Control method and system for coal power unit to participate in peak regulation of power grid

Abstract

According to the control method and system for participation of the coal power unit in peak regulation of the power grid, input-output characteristics of participation of the coal power unit in peak regulation are quantified, energy storage capacity and power are taken as constraint conditions, profit model maximization and carbon emission minimization of a coal power unit power generator are taken as targets, optimization solution is carried out, and the peak regulation accuracy of the power grid is improved. Rationality of participation of the coal power unit in peak regulation of the power grid is enhanced, and economic benefits and carbon reduction benefits are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field related to power grid peak regulation, and in particular relates to a control method and system for coal-fired power generation units to participate in power grid peak regulation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous expansion of the installed capacity of clean energy such as wind power and solar energy, the power structure is developing in a more diversified and low-carbon direction. New energy power generation mainly includes clean energy such as wind power, photovoltaics, hydropower and biomass energy. The continuous advancement of technology has promoted the rapid increase of its installed capacity. The proportion of new energy in the power market has increased year by year, especially the growth of wind power and photovoltaic power generation. At the same time, the accelerated development of emerging technologies such as distributed energy, energy storage and smart grids has provided technical support for better acceptance and absorption of new energy. However, the volatility and instability of new energy power generation have also put forward new requirements for the flexible adjustment ability of the power system. Thermal storage technology uses energy storage technology to store excess thermal energy when the new energy power generation is at its peak, and then release the stored heat when the new energy is insufficient. This technology effectively balances the load of the power grid and realizes the time shift of energy to stabilize the power output and alleviate the pressure on the power grid caused by the volatility of new energy.

[0004] Traditional economic analysis methods have exposed many limitations when evaluating the flexibility transformation of thermal power units. Its simplified assumptions and neglect of key factors make it difficult to cope with the complex operation requirements of modern power grids, and it is also unable to fully evaluate the long-term impact of frequent start-stop and deep peak load regulation on the economic efficiency of units. The main shortcomings are as follows:

[0005] (1) The economic accounting model of traditional power plants has, to a certain extent, ignored the significant impact of seasons and time periods on the economic performance of power plants. Since electricity demand and electricity prices fluctuate greatly in different seasons and time periods, especially when the peak load and electricity consumption demand changes, the income and operating costs of power plants also fluctuate accordingly. This neglect has led to a one-sided analysis of the actual economic performance of power plants, making it difficult to fully evaluate the profitability of power plants under the background of power load fluctuations and electricity price fluctuations, and thus failing to accurately reflect the complex actual operating status.

[0006] (2) In the flexibility transformation, there are complex relationships between unit parameters, environmental parameters and economic parameters, which are difficult to accurately represent in the original model. When the number of parameters increases, the original model is difficult to meet the constraints between the parameters, and the accuracy rate decreases rapidly, which has an adverse impact on the implementation of deep peak regulation.

[0007] Therefore, how to improve the rationality of coal-fired power units participating in grid peak regulation and improve economic efficiency while reducing carbon emissions is a problem that needs to be solved at present. Summary of the invention

[0008] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a control method and system for coal-fired power units to participate in power grid peak regulation, thereby enhancing the rationality of coal-fired power units participating in power grid peak regulation and improving economic benefits and carbon reduction benefits.

[0009] In order to achieve the above object, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a control method for a coal-fired power unit to participate in power grid peak regulation, comprising:

[0011] Calculate the total cost of coal-fired power generators based on the coal consumption cost, energy storage equipment cost, pollutant treatment cost and carbon emission cost during the power generation process of coal-fired power units;

[0012] Based on the total cost of coal-fired power generators, combined with the income from coal-fired power generation and market subsidies, a profit model for coal-fired power generators is established;

[0013] With energy storage capacity and power as constraints, and with the goal of maximizing the profit model of coal-fired power generators and minimizing carbon emissions, the optimization algorithm is used to solve the problem and obtain the power generation and storage capacity of coal-fired power units participating in grid peak regulation.

[0014] In a second aspect, the present invention provides a control system for a coal-fired power unit to participate in power grid peak regulation, characterized in that it includes:

[0015] The first calculation unit is configured to calculate the total cost of the coal-fired power generator according to the coal consumption cost, energy storage equipment cost, pollutant treatment cost and carbon emission cost during the power generation process of the coal-fired power generator;

[0016] The second calculation unit is configured to: establish a profit model for the coal-fired power generator according to the total cost of the coal-fired power generator, combined with the income of the coal-fired power generator and the market subsidy;

[0017] The solving unit is configured to: use energy storage capacity and power as constraints, and maximize the profit model of coal-fired power generators and minimize carbon emissions as goals, and solve through an optimization algorithm to obtain the power generation and storage capacity of the coal-fired power units participating in the peak load regulation of the power grid.

[0018] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0020] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0021] One or more of the above technical solutions have the following beneficial effects:

[0022] The present invention quantifies the input-output characteristics of coal-fired power units participating in peak load regulation, takes energy storage capacity and power as constraints, and optimizes the solution with the goal of maximizing the profit model of coal-fired power generators and minimizing carbon emissions. This strengthens the rationality of coal-fired power units participating in grid peak load regulation and improves economic benefits and carbon reduction benefits.

[0023] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1 The monthly profit difference of the power plant before and after the establishment of the molten salt tank calculated in the first embodiment of the present invention changes with the month trend chart;

[0026] Figure 2 This is a monthly carbon emission trend chart of the power plant before and after the establishment of the molten salt tank calculated in Example 1 of the present invention. DETAILED DESCRIPTION

[0027] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0028] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0029] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0030] Embodiment 1

[0031] This embodiment discloses a control method for coal-fired power generation units to participate in power grid peak regulation, including:

[0032] Calculate the total cost of coal-fired power generators based on the coal consumption cost, energy storage equipment cost, pollutant treatment cost and carbon emission cost during the power generation process of coal-fired power units;

[0033] Based on the total cost of coal-fired power generators, combined with the income from coal-fired power generation and market subsidies, a profit model for coal-fired power generators is established;

[0034] With energy storage capacity and power as constraints, and with the goal of maximizing the profit model of coal-fired power generators and / or minimizing carbon emissions, an optimization algorithm is used to solve the problem and obtain the power generation plan of the power grid on a certain day, that is, the power generation and storage capacity of the coal-fired power units participating in the peak regulation of the power grid at each moment.

[0035] Of course, corresponding targets can also be set according to the specific needs of coal-fired power generators.

[0036] The following is a detailed description of a control method for a coal-fired power unit participating in power grid peak regulation proposed in this embodiment:

[0037] First, construct the profit model of coal-fired power generators:

[0038] Total profit formula:

[0039] P=RC

[0040] R=(r s ×E+r h ×Q h +r p ×E)

[0041]

[0042] Among them, P is the total profit (yuan); R is the total revenue (yuan); C is the total cost (yuan); s is the subsidy rate per unit of electricity generation (yuan / MWh); r h is the income per unit of heat (yuan / MWh); r p is the electricity price (yuan / MWh); E is the total power generation (MWh); Q h is the total heat supply (MWh); f is the coal price (yuan / ton); H s is the coal consumption rate (tons / MWh); C install is the installation cost of the molten salt system (yuan); C maintain is the maintenance cost of the molten salt system (yuan); is the cost of treating or discharging each unit of pollutant (yuan / ton); M i is the amount of pollutants emitted (tons); r cis the carbon price (yuan / ton CO2); M is the total coal consumption (ton); F c is the carbon emission factor (tons of CO2 / tons of coal).

[0043] Total revenue formula:

[0044] R=S+I s +I p

[0045] Among them, S is subsidy (yuan); I s is the income from energy storage use (yuan); I p is the income from power generation (Yuan).

[0046] The subsidy calculation formula is:

[0047] S=∑r s ×E

[0048] Among them, r s is the subsidy rate per unit of electricity generation (yuan / MWh); E is the total electricity generation (MWh). If the energy storage is molten salt, the use income is:

[0049] I s =∑r g ×Q h ×v coal

[0050] Among them, r g is the reduced coal consumption per unit of heat (tons / MWh); Q h is the total heat supply (MWh); v coal is the coal price (yuan / ton).

[0051] For electric boilers:

[0052] I s =∑r h ×Q j

[0053] Among them, r h is the income per unit of heat (yuan / MWh); Q h is the total heat supply (MWh).

[0054] The revenue from power generation is:

[0055] I p =∑r p ×E

[0056] Among them, r p is the electricity price (yuan / MWh); E is the total power generation (MWh).

[0057] Total cost formula:

[0058] C=C f +C m +C p +C e

[0059] Among them, C f is the coal consumption cost (yuan); C m is the maintenance cost (yuan); C p is the pollutant cost (yuan); C e is the carbon emission cost (yuan).

[0060] The coal consumption cost is calculated as follows:

[0061] C f =∑r f ×E×H s

[0062] Among them, r f is the coal price (yuan / ton); E is the total power generation (MWh); H s is the coal consumption rate (tons / MWh).

[0063] The maintenance cost of molten salt is:

[0064] C m =C install +C maintain

[0065] Among them, C install is the installation cost of the energy storage system; C maintain The maintenance cost of the energy storage system.

[0066] C install =V x ·P ri +C H

[0067] Among them, C install is the initial investment cost (10,000 yuan); V x is the volume of the energy storage device (m 3 );P ri is the volume and cost of energy storage equipment (10,000 yuan); C H is the cost of other equipment (heat exchanger, electric heater, molten salt pump) (10,000 yuan).

[0068] Among them, the calculation formula for energy storage equipment maintenance cost is:

[0069]

[0070] Where ΔT is the heat storage temperature difference (°C); ε is the heat storage volume ratio; σ is a constant, generally taken as 1.163; k loss is the heat loss rate, S his the molten salt tank capacity (MWh).

[0071] Energy storage equipment operating costs:

[0072]

[0073] Where n is the service life of the energy storage equipment (years); r is the annual interest rate of capital.

[0074] C maintain =C in ·ξ

[0075] The maintenance cost of energy storage equipment is C maintain , based on existing engineering examples, the available construction cost C in Multiply by ξ (usually 0.005) to select.

[0076]

[0077] Among them, k loss is the heat loss rate; S t The thermal storage required for dispatch (MWh).

[0078] The operating costs of energy storage equipment mainly include heat loss and power consumption of the pump.

[0079]

[0080] Where ρ is the power consumption rate of the heat storage and heat release pump per unit time. pump The amount of electricity consumed by the heat storage and heat release pumps per unit time (MWh).

[0081]

[0082] Where ω is the average standard coal consumption of thermal power units (t / MWh). coal is the price of coal (RMB).

[0083] The same equation applies to other energy storage devices.

[0084] SO2 and NO are produced during coal combustion. x , dust and other pollutants, the operation of pollution control equipment will incur certain costs, and the calculation formula is:

[0085]

[0086] in, is the cost of treating or discharging each unit of pollutant (yuan / ton); M i is the amount of pollutants emitted (tons).

[0087] M i =M×Fi

[0088] Among them, F i is the emission factor of the pollutant (tons / ton of coal).

[0089] As my country's carbon market gradually improves, CO2 emissions from power plants will be included in the accounting system and become an important part of operating costs. The carbon emission cost is calculated as follows:

[0090] C e =r c ×M×F c

[0091] Among them, r c is the carbon price (yuan / ton CO2); M is the total coal consumption (ton); F c is the carbon emission factor (tons of CO2 / tons of coal).

[0092] The objective function established in this embodiment is:

[0093] 1. If the goal is to maximize economic benefits, the maximum benefit value is found through genetic algorithm, and the objective function is:

[0094] max P=RC

[0095] 2. If the goal is to minimize carbon emissions, the minimum carbon emissions value is found through genetic algorithm, and the objective function is:

[0096] min C e =r c ×M×F c

[0097] 3. If both enterprise needs are taken into consideration, then set the weight α of the two. Consider the above two factors comprehensively. The weight α is specified according to the enterprise needs. The optimization target is the final score value. The higher the score, the more it meets the standard.

[0098]

[0099] Among them, P min is the minimum profit value, P max is the maximum profit value, is the maximum carbon emission value, is the minimum carbon emission value, and α is the weight.

[0100] The constraint conditions of the objective function established in this embodiment include power constraint and energy storage capacity constraint.

[0101] Power Constraints:

[0102]

[0103] 0≤Pi ≤P LD,t

[0104] Among them, P i is the power output of the thermal power unit at time t (MW); P KD , t is the total load value of the power system at time t (MW). C is the number of units.

[0105] Energy storage capacity constraints:

[0106] 0≤S h ≤S max

[0107] Among them, S h The amount of energy stored at the current moment (MWh).

[0108] Genetic algorithms are a series of computational models inspired by evolution, which are usually regarded as function optimizers and can study a wide range of problems. Genetic algorithms are based on population models, taking into account inheritance, mutation and recombination, and selecting according to fitness to generate and search sample points in space and optimize the final result. The first step of the algorithm is to generate an initial population, in which each member is a binary string of length L, which corresponds to the encoding of a specific problem. Next, these binary strings are evaluated and a fitness value is assigned to each string. Fitness is defined as: is the fitness associated with the string, is the average fitness of all strings in the population. Selection is applied to the current population to create an intermediate population, and the selection tends to retain the top 25% of high-quality individuals, and then the intermediate population is recombined and mutated to produce the next generation population to restore the population size. Mutation random sampling adopts a roulette game, which requires assuming that the population is arranged in a random order in a pie chart, where each individual is allocated a corresponding space on the pie chart according to its fitness ratio. Then, an outer roulette wheel with N equidistant pointers is placed around the pie chart. By rotating the roulette wheel, all N members of the population can be selected at the same time. Since the position of each pointer is equidistant, this selection process is unbiased, ensuring that all individuals have a chance to be selected. Crossover recombines these strings with probability, forming two new strings to be inserted into the next generation population. Each time we randomly generate the initial population within the variable range of parameters and perform iterative optimization until the iteration is completed and the best individual is selected.

[0109] In this embodiment, at the initial stage of program operation, the unit output power data at each time point is first read, and random initialization is performed based on this data to generate 50 sets of unit power and corresponding heat storage power after deep peak regulation. These initial data will be used as the first generation of individuals in the algorithm, and the first generation of individuals represent different solutions. The program evaluates each set of data according to the set objective function and calculates its overall economic benefits and carbon emission levels. To achieve optimization, the program adopts a screening and mutation strategy in each generation. After the evaluation is completed, the initial 50 sets of data are sorted according to the comprehensive standards of maximizing economic benefits and minimizing carbon emissions, and the top 30% of individuals are selected as excellent solution groups. The program introduces random changes within a certain range (within 10%) to the deep peak regulation power and heat storage power of these high-quality solutions to generate offspring individuals. In order to maintain the population quality and convergence speed, the generated individual population will be eliminated at the end in each generation to remove solutions with poor performance and ensure that only high-quality solutions are retained to the next generation. By continuously iterating this process, the program gradually optimizes individual performance. After the predetermined number of iterations is reached, the best performing individual is selected from the final population as the final result of the optimization.

[0110] The 300MW unit was selected as the analysis object to compare the impact of the molten salt heat storage system on the operation economy. The unit output is calculated according to the monthly average value, and the electricity price is calculated according to the daily report of Shandong Power Market Operation. A price is taken every 10 days, and 3 prices are calculated every month. Taking a unit as an example, the relationship between its daily output and time of day in each month is shown in Table 1-Table 3:

[0111] Table 1 Relationship between the output power of a certain unit and the month and time

[0112]

[0113]

[0114] Table 2 Relationship between the output power of a certain unit and the month and time

[0115]

[0116]

[0117] Table 3 Relationship between the output power of a certain unit and the month and time

[0118]

[0119]

[0120] According to the data and analysis in the literature, for the flexibility requirements of coal-fired power units, the parameter settings of molten salt boilers and electrode boilers need to meet different heat storage requirements and response requirements to improve the peak load capacity and efficiency of the system. The following are the parameters of molten salt boilers and electrode boilers set in this study for accurate economic calculation:

[0121] Table 4 Molten salt boiler parameter settings

[0122]

[0123] Taking a 330MW subcritical unit as an example, the economic benefits of the unit before and after the establishment of the molten salt heat storage system are calculated.

[0124] like Figure 1 As shown in the figure, from the overall fluctuation point of view, the monthly profit difference of the power plant before and after the establishment of the molten salt tank shows an obvious fluctuation trend with seasonal changes. After the introduction of the molten salt heat storage system, the power plant showed significant profitability in the spring (March to May). Specifically, each month in spring achieved positive returns, with a cumulative profit of 4.2088 million yuan. In particular, in April, the power plant's profit performance was the most outstanding, with a profit difference of 2.1872 million yuan, indicating that in this month, the molten salt heat storage system had a significant positive impact on the economy of the power plant. In summer (June to August) and autumn (September to November), the overall seasonal profit difference is still positive, with a cumulative profit of 306,700 yuan in summer and 1.7815 million yuan in autumn. In October, the power plant's profit performance was relatively outstanding, reaching 1.7902 million yuan, but negative returns appeared in August and September, at -948,600 yuan and -128,600 yuan respectively. This phenomenon shows that in August and September, the fixed operation period of the heat storage system failed to give full play to its advantages, resulting in the failure to fully release the power generation in some high-price periods, thus affecting the income in the high-price periods. Therefore, in the actual operation process, it is necessary to predict the trend of electricity price changes. When the cost of molten salt heat storage is higher than the power generation income, the heat storage capacity should be reduced to improve the overall efficiency of the enterprise. In winter (December to February), the profitability of the power plant is more severe. During this period, the monthly profits of the power plant were negative, and the cumulative negative income was -2.6575 million yuan. In particular, in January, the negative income was as high as -1.6676 million yuan, indicating that during this period, the molten salt heat storage system failed to effectively respond to the market environment of high electricity prices and low demand, resulting in the failure of the power plant's operating costs to be effectively offset by power generation during high electricity price periods.

[0125] like Figure 2As shown in the graph, the carbon emissions of the power plant vary in different months of the year. After the introduction of molten salt, the power plant achieved carbon emission reduction throughout the year. The emission reduction reached the lowest value of the year (about -35,000 tons) in April and September, which was closely related to the seasonal peak demand. In November, the emission reduction rebounded because part of the stored energy in November was withdrawn for heating and no longer returned to the boiler.

[0126] The heat storage time is set to 6h-9h, and the heat release time is set to 6h-9h. The genetic algorithm is used to optimize the power plant in November to find the optimal storage and allocation plan. The population size is set to 50; the maximum iteration generation is set to 100; the mutation probability is set to 0.1 (10%). The heat storage and heat release time range is constrained to 6 hours to 9 hours, and the optimization goal is set to maximize monthly profit. We found that the economic benefit is maximized when the heat storage is 9h and the heat release is 6h, and the maximum monthly profit is 3.502 million yuan.

[0127] Embodiment 2

[0128] The purpose of this embodiment is to provide a control system for coal-fired power generation units to participate in power grid peak regulation, including:

[0129] The first calculation unit is configured to calculate the total cost of the coal-fired power generator according to the coal consumption cost, energy storage equipment cost, pollutant treatment cost and carbon emission cost during the power generation process of the coal-fired power generator;

[0130] The second calculation unit is configured to: establish a profit model for the coal-fired power generator according to the total cost of the coal-fired power generator, combined with the income of the coal-fired power generator and the market subsidy;

[0131] The solving unit is configured to: use energy storage capacity and power as constraints, and maximize the profit model of coal-fired power generators and minimize carbon emissions as goals, and solve through an optimization algorithm to obtain the power generation of coal-fired power units participating in power grid peak regulation.

[0132] In further embodiments, there is also provided:

[0133] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0134] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0135] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0136] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0137] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0138] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in the first embodiment is implemented.

[0139] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0140] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0141] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0142] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0143] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A control method for coal-fired power units participating in power grid peak regulation, characterized in that: include: Calculate the total cost of coal-fired power generators based on the coal consumption cost, energy storage equipment cost, pollutant treatment cost and carbon emission cost during the power generation process of coal-fired power units; Based on the total cost of coal-fired power generators, combined with the income from coal-fired power generation and market subsidies, a profit model for coal-fired power generators is established; With energy storage capacity and power as constraints, and with the goal of maximizing the profit model of coal-fired power generators and minimizing carbon emissions, the optimization algorithm is used to solve the problem and obtain the power generation and storage capacity of coal-fired power units participating in grid peak regulation.

2. A control method for coal-fired power generation units participating in power grid peak regulation according to claim 1, characterized in that: include: According to different forms of energy storage, the energy storage efficiency of different energy storage is calculated separately; according to the energy storage efficiency of different energy storage, combined with power generation income and market subsidies, the total revenue model of coal-fired power generators is calculated.

3. A control method for coal-fired power generation units participating in power grid peak regulation according to claim 1, characterized in that: The total cost of coal-fired power generators is calculated based on the installation cost, maintenance cost and operating cost of energy storage equipment, as well as coal consumption cost, pollutant treatment cost and carbon emission cost.

4. A control method for coal-fired power generation units participating in power grid peak regulation as claimed in claim 3, characterized in that: The operating costs of the energy storage device include heat loss and power consumption of the pump.

5. A control method for coal-fired power generation units participating in power grid peak regulation as claimed in claim 1, characterized in that: With the goal of maximizing the profit model of coal-fired power generators and minimizing carbon emissions, the objective function constructed is: Among them, P min is the minimum profit value, P max is the maximum profit value, is the maximum carbon emission value, is the minimum carbon emission value, and α is the weight.

6. A control method for coal-fired power generation units participating in power grid peak regulation as claimed in claim 1, characterized in that: With energy storage capacity and power as constraints, and with the goal of maximizing the profit model of coal-fired power generators and minimizing carbon emissions, the solution is obtained through an optimization algorithm, specifically: Read the unit output power data at each time point and initialize it to generate multiple sets of unit power after deep peak regulation and the corresponding heat storage power; According to the set objective function, the generated multiple groups of deep peak-shaving unit powers and corresponding thermal storage powers are sorted, and individuals whose ranking exceeds the set threshold are selected as the excellent solution group; Randomly change the excellent solution group to generate offspring individuals; Through continuous iteration, individuals are optimized. After the set number of iterations is reached, the individual with the best performance is obtained as the final result of the optimization.

7. A control system for coal-fired power units participating in power grid peak regulation, characterized in that: include: The first calculation unit is configured to calculate the total cost of the coal-fired power generator according to the coal consumption cost, energy storage equipment cost, pollutant treatment cost and carbon emission cost during the power generation process of the coal-fired power generator; The second calculation unit is configured to: establish a profit model for the coal-fired power generator according to the total cost of the coal-fired power generator, combined with the income of the coal-fired power generator and the market subsidy; The solving unit is configured to: use energy storage capacity and power as constraints, and maximize the profit model of coal-fired power generators and minimize carbon emissions as goals, and solve through an optimization algorithm to obtain the power generation and storage capacity of the coal-fired power units participating in the peak load regulation of the power grid.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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