CN113655762A - Operation optimization control method and system for gas energy supply system - Google Patents
Operation optimization control method and system for gas energy supply system Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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Abstract
The invention discloses a method and a system for optimizing and controlling the operation of a gas energy supply system, which comprises the following steps: and B1, acquiring environmental information and actual operation data of the boiler and the accessory equipment to obtain the change rules of the cold and heat loads of the user along with seasons and moments, and establishing a cold and heat load prediction model according to the change rules. The invention has the advantages of intelligent optimal load distribution, higher load response rate and adjustment precision, optimal combination of economic benefit and energy comprehensive utilization efficiency, more stable boiler operation and obviously improved safety and reliability, can obtain high-precision load prediction information, form a corresponding optimization command, perform online optimization control on load dynamic and equipment operation, and solve the problem that the existing operation control method of the gas energy supply system cannot achieve the purpose of optimizing operation and has certain use limitation, thereby being capable of meeting the requirements of users.
Description
Technical Field
The invention relates to the technical field of gas energy supply, in particular to a method and a system for optimally controlling the operation of a gas energy supply system.
Background
The gas energy supply system passes through a gas boiler in the energy station, takes natural gas as main driving energy, takes steam as a technical basis, realizes the direct demand of a user on cold and hot loads, has the characteristics of short energy supply distance and small heat loss, and is an energy supply system with high efficiency and cleanness as characteristics.
The obvious characteristics of user energy use are that load change is large, especially heat load change is large, randomness is strong, in order to meet cold and heat load requirements, a system is often in a variable working condition running state, because of lack of a load forecasting link or low forecasting precision, the deviation of load optimization distribution is continuously increased, the response characteristics of a boiler and a control system thereof can generate hysteresis, overshoot and even oscillation, so that the system is difficult to meet the requirement of optimal boiler running efficiency, and economic benefit is difficult to guarantee.
Because the cold and heat loads at the user side are continuously changed, the collocation operation modes of all equipment of the energy station are more, more boilers are thrown in/withdrawn from the energy station or the equipment collocation is unreasonable, the energy consumption is increased, the power consumption is increased, and the economy is reduced, so how to reasonably optimize the operation modes of the multiple equipment is also an urgent problem to be solved by the energy supply system, but at present, the SIS system of the energy station is only responsible for real-time production data transmission and report forms and is not responsible for site decision optimization control and optimization management, so the operation optimization management of the site equipment cannot be guided; however, the existing DCS control system and the SIS production management system of the energy station operate independently, the production management system is not associated with the control system in real time on line, and the real time on line operation optimization function of the whole energy station is not provided.
Disclosure of Invention
The invention aims to provide a method and a system for optimizing and controlling the operation of a gas energy supply system, which have the advantages of intelligent optimal load distribution, higher load response rate and adjustment precision, optimal combination of economic benefit and energy comprehensive utilization efficiency, more stable boiler operation and obviously improved safety and reliability, can obtain high-precision load prediction information, form a corresponding optimization command, and perform online optimization control on load dynamics and equipment operation.
In order to achieve the purpose, the invention provides the following technical scheme: an operation optimization control method for a gas energy supply system comprises the following steps:
b1, collecting environmental information and actual operation data of the boiler and the accessory equipment to obtain the change rules of the cold and heat loads of the user along with seasons and moments, and establishing a cold and heat load prediction model according to the change rules;
b2, optimizing a cold and heat load prediction model on line by introducing real-time check factors and actual operation data of each device, and predicting dynamic requirements of cold and heat loads time by time;
b3, on the premise of meeting the optimal energy supply efficiency of the energy station, establishing a load dynamic optimization distribution model according to the predicted dynamic demands of the cold and heat loads and outputting a load dynamic optimization distribution result by taking the optimal economic benefit of the energy station as a target;
and B4, based on the optimal economic benefit of the energy station, establishing a combined optimization model according to the load dynamic optimization distribution result, outputting a boiler operation optimization command, and optimizing and regulating the load implementation mode.
Preferably, the environmental information includes local atmospheric environmental temperature and humidity information; the actual operation data of each device comprises the load factor and the working efficiency of the boiler.
Preferably, the cooling load in step B1 is an air-conditioning cooling load, the heating load includes an industrial steam load and an air-conditioning heating load, and the cooling-heating load prediction model is built by the following steps:
b11, collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining a time-by-time weather change curve by using an exponential smoothing algorithm, then comparing and correcting the time-by-time weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the time-by-time weather change curve in the next day;
b12, according to the time-by-time weather change curve of the future day, counting the energy consumption (cold and heat) data of the user in no less than one year, calculating the time-by-time change curve of the cold and heat loads along with the season and the time of the future day, and then according to the time-by-time change curve of the cold and heat loads along with the season and the time of the future day, establishing a cold and heat load prediction model.
Preferably, in step B2, the real-time verification factor is a load predicted value obtained by online correction through instant data, where the instant data includes steam pressure, temperature, and flow rate provided by the energy station to the user, and temperature and flow rate of the air conditioner cold and hot water supply and return water main pipes.
Preferably, in step B2, the dynamic demand of the cold and hot loads includes the future load distribution of the user and the occurrence time and duration of the peak-to-valley load.
Preferably, in the step B3, the load dynamic optimization allocation model outputs a load dynamic optimization allocation result according to the predicted cold and heat load values, and allocates load capacities of the boilers in the energy station; the load dynamic optimization distribution model is established according to the following steps:
b31, taking the sum of expenditure expenses of the energy station as an objective function, wherein the expenditure expenses comprise electricity consumption expenses, gas consumption expenses, water consumption expenses and operation and maintenance expenses;
b32, taking the boiler efficiency as constraint conditions, wherein the constraint conditions comprise a balance condition for system cold and hot products required by users (cold and hot products provided by an energy station must be balanced in production and use (consumption)), a boiler capacity limiting condition and an environmental emission limiting condition;
and B33, obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal load dynamic optimization distribution result.
Preferably, in the step B4, according to the load required to be completed by each boiler obtained from the load dynamic optimization allocation result, performing combination optimization and adjustment control of relevant parameters on all possible energy supply heat manners in a single boiler to meet the load supply; the combined optimization model is established according to the following steps:
b41, taking the sum of boiler expenditure expenses as an objective function, wherein the boiler expenditure expenses comprise the electricity consumption, the water consumption and the gas consumption of the boiler;
b42, constraint conditions comprise that the cold and hot loads meet supply value conditions, capacity regulation up-slope and down-slope rate limiting conditions of a boiler energy supply unit (when the boiler operates under variable working conditions and variable loads, the capacity or load regulation change rate, namely the up-slope and down-slope rates, cannot be too fast, otherwise, the system is unstable, the boiler is damaged, and therefore the rate limitation is required) and working interval limiting conditions;
and B43, obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal combination mode.
Preferably, a gas energy supply system operation optimization control system includes:
the information acquisition module is used for acquiring environmental information and actual operation data of the boiler;
the cold and heat load prediction model is used for acquiring the change rules of the cold and heat loads of the user along with seasons and moments according to the data acquired by the information acquisition module and establishing the cold and heat load prediction model according to the change rules;
the load prediction optimization module is used for optimizing a cold and heat load prediction model on line by introducing a real-time check factor and actual operation data of the boiler, and predicting dynamic requirements of cold and heat loads time by time;
the load dynamic optimization distribution module is used for establishing a load dynamic optimization distribution model according to predicted dynamic demands of cold and hot loads and outputting a load dynamic optimization distribution result on the premise of meeting the optimal energy supply efficiency of the energy station and aiming at realizing the optimal economic benefit of the energy station;
the load implementation mode optimization regulation and control module is used for establishing a combined optimization model according to a load dynamic optimization distribution result based on the optimal economic benefit of the energy station, outputting a boiler operation optimization command and optimizing and regulating the load implementation mode;
the cold and heat load time-varying curve calculation module is used for counting energy consumption (cold and heat) data of the energy station user in not less than one year according to a time-varying weather curve of a future day and calculating the time-varying curves of the cold and heat loads along with seasons and moments in the future day.
Preferably, the cold and heat load prediction model includes: the calculation module of the hourly weather change curve is used for collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining the hourly weather change curve by utilizing an exponential smoothing algorithm, then comparing and correcting the hourly weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the hourly weather change curve in the next day.
Compared with the prior art, the optimization control method can be summarized into 'fixed and double optimization'; "certain" means that the cold and heat loads required by the user are constant every day, the energy station must supply in time according to the quantity, namely the load constraint is constant; "certain" means "determined" rather than "fixed" herein, i.e., the load demand at each moment is a certain value, which is provided by the cold and hot load prediction module of the method; "double optimization" means the optimization of the use of the industrial steam and the warm-ventilation steam, namely how to optimize the going direction and distribution amount of the two types of steam under the condition of meeting the load requirement so as to optimize the economic benefit of the energy station; the control strategy is as follows: firstly, optimizing and distributing the energy station level load, and then optimizing and regulating the load implementation mode.
The invention aims to realize the optimal economic benefit and energy supply efficiency index of the operation of an energy supply system, applies the technologies of load prediction, online identification, mathematical modeling, intelligent calculation, safe communication and the like, combines the real-time data and the historical data of the operation of the system, establishes and solves an optimal load dynamic optimization distribution model by evaluating the energy efficiency data of two energy supply systems of refrigeration and heat supply, gives an optimal control and scheduling command (comprising a working mode and a corresponding numerical value), sends the command to a Distributed Control System (DCS), and controls a refrigeration unit and a heat supply unit through the DCS, thereby realizing the optimal operation of the energy supply system and achieving the operation aims of high efficiency, low consumption, economic matching, reliability and safety.
The invention has the following beneficial effects:
(1) intelligent optimal load distribution
The heat load of the energy station automatically realizes the optimal distribution among the unit boilers in real time;
(2) higher load response rate and regulation accuracy
Reasonable boiler addition and subtraction and start-stop time are given, and load overshoot is optimized, so that the system has higher thermal load response rate improved by about 5% and regulation precision improved by about 3%;
(3) optimum combination of economic benefit and comprehensive utilization efficiency of energy
The system can operate according to the optimal matching mode of economic benefit and energy utilization efficiency, wherein the economic benefit is improved by about 3%, and the energy utilization efficiency is improved by about 2%;
(4) the boiler operates more stably, and the safety and reliability are obviously improved
By optimizing the combined use mode of the boiler, the load supply is regulated and controlled smoothly, so that the operation of the boiler is more stable, the safety and the reliability are obviously improved, and the service life of the boiler is prolonged.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system architecture of an embodiment of the present invention;
fig. 3 is a system architecture diagram of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, a method for optimizing and controlling the operation of a gas energy supply system includes the following steps:
b1, collecting environmental information and actual operation data of the boiler and the accessory equipment to obtain the change rules of the cold and heat loads of the user along with seasons and moments, and establishing a cold and heat load prediction model according to the change rules;
b2, optimizing a cold and heat load prediction model on line by introducing real-time check factors and actual operation data of each device, and predicting dynamic requirements of cold and heat loads time by time;
b3, on the premise of meeting the optimal energy supply efficiency of the energy station, establishing a load dynamic optimization distribution model according to the predicted dynamic demands of the cold and heat loads and outputting a load dynamic optimization distribution result by taking the optimal economic benefit of the energy station as a target;
b4, based on the optimal economic benefit of the energy station, establishing a combined optimization model according to the load dynamic optimization distribution result, outputting a boiler operation optimization command, and optimizing and regulating the load implementation mode;
the environment information comprises local atmospheric environment temperature and humidity information; the actual operation data of each device comprises the load factor and the working efficiency of the boiler;
the cold load in the step B1 is an air conditioner cold load, the heat load comprises an industrial steam load and an air conditioner heat load, and a cold and heat load prediction model is established through the following steps:
b11, collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining a time-by-time weather change curve by using an exponential smoothing algorithm, then comparing and correcting the time-by-time weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the time-by-time weather change curve in the next day;
b12, according to the time-by-time weather change curve of the future day, counting the energy consumption (cold and heat) data of the user in no less than one year, calculating the time-by-time change curve of the cold and heat loads along with the season and the time of the future day, and then according to the time-by-time change curve of the cold and heat loads along with the season and the time of the future day, establishing a cold and heat load prediction model;
weather forecast usually predicts the weather condition of the future week, and for an energy supply system, the practicability of the energy supply system is mainly considered, and the future three days are selected as prediction step length; if the time-varying curve is too long, the practical value is not high, if the time-varying curve is too short, the support for the next accurate prediction of the data of the future day is insufficient, and the time-varying curve of the future day is calculated and predicted so as to provide the operating load arrangement of each period of the boiler for the next day for the operating personnel of the energy station;
in the step B2, the real-time check factor is a load predicted value obtained by online correction of instant data, wherein the instant data comprise steam pressure, temperature and flow provided by the energy station to a user and temperature and flow of a main pipe for supplying and returning cold water and hot water of an air conditioner;
in step B2, the dynamic demands of the cold and hot loads include the future load distribution of the user and the occurrence time and duration of the peak-to-valley load;
in the step B3, the load dynamic optimization distribution model outputs a load dynamic optimization distribution result according to the cold and heat load values obtained by prediction, and load capacity of each boiler of the energy station needs to be allocated; the load dynamic optimization distribution model is established according to the following steps:
b31, taking the sum of expenditure expenses of the energy station as an objective function, wherein the expenditure expenses comprise electricity consumption expenses, gas consumption expenses, water consumption expenses and operation and maintenance expenses;
b32, taking the boiler efficiency as constraint conditions, wherein the constraint conditions comprise a balance condition for system cold and hot products required by users (cold and hot products provided by an energy station must be balanced in production and use (consumption)), a boiler capacity limiting condition and an environmental emission limiting condition;
b33, obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal load dynamic optimization distribution result;
in step B4, according to the load required to be completed by each boiler obtained by the load dynamic optimization distribution result, performing combination optimization and regulation control of relevant parameters on all possible energy supply heat modes in a single boiler to meet the load supply; the combined optimization model is established according to the following steps:
b41, taking the sum of boiler expenditure expenses as an objective function, wherein the boiler expenditure expenses comprise the electricity consumption, the water consumption and the gas consumption of the boiler;
b42, constraint conditions comprise that the cold and hot loads meet supply value conditions, capacity regulation up-slope and down-slope rate limiting conditions of a boiler energy supply unit (when the boiler operates under variable working conditions and variable loads, the capacity or load regulation change rate, namely the up-slope and down-slope rates, cannot be too fast, otherwise, the system is unstable, the boiler is damaged, and therefore the rate limitation is required) and working interval limiting conditions;
and B43, obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal combination mode.
An operation optimization control system of a gas energy supply system comprises:
the information acquisition module is used for acquiring environmental information and actual operation data of the boiler;
the cold and heat load prediction model is used for acquiring the change rules of the cold and heat loads of the user along with seasons and moments according to the data acquired by the information acquisition module and establishing the cold and heat load prediction model according to the change rules;
the load prediction optimization module is used for optimizing a cold and heat load prediction model on line by introducing a real-time check factor and actual operation data of the boiler, and predicting dynamic requirements of cold and heat loads time by time;
the load dynamic optimization distribution module is used for establishing a load dynamic optimization distribution model according to predicted dynamic demands of cold and hot loads and outputting a load dynamic optimization distribution result on the premise of meeting the optimal energy supply efficiency of the energy station and aiming at realizing the optimal economic benefit of the energy station;
the load implementation mode optimization regulation and control module is used for establishing a combined optimization model according to a load dynamic optimization distribution result based on the optimal economic benefit of the energy station, outputting a boiler operation optimization command and optimizing and regulating the load implementation mode;
the cold and heat load time-varying curve calculation module is used for counting energy consumption (cold and heat) data of the energy station user in not less than one year according to a time-varying weather curve of a future day and calculating the time-varying curves of the cold and heat loads along with seasons and moments in the future day;
the cold and hot load prediction model comprises: the calculation module of the hourly weather change curve is used for collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining the hourly weather change curve by utilizing an exponential smoothing algorithm, then comparing and correcting the hourly weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the hourly weather change curve in the next day.
The first embodiment is as follows:
an operation optimization control method of a gas boiler energy supply system is shown in figure 1, and comprises the following steps:
first, data acquisition
Collecting environmental information and actual operation data of a boiler; the environment information comprises local atmospheric environment temperature and humidity information; the actual operation data of the boiler comprises the load factor and the working efficiency of the boiler;
secondly, establishing a cold and hot load prediction model
In the energy station design stage, the boiler capacity configuration is determined according to the requirements of cold or heat loads, so that the optimal distribution of the cold and heat loads is actually the optimal distribution problem of the cold and heat loads, wherein the cold load is defined as an air conditioner cold load, and the heat load comprises an industrial steam load and an air conditioner heat load, wherein the air conditioner cold and heat loads are respectively realized by an absorption refrigeration device and a plate heat exchanger by taking steam as a driving source; industrial steam load is realized by taking steam as a driving source and adopting a temperature and pressure reducing device;
the method comprises the following steps of obtaining the change rule of the cold and heat loads of an energy station user along with seasons and moments according to collected environment information, and establishing a cold and heat load prediction model according to the change rule, wherein the change rule comprises the following specific steps:
1. collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining a time-by-time weather change curve by using an exponential smoothing algorithm, then comparing and correcting the time-by-time weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the time-by-time weather change curve in the next day;
2. according to the time-by-time weather change curve of the future day, energy consumption (cold and heat) data of a user in no less than one year are counted, the time-by-time change curve of the cold and heat loads along with the season and the time of the future day is calculated, and then a cold and heat load prediction model is established according to the time-by-time change curve of the cold and heat loads along with the season and the time of the future day;
by time-wise in this application is meant listed hourly;
third, optimizing prediction model
In order to improve the accuracy of a cold and heat load prediction model, real-time check factors and actual boiler operation data are introduced, and the cold and heat load prediction model is optimized on line in real time to predict the dynamic requirements of cold and heat loads time by time;
the real-time check factor is a load predicted value obtained by online correction of instant data, wherein the instant data comprise the pressure, the temperature and the flow of steam provided by the energy station to a user and the temperature and the flow of a cold water supply and return water main pipe of an air conditioner;
the dynamic requirements of the cold load and the hot load comprise the future load distribution condition of a user and the occurrence time and duration of peak-to-valley load;
fourthly, load dynamic optimization distribution
On the premise of meeting the optimal energy supply efficiency, aiming at realizing the optimal economic benefit of the energy station, establishing a load dynamic optimization distribution model according to the predicted dynamic demands of cold and heat loads, outputting a load dynamic optimization distribution result, allocating the load capacity of each boiler of the energy station, and mainly completing the optimal distribution among a plurality of boilers of steam load; the optimized distribution of the steam load is the load distribution among all boilers and the opening degree of the temperature and pressure reducing device; the optimized distribution of heating and ventilation steam load is optimized distribution between the plate heat exchanger and the absorption refrigeration equipment;
the load dynamic optimization distribution model is established according to the following steps:
1. taking the sum of expenditure costs of the energy station as an objective function, wherein the expenditure costs comprise power consumption, water consumption, gas consumption and operation and maintenance costs;
2. the method comprises the following steps of taking boiler efficiency as constraint conditions, wherein the constraint conditions comprise a system cold and hot production balance condition required by a user, a boiler capacity limiting condition and an environmental emission limiting condition;
3. obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal load dynamic optimization distribution result;
fifthly, optimization and regulation of load implementation mode
Based on the optimal economic benefit of the energy station, a combined optimization model is established according to the dynamic load optimization distribution result, the obtained load required to be completed by each boiler is output, a boiler operation optimization command is output, all possible energy supply modes in a single boiler are subjected to combined optimization and relevant parameters (such as the opening degree of a temperature and pressure reducing valve) are adjusted and controlled, the load supply is met, and the smoothness requirement of parameter change is met; the method mainly completes mode regulation among all regulation units in the energy station, mainly aims at optimizing the operation mode of each boiler unit on the premise of meeting the heat supply requirement, and aims at realizing the maximization of the benefit of a single boiler and determining the working mode of the single boiler; for example, under a certain steam demand condition, the boiler, the temperature and pressure reducing device, the feed pump and the like are optimally combined, and the optimal load capacity of each boiler, the opening degree of the temperature and pressure reducing valve and the like are calculated, so that the economic operation of the boiler is realized; under the working condition of a certain refrigeration load requirement, the opening degree of a steam supply steam valve is optimally adjusted, and the like;
the combined optimization model is established according to the following steps:
1. taking the sum of boiler expenditure costs as an objective function, wherein the boiler expenditure costs comprise the power consumption, the water consumption and the gas consumption of the boiler;
2. the constraint conditions comprise that the cold and hot loads meet a supply value condition, a capacity regulation up-slope and down-slope speed limiting condition of a boiler energy supply unit and a working interval limiting condition;
3. and calculating to obtain a solution when the objective function under the constraint condition is minimum, thereby obtaining an optimal combination mode.
Example two:
an operation optimization control system of an energy supply system for implementing the method of embodiment 1, as shown in fig. 2, comprises:
the information acquisition module is used for acquiring environmental information and actual operation data of the boiler;
the cold and heat load prediction model is used for acquiring the change rule of the cold and heat loads of the energy station users along with seasons and moments according to the data acquired by the information acquisition module, and then establishing the cold and heat load prediction model according to the change rule;
the load prediction optimization module is used for optimizing a cold and heat load prediction model on line by introducing a real-time check factor and actual operation data of the boiler, and predicting dynamic requirements of cold and heat loads time by time;
the load dynamic optimization distribution module is used for establishing a load dynamic optimization distribution model according to predicted dynamic demands of cold and heat loads and outputting a load dynamic optimization distribution result on the premise of meeting the optimal energy supply efficiency of the energy station and aiming at realizing optimal economic benefit;
the load implementation mode optimization regulation and control module is used for establishing a combined optimization model according to a load dynamic optimization distribution result based on the optimal economic benefit of the energy station, outputting a boiler operation optimization command and optimizing and regulating the load implementation mode;
wherein, cold and hot load prediction model includes: the hourly weather change curve calculation module is used for collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining a hourly weather change curve by utilizing an exponential smoothing algorithm, then comparing and correcting the hourly weather change curve with the current weather forecast information, establishing a weather prediction model, predicting weather conditions in three days in the future, and calculating the hourly weather change curve in the next day; the cold and heat load time-varying curve calculation module is used for counting energy consumption (cold and heat) data of the energy station user in not less than one year according to a time-varying weather curve of a future day and calculating the time-varying curves of the cold and heat loads along with seasons and moments in the future day;
the load forecasting and optimizing module provides a determined value of the time-by-time cold and heat load demand, then the load dynamic optimizing and distributing module performs load optimizing and distributing on the energy station, a reasonable heat load set value is distributed to each boiler based on the real-time performance of the boiler, and finally the load implementation mode optimizing and regulating module completes mode regulation and control among all the regulating and controlling units in the single boiler, so that the online operation decision optimization of the energy station is realized;
the system can be divided into a decision optimization system and an energy efficiency management system, and mainly comprises a set of real-time configurable decision optimization hardware system, a set of energy efficiency management and load prediction computing platform and related software packages; in order to enable the system to operate in the optimal state of energy efficiency data, the system is arranged in a DCS public system part;
the decision optimization system is responsible for calculating and optimizing real-time operation data and finally forming a decision optimization command; the energy efficiency management system is responsible for classifying and managing system operation data;
the hardware equipment mainly comprises decision optimization stations, energy efficiency management stations, optimization controllers, power supply switching devices and the like; the decision optimization station and the energy efficiency management station are arranged at an engineer station (comprising one host and one display per station); the optimization controller is arranged between the electronic equipment and is arranged in parallel with other cabinets of the DCS public system; the operation desk and the two power supply switching devices are arranged at an engineer station and provide an uninterrupted power supply for the decision optimization station and the energy efficiency management station;
the relation between the system and the DCS is summarized as 'clear division of work and mutual cooperation', the system emphasizes real-time optimization, and the DCS emphasizes real-time control; as shown in fig. 3, the system obtains actual field operation data through the DCS, then performs calculation and optimization, forms an optimization command, and transmits the optimization command to the DCS to perform, thereby completing optimization and control of the distributed energy station;
further, the present system was verified by the following steps:
1. online open-loop debugging: connecting the system with a DCS (distributed control system), testing the data communication rate and the communication capacity between the system and the DCS, and checking the correctness of data communication;
2. online closed-loop debugging: the system is debugged in an online operation mode, and after operation tests of each load section, field data and boiler operation parameters are collected and analyzed, and relevant control parameters of the system are corrected.
In summary, the following steps: the operation optimization control method and the operation optimization control system for the gas energy supply system have the advantages of intelligent optimal load distribution, higher load response rate, higher adjustment precision, optimal combination of economic benefit and energy comprehensive utilization efficiency, more stable boiler operation and obviously improved safety and reliability, and solve the problems that the operation of the existing operation control method for the gas energy supply system cannot achieve the purpose of optimized operation and certain use limitation exists.
Claims (9)
1. An operation optimization control method of a gas energy supply system is characterized by comprising the following steps:
b1, collecting environmental information and actual operation data of the boiler and the accessory equipment to obtain the change rules of the cold and heat loads of the user along with seasons and moments, and establishing a cold and heat load prediction model according to the change rules;
b2, optimizing a cold and heat load prediction model on line by introducing real-time check factors and actual operation data of each device, and predicting dynamic requirements of cold and heat loads time by time;
b3, on the premise of meeting the optimal energy supply efficiency of the energy station, establishing a load dynamic optimization distribution model according to the predicted dynamic demands of the cold and heat loads and outputting a load dynamic optimization distribution result by taking the optimal economic benefit of the energy station as a target;
and B4, based on the optimal economic benefit of the energy station, establishing a combined optimization model according to the load dynamic optimization distribution result, outputting a boiler operation optimization command, and optimizing and regulating the load implementation mode.
2. The operation optimization control method of the gas energy supply system according to claim 1, characterized in that: the environment information comprises local atmospheric environment temperature and humidity information; the actual operation data of each device comprises the load factor and the working efficiency of the boiler.
3. The operation optimization control method of the gas energy supply system according to claim 1, characterized in that: the cold load in the step B1 is an air conditioner cold load, the heat load comprises an industrial steam load and an air conditioner heat load, and the cold and heat load prediction model is established through the following steps:
b11, collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining a time-by-time weather change curve by using an exponential smoothing algorithm, then comparing and correcting the time-by-time weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the time-by-time weather change curve in the next day;
b12, according to the time-by-time weather change curve of the future day, counting the energy consumption (cold and heat) data of the user in no less than one year, calculating the time-by-time change curve of the cold and heat loads along with the season and the time of the future day, and then according to the time-by-time change curve of the cold and heat loads along with the season and the time of the future day, establishing a cold and heat load prediction model.
4. The operation optimization control method of the gas energy supply system according to claim 1, characterized in that: in the step B2, the real-time verification factor is a load prediction value obtained by online correction through instant data, and the instant data includes steam pressure, temperature, and flow rate provided by the energy station to the user, and temperature and flow rate of the air conditioner cold and hot water supply and return water main pipes.
5. The operation optimization control method of the gas energy supply system according to claim 1, characterized in that: in step B2, the dynamic demands of the cold and hot loads include the future load distribution of the user and the occurrence time and duration of the peak-to-valley load.
6. The operation optimization control method of the gas energy supply system according to claim 1, characterized in that: in the step B3, the load dynamic optimization allocation model outputs a load dynamic optimization allocation result according to the predicted cold and heat load values, and allocates load capacities of the boilers of the energy station; the load dynamic optimization distribution model is established according to the following steps:
b31, taking the sum of expenditure expenses of the energy station as an objective function, wherein the expenditure expenses comprise electricity consumption expenses, gas consumption expenses, water consumption expenses and operation and maintenance expenses;
b32, taking the boiler efficiency as constraint conditions, wherein the constraint conditions comprise a balance condition for system cold and hot products required by users (cold and hot products provided by an energy station must be balanced in production and use (consumption)), a boiler capacity limiting condition and an environmental emission limiting condition;
and B33, obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal load dynamic optimization distribution result.
7. The operation optimization control method of the gas energy supply system according to claim 1, characterized in that: in the step B4, according to the load required to be completed by each boiler obtained from the load dynamic optimization distribution result, performing combination optimization and adjustment control of relevant parameters on all possible energy supply heat manners in a single boiler to meet the load supply; the combined optimization model is established according to the following steps:
b41, taking the sum of boiler expenditure expenses as an objective function, wherein the boiler expenditure expenses comprise the electricity consumption, the water consumption and the gas consumption of the boiler;
b42, constraint conditions comprise that the cold and hot loads meet supply value conditions, capacity regulation up-slope and down-slope rate limiting conditions of a boiler energy supply unit (when the boiler operates under variable working conditions and variable loads, the capacity or load regulation change rate, namely the up-slope and down-slope rates, cannot be too fast, otherwise, the system is unstable, the boiler is damaged, and therefore the rate limitation is required) and working interval limiting conditions;
and B43, obtaining a solution when the objective function under the constraint condition is minimum through calculation, thereby obtaining an optimal combination mode.
8. A gas powered system operation optimization control system as claimed in any one of claims 1 to 7 characterised by comprising:
the information acquisition module is used for acquiring environmental information and actual operation data of the boiler;
the cold and heat load prediction model is used for acquiring the change rules of the cold and heat loads of the user along with seasons and moments according to the data acquired by the information acquisition module and establishing the cold and heat load prediction model according to the change rules;
the load prediction optimization module is used for optimizing a cold and heat load prediction model on line by introducing a real-time check factor and actual operation data of the boiler, and predicting dynamic requirements of cold and heat loads time by time;
the load dynamic optimization distribution module is used for establishing a load dynamic optimization distribution model according to predicted dynamic demands of cold and hot loads and outputting a load dynamic optimization distribution result on the premise of meeting the optimal energy supply efficiency of the energy station and aiming at realizing the optimal economic benefit of the energy station;
the load implementation mode optimization regulation and control module is used for establishing a combined optimization model according to a load dynamic optimization distribution result based on the optimal economic benefit of the energy station, outputting a boiler operation optimization command and optimizing and regulating the load implementation mode;
the cold and heat load time-varying curve calculation module is used for counting energy consumption (cold and heat) data of the energy station user in not less than one year according to a time-varying weather curve of a future day and calculating the time-varying curves of the cold and heat loads along with seasons and moments in the future day.
9. The operation optimization control system of the gas energy supply system according to claim 8, characterized in that: the cold and hot load prediction model comprises: the calculation module of the hourly weather change curve is used for collecting weather forecast information (including temperature, humidity and the like) in the last three years by taking the current time as a starting point, obtaining the hourly weather change curve by utilizing an exponential smoothing algorithm, then comparing and correcting the hourly weather change curve with the current weather forecast information, establishing a weather prediction model, predicting the weather conditions in the next three days, and calculating the hourly weather change curve in the next day.
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