Feasible region construction method and device for load distribution of two-in-one combined cycle cogeneration unit

Abstract

This invention discloses a feasible region construction method for load allocation in a two-on-one combined cycle cogeneration unit, comprising: constructing a unit model reflecting the complete thermoelectric characteristics and coupling relationships between equipment under all operating conditions; setting operating constraints for the two-on-one combined cycle cogeneration unit to form boundary constraint conditions including equipment parameter adjustment boundaries and the unit's joint feasible region; constructing an objective function; dividing the unit's joint feasible region into grids, and sequentially selecting the thermoelectric load combinations corresponding to the grid points to obtain the optimal sub-unit thermoelectric load combinations corresponding to the optimal objective function; and outputting the joint feasible region of the optimal sub-unit thermoelectric load combinations corresponding to each load of the two-on-one combined cycle cogeneration unit through a visualization method. This invention also provides a feasible region construction device. The method provided by this invention enables visualization in engineering, allowing dispatchers to intuitively locate the optimal operating point.

Description

Technical Field

[0001] This invention belongs to the field of feasible domain mapping for combined cycle cogeneration units, and particularly relates to a method and apparatus for constructing feasible domains for load allocation of two-to-one combined cycle cogeneration units. Background Technology

[0002] Combined cycle cogeneration units are clean, efficient, and flexible thermal power units, representing an important future direction for thermal power development. These units primarily use natural gas as fuel, which undergoes complete combustion in the combustion chamber. This combustion efficiency is higher than that of decentralized coal-fired boilers, resulting in lower particulate emissions and making it a cleaner form of cogeneration. Based on the principle of energy cascade utilization, the heat generated from fuel combustion is used for power generation via a gas turbine, transferred to a steam turbine via a waste heat boiler, and then further supplied to users with lower-grade waste heat, making it an even more efficient form of cogeneration.

[0003] To maximize energy efficiency, the operation of combined cycle cogeneration units requires consideration of how to most effectively allocate and utilize different forms of energy. With the diversification of regional energy systems, the characteristics of heat and power demand from different end users vary significantly: public building users require periodic electricity and hot water supplies, while industrial steam users often need a continuous output of high-grade heat energy. This structural difference on the demand side places high demands on the supply-side capacity for coordinated heat and power supply.

[0004] While the combined cycle power plant (2-to-1) significantly improves overall energy efficiency through the cascaded utilization of energy from the gas turbine, waste heat boiler, and steam turbine, its thermal system suffers from strong coupling constraints. During thermal power generation, the high-temperature flue gas from the gas turbine outlet is reheated by the waste heat boiler, and the resulting high-temperature steam enters the steam turbine for power generation. A portion of the steam from the steam turbine is extracted and fed into the primary heating network for heating. Key parameters such as combustion chamber load, waste heat boiler evaporation rate, and steam turbine extraction rate are mutually restrictive, resulting in a nonlinear correlation between the heat and power output ratio. This coupling relationship makes determining the feasible operating region of the unit a fundamental challenge for optimal scheduling.

[0005] Patent document CN116345567A discloses a load allocation method for gas-fired steam combined cycle cogeneration units. Based on the principles of modularization, generalization, and hierarchy, and according to the demand analysis of the regional unit load optimization allocation system, the regional unit load optimization allocation system is a real-time load allocation strategy system for units built on the basis of automatic control systems such as AGC, SIS, and DCS. The system consists of three modules: a unit model simulation module, a gas consumption characteristic curve module, and a load optimization allocation module.

[0006] Patent document CN121076750A discloses an intelligent method for allocating heat and power loads in a combined heat and power (CHP) system, comprising the following steps: S1. Establishing CHP units and performing system analysis; S2. Determining the optimization objective, constraints, and operating domain of the units for CHP load allocation; S3. Utilizing a random forest algorithm to couple an economic calculation model for the CHP load power plant; S4. Establishing an improved particle swarm optimization (PSO) model; S5. Using the improved PSO model established in step S4 to allocate heat and power loads in the CHP system. Summary of the Invention

[0007] The purpose of this invention is to provide a feasible domain construction method and apparatus for load distribution of a two-to-one combined cycle cogeneration unit. This method can enable visualization of the project, allowing dispatchers to intuitively locate the optimal operating point.

[0008] To achieve the first objective of this invention, the following technical solution is provided: a method for constructing a feasible domain for load allocation in a two-on-one combined cycle cogeneration unit, comprising the following steps: Step 1: Construct a unit model that reflects the complete thermoelectric characteristics of the unit under all operating conditions and the coupling relationships between equipment; Step 2: Set the operating constraints of the two-to-one combined cycle cogeneration unit to form boundary constraint conditions that include equipment parameter adjustment boundaries and the combined feasible domain of the unit. Step 3: Based on the equipment parameter adjustment boundary and unit model, construct the objective function with the load of all units in the two-on-one combined cycle cogeneration unit as the basic optimization variable; Step 4: Divide the grid into the joint feasible domain of the units obtained in Step 2, and select the heat and power load combination corresponding to the grid points in sequence. Substitute each heat and power load combination into the objective function for iterative calculation to obtain the sub-unit heat and power load combination corresponding to the optimal objective function. Step 5: Output the joint feasible domain, which shows the optimal combination of heat and power loads of each sub-unit corresponding to each load of the two-to-one combined cycle cogeneration unit, through visualization methods.

[0009] This invention characterizes the Pareto optimal solution set for thermoelectric load allocation in a joint feasible domain expressed in three-dimensional space, and solves the thermoelectric load allocation problem of sub-units through multi-objective optimization using a genetic algorithm.

[0010] Specifically, the equipment parameter adjustment boundaries include constraints on various operating parameters in gas turbines, steam turbines, and waste heat boilers.

[0011] Specifically, the feasible domain of the combined unit includes when the steam turbine is used for extraction heating and when the steam turbine is used for back pressure heating.

[0012] Specifically, the feasible domain for the steam turbine to perform extraction heating is as follows: Under the maximum load conditions of two gas turbines, the maximum electrical load boundary of the unit in the two-to-one mode is obtained by different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under the minimum load conditions of two gas turbines, the minimum electrical load boundary of the unit in the two-to-one mode is obtained by different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under different load conditions of two gas turbines, the maximum steam extraction rate of the steam turbine and the corresponding thermal and electrical load are used to obtain the maximum heat load boundary of the unit in the two-to-one mode. Under the maximum load condition of a single gas turbine, the maximum electrical load boundary of the unit in the one-to-one mode is obtained by considering different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under the minimum load condition of a single gas turbine, the minimum electrical load boundary of the unit in the one-to-one mode is obtained by considering different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under different load conditions of a single gas turbine, the maximum steam extraction rate of the steam turbine and the corresponding thermal and electrical load are used to obtain the maximum thermal load boundary of the unit in the one-to-one mode.

[0013] Specifically, the feasible domain for the steam turbine to provide back pressure heating is as follows: The thermal and electrical loads corresponding to the two gas turbines under different load conditions are used to obtain the unit thermal and electrical load boundary in the two-to-one mode. The thermal and electrical loads corresponding to different load conditions of a single gas turbine are used to obtain the thermal and electrical load boundary of the unit in the one-to-one mode.

[0014] Specifically, the optimization objective of the objective function includes one or more of thermal efficiency, heat loss rate, or heat efficiency.

[0015] Specifically, the expression for the objective function is as follows: ; Among them, P gt1 P gt2 P st、 P cp And Q e These represent the output electrical power of the gas turbine and steam engine, the power consumption of the compressor and water pump, and the heat output of the unit, respectively, in kW; m f The total mass of natural gas consumed includes the natural gas input when the waste heat boiler is a supplementary combustion type, and the unit is kg / s; q l Q represents the lower heating value of a fuel, expressed in kJ / kg; e This indicates the unit's heat output, which is obtained by measuring the temperature and pressure at the inlet and outlet of the heating network water, and is measured in kW.

[0016] Specifically, the iterative calculation process is as follows: Determine the formula parameters of the iterative algorithm; After selecting the thermoelectric load combination and optimization variables corresponding to the grid points, as well as the objective function, optimization is performed under the boundary constraints. Repeat the above process until all thermoelectric load combinations have been traversed.

[0017] To achieve the second objective of the present invention, the following technical solution is provided: a feasible domain construction apparatus for performing the steps of the above-described feasible domain construction method for load allocation of a two-to-one combined cycle cogeneration unit.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: In power plant dispatching, the first step is often to treat combined cycle cogeneration units as a whole, which effectively reduces the number of dispatchable objects. The second step is to perform load dispatching within the combined cycle. Previous joint feasible domains could only represent the thermoelectric regulation range of multiple units, failing to reflect the load distribution of sub-units under specific thermoelectric loads. The thermoelectric load distribution among sub-units is a complex problem that comprehensively considers system thermoelectric coupling and equipment coupling. This invention selects multiple unit evaluation indicators, considers equipment constraints during unit operation, and uses a genetic algorithm to perform multi-objective optimization of the units. It improves the joint feasible domain drawing method for two-to-one cogeneration units and couples the Pareto optimal solution set for thermoelectric distribution onto the three-dimensional feasible domain using visualization technology, enabling direct application in production dispatching. This provides operators with an intuitive understanding of thermoelectric load distribution relationships and simplifies the thermoelectric load dispatching process from the grid to the unit. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a feasible domain construction method for load allocation of a two-to-one combined cycle cogeneration unit provided in this embodiment; Figure 2 This is a flowchart of the method for finding optimal constraints for a two-to-one combined heat and power unit provided in this embodiment; Figure 3 This is a flowchart of the optimization process for a two-to-one combined heat and power unit provided in this embodiment; Figure 4 This is a three-dimensional diagram of the joint feasible region provided in this embodiment. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] like Figure 1 As shown in this embodiment, a feasible domain construction method for load allocation of a two-on-one combined cycle cogeneration unit is provided, which specifically includes the following steps: A mechanistic model of a two-to-one combined cycle cogeneration unit was obtained. The model accurately reflects the connection relationships of the reaction equipment, including the compressor, combustion chamber, gas turbine, steam turbine, boiler, and deaerator. The embodiment is a supplementary combustion combined cycle, where flue gas from the gas turbine is combined with other gases and then enters the waste heat boiler after supplementary combustion. The model accurately reflects the internal physicochemical processes of the equipment, including compression, combustion, expansion, heat transfer, evaporation, condensation, and throttling. The model accurately reflects changes in the equipment's operating conditions, including changes in working fluid composition, calorific value, flow rate, temperature, and pressure. The model can reflect the unit's operating status under all operating conditions.

[0022] Determine the operating constraints of the two-on-one combined cycle cogeneration unit: To determine the boundary constraints for optimization, it is necessary to obtain the unit's operational constraints, including both equipment parameter adjustment boundaries and the unit's joint feasible region. The specific method and process are as follows: Figure 2 As shown. The equipment parameter adjustment boundaries include the adjustment ranges and operating constraints of each piece of equipment provided by the equipment manufacturer and in actual operation. For gas turbines, these include, but are not limited to, the compressor IGV adjustment range, gas turbine load adjustment range, TCA cooling air temperature constraint, natural gas temperature constraint after throttling, combustion chamber air-fuel ratio constraint, and gas turbine speed constraint. For steam turbines, these include, but are not limited to, the main steam valve adjustment range, extraction steam valve adjustment range, steam turbine load adjustment range, inlet steam temperature constraint, inlet steam pressure constraint, and inlet steam flow constraint. For waste heat boilers, these include, but are not limited to, deaerator operating pressure constraint, steam drum operating pressure constraint, and superheater inlet steam temperature constraint.

[0023] The boundary constraints for optimization include two parts: equipment parameter adjustment boundaries and the combined feasible region of the unit. Equipment parameter adjustment boundaries include the adjustment ranges and operating constraints of each piece of equipment provided by the equipment manufacturers and based on actual operation. For gas turbines, these include, but are not limited to, compressor IGV adjustment range, gas turbine load adjustment range, TCA cooling air temperature constraints, post-throttling natural gas temperature constraints, combustion chamber air-fuel ratio constraints, and gas turbine speed constraints. For steam turbines, these include, but are not limited to, main steam valve adjustment range, extraction steam valve adjustment range, steam turbine load adjustment range, inlet steam temperature constraints, inlet steam pressure constraints, and inlet steam flow constraints. For waste heat boilers, these include, but are not limited to, deaerator operating pressure constraints, drum operating pressure constraints, superheater inlet steam temperature constraints, and two-to-one combined cycle steam generation constraints. The data for plotting the combined feasible region of the two-to-one combined cycle cogeneration unit is calculated using the model obtained in step S1. When the steam turbine is extracting steam for heating, the specific steps are as follows: S201. The model calculates the maximum power load boundary of the unit under the maximum load conditions of two gas turbines, with different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. S202. The model calculates the minimum steam extraction rate of the steam turbine and the corresponding thermal and electrical load under the minimum load conditions of two gas turbines, and obtains the minimum electrical load boundary of the unit in the two-to-one mode. S203. The model calculates the maximum steam extraction rate of the steam turbine and the corresponding thermal and electrical load under different load conditions of the two gas turbines, and obtains the maximum heat load boundary of the unit in the two-to-one mode. S204. Model calculation of the maximum load of a single gas turbine, the steam turbine extraction rate and corresponding thermal and electrical load, to obtain the maximum electrical load boundary of the unit in the one-to-one mode. S205. Model calculation of the minimum load of a single gas turbine, the steam turbine extraction rate and corresponding thermal and electrical load, to obtain the minimum electrical load boundary of the unit in the one-to-one mode. S206. The model calculates the maximum steam extraction capacity of the steam turbine and the corresponding thermal and electrical load under different load conditions of a single gas turbine, and obtains the maximum thermal load boundary of the unit in the one-to-one mode. When the steam turbine is used for back pressure heating, the specific steps for drawing the joint feasible region are as follows: S207. The model calculates the thermoelectric load corresponding to the two gas turbines under different load conditions, and obtains the unit thermoelectric load boundary under the two-to-one mode. S208. The model calculates the thermoelectric load corresponding to a single gas turbine under different load conditions, and obtains the unit thermoelectric load boundary in the one-to-one mode.

[0024] The data for the combined feasible region of the two-to-one combined cycle cogeneration unit were calculated using the obtained mechanistic model. In this embodiment, the steam turbine extracts steam for heating. The specific calculation of the combined feasible region boundary is shown below: (1) Calculate the steam turbine extraction rate and corresponding thermoelectric load under the maximum load conditions of the two gas turbines to obtain the maximum electrical load boundary of the unit in the two-to-one mode. Set the gas turbine load in the model to the maximum operating load, set the steam turbine extraction rate to start from 0, and increase the model simulation by a fixed step size each time until the low-pressure cylinder inlet flow reaches the minimum flow limit provided by the manufacturer. Save and plot the thermoelectric load curve of the unit to obtain the maximum electrical load boundary of the unit in the two-to-one mode.

[0025] (2) The model calculates the minimum load of the steam turbine and the corresponding thermal and electrical load under the minimum load conditions of the two gas turbines, and obtains the minimum electrical load boundary of the unit in the two-to-one mode. The gas turbine load in the model is set to the minimum operating load. The steam turbine extraction rate is set to start from 0 and the model simulation is performed by increasing the fixed step size each time until the low-pressure cylinder inlet flow reaches the minimum flow limit provided by the manufacturer. The thermal-electric load curve of the unit is saved and plotted to obtain the minimum electrical load boundary of the unit in the two-to-one mode.

[0026] (3) The model calculates the maximum steam extraction rate of the steam turbine and the corresponding thermoelectric load under different load conditions of the two gas turbines to obtain the maximum thermal load boundary of the unit in the two-to-one mode. The gas turbine load in the model is set to start from the minimum operating load, and the simulation is performed by increasing the fixed step size each time until the maximum operating load is reached. The steam extraction rate of the steam turbine is set to make the low-pressure cylinder inlet flow reach the minimum flow limit provided by the manufacturer in each simulation. The thermoelectric load curve of the unit is saved and plotted to obtain the maximum thermal load boundary of the unit in the two-to-one mode.

[0027] (4) The model calculates the maximum electrical load boundary of the unit under the maximum load condition of a single gas turbine, with different steam extraction rates of the steam turbine and the corresponding thermoelectric load. One gas turbine in the model is set to the maximum operating load, and the other gas turbine is shut down. The steam extraction rate of the steam turbine is set to start from 0, and the model simulation is performed by increasing the fixed step size each time until the low-pressure cylinder inlet flow reaches the minimum flow limit provided by the manufacturer. The thermoelectric load curve of the unit is saved and plotted to obtain the maximum electrical load boundary of the unit under the one-to-one mode.

[0028] (5) The model calculates the minimum load of a single gas turbine, the steam turbine extraction rate and the corresponding thermoelectric load, and obtains the minimum electrical load boundary of the unit in the one-to-one mode. Set the load of one gas turbine in the model to the minimum operating load, and shut down the other gas turbine. Set the steam turbine extraction rate to 0, and increase the model simulation by a fixed step size each time until the low-pressure cylinder inlet flow reaches the minimum flow limit provided by the manufacturer. Save and plot the thermoelectric load curve of the unit to obtain the minimum electrical load boundary of the unit in the one-to-one mode.

[0029] (6) The model calculates the maximum steam extraction rate of the steam turbine and the corresponding thermoelectric load under different load conditions of a single gas turbine to obtain the maximum thermal load boundary of the unit in the one-to-one mode. Set one gas turbine in the model to be shut down, and the load of the other gas turbine starts from the minimum operating load. Each simulation is performed with a fixed step size until the maximum operating load is reached. The steam extraction rate of the steam turbine is set in each simulation so that the inlet flow of the low-pressure cylinder reaches the minimum flow limit provided by the manufacturer. Save and plot the thermoelectric load curve of the unit to obtain the maximum thermal load boundary of the unit in the one-to-one mode.

[0030] Determine the optimization variables and objective function: In this embodiment, the optimization variables are selected as the loads of the two gas turbines and the steam turbine, and the objective function is selected as thermal efficiency.

[0031] Setting optimization parameters and optimizing using a genetic algorithm: In this embodiment, as shown... Figure 3 As shown, it includes: S401. Set appropriate genetic algorithm population size, mutation probability, crossover probability, replacement rate, maximum number of generations, and convergence index; S402, Mesh the joint feasible region drawn in step S2; S403. Select the thermoelectric combination (Pi, Qi) and the optimization parameter X, select the objective function F(Pi, Qi, X), and perform optimization within the constraint range ∑Ci obtained in step S2.

[0032] S404. Check and save the results. Repeat step S403 until the thermoelectric combination (Pi, Qi) is traversed.

[0033] In some embodiments of the present invention, in step S401, the genetic algorithm is a search heuristic algorithm that simulates the mechanisms of natural selection and genetics. It searches for the optimal or near-optimal solution to a problem by mimicking operations such as selection, mutation, and crossover in biological evolution. The genetic algorithm parameters that need to be determined include the number of individuals in the population, mutation probability, crossover probability, retention rate, maximum number of generations, and convergence index. Among these, a larger population size results in a larger search space and longer computation time; a higher mutation probability leads to better population diversity and a more random search process; a higher crossover probability allows for new parameter combinations but may lead to excessively fast convergence; a higher retention rate results in more surviving superior individuals from the previous generation but may also lead to excessively fast convergence; the maximum number of generations, combined with the convergence index, limits the optimization time. Through several pre-optimization trials, the parameter combinations are adjusted while ensuring accuracy, thus saving computational resources.

[0034] 1) Set the population size, mutation probability, crossover probability, replacement rate, maximum number of generations, and convergence index for the genetic algorithm. The number of individuals in the population affects the search space and computation time; the mutation probability improves population diversity and the randomness of the search process; the crossover probability affects the generation of new parameter combinations and the convergence speed; the retention rate affects the survival rate of superior individuals from the previous generation and the convergence speed; the maximum number of generations and the convergence index determine when the optimization stops. Through several preliminary optimization experiments, the above parameter combinations were adjusted while ensuring accuracy and saving computational resources. Finally, a population size of 30 individuals was selected, with mutation probability, crossover probability, and retention rate of 0.9, 0.5, and 0.2 respectively, a maximum number of generations of 30, and a convergence index of 0.03.

[0035] (2) Perform grid generation for the joint feasible region. Let the maximum heat load of the unit be denoted as Q. max The steam turbine extracts steam for heating, dividing the heating regulation range into 16 equal parts, resulting in [0, Q]. max / 16,…,15Q max / 16,Q max Furthermore, the electrical load regulation boundary under each heat load Hi is divided into 15 equal parts to obtain [P]. i,min ,(P i,max -P i,min ) / 15,…,14(P i,max -P i,min ) / 15,P i,max The feasible region is then meshed.

[0036] (3) Perform formal optimization by traversing the thermoelectric combinations on the grid points of the joint feasible domain.

[0037] (4) Result verification and storage. The load of gas turbine No. 1, the load of gas turbine No. 2, the load of steam turbine, and the optimized unit heat rate are obtained through optimization. The results are stored together with the total thermal and electrical load. Before saving the results, the validity of the results is checked to ensure that the optimization process should end due to convergence rather than reaching the maximum algebra. The unit can be tested through the model to ensure that it can operate normally under the specified parameters.

[0038] In this embodiment, the selected optimization parameters include the loads of the two gas turbines, the steam turbine load, and the aforementioned equipment adjustment parameters. Selectable optimization objectives include thermal efficiency, heat rate, thermal efficiency, and their weighted combinations. The specific calculation methods for these evaluation indicators are as follows: Thermal efficiency calculation: ; Among them, P gt1 P gt2 P st、 P cp And Q e These represent the output electrical power of the gas turbine and steam engine, the power consumption of the compressor and water pump, and the heat output of the unit, respectively, in kW; m f The total mass of natural gas consumed includes the natural gas input when the waste heat boiler is a supplementary combustion type, and the unit is kg / s; q l Q represents the lower heating value of a fuel, expressed in kJ / kg; e This indicates the unit's heat output, which is obtained by measuring the temperature and pressure at the inlet and outlet of the heating network water, and is measured in kW.

[0039] The unit's heat supply is obtained by measuring the temperature and pressure at the inlet and outlet of the heating network water. ; In the formula, m net The flow rate of the heating network is expressed in kg / s or h. e h i These represent the enthalpy values ​​of the water flowing out of and into the heat exchanger of the heating network, respectively, in kJ / kg.

[0040] Heat rate calculation: ; Efficiency calculation: ; Among them, E heat The power supply capacity is expressed in kW, determined by measuring the temperature and pressure at the inlet and outlet of the heating network water. f Fuel is a component of γ, expressed in kJ / kg*K, and is composed of physical γ and chemical γ.

[0041] ; ; In the formula, T0 represents the ambient temperature in K; s e s i These represent the entropy values ​​of water flowing out of and into the heat exchanger of the heating network, respectively, in kJ / kg*K, and q. h This indicates the higher heating value of the fuel, expressed in kJ / kg.

[0042] The general form of the optimization objective is: ; In the formula, α i F represents the weight of each indicator. i This indicates the above indicators.

[0043] Drawing the joint feasible region to reflect ideal load distribution: i.e., P gt1 P gt2 P st Choose P st As a contour line indicator, C=P is calculated. gt1 / P gt2 As a color indicator.

[0044] The specific steps for visualizing plots using the matplotlib library on the Python platform are as follows: S501. Select a suitable visualization platform; S502. Data Preprocessing. This mainly includes transforming the resulting data to ensure it is suitable for visualization. S503, Configure and calculate drawing parameters.

[0045] S504, Drawing and Adjustment.

[0046] More specifically, read the obtained plotting parameters [Q,P,η] e ,P st [,C], using Q, P, η e Establish a three-dimensional image of the joint feasible region; select the steam turbine load P. st Connect identical points and draw contour lines using linear interpolation; let the minimum and maximum values ​​of the calculated color index C be Cmin and Cmax, respectively. min C max Adjust the color mapping range to C min ~C maxFurther adjustments were made to the perspective factor, scaling ratio, viewing angle, and data label position to ensure a clear and complete representation of the unit's thermoelectric regulation range, optimal performance under different thermoelectric loads, and the corresponding load distribution of sub-units, resulting in a three-dimensional schematic diagram of the unit's joint feasible region. The viewing angle was then aligned perpendicular to the xy-plane, and the perspective factor was set to 0 to obtain a projection diagram of the joint feasible region representing the unit's thermoelectric regulation range. Finally, the viewing angle was aligned perpendicular to the yz-plane, and the perspective factor was set to 0 to obtain a projection diagram of the joint feasible region representing the unit's thermal efficiency range, as shown below. Figure 4 As shown. Finally, the joint feasible domain graph is exported as a static URL and stored.

[0047] This embodiment also provides a feasible domain construction apparatus for performing the steps of the feasible domain construction method for load allocation of a two-to-one combined cycle cogeneration unit as provided in the above embodiments.

[0048] Furthermore, the terms "upper," "lower," "inner," "outer," "front," and "rear" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0049] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes or modifications made to the structure, features and principles described in the claims of the present invention should be included in the scope of the claims of the present invention.

[0050] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing a feasible domain for load allocation in a two-on-one combined cycle cogeneration unit, characterized in that, Includes the following steps: Step 1: Construct a unit model that reflects the complete thermoelectric characteristics of the unit under all operating conditions and the coupling relationships between equipment; Step 2: Set the operating constraints of the two-to-one combined cycle cogeneration unit to form boundary constraint conditions that include equipment parameter adjustment boundaries and the combined feasible domain of the unit. Step 3: Based on the equipment parameter adjustment boundary and unit model, construct the objective function with the load of all units in the two-on-one combined cycle cogeneration unit as the basic optimization variable; Step 4: Divide the grid into the joint feasible domain of the units obtained in Step 2, and select the heat and power load combination corresponding to the grid points in sequence. Substitute each heat and power load combination into the objective function for iterative calculation to obtain the sub-unit heat and power load combination corresponding to the optimal objective function. Step 5: Output the joint feasible region of the optimal combination of heat and power loads of each sub-unit corresponding to each load of the two-to-one combined cycle cogeneration unit through visualization method.

2. The feasible domain construction method for load distribution of a two-drive-one combined cycle cogeneration unit according to claim 1, characterized in that, The equipment parameter adjustment boundaries include constraints on various operating parameters in gas turbines, steam turbines, and waste heat boilers.

3. The feasible domain construction method for load distribution of a two-drive-one combined cycle cogeneration unit according to claim 1, characterized in that, The combined feasible domain of the units includes when the steam turbine is used for extraction heating and when the steam turbine is used for back pressure heating.

4. The feasible domain construction method for load distribution of a two-on-one combined cycle cogeneration unit according to claim 3, characterized in that, The feasible region for the steam turbine to perform extraction heating is as follows: Under the maximum load conditions of two gas turbines, the maximum electrical load boundary of the unit in the two-to-one mode is obtained by different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under the minimum load conditions of two gas turbines, the minimum electrical load boundary of the unit in the two-to-one mode is obtained by different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under different load conditions of two gas turbines, the maximum steam extraction rate of the steam turbine and the corresponding thermal and electrical load are used to obtain the maximum heat load boundary of the unit in the two-to-one mode. Under the maximum load condition of a single gas turbine, the maximum electrical load boundary of the unit in the one-to-one mode is obtained by considering different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under the minimum load condition of a single gas turbine, the minimum electrical load boundary of the unit in the one-to-one mode is obtained by considering different steam extraction rates of the steam turbine and the corresponding thermal and electrical loads. Under different load conditions of a single gas turbine, the maximum steam extraction rate of the steam turbine and the corresponding thermal and electrical load are used to obtain the maximum thermal load boundary of the unit in the one-to-one mode.

5. The feasible domain construction method for load distribution of a two-on-one combined cycle cogeneration unit according to claim 3, characterized in that, The feasible region for the steam turbine to provide back pressure heating is as follows: The thermal and electrical loads corresponding to the two gas turbines under different load conditions are used to obtain the unit thermal and electrical load boundary in the two-to-one mode. The thermal and electrical loads corresponding to different load conditions of a single gas turbine are used to obtain the thermal and electrical load boundary of the unit in the one-to-one mode.

6. The feasible domain construction method for load distribution of a two-on-one combined cycle cogeneration unit according to claim 1, characterized in that, The optimization objective of the objective function includes one or more of thermal efficiency, heat loss rate, or heat efficiency.

7. The feasible domain construction method for load distribution of a two-drive-one combined cycle cogeneration unit according to claim 1, characterized in that, The expression for the objective function is as follows: Thermal efficiency: ; Heat loss rate: ; Efficiency: ; Among them, P gt1 P gt2 P st、 P cp And Q e These represent the output electrical power of the gas turbine and steam engine, the power consumption of the compressor and water pump, and the heat output of the unit, respectively, in kW; m f The total mass of natural gas consumed includes the natural gas input when the waste heat boiler is a supplementary combustion type, and the unit is kg / s; q l Q represents the lower heating value of a fuel, expressed in kJ / kg; e This indicates the unit's heating capacity, which is obtained by measuring the temperature and pressure at the inlet and outlet of the heating network water, and is measured in kW; E heat The power supply capacity is expressed in kW, determined by measuring the temperature and pressure at the inlet and outlet of the heating network water. f Fuel is a component of γ, expressed in kJ / kg*K, and is composed of physical γ and chemical γ.

8. The feasible domain construction method for load distribution of a two-drive-one combined cycle cogeneration unit according to claim 1, characterized in that, The iterative calculation process is as follows: Determine the formula parameters of the iterative algorithm; After selecting the thermoelectric load combination and optimization variables corresponding to the grid points, as well as the objective function, optimization is performed under the boundary constraints. Repeat the above process until all thermoelectric load combinations have been traversed.

9. A feasible domain construction apparatus, characterized in that, The steps are for performing the feasible domain construction method for load allocation of a two-to-one combined cycle cogeneration unit as described in any one of claims 1 to 8.

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