Energy consumption analysis and optimization method for sliding pressure optimization control of thermal power generating unit

Abstract

The invention relates to the technical field of automatic control, and discloses an energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power generating unit. According to the method, the steam pressure, the energy consumption and the efficiency of the unit are accurately reflected by establishing a mathematical model, influence factors are recognized in real time, scientific and reasonable control targets can be formulated, the control strategy can adapt to changes of actual operation conditions due to operation parameters collected and processed in real time, and therefore energy consumption and emission are effectively reduced; a genetic algorithm is adopted to optimize control parameters, dynamic regulation and control of steam pressure can be achieved, the optimal operation state of a unit under different working conditions is ensured, a continuous monitoring and evaluation mechanism provides timely feedback for the optimization process, parameter adjustment and control strategies can be continuously optimized, and the optimization efficiency is improved. And finally, the purposes of energy conservation, consumption reduction and environmental protection of the thermal power generating unit are achieved, and the thermal power generating unit is promoted to develop towards the more efficient and low-emission direction.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, in particular to an energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit. Background Art

[0002] A thermal power unit is a device that generates electricity by burning fossil fuels (such as coal, natural gas or oil). Its core principle is to convert the heat energy generated by fuel combustion into mechanical energy, and then convert the mechanical energy into electrical energy through a generator. Thermal power units occupy an important position in power production and can provide stable baseload power to meet the energy needs of social and economic development. However, the operation of thermal power units is also accompanied by energy consumption and environmental pollution problems, especially the emission of carbon dioxide and other harmful gases. Therefore, optimizing the energy consumption of thermal power units and improving their operating efficiency have become important means to reduce production costs and environmental impacts.

[0003] Traditional thermal power unit control methods mostly use fixed parameter control, which ignores the real-time changing operating conditions and their impact on energy efficiency, leading to problems such as high energy consumption and excessive emissions. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit. By establishing a mathematical model to accurately reflect the steam pressure, energy consumption and efficiency of the unit, and identifying influencing factors in real time, it is helpful to formulate scientific and reasonable control targets. The operating parameters collected and processed in real time enable the control strategy to adapt to changes in actual operating conditions, thereby effectively reducing energy consumption and emissions. The use of genetic algorithms to optimize control parameters can achieve dynamic regulation of steam pressure and ensure the optimal operating state of the unit under different operating conditions. The continuous monitoring and evaluation mechanism provides timely feedback for the optimization process, so that parameter adjustment and control strategy can be continuously optimized, ultimately achieving the energy conservation and consumption reduction and environmental protection goals of the thermal power unit, and promoting its development towards higher efficiency and lower emissions.

[0006] (2) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: an energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit, comprising the following steps:

[0008] S1. Utilize equipment technical parameters, thermodynamic characteristics, and historical operating data of thermal power units to establish a mathematical model reflecting the unit's steam pressure, energy consumption, and efficiency. Identify the key factors affecting sliding pressure and energy consumption, and set control objectives and constraints.

[0009] S2. Collect unit operating parameters in real time through sensors. These parameters include steam pressure, flow, temperature, fuel consumption, and power output data. These parameters are filtered and converted to form unified time series data.

[0010] S3. Calculate the energy consumption indicators of the current unit using the noise-filtered and converted unit operating parameters. The energy consumption indicators of the current unit include fuel consumption, thermal efficiency, and emission indicators. Compare the energy consumption indicators of the current unit with industry standards and set optimization targets.

[0011] S4. Based on the established mathematical model and the set optimization objectives, a dynamic control strategy is designed to stabilize the steam pressure within a predetermined range;

[0012] S5. Use genetic algorithms to find the optimal control parameters and apply the resulting control strategy to the actual regulation process, dynamically controlling the sliding pressure by adjusting the steam valve opening and fuel input;

[0013] S6. Continuously monitor the operating status of the unit, evaluate the energy-saving effect and pressure stability of the adjustment measures in real time, and feed the evaluation results back to the mathematical model and dynamic control strategy for parameter optimization and dynamic adjustment of the adjustment strategy.

[0014] Preferably, the mathematical model of the steam pressure is as follows:

[0015] P steam (t) = f1(m steam (t),T water (t),Q input (t),θ(t))

[0016] In the formula, P steam (t) represents the steam pressure, m steam (t) represents the steam flow rate, T water (t) represents the feed water temperature, Q input (t) represents the fuel input heat, θ(t) represents the boiler parameters, and f1(*) represents the steam pressure model function.

[0017] Preferably, the mathematical model of energy consumption is as follows:

[0018] Q fuel (t) = f2(P steam (t),η boiler (t),m steam (t))

[0019] In the formula, Q fuel (t) represents the fuel heat input power, P steam (t) represents the steam pressure, m steam(t) represents the steam flow rate, η boiler (t) represents efficiency, and f2(*) represents the energy consumption model function.

[0020] Preferably, the mathematical model of the efficiency is as follows:

[0021]

[0022] In the formula, η boiler (t) represents efficiency, P electric (t) represents the power output, Q fuel (t) represents the fuel heat input power.

[0023] Preferably, the formula for filtering noise of the unit operating parameters is as follows:

[0024] x filter (t) = a*x raw (t)+(1-a)*x filter (t-1)

[0025] In the formula, x filter (t) represents the data at time t after filtering, a represents the smoothing coefficient, and its value range is (0-1), x raw (t) represents the original data without filtering, x filter (t-1) represents the smoothed data at the previous sampling time (t-1).

[0026] Preferably, the formula for converting the unit operating parameters is as follows:

[0027]

[0028] In the formula, x normal (t) represents the normalized data value, x filter (t) represents the data at time t after filtering, x min Indicates the minimum value of the corresponding parameter in the data set to be normalized, x max Indicates the maximum value of the corresponding parameter in the data set to be normalized.

[0029] Preferably, the calculation formula of the fuel consumption is as follows:

[0030]

[0031] In the formula, Qrxl represents fuel consumption, represents the fuel mass flow rate, CV represents the calorific value of the fuel, n combusion Indicates combustion efficiency.

[0032] Preferably, the calculation formula of the thermal efficiency is as follows:

[0033]

[0034] In the formula, Rxcl represents thermal efficiency, P electric (t) represents the power output, and Qrxl represents the fuel consumption.

[0035] Preferably, the calculation formula of the emission index is as follows:

[0036]

[0037] In the formula, represents the carbon dioxide emission index, represents the fuel mass flow rate, Indicates the carbon dioxide emission coefficient produced by fuel combustion.

[0038] Preferably, the optimization objective is defined as follows:

[0039]

[0040] In the formula, represents the overall optimization goal, w1 represents the coefficient of the influence of adjusting fuel consumption, w2 represents the penalty coefficient of adjusting pressure deviation, w3 represents the penalty coefficient of adjusting carbon dioxide emissions, Q fuel (t) represents the fuel heat input power, P steam (t) represents the steam pressure, P set Indicates the ideal steam pressure target value, Represents the carbon dioxide emission index, and the integral symbol represents the cumulative value accumulated in the time interval [0-T].

[0041] Compared with the prior art, the present invention provides an energy consumption analysis and optimization method for sliding pressure optimization control of thermal power units, which has the following beneficial effects:

[0042] The present invention accurately reflects the steam pressure, energy consumption and efficiency of the unit by establishing a mathematical model, identifies influencing factors in real time, and helps to formulate scientific and reasonable control targets. The operating parameters collected and processed in real time enable the control strategy to adapt to changes in actual operating conditions, thereby effectively reducing energy consumption and emissions. The use of genetic algorithms to optimize control parameters can achieve dynamic regulation of steam pressure and ensure the optimal operating state of the unit under different operating conditions. The continuous monitoring and evaluation mechanism provides timely feedback for the optimization process, allowing parameter adjustment and control strategy to be continuously optimized, ultimately achieving the energy conservation and consumption reduction and environmental protection goals of thermal power units, and promoting their development towards higher efficiency and lower emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Traditional thermal power plant control methods mostly use fixed parameter control. This method ignores the real-time changing operating conditions and their impact on energy efficiency, leading to problems such as high energy consumption and excessive emissions. To this end, an energy consumption analysis and optimization method for thermal power plant sliding pressure optimization control is proposed. Figure 1 , the method comprises the following steps:

[0046] S1. Utilize equipment technical parameters, thermodynamic characteristics, and historical operating data of thermal power units to establish a mathematical model reflecting the unit's steam pressure, energy consumption, and efficiency. Identify the key factors affecting sliding pressure and energy consumption, and set control objectives and constraints.

[0047] By using the technical parameters of the equipment (such as the rated parameters and equipment characteristics of boilers, steam turbines, and generators), thermodynamic characteristics (coal calorific value, thermal efficiency, heat transfer coefficient, etc.) and historical operating data of thermal power units, a multi-level and multi-variable mathematical model can be established to fully reflect the dynamic state and energy consumption performance of the unit. Thermodynamic models such as P steam (t) = f1(m steam (t),T water (t),Q input (t),θ(t)), which helps to reveal the core parameters affecting pressure; dynamic models such as Q fuel (t) = f2(P stean (t),η boile (t),m stean (t)), the dynamic response characteristics of pressure can be described; the energy efficiency model is as follows Used to reflect the relationship between fuel input and power output, the establishment of these models not only helps identify key factors affecting sliding pressure changes and energy consumption levels, such as fuel calorific value, boiler heat transfer efficiency, steam flow rate, and control valve response, but also lays the foundation for formulating scientific and reasonable control targets;

[0048] On this basis, it is also necessary to clarify the control objectives of the system:

[0049] Maintain steam pressure P steam At the set ideal value Pset Nearby, ensuring stable operation of boilers and turbines;

[0050] Minimize the fuel consumption per unit of electricity consumption to achieve the lowest energy consumption;

[0051] Control harmful emissions such as carbon dioxide within industry standards to ensure environmental protection requirements;

[0052] Improve thermal efficiency and reduce energy waste.

[0053] At the same time, in order to ensure the safe and stable operation of the system, a series of constraints must be set:

[0054] Steam pressure should be kept within a safe range (such as P min ≤P steam ≤P max );

[0055] Fuel input and heat energy input must not exceed the maximum limits allowed by the equipment;

[0056] Emission indicators must not exceed the standard values ​​stipulated by national environmental protection regulations;

[0057] The regulating variables of the unit (such as valve opening, fuel input, etc.) should be within a reasonable adjustment range (such as [0,1] or a specific numerical interval);

[0058] Consider the system's dynamic response capabilities to ensure that adjustments do not cause oscillation or instability in the equipment;

[0059] By establishing such a model system, with clear objectives and constraints, it is possible to achieve intelligent and optimized scheduling and energy management of thermal power units, which not only enhances the operational efficiency of the system but also effectively controls environmental pollution and meets increasingly stringent environmental regulations and energy efficiency requirements.

[0060] S2. Collect unit operating parameters in real time through sensors. These parameters include steam pressure, flow, temperature, fuel consumption, and power output data. These parameters are filtered and converted to form unified time series data.

[0061] By building a multi-channel sensor system, we can achieve real-time high-frequency acquisition of key operating parameters of thermal power units, including steam pressure, steam flow, water temperature, fuel consumption rate, and electrical output power. The collected raw data generally has interference such as noise, mutation, and drift. To ensure the accuracy of subsequent analysis and control, the data needs to be filtered (signal smoothing) and converted (normalized). The noise filtering method commonly used is the exponential moving average (EWMA) filter, and its mathematical expression is:

[0062] x filter (t) = a*x raw(t)+(1-a)*x filter (t-1)

[0063] Among them, x raw (t) represents the original sensor data collected at time t, such as pressure or temperature, x filter (t) is the smoothed value after filtering, and a is the smoothing coefficient, which is usually between (0-1) (for example, 0.1 to 0.3) to find a good balance between filtering noise and maintaining signal changes. In order to make different parameters comparable in numerical range, normalization is also used to convert the noise-filtered data into a unified scale. The formula is:

[0064]

[0065] Here, x min and x max are the historical minimum and maximum values ​​of the parameter, respectively. This operation maps all parameters to the range of [0-1], facilitating the design of subsequent control strategies and the unification of model inputs. Overall, this series of technical measures, including high-frequency data acquisition, exponential filtering, and normalization processing, not only effectively suppresses sensor noise and interference, but also ensures the consistency and comparability of data from different sensors. This provides a reliable foundation for accurate modeling, state estimation, and real-time control, significantly improving the efficiency and effectiveness of dynamic optimization of thermal power units.

[0066] S3. Calculate the energy consumption indicators of the current unit using the noise-filtered and converted unit operating parameters. The energy consumption indicators of the current unit include fuel consumption, thermal efficiency, and emission indicators. Compare the energy consumption indicators of the current unit with industry standards and set optimization targets.

[0067] Using the filtered and normalized unit operating parameters, such as steam pressure, steam flow, fuel input, and electrical output power, a series of thermodynamic and performance analysis models can be used to accurately calculate the current unit's energy consumption indicators. Fuel consumption is usually calculated based on fuel mass flow and calorific value, using the formula:

[0068]

[0069] in, is the fuel mass flow rate (e.g., tons / hour), and CV is the fuel calorific value (MJ / ton). From this, the fuel heat input energy can be obtained, which provides the basis for the subsequent calculation of thermal efficiency. Thermal efficiency is usually defined as:

[0070]

[0071] Among them, Rxcl represents the combustion efficiency, and the emission index (such as carbon dioxide emissions) can be calculated by multiplying the total amount of fuel burned by the emission coefficient:

[0072]

[0073] The energy consumption indicators calculated in real time should be compared with industry standards (such as energy conservation, emissions, and other industry-specified indicators). For example, fuel consumption should not exceed the industry maximum, and emissions should also be lower than the industry-specified emission standards. Based on the comparison results, specific optimization goals are formulated. The objective function for multi-objective optimization is set as follows:

[0074]

[0075] Among them, w1, w2, and w3 are the importance weights of the corresponding indicators. The goal is to minimize J, that is, to maximize thermal efficiency while ensuring that fuel consumption and emissions do not exceed standards. By continuously optimizing J, the operating goals of optimal fuel utilization, emission compliance, and improved energy efficiency are achieved. This series of technical means provides a scientific basis and effective approach for the performance optimization of thermal power units through precise calculations and strict benchmarking.

[0076] S4. Based on the established mathematical model and the set optimization objectives, a dynamic control strategy is designed to stabilize the steam pressure within a predetermined range;

[0077] According to the established thermodynamic, kinetic and energy efficiency models, and combined with multi-objective optimization goals such as maximizing thermal efficiency, minimizing fuel consumption and emission indicators, a dynamic control strategy based on model predictive control is designed to achieve precise regulation and stabilization of the steam pressure of thermal power units. Specifically, the real-time sensor data (after noise filtering and normalization) is first input into the model, and the prediction algorithm is used to simulate the unit operation status within the prediction time domain (such as the next 10 seconds). Then, the objective function is constructed to minimize the pressure deviation while meeting the operational constraints of fuel input and control valve opening. The controller optimizes the next operation input in each control cycle (such as every 1 second) to generate the optimal control strategy and dynamically adjust the opening of the fuel valve and control valve. During the implementation process, real-time feedback adjustment is used to continuously correct the prediction deviation to ensure that the pressure is within the predetermined range (for example, 0.9MPa≤P steam ≤1.1MPa), greatly reducing pressure fluctuations and system disturbances while taking into account the optimization of energy efficiency and emission indicators. Through this efficient model predictive control method, it not only ensures the safe and stable operation of the unit, but also achieves the minimum energy consumption and the optimization of emissions, providing strong technical support for the intelligent operation of thermal power units;

[0078] S5. Use genetic algorithms to find the optimal control parameters and apply the resulting control strategy to the actual regulation process, dynamically controlling the sliding pressure by adjusting the steam valve opening and fuel input;

[0079] In order to achieve optimal control of the steam pressure of thermal power units, a genetic algorithm is used to globally optimize the control parameters. First, based on the previously established thermodynamic and dynamic models, the control parameters (such as the control valve opening coefficient, fuel input ratio, and control time step) are used as individual codes of the genetic algorithm. The fitness function is defined as a comprehensive indicator of pressure deviation, fuel consumption, and emission indicators during operation to ensure that the control parameters take into account both system stability and economy. Through a series of genetic operations (selection, crossover, and mutation), continuous iterations are performed within the preset parameter space to find the optimal control parameter combination that minimizes pressure deviation, minimizes energy consumption, and meets emission standards. After obtaining the optimal parameters, they are applied to the actual regulation system to adjust the steam valve opening and fuel input in real time to achieve dynamic regulation of sliding pressure. In specific operations, the control input is calculated based on the current system status (pressure, temperature, flow rate, etc.), and the valve opening is adjusted to stabilize the steam pressure within the predetermined range while ensuring optimal fuel consumption and emission indicators. The global optimal parameters solved by the genetic algorithm guide actual operations, which not only significantly improves the stability and response speed of the system, but also minimizes energy consumption and emissions, achieving intelligent and optimized operation of the thermal power unit. This process combines global search capabilities with real-time control to ensure the efficient and stable operation of the thermal power system under complex operating conditions, meeting the dual needs of economy and environmental protection.

[0080] S6. Continuously monitor the unit's operating status, evaluate the energy-saving effects and pressure stability of regulatory measures in real time, and feed the evaluation results back into the mathematical model and dynamic control strategy for parameter optimization and dynamic adjustment of the regulation strategy;

[0081] To ensure efficient and stable operation of thermal power units, a real-time monitoring system continuously collects key operating parameters. High-precision sensors and multi-source data fusion technology ensure data integrity and accuracy. Subsequently, the effectiveness of regulatory measures is evaluated in real time using predefined metrics (such as energy savings, pressure deviation, and emissions compliance). Advanced algorithms (such as rolling window analysis, multi-factor regression, and machine learning models) are applied to dynamically assess energy savings and pressure stability. For example, energy consumption reduction and pressure deviation are used as evaluation metrics. Evaluation results are immediately fed back to the thermodynamic model and dynamic control strategy via a data management platform to achieve closed-loop control. At the control strategy level, feedback information is used to adaptively adjust model parameters. Gain adjustment, parameter identification, or online tuning based on optimization algorithms are used to modify control parameters or regulation strategies in real time. This continuous optimization of model predictions and control schemes based on evaluation results improves regulation efficiency and pressure stability under different operating conditions, maximizing energy conservation, emission reduction, and operational optimization. This continuous feedback and adjustment mechanism, leveraging big data analysis and intelligent algorithms, ensures that thermal power units maintain optimal operation under complex operating conditions and is a key enabler of intelligent power generation management.

[0082] By establishing accurate thermodynamic and kinetic models, combining the steady-state and dynamic operating parameters collected by sensors in real time, adopting noise filtering and normalization techniques to ensure data accuracy, and using genetic algorithms to globally optimize control parameters, guiding the dynamic regulation of regulating valves and fuel input, ensuring that steam pressure is stable within a predetermined range, and introducing model predictive control (MPC) combined with multi-objective optimization strategies to achieve balanced regulation of pressure, energy consumption and emissions, while real-time monitoring of operating status, evaluation of regulation effects, forming a closed-loop feedback mechanism, and dynamically adjusting model parameters and control strategies to significantly improve energy efficiency and environmental protection levels. The application of this method provides scientific and feasible solutions for energy conservation, emission reduction and operating efficiency of thermal power units, and promotes the intelligent and green development of the thermal power industry.

[0083] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An energy consumption analysis and optimization method for sliding pressure optimization control of thermal power units, characterized in that: The following steps are involved: S1. Utilize equipment technical parameters, thermodynamic characteristics, and historical operating data of thermal power units to establish a mathematical model reflecting the unit's steam pressure, energy consumption, and efficiency. Identify the key factors affecting sliding pressure and energy consumption, and set control objectives and constraints. S2. Collect unit operating parameters in real time through sensors. These parameters include steam pressure, flow, temperature, fuel consumption, and power output data. These parameters are filtered and converted to form unified time series data. S3. Calculate the energy consumption indicators of the current unit using the noise-filtered and converted unit operating parameters. The energy consumption indicators of the current unit include fuel consumption, thermal efficiency, and emission indicators. Compare the energy consumption indicators of the current unit with industry standards and set optimization targets. S4. Based on the established mathematical model and the set optimization objectives, a dynamic control strategy is designed to stabilize the steam pressure within a predetermined range; S5. Use genetic algorithms to find the optimal control parameters and apply the resulting control strategy to the actual regulation process, dynamically controlling the sliding pressure by adjusting the steam valve opening and fuel input; S6. Continuously monitor the operating status of the unit, evaluate the energy-saving effect and pressure stability of the adjustment measures in real time, and feed the evaluation results back to the mathematical model and dynamic control strategy for parameter optimization and dynamic adjustment of the adjustment strategy.

2. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 1, characterized in that: The mathematical model of the vapor pressure is as follows: P steam (t)=f1(m steam (t),T water (t),Q input (t),θ(t)) In the formula, P steam (t) represents the steam pressure, m steam (t) represents the steam flow rate, T water (t) represents the feed water temperature, Q input (t) represents the fuel input heat, θ(t) represents the boiler parameters, and f1(*) represents the steam pressure model function.

3. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 2, characterized in that: The mathematical model of energy consumption is as follows: Q fuel (t)=f2(P steam (t),η boiler (t),m steam (t)) In the formula, Q fuel (t) represents the fuel heat input power, P steam (t) represents the steam pressure, m steam (t) represents the steam flow rate, η boiler (t) represents efficiency, and f2(*) represents the energy consumption model function.

4. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 3 is characterized by: The mathematical model of the efficiency is as follows: In the formula, η boiler (t) represents efficiency, P electric (t) represents the power output, Q fuel (t) represents the fuel heat input power.

5. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 4 is characterized in that: The formula for filtering noise of the unit operating parameters is as follows: x filter (t)=a*x raw (t)+(1-a)*x filter (t-1) In the formula, x filter (t) represents the data at time t after filtering, a represents the smoothing coefficient, and its value range is (0-1), x raw (t) represents the original data without filtering, x filter (t-1) represents the smoothed data at the previous sampling time (t-1).

6. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 5, characterized in that: The formula for converting the unit operating parameters is as follows: In the formula, x normal (t) represents the normalized data value, x filter (t) represents the data at time t after filtering, x min Indicates the minimum value of the corresponding parameter in the data set to be normalized, x max Indicates the maximum value of the corresponding parameter in the data set to be normalized.

7. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 6, characterized in that: The calculation formula of the fuel consumption is as follows: In the formula, Qrxl represents fuel consumption, represents the fuel mass flow rate, CV represents the calorific value of the fuel, n combusion Indicates combustion efficiency.

8. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 7, characterized in that: The calculation formula of the thermal efficiency is as follows: In the formula, Rxcl represents thermal efficiency, P electric (t) represents the power output, and Qrxl represents the fuel consumption.

9. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 8, characterized in that: The calculation formula for the emission index is as follows: In the formula, represents the carbon dioxide emission index, represents the fuel mass flow rate, Indicates the carbon dioxide emission coefficient produced by fuel combustion.

10. The energy consumption analysis and optimization method for sliding pressure optimization control of a thermal power unit according to claim 9, characterized in that: The optimization goal is defined as follows: In the formula, represents the overall optimization goal, w1 represents the coefficient of the influence of adjusting fuel consumption, w2 represents the penalty coefficient of adjusting pressure deviation, w3 represents the penalty coefficient of adjusting carbon dioxide emissions, Q fuel (t) represents the fuel heat input power, P steam (t) represents the steam pressure, P set Indicates the ideal steam pressure target value, Represents the carbon dioxide emission index, and the integral symbol represents the cumulative value accumulated in the time interval [0-T].

Images