating the inlet valve opening and the outlet valve opening of the heat storage tank, and the operation of the thermoelectric unit is controlled according to the output of the model.
The initial value Ni0 of the heat load of the current area in the step S1 is the initial value of the heat load obtained by the big data management cloud platform through induction analysis according to historical data.
In step S2, the initial thermal load Gi0 of the thermal pipeline is the initial thermal load value obtained by the big data management cloud platform through inductive analysis according to the historical data.
Specifically, the value of Ni0 may be predicted by constructing a deep learning prediction model; specifically, historical data can be collected, the processed historical data is trained through data cleaning, standardization and normalization data processing, and then a deep learning prediction model is finally obtained through model testing and updating;
specifically, the history data includes: heating area, heating area number of houses, heating area building materials and thickness, heat load demand value; therefore, the deep learning prediction model not only can consider the influence of the heat supply area and the number of households on the heat load demand, but also can comprehensively consider the heat dissipation influence of the building materials and the thickness of the heat supply area, so that a more accurate initial value Ni0 of the heat load can be predicted.
Specifically, in step S1, the historical thermal load demand is obtained according to the historical record data of the city, where the historical data includes: heat supply quantity, temperature data, heat supply point distribution, thermal power plant distribution, heating power line data, heat supply period includes: the first cold stage, the severe cold stage and the last cold stage.
In step S32, the plurality of modes include: the system comprises a dead steam heating mode, a dead steam pumping heating mode, an idle heating mode, a high back pressure heating mode for steam pumping, a high back pressure heating mode and a high and low side heating mode.
The thermoelectric set energy efficiency fusion model in the step S4 is a neural network model and further comprises the steps of historical data collection, training, testing, updating and prediction.
The thermoelectric unit energy efficiency fusion model is a conventional model established according to the transfer function of the thermoelectric unit and the heat storage tank; the model inputs are: extraction power, generation power, storage capacity of a heat storage tank and heat release capacity of the heat storage tank; the model output is: the fuel quantity, the steam door opening and the steam extraction valve opening of the thermoelectric unit, and the inlet valve opening and the outlet valve opening of the heat storage tank.
And (4) predicting the heat release amount of the heat storage tank in the step (S4) according to the demand, or determining according to the instructions of the secondary thermoelectric management platform and/or the primary thermoelectric management platform.
The method for monitoring and controlling the operation energy efficiency of the thermoelectric unit can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The method for monitoring and controlling the operation energy efficiency of the thermoelectric unit can be stored in a computer readable storage medium if the method is realized in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals. The computer storage media may be any available media or data storage device that can be accessed by a computer, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), and semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NANDFLASH), solid State Disk (SSD)), etc.
In an exemplary embodiment, a computer device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of monitoring and controlling the operating energy efficiency of the thermoelectric unit when the computer program is executed. The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (7)
1. The method for monitoring and controlling the operation energy efficiency of the thermoelectric unit is characterized by comprising the following steps of:
step S1, updating and correcting the historical heat load demand of the city, and calculating the whole heat load demand of the current city;
the calculation method of the step S1 is as follows:
Ht＝H0+∑Ni；
wherein H0 is the historical heat load demand of the city, ni is the heat load demand correction value of the ith newly added heat supply area, and Ht is the whole heat load demand of the current city;
ni is calculated according to the following formula:
Ni＝Ni0*α s *|Tin-Tout|*β m * Sa; wherein Ni0 is the initial value of the heat load of the current area, alpha s For seasonal factors, tout is regional outdoor temperature, tin is the indoor temperature weighted average, β m As a user satisfaction coefficient, sa is an occupancy rate; s2, distributing heat loads of the thermal power plants contained in the current city according to the whole heat load demand of the current city calculated in the step S1, and calculating the heat generation quantity of each thermal power plant;
the step S2 specifically comprises the following steps:
on the premise of meeting the current urban integral heat load demand, optimizing with the aim of highest integral coal-fired energy efficiency, comprehensively considering the maximum heat generation amount and energy efficiency coefficients of different thermal power plants, the conveying distance between the thermal power plants and a heat supply area, and the heat loss coefficients of heating power pipelines of different conveying routes, and distributing and optimizing the heat generation amount of each thermal power plant, thereby calculating the heat generation amount Hi of each thermal power plant according to the following formula;
Maxη＝(∑Hi+∑Hei)/(∑Qri)；
∑Hi+∑Gi≥Ht；
wherein η is an energy efficiency coefficient of the whole thermal power plant, hei is an electric power generation amount of the ith thermal power plant, qri is a fuel consumption amount of the ith thermal power plant;
gi is the i-th thermodynamic line thermal load correction value calculated according to the following equation:
Gi＝Gi0*α s *|Tin-Tout|*Lg*β s the method comprises the steps of carrying out a first treatment on the surface of the Wherein Gi0 is the initial value of the thermal load of the heating power pipeline, alpha s Is the seasonal coefficient, lg is the pipe network conveying distance, beta s For the transport loss factor, tout is the regional chamberThe external temperature, tin, is the weighted average of the indoor temperature;
step S3, collecting and counting historical data of each thermoelectric unit, constructing an energy efficiency index model of each thermoelectric unit according to the heat generation amount of the current thermoelectric unit calculated in the step S2, searching an optimal working mode of an energy efficiency index, and planning the working mode and the working time of each thermoelectric unit;
the step S3 specifically comprises the following steps:
step S31, collecting and counting historical data of each thermoelectric unit, and recording average power Pi and working time Ti of each thermoelectric unit in n thermoelectric units in various heating modes respectively;
s32, constructing an energy efficiency index model of each thermoelectric unit;
e=a+ab; wherein E is an energy efficiency index parameter, A is the efficiency of the thermoelectric unit, B is a risk index parameter, and a is a risk correction coefficient;
B＝c*Q u +d.Hi, where c is the failure rate of the thermoelectric unit, Q u D is a heat supply correction coefficient, and Hi is the heat generation amount of the current thermal power plant calculated according to the step S2;
step S33, obtaining a thermoelectric unit mode combination with optimal energy efficiency indexes;
on the premise that the heat generation amount or the electricity generation amount of the thermal power plant meets the heat supply demand amount of the current thermal power plant, taking the power weighted average value of the energy efficiency indexes of n thermoelectric units in the thermal power plant as the maximum optimization target, and planning the working mode and the working time length of each thermoelectric unit;
that is, max et= Σ (e×qj)/Σqj;
∑Qj*Tj≥H；
wherein Et is an energy efficiency index comprehensive parameter, H is the heat supply demand of the current thermal power plant, E is the energy efficiency index parameter of each thermoelectric unit obtained by calculation in the step S32, qj is the average heat generation power of each thermoelectric unit, and Tj is the operation time of each thermoelectric unit; s4, acquiring the operation parameters of each thermoelectric unit according to the working mode and the working time length of each thermoelectric unit obtained in the step S3; according to the operation parameters of the thermoelectric unit and the characteristics of the heat storage tank, an energy efficiency fusion model of the thermoelectric unit is established; collecting the extraction power, the generation power, the storage capacity of the heat storage tank and the heat release capacity of the heat storage tank, inputting the energy efficiency fusion model of the thermoelectric unit, and calculating to obtain the fuel quantity, the opening of the steam door and the opening of the steam extraction valve, and the opening of the inlet valve and the opening of the outlet valve of the heat storage tank.
2. The method according to claim 1, wherein in step S1, the historical heat load demand of the city is obtained according to the historical record data of the city, the historical data includes heat supply amount, temperature data, heat supply point distribution, thermal power plant distribution, heat line data and heat supply period, and the heat supply period includes an initial cold period, a severe cold period and a final cold period.
3. The method for monitoring and controlling the operation energy efficiency of a thermoelectric unit according to claim 1, wherein in the step S2, the initial value Gi0 of the thermal load of the thermal pipeline is the initial value of the thermal load obtained by the big data management cloud platform through inductive analysis according to the historical data; and predicting the initial value Gi0 of the heat load of the heating power pipeline by constructing a deep learning prediction model.
4. The method according to claim 1, wherein in step S3, the operation modes include a dead steam heating mode, a dead steam pumping heating mode, an idle heating mode, a high back pressure heating mode for pumping steam, a high back pressure heating mode, and a high-low side heating mode;
in step S4, the parameters of the operation energy efficiency change include the fuel amount, the steam door opening and the steam extraction valve opening of the thermoelectric unit, and the inlet valve opening and the outlet valve opening of the heat storage tank.
5. The operation energy efficiency monitoring control system of a thermoelectric unit for realizing the operation energy efficiency monitoring control method according to any one of claims 1 to 4, which is characterized by comprising a heating power pipe network, a big data management cloud platform, a data server, a primary thermoelectric management platform, a secondary thermoelectric management platform and a plurality of thermoelectric power plants;
each thermal power plant comprises a plurality of thermoelectric units, a heat storage tank and a thermoelectric unit energy efficiency control system;
the big data management cloud platform is used for acquiring energy efficiency data of the thermal power plant, the heating power pipe network and the heat and power supply area, analyzing and processing the acquired energy efficiency data to obtain urban historical heat load demands;
the data server is connected with the big data management cloud platform and is used for storing the urban historical heat load demand;
the primary thermoelectric management platform is in communication connection with the big data management cloud platform and is used for acquiring the historical heat load demand of the city, updating and correcting the historical heat load demand of the city and calculating the whole heat load demand of the current city; according to the whole heat load demand of the current city, carrying out heat load distribution on the heat power plants contained in the current city, and calculating the heat generation quantity of each heat power plant;
the second-level thermoelectric management platform is connected with the first-level thermoelectric management platform, and the first-level thermoelectric management platform is in communication connection with the thermal power plant and is used for distributing and managing the generated energy and the generated heat of the thermal power plant;
the thermoelectric unit energy efficiency control system is used for analyzing and controlling a plurality of thermoelectric units in any thermoelectric power plant, searching for a working mode with the best energy efficiency index, and planning out the working mode and the working time length of each thermoelectric unit; and the fuel quantity, the steam door opening and the steam extraction valve opening of the thermoelectric unit, and the inlet valve opening and the outlet valve opening of the heat storage tank are calculated through the thermoelectric unit energy efficiency fusion model according to the steam extraction power, the power generation power, the heat storage amount of the heat storage tank and the heat release amount of the heat storage tank.
6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method for monitoring and controlling the operation energy efficiency of a thermoelectric unit according to any one of claims 1 to 4.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the method for monitoring and controlling the operation energy efficiency of a thermoelectric power unit according to any one of claims 1 to 4.
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