Gas supercritical carbon dioxide cycle variable load multi-objective optimization method and device

Abstract

This invention relates to a multi-objective optimization method and apparatus for supercritical carbon dioxide cycle load variation in a gas-fired power generation system. Based on an AGC (Automatic Load Control) command, it optimizes the load allocation for gas-fired turbines, supercritical turbines driven by waste heat from gas-fired turbines, and transcritical turbines in a power generation system. The method includes: receiving the current AGC command; performing a load allocation based on the current AGC command, with speed and environmental friendliness as optimization objectives, obtaining a first set load for each turbine, and determining the maximum value T of the time required to reach each turbine's first set load. max The time T between two consecutive AGC commands, if T max ≥T, adjust the load of each turbine according to the first set load, if T max <T, enter the secondary allocation stage; secondary allocation, based on the first set load, with economy and environmental protection as optimization goals, performs secondary allocation of the load to obtain the second set load of each turbine, and adjusts the load of each turbine according to the second set load to achieve the optimization of load allocation in terms of environmental protection, speed and economy.

Description

Technical Field

[0001] This invention relates to the field of multi-objective optimization technology for rapid load change in power generation systems, and in particular to a method and apparatus for multi-objective optimization of supercritical carbon dioxide cycle power generation systems. Background Technology

[0002] The grid connection of renewable energy sources, such as wind power, places higher demands on the flexibility and speed of load regulation for generating units. Gas-fired supercritical CO2 thermal cycle units have significant advantages in terms of the flexibility and speed of absorbing renewable energy, and their ability to quickly change loads is conducive to the large-scale and rapid absorption of renewable energy.

[0003] When a gas-fired supercritical CO2 thermal cycle unit operates under rapid load changes, it is not only necessary to improve the economy and safety throughout the entire load change process, but also to respond quickly to changes in load commands and achieve synergy between efficient supply and rapid absorption. Therefore, there is a problem of distributing load commands among the gas turbine, supercritical (s-CO2) turbine, and transcritical (t-CO2) turbine. Furthermore, with the large-scale absorption of grid-connected renewable energy, rapid load change operation of gas-fired supercritical CO2 thermal cycles will become the norm, and improving their peak-shaving capacity must comprehensively consider both economy and carbon emissions within the safe operating boundary. Therefore, the control concept of multivariable decoupling and multi-source disturbance suppression should organically combine multi-objective coordinated control of the load controlled variable reference value.

[0004] Because the economic, environmental, and responsiveness indicators of a generating unit are not positively correlated, the load corresponding to the optimal performance of each indicator is usually inconsistent. Generally, the larger the load of a generating unit, the better its economic performance, but the environmental performance may not be optimal at this time. The responsiveness indicator is related to the load increase and decrease rate of the generating unit; the generating unit with the best economic performance may not have the best load response capability. When more than one indicator is considered in the plant-level energy-saving dispatch process, it is called multi-objective plant-level energy-saving dispatch. Existing control methods cannot handle multi-objective optimization regulation. Usually, a unified multi-objective optimization function is established for one-time conformal optimization regulation. Although the corresponding optimal allocation results can be obtained by selecting appropriate algorithms and optimizing the algorithms, the nonlinear relationship between the objectives leads to insufficient precision in the collaborative control optimization results and a large time control scale, which cannot effectively achieve the goal of rapid load change. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-objective optimization method and apparatus for supercritical carbon dioxide cycle gas turbines under variable load conditions, achieving optimization in terms of speed, economy, and environmental friendliness for load distribution among gas turbines, supercritical turbines, and transcritical turbines under rapid load change operation conditions.

[0006] The technical solution adopted in this invention is as follows:

[0007] This application provides a rapid load-changing multi-objective optimization method for a gas-fired supercritical carbon dioxide cycle system. Based on AGC instructions, the method optimizes the load allocation for the gas turbines in the power generation system, the supercritical turbines driven by the waste heat of the gas turbines, and the transcritical turbines, including:

[0008] Received the current AGC instruction;

[0009] In the initial allocation phase, based on the current AGC instructions and with speed and environmental friendliness as optimization objectives, the load is allocated in one go to obtain the first set load for each turbine, and the time required for each turbine to reach its respective first set load is calculated. The maximum value T of the time required to reach the first set load is then determined. max Determine T max The size of the reception period T between two adjacent AGC commands, if T max >T, adjust the load of each turbine according to the first set load, if T max <T, enter the secondary distribution phase;

[0010] In the secondary distribution stage, based on the first set load, with economic and environmental benefits as optimization objectives, the load is distributed in a secondary manner to obtain the second set load for each turbine, and the load of each turbine is adjusted according to the second set load.

[0011] The further technical solution is as follows:

[0012] The method of allocating the load in one go, with the optimization goals of speed and environmental friendliness, includes:

[0013] Establish the first objective function:

[0014] min T and min G are used to describe the minimum load variation time and the minimum pollutant emission, respectively.

[0015] in, P i,now v i T represents the current load and load variation rate of the i-th turbine, respectively. ideal This represents the shortest time required for load changes to reach the ideal state.

[0016] in, α, β, γ are emission characteristic coefficients, P i The set load for the i-th turbine;

[0017] Using the current load as the initial solution, the Grey Wolf algorithm is used to solve the first objective function to obtain the optimal solution of the first set load that simultaneously satisfies the minimum pollutant emissions and the minimum load variation time.

[0018] The minimum time T required for load variation under ideal conditions ideal Defined as:

[0019]

[0020] In the formula, P D This indicates the total load value of the AGC command.

[0021] The secondary load allocation, optimized for both economic and environmental benefits, includes:

[0022] Establish a second objective function:

[0023] min F and min G are used to describe minimum cost and minimum pollutant emissions, respectively;

[0024] in, a i ,b i ,c i This is the characteristic coefficient of gas consumption variation;

[0025] in, α, β, γ are emission characteristic coefficients, P i The set load for the i-th turbine;

[0026] Using the first set load as the initial solution, the Grey Wolf algorithm is used to solve the second objective function, and the optimal solution of the load value that simultaneously satisfies the minimum cost and the minimum load change time is obtained as the second set load.

[0027] The constraints for the first objective function and the second objective function are as follows:

[0028] P D This represents the total load value of the AGC command.

[0029] P i,min ≤P i ≤P i,max P i,min P i,max These are the lower and upper load limits for the i-th turbine, respectively.

[0030] max(P i,min ,P i,now -UR i )≤P i ≤min(P i,max ,P i,now +UR i ), DR i Let be the load variable of the i-th turbine per unit time.

[0031] The method for adjusting the turbine load according to the first set load and the method for adjusting the turbine load according to the second set load are the same, including:

[0032] Determine operating parameters: Calculate the inlet pressure, inlet temperature, circulating working fluid flow rate, circulating compression ratio, and supercritical turbine valve opening based on the load. Calculate the circulating working fluid flow rate, carbon dioxide condensation pressure, circulating compression ratio, and transcritical turbine valve opening for the transcritical turbine.

[0033] The proportion of exhaust waste heat from the gas turbine allocated to the supercritical CO2 heater and the transcritical CO2 heater is determined based on the operating parameters. The supercritical CO2 heater and the transcritical CO2 heater are used to heat the working fluid entering the supercritical turbine and the transcritical turbine to a set temperature, respectively.

[0034] This application also provides a rapid load-changing multi-objective optimization device for a gas-fired supercritical carbon dioxide cycle system, used to optimize the load allocation of the gas turbine, the supercritical turbine driven by the waste heat of the gas turbine, and the transcritical turbine of the power generation system according to AGC instructions, including:

[0035] The receiving module is used to receive the current AGC command;

[0036] The primary allocation module, based on the current AGC instructions and with speed and environmental friendliness as optimization objectives, performs a primary allocation of the load, obtains the first set load for each turbine, calculates the time required for each turbine to reach its respective first set load, and determines the maximum value T of the time required to reach each turbine's first set load. max Determine T max The size of the reception period T between two adjacent AGC commands, if T max >T, adjust the load of each turbine according to the first set load, if T max <T, enter the secondary distribution phase;

[0037] The secondary distribution module, based on the first set load and with economic and environmental optimization as the optimization objectives, performs secondary distribution of the load to obtain the second set load for each turbine, and adjusts the load of each turbine according to the second set load.

[0038] The beneficial effects of this invention are as follows:

[0039] This invention regulates the energy distribution of waste heat from gas in each heat exchange device and the heat transfer between two turbines based on a two-stage optimization process. By allocating in stages, the time interval between two AGC commands can be used to decompose the multi-objective problem, ensuring that the optimization objective of the first stage is achieved. At the same time, it ensures that the second stage objective optimization is carried out with sufficient adjustment time. It can match the input energy of supercritical turbines and transcritical turbines in real time and coordinate the optimization results.

[0040] The phased optimization method of this invention selects to perform primary allocation or primary and secondary allocation based on the relationship between the actual load of the unit and the commanded load. This shortens the time control scale of the supercritical cycle, reduces the total time of the optimization process and the adjustment process, and improves the economy of secondary allocation of AGC control commands.

[0041] This invention applies the Grey Wolf algorithm when solving the objective function. It leverages the Grey Wolf algorithm's characteristics of fast convergence, good stability, and low complexity to obtain high-precision load allocation results. This enables the unit to respond quickly to load command changes, achieving synergy between efficient supply and rapid absorption. It also improves the unit's peak-shaving capacity within the safe operating boundary during normal rapid load changes.

[0042] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0043] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0044] The specific embodiments of the present invention are described below with reference to the accompanying drawings.

[0045] See Figure 1 This application provides a method for rapid load change and multi-objective optimization of a gas-fired supercritical carbon dioxide cycle system. Based on AGC instructions, the method optimizes the load allocation for the gas turbines in the power generation system, the supercritical turbines driven by the waste heat of the gas turbines, and the transcritical turbines. The method includes:

[0046] Received the current AGC instruction;

[0047] In the initial allocation phase, based on the current AGC instructions and with speed and environmental friendliness as optimization objectives, the load is allocated in one go to obtain the first set load for each turbine, and the time required for each turbine to reach its respective first set load is calculated. The maximum value T of the time required to reach the first set load is then determined. max Determine T max The size of the reception period T between two adjacent AGC commands, if T max >T, adjust the load of each turbine according to the first set load, if T max <T, enter the secondary distribution phase;

[0048] In the secondary distribution stage, based on the first set load, with economic and environmental benefits as optimization objectives, the load is distributed in a secondary manner to obtain the second set load for each turbine, and the load of each turbine is adjusted according to the second set load.

[0049] This application regulates the energy distribution of waste heat from the gas in each heat exchanger and the heat transfer between the two turbines based on a two-stage optimization process. By using phased allocation, the time interval between two AGC commands can be used to decompose a multi-objective problem, ensuring the achievement of the first-stage optimization objective. Simultaneously, sufficient adjustment time is ensured before proceeding to the second-stage objective optimization. This allows for real-time matching of the input energy of supercritical and transcritical turbines, and coordinated control of the optimization results. The phased optimization method selects primary allocation or a combination of primary and secondary allocation based on the relationship between the actual unit load and the commanded load, shortening the time control scale of the supercritical cycle, reducing the total time of the optimization and adjustment processes, and improving the economy of secondary allocation of AGC control commands.

[0050] Those skilled in the art will understand that, in this application, supercritical turbine and transcritical turbine refer to turbines that use supercritical CO2 and transcritical CO2 as circulating working fluids, respectively, and the heat source of the CO2 working fluid in these two turbines comes from the exhaust waste heat of the gas turbine.

[0051] Under certain implementation conditions, the reception period T = 1 minute between two consecutive AGC commands.

[0052] The method of allocating the load in one go, with the optimization goals of speed and environmental friendliness, includes:

[0053] Establish the first objective function:

[0054] min T and min G are used to describe the minimum load variation time and the minimum pollutant emission, respectively.

[0055] in, P i,now v i T represents the current load and load variation rate of the i-th turbine, respectively. ideal This represents the shortest time required for load changes to reach the ideal state.

[0056] in, α, β, γ are emission characteristic coefficients, P i The set load for the i-th turbine;

[0057] in:

[0058]

[0059] In the formula, P D This is represented as the total load value of the AGC command.

[0060] The constraints are:

[0061] (1) That is, the sum of the loads of all turbine units equals the total load value of the AGC command;

[0062] (2)P i,min ≤P i ≤P i,max P i,min P i,max These are the lower and upper load limits of the i-th turbine, respectively, meaning that the output of each turbine must be within its load capacity.

[0063] (3) max(P) i,min ,P i,now -UR i )≤P i ≤min(P i,max ,P i,now +UR i ), DR i Let be the load variable of the i-th turbine per unit time. That is, the difference between the set load and the real-time load of each turbine unit must be within the regulation rate range, and the load change is limited by the rate of increase and decrease of load.

[0064] Using the current load as the initial solution, the Grey Wolf algorithm is used to solve the first objective function to obtain the optimal solution of the first set load that simultaneously satisfies the minimum pollutant emissions and the minimum load variation time.

[0065] The secondary load allocation, optimized for both economic and environmental benefits, includes:

[0066] Establish a second objective function:

[0067] min F and min G are used to describe minimum cost and minimum pollutant emissions, respectively;

[0068] in, a i ,b i ,c i The characteristic coefficient of gas variation can be obtained from the energy consumption curve of the unit. Specifically, the energy consumption characteristic data of the unit can be obtained by means of equivalent enthalpy drop method, thermodynamic performance test and online identification of unit energy consumption characteristics, and then obtained by multiple linear regression.

[0069] in, α, β, and γ are emission characteristic coefficients that describe the proportion of a certain type of air pollutant generated under a certain activity. They can be calculated from the ratio of pollutant generation to activity level. i The set load for the i-th turbine;

[0070] The constraints are:

[0071] (1) P D The total load value of the AGC command is equal to the sum of the loads of all turbine units.

[0072] (2)P i,min ≤P i ≤P i,max P i,min P i,max These are the lower and upper load limits of the i-th turbine, respectively, meaning that the output of each turbine must be within its load capacity.

[0073] (3) max(P) i,min ,P i,now -UR i )≤P i ≤min(P i,max ,P i,now +UR i ), DR i The load variable is the load of the i-th turbine per unit time. That is, the difference between the set load and the real-time load of each turbine unit must be within the regulation rate range, and the load change is limited by the rate of increase and decrease of load.

[0074] Using the first set load as the initial solution, the Grey Wolf algorithm is used to solve the second objective function, and the optimal solution of the load value that simultaneously satisfies the minimum cost and the minimum load change time is obtained as the second set load.

[0075] After the second stage of economic optimization, the optimal power generation load commands for the gas turbine, supercritical turbine, and transcritical turbine of the final unit are obtained, realizing the secondary distribution of load commands among the unit's power generation equipment during rapid load changes and completing the regulation of the output power of the controlled variable. Specifically, the method of adjusting the load of each turbine according to the first set load and the method of adjusting the load of each turbine according to the second set load are the same, including:

[0076] Determine operating parameters: Calculate the inlet pressure, inlet temperature, circulating working fluid flow rate, circulating compression ratio, and supercritical turbine valve opening based on the set load; calculate the circulating working fluid flow rate, carbon dioxide condensation pressure, circulating compression ratio, and transcritical turbine valve opening for the transcritical turbine.

[0077] The proportion of exhaust waste heat from the gas turbine allocated to the supercritical CO2 heater and the transcritical CO2 heater is determined based on the operating parameters. The supercritical CO2 heater and the transcritical CO2 heater are used to heat the working fluid entering the supercritical turbine and the transcritical turbine to a set temperature, respectively.

[0078] This application also provides a rapid load-changing multi-objective optimization device for a gas-fired supercritical carbon dioxide cycle system, used to optimize the load allocation of the gas turbine, the supercritical turbine driven by the waste heat of the gas turbine, and the transcritical turbine of the power generation system according to AGC instructions, including:

[0079] The receiving module is used to receive the current AGC command;

[0080] The primary allocation module, based on the current AGC instructions and with speed and environmental friendliness as optimization objectives, performs a primary allocation of the load, obtains the first set load for each turbine, calculates the time required for each turbine to reach its respective first set load, and determines the maximum value T of the time required to reach each turbine's first set load. max Determine T max The size of the reception period T between two adjacent AGC commands, if T max >T, adjust the load of each turbine according to the first set load, if T max <T, enter the secondary distribution phase;

[0081] The secondary distribution module, based on the first set load and with economic and environmental optimization as the optimization objectives, performs secondary distribution of the load to obtain the second set load for each turbine, and adjusts the load of each turbine according to the second set load.

[0082] It will be understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rapid variable-load multi-objective optimization method for a supercritical carbon dioxide cycle system, characterized in that, The load allocation for the gas turbines, supercritical turbines driven by the waste heat of the gas turbines, and transcritical turbines in the power generation system is optimized according to AGC instructions, including: Received the current AGC instruction; In the primary allocation phase, based on the current AGC instructions and with speed and environmental friendliness as optimization objectives, the load is allocated in one go to obtain the first set load for each turbine, and the time required for each turbine to reach its respective first set load is calculated. The maximum value of the time required to reach the first set load is then determined. ,judge Receive cycle between two adjacent AGC commands The size, if Adjust the load of each turbine according to the first set load. It enters the secondary distribution stage; In the secondary distribution stage, based on the first set load, with economic efficiency and environmental protection as optimization objectives, the load is distributed in a secondary manner to obtain the second set load for each turbine, and the load of each turbine is adjusted according to the second set load. The method of allocating the load in one go, with the optimization goals of speed and environmental friendliness, includes: Establish the first objective function: , , These are used to describe the minimum load variation time and the minimum pollutant emissions, respectively. in, , , The first The current load and load change rate of a turbine. This represents the shortest time required for load changes to reach the ideal state. in, , Emission characteristic coefficient, For the first The set load of the turbine; Using the current load as the initial solution, the Grey Wolf algorithm is used to solve the first objective function to obtain the optimal solution of the first set load that simultaneously satisfies the minimum pollutant emissions and the minimum load variation time. The shortest time required for load change under ideal conditions Defined as: ， In the formula, This indicates the total payload value of the AGC command; The secondary load allocation, optimized for both economic and environmental benefits, includes: Establish a second objective function: , , These are used to describe minimum cost and minimum pollutant emissions, respectively. in, , This is the characteristic coefficient of gas consumption variation; in, , Emission characteristic coefficient, For the first The set load of the turbine; Using the first set load as the initial solution, the Grey Wolf algorithm is used to solve the second objective function, and the optimal solution of the load value that simultaneously satisfies the minimum cost and the minimum load change time is obtained as the second set load.

2. The rapid load-changing multi-objective optimization method for a supercritical carbon dioxide cycle system according to claim 1, characterized in that, The constraints for the first objective function and the second objective function are as follows: , This represents the total load value of the AGC command. , , They are the first The lower and upper load limits for a turbine; , For the first The load variable of a turbine per unit time.

3. The rapid load-changing multi-objective optimization method for a supercritical carbon dioxide cycle system according to claim 1, characterized in that, The method for adjusting the turbine load according to the first set load and the method for adjusting the turbine load according to the second set load are the same, including: Determine operating parameters: Calculate the inlet pressure, inlet temperature, circulating working fluid flow rate, circulating compression ratio, and supercritical turbine valve opening based on the load; calculate the circulating working fluid flow rate, carbon dioxide condensation pressure, circulating compression ratio, and transcritical turbine valve opening for the transcritical turbine. The proportion of exhaust waste heat from the gas turbine allocated to the supercritical CO2 heater and the transcritical CO2 heater is determined based on the operating parameters. The supercritical CO2 heater and the transcritical CO2 heater are used to heat the working fluid entering the supercritical turbine and the transcritical turbine to a set temperature, respectively.

4. An apparatus for a rapid load-changing multi-objective optimization method for a gas-fired supercritical carbon dioxide cycle system according to any one of claims 1 to 3, used to optimize the load allocation of the gas turbine, the supercritical turbine driven by the waste heat of the gas turbine, and the transcritical turbine of the power generation system according to AGC instructions, characterized in that, include: The receiving module is used to receive the current AGC command; The primary allocation module, based on the current AGC instructions and with speed and environmental friendliness as optimization objectives, performs a primary allocation of load to obtain the first set load for each turbine, calculates the time required for each turbine to reach its respective first set load, and determines the maximum value of the time required to reach each turbine's first set load. ,judge Receive cycle between two adjacent AGC commands The size, if Adjust the load of each turbine according to the first set load. It enters the secondary distribution stage; The secondary distribution module, based on the first set load and with economic and environmental optimization as the optimization objectives, performs secondary distribution of the load to obtain the second set load for each turbine, and adjusts the load of each turbine according to the second set load.

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