CN-122022352-A - Operation optimization scheduling method and system for cogeneration unit

CN122022352ACN 122022352 ACN122022352 ACN 122022352ACN-122022352-A

Abstract

The invention relates to the technical field of energy management, and discloses a heat and power cogeneration unit operation optimizing and scheduling method and system, which realize high-efficiency matching of heat supply and power generation by optimizing heat-electricity ratio scheduling, improve the overall energy efficiency, optimize unit load distribution by adopting a dynamic energy efficiency evaluation method, and ensure high-efficiency operation; the method comprises the steps of optimizing heat-supply and power generation efficient matching through optimizing heat-electricity ratio dispatching, improving overall energy efficiency, optimizing unit load distribution by adopting a dynamic energy efficiency evaluation method, ensuring efficient operation, optimizing heat-supply temperature and pressure by adopting self-adaptive PID control, fuzzy control and the like, ensuring stable heat-supply quality, improving heat-supply load prediction precision by combining factors such as weather prediction, building thermal inertia and the like, improving heat-supply service quality, calculating and optimizing unit carbon emission in real time through a carbon emission monitoring module, reducing carbon transaction cost, optimizing fuel consumption by combining carbon market dynamics, reducing pollutant emission and realizing environmental protection compliance.

Inventors

HU ZUNMIN
XU YIRAN
YANG XIAOLONG
CHEN BO
LU YANNING
SUN ZIWEN
Geng Chamin
GUAN SHIPIAN
HE PENGFEI
JIN YAWEI

Assignees

江苏方天电力技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260205

Claims (9)

1. An optimal scheduling system for operation of a cogeneration unit, comprising: The data acquisition and monitoring module is used for acquiring operation parameters of the cogeneration unit, including power generation load, heat load, fuel consumption, steam pressure, temperature and environmental parameters, and carrying out real-time monitoring through the SCADA system, the PLC controller and the Internet of things sensor; The load prediction and optimization scheduling module is used for predicting future heat supply loads and power loads based on historical data, weather forecast and a user demand model, and adopting mixed integer linear programming, dynamic programming, genetic algorithm and particle swarm optimization algorithm to formulate an optimized unit scheduling strategy; The economic optimization module is used for calculating fuel cost and carbon emission cost, optimizing unit fuel consumption and comprehensive energy efficiency by combining fuel price, unit efficiency and carbon transaction market information, and improving economic efficiency; the real-time control and execution module is used for dynamically adjusting the start and stop of the unit according to an optimal scheduling strategy, optimizing heat supply parameters based on a feedback control algorithm, realizing the accurate control of heat supply temperature and pressure, detecting abnormal working conditions through an intelligent fault diagnosis algorithm and providing early warning; the power supply and heat supply cooperative dispatching module is used for optimizing the thermoelectric ratio, improving the power output income on the premise of meeting the heat supply requirement, interacting with a power grid, responding to management on a requirement side, and simultaneously combining an energy storage system to improve the dispatching flexibility; The visual management and decision support module is used for providing real-time monitoring, trend analysis, alarm management and optimal scheduling decision support, and realizing remote monitoring and intelligent scheduling through cloud computing and big data analysis.
2. The optimal scheduling system for operation of a cogeneration unit according to claim 1, wherein the load prediction and optimal scheduling module adopts a machine learning algorithm, and comprises a support vector machine, a long and short time memory network (LSTM) and a random forest, so that the load prediction accuracy is improved.
3. The optimal scheduling system for the operation of the cogeneration unit according to claim 1, wherein the economic optimization module optimizes the unit operation efficiency in real time by adopting a dynamic energy efficiency evaluation method and combining historical operation data of the cogeneration unit.
4. The optimal scheduling system for the operation of the cogeneration unit according to claim 1, wherein the real-time control and execution module adopts a fuzzy control algorithm and an adaptive PID control algorithm to optimize the dynamic adjustment of heat supply parameters and improve the heat supply stability.
5. The optimal scheduling system for operation of a cogeneration unit according to claim 1, wherein the power supply and heat supply cooperative scheduling module integrates a heat energy storage system and an electric energy storage system to relieve thermoelectric load fluctuation and improve energy utilization efficiency.
6. The optimal scheduling system for operation of a cogeneration unit according to claim 1, wherein the visual management and decision support module comprises a Web-based remote monitoring interface for providing real-time operation data visualization, optimal scheduling strategy recommendation and alarm pushing functions.
7. The optimal dispatching system for the operation of the cogeneration unit according to claim 1, wherein the optimal dispatching system is connected with a power grid dispatching system and can participate in electric market transaction, and the power generation capacity is dynamically adjusted according to electricity price fluctuation, so that the income is improved.
8. The optimal scheduling system for the operation of the cogeneration unit according to claim 1, wherein the system is integrated with a carbon emission monitoring module, can calculate the carbon emission in the operation process of the unit in real time, and is combined with a carbon transaction market strategy to optimize the operation mode of the unit so as to reduce the carbon emission cost.
9. The optimal scheduling method and system for operation of a cogeneration unit according to claims 1 to 8, wherein the optimal scheduling method and method steps are as follows: The method comprises the steps of S1, establishing an optimal scheduling model of a cogeneration unit, wherein the optimal scheduling model comprises but is not limited to power generation load, power grid scheduling instruction, heat supply load, steam flow, temperature, pressure, fuel consumption, unit efficiency, carbon emission data, environmental temperature, humidity and meteorological data; S2, based on historical operation data, predicting future heat supply load and power load by adopting a machine learning algorithm (such as a support vector machine, an LSTM (least squares), a random forest and the like) or a time sequence analysis method, optimizing a prediction result by combining weather forecast, a user demand model and industrial load fluctuation, improving precision, and calculating short-term (minute/hour level), medium-term (day level) and long-term (month level) load demands to provide reference for subsequent scheduling; S3, establishing an optimal scheduling model of the cogeneration unit, wherein constraint conditions comprise unit start-stop constraint, minimum output of given maximum output limit, thermoelectric ratio balance constraint and fuel consumption and carbon emission constraint; an optimal scheduling scheme is solved by adopting an optimization algorithm (such as mixed integer linear programming MILP, dynamic programming DP, genetic algorithm GA, particle swarm optimization PSO and the like), an optimal scheduling instruction is generated, and the optimal scheduling instruction is compared with the actual running condition to dynamically adjust the scheduling scheme; S4, the running cost of the unit is calculated, including fuel cost, carbon emission cost, start-stop cost and the like, and the fuel consumption and start-stop strategy of the unit are optimized by combining market electricity price, fuel price and carbon transaction market data, and the running efficiency of the unit is analyzed in real time by adopting a dynamic energy efficiency evaluation method, so that the economy of the unit is improved; S5, according to an optimal scheduling scheme, the start-stop state of the unit is adjusted, the air extraction proportion of the steam turbine is optimized, the cogeneration efficiency is improved, the heat supply temperature and the heat supply pressure are optimized by adopting a fuzzy control algorithm or a self-adaptive PID control algorithm, the heat supply quality is ensured, the running state of the unit is monitored in real time through an intelligent fault diagnosis system, abnormal conditions are identified, and early warning and optimization suggestions are provided; s6, optimizing the thermoelectric ratio on the premise of meeting the heat supply requirement, maximizing the electric power income, combining a thermal energy storage system and an electric energy storage system, reducing load fluctuation and improving scheduling flexibility; And S7, displaying the running state of the unit, the load prediction result and the optimal scheduling scheme in real time through a Web remote monitoring interface, generating an operation analysis report, wherein the operation analysis report comprises unit energy efficiency evaluation, economic analysis, carbon emission monitoring and the like, providing optimal scheduling decision support, adopting cloud computing and big data analysis, realizing remote optimal scheduling and improving the intelligent scheduling level.

Description

Operation optimization scheduling method and system for cogeneration unit Technical Field The invention relates to the technical field of energy management, in particular to a heat and power cogeneration unit operation optimization scheduling method and system. Background Cogeneration (Combined Heat and Power, CHP) is a highly efficient energy utilization technology that can produce both electricity and heat. Compared with the traditional scattered heat supply and power supply mode, the cogeneration system can obviously improve the energy utilization rate and reduce the fuel consumption and the pollution emission. Therefore, the technology is widely applied to the fields of industrial production, central heating, urban energy supply and the like. Although the cogeneration system has higher energy efficiency, many challenges still exist in the operation process, and the system is influenced by factors such as weather change, user demand fluctuation, electric market transaction and the like, the thermoelectric load has larger uncertainty, and the traditional prediction method has limited precision, so that the scheduling decision is not accurate enough, and the system stability and economic benefit are influenced. The fuel cost fluctuation of fire coal, natural gas and the like is large, and how to optimize the fuel ratio to reduce the running cost and reduce the carbon emission while meeting the heat supply requirement is a problem to be solved urgently. In order to improve the operation accuracy of the cogeneration system, reduce the cost and realize green low-carbon operation, an operation management system of the cogeneration unit based on intelligent optimal scheduling needs to be constructed. Disclosure of Invention The invention provides a method and a system for optimizing and scheduling operation of a cogeneration unit, which are used for solving the technical problems in the background art. The invention provides the following technical scheme: an optimal scheduling system for operation of a cogeneration unit, the system comprising: The data acquisition and monitoring module is used for acquiring operation parameters of the cogeneration unit, including power generation load, heat load, fuel consumption, steam pressure, temperature and environmental parameters, and carrying out real-time monitoring through the SCADA system, the PLC controller and the Internet of things sensor; The load prediction and optimization scheduling module is used for predicting future heat supply loads and power loads based on historical data, weather forecast and a user demand model, and adopting mixed integer linear programming, dynamic programming, genetic algorithm and particle swarm optimization algorithm to formulate an optimized unit scheduling strategy; The economic optimization module is used for calculating fuel cost and carbon emission cost, optimizing unit fuel consumption and comprehensive energy efficiency by combining fuel price, unit efficiency and carbon transaction market information, and improving economic efficiency; the real-time control and execution module is used for dynamically adjusting the start and stop of the unit according to an optimal scheduling strategy, optimizing heat supply parameters based on a feedback control algorithm, realizing the accurate control of heat supply temperature and pressure, detecting abnormal working conditions through an intelligent fault diagnosis algorithm and providing early warning; the power supply and heat supply cooperative dispatching module is used for optimizing the thermoelectric ratio, improving the power output income on the premise of meeting the heat supply requirement, interacting with a power grid, responding to management on a requirement side, and simultaneously combining an energy storage system to improve the dispatching flexibility; The visual management and decision support module is used for providing real-time monitoring, trend analysis, alarm management and optimal scheduling decision support, and realizing remote monitoring and intelligent scheduling through cloud computing and big data analysis. Preferably, the load prediction and optimization scheduling module adopts a machine learning algorithm, including a support vector machine, a long and short time memory network (LSTM) and a random forest, so as to improve the load prediction precision. Preferably, the economic optimization module adopts a dynamic energy efficiency evaluation method and combines historical operation data of the cogeneration unit to optimize the unit operation efficiency in real time. Preferably, the real-time control and execution module adopts a fuzzy control algorithm and a self-adaptive PID control algorithm to optimize the dynamic adjustment of heat supply parameters and improve the heat supply stability. Preferably, the power supply and heat supply cooperative dispatching module integrates a thermal energy storage system and an electric energy storage system so a