Cogeneration unit operation optimization scheduling method and system
By integrating data acquisition, load forecasting, optimized scheduling, and intelligent control, the cogeneration system solves the problems of uncertainty in heating and power generation loads and fluctuations in fuel costs, achieving efficient, low-carbon, and economical operation optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU FRONTIER ELECTRIC TECH
- Filing Date
- 2026-02-05
- Publication Date
- 2026-05-12
AI Technical Summary
Cogeneration systems face uncertainties in heating and power generation loads, large fluctuations in fuel costs, and limited accuracy of traditional forecasting methods during operation, leading to inaccurate dispatching decisions and impacting system stability and economic benefits.
It employs modules for data acquisition and monitoring, load forecasting and optimized scheduling, economic optimization, real-time control and execution, power supply and heating coordinated scheduling, and visualization management and decision support. By combining machine learning, optimization algorithms, and energy storage systems, it optimizes fuel ratio, heating parameters, and power output to achieve precise scheduling.
It improves energy efficiency, reduces operating costs and carbon emissions, enhances system stability and economic benefits, and improves dispatch flexibility and user experience.
Smart Images
Figure CN122022352A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of energy management technology, specifically to a method and system for optimizing the operation and scheduling of combined heat and power (CHP) units. Background Technology
[0002] Combined heat and power (CHP) is a highly efficient energy utilization technology that can simultaneously produce electricity and heat. Compared to traditional decentralized heating and power supply methods, CHP systems can significantly improve energy efficiency and reduce fuel consumption and pollution emissions. Therefore, this technology is widely used in industrial production, district heating, and urban energy supply.
[0003] Despite the high energy efficiency of combined heat and power (CHP) systems, there are still many challenges in their operation. Affected by factors such as weather changes, fluctuations in user demand, and electricity market transactions, CHP loads are highly uncertain. Traditional forecasting methods have limited accuracy, resulting in inaccurate dispatching decisions that affect system stability and economic benefits.
[0004] The cost of fuels such as coal and natural gas fluctuates greatly. How to optimize the fuel ratio to reduce operating costs and carbon emissions while meeting heating demand is an urgent problem to be solved.
[0005] To improve the accuracy of cogeneration system operation, reduce costs, and achieve green and low-carbon operation, it is necessary to build a cogeneration unit operation management system based on intelligent optimization scheduling. Summary of the Invention
[0006] This invention provides a method and system for optimizing the operation and scheduling of combined heat and power (CHP) units, which is used to solve the technical problems mentioned in the background section.
[0007] This invention provides the following technical solution: A combined heat and power (CHP) unit operation optimization and scheduling system, the system comprising: The data acquisition and monitoring module is used to collect the operating parameters of the cogeneration unit, including power generation load, heat load, fuel consumption, steam pressure, temperature and environmental parameters, and to monitor them in real time through the SCADA system, PLC controller and IoT sensors. The load forecasting and optimization scheduling module is used to predict future heating and power loads based on historical data, weather forecasts and user demand models, and to formulate optimized unit scheduling strategies using mixed integer linear programming, dynamic programming, genetic algorithms and particle swarm optimization algorithms. The economic optimization module is used to calculate fuel costs and carbon emission costs, and combine fuel prices, unit efficiency, and carbon trading market information to optimize unit fuel consumption and overall energy efficiency, thereby improving economic efficiency. The real-time control and execution module is used to dynamically adjust the start and stop of the unit according to the optimized scheduling strategy, and optimize the heating parameters based on the feedback control algorithm to achieve precise control of heating temperature and pressure. At the same time, it detects abnormal operating conditions and provides early warnings through intelligent fault diagnosis algorithm. The power supply and heating coordinated dispatch module is used to optimize the heat-to-power ratio, increase power output revenue while meeting heating demand, interact with the power grid, respond to demand-side management, and combine with energy storage systems to improve dispatch flexibility. The visualization management and decision support module provides real-time monitoring, trend analysis, alarm management, and optimized scheduling decision support. It also enables remote monitoring and intelligent scheduling through cloud computing and big data analysis.
[0008] Preferably, the load forecasting and optimized scheduling module employs machine learning algorithms, including support vector machines, long short-term memory networks (LSTM), and random forests, to improve load forecasting accuracy.
[0009] Preferably, the economic optimization module adopts a dynamic energy efficiency evaluation method, which combines historical operating data of the cogeneration unit to optimize the unit's operating efficiency in real time.
[0010] Preferably, the real-time control and execution module employs fuzzy control algorithm and adaptive PID control algorithm to optimize the dynamic adjustment of heating parameters and improve heating stability.
[0011] Preferably, the power supply and heating coordinated scheduling module integrates thermal energy storage and electrical energy storage systems to mitigate thermal and electrical load fluctuations and improve energy utilization efficiency.
[0012] Preferably, the visualization management and decision support module includes a web-based remote monitoring interface, providing real-time operational data visualization, optimized scheduling strategy recommendations, and alarm push functions.
[0013] Preferably, the system is connected to the power grid dispatching system, enabling it to participate in electricity market transactions and dynamically adjust power generation output according to electricity price fluctuations, thereby increasing revenue.
[0014] Preferably, the system integrates a carbon emission monitoring module, which can monitor the carbon emissions during the unit's operation in real time and optimize the unit's operation mode in conjunction with carbon trading market strategies to reduce carbon emission costs.
[0015] A method and system for optimizing the operation and scheduling of combined heat and power (CHP) units, comprising the following steps: S1: Establish an optimized scheduling model for cogeneration units, including but not limited to power generation load, grid dispatch instructions, heating load, steam flow, temperature, pressure, fuel consumption, unit efficiency, carbon emission data, ambient temperature, humidity, and meteorological data; use SCADA systems, PLCs, and IoT sensors for real-time data acquisition and store the data in a database; improve data quality through data cleaning and outlier handling. S2: Based on historical operating data, machine learning algorithms (such as support vector machine, LSTM, random forest, etc.) or time series analysis methods are used to predict future heating and power loads. Combined with weather forecasts, user demand models, and industrial load fluctuations, the prediction results are optimized and the accuracy is improved. Short-term (minute / hour level), medium-term (daily level), and long-term (monthly level) load demands are calculated to provide a reference for subsequent scheduling. S3: Establish an optimal scheduling model for cogeneration units, with constraints including unit start-up and shutdown constraints, minimum output given a maximum output limit, heat-to-power ratio balance constraints, and fuel consumption and carbon emission constraints; use optimization algorithms (such as mixed integer linear programming (MILP), dynamic programming (DP), genetic algorithm (GA), particle swarm optimization (PSO), etc.) to solve for the optimal scheduling scheme; generate optimized scheduling instructions and compare them with actual operating conditions to dynamically adjust the scheduling scheme; S4: Computer unit operating costs, including fuel costs, carbon emission costs, start-up and shutdown costs, etc., combined with market electricity prices, fuel prices, carbon trading market data, optimize the unit's fuel consumption and start-up and shutdown strategies, adopt dynamic energy efficiency assessment methods, and conduct real-time analysis of unit operating efficiency to improve unit economy; S5: Based on the optimized scheduling plan, adjust the start-up and shutdown status of the units, optimize the turbine extraction ratio, improve the efficiency of cogeneration, and use fuzzy control algorithm or adaptive PID control algorithm to optimize heating temperature and pressure, ensure heating quality, and monitor the unit operating status in real time through the intelligent fault diagnosis system, identify abnormal situations, and provide early warning and optimization suggestions. S6: Under the premise of meeting heating demand, optimize the heat-to-power ratio to maximize electricity revenue, and combine thermal energy storage and electric energy storage systems to reduce load fluctuations and improve dispatch flexibility. S7: Through the web-based remote monitoring interface, the unit's operating status, load forecast results, and optimized scheduling schemes are displayed in real time. It generates operation analysis reports, including unit energy efficiency assessment, economic analysis, and carbon emission monitoring, providing support for optimized scheduling decisions. It uses cloud computing and big data analysis to achieve remote optimized scheduling and improve the level of intelligent scheduling.
[0016] The present invention has the following beneficial effects: 1. Improve energy efficiency: By optimizing the heat-to-power ratio scheduling, achieve efficient matching between heating and power generation to improve overall energy efficiency; adopt dynamic energy efficiency assessment methods to optimize unit load distribution and ensure efficient operation.
[0017] 2. Reduce operating costs: Adopt a fuel cost optimization strategy, rationally allocate coal, natural gas and biomass fuels to reduce fuel costs; reduce unnecessary unit start-ups and shutdowns to reduce maintenance and start-up / shutdown costs and improve unit lifespan.
[0018] 3. Optimize scheduling and improve economic benefits: Combine machine learning and optimization algorithms (LSTM, SVM, random forest, etc.) to improve load forecasting accuracy and reduce scheduling errors; participate in electricity market transactions and adjust power generation output in accordance with electricity price fluctuations to maximize economic benefits; use energy storage systems to optimize cogeneration scheduling, improve revenue and enhance scheduling flexibility.
[0019] 4. Intelligent fault diagnosis to improve system stability: Using methods such as neural networks and expert systems, intelligent fault detection and early warning are achieved, reducing unplanned downtime; combined with SCADA, PLC, and IoT sensors, real-time monitoring and anomaly detection are achieved, improving operational reliability.
[0020] 5. Precise heating to improve user experience: Adaptive PID control and fuzzy control are used to optimize heating temperature and pressure to ensure stable heating quality; combined with weather forecasts and building thermal inertia, the accuracy of heating load forecasting is improved, thereby enhancing the quality of heating services.
[0021] 6. Green and low-carbon operation, reducing carbon emissions: Through the carbon emission monitoring module, the unit's carbon emissions are calculated and optimized in real time, reducing carbon trading costs; combined with carbon market dynamics, fuel consumption is optimized to reduce pollutant emissions and achieve environmental compliance.
[0022] 7. Intelligent and visual management to improve scheduling efficiency: Provides functions such as real-time monitoring, alarm management, and historical data analysis to assist in decision optimization and improve the level of intelligent operation.
[0023] 8. Enhance grid interaction capabilities and improve market competitiveness: Combine demand-side response strategies to increase power generation during peak grid load periods to improve revenue; adopt AGC, SCADA, and EMS systems to achieve real-time data interaction with the grid and improve competitiveness in the ancillary services market.
[0024] A combined heat and power (CHP) unit operation optimization and scheduling system can improve the overall operating efficiency of CHP units by 5% to 15%, reduce operating costs, reduce unplanned downtime by 30%, increase market transaction revenue, and promote green and low-carbon development. Attached Figure Description
[0025] Figure 1This is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram of the optimized scheduling method of the present invention. Detailed Implementation
[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0027] Example Please see Figure 1 and Figure 2 A combined heat and power (CHP) unit operation optimization and scheduling system, the system comprising: The data acquisition and monitoring module is used to collect operating parameters of the cogeneration unit, including power generation load (real-time monitoring of the unit's power generation to ensure that the grid load demand is met), heat load (monitoring heat supply to ensure that industrial and residential heating needs are met), fuel consumption (recording the consumption of different fuels, such as coal, natural gas, biomass fuel, etc., to optimize fuel ratio), steam pressure, temperature (monitoring the unit's steam parameters to ensure heating quality and optimize the heat-to-power ratio), and environmental parameters (including outdoor temperature, humidity, meteorological information, etc., to optimize heating forecasting). Real-time monitoring is achieved through a SCADA system (for centralized management and remote data acquisition and monitoring), a PLC controller (for on-site control and precise adjustment of the unit's status), and IoT sensors (for distributed monitoring and high-precision data acquisition and real-time transmission).
[0028] The load forecasting and optimized scheduling module is used to predict future heating and power loads based on historical data, weather forecasts, and user demand models. It then uses mixed-integer linear programming (MILP), dynamic programming (DP), genetic algorithms (GA), and particle swarm optimization (PSO) to formulate optimized unit scheduling strategies. Furthermore, it employs optimization methods such as mixed-integer linear programming (MILP), dynamic programming (DP), genetic algorithms (GA), and particle swarm optimization (PSO) to optimize generator start-up and shutdown strategies while meeting heating demands, thereby improving economy and operational efficiency.
[0029] Load forecasting is divided into two categories: heating load forecasting, which uses time series analysis and machine learning algorithms (such as support vector machine SVM and long short-term memory network LSTM) to predict future heating demand, taking into account historical data, ambient temperature, building thermal inertia and other factors to improve forecast accuracy; and electricity load forecasting, which combines grid load curves, electricity price fluctuations and electricity market transactions to predict changes in electricity demand.
[0030] The economic optimization module calculates fuel costs and carbon emission costs, and combines fuel prices, unit efficiency, and carbon trading market information to optimize unit fuel consumption and overall energy efficiency, thereby improving economic efficiency. The main optimization objectives are: Fuel cost optimization involves calculating the prices of different fuels (coal, natural gas, biomass, etc.) and selecting the optimal fuel combination. Carbon emission cost optimization involves monitoring carbon emissions during unit operation and, in conjunction with carbon trading market prices, optimizing unit operation methods to reduce carbon emission costs. Unit efficiency optimization involves using dynamic energy efficiency assessment methods to analyze unit operating data and improve operating efficiency. Start-up and shutdown cost optimization involves analyzing start-up and shutdown losses of the computer unit, reducing unnecessary start-ups and shutdowns, extending unit lifespan, and lowering maintenance costs.
[0031] The real-time control and execution module dynamically adjusts the start-up and shutdown of the units according to the optimized scheduling strategy, and optimizes heating parameters based on feedback control algorithms to achieve precise control of heating temperature and pressure. It also detects abnormal operating conditions and provides early warnings through intelligent fault diagnosis algorithms. The main functions of the real-time control and execution module include: Unit start-up and shutdown optimization: Based on the optimized scheduling results, the start-up and shutdown of the units are automatically controlled to reduce the damage to the equipment caused by frequent start-ups and shutdowns; heating parameter optimization: Fuzzy control algorithm or adaptive PID control algorithm is used to optimize heating temperature and pressure to ensure heating quality; intelligent fault diagnosis: Neural network algorithm, expert system and other technologies are used to analyze equipment operation data, detect faults in advance, provide early warnings and reduce the risk of unplanned shutdowns.
[0032] The power supply and heating coordinated dispatch module is used to optimize the heat-to-power ratio, increase power output revenue while meeting heating demand, interact with the power grid, respond to demand-side management, and integrate with energy storage systems to enhance dispatch flexibility. The core functions of the power supply and heating coordinated dispatch module include: Optimize the heat-to-power ratio by adjusting unit output to increase power generation revenue while ensuring heat load demand; interact with the grid by participating in electricity market transactions in accordance with grid dispatch requirements to optimize unit power generation; respond to demand by increasing power generation during peak grid load periods and reducing power generation during off-peak periods to increase revenue; and integrate energy storage systems by combining thermal and electrical energy storage systems to store energy during off-peak periods and release it during peak periods to improve dispatch flexibility.
[0033] The Visual Management and Decision Support module provides real-time monitoring, trend analysis, alarm management, and optimized scheduling decision support. It leverages cloud computing and big data analytics to achieve remote monitoring and intelligent scheduling. The core functions of the Visual Management and Decision Support module include: Real-time monitoring: Displays unit operating status, load forecast, and scheduling strategies in real time through web interfaces and mobile applications; Trend analysis: Utilizes big data analytics to generate historical trend charts and optimize scheduling strategies; Alarm management: Combines intelligent fault diagnosis to automatically detect abnormal states and push alarm information; Cloud computing and remote scheduling: Enables remote optimization scheduling through a cloud computing platform, improving the level of intelligent scheduling.
[0034] The effects of optimizing the operation of combined heat and power (CHP) units include: improving energy efficiency and reducing operating costs such as coal and gas consumption; optimizing the heat-to-power ratio dispatch to increase electricity revenue while meeting heating demand; improving load forecasting accuracy and reducing dispatching errors by combining intelligent algorithms; improving system stability and reducing unplanned outages through intelligent fault diagnosis; and supporting electricity market transactions to maximize economic benefits through electricity price optimization.
[0035] This system can significantly improve the operating efficiency of combined heat and power units, while reducing operating costs and achieving green and low-carbon operation.
[0036] Specifically, load forecasting is the foundation of optimized scheduling. Accurate forecasting can reduce the number of unit start-ups and shutdowns and improve energy efficiency. The load forecasting and optimized scheduling module uses machine learning algorithms, including support vector machines, long short-term memory networks (LSTM), and random forests, to improve the accuracy of load forecasting.
[0037] Support vector machines are suitable for load forecasting of linear or nonlinear data, especially short-term heating and electricity load forecasting. They map nonlinear data to a high-dimensional space through kernel functions, thereby constructing a high-precision prediction model. Their advantages are that they can be applied to small sample data, effectively avoid overfitting, and have strong generalization ability in complex environments. They are used in the form of historical temperature, humidity, and load data as input to predict future heating load change trends.
[0038] Long Short-Term Memory (LSTM) networks are used for long-term load trend forecasting, such as seasonal heating demand and electricity load fluctuations. They have memory capabilities and can learn long-term time series dependencies, making them suitable for complex nonlinear load forecasting tasks. Their advantage lies in their ability to effectively capture trends and periodic changes in time series. When dealing with long-term trend forecasting, their accuracy is better than traditional statistical models (such as ARIMA). The application method uses historical load, weather factors (temperature, humidity, wind speed), and user demand models as inputs to predict future load changes.
[0039] Random forests (RF) are suitable for load forecasting of high-dimensional data, especially when the data contains multiple influencing factors (such as weather, user behavior, electricity price fluctuations, etc.). They use a voting method with multiple decision trees to improve the stability and noise resistance of the forecast. They have good generalization ability when there are many data features and a large amount of data. The application can automatically select the most important influencing factors to improve the accuracy of the forecast. They can still maintain high forecast performance even when the data noise is high. The application combines weather factors, grid load, and historical operating data to make short-term forecasts of heating and power generation loads.
[0040] Specifically, the main objective of the economic optimization module is to optimize fuel consumption, reduce operating costs, improve unit energy efficiency, and reduce carbon emissions while meeting heating and power generation needs. The economic optimization module adopts a dynamic energy efficiency assessment method and combines historical operating data of the cogeneration unit to optimize the unit's operating efficiency in real time.
[0041] The dynamic energy efficiency assessment method analyzes historical data, collects unit operating data (including load level, fuel consumption, temperature, pressure, etc.) under different operating conditions, establishes a benchmark energy efficiency curve, and identifies the optimal operating state of the unit through regression analysis, cluster analysis, and other methods. It then employs machine learning algorithms such as neural networks (ANN), random forests (RF), and support vector regression (SVR) to establish a unit energy efficiency prediction model. Combining historical data and real-time monitoring data, it predicts key indicators such as fuel consumption, boiler efficiency, and turbine efficiency under different operating conditions, calculates the optimal load allocation scheme, and ensures that the unit operates within its high-efficiency range. Finally, through SCADA systems, PLCs, and IoT sensors, it monitors the unit's fuel consumption, heat supply, and power generation in real time, calculates the unit's current energy efficiency indicators, compares them with the benchmark energy efficiency curve, and identifies efficiency deviations. When the system detects that the unit is deviating from the optimal energy efficiency state, it automatically adjusts: optimizing combustion parameters (fuel injection ratio, excess air coefficient, etc.) to improve boiler efficiency; optimizing heating steam parameters (pressure, temperature) to reduce internal turbine losses and improve heat utilization; adjusting unit load distribution to ensure that the unit operates in the high energy efficiency range; and reducing unnecessary start-ups and shutdowns to avoid start-up and shutdown losses and improve unit lifespan.
[0042] Specifically, the main task of the real-time control and execution module is to dynamically adjust the operating parameters of the unit according to the optimized scheduling strategy to ensure the stability of key parameters such as heating temperature and pressure, and at the same time optimize the start-up and shutdown strategy of the unit. To this end, the real-time control and execution module adopts fuzzy control algorithm and adaptive PID control algorithm to optimize the dynamic adjustment of heating parameters and improve heating stability.
[0043] Fuzzy control is a method based on expert experience and fuzzy logic reasoning, suitable for controlling complex, nonlinear systems. In heating optimization, fuzzy control can be used to automatically adjust heating temperature and steam pressure to ensure the stability of the heating system. The principle of fuzzy control of heating parameters is as follows: Input variables: Heating temperature deviation (ΔT): The deviation between the set temperature and the actual temperature.
[0044] Heating pressure deviation (ΔP): The deviation between the set pressure and the actual pressure.
[0045] Load change rate (ΔL): The rate of change of heating load.
[0046] Fuzzy rules: If ΔT is too large and ΔL increases, then increase the steam flow rate.
[0047] If ΔT is too small and ΔL decreases, then reduce the steam flow rate.
[0048] If ΔP is too large, the combustion rate will be reduced, thus decreasing steam output.
[0049] If ΔP is too small, increase the combustion rate to improve steam output.
[0050] Output variables: Adjust the steam flow rate to control the heating temperature and pressure.
[0051] Adjust the boiler combustion rate to optimize fuel consumption.
[0052] Fuzzy control optimization can adapt to complex and nonlinear systems, reduce temperature and pressure fluctuations, and adaptively adjust heating parameters when external conditions change (such as load fluctuations and changes in ambient temperature), thereby improving heating stability, ensuring smooth operation, avoiding drastic control fluctuations, and enhancing the user's heating experience.
[0053] PID control (Proportional-Integral-Derivative control) is a classic automatic control method that can precisely adjust heating parameters. However, traditional PID control may struggle to maintain optimal control performance when external environmental conditions change (such as load fluctuations or weather changes). Therefore, the real-time control and execution module employs an adaptive PID control algorithm to adjust PID parameters in real time, improving the dynamic response capability of the heating system. The adaptive PID control strategy is as follows: Parameter self-tuning: Real-time monitoring of heating temperature and pressure, and dynamic adjustment of PID parameters (P, I, D) to adapt to different load conditions.
[0054] Variable gain PID control: When the heating load fluctuates slightly, the control gain is reduced to decrease regulation fluctuations; when the load changes significantly, the control gain is increased to accelerate the response speed.
[0055] PID optimization based on genetic algorithm: The genetic algorithm (GA) is used to automatically optimize PID parameters, thereby improving the stability and robustness of the system.
[0056] The optimization effect of adaptive PID control lies in improving the precise control capability of heating parameters, making heating temperature and pressure more stable, and enabling rapid adjustment under load fluctuations, thereby improving the system response speed. Combined with fuzzy control, the control strategy is further optimized to improve the adjustment accuracy.
[0057] Specifically, the main task of the power supply and heating coordinated dispatch module is to optimize the power supply and heating balance of the cogeneration system, improve electricity revenue while meeting heating demand, and enhance the system's operational flexibility. The power supply and heating coordinated dispatch module integrates thermal energy storage and electrical energy storage systems to alleviate fluctuations in thermal and electrical loads and improve energy utilization efficiency.
[0058] Thermal energy storage systems are used to store surplus thermal energy and release it during peak demand periods, achieving balanced scheduling of heat load. A thermal energy storage system consists of a heat storage tank (for storing high-temperature hot water or steam, reducing frequent boiler start-ups and shutdowns), phase change material thermal storage (achieving more efficient thermal energy storage through the heat absorption and release of phase change materials), and a high-temperature molten salt thermal storage system (suitable for large-scale thermal energy storage, improving the regulation capability of combined heat and power systems). The optimized control of thermal energy storage is as follows: Off-peak heat storage: When heating demand is low and unit load is low, excess heat is stored in the heat storage system.
[0059] Peak-hour heat release: During peak heating demand periods, stored heat energy is released to reduce the additional combustion burden on boilers and improve heating stability.
[0060] Intelligent heat load forecasting: Based on weather data, user heat load models, and historical operating data, predict future heating demand and optimize thermal energy storage strategies.
[0061] The advantages of thermal energy storage include reducing boiler start-up and shutdown, improving operating efficiency, extending equipment life, reducing fuel consumption, lowering heating costs, improving overall energy efficiency, enhancing system flexibility, adapting to different operating conditions, and improving dispatching capabilities.
[0062] Electric energy storage systems are used to store excess electrical energy and release it during peak electricity price periods, increasing electricity revenue and enhancing the system's grid interaction capabilities. These systems consist of lithium-ion battery storage (suitable for short-term power regulation, improving system flexibility), flywheel storage (suitable for short-term frequency regulation, improving power quality), and pumped hydro storage (suitable for large-scale energy storage, improving renewable energy utilization). The optimized control of electric energy storage is as follows: Charging during periods of low electricity demand: When electricity prices are low and load demand is low, excess electricity is stored in an energy storage system.
[0063] Peak-demand discharge: When electricity prices are high and load demand is high, the stored electrical energy is released to reduce the grid's electricity purchase costs and increase revenue.
[0064] Intelligent load forecasting: By combining electricity market prices, grid dispatch signals, and historical load data, energy storage strategies are optimized to improve economic efficiency.
[0065] The advantages of energy storage are to reduce the cost of purchasing electricity from the grid, improve economic efficiency, enhance grid interaction capabilities, support demand response, reduce unit start-up and shutdown, and improve system stability.
[0066] Specifically, the visualization management and decision support module includes a web-based remote monitoring interface that provides real-time operational data visualization, optimized scheduling strategy recommendations, and alarm push notifications.
[0067] The web-based remote monitoring system adopts a B / S (Browser / Server) architecture and includes the following core components: Frontend (Web UI): Developed using Vue.js / React, providing an interactive and visual interface.
[0068] Backend (Data Processing and API): Based on Node.js / Python (Flask / Django), responsible for data processing, API interfaces, user management and other functions.
[0069] Database: MySQL / PostgreSQL is used to store historical data, and InfluxDB / TimescaleDB is used to optimize time-series data queries.
[0070] Message push service: Real-time data updates are achieved based on WebSocket / MQTT, and multiple alarm push methods such as SMS, email, and APP notifications are supported.
[0071] The main functions of a web-based remote monitoring system are: Homepage Overview: Displays key data such as unit operating status, power and heat load, and economic indicators.
[0072] Real-time monitoring: Visualizes parameters such as temperature, pressure, power generation, and heat supply of each unit, and supports custom filtering and data comparison.
[0073] Scheduling optimization recommendation: Based on machine learning and optimization algorithms, the system dynamically recommends the optimal scheduling strategy and provides feasibility analysis.
[0074] Historical data analysis: Users can query historical data to perform trend analysis, anomaly analysis, energy efficiency assessment, etc.
[0075] Alarms and warnings: Real-time monitoring of abnormal situations, such as equipment failures and overload, and push alarm information.
[0076] Specifically, the system is connected to the power grid dispatching system, can participate in electricity market transactions, and dynamically adjust power generation output according to electricity price fluctuations to increase revenue.
[0077] By using AGC (Automatic Generation Control), SCADA (Supervisory Control and Data Acquisition), and EMS (Energy Management System), real-time data interaction with the power grid is achieved. Based on market electricity prices, power generation output is flexibly adjusted to increase revenue. By combining machine learning and optimization algorithms, electricity price trends are predicted, and optimal dispatch strategies are formulated. Based on grid demand, load is reduced during peak hours and power generation is increased during off-peak hours to obtain additional subsidies. Ancillary services such as frequency regulation, peak shaving, and reserve are provided to improve economic efficiency.
[0078] Connecting with the power grid dispatching system can increase market transaction revenue by 5%-15%, reduce operating costs, reduce unplanned outages by 30%, improve system stability, optimize grid interaction, enhance dispatching flexibility, and improve the competitiveness of ancillary services markets. Through grid interaction, market transaction optimization, and intelligent dispatching, the economic benefits and operating efficiency of cogeneration systems have been significantly improved.
[0079] Specifically, the system integrates a carbon emission monitoring module, which can monitor the carbon emissions during the unit's operation in real time and optimize the unit's operation mode in conjunction with carbon trading market strategies to reduce carbon emission costs.
[0080] The carbon emission monitoring module monitors carbon emissions in real time, including carbon dioxide (CO2) and nitrogen oxides (NOx) produced during the computer group's fuel consumption. x The carbon emission monitoring module monitors emissions of pollutants such as sulfur dioxide (SO2); it statistically analyzes carbon emission characteristics under different unit load conditions and assesses the carbon emission intensity per unit of electricity / heat; it obtains real-time market information such as carbon allowance prices and carbon credit prices, and optimizes unit operation strategies; it seeks the optimal balance between economic efficiency and carbon emission costs to reduce operating costs; and it formulates optimal carbon trading strategies based on the company's carbon allowance situation to reduce penalties for exceeding emission limits. Through carbon emission monitoring, carbon trading optimization, and low-carbon dispatch strategies, the carbon emission monitoring module significantly improves the environmental compliance and economic benefits of cogeneration systems.
[0081] A method and system for optimizing the operation and scheduling of combined heat and power (CHP) units, comprising the following steps: S1: Establish an optimized scheduling model for cogeneration units, including but not limited to power generation load, grid dispatch instructions, heating load, steam flow, temperature, pressure, fuel consumption, unit efficiency, carbon emission data, ambient temperature, humidity, and meteorological data; use SCADA systems, PLCs, and IoT sensors for real-time data acquisition and store the data in a database; improve data quality through data cleaning and outlier handling.
[0082] S2: Based on historical operating data, machine learning algorithms (such as support vector machine, LSTM, random forest, etc.) or time series analysis methods are used to predict future heating and power loads. Combined with weather forecasts, user demand models, and industrial load fluctuations, the prediction results are optimized and the accuracy is improved. Short-term (minute / hour level), medium-term (daily level), and long-term (monthly level) load demands are calculated to provide a reference for subsequent scheduling.
[0083] S3: Establish an optimal scheduling model for cogeneration units, with constraints including unit start-up and shutdown constraints, minimum output given a maximum output limit, heat-to-power ratio balance constraints, and fuel consumption and carbon emission constraints; use optimization algorithms (such as mixed integer linear programming (MILP), dynamic programming (DP), genetic algorithm (GA), particle swarm optimization (PSO), etc.) to solve for the optimal scheduling scheme; generate optimized scheduling instructions and compare them with actual operating conditions to dynamically adjust the scheduling scheme.
[0084] S4: Operating costs of the computer unit, including fuel costs, carbon emission costs, start-up and shutdown costs, etc. By combining market electricity prices, fuel prices, and carbon trading market data, the fuel consumption and start-up and shutdown strategies of the unit are optimized. Dynamic energy efficiency assessment methods are used to analyze the unit's operating efficiency in real time and improve the unit's economy.
[0085] S5: Based on the optimized scheduling plan, adjust the start-up and shutdown status of the units, optimize the turbine extraction ratio, improve the efficiency of cogeneration, and use fuzzy control algorithm or adaptive PID control algorithm to optimize heating temperature and pressure to ensure heating quality. Through the intelligent fault diagnosis system, monitor the unit operating status in real time, identify abnormal situations, and provide early warnings and optimization suggestions.
[0086] S6: While meeting heating demand, optimize the heat-to-power ratio to maximize electricity revenue. Combine thermal energy storage and electric energy storage systems to reduce load fluctuations and improve dispatch flexibility.
[0087] S7: Through the web-based remote monitoring interface, the unit's operating status, load forecast results, and optimized scheduling schemes are displayed in real time. It generates operation analysis reports, including unit energy efficiency assessment, economic analysis, and carbon emission monitoring, providing support for optimized scheduling decisions. It uses cloud computing and big data analysis to achieve remote optimized scheduling and improve the level of intelligent scheduling.
[0088] This method has been optimized in multiple aspects, including data acquisition, load forecasting, optimized scheduling, economic analysis, intelligent control, collaborative scheduling, and visual management, and has the following main advantages: Data-driven approach to improve scheduling accuracy: Multi-source data fusion, combined with SCADA system, PLC, and IoT sensors, collects unit load, fuel consumption, environmental parameters, etc. in real time to improve data integrity; data cleaning and outlier handling improve data quality, reduce errors, and improve scheduling reliability; machine learning-based load forecasting, using algorithms such as LSTM, support vector machine (SVM), and random forest, combined with weather forecasts and user demand models, significantly improves load forecasting accuracy.
[0089] Intelligent optimization to improve economic efficiency: Taking into account factors such as unit start-up and shutdown, fuel consumption, heat-to-power ratio, and carbon emissions, optimization algorithms such as mixed-integer linear programming (MILP), dynamic programming (DP), genetic algorithm (GA), and particle swarm optimization (PSO) are used to solve the optimal scheduling scheme; intelligent calculation of operating costs, combined with fuel price, electricity price, and carbon trading market data, dynamically optimizes fuel consumption and start-up and shutdown strategies to reduce operating costs; and dynamic energy efficiency assessment methods are used to analyze unit operating efficiency in real time, optimize energy consumption, and improve overall economic efficiency.
[0090] Intelligent control enhances operational stability: Adaptive control optimization employs fuzzy control and adaptive PID control algorithms to dynamically adjust heating temperature and pressure, thereby improving heating stability; Intelligent fault diagnosis monitors unit operating status in real time, identifies abnormal operating conditions, provides early warnings and optimization suggestions, reduces unplanned downtime, and improves system reliability.
[0091] Coordinated dispatching enhances flexibility: Optimize the heat-to-power ratio to increase electricity revenue. While meeting heating demand, optimize the heat-to-power ratio to maximize electricity market revenue; integrate energy storage systems to reduce load fluctuations. Combine thermal energy storage and electrical energy storage to smooth load fluctuations and improve dispatching flexibility and energy utilization efficiency; participate in electricity market transactions. Optimize power generation output based on electricity price fluctuations to increase revenue and respond to demand-side management.
[0092] Intelligent visualization enhances decision-making efficiency: The web-based remote monitoring interface displays real-time unit operating status, load forecast results, and optimized scheduling schemes, improving management efficiency; it generates operation analysis reports, including unit energy efficiency assessment, economic analysis, and carbon emission monitoring, providing support for optimized scheduling decisions; cloud computing enhances data analysis, enabling remote optimized scheduling and improving the level of intelligence.
[0093] Environmentally friendly and low-carbon, reducing carbon emission costs: Carbon emission monitoring and optimization, real-time calculation of carbon emissions, and optimization of unit operation modes in conjunction with carbon trading market strategies to reduce carbon emission costs; Fuel optimization scheduling, prioritizing low-carbon fuels (such as natural gas and biomass fuels) to reduce carbon emissions and improve environmental friendliness.
[0094] This method integrates multiple technologies such as big data analysis, machine learning, optimization algorithms, intelligent control, and carbon emission optimization, taking into account economy, stability, flexibility, intelligence, and environmental protection. It significantly improves the operating efficiency and revenue of cogeneration units while reducing operating costs and carbon emissions, and has important value for the electricity market, energy management, and environmental compliance.
[0095] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0096] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A combined heat and power (CHP) unit operation optimization and scheduling system, characterized in that the system... include: The data acquisition and monitoring module is used to collect the operating parameters of the cogeneration unit, including power generation load, heat load, fuel consumption, steam pressure, temperature and environmental parameters, and to monitor them in real time through the SCADA system, PLC controller and IoT sensors. The load forecasting and optimized scheduling module is used to predict future heating and power loads based on historical data, weather forecasts, and user demand models, and to formulate optimized unit scheduling strategies using mixed integer linear programming, dynamic programming, genetic algorithms, and particle swarm optimization algorithms. The economic optimization module is used to calculate fuel costs and carbon emission costs, and combine fuel prices, unit efficiency, and carbon trading market information to optimize unit fuel consumption and overall energy efficiency, thereby improving economic efficiency. The real-time control and execution module is used to dynamically adjust the start and stop of the unit according to the optimized scheduling strategy, and optimize the heating parameters based on the feedback control algorithm to achieve precise control of heating temperature and pressure. At the same time, it detects abnormal operating conditions and provides early warnings through intelligent fault diagnosis algorithm. The power supply and heating coordinated dispatch module is used to optimize the heat-to-power ratio, increase power output revenue while meeting heating demand, interact with the power grid, respond to demand-side management, and combine with energy storage systems to improve dispatch flexibility. The visualization management and decision support module provides real-time monitoring, trend analysis, alarm management, and optimized scheduling decision support. It also enables remote monitoring and intelligent scheduling through cloud computing and big data analysis.
2. The combined heat and power (CHP) unit operation optimization and scheduling system according to claim 1, characterized in that: The load forecasting and optimized scheduling module employs machine learning algorithms, including support vector machines, long short-term memory networks (LSTM), and random forests, to improve load forecasting accuracy.
3. The combined heat and power unit operation optimization and scheduling system according to claim 1, characterized in that: The economic optimization module adopts a dynamic energy efficiency evaluation method, which combines historical operating data of the cogeneration unit to optimize the unit's operating efficiency in real time.
4. The combined heat and power unit operation optimization and scheduling system according to claim 1, characterized in that: The real-time control and execution module employs fuzzy control algorithm and adaptive PID control algorithm to optimize the dynamic adjustment of heating parameters and improve heating stability.
5. The combined heat and power unit operation optimization and scheduling system according to claim 1, characterized in that: The power supply and heating coordinated scheduling module integrates thermal energy storage and electrical energy storage systems to mitigate thermal and electrical load fluctuations and improve energy utilization efficiency.
6. The combined heat and power unit operation optimization and scheduling system according to claim 1, characterized in that: The visualization management and decision support module includes a web-based remote monitoring interface, providing real-time operational data visualization, optimized scheduling strategy recommendations, and alarm push notifications.
7. The combined heat and power unit operation optimization and scheduling system according to claim 1, characterized in that: The system is connected to the power grid dispatching system and can participate in electricity market transactions. It can dynamically adjust power generation output according to electricity price fluctuations to increase revenue.
8. The combined heat and power unit operation optimization and scheduling system according to claim 1, characterized in that: The system integrates a carbon emission monitoring module, which can monitor the carbon emissions during the operation of the power unit in real time and optimize the unit's operation mode in conjunction with carbon trading market strategies to reduce carbon emission costs.
9. A method and system for optimizing the operation and scheduling of a combined heat and power (CHP) unit according to claims 1 to 8, characterized in that, The optimized scheduling method and steps are as follows: S1: Establish an optimized scheduling model for cogeneration units, including but not limited to power generation load, grid dispatch instructions, heating load, steam flow, temperature, pressure, fuel consumption, unit efficiency, carbon emission data, ambient temperature, humidity, and meteorological data; use SCADA systems, PLCs, and IoT sensors for real-time data acquisition and store the data in a database; improve data quality through data cleaning and outlier handling. S2: Based on historical operating data, machine learning algorithms (such as support vector machine, LSTM, random forest, etc.) or time series analysis methods are used to predict future heating and power loads. Combined with weather forecasts, user demand models, and industrial load fluctuations, the prediction results are optimized and the accuracy is improved. Short-term (minute / hour level), medium-term (daily level), and long-term (monthly level) load demands are calculated to provide a reference for subsequent scheduling. S3: Establish an optimal scheduling model for cogeneration units, with constraints including unit start-up and shutdown constraints, minimum output given a maximum output limit, heat-to-power ratio balance constraints, and fuel consumption and carbon emission constraints; use optimization algorithms (such as mixed integer linear programming (MILP), dynamic programming (DP), genetic algorithm (GA), particle swarm optimization (PSO), etc.) to solve for the optimal scheduling scheme; generate optimized scheduling instructions and compare them with actual operating conditions to dynamically adjust the scheduling scheme; S4: Computer unit operating costs, including fuel costs, carbon emission costs, start-up and shutdown costs, etc., combined with market electricity prices, fuel prices, carbon trading market data, optimize the unit's fuel consumption and start-up and shutdown strategies, adopt dynamic energy efficiency assessment methods, and conduct real-time analysis of unit operating efficiency to improve unit economy; S5: Based on the optimized scheduling plan, adjust the start-up and shutdown status of the units, optimize the turbine extraction ratio, improve the efficiency of cogeneration, and use fuzzy control algorithm or adaptive PID control algorithm to optimize heating temperature and pressure, ensure heating quality, and monitor the unit operating status in real time through the intelligent fault diagnosis system, identify abnormal situations, and provide early warning and optimization suggestions. S6: Under the premise of meeting heating demand, optimize the heat-to-power ratio to maximize electricity revenue, and combine thermal energy storage and electric energy storage systems to reduce load fluctuations and improve dispatch flexibility. S7: Through the web-based remote monitoring interface, the unit's operating status, load forecast results, and optimized scheduling schemes are displayed in real time. It generates operation analysis reports, including unit energy efficiency assessment, economic analysis, and carbon emission monitoring, providing support for optimized scheduling decisions. It uses cloud computing and big data analysis to achieve remote optimized scheduling and improve the level of intelligent scheduling.