CN-122022223-A - Multi-unit carbon emission collaborative optimization method, device and medium for coal-fired power plant

CN122022223ACN 122022223 ACN122022223 ACN 122022223ACN-122022223-A

Abstract

The invention relates to the technical field of energy environment, in particular to a method, a device and a medium for collaborative optimization of carbon emission of multiple units of a coal-fired power plant. The method comprises the steps of constructing a carbon emission cost function based on the deviation degree of unit output and optimal working conditions, the climbing rate of the unit and a carbon emission factor, constructing an optimization model by minimizing the carbon emission cost function and combining preset constraint conditions, solving the optimization model by adopting an improved particle swarm optimization algorithm with inertia weight self-adaptive adjustment to obtain an output planning curve of each unit at a preset time in the future, and generating executable instructions and sending the executable instructions to each unit. In the invention, multiple factors such as the deviation of the unit output from the optimal working condition, the climbing speed, the thermal efficiency and the like are innovatively and comprehensively considered, and the accurate and dynamic quantification of the carbon emission cost is realized. By means of an inertia weight self-adaptive adjustment mechanism, global and local searching capacity is effectively balanced by means of an improved algorithm, and the convergence speed and optimization accuracy of solving are greatly improved.

Inventors

PENG YUJUN
WANG HAIJUN
YANG XIN
WANG JING
TAO LIANG
ZHENG WENGUANG
ZHUO DEYONG
SUN YOUYUAN
Guo Xuekuan
CHEN JING
KE JUN

Assignees

华电国际电力股份有限公司奉节发电厂
华电电力科学研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20251204

Claims (10)

1. The method for optimizing the carbon emission cooperation of the multiple units of the coal-fired power plant is characterized by comprising the following steps of: constructing a carbon emission cost function based on the deviation degree of the unit output and the optimal working condition, the unit climbing rate and the carbon emission factor; constructing an objective function by minimizing the carbon emission cost function, and constructing an optimization model by combining preset constraint conditions; solving the optimization model by adopting an improved particle swarm optimization algorithm with inertia weight self-adaptive adjustment to obtain an output planning curve of each unit at a preset time in the future; and generating executable instructions based on the output plan curve and sending the executable instructions to each unit.
2. The method of claim 1, wherein the carbon emission cost function further comprises a first coefficient of degree of deviation and a second coefficient of unit hill climbing rate, the first coefficient and the second coefficient being determined by: acquiring historical output data, corresponding carbon emission intensity, carbon emission cost and unit climbing rate; Screening historical output data of the historical output data when the unit operates under preset conditions, and determining an output point when the carbon emission intensity is lowest by combining the corresponding carbon emission intensity, wherein the output point is an optimal working condition; and fitting the optimal working condition, the historical output data, the climbing rate and the carbon emission factor serving as independent variables, wherein the carbon emission cost serving as the dependent variable to obtain a first coefficient and a second coefficient.
3. The method according to claim 1, wherein the preset constraints are constructed in the following manner: constructing power balance constraint based on matching of unit output and grid load output; Constructing a unit output limit constraint based on a safe output range of unit output; Constructing climbing rate constraint based on the output change rate of the unit; and constructing peak shaving depth constraint based on the peak shaving depth of the unit and the peak shaving demand variation of renewable energy sources.
4. The method of claim 1, wherein solving the optimization model by using an improved particle swarm optimization algorithm with inertia weight self-adaptive adjustment to obtain an output planning curve of each set at a preset time in the future comprises: Discretizing future preset time to be optimized to obtain a discretized objective function; and taking the output of each unit at each discrete step length as a particle position code, and adopting an improved particle swarm optimization algorithm of inertia weight self-adaptive adjustment to adjust the particle position, so as to obtain an output planning curve of each unit at a future preset time when the carbon emission cost is minimum.
5. The method according to claim 2, wherein the method further comprises: Acquiring output data of a unit in real time; and when the deviation between the output data of the unit obtained in real time and the output data in the output plan curve is larger than a threshold value, correcting the second coefficient.
6. The method of claim 1, wherein the carbon emission cost function is expressed using the formula: In the formula, Represent the first The station unit is at moment Is added to the dynamic carbon emission costs of (a), The output of the ith machine set at the time t is shown, Represent the first The output of the machine set under the optimal working condition, A first coefficient representing the degree of deviation is provided, A second coefficient representing the rate of climbing of the unit, Represent the first The maximum output limit of the bench set, Represent the first The minimum output limit of the bench set, Represent the first The climbing speed of the bench unit, Represents the carbon emission coefficient weight of the unit relative to the coal quality, Represent the first The thermal efficiency of the set of stations at time t, Representing the carbon emission factor of coal.
7. The method of claim 1, wherein the inertial weight is adjusted using the formula: In the formula, Representing the inertial weights in the particle swarm algorithm, Representing the maximum value of the inertial weight, Representing the minimum value of the inertial weight, Representing the number of current iterations and, Representing the maximum number of iterations.
8. A coal-fired power plant multi-unit carbon emission collaborative optimization device, characterized in that the device comprises: The function construction module is used for constructing a carbon emission cost function based on the deviation degree of the unit output and the optimal working condition, the unit climbing rate and the carbon emission factor; the model construction module is used for constructing an objective function by minimizing the carbon emission cost function and constructing an optimization model by combining preset constraint conditions; the output optimization module is used for solving the optimization model by adopting an improved particle swarm optimization algorithm of inertia weight self-adaptive adjustment to obtain an output planning curve of each unit at a preset time in the future; and the instruction sending module is used for generating executable instructions based on the output plan curve and sending the executable instructions to each unit.
9. An electronic device, comprising: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the method for optimizing the carbon emission cooperation of multiple units of the coal-fired power plant is executed.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the multi-unit carbon emission collaborative optimization method of a coal-fired power plant of any of claims 1-7.

Description

Multi-unit carbon emission collaborative optimization method, device and medium for coal-fired power plant Technical Field The invention relates to the technical field of energy environment, in particular to a method, a device and a medium for collaborative optimization of carbon emission of multiple units of a coal-fired power plant. Background In the current power industry, coal-fired power generation is a major source of carbon emissions. Therefore, reducing carbon emissions from coal-fired power plants is critical to drive the power industry to lower carbon conversion. In a regional power system, a plurality of coal-fired power plants participate in power dispatching together, and the power generation power of each power plant is different but interdependent. The operation state of each power plant generator set can be timely adjusted, and low-carbon operation is realized on the basis of ensuring safe operation. In the prior art, when a plurality of units of a coal-fired power plant are used for coping with peak regulation, the load demand of a power distribution network and the peak regulation change of a renewable energy power station are mainly adapted through a conventional unit output distribution mode, the operation adjustment is carried out by relying on basic data acquisition and a simple carbon emission cost accounting model, the problems of inaccurate carbon emission accounting and insufficient cooperative optimization among the units are solved, the peak regulation output change of the power distribution network and the total load demand of the power distribution network are difficult to dynamically adapt, and the carbon emission is high. Disclosure of Invention The invention provides a method, a device, electronic equipment and a storage medium for collaborative optimization of carbon emission of multiple units of a coal-fired power plant, which are used for solving the problems of inaccurate calculation of carbon emission and insufficient collaborative optimization among units in the prior art. The invention provides a multi-unit carbon emission collaborative optimization method of a coal-fired power plant, which comprises the steps of constructing a carbon emission cost function based on the deviation degree of unit output and optimal working conditions, the unit climbing rate and a carbon emission factor, constructing an optimization model by minimizing the carbon emission cost function and combining preset constraint conditions, solving the optimization model by adopting an improved particle swarm optimization algorithm with inertia weight self-adaptive adjustment to obtain an output planning curve of each unit at a future preset time, and generating executable instructions based on the output planning curve and sending the executable instructions to each unit. According to the invention, the dynamic carbon emission cost function fused with the unit output deviation, the climbing rate and the carbon emission factor is constructed, the objective function and the optimization model for minimizing the total carbon cost are built, and the improved particle swarm algorithm with the inertia weight self-adaptive adjustment is adopted to carry out efficient solution, so that the optimal output plan of the coordinated operation of each unit in the future period can be generated. According to the method, carbon emission is used as a direct optimization target, so that when the multiple units cooperatively respond to the peak shaving requirement of the power grid, the overall power generation efficiency of the power plant can be remarkably improved, the total carbon emission can be effectively reduced, and the unification of economy and environmental friendliness is achieved. In an alternative implementation mode, the carbon emission cost function further comprises a first coefficient of deviation degree and a second coefficient of unit climbing speed, wherein the first coefficient and the second coefficient are determined in the mode that historical output data, corresponding carbon emission intensity, carbon emission cost and unit climbing speed are obtained, historical output data of the unit in the historical output data when running under preset conditions are screened, an output point when the carbon emission intensity is the lowest is determined by combining the corresponding carbon emission intensity, the output point is the optimal working condition, the historical output data, the climbing speed and the carbon emission factor are used as independent variables, and the carbon emission cost is used as a dependent variable to be fitted to obtain the first coefficient and the second coefficient. According to the invention, the penalty coefficients deviating from the optimal working condition and the climbing rate in the carbon emission cost function are accurately calibrated by adopting a mathematical fitting method based on historical operation data, so that the cost function can more truly