CN-121984038-A - Automatic deployment and performance evaluation system for wind power base camera

CN121984038ACN 121984038 ACN121984038 ACN 121984038ACN-121984038-A

Abstract

The invention discloses an automatic deployment and performance evaluation system of a wind power base phase modulation machine, relates to the field of intelligent deployment and operation evaluation of power system phase modulation equipment, and is used for improving scientificity and economy of deployment decisions of the wind power base phase modulation machine. The self-adaptive deployment optimization module takes installation node, single machine capacity and operation time as decision variables, combines overvoltage risk and investment cost to construct a multi-objective optimization problem, generates a non-dominant solution by simulating an annealing particle swarm algorithm, carries out time sequence tide calculation and transient stability simulation, quantifies power transmission capacity improvement, wind abandoning income, reactive compensation and auxiliary service income, calculates a net present value and a conditional risk value, and the decision feedback module sorts scheme scores and feeds back weight adjustment to the optimization module to realize closed-loop decision support.

Inventors

WU ZHE
SUN FUYUAN
GAO QINGZHONG
ZHOU SHUAI
LI HAOLUAN
LIU BAOLIANG
SHEN LI
LENG XUE
SUN MINGZE
CHEN FANGDI

Assignees

沈阳工程学院
辽宁新能数智科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251124

Claims (8)

1. An automated deployment and performance evaluation system for a wind power base dispatching machine is characterized by comprising the following modules: the scene generation module is used for acquiring real-time measurement data of the power grid, updating a power grid calculation model, identifying a voltage stability weak area through calculating a node tide entropy value, generating a key section list, acquiring probability distribution of power prediction data of a wind power plant group, generating an initial scene set through Latin hypercube sampling, and outputting a representative operation scene set through clustering reduction processing; the self-adaptive deployment optimization module is used for taking the installation node, the single-machine capacity and the operation time of the camera as decision variables, taking the minimum transient overvoltage risk and the minimum total investment cost in a key section list as optimization targets, carrying out iterative solution under an operation scene set through a multi-target particle swarm algorithm with a simulated annealing mutation operator, and outputting a deployment scheme set of the camera comprising a plurality of non-dominant solutions; The full life cycle assessment module is used for carrying out time sequence tide calculation and transient stability simulation on the deployment scheme set, quantifying economic benefits brought by the power transmission capacity improvement and reduction of the abandoned wind, assessing reactive compensation provided by a regulator and expected benefits participated in auxiliary service, and calculating a net present value and a conditional risk value of each scheme by combining the full life cycle cost of the equipment; The decision feedback module is used for carrying out multi-dimensional visual display on the net present value and the conditional risk value of each deployment scheme, calculating the comprehensive score through a multi-attribute utility function according to the weight parameters set by a decision maker, generating scheme ordering, and updating the weight proportion of the total investment cost and the transient overvoltage risk in the objective function according to the weight coefficients adjusted by the decision maker.
2. The automatic deployment and performance evaluation system for wind power base dispatching machines of claim 1, wherein the specific process of acquiring real-time measurement data of a power grid, updating a power grid calculation model, identifying a voltage stability weak area through calculating a node tide entropy value and generating a key section list is as follows: Acquiring real-time measurement data from a power grid dispatching system, wherein the real-time measurement data comprise node voltage amplitude, phase angle and line power data, updating running state parameters in a power grid calculation model, calculating a power flow entropy value of each node based on the updated power grid model, and identifying a node set which is most sensitive to system voltage stability by analyzing the influence degree of node power injection change on system power flow distribution; And determining nodes with high tide entropy values and associated power transmission channels as voltage stability weak areas, extracting key power transmission lines connecting the areas, forming a key section list containing line parameters and stability limits, and taking transient overvoltage risk indexes in the key section list as core constraint conditions of subsequent optimization deployment.
3. The automated deployment and performance evaluation system for wind power base dispatching machines of claim 2, wherein the method is characterized in that probability distribution of wind power plant group power prediction data is obtained, latin hypercube sampling is adopted to generate an initial scene set, and a specific process of outputting a representative operation scene set through cluster reduction processing is as follows: Establishing a multidimensional joint probability distribution model based on historical power data of a wind power plant group, generating an initial scene set with complete coverage in a power prediction error space through Latin hypercube sampling, and carrying out load flow calculation on the initial scene set to obtain system running state characteristic quantities under each scene, wherein the system running state characteristic quantities comprise key section power margin, node voltage deviation and system network loss indexes; the method comprises the steps of constructing scene clustering feature vectors based on system running state feature quantity, identifying typical running modes and extreme running conditions in a scene set through a clustering algorithm, and selecting a scene closest to a clustering center from each cluster as a representative scene on the premise of keeping scene diversity to form a representative running scene set capable of reflecting main running features of the system.
4. The automated deployment and performance evaluation system of wind power base dispatching machine of claim 1, wherein the adaptive deployment optimization module comprises the following steps: Determining the priority order of candidate installation nodes of the camera based on the key section list, determining an optional single machine capacity grade sequence by combining with equipment manufacturing standards, and dividing the operation time period according to the power grid construction planning; Constructing a multi-objective optimization problem with critical section transient overvoltage severity and total investment cost of a whole life cycle as cores, wherein the total investment cost comprises equipment purchase, installation construction, operation maintenance and retirement treatment cost; under a representative operation scene set, performing collaborative optimization through a multi-objective particle swarm algorithm fused with a simulated annealing mechanism, and synchronously evaluating convergence and distribution indexes of the particle swarm in each iteration; When the particle swarm is detected to be in local optimum, receiving an inferior solution according to the simulated annealing probability, and expanding the searching range by adaptively adjusting a mutation operator to ensure that the pareto optimum front edge with uniform distribution is obtained; and archiving and screening non-dominant solutions generated in the optimization process, and outputting a camera deployment scheme set which achieves the best balance between investment cost and system stability.
5. The automatic deployment and performance evaluation system of the wind power base dispatching machine according to claim 1, wherein the specific process of carrying out time sequence tide calculation and transient stability simulation on a deployment scheme set and quantifying economic benefits brought by improving power transmission capability and reducing abandoned wind is as follows: Performing time sequence power flow calculation on each deployment scheme of the phase regulator based on a representative operation scene set, tracking the voltage stability limit of the system through a continuous power flow method, recording the maximum lifting amplitude of the transmission power of the key section, simulating the typical fault form comprising three-phase short circuit and line break through a transient stability simulation analysis system based on the lifting data of the transmission power of the key section, and recording the voltage recovery characteristic and the power angle rocking curve of the system; and calculating the power quantity capable of being increased and transmitted according to the power transmission capacity lifting amplitude, and combining the power grid wind curtailment statistical data and the pole internet power price to quantitatively reduce the power generation income caused by wind curtailment.
6. The automated deployment and performance evaluation system for wind power base cameras of claim 5, wherein the system is characterized by simultaneously evaluating reactive compensation provided by the cameras and expected benefits of participation in auxiliary services, and calculating the net present value and conditional risk value of each scheme by combining the full life cycle cost of the equipment comprises the following specific processes: the dynamic reactive capacity provided by the regulating camera in various operation scenes is counted, the capacity saving of the static reactive compensation equipment is replaced based on dynamic reactive equivalent calculation, and investment saving benefits are converted according to equipment investment unit price; According to the auxiliary service market rules and the clearing prices, calculating expected benefits of dynamic reactive support and voltage stabilization service provided by the camera, and considering service duration and output levels in different operation scenes; The method comprises the steps of establishing a full life cycle cost model, covering the cost of equipment purchase, installation, operation and maintenance, overhaul and retirement treatment at each stage, evaluating the scheme economy by means of a benefit present value analysis by combining power generation benefits, investment saving benefits and auxiliary service benefits, and calculating expected losses under a given confidence level based on risk scene analysis.
7. The automated deployment and performance evaluation system of wind power base dispatching machine according to claim 1, wherein the specific process of calculating comprehensive scores and generating scheme ordering through multi-attribute utility functions according to weight parameters set by a decision maker is as follows: Constructing an evaluation index system comprising three dimensions of technical performance, economic benefit and risk control, and normalizing a time sequence load flow calculation result, transient stability simulation data and economic benefit indexes; according to weight parameters set by a decision maker, determining comprehensive weights of all evaluation indexes by a combined weight method, establishing an inter-index correlation matrix analysis index mutual influence relation, calculating utility values of all schemes under different evaluation dimensions, obtaining comprehensive scores by weighted summation, generating scheme priority sequences according to the scores, and outputting scheme sequencing results.
8. The automated deployment and performance evaluation system for wind power base dispatching machines of claim 7, wherein the specific process of updating the weight ratio of total investment cost and transient overvoltage risk in the objective function by the weight coefficient adjusted by the decision maker is as follows: analyzing decision preference reflected in the scheme sequencing result, and extracting the importance degree of a decision maker on three dimensions of technical performance, economic benefit and risk control; and establishing a conversion relation from the evaluation weight to the optimization weight through a weight mapping rule, converting the decision preference into a weight parameter of an optimization objective function, and reallocating the weight coefficients of the total investment cost and the transient overvoltage risk in the objective function according to the updated weight coefficient.

Description

Automatic deployment and performance evaluation system for wind power base camera Technical Field The invention relates to the field of intelligent deployment and operation evaluation of phase modulation equipment of a power system, in particular to an automatic deployment and performance evaluation system of a wind power base phase modulation machine. Background With the rapid development of new energy, wind power has become an important renewable energy component in an electric power system. The uncertainty and the volatility of the operation of the power grid are obviously increased by large-scale wind power grid connection, and the voltage stability, the power transmission capacity and the economic benefit of power equipment become key problems to be solved in power grid planning and operation management. Meanwhile, the control of the investment and operation cost of the power grid and the optimization of the income of auxiliary services also put higher requirements on decision support systems. Under the background, on the premise of ensuring the safety and stability of the power grid, reasonable deployment, optimal cost and maximum operation income of the camera adjusting equipment are realized through scientific decisions, and the method becomes an important technical problem of operation management of the wind power base. The existing method mainly relies on manual experience or a single optimization strategy for planning in the aspects of deployment and performance evaluation of a camera, and lacks comprehensive analysis capability on dynamic behavior of a power grid in a complex operation scene. In addition, some methods are prone to local optimality or uneven solution set distribution when dealing with large-scale scenes and multi-objective optimization problems, so that the deployment scheme of the camera is lack of diversity and feasibility. These drawbacks limit the ability of existing methods to achieve automated, intelligent deployment and comprehensive performance assessment in large-scale wind power bases, and also make it difficult to provide comprehensive decision support for grid investment and operation management. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an automatic deployment and performance evaluation system for a wind power base camera, which solves the problems of the background art. The invention aims at achieving the purposes by adopting the following technical scheme, the automatic deployment and performance evaluation system of the wind power base dispatching camera comprises a scene generation module, a full life cycle evaluation module, an adaptive deployment optimization module, a full life cycle evaluation module, a comprehensive life cycle evaluation module and a comprehensive life cycle evaluation module, wherein the scene generation module is used for acquiring real-time measurement data of a power grid, updating a power grid calculation model, identifying a voltage stability weak area through a calculation node trend entropy value and generating a key section list, acquiring probability distribution of power prediction data of a wind power plant group, generating an initial scene set through Latin hypercube sampling, processing and outputting a representative operation scene set through clustering, the adaptive deployment optimization module is used for taking an installation node, a single machine capacity and a commissioning time of the dispatching camera as decision variables, taking the minimum risk of transient overvoltage and the minimum total investment cost in the key section list as optimization targets, carrying out iterative solution under the operation scene set through a multi-objective particle swarm algorithm with a simulated annealing mutation operator, outputting a deployment scheme set comprising a plurality of non-dominant solutions, the full life cycle evaluation module is used for carrying out time sequence trend calculation and stable simulation on the deployment scheme set, quantifying the power transmission capacity and reducing the economic cost brought by wind power, and simultaneously dispatching the provided by the dispatching camera is processed through clustering reduction, the installation node, the self-adaptive deployment optimization module is used for taking the minimum transient overvoltage risk and single machine capacity and the operation time as decision variables in the decision variables, the minimum risk and minimum investment cost in the key section calculation plan as optimization targets, and updating the weight proportion of the total investment cost and the transient overvoltage risk in the objective function by the weight coefficient adjusted by the decision maker. The method comprises the specific processes of acquiring real-time measurement data of a power grid, updating a power grid calculation model, identifying voltage stability weak areas through calculating node power f