Method and system for optimizing in-plant load distribution of gas generator set

Abstract

The invention relates to an in-plant load distribution optimization method for a generator set, and the method comprises the steps: building a gas turbine power generation power consumption model and a steam turbine power generation power model in each unit, and accurately calculating the power generation cost; further, a unit income model is constructed in combination with power supply income and heat supply income, and an operation boundary is determined according to a power plant combustion plan demand, a heat supply demand and unit characteristics; and taking an operation boundary as a constraint, and obtaining an optimal load distribution strategy which meets the combustion and heat supply requirements and has the maximum theoretical value of the income model by using an optimization algorithm. The problem that a cost load distribution mode in a traditional method is poor in economical performance is solved, accurate cost accounting, comprehensive income consideration and load distribution optimization are achieved, and the energy utilization efficiency and decision scientificity of a power plant are improved.

Description

Technical Field

[0001] The present application relates to the field of gas turbine operation and maintenance deployment, and in particular to a method, system, computer equipment and computer-readable storage medium for optimizing load distribution within a gas-fired power generation unit plant. Background Art

[0002] For gas power plants with internal unit load distribution margin, when adjusting unit load, the amount of load distributed to the unit is generally determined based on the power generation efficiency. This distribution method often fails to minimize the power generation cost.

[0003] The gas consumption of gas turbines is the main factor affecting the power generation cost of gas-fired power plants. There are many external factors affecting it, including: (1) natural gas calorific value, (2) environmental parameters, and (3) gas turbine load rate. How to optimize the load distribution between units while considering the above factors and thus significantly reduce the power generation cost of gas-fired power plants is an issue that needs to be solved urgently in this field. Summary of the invention

[0004] The embodiments of the present application provide a method, system, computer device and computer-readable storage medium for optimizing load distribution within a power generation plant, so as to at least solve the problem that the load distribution method in the related art is less economical.

[0005] In a first aspect, an embodiment of the present application provides a method for optimizing load distribution within a power generation unit plant, the method comprising:

[0006] Construct the power consumption model of the gas turbines of each generator set in the power plant, and the power generation model of the steam turbines;

[0007] Based on the power generation consumption model, the power generation power model and the unit operation reference information, obtaining the power generation cost of each power generation unit;

[0008] Constructing a unit profit model based on the power generation cost and its corresponding power supply income and heat supply income;

[0009] The operating boundary is determined in combination with the combustion plan requirements, heating requirements and unit characteristics of the power plant. Taking the operating boundary as a constraint, an optimization algorithm is used to obtain an optimized load distribution strategy that can meet the combustion plan requirements and heating requirements and maximize the unit profit model.

[0010] In some embodiments, constructing a power generation and power consumption model of a gas turbine of each power generation unit in a power plant includes:

[0011] Obtaining the atmospheric temperature of the area where the power plant is located and the combustion engine power of the gas turbine;

[0012] Performing mathematical modeling and regression analysis based on historical operating data of the gas turbine to obtain a first regression coefficient related to the atmospheric temperature, a second regression coefficient related to the engine power of the gas turbine, and a gas consumption constant;

[0013] The power generation power consumption model is constructed based on the atmospheric temperature and its corresponding first regression coefficient, the second regression coefficient corresponding to the gas turbine power of each gas turbine, and the gas consumption constant.

[0014] In some embodiments, constructing a power generation model of a steam turbine of each generator set in a power plant includes:

[0015] Determine the steam turbine and its associated target gas turbine, obtain the gas turbine power of the target gas turbine and the heat supply function between the steam turbine and the target gas turbine;

[0016] Performing mathematical modeling and regression analysis based on historical operating data of the gas turbine and the steam turbine to obtain a third regression coefficient related to the target gas turbine, a fourth regression coefficient related to the heat supply function, and a power generation constant corresponding to the steam turbine;

[0017] The power generation model is constructed based on the third regression coefficient corresponding to the gas turbine power of the target gas turbine, the fourth regression coefficient related to the heat supply function, and the power generation constant.

[0018] In some embodiments, the unit operation reference information includes fuel price, electricity price, fixed cost and average heating demand and heating price;

[0019] The power generation cost includes combustion cost and fixed cost, wherein the combustion cost is determined according to the power generation power consumption and fuel cost of each gas-fired unit, and the fixed cost corresponding to the unit power generation is estimated by analyzing the historical data of the power generation unit.

[0020] In some of the embodiments, a unit profit model is constructed according to the power generation cost and its corresponding power supply income and heat supply income, wherein the calculation expression of the unit profit model of any power generation unit is as follows:

[0021]

[0022] in, is the power of the i-th gas turbine, f1 is the electricity price, q i is the gas consumption of the i-th gas turbine, m is the fuel price, f0 is the fixed cost, C1 is the average heating demand, and n is the heating price.

[0023] In some embodiments, determining the operation boundary in combination with the combustion plan requirements, heating requirements, and unit characteristics of the power plant includes:

[0024] According to the unit characteristics, the mutual constraint relationship between the heat supply demand and the combustion plan demand is obtained, as well as the minimum stable combustion load of the gas turbine and the maximum limit of the heat supply of the generator set are obtained;

[0025] The operating boundary is determined according to the mutual restriction relationship, the minimum stable combustion load, and the maximum limit of the heating amount.

[0026] In some embodiments, taking the operation boundary as a constraint condition, obtaining an optimized load distribution strategy that can meet the combustion plan requirements and the heating requirements and maximize the unit revenue through an optimization algorithm includes:

[0027] Randomly generate initial solutions, wherein the initial solutions represent different load distribution strategies;

[0028] According to the objective function corresponding to the unit profit model and the constraint conditions, the fitness value of the initial solution is updated and iterated, and when the iteration meets the stop condition, the optimized load distribution strategy is determined according to the current solution.

[0029] In a second aspect, an embodiment of the present application provides a system for optimizing load distribution within a power generation unit plant, the system comprising: a construction module and a distribution strategy generation module, wherein:

[0030] The construction module is used to construct a power generation and consumption model of the gas turbine of each generator set in the power plant, and a power generation model of the steam turbine;

[0031] And, based on the power generation consumption model, the power generation power model and the unit operation reference information, the power generation cost of each power generation unit is obtained, and according to the power generation cost and its corresponding power supply income and heat supply income, a unit profit model is constructed;

[0032] The allocation strategy generation module is used to determine the operating boundary in combination with the combustion plan requirements, heating requirements and unit characteristics of the power plant, and to obtain an optimized load allocation strategy that can meet the combustion plan requirements and heating requirements and maximize the unit profit model through an optimization algorithm, taking the operating boundary as a constraint condition.

[0033] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0035] Compared with the related art, the method for optimizing load distribution within the power generation unit provided in the embodiment of the present application accurately calculates the power generation cost by constructing the power consumption model of the gas turbine power generation and the power generation model of the steam turbine in each unit; further, the unit profit model is constructed by combining the power supply income and the heat supply income, and the operation boundary is determined according to the combustion plan requirements, heat supply requirements and unit characteristics of the power plant; and the optimization algorithm is used to obtain the optimized load distribution strategy that meets the combustion and heating requirements and has the maximum theoretical value of the profit model with the operation boundary as a constraint. The problem of poor economy of the cost load distribution method in the traditional method is solved, and accurate cost accounting, comprehensive benefit consideration and optimized load distribution are realized, which improves the energy utilization efficiency of the power plant and the scientific nature of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 is a flow chart of a method for optimizing load distribution within a generator set according to an embodiment of the present application;

[0038] Figure 2 is a structural block diagram of a load distribution optimization system for a generator set in a plant according to an embodiment of the present application; Figure 3 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0040] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0041] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0042] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0043] When adjusting the load of a gas-fired power plant with a margin for load distribution of units within the plant, the load distribution of the units is generally determined by the power generation efficiency. This distribution method is often not the lowest power generation cost. The gas consumption of the gas turbine is the main factor affecting the power generation cost of a gas-fired power plant. There are many external factors that affect it, including: (1) the calorific value of natural gas, (2) environmental parameters, and (3) the load rate of the gas turbine. Under the condition that the power supply load and heating load of the entire plant are certain, optimizing the load distribution between units can significantly reduce the power generation cost of the gas-fired power plant.

[0044] In view of this, the present application provides a method for optimizing load distribution within a power generation unit plant. Figure 1 is a flow chart of a method for optimizing load distribution within a generator set according to an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0045] S101, constructing a power generation and consumption model of the gas turbine of each generator set in the power plant and a power generation model of the steam turbine;

[0046] The power plant may be any chemical fuel power plant, and further, the power plant should include at least two generating units.

[0047] In this embodiment, the generator set can be a gas-steam combined cycle generator set; wherein the gas turbine and the steam turbine usually form an integrated power generation system. The gas turbine first burns fuel to produce high-temperature and high-pressure gas, which drives the gas turbine to generate power; on the other hand, the high-temperature exhaust gas discharged by the gas turbine enters the waste heat boiler to generate steam, which in turn drives the steam turbine to generate power. Such a combination can make full use of the characteristics of different energy forms and improve energy utilization efficiency and power generation.

[0048] Specifically, accurate evaluation of the operating efficiency and energy consumption of gas turbines is crucial for the economic operation and energy management of power plants. By building a power generation energy consumption model, we can better understand the performance of gas turbines under different operating conditions and provide a basis for optimizing operating strategies.

[0049] In this embodiment, considering the important influence of atmospheric temperature and gas turbine power on gas consumption, the gas consumption of gas turbine is expressed as The functional relationship between these two key parameters and gas consumption can be clarified, which facilitates quantitative analysis and prediction.

[0050] Among them, the change of atmospheric temperature (T) will affect the inlet temperature and density of the gas turbine, and then affect the combustion process and thermal efficiency; generally speaking, as the atmospheric temperature increases, the efficiency of the gas turbine will decrease and the power consumption of power generation will increase;

[0051] There is also a certain relationship between the power generation power (P1) of the gas turbine and the gas consumption. At different power output levels, the operating efficiency of the gas turbine may be different, resulting in changes in gas consumption.

[0052] In addition, due to performance differences in the design, manufacturing, maintenance and aging of gas turbine equipment, different gas turbines may have different gas consumption performance under the same operating conditions. Therefore, modeling and calculating the relationship between the power generation consumption of each gas turbine and the power generation power of the gas turbine and the atmospheric temperature can more accurately reflect the actual performance of each gas turbine; this modeling method can better adapt to the characteristics of different gas turbines and provide more targeted suggestions for the optimized operation of each gas turbine.

[0053] In addition, after the exhaust gas of the gas turbine enters the boiler for heat exchange, high-pressure superheated steam is generated. The distribution of steam determines the power generation of the steam turbine. Part of the steam is extracted for heat supply, and the rest drives the steam turbine to generate power. Therefore, the power generation of the steam turbine is mainly affected by the power of the gas turbine and the heat supply. The power of the steam turbine is expressed as P2 = ω (P1, Q); among which, the power of the gas turbine (P1) determines the heat of the exhaust gas and the amount of steam generated, while the heat supply (Q) directly affects the amount of steam used to drive the steam turbine to work.

[0054] It should be noted that the performance of steam turbine equipment is also affected by many factors such as design, maintenance, manufacturing, back pressure, etc. Different steam turbines may show obvious differences under the same operating conditions. Therefore, the functional relationship between the power generation of each steam turbine and the power and heat supply of the gas turbine is modeled and calculated separately, which can more accurately reflect the actual performance of each steam turbine.

[0055] The steam turbine power generation model modeling method provided in this application can accurately evaluate the power generation of the steam turbine under different operating conditions, providing a basis for the power production and scheduling of the power plant; at the same time, by understanding the relationship between the steam turbine power and the gas turbine power and heat supply, the steam distribution strategy can be optimized and the energy utilization efficiency can be improved.

[0056] Specifically, constructing the power generation and power consumption model of each gas turbine in the power plant includes the following subdivision steps:

[0057] Step 1, obtain the atmospheric temperature of the area where the power plant is located and the gas turbine power of the gas turbine;

[0058] Step 2, mathematical modeling and regression analysis are performed based on the historical operating data of the gas turbine to obtain the first regression coefficient related to the atmospheric temperature, the second regression coefficient related to the gas turbine power, and the gas consumption constant;

[0059] Step 3, based on the atmospheric temperature and its corresponding first regression coefficient, the second regression coefficient corresponding to the gas turbine power of each gas turbine, and the gas consumption constant, a power generation consumption model is constructed.

[0060] In an exemplary embodiment, if there are two generator sets in the power plant, 1# and 2#, the relationship between the power consumption of the gas turbine and the power generation of the gas turbine and the atmospheric temperature is modeled, and the mathematical expression is as follows:

[0061]

[0062]

[0063] It should be noted that 0.2092&0.5148, 0.837&1.06 and 410.3&443.3 are respectively the first regression coefficient, the second regression coefficient and the gas consumption constant obtained by mathematical modeling and regression analysis of a large amount of actual operating data of Unit 1# and Unit 2#.

[0064] Specifically, different atmospheric temperatures T and different gas turbine power generation powers P1 can be collected. 1 The data of gas turbine power consumption q1 under the condition are then fitted using mathematical statistical methods, such as the least square method, to determine the mathematical model that can best describe the relationship between gas turbine power consumption, atmospheric temperature and gas turbine power generation. In this process, these specific coefficient values ​​will be obtained.

[0065] The above coefficients reflect the influence of atmospheric temperature and gas turbine power on gas turbine power consumption. For example, the coefficient 0.2092 indicates the influence of one unit change in atmospheric temperature on gas turbine power consumption; the coefficient 0.837 reflects the influence of gas turbine power change on gas turbine power consumption, and the constant term 410.3 can be a basic value of gas turbine power consumption under specific reference conditions.

[0066] Specifically, constructing the power generation model of each steam turbine in the power plant includes the following subdivision steps:

[0067] Step 1, determine the steam turbine and its associated target gas turbine, obtain the gas turbine power of the target gas turbine and the heat supply function between the steam turbine and the target associated gas turbine;

[0068] Step 2, mathematical modeling and regression analysis are performed based on the historical operating data of gas turbines and steam turbines to obtain three regression coefficients related to the target gas turbine, the fourth regression coefficient related to the heat supply function, and the power generation constant corresponding to the steam turbine;

[0069] Step 3, construct a power generation model based on the third regression coefficient corresponding to the target gas turbine's gas turbine power, the fourth regression coefficient related to the heat supply function, and the power generation constant.

[0070] In an exemplary embodiment, if the power plant has two units, Unit 1 and Unit 2, the functional relationship between the steam turbine power generation and the gas turbine power and heat supply is modeled and calculated, and the mathematical expression is as follows:

[0071]

[0072] In the above expression, taking unit 1# as an example, 0.2597 and -0.06285 are the third and fourth regression coefficients respectively, where 0.2597 represents the gas turbine power P1 1 Power generation for steam turbine P2 1 The degree of influence is that for every unit change in gas turbine power, the steam turbine power generation power changes by 0.2597 units; 0.06285 means the heat supply Q1 has an influence on the steam turbine power generation power P2 1 Q1 represents the heat supply function of unit 1, which is a variable affecting the power generation of the steam turbine; and 31.77 is a constant term, which represents the power of the gas turbine when P1 1 And the heating amount Q1 are both specific values, a basic value of the steam turbine power generation capacity.

[0073] S102, based on the power consumption model, the power generation model and the unit operation reference information, the power generation cost of each power generation unit is obtained, and the unit profit model is constructed according to the power generation cost and its corresponding power supply profit and heat supply profit;

[0074] Specifically, the total revenue is the difference between the power supply and heat supply revenue and the power generation cost. The total revenue of the whole plant is obtained by calculating the revenue of each unit and summing them up, and adding the heat supply revenue;

[0075] Among them, the power generation cost includes fuel cost and fixed cost. The fuel cost is calculated based on the real-time gas price and the power consumption of the gas turbine. The fixed cost is allocated to other costs such as depreciation, maintenance, wages, etc. required for each kilowatt-hour of electricity generated, which can be estimated based on data from previous years.

[0076] Specifically, the power consumption of a gas turbine represents the amount of natural gas consumed per megawatt-hour of electricity (in ), which can be multiplied by the real-time gas price to get the fuel cost. For example, if the power consumption of a gas turbine is cubic meters per megawatt-hour and the gas price is yuan per cubic meter, then the fuel cost of this gas turbine for each megawatt-hour of electricity generated is yuan;

[0077] Fixed costs are other costs such as depreciation, maintenance, and wages that are allocated to each kilowatt-hour of electricity generated. These costs are relatively stable and will not fluctuate dramatically with real-time changes in power generation. An average fixed cost value can be estimated by analyzing historical data from previous years. It can be understood that by considering fuel costs and fixed costs, the cost of power generation can be calculated more comprehensively, thereby accurately evaluating the profitability of the power plant.

[0078] In this embodiment, the unit revenue model is constructed by the following expression:

[0079]

[0080] in, is the power of the i-th gas turbine, f1 is the electricity price, qi is the gas consumption of the i-th gas turbine, Nm3 / MWh; m is the gas price, yuan / Nm3; f0 is the fixed cost, yuan / MWh; C1 is the average heating demand, n is the heating price, yuan / t.

[0081] In a specific example, the ambient temperature is set to 25°C, the electricity price is set to 800 yuan / MWh, the gas price is set to 3 yuan / Nm3, the fixed cost is 25 yuan / MWh, the average heating demand is 37t / h, the planned fuel volume is 19000Nm3 / h, the heating price is 400 yuan / t, and the profit calculation model of the i-th unit in the whole plant is as follows:

[0082]

[0083] Through this step, various revenue and cost factors are taken into consideration, including both power supply revenue (electricity price revenue from gas turbine and steam turbine power generation) and heating revenue. At the same time, the fuel cost and fixed cost in the power generation cost are comprehensively considered to construct a unit revenue model that can comprehensively reflect the economic status of the entire plant.

[0084] It can be understood that this model can provide powerful decision-making support for the operation and management of power plants. Through this model, power plant managers can clearly understand the impact of different factors on revenue, so as to formulate reasonable production plans, adjust electricity and heating price strategies, optimize fuel procurement, etc., to maximize the economic benefits of power plants.

[0085] S103, determining the operation boundary in combination with the combustion plan demand, heating demand and unit characteristics of the power plant, taking the operation boundary as a constraint condition, and obtaining an optimized load distribution strategy that can meet the combustion plan demand and heating demand and maximize the theoretical value of the unit profit model through an optimization algorithm;

[0086] The average heat demand of the whole plant is represented by the symbol C1, and the unit is t / h. The expression is ∑Q i= C1, which means that the total heat supply of all units in the plant is equal to the average heat demand of the whole plant

[0087] The planned fuel quantity for the whole plant is represented by the symbol C2, and the unit is The expression is Among them, qi is the power consumption of the first combustion engine (in units of ), is the power of the i-th gas turbine. This expression means that half of the sum of the product of the power consumption of the gas turbines of all units in the plant and the power of the gas turbines is equal to the planned fuel quantity of the whole plant. This condition limits the total amount of fuel that can be used in the whole plant within a certain period of time, which helps to reasonably arrange the power generation tasks of each unit to ensure that the planned fuel quantity is not exceeded.

[0088] Furthermore, the heat supply and gas engine power are subject to the influence of equipment characteristics and have boundaries, and they restrict each other. Because in actual production, the performance and design parameters of the equipment determine the range of heat supply and gas engine power. For example, some equipment may only provide a certain amount of heat supply at a specific gas engine power, and beyond this power range, the heat supply may not be able to continue to increase or may be limited.

[0089] In addition, there is a minimum stable combustion load for the gas turbine power. This condition is to ensure the stable operation of the gas turbine. The gas turbine power cannot be lower than a certain value. If it is lower than this minimum stable combustion load, the gas turbine may have problems such as unstable combustion and flameout, affecting the normal operation of the power plant.

[0090] In addition, the heat supply has a maximum limit due to the constraints of the equipment, and increases with the increase of the power of the gas turbine until it reaches the maximum value. This means that the heat supply cannot be increased indefinitely. When the power of the gas turbine increases, the heat supply will increase accordingly, but after increasing to a certain extent, it will be limited by the equipment and reach a maximum value. This is because the heating capacity of the equipment is limited. If it exceeds this maximum value, the equipment may not be able to continue to provide more heat.

[0091] In an exemplary embodiment, the boundary conditions are specifically:

[0092]

[0093] Q1+Q2=37

[0094] or

[0095] or

[0096] 16≤Q1≤21 or Q1=0

[0097] 16≤Q2≤21 or Q2=0

[0098] The limiting condition of heat supply on the lower limit of gas turbine power is:

[0099] The above-mentioned operating boundary conditions are very important for the operation and management of power plants. In actual production, it is necessary to reasonably arrange the operation of each unit according to these conditions to ensure that the power plant can achieve safe, stable and efficient operation and obtain the maximum economic benefits while meeting the heat supply demand and fuel planning amount. Corresponding to this application, combined with the above-mentioned unit profit calculation model, this boundary condition can help power plant managers better formulate production plans and decisions, avoid deviations from actual operation and maintenance management, and optimize the economic performance of the power plant.

[0100] After obtaining the operating boundary, the operating boundary is used as a constraint condition, and an optimization algorithm is used to obtain an optimized load distribution strategy that can meet the combustion plan requirements and heating requirements and maximize the theoretical value of the unit profit model:

[0101] Among them, before starting the optimization, the goal of the optimization is to first determine that the optimization goal is to find an optimal load distribution strategy that can meet the combustion plan requirements and heating requirements and maximize the theoretical value of the unit profit model under the operating boundary conditions determined by the combustion plan requirements, heating requirements and unit characteristics of the given power plant.

[0102] After obtaining the data required for the unit revenue model, a mathematical expression of the unit revenue model in an actual power plant is established based on the collected data. This model should be able to reflect the relationship between load distribution and revenue.

[0103] Furthermore, the operating boundary conditions are converted into mathematical constraints, such as heat supply constraint (∑Qi=C1), fuel planning constraint (∑q i P1 2 =C2), minimum stable combustion load constraint of gas turbine power, maximum limit constraint of heat supply, etc.

[0104] In the optimization process, firstly, a suitable optimization algorithm is selected. Common optimization algorithms include genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm, etc. In this embodiment, a suitable optimization algorithm can be selected according to the actual working conditions and data scale of the power plant;

[0105] After determining the optimization algorithm, the specific optimization process includes the following steps:

[0106] Step 1, randomly generate initial solutions, where the initial solutions represent different optimal load distribution strategies;

[0107] In addition, the fitness value of each solution needs to be evaluated based on the objective function and constraints. The fitness value reflects the degree to which the solution meets the objectives and constraints. In this implementation, the objective function is the mathematical calculation formula corresponding to the above-mentioned unit profit model.

[0108] Step 2: Update the fitness value of the initial solution of the iteration according to the objective function and constraints corresponding to the unit profit model. When the iteration meets the stopping conditions, determine the optimal load distribution strategy based on the current solution.

[0109] Specifically, through the iterative process of the algorithm, the solution or population is continuously updated to find a better optimization load distribution strategy, which includes selection, crossover and mutation operations for the genetic algorithm to generate new offspring individuals. The simulated annealing algorithm avoids falling into the local optimal solution by accepting a certain probability of poor solutions and gradually lowers the temperature to converge to the optimal solution. The particle swarm optimization algorithm finds the optimal solution through information sharing and speed update between particles.

[0110] In addition, during the optimization process, it is necessary to monitor the convergence and running time of the algorithm. If the algorithm converges too slowly or falls into a local optimal solution, you can adjust the algorithm parameters or try other optimization algorithms. At the same time, it is also necessary to check whether the solution meets all the constraints. If there is a solution that does not meet the constraints, it needs to be corrected or a new solution needs to be generated.

[0111] Finally, when the algorithm meets the stopping condition, such as reaching the maximum number of iterations or the objective function value no longer changes significantly, the optimal load distribution strategy is determined. The optimal solution is analyzed and verified to ensure its feasibility and effectiveness in practical applications.

[0112] Through the above steps, it is possible to use the operating boundary as a constraint and obtain an optimized load distribution strategy that can meet the combustion plan requirements and heating requirements and maximize the theoretical value of the unit profit model through an optimization algorithm.

[0113] In an exemplary embodiment, due to the limitations of equipment and control systems, there is a reasonable fluctuation range for unit load and heating supply in actual operation. The optimization granularity of the present application is set to 5t / h of heating supply and 5MW of power. Within the boundary range, the economy of the whole plant is traversed and calculated according to the granularity to search for the maximum value. The calculation results are shown in Table 1. The best economic operation mode is that unit 1 supplies 16t / h of heat, the power of the gas turbine is 55.9MW, unit 2 supplies 21t / h of heat, the power of the gas turbine is 45MW, and the economy of the whole plant is 17489 yuan / h.

[0114] Table 1 Ranking of the economy of the whole plant

[0115]

[0116] Through the above steps S101 to S103, compared with the load distribution method in traditional technology, the present application scheme accurately calculates the power generation cost by constructing the gas turbine power consumption model and steam turbine power generation model in each unit; further, the unit profit model is constructed in combination with the power supply income and heating income, and the operation boundary is determined according to the power plant combustion plan requirements, heating requirements and unit characteristics; and the operation boundary is used as a constraint to use the optimization algorithm to obtain the optimal load distribution strategy that meets the combustion and heating requirements and has the maximum theoretical value of the profit model. The problem of poor economic performance of the cost load distribution method in the traditional method is solved, and accurate cost accounting, comprehensive benefit consideration and optimized load distribution are achieved, which improves the energy utilization efficiency of power plants and the scientific nature of decision-making.

[0117] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0118] The present application also provides a system for optimizing load distribution within a power generation unit plant. Figure 2 is a structural block diagram of a load distribution optimization system for a generator set in a plant according to an embodiment of the present application, such as Figure 2 As shown, the system includes: a construction module 20 and an allocation strategy generation module 21, wherein:

[0119] The construction module 20 is used to construct the power generation and consumption model of the gas turbine of each generator set in the power plant, and the power generation model of the steam turbine;

[0120] Furthermore, based on the power consumption model, power generation model and unit operation reference information, the power generation cost of each power generation unit is obtained, and the unit profit model is constructed according to the power generation cost and its corresponding power supply income and heat supply income;

[0121] The allocation strategy generation module 21 is used to determine the operating boundary in combination with the combustion plan requirements, heating requirements and unit characteristics of the power plant, and to obtain an optimized load allocation strategy that can meet the combustion plan requirements and heating requirements and maximize the unit profit model through an optimization algorithm, taking the operating boundary as a constraint condition.

[0122] Through the above system, the gas turbine power consumption model and steam turbine power model in each unit are constructed to accurately calculate the power generation cost; further, the unit profit model is constructed in combination with the power supply income and heating income, and the operation boundary is determined according to the power plant combustion plan requirements, heating requirements and unit characteristics; and the operation boundary is used as a constraint to use the optimization algorithm to obtain the optimal load distribution strategy that meets the combustion and heating requirements and has the maximum theoretical value of the profit model. The problem of poor economic performance of the cost load distribution method in the traditional method is solved, and accurate cost accounting, comprehensive benefit considerations and optimized load distribution are achieved, which improves the energy utilization efficiency of power plants and the scientific nature of decision-making.

[0123] In one embodiment, Figure 3 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 3 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 3 The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a method for optimizing load distribution within a generator set plant.

[0124] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0126] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing load distribution within a power generation unit plant, characterized in that: The method comprises: Construct the power consumption model of the gas turbines of each generator set in the power plant, and the power generation model of the steam turbines; Based on the power generation consumption model, the power generation power model and the unit operation reference information, obtaining the power generation cost of each power generation unit; Constructing a unit profit model based on the power generation cost and its corresponding power supply income and heat supply income; The operating boundary is determined in combination with the combustion plan requirements, heating requirements and unit characteristics of the power plant. Taking the operating boundary as a constraint, an optimization algorithm is used to obtain an optimized load distribution strategy that can meet the combustion plan requirements and heating requirements and maximize the unit profit model.

2. The method according to claim 1, characterized in that: The power generation and power consumption model of the gas turbines of each generator unit in the power plant is constructed including: Obtaining the atmospheric temperature of the area where the power plant is located and the combustion engine power of the gas turbine; Performing mathematical modeling and regression analysis based on historical operating data of the gas turbine to obtain a first regression coefficient related to the atmospheric temperature, a second regression coefficient related to the engine power of the gas turbine, and a gas consumption constant; The power generation power consumption model is constructed based on the atmospheric temperature and its corresponding first regression coefficient, the second regression coefficient corresponding to the gas turbine power of each gas turbine, and the gas consumption constant.

3. The method according to claim 1, characterized in that Constructing the power generation model of each turbine in the power plant includes: Determine the steam turbine and its associated target gas turbine, obtain the gas turbine power of the target gas turbine and the heat supply function between the steam turbine and the target gas turbine; Performing mathematical modeling and regression analysis based on historical operating data of the gas turbine and the steam turbine to obtain a third regression coefficient related to the target gas turbine, a fourth regression coefficient related to the heat supply function, and a power generation constant corresponding to the steam turbine; The power generation model is constructed based on the third regression coefficient corresponding to the gas turbine power of the target gas turbine, the fourth regression coefficient related to the heat supply function, and the power generation constant.

4. The method according to claim 1, characterized in that: The unit operation reference information includes fuel price, electricity price, fixed cost and average heating demand and heating price; The power generation cost includes combustion cost and fixed cost, wherein the combustion cost is determined according to the power generation power consumption and fuel cost of each gas-fired unit, and the fixed cost corresponding to the unit power generation is estimated by analyzing the historical data of the power generation unit.

5. The method according to claim 4, characterized in that According to the power generation cost and its corresponding power supply income and heat supply income, a unit income model is constructed, wherein the calculation expression of the unit income model of any power generation unit is as follows: in, is the power of the i-th gas turbine, f1 is the electricity price, qi is the gas consumption of the i-th gas turbine, m is the fuel price, f0 is the fixed cost, C1 is the average heating demand, and n is the heating price.

6. The method according to claim 1, characterized in that The operating boundaries determined by combining the combustion plan requirements, heating requirements and unit characteristics of the power plant include: According to the unit characteristics, the mutual constraint relationship between the heat supply demand and the combustion plan demand is obtained, as well as the minimum stable combustion load of the gas turbine and the maximum limit of the heat supply of the generator set are obtained; The operating boundary is determined according to the mutual restriction relationship, the minimum stable combustion load, and the maximum limit of the heating amount.

7. The method according to claim 6, characterized in that Taking the operation boundary as a constraint condition, an optimization load distribution strategy that can meet the combustion plan requirements and the heating requirements and maximize the unit revenue is obtained through an optimization algorithm, including: Randomly generate initial solutions, wherein the initial solutions represent different load distribution strategies; According to the objective function corresponding to the unit profit model and the constraint conditions, the fitness value of the initial solution is updated and iterated, and when the iteration meets the stop condition, the optimized load distribution strategy is determined according to the current solution.

8. A load distribution optimization system for a generator set in a plant, characterized in that: The system comprises: a construction module and an allocation strategy generation module, wherein: The construction module is used to construct a power generation and consumption model of the gas turbine of each generator set in the power plant, and a power generation model of the steam turbine; And, based on the power generation consumption model, the power generation power model and the unit operation reference information, the power generation cost of each power generation unit is obtained, and according to the power generation cost and its corresponding power supply income and heat supply income, a unit profit model is constructed; The allocation strategy generation module is used to determine the operating boundary in combination with the combustion plan requirements, heating requirements and unit characteristics of the power plant, and to obtain an optimized load allocation strategy that can meet the combustion plan requirements and heating requirements and maximize the unit profit model through an optimization algorithm, taking the operating boundary as a constraint condition.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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