CN-115663893-B - Multi-target optimal control method and system for new energy unit

CN115663893BCN 115663893 BCN115663893 BCN 115663893BCN-115663893-B

Abstract

The invention provides a multi-target optimization control method and system for new energy units, wherein the method comprises the steps of acquiring running state information of each new energy unit in real time when grid faults are determined to occur according to grid voltage signals, enabling the running state information to be a multi-dimensional vector, carrying out normalization processing on the running state information of each new energy unit to obtain running characteristic information, carrying out optimizing analysis on weights of the running characteristic information by using a preset multi-target optimization algorithm, and determining a cutting unit in each new energy unit, wherein the multi-target optimization algorithm converges when the number of cutting units, the number of fault frequencies and the residual service lives in each new energy unit are combined and optimal, generating a cutting command, and sending the cutting command to the cutting unit to carry out cutting operation.

Inventors

ZHONG MING
TAO JUN
HAN RULEI
ZHANG WEI
TANG LI

Assignees

内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司

Dates

Publication Date: 20260505
Application Date: 20221104

Claims (4)

1. The multi-target optimization control method of the new energy unit is characterized by comprising the following steps of: when the occurrence of power grid faults is determined according to power grid voltage signals, the running state information of each new energy unit is obtained in real time, wherein the running state information is a multidimensional vector; Normalizing the running state information of each new energy unit to obtain running characteristic information; The method comprises the steps of optimizing the weight of operation characteristic information by using a preset multi-objective optimization algorithm, and determining a cut-out unit in each new energy unit, wherein the multi-objective optimization algorithm converges when the number of cut-out units in each new energy unit, the number of fault frequencies and the residual service life are combined to be optimal, the constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of a reserved operation unit in each new energy unit and the real-time demand of a power grid is smaller than a preset threshold, the weight of the operation characteristic information comprises 0 or 1, and when the multi-objective optimization algorithm converges, the new energy unit to which the operation characteristic information corresponding to the weight of 0 belongs is a cut-out unit object, and the new energy unit to which the operation characteristic information corresponding to the weight of 1 belongs is the reserved operation unit; generating a cutting command, sending the cutting command to the cutting unit for cutting operation, wherein the running state information comprises power generation capacity information, fault frequency information and residual service life information, the preset optimization algorithm is a multi-objective genetic algorithm, The step of determining the cut-out unit in each new energy unit comprises the following steps of: Generating an individual to be optimized, wherein the individual is used as the weight of the operation characteristic information of each new energy unit, the dimension is 3 the number of the new energy units, and the value of each dimension is 0 or 1; Calculating the number of new energy units corresponding to the operation characteristic information with the weight of 0, and taking the number as a first fitness function of the individual to be optimized; Calculating the sum of the fault frequency information corresponding to the weight of 1, and taking the sum as a second fitness function of the individual to be optimized; calculating the sum of the residual service life information corresponding to the weight of 1, and taking the reciprocal of the sum of the residual service life information as a third fitness function of the individual to be optimized; And carrying out joint optimization on the individual by utilizing the first fitness function, the second fitness function and the third fitness function, and determining a cutting unit in each new energy unit when the multi-objective genetic algorithm converges.
2. The multi-objective optimal control method of a new energy unit according to claim 1, wherein the new energy unit comprises a wind power generation device and a photovoltaic power generation device.
3. A multi-target optimization control system of a new energy unit is characterized by comprising: The system comprises a state information acquisition module, a power grid voltage signal acquisition module and a power grid control module, wherein the state information acquisition module is used for acquiring the operation state information of each new energy unit in real time when the power grid fault is determined to occur according to the power grid voltage signal; The operation characteristic processing module is used for carrying out normalization processing on the operation state information of each new energy unit to obtain operation characteristic information; The multi-objective optimization module is used for carrying out optimization analysis on the weight of the operation characteristic information by utilizing a preset multi-objective optimization algorithm to determine a cut-out unit in each new energy unit, wherein the multi-objective optimization algorithm converges when the number of the cut-out units, the number of fault frequencies and the residual service lives in each new energy unit are combined and optimal, the constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the reserved operation units in each new energy unit and the real-time demand of a power grid is smaller than a preset threshold, the weight of the operation characteristic information comprises 0 or 1, when the multi-objective optimization algorithm converges, the new energy unit to which the operation characteristic information corresponding to the weight of 0 belongs is a cut-out unit object, and the new energy unit to which the operation characteristic information corresponding to the weight of 1 belongs is the reserved operation unit; The cutting control module is used for generating a cutting command, sending the cutting command to the cutting unit for cutting operation, wherein the running state information comprises power generation capacity information, fault frequency information and residual service life information, and the preset optimization algorithm is a multi-objective genetic algorithm; The multi-objective optimization module is specifically configured to generate an individual to be optimized, calculate the number of new energy units to which the operation feature information corresponding to the weight 0 belongs, take the number as a first fitness function of the individual to be optimized, calculate the sum of fault frequency information corresponding to the weight 1 as a second fitness function of the individual to be optimized, calculate the sum of residual service life information corresponding to the weight 1 as a third fitness function of the individual to be optimized, and determine a cut-out unit in each new energy unit when the multi-objective genetic algorithm converges by using the first fitness function, the second fitness function and the third fitness function; The individual serves as the weight of the operation characteristic information of each new energy unit, the dimension is 3 times the number of the new energy units, and the value of each dimension is 0 or 1.
4. The multi-objective optimal control system of a new energy unit according to claim 3, wherein the new energy unit comprises a wind power generation device and a photovoltaic power generation device.

Description

Multi-target optimal control method and system for new energy unit Technical Field The disclosure relates to the field of new energy, and more particularly, to a multi-objective optimization control method and system of a new energy unit. Background When the traditional power grid stability control system adopts control measures, the whole photovoltaic power station or part of the current collecting circuit inside the photovoltaic power station is directly cut off according to the over-cut principle, so that the defect that the new energy unit is required to be re-connected in a grid mode is caused, and the measure matching amount is not accurate. After the new energy unit has the capability of quickly adjusting the output, the control resources of the stability control system are enriched, namely, the stability control system has the capability of continuous adjustment and the capability of direct cutting, and when the system fails, the power grid requirement can be met at the same time and the stable operation of the unit can be maintained, so that the technical problem to be solved is urgent. Disclosure of Invention The embodiment of the disclosure aims to provide a multi-objective optimization control method and system for a new energy unit, so as to simultaneously meet the power grid requirement and maintain the stable operation of the unit in a power grid fault state. The invention provides a multi-target optimal control method of new energy units, which comprises the steps of acquiring the running state information of each new energy unit in real time when the occurrence of power grid faults is determined according to power grid voltage signals, wherein the running state information is a multidimensional vector; Normalizing the running state information of each new energy unit to obtain running characteristic information; The method comprises the steps of optimizing the weight of operation characteristic information by using a preset multi-objective optimization algorithm, and determining a cut-out unit in each new energy unit, wherein the multi-objective optimization algorithm converges when the number of cut-out units in each new energy unit, the number of fault frequencies and the residual service life are combined to be optimal, the constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of a reserved operation unit in each new energy unit and the real-time demand of a power grid is smaller than a preset threshold, the weight of the operation characteristic information comprises 0 or 1, and when the multi-objective optimization algorithm converges, the new energy unit to which the operation characteristic information corresponding to the weight of 0 belongs is a cut-out unit object, and the new energy unit to which the operation characteristic information corresponding to the weight of 1 belongs is the reserved operation unit; And generating a cutting command, and sending the cutting command to the cutting unit for cutting operation. Further, the operation state information includes power generation capability information, failure frequency information, and remaining life information. Further, the preset optimization algorithm is a multi-objective genetic algorithm. Further, the step of determining the cut-out unit in each new energy unit by optimizing the weight of the operation characteristic information by using a preset multi-objective optimization algorithm includes: Generating an individual to be optimized, wherein the individual is used as the weight of the operation characteristic information of each new energy unit, the dimension is 3 the number of the new energy units, and the value of each dimension is 0 or 1; Calculating the number of new energy units corresponding to the operation characteristic information with the weight of 0, and taking the number as a first fitness function of the individual to be optimized; Calculating the sum of the fault frequency information corresponding to the weight of 1, and taking the sum as a second fitness function of the individual to be optimized; calculating the sum of the residual service life information corresponding to the weight of 1, and taking the reciprocal of the sum of the residual service life information as a third fitness function of the individual to be optimized; And carrying out joint optimization on the individual by utilizing the first fitness function, the second fitness function and the third fitness function, and determining a cutting unit in each new energy unit when the multi-objective genetic algorithm converges. Further, the new energy unit comprises wind power generation equipment and photovoltaic power generation equipment. The invention provides a multi-target optimization control system of new energy units, which comprises a state information acquisition module, a control module and a control module, wherein the state information acquisition module i