CN-122026482-A - Photovoltaic output fluctuation-oriented active power distribution network dynamic reconstruction optimization method

CN122026482ACN 122026482 ACN122026482 ACN 122026482ACN-122026482-A

Abstract

The invention discloses a photovoltaic output fluctuation-oriented active power distribution network dynamic reconstruction optimization method which comprises the steps of constructing a joint probability density model of different working conditions of photovoltaic output based on a Copula function according to preprocessed power distribution network data, screening out photovoltaic output influence factors, dividing typical fluctuation scenes, constructing an active power distribution network dynamic reconstruction multi-objective optimization model containing photovoltaic access by taking the safety and reliability of a power distribution network as constraint conditions, solving the active power distribution network dynamic reconstruction multi-objective optimization model to obtain an optimal economic topology reconstruction result, performing closed-loop optimization and real-time correction on the optimal economic topology reconstruction result based on a rolling time domain, and outputting a final reconstruction optimization result. The invention solves the problems of uncertainty fluctuation of photovoltaic output affected by meteorological factors, easy occurrence of voltage out-of-limit, branch overload and network loss increase of the active power distribution network, and insufficient suitability and instantaneity of the traditional reconstruction scheme.

Inventors

SUN SHIQI
MA GANG
XU DONGLIANG
Wang Chaomeng
ZHAN XIAOSHENG
CHEN XUDONG
LU HAO

Assignees

南京师范大学

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. The active power distribution network dynamic reconstruction optimization method for photovoltaic output fluctuation is characterized by comprising the following steps of: S1, constructing a joint probability density model of different working conditions of photovoltaic output based on a Copula function according to preprocessed power distribution network data, and screening out photovoltaic output influence factors; S2, dividing typical fluctuation scenes based on photovoltaic output influence factors; S3, based on the divided typical fluctuation scene, constructing an active power distribution network dynamic reconstruction multi-objective optimization model containing photovoltaic access by taking the safety and the reliability of the power distribution network as constraint conditions; S4, solving a dynamic reconfiguration multi-objective optimization model of the active power distribution network based on an improved self-adaptive particle swarm algorithm to obtain an optimal economic topology reconfiguration result for guaranteeing the safety of the power distribution network; and S5, performing closed-loop optimization and real-time correction on the optimal economic topology reconstruction result obtained in the step S4 based on a rolling time domain, and outputting a final reconstruction optimization result.
2. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 1, wherein the screening method for the photovoltaic output influence factors in step S1 comprises the following steps: a1 construction of reference sequences With comparison sequence A matrix; A2, calculating an association coefficient according to the reference sequence and the comparison sequence matrix: ; Wherein, the As the correlation coefficient of the ith moment of the kth weather feature and the photovoltaic output, In order to resolve the coefficient of the difference, For a two-stage minimum difference of the reference sequence and the comparison sequence, Two-stage maximum difference between the reference sequence and the comparison sequence; a3 according to the association coefficient Gray correlation is calculated: ; Wherein, the For gray correlation of class k meteorological features with photovoltaic output, ; And A4, setting a relevance threshold r 0 , and screening out meteorological features of r k ≥r 0 as key influence factors.
3. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 2, wherein the constructing process of the joint probability density model in step S1 comprises the following steps: B1, edge distribution fitting: Respectively to the normalized photovoltaic output sequences Class 5 weather feature sequences 、、、、 Performing edge distribution fitting; taking the non-normal characteristic of the data into consideration, constructing an edge distribution function of each variable by adopting a nuclear density estimation method, wherein the expression is as follows: ; Wherein X is a variable to be fitted, n is the number of samples, h is the window width, The Gaussian kernel function is the ith sample data, and x is the point to be estimated; obtaining an edge distribution function F P (p)、F G (g)、F T (t)、F C (c)、F V (v)、F H (h) and a corresponding probability density function F P (p)、f G (g)、f T (t)、f C (c)、f V (v)、f H (h) of each variable respectively through kernel density estimation; B2:Copula function selection: The method comprises the steps of constructing the joint distribution of photovoltaic output and meteorological factors by using a T-Copula function, and constructing a four-dimensional T-Copula joint distribution function by using photovoltaic output P, irradiance G, ambient temperature T and cloud cover C as core variables: ; Wherein ,u 1 =F P (p)、u 2 =F G (g)、u 3 =F T (t)、u 4 =F C (c) is the cumulative probability value of each variable edge distribution, θ is the correlation parameter matrix, v is the degree of freedom parameter, As a cumulative distribution function of the four-dimensional t distribution, Is an inverse function of univariate t distribution; B3, solving parameters theta and v by adopting a maximum likelihood estimation method: constructing a log-likelihood function: ; Wherein, the Representing the edge distribution cumulative probability value of the 1 st random variable in the ith sample, wherein the edge distribution cumulative probability value corresponds to the photovoltaic active power output; representing the cumulative probability value of the edge distribution of the 2 nd random variable in the ith sample, and corresponding to the cumulative probability of the edge distribution of irradiance; representing the cumulative probability value of the edge distribution of the 3 rd random variable in the ith sample, wherein the cumulative probability value corresponds to the edge distribution of the ambient temperature; Representing the edge distribution cumulative probability value of the 4 th random variable in the ith sample, and corresponding to the edge distribution cumulative probability of the wind speed; The joint probability density function, which is a t-Copula function, is expressed as: ; Wherein, the As a probability density function of a four-dimensional t distribution, Probability density function for univariate t distribution; maximizing the log likelihood function through a BFGS numerical optimization algorithm to obtain optimal parameters And (3) with And finally constructing a joint probability model: 。
4. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 3, wherein the process of dividing the typical fluctuation scene in step S2 comprises the following steps: C1, determining the number of clusters and initial parameters; c2, constructing an objective function: the core of fuzzy C-means clustering is to minimize an objective function J, and the expression is as follows: ; Wherein, the For the membership degree of the ith sample to the jth scene, x i is the key meteorological feature vector of the ith sample, v j is the cluster center vector of the jth scene, The Euclidean distance between the sample x i and the clustering center v j is calculated, c is the clustering category number, m is the fuzzy weighting index to control the fuzzy degree of the clustering result; And C3, iteratively solving the cluster center and the membership degree: ; and C4, after iteration convergence, determining the scene category to which each sample belongs according to the maximum membership u ij,max of the sample.
5. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic power output fluctuation according to claim 4, wherein in the step S3, the dynamic reconfiguration multi-objective optimization model of the active power distribution network is combined with the operation requirement of the active power distribution network, and three optimization objectives, namely an economic objective, a voltage safety objective and an operation stability objective, are set with economic optimality and safety controllability as cores.
6. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 5, wherein the expression of three optimization targets is as follows: Economic objectives of minimizing network loss and switching costs The objective function is: ; P ij,t 、Q ij,t is the active power and reactive power of a branch i-j at the moment T respectively, U i,t is the voltage of a node i at the moment T, R ij is the resistance of the branch i-j, deltat is the time period, C s is the single switch operation cost, x ij,t is the switch state of the branch i-j at the moment T, x ij,t =1 is closed, x ij,t =0 is open, x ij,t+1 -x ij,t is the change of the switch state at the adjacent time period, 1 is the change, and 0;E is the branch set of the power distribution network; Voltage safety objective node voltage offset minimization The objective function is: ; N is the total number of nodes of the power distribution network, and U N is the rated voltage of the nodes; The voltage offset rate of the node i at the moment T is the total time period number in the optimization period; Operation stability goal, branch load rate equalization The objective function is: ; Wherein |E| is the total number of branches of the power distribution network, S ij,t is the apparent power of the branch i-j at the moment t, S ij,max is the rated apparent power of the branch i-j, And the average load rate of the whole network branch circuit at the moment t is obtained.
7. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 6, wherein the constraint conditions in step S3 include: And (3) load flow constraint: ; Wherein, P i,t 、Q i,t is the injection active power and reactive power of the node i at the time t, and P PV,i,t is the output of the photovoltaic connected to the node i at the time t; A set of contiguous nodes that is node i; The load active power of the node i at the time t, The reactive power of the load of the node i at the moment t; Node voltage constraint: ; Wherein, U min and U max are respectively the lower limit value and the upper limit value of the node voltage; Branch capacity constraint: ; Wherein S ij,max is the branch rated capacity; topological structure constraints including switch state constraints, radial constraints and switch operating frequency constraints; The switch state constraint is that x ij,t epsilon {0,1}, i.e. the switch has only two states of on or off; Radial constraint, namely the power distribution network needs to keep radial topology after reconstruction, and has no ring network and no island, namely all load nodes are communicated with a power supply point through a closed branch; The switch operation frequency constraint is that the operation times of a single switch in one optimization period are not more than 3 times; Photovoltaic output constraint: ; wherein P PV,max,i is the rated output of the photovoltaic accessed to the node i.
8. The method for optimizing the dynamic reconfiguration of the active power distribution network for the fluctuation of the photovoltaic output according to claim 7, wherein the process of solving the dynamic reconfiguration multi-objective optimization model of the active power distribution network based on the improved adaptive particle swarm algorithm in the step S4 comprises the following steps: d1, sequentially executing coding mode design, chaotic initialization population and feasible solution screening; d2, introducing dynamic inertia weight and self-adaptive learning factors; The particle speed and position updating rule is that a discretization speed and position updating formula is designed by combining binary coding characteristics; A photovoltaic fluctuation robustness enhancement strategy, namely introducing a scene robustness penalty factor and dynamic search step length adjustment for improving the suitability of the algorithm to photovoltaic output fluctuation; And D5, after reaching the iteration termination condition, performing optimal solution screening.
9. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 8, wherein the step S5 comprises: E1. constructing a rolling time domain optimization framework, which comprises the following steps: e1-1, dividing a time domain window, namely dividing a total optimization period such as 24 hours into a plurality of continuous rolling windows, wherein the duration of each window is tau, and dynamically adjusting tau according to a photovoltaic fluctuation scene; E1-2, updating data in windows, namely collecting running data of the power distribution network in real time when each rolling window is started; E1-3, dynamically updating the optimization target and the constraint, namely adjusting the weight of the optimization target and the constraint threshold of the current window based on the updated photovoltaic output data and the running state; E2, executing rolling iteration optimization of the initial reconstruction scheme, and specifically comprising the following steps: E2-1, solving the model in the window, namely taking updated photovoltaic output data and running state data as input for each rolling window, and calling an improved self-adaptive particle swarm algorithm to solve the multi-target optimization model again to obtain an optimal reconstruction scheme in the current window ; E2-2, scheme smooth transition constraint, namely introducing scheme smooth constraint to limit the change amount of the switching state of the adjacent window: ; Wherein, the K max is the maximum allowable switching change times for the switching state at the middle moment of the previous window; e2-3, iteratively updating the reconstruction scheme, namely outputting the optimal reconstruction scheme of the second half period of the current window and executing the optimal reconstruction scheme after each rolling window is finished; e3, carrying out emergency correction on the reconstruction scheme based on real-time monitoring, wherein the method comprises the following steps: E3-1, monitoring the running state in real time, namely monitoring key running indexes in real time through a synchronous phasor measurement device of the power distribution network and a data acquisition and monitoring system in the execution process of each rolling window; E3-2, triggering an emergency correction mechanism when the correction triggering condition is met; And E3-3, adopting a rapid gradient descent algorithm to realize local adjustment of the reconstruction scheme.
10. The method for optimizing dynamic reconfiguration of an active power distribution network for photovoltaic output fluctuation according to claim 9, wherein the local adjustment process of the reconfiguration scheme in step E3-3 comprises: E3-3-1, identifying the switch states of adjacent branches aiming at voltage out-of-limit nodes, and determining adjustable key switches; E3-3-2, setting a local optimization target minviolation (X) by taking the fastest elimination constraint violation as a core, wherein the localization (X) is the constraint violation degree; E3-3-3, locally adjusting the switch states, namely performing state optimization on the key switches only, and searching for a switch state combination which minimizes constraint violation degree through a gradient descent method; E3-3-4, scheme verification, namely verifying whether the topology of the power distribution network meets radial constraint and island constraint-free after correction, and ensuring that the correction scheme can be executed immediately after the correction scheme is feasible.

Description

Photovoltaic output fluctuation-oriented active power distribution network dynamic reconstruction optimization method Technical Field The invention belongs to the field of power grids, relates to a power distribution network operation optimization technology, and particularly relates to a photovoltaic output fluctuation-oriented active power distribution network dynamic reconstruction optimization method. Background Along with large-scale grid connection of new energy sources such as distributed photovoltaic and wind power and rapid increase of random loads such as electric vehicles and flexible loads, uncertainty of power output and load demand of a power distribution network source side is remarkably enhanced, so that the running state of the power distribution network frequently fluctuates, and a traditional static reconstruction strategy is difficult to adapt to dynamically-changed running conditions. The existing reconstruction method of the power distribution network is mostly based on a deterministic model, influences of uncertainty of source load on reconstruction results are not fully considered, problems of poor adaptability of the reconstruction scheme, high network loss rate, increased risk of voltage out-of-limit and the like are easily caused, meanwhile, the reconstruction method which partly considers uncertainty mostly adopts a single prediction model or a fixed optimization period, cannot accurately capture the time sequence fluctuation characteristics of the source load, and has the defects of insufficient optimization timeliness, high calculation complexity and the like, so that the actual requirements of safety, economy and stable operation of the power distribution network are difficult to meet. Therefore, developing a power distribution network reconstruction optimization strategy capable of accurately quantifying source load uncertainty and dynamically adapting to operation conditions has important engineering application value. Disclosure of Invention The invention aims to solve the problems that the photovoltaic output is affected by the meteorological factors and has uncertainty fluctuation, so that the active power distribution network is easy to have voltage out-of-limit, branch overload and network loss increase, and the suitability and instantaneity of the traditional reconstruction scheme are insufficient, and provides an active power distribution network dynamic reconstruction optimization method for the photovoltaic output fluctuation. In order to achieve the purpose, the invention provides an active power distribution network dynamic reconstruction optimization method oriented to photovoltaic output fluctuation, which comprises the following steps: S1, constructing a joint probability density model of different working conditions of photovoltaic output based on a Copula function according to preprocessed power distribution network data, and screening out photovoltaic output influence factors; S2, dividing typical fluctuation scenes based on photovoltaic output influence factors; S3, based on the divided typical fluctuation scene, constructing an active power distribution network dynamic reconstruction multi-objective optimization model containing photovoltaic access by taking the safety and the reliability of the power distribution network as constraint conditions; S4, solving a dynamic reconfiguration multi-objective optimization model of the active power distribution network based on an improved self-adaptive particle swarm algorithm to obtain an optimal economic topology reconfiguration result for guaranteeing the safety of the power distribution network; and S5, performing closed-loop optimization and real-time correction on the optimal economic topology reconstruction result obtained in the step S4 based on a rolling time domain, and outputting a final reconstruction optimization result. Further, the screening method of the photovoltaic output influencing factors in the step S1 includes: a1 construction of reference sequences With comparison sequenceA matrix; A2, calculating an association coefficient according to the reference sequence and the comparison sequence matrix: Wherein, the As the correlation coefficient of the ith moment of the kth weather feature and the photovoltaic output,In order to resolve the coefficient of the difference,For a two-stage minimum difference of the reference sequence and the comparison sequence,Two-stage maximum difference between the reference sequence and the comparison sequence; a3 according to the association coefficient Gray correlation is calculated: Wherein, the For gray correlation of class k meteorological features with photovoltaic output,; And A4, setting a relevance threshold r 0, and screening out meteorological features of r k ≥r0 as key influence factors. Further, the constructing process of the joint probability density model in the step S1 includes: B1, edge distribution fitting: Respectively to the normalized photovo