Method and system for determining generated power limit interval of heat supply unit with multiple steam extraction positions based on genetic algorithm

Abstract

The invention relates to the technical field of power system flexibility improvement, in particular to a method and a system for determining a generation power limit interval of a multi-steam extraction position heat supply unit based on a genetic algorithm. According to the method, through the global optimization characteristic of the genetic algorithm, the dynamic coupling relation among multiple steam extraction points and the sudden change boundary triggered by constraint can be effectively captured, and the problem of systematic errors existing in a traditional univariate analysis or discrete sampling method is solved. The method can reduce the relative error of the calculation of the generated power extreme value, supports the dynamic prediction of the power interval in a heat supply parameter continuous adjustment scene, and provides a more accurate evaluation basis for the adjustable capacity of the unit for power grid dispatching. Besides, by optimizing the steam extraction distribution strategy, on the premise that the heat supply requirement is guaranteed, the deep peak regulation capacity of the unit can be maximized, the renewable energy consumption space can be improved, the unit operation coal consumption can be reduced, and the double benefits of flexibility improvement, energy conservation and emission reduction are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of improving power system flexibility, and in particular to a method and system for determining a power generation power limit range of a multi-extraction steam position heating unit based on a genetic algorithm. Background Art

[0002] As new power systems continue to demand more flexible regulation capabilities, the power regulation range of coal-fired heating units directly affects the grid's ability to absorb renewable energy. The complex extraction network coupling of heating units with multiple extraction locations results in a highly nonlinear power limit for the units. Accurately defining this critical operating boundary has become a core challenge restricting the units' deep peak-shaving capabilities. The current technical system faces the following bottlenecks in determining the power limit range:

[0003] Existing methods are often based on single extraction locations or simplified extraction network modeling, failing to accurately characterize the dynamic coupling relationships among multiple extraction points, such as the intermediate and low-pressure interconnecting pipes and the reheat cooling system. In actual operation, steam flow redistribution between different extraction points can significantly alter turbine flow characteristics and boiler combustion stability. Traditional single-variable analysis methods struggle to capture the synergistic and competitive effects of multiple extraction locations, leading to systematic biases in power limit boundary calculations.

[0004] Existing technologies often use discrete operating point interpolation to approximate the power range (e.g., based on extreme sampling of typical heating loads). This method is unable to construct the power envelope for continuously varying heating parameters. This is especially true when multiple steam extraction flows are adjusted non-proportionally. Traditional methods struggle to capture the sudden changes triggered by constraints, leading to errors in the dispatch system's adjustable capacity assessment.

[0005] In order to overcome the problem that the power generation limit range of a heating unit with multiple extraction steam positions is difficult to determine, a method and system for determining the power generation limit range of a heating unit with multiple extraction steam positions based on a genetic algorithm is urgently needed to improve energy utilization efficiency, reduce environmental pollution, and enhance system flexibility. Summary of the Invention

[0006] In response to the problems in the prior art, the present invention provides a method and system for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm. The specific technical solution is as follows:

[0007] A method for determining a power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm comprises the following steps:

[0008] Step S1: Establish a simulation model based on the design parameters of the heating unit components, wherein the design parameters include: component efficiency η, component inlet flow M in , component outlet flow M out 、Component inlet temperature Tin , component outlet temperature T out 、Component inlet pressure P in , component outlet pressure P out 、Component inlet enthalpy H in , component outlet enthalpy H out The heating unit components include: boiler, steam turbine, generator, water pump, condenser, deaerator, regenerative heater, and steam extraction pipeline;

[0009] Step S2, constructing a thermal power load optimization model for a heating unit with multiple extraction steam locations, including an optimization target, an optimization algorithm, constraints, and variable parameters; the optimization target of the optimization model is the maximum power generation capacity Pmax and the minimum power generation capacity Pmin of the heating unit; the optimization algorithm is a genetic algorithm;

[0010] Step S3, solving the thermal power load optimization model of the heating unit with multiple extraction positions with the maximum power generation power of the heating unit as the optimization goal, and obtaining the maximum power generation power Pmax of the heating unit;

[0011] Step S4, solving the thermal power load optimization model of the heating unit with multiple extraction positions with the minimization of the heating unit's power generation as the optimization goal, and obtaining the minimum power generation value Pmin of the heating unit;

[0012] Step S5 , determining the power generation range of the heating unit with multiple steam extraction positions according to the obtained maximum power generation value Pmax and minimum power generation value Pmin of the heating unit.

[0013] Preferably, the constraint condition in step S2 includes: the minimum steam inlet flow rate of the low-pressure cylinder is not less than the minimum required flow rate for safe operation of the low-pressure cylinder, which is specifically defined as:

[0014] M LP,min ≤M LP ;

[0015] Where M LP is the air intake flow rate of the low-pressure cylinder of the heating unit, t / h; M LP,min It is the minimum air intake flow of the low-pressure cylinder of the heating unit, t / h.

[0016] Preferably, the constraint condition in step S2 includes: the upper limit of the pipeline extraction flow rate does not exceed the maximum extraction flow rate of the pipeline, which is specifically defined as:

[0017] 0≤MM max ;

[0018] Where, M is the heat supply of the unit, t / h; M max The maximum heating capacity of the unit, t / h.

[0019] Preferably, the constraint condition in step S2 includes: the heating amount supplied by the heating unit is equal to the required flow rate of the industrial heat user, which is specifically defined as:

[0020] M=M req ;

[0021] Where, M is the heat supply of the unit, t / h; M req The maximum heating capacity of the unit, t / h.

[0022] Preferably, the variable parameters in step S2 specifically include the steam extraction flow rate of the heating extraction steam pipeline at the medium and low pressure connecting pipelines and the steam extraction flow rate of the heating extraction steam pipeline at the reheat steam cold section.

[0023] Preferably, the step S3 specifically includes the following steps:

[0024] Step S31, randomly generating an initial population, specifically randomly generating the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0025] Step S32, evaluating the quality of each individual through the fitness function, specifically comparing the power generation of the heating units;

[0026] Step S33: performing a selection operation based on fitness, retaining high-quality individuals and eliminating low-quality individuals, specifically retaining the steam extraction flow of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow at the reheat steam cold section when the heating unit has a large power generation capacity;

[0027] Step S34, simulating biological genetic variation through crossover and mutation to generate a new generation of populations, specifically by changing the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0028] Step S35, screening the optimal solution that satisfies all constraints, specifically: excluding from the solution set the case where the minimum steam inlet flow rate of the low-pressure cylinder is lower than the minimum required flow rate for safe operation of the low-pressure cylinder, excluding from the solution set the case where the upper limit of the pipeline extraction flow rate exceeds the maximum pipeline extraction flow rate, and excluding from the solution set the case where the heating unit heat supply is not equal to the required flow rate of the industrial heat user;

[0029] Step S36, repeating steps S31 to S34 to make the population evolve generation by generation and eventually converge to the individual with the highest fitness, that is, the optimal or approximately optimal solution to the problem, specifically, the power generation power of the heating unit reaches the maximum value Pmax.

[0030] Preferably, the step S4 specifically includes the following steps:

[0031] Step S41, randomly generating an initial population, specifically randomly generating the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0032] Step S42, evaluating the quality of each individual through the fitness function, specifically comparing the power generation of the heating units;

[0033] Step S43: Perform a selection operation based on fitness, retain high-quality individuals and eliminate low-quality individuals. Specifically, retain the steam extraction flow of the heating extraction pipe at the medium and low pressure connecting pipes and the heating extraction pipe flow at the reheat steam cold section where the power generation of the heating unit is relatively small.

[0034] Step S44, then, by simulating biological genetic variation through crossover and mutation, a new generation of population is generated, specifically by changing the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0035] Step S45, screening the optimal solution that satisfies all constraints, specifically: excluding from the solution set the case where the minimum steam inlet flow rate of the low-pressure cylinder is lower than the minimum required flow rate for safe operation of the low-pressure cylinder, excluding from the solution set the case where the upper limit of the pipeline extraction flow rate exceeds the maximum pipeline extraction flow rate, and excluding from the solution set the case where the heating unit heat supply is not equal to the required flow rate of the industrial heat user;

[0036] Step S46, iterate steps S41 to S44 repeatedly, and finally converge to the individual with the highest fitness, that is, the optimal or approximately optimal solution to the problem, specifically, the power generation power of the heating unit reaches the minimum value Pmin.

[0037] A system for determining a power generation power limit range of a multi-extraction steam heating unit based on a genetic algorithm, applying the method described, comprises:

[0038] The simulation modeling module is used to establish a simulation model based on the design parameters of the heating unit components, including: component efficiency η, component inlet flow M in , component outlet flow M out 、Component inlet temperature T in , component outlet temperature T out 、Component inlet pressure P in , component outlet pressure P out 、Component inlet enthalpy H in , component outlet enthalpy H out The heating unit components include: boiler, steam turbine, generator, water pump, condenser, deaerator, regenerative heater, and steam extraction pipeline;

[0039] An optimization model construction module is used to construct a thermal power load optimization model for a heating unit with multiple extraction steam locations, including an optimization objective, an optimization algorithm, constraints, and variable parameters; the optimization objective of the optimization model is the maximum power generation capacity Pmax and the minimum power generation capacity Pmin of the heating unit; the optimization algorithm is a genetic algorithm;

[0040] The first solving module is used to solve the thermal power load optimization model of the heating unit with multiple extraction positions with the maximization of the heating unit power generation as the optimization goal, and obtain the maximum power generation power Pmax of the heating unit;

[0041] The second solving module is used to solve the thermal power load optimization model of the heating unit with multiple extraction positions with the minimization of the heating unit's power generation as the optimization goal, and obtain the minimum power generation power Pmin of the heating unit;

[0042] The interval determination module is used to determine the power generation range of the heating unit with multiple extraction steam positions according to the obtained maximum power generation value Pmax and minimum power generation value Pmin of the heating unit.

[0043] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the method for determining the power generation power limit range of a multi-extraction steam position heating unit based on a genetic algorithm.

[0044] A processor is used to run a program, wherein when the program is run, the method for determining the power generation power limit range of a multi-extraction steam position heating unit based on a genetic algorithm is executed.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The technical effect of the present invention is reflected in the significant improvement of the calculation accuracy and applicability of the power generation limit range of the heating unit with multiple steam extraction positions. Through the global optimization characteristics of the genetic algorithm, the dynamic coupling relationship between multiple steam extraction points and the mutation boundary triggered by the constraint can be effectively captured, solving the systematic error problem existing in the traditional single variable analysis or discrete sampling method. This method can reduce the relative error of the calculation of the extreme value of the power generation, and at the same time support the dynamic prediction of the power range under the scenario of continuous adjustment of the heating parameters, providing a more accurate basis for the evaluation of the adjustable capacity of the unit for the power grid dispatch. In addition, by optimizing the steam extraction distribution strategy, the present invention can maximize the deep peak-shaving capability of the unit, improve the space for renewable energy absorption, and reduce the coal consumption of the unit under the premise of ensuring the heating demand, and has the dual benefits of flexibility improvement and energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0048] Figure 1 Flow chart of the method of the present invention.

[0049] Figure 2 This is the structural diagram of a heating unit with multiple steam extraction positions.

[0050] Figure 3 This is a schematic diagram of the power generation range of the unit in the implementation case.

[0051] Figure 4 This is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0052] 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 them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0054] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0055] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0056] Example 1:

[0057] like Figure 1 As shown, this embodiment provides a method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm, comprising the following steps:

[0058] Step S1: Establish a simulation model based on the design parameters of the heating unit components, wherein the design parameters include: component efficiency η, component inlet flow M in , component outlet flow M out 、Component inlet temperature T in , component outlet temperature T out 、Component inlet pressure P in , component outlet pressure P out 、Component inlet enthalpy H in , component outlet enthalpy H out ; The heating unit components include: boiler, steam turbine, generator, water pump, condenser, deaerator, regenerative heater, and steam extraction pipeline.

[0059] The simulation model establishment process is described in detail as follows:

[0060] Through the phase change process of the working fluid, the system achieves efficient conversion of thermal energy into mechanical energy, and ultimately into electrical energy. It consists of key components such as the boiler, turbine, condenser, deaerator, feedwater pump, and heat recovery system. Each component achieves closed and optimized circulation through precise thermodynamic coupling and energy transfer. The initial working fluid of the cycle is chemically treated desalted water, which first enters the boiler system. The boiler serves as the heat sink of the cycle. Its internal structure is functionally divided into three parts: the economizer, the evaporator, and the superheater. The economizer uses waste heat from the flue gas to preheat the feedwater, bringing its temperature close to the saturation point but not yet vaporizing. This process significantly improves the overall thermodynamic perfection of the boiler. The working fluid then enters the evaporator, where it absorbs the high-temperature heat released by combustion under constant pressure to complete the liquid-to-vapor phase transition, generating saturated steam. To prevent erosion of the turbine blades caused by wet steam, the saturated steam is further heated through the superheater, becoming superheated steam with a higher enthalpy value and dryness. This stage effectively increases the available enthalpy drop within the turbine by increasing the steam superheat. The superheated steam's temperature and pressure are significantly increased, resulting in higher thermal energy quality, providing an ideal working fluid for efficient turbine operation and directly improving cycle efficiency.

[0061] The superheated steam is then introduced into the turbine's high-pressure (HP) turbine, where it undergoes an isentropic expansion process, converting thermal energy into rotor kinetic energy. The pressure drops sharply to the intermediate pressure level, and the temperature also drops accordingly. To prevent excessive steam humidity in the low-pressure stage, which can lead to blade corrosion and efficiency loss, modern large-scale turbines commonly use a reheat cycle. The HP exhaust steam is redirected back to the boiler's reheater for secondary heating. This process not only restores the steam's superheat but also increases the inlet enthalpy of the intermediate-pressure (IP) turbine, allowing the steam to continue expanding efficiently there to intermediate and low pressures. Finally, the steam enters the low-pressure (LP) turbine, where it completes its final expansion and performs work under a high vacuum. The tandem design of multi-stage steam turbines significantly reduces the thermodynamically irreversible losses of single-stage expansion by extracting steam energy in stages. The introduction of reheat technology also improves cycle efficiency.

[0062] The exhaust steam that has completed its work then enters the condenser, where it releases latent heat through indirect heat exchange with cooling water (circulating water or seawater) and rapidly changes phase into condensate. The vacuum environment of the condenser (maintained by the sudden reduction in the volume of condensed steam and the vacuum pump) not only reduces the condensation temperature of the working fluid, but also enhances the net output power of the system by maximizing the pressure difference between the inlet and outlet of the turbine. After being pressurized by the condensate pump, the condensate flows sequentially through multiple low-pressure feedwater heaters. These surface heat exchangers use steam extracted from the low-pressure sections of the turbine to preheat the condensate. This process, as a key link in the regenerative cycle, effectively increases the average heat absorption temperature of the cycle by reducing the working fluid's low-temperature heat absorption demand in the boiler, thereby significantly reducing cold source losses. The preheated condensate then enters the deaerator. This device, which combines physical deoxygenation with mixed heating, introduces steam extraction from the turbine to heat the water to saturation at the corresponding pressure. At this point, dissolved oxygen precipitates from the water due to Henry's Law and is discharged from the system through the exhaust valve, completely eliminating the potential threat of oxygen corrosion to the boiler tube walls and turbine flow components, thereby improving the safety and reliability of the circulation system. As the core node of the heat recovery system, the deaerator is typically strategically located at the front end of the feedwater pump inlet. This not only prevents cavitation in the pump body, but also integrates heat input from the condensate system and extraction steam, achieving precise control of working fluid parameters.

[0063] The deoxygenated feedwater is pressurized to the boiler's operating pressure by a multi-stage centrifugal feedwater pump. This pump is driven either by grid electricity or by a dedicated small steam turbine (typically driven by extraction steam from the main steam turbine) to achieve energy self-consistency. The latter further reduces plant electricity consumption. Before returning to the boiler, the high-pressure feedwater passes through high-pressure feedwater heaters (HPFEHs). These heat exchangers utilize extraction steam from the turbine's high-pressure section for a final preheating of the feedwater, bringing its temperature close to the economizer inlet design value, thus completing the heat recovery chain.

[0064] The unit selected in this case is a subcritical, single-intermediate reheat, single-shaft, two-cylinder, two-exhaust, condensing steam turbine. The rated power of the unit before the heating transformation is 300MW.

[0065] The structure diagram of the multi-extraction position heating unit is as follows: Figure 2 shown.

[0066] Step S2, constructing a thermal power load optimization model for a heating unit with multiple steam extraction locations, including optimization objectives, optimization algorithms, constraints, and variable parameters; wherein multiple steam extraction locations are specifically defined as having at least one steam extraction heating location in the heating unit.

[0067] The optimization objectives of the optimization model are the maximum power generation value Pmax and the minimum power generation value Pmin of the heating unit.

[0068] The optimization algorithm is a genetic algorithm. The population size is assumed to remain constant (steady state). Since this optimization method is probabilistic, each optimization run will produce different results. According to the genetic algorithm, the following parameters can be set for the optimization sequence:

[0069] Population size: The size of a population determines the scope of an optimization. A population is generated arbitrarily. For example, an output dataset is randomly selected, and its successors are also randomly determined from it. The dataset is slightly altered through mutation. The greater the number of available variables, the larger the population.

[0070] Probability of crossover: Crossover usually only makes sense when there is more than one variable, taking into account the mutual influence of several variables.

[0071] Probability of mutation: The change of a single value pair is based on a Gaussian distribution. This decreases with the number of generations.

[0072] The fraction of the population to be replaced in each generation: This parameter determines the fraction of the best results that will be adopted in the next generation.

[0073] Termination criteria: The optimization can be performed up to the specified number of calculation generations.

[0074] The specific definition of the constraint conditions is that the minimum steam inlet flow rate of the low-pressure cylinder is not lower than the minimum required flow rate for safe operation of the low-pressure cylinder, the upper limit of the pipeline steam extraction flow rate does not exceed the maximum extraction flow rate of the pipeline, and the heating unit's heat supply is equal to the required flow rate of the industrial heat user.

[0075] A thermal power load optimization model for a heating unit with multiple steam extraction locations was constructed, and the optimization target was determined to be the power generation limit. The genetic algorithm was selected as the optimization method, and the genetic algorithm parameters were configured. The genetic algorithm parameters included: an initial population size of 100, a crossover probability of 0.8, a mutation probability of 0.1, a population fraction to be replaced in each generation of 0.75, and a termination criterion of 300 iterations.

[0076] Set constraints. The specific definitions of the constraints are that the minimum steam inlet flow of the low-pressure cylinder is not lower than the minimum required flow for safe operation of the low-pressure cylinder, the upper limit of the pipeline extraction flow does not exceed the maximum extraction flow of the pipeline, and the heating unit heat supply is equal to the required flow of the industrial heat user.

[0077] The constraints include:

[0078] (1) The minimum steam inlet flow rate of the low-pressure cylinder shall not be lower than the minimum required flow rate for safe operation of the low-pressure cylinder, which is specifically defined as: M LP,min ≤M LP ;

[0079] Where M LP is the air intake flow rate of the low-pressure cylinder of the heating unit, t / h; M LP,min is the minimum air flow rate of the low-pressure cylinder of the heating unit, t / h. The case unit is set to 120t / h.

[0080] (2) The upper limit of pipeline extraction flow rate shall not exceed the maximum extraction flow rate of the pipeline, which is specifically defined as: 0≤M≤M max ;

[0081] Where, M is the heat supply of the unit, t / h; M max The maximum heating capacity of the unit, t / h.

[0082] The upper limit of the flow rate at extraction position 1 of the case unit is set to 400t / h, and the upper limit of the flow rate at extraction position 2 is set to 400t / h.

[0083] (3) The heat supply of the heating unit is equal to the demand flow of industrial heat users, which is specifically defined as:

[0084] M=M req ;

[0085] Where: M is the heat supply of the unit, t / h; M req The maximum heat supply of the unit, t / h. The demand of industrial heat users around the unit is 450t / h.

[0086] The changing parameters specifically include the steam extraction flow rate of the heating extraction steam pipeline at the medium and low pressure connecting pipelines, and the steam extraction flow rate of the heating extraction steam pipeline at the reheat steam cold section.

[0087] Step S3, solving the thermal power load optimization model of the heating unit with multiple extraction positions with the maximum power generation of the heating unit as the optimization goal, and obtaining the maximum power generation capacity Pmax of the heating unit. Specifically, the following steps are included:

[0088] Step S31, randomly generating an initial population (candidate solution set), specifically randomly generating the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0089] Step S32, evaluating the quality of each individual through the fitness function, specifically comparing the power generation of the heating units;

[0090] Step S33: performing a selection operation based on fitness, retaining high-quality individuals and eliminating low-quality individuals, specifically retaining the steam extraction flow of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow at the reheat steam cold section when the heating unit has a large power generation capacity;

[0091] Step S34: Generate a new generation of population by simulating biological genetic variation through crossover (exchanging some genes of different individuals) and mutation (randomly changing individual genes). Specifically, the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section are changed.

[0092] Step S35, screening the optimal solution that satisfies all constraints, specifically: excluding from the solution set the case where the minimum steam inlet flow rate of the low-pressure cylinder is lower than the minimum required flow rate for safe operation of the low-pressure cylinder, excluding from the solution set the case where the upper limit of the pipeline extraction flow rate exceeds the maximum pipeline extraction flow rate, and excluding from the solution set the case where the heating unit heat supply is not equal to the required flow rate of the industrial heat user;

[0093] Step S36 repeats steps S31 to S34, specifically first randomly generating an initial population (a set of candidate solutions), evaluating the quality of each individual using a fitness function, then performing a selection operation based on fitness, retaining high-quality individuals and eliminating low-quality individuals, and then simulating biological genetic variation through crossover (exchanging some genes of different individuals) and mutation (randomly changing individual genes) to generate a new generation of populations. The population evolves generation by generation, eventually converging to the individual with the highest fitness, i.e., the optimal or near-optimal solution to the problem, specifically, the maximum power generation power of the heating unit, Pmax, which in this embodiment is 182.765MW.

[0094] Step S4, solving the thermal power load optimization model of the heating unit with multiple extraction positions with the minimum power generation of the heating unit as the optimization goal, and obtaining the minimum power generation value Pmin of the heating unit. Specifically, the following steps are included:

[0095] Step S41, randomly generating an initial population, specifically randomly generating the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0096] Step S42, evaluating the quality of each individual through the fitness function, specifically comparing the power generation of the heating units;

[0097] Step S43: Perform a selection operation based on fitness, retain high-quality individuals and eliminate low-quality individuals. Specifically, retain the steam extraction flow of the heating extraction pipe at the medium and low pressure connecting pipes and the heating extraction pipe flow at the reheat steam cold section where the power generation of the heating unit is relatively small.

[0098] Step S44, then, by simulating biological genetic variation through crossover and mutation, a new generation of population is generated, specifically by changing the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section;

[0099] Step S45, screening the optimal solution that satisfies all constraints, specifically: excluding from the solution set the case where the minimum steam inlet flow rate of the low-pressure cylinder is lower than the minimum required flow rate for safe operation of the low-pressure cylinder, excluding from the solution set the case where the upper limit of the pipeline extraction flow rate exceeds the maximum pipeline extraction flow rate, and excluding from the solution set the case where the heating unit heat supply is not equal to the required flow rate of the industrial heat user;

[0100] Step S46: Repeat steps S41 through S44 until the optimal or near-optimal solution is found. Specifically, the heating unit power generation reaches its minimum value, Pmin. In this example, Pmin is 133.283 MW. Step S5: Based on the maximum and minimum power generation values, Pmax, Pmin, obtained for the heating unit, determine the power generation range for the heating unit at multiple extraction locations.

[0101] According to the upper and lower limits of power generation, draw the power generation range of the heating unit with multiple extraction steam positions, such as Figure 3 shown.

[0102] The principle of solving the technical problem of the present invention lies in the use of the efficient global search capability of the genetic algorithm, combined with the precise modeling of the thermoelectric coupling system of the heating unit with multiple extraction positions, to achieve dynamic optimization solution of the power generation limit boundary under complex nonlinear constraints. The traditional method is limited by the single extraction point assumption or discrete working condition interpolation, and cannot characterize the steam flow redistribution effect between multiple extraction positions and its comprehensive impact on the turbine flow characteristics and boiler combustion stability. The present invention establishes a thermodynamic simulation model covering key components such as boilers, turbines, and heat recovery systems, and uses variables such as the extraction flow of medium and low pressure connecting pipelines and the extraction flow of reheating cold sections as optimization parameters, and simulates the multivariable co-evolution process with the population iteration mechanism of the genetic algorithm. The genetic algorithm evaluates, selects, crosses and mutates the fitness function (power generation), and under the premise of meeting hard constraints such as the minimum steam inlet flow of the low-pressure cylinder, the upper limit of pipeline extraction and the heat supply demand, the optimal extraction configuration scheme corresponding to the maximum and minimum power generation power is screened out generation by generation, and finally the power generation power envelope under continuous heating parameter changes is constructed, breaking through the boundary deviation problem caused by simplified modeling of traditional methods.

[0103] Example 2:

[0104] Based on the same inventive concept as Example 1, this embodiment provides a system for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm, and the method described herein includes:

[0105] The simulation modeling module is used to establish a simulation model based on the design parameters of the heating unit components, including: component efficiency η, component inlet flow M in , component outlet flow M out 、Component inlet temperature T in , component outlet temperature T out 、Component inlet pressure P in , component outlet pressure P out 、Component inlet enthalpy H in , component outlet enthalpy H out The heating unit components include: boiler, steam turbine, generator, water pump, condenser, deaerator, regenerative heater, and steam extraction pipeline;

[0106] An optimization model construction module is used to construct a thermal power load optimization model for a heating unit with multiple extraction steam locations, including an optimization objective, an optimization algorithm, constraints, and variable parameters; the optimization objective of the optimization model is the maximum power generation capacity Pmax and the minimum power generation capacity Pmin of the heating unit; the optimization algorithm is a genetic algorithm;

[0107] The first solving module is used to solve the thermal power load optimization model of the heating unit with multiple extraction positions with the maximization of the heating unit power generation as the optimization goal, and obtain the maximum power generation power Pmax of the heating unit;

[0108] The second solving module is used to solve the thermal power load optimization model of the heating unit with multiple extraction positions with the minimization of the heating unit's power generation as the optimization goal, and obtain the minimum power generation power Pmin of the heating unit;

[0109] The interval determination module is used to determine the power generation range of the heating unit with multiple extraction steam positions according to the obtained maximum power generation value Pmax and minimum power generation value Pmin of the heating unit.

[0110] Example 3:

[0111] Based on the same inventive concept as Example 1, this embodiment provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining the power generation power limit range of the multi-extraction position heating unit based on the genetic algorithm.

[0112] Example 4:

[0113] Based on the same inventive concept as Example 1, this embodiment provides a processor for running a program, wherein when the program is running, the method for determining the power generation power limit range of the multi-extraction steam position heating unit based on the genetic algorithm is executed.

[0114] Those skilled in the art will appreciate that the modules of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0115] In the embodiments provided by the present invention, it should be understood that the division of modules is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple modules can be combined into one module, one module can be split into multiple modules, or some features can be ignored, etc.

[0116] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0117] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm, characterized in that: The following steps are involved: Step S1: Establish a simulation model based on the design parameters of the heating unit components, wherein the design parameters include: component efficiency η, component inlet flow M in , component outlet flow M out 、Component inlet temperature T in , component outlet temperature T out 、Component inlet pressure P in , component outlet pressure P out 、Component inlet enthalpy H in , component outlet enthalpy H out The heating unit components include: boiler, steam turbine, generator, water pump, condenser, deaerator, regenerative heater, and steam extraction pipeline; Step S2, constructing a thermal power load optimization model for a heating unit with multiple extraction steam locations, including an optimization target, an optimization algorithm, constraints, and variable parameters; the optimization target of the optimization model is the maximum power generation capacity Pmax and the minimum power generation capacity Pmin of the heating unit; the optimization algorithm is a genetic algorithm; Step S3, solving the thermal power load optimization model of the heating unit with multiple extraction positions with the maximum power generation power of the heating unit as the optimization goal, and obtaining the maximum power generation power Pmax of the heating unit; Step S4, solving the thermal power load optimization model of the heating unit with multiple extraction positions with the minimization of the heating unit's power generation as the optimization goal, and obtaining the minimum power generation value Pmin of the heating unit; Step S5 , determining the power generation range of the heating unit with multiple steam extraction positions according to the obtained maximum power generation value Pmax and minimum power generation value Pmin of the heating unit.

2. The method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm according to claim 1, characterized in that: The constraint conditions in step S2 include: the minimum steam inlet flow rate of the low-pressure cylinder is not less than the minimum required flow rate for safe operation of the low-pressure cylinder, which is specifically defined as: M LP,min ≤M LP ; Where M LP is the air intake flow rate of the low-pressure cylinder of the heating unit, t / h; M LP,min It is the minimum air intake flow of the low-pressure cylinder of the heating unit, t / h.

3. The method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm according to claim 1, characterized in that: The constraint condition in step S2 includes: the upper limit of the pipeline extraction flow rate does not exceed the maximum extraction flow rate of the pipeline, which is specifically defined as: 0≤MM max ; Where, M is the heat supply of the unit, t / h; M max The maximum heating capacity of the unit, t / h.

4. The method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm according to claim 1, characterized in that: The constraint condition in step S2 includes: the heating supply of the heating unit is equal to the required flow of the industrial heat user, which is specifically defined as: M=M req ; Where, M is the heat supply of the unit, t / h; M req The maximum heating capacity of the unit, t / h.

5. The method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm according to claim 1, characterized in that: The parameters changed in step S2 specifically include the steam extraction flow rate of the heating steam extraction pipeline at the medium and low pressure connecting pipelines and the steam extraction flow rate of the heating steam extraction pipeline at the reheat steam cold section.

6. The method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm according to claim 1, characterized in that: The step S3 specifically includes the following steps: Step S31, randomly generating an initial population, specifically randomly generating the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section; Step S32, evaluating the quality of each individual through the fitness function, specifically comparing the power generation of the heating units; Step S33: performing a selection operation based on fitness, retaining high-quality individuals and eliminating low-quality individuals, specifically retaining the steam extraction flow of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow at the reheat steam cold section when the heating unit has a large power generation capacity; Step S34, simulating biological genetic variation through crossover and mutation to generate a new generation of populations, specifically by changing the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section; Step S35, screening the optimal solution that satisfies all constraints, specifically: excluding from the solution set the case where the minimum steam inlet flow rate of the low-pressure cylinder is lower than the minimum required flow rate for safe operation of the low-pressure cylinder, excluding from the solution set the case where the upper limit of the pipeline extraction flow rate exceeds the maximum pipeline extraction flow rate, and excluding from the solution set the case where the heating unit heat supply is not equal to the required flow rate of the industrial heat user; Step S36, repeating steps S31 to S34 to make the population evolve generation by generation and eventually converge to the individual with the highest fitness, that is, the optimal or approximately optimal solution to the problem, specifically, the power generation power of the heating unit reaches the maximum value Pmax.

7. The method for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm according to claim 1, characterized in that: The step S4 specifically includes the following steps: Step S41, randomly generating an initial population, specifically randomly generating the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section; Step S42, evaluating the quality of each individual through the fitness function, specifically comparing the power generation of the heating units; Step S43: Perform a selection operation based on fitness, retain high-quality individuals and eliminate low-quality individuals. Specifically, retain the steam extraction flow of the heating extraction pipe at the medium and low pressure connecting pipes and the heating extraction pipe flow at the reheat steam cold section where the power generation of the heating unit is relatively small. Step S44, then, by simulating biological genetic variation through crossover and mutation, a new generation of population is generated, specifically by changing the steam extraction flow rate of the heating extraction pipe at the medium and low pressure connecting pipes and the steam extraction pipe flow rate at the reheat steam cold section; Step S45, screening the optimal solution that satisfies all constraints, specifically: excluding from the solution set the case where the minimum steam inlet flow rate of the low-pressure cylinder is lower than the minimum required flow rate for safe operation of the low-pressure cylinder, excluding from the solution set the case where the upper limit of the pipeline extraction flow rate exceeds the maximum pipeline extraction flow rate, and excluding from the solution set the case where the heating unit heat supply is not equal to the required flow rate of the industrial heat user; Step S46, iterate steps S41 to S44 repeatedly, and finally converge to the individual with the highest fitness, that is, the optimal or approximately optimal solution to the problem, specifically, the power generation power of the heating unit reaches the minimum value Pmin.

8. A system for determining the power generation limit range of a multi-extraction steam heating unit based on a genetic algorithm, characterized in that: Applying the method according to any one of claims 1 to 7, comprising: The simulation modeling module is used to establish a simulation model based on the design parameters of the heating unit components, including: component efficiency η, component inlet flow M in , component outlet flow M out 、Component inlet temperature T in , component outlet temperature T out 、Component inlet pressure P in , component outlet pressure P out 、Component inlet enthalpy H in , component outlet enthalpy H out The heating unit components include: boiler, steam turbine, generator, water pump, condenser, deaerator, regenerative heater, and steam extraction pipeline; An optimization model construction module is used to construct a thermal power load optimization model for a heating unit with multiple extraction steam locations, including an optimization objective, an optimization algorithm, constraints, and variable parameters; the optimization objective of the optimization model is the maximum power generation capacity Pmax and the minimum power generation capacity Pmin of the heating unit; the optimization algorithm is a genetic algorithm; The first solving module is used to solve the thermal power load optimization model of the heating unit with multiple extraction positions with the maximization of the heating unit power generation as the optimization goal, and obtain the maximum power generation power Pmax of the heating unit; The second solving module is used to solve the thermal power load optimization model of the heating unit with multiple extraction positions with the minimization of the heating unit's power generation as the optimization goal, and obtain the minimum power generation power Pmin of the heating unit; The interval determination module is used to determine the power generation range of the heating unit with multiple extraction steam positions according to the obtained maximum power generation value Pmax and minimum power generation value Pmin of the heating unit.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining the power generation power limit range of a multi-extraction position heating unit based on a genetic algorithm as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein when the program is run, the method for determining the power generation power limit range of a multi-extraction position heating unit based on a genetic algorithm as described in any one of claims 1 to 7 is executed.

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