CN-122026518-A - AGC coordinated optimization control system based on multi-source data fusion

CN122026518ACN 122026518 ACN122026518 ACN 122026518ACN-122026518-A

Abstract

The invention relates to the technical field of AGC coordinated control, and discloses an AGC coordinated optimization control system based on multi-source data fusion, wherein the system is used for carrying out multi-source data acquisition and normalization preprocessing through a multi-source data acquisition module to form a global multi-dimensional data set; the method comprises the steps of constructing a risk model through a data driving module, outputting minimum spare capacity requirements adapting to the current system risk level, simultaneously determining safety requirements and economic boundaries under different working conditions through working condition discrimination and economic cost reference calculation, constructing a layered multi-objective optimization model through an intelligent optimization module, solving a resource allocation scheme considering both a safety base line and economic optimization, and disassembling the optimal decision scheme into personalized instructions adapting to physical characteristics of various resources through a personalized regulation module. Finally, an AGC coordinated optimization mechanism for safety and economic dynamic balance is constructed, and the difficulty that the spare capacity guarantee safety and the control cost are contradicted is accurately solved.

Inventors

XU WEI
WU GUOXING
WU XUANCHEN
WANG FENG
Yuan Cixian
LU JUN

Assignees

国能常州第二发电有限公司

Dates

Publication Date: 20260512
Application Date: 20260209

Claims (10)

1. The AGC coordinated optimization control system based on multi-source data fusion is characterized by comprising a multi-source data acquisition module, a data driving module, an intelligent optimization module and a personality adjustment module; the multi-source data acquisition module is used for acquiring power grid operation data, adjustment resource data, market transaction data and risk scene data, and carrying out normalization preprocessing on the acquired data to form a global multi-dimensional data set; the data driving module builds a risk model based on the global multidimensional data set introducing condition risk value indexes, outputs minimum spare capacity requirements, synchronously judges the working condition types, and calculates economic cost references under different working conditions; The intelligent optimization module takes the minimum spare capacity as hard constraint, takes an economic cost reference as a target basis, merges the global multi-dimensional data set to construct a layered multi-target optimization model, and solves an optimal decision scheme; And the personality regulating module disassembles the optimal decision scheme into personalizing instructions adapting to all resources based on the resource characteristic data in the global multidimensional data, and outputs the personalizing instructions, and all local scheduling nodes receive the personalizing instructions and execute the processing regulating instructions.
2. The AGC coordinated optimization control system based on multi-source data fusion of claim 1, wherein the data driving module screens out core data strongly related to risk assessment, condition discrimination, cost calculation from a global multi-dimensional data set, and specifically comprises a risk association data set, a condition discrimination data set, and a cost calculation data set; The risk association data set comprises unit tripping power, new energy dip amplitude, load mutation, new energy permeability, unit health and tie line transmission margin; The working condition judging data set comprises real-time frequency, rated frequency, real-time load and maximum load; The cost calculation data set comprises a conventional unit resource type, an energy storage resource type, a market resource type, various resource minimum output limit values, various resource maximum output limit values and unit adjustment cost parameters.
3. The AGC coordinated optimization control system based on multi-source data fusion of claim 2, wherein in the data driven module, a risk model expression is: ; Wherein, the Representing the output result of the risk model, namely the minimum spare capacity requirement; Representing the risk quantifying core term, Represents the tripping power of the unit, the dip amplitude of new energy and the random variable of the power deficiency under each single disturbance scene corresponding to the abrupt change of load, Representing risk quantization coefficients; representing a risk deviation correction term; representing the permeability weight coefficient of new energy; Represents the permeability of new energy; representing a health degree weight coefficient of the unit; Representing the health degree of the unit; representing a transmission margin weight coefficient of the connecting line; representing the link transmission margin.
4. The AGC coordinated optimization control system based on multi-source data fusion of claim 2, wherein the data driving module calculates a frequency deviation and a load factor based on a working condition discrimination data set to discriminate a working condition type, and a calculation formula of the frequency deviation and the load factor is: ; ; Wherein, the Representing the frequency deviation; representing real-time frequency; Representing a nominal frequency; Representing the load factor; Representing real-time load; Representing the maximum load.
5. The AGC coordinated optimization control system based on multi-source data fusion of claim 4 wherein the method for discriminating the operating mode type is: When (when) Representing disturbance conditions when And is also provided with Representing peak working conditions when And is also provided with Representing the valley condition when And is also provided with Representing a flat condition.
6. The AGC coordinated optimization control system based on multi-source data fusion of claim 5 wherein the data driven module has an economic cost reference formula under different conditions: ; Wherein, the Represents the first Economic cost standard of similar working conditions; representing the adjustment of the type of resource, Representing a conventional unit of the machine, Represents the energy storage capacity of the electric vehicle, Representing market resources; represents the first Class resources are at the first Unit adjustment cost under similar conditions; Representing an acceptable maximum unit standby cost; Representing an indication function; Represents that the standby redundant cost is superposed only under the disturbance working condition or the peak working condition, Representing spare redundancy costs.
7. The AGC coordinated optimization control system based on multi-source data fusion of claim 6, wherein in the intelligent optimization module, a hierarchical multi-objective optimization model expression formula is: ; ; Wherein, the Representing an optimization objective function; represents the first Within the scheduling period Class resources are at the first The optimal decision scheme under the class working condition is the output result of the layered multi-objective optimization model; represents the first Total system spare capacity during each scheduling period; 、 Representing the weight; Representing constraint conditions; Representing a core safety hard constraint, forcing the total standby capacity of the system to be not lower than the minimum standby capacity; representing that the optimal dispense capacity is limited between a minimum output limit and a maximum output limit, Represents the first Class resources are at the first The minimum output limit under the similar operating conditions, Represents the first Class resources are at the first Maximum output limit under class of working conditions.
8. The AGC coordinated optimization control system based on multi-source data fusion of claim 7, wherein the The calculation formula of (2) is as follows; 。
9. the AGC coordinated optimization control system based on multi-source data fusion of claim 8, wherein the personality adjustment module extracts all resources from a global multi-dimensional data set And are classified by resource type: Conventional unit Minimum start-stop time, climbing speed, coal consumption curve and peak regulation range; Energy storage The charge and discharge efficiency, the upper limit of capacity, response time delay and cycle life constraint; Market resources Quotation curves, calling response time and capacity adjustable intervals; Matching the working condition type of the current scheduling period And calling the resource characteristic parameter threshold under the corresponding working condition.
10. The AGC coordinated optimization control system based on multi-source data fusion of claim 9, wherein the personality adjustment module outputs a hierarchical multi-objective optimization model Splitting according to the resource types to obtain the target allocation capacity of the single resource under the current working condition, and marking as Then to Performing hard constraint verification, and correcting according to rules if the physical limit value is exceeded: If it is Then The balance part is distributed to other resources according to the principle of optimal cost; If it is Then The excess part counts into the standby redundancy of the system; If it is Then ; Represents the first Within the scheduling period, the first Class resources are at the first Final executable allocation capacity under similar working condition, and corrected Converting into executable instruction parameters of each resource: The conventional unit is converted into an output adjustment instruction, stores energy, is converted into a charge and discharge mode instruction, and converts market resources into a capacity calling instruction.

Description

AGC coordinated optimization control system based on multi-source data fusion Technical Field The invention relates to the technical field of AGC coordinated control, in particular to an AGC coordinated optimization control system based on multi-source data fusion. Background AGC coordinated optimization control is a key technology used for automatically adjusting the output of a generator set in a power system to maintain the stability of the frequency of a power grid and the operation of the power of a tie line according to a planned value. The power generation output adjustment quantity is calculated by monitoring parameters such as power grid frequency, tie line power and the like in real time and comparing the parameters with a set value, and an instruction is issued to each generator set 14. In actual operation, the load of the power grid changes at moment, such as peak electricity consumption in daytime and valley electricity consumption at night, and the AGC coordinated optimization control can ensure that the generator set responds to the changes in time, avoid the fluctuation of the frequency of the power grid and ensure the safe and stable operation 1 of the power system. Meanwhile, the control technology can dynamically optimize the combination and the operation mode of the generator set according to the load condition of the power grid, so that the generator set operates under the efficient working condition, the power generation cost is reduced, the energy utilization efficiency is improved, and the economic operation of the power system is realized. In order to ensure stable frequency, enough spare capacity is reserved, but the spare capacity can increase the power generation cost, if economic optimization is pursued, the spare margin can be reduced, so that the disturbance rejection capability of the system is reduced, and the current AGC coordinated optimization control system is difficult to find a proper balance point before the safety priority and the economic optimization. Disclosure of Invention (One) solving the technical problems Aiming at the defects of the prior art, the invention provides an AGC coordinated optimization control system based on multi-source data fusion, which is provided with an AGC coordinated optimization mechanism for safety and economic dynamic balance through quantitative decision, hierarchical optimization and individual execution under data driving, and is used for accurately solving the problem that the reserve capacity guarantees the contradiction between safety and control cost, thereby not only guaranteeing the reserve margin required by the system frequency stabilization, but also compressing the power generation cost caused by redundancy reserve to the maximum extent, and finally finding the optimal solution of the dynamic balance between the safety priority and the economic optimum. (II) technical scheme In order to achieve the aim, the invention provides the technical scheme that the AGC coordinated optimization control system based on multi-source data fusion comprises a multi-source data acquisition module, a data driving module, an intelligent optimization module and a personality adjustment module; the multi-source data acquisition module is used for acquiring power grid operation data, adjustment resource data, market transaction data and risk scene data, and carrying out normalization preprocessing on the acquired data to form a global multi-dimensional data set; the data driving module builds a risk model based on the global multidimensional data set introducing condition risk value indexes, outputs minimum spare capacity requirements, synchronously judges the working condition types, and calculates economic cost references under different working conditions; The intelligent optimization module takes the minimum spare capacity as hard constraint, takes an economic cost reference as a target basis, merges the global multi-dimensional data set to construct a layered multi-target optimization model, and solves an optimal decision scheme; And the personality regulating module disassembles the optimal decision scheme into personalizing instructions adapting to all resources based on the resource characteristic data in the global multidimensional data, and outputs the personalizing instructions, and all local scheduling nodes receive the personalizing instructions and execute the processing regulating instructions. Preferably, the data driving module screens out core data which are strongly related to risk assessment, working condition discrimination and cost calculation from the global multidimensional data set, and specifically comprises a risk association data set, a working condition discrimination data set and a cost calculation data set; The risk association data set comprises unit tripping power, new energy dip amplitude, load mutation, new energy permeability, unit health and tie line transmission margin; The working condition judging data s