CN-120474034-B - Active power distribution network voltage optimization method considering distributed photovoltaic cluster characteristics

CN120474034BCN 120474034 BCN120474034 BCN 120474034BCN-120474034-B

Abstract

The invention discloses an active power distribution network voltage optimization method considering distributed photovoltaic cluster characteristics, which comprises the steps of giving initial operation data of a power distribution network, carrying out cluster division according to the initial operation data, determining each cluster autonomous optimization objective function according to cluster division results, selecting the minimum voltage deviation as the cluster autonomous optimization objective function if reactive power regulation equipment and energy storage are not available in the clusters, selecting the minimum network loss as the cluster autonomous optimization objective function if the reactive power regulation equipment or the energy storage is available in the clusters, and independently carrying out cluster autonomous optimization according to the respective objective function by each cluster. According to the invention, through two links of cluster autonomous optimization and inter-cluster coordination optimization, different objective functions are adopted to optimize according to different cluster characteristics, so that the safety economy and flexibility of the whole network are improved.

Inventors

XU YONG
GAO JIAN
Yao ben
LI JIA
YIN YU
WANG JING
CHEN HONG
YANG CHUAN
XU ZHANYANG
SUN XINCHENG
Hu Xueman
Sun Jianshuo

Assignees

国网江苏省电力有限公司高邮市供电分公司
国网江苏省电力有限公司扬州供电分公司
国网江苏省电力有限公司

Dates

Publication Date: 20260512
Application Date: 20250513

Claims (9)

1. The active power distribution network voltage optimization method considering the characteristics of the distributed photovoltaic clusters is characterized by comprising the following steps of: Giving initial operation data of a power distribution network, and carrying out cluster division according to a cluster comprehensive division index based on a K-means algorithm according to the initial operation data; Step two, determining each cluster autonomous optimization objective function according to the cluster dividing result and the characteristics of each cluster, and selecting the minimum voltage deviation as the cluster autonomous optimization objective function if reactive power regulation equipment and energy storage are not available in the cluster; Introducing virtual edge nodes among clusters, constructing a virtual power transmission line, establishing a relation among the clusters, and decoupling the clusters; Initializing Lagrange penalty items to be zero, and determining active power, reactive power and voltage of cluster edge nodes according to initial operation data of the distribution network; step five, each cluster independently performs cluster autonomous optimization according to respective objective functions to obtain the optimal solution of each current cluster; Step six, calculating Lagrange penalty items of cluster edge nodes, and updating objective functions of inter-cluster coordination optimization; Adding Lagrange penalty items on the basis of the self-discipline optimization objective function of each cluster, carrying out inter-cluster coordination optimization scheduling by adopting an alternate direction multiplier method, and constructing a solution of the global distribution network by integrating solutions of each cluster; Step eight, judging whether the result is converged according to the cluster edge node data, if so, ending, otherwise, updating the active power, the reactive power and the voltage value of the cluster edge node, and turning to step five to continue until an optimal solution is obtained; The third step comprises: When the cooperative optimization regulation and control are carried out among the clusters, the three clusters of the upstream cluster I, the cluster K and the downstream cluster J finish cluster decoupling by adding virtual edge nodes and virtual power transmission lines among the clusters; the cluster I and the cluster K are connected through a line ab, the cluster K and the cluster J are connected through a line cl, the edge node is a node a of the cluster I and a node c of the cluster K, a virtual edge node a 1 is correspondingly added in the line ab of the cluster K, and a virtual edge node c 1 is added in the line cl of the cluster J; Virtual edge node a 1 belongs to cluster K and virtual edge node c 1 belongs to cluster J; Firstly, information is interacted through an edge node a of an upstream cluster I and a virtual edge node a 1 of a cluster K, intra-cluster self-discipline optimization of the next round is carried out, the cluster K receives the accurate voltage of the edge node a of the upstream cluster I, the active power and reactive power transmitted by a virtual power transmission line a 1 b, the voltage of a virtual edge node c 1 of a downstream cluster J and the real active power and reactive power transmitted by a line cl, simultaneously, the virtual edge node a 1 node voltage and the accurate active power and reactive power transmitted by a line ab are sent to the upstream cluster I, and the real voltage of a node c and the active power and reactive power transmitted by a virtual power transmission line c 1 l are sent to the downstream cluster J.
2. The method for optimizing the voltage of the active power distribution network by considering the characteristics of a distributed photovoltaic cluster according to claim 1, wherein in the first step, the cluster comprehensive division index adopted by the cluster division comprises an electrical distance and a cluster voltage regulation potential index by considering approximate voltage sensitivity; The method comprises the following steps: Wherein gamma is a cluster division comprehensive index, l ij is the electrical distance between a node i and a node j, l ij,max is the maximum value in l ij , rho K is cluster voltage regulation potential, and n c is the number of clusters after network division.
3. The method for optimizing the voltage of an active distribution network taking into account characteristics of distributed photovoltaic clusters according to claim 1, characterized in that: In step one, cluster division includes: (1) For any node i in the network, the electrical distance between the node i and other nodes in the network is calculated to form a set L, namely (2) Aiming at all elements in the set L, the elements are arranged in an ascending order, and one element is randomly selected as an index d i of the node i, and the node index d i is used for describing the node density around the node i; (3) For indexes of all nodes in a network, arranging according to ascending order, randomly selecting an element as a threshold value, selecting nodes smaller than the threshold value to form a set E, and selecting a cluster center from the interior of the set; (4) Selecting a node with the highest index in the set E as a first cluster node E 1 ; (5) Selecting a node far away from the existing cluster center from the rest elements in the set E as a next cluster center; (6) If the division result is changed, repeating the steps (2) - (5), and randomly selecting a cluster center until the result is not changed; (7) The calculation cluster division quantization evaluation index SSE specifically comprises the following steps: Wherein l jεK represents the electrical distance between the node j and the cluster center epsilon K , and n K represents the number of nodes in the cluster K; (8) And after the number of the clusters in the elbow section is selected according to the elbow rule, respectively calculating the comprehensive cluster dividing indexes, and selecting the cluster dividing number with the highest index value and the dividing result.
4. The method for optimizing the voltage of the active power distribution network by considering the characteristics of the distributed photovoltaic clusters according to claim 1, wherein in the second step, an objective function with the minimum voltage deviation and an objective function with the minimum network loss are respectively: Wherein f s represents an objective function with minimum voltage deviation, f e represents an objective function with minimum network loss, U ref represents a voltage reference value, n K represents the number of nodes in the cluster, r ij represents the resistance of a line ij, P ij,t 、Q ij,t represents the active power and the reactive power of the line ij at the time t respectively, and U i,t represents the voltage of the node i at the time t.
5. The method for optimizing the voltage of the active power distribution network by considering the characteristics of the distributed photovoltaic clusters, which is disclosed in claim 1, is characterized in that the fifth step comprises the steps of adopting different objective functions by each cluster according to the characteristics of the clusters, and independently optimizing by considering line flow constraint, node voltage constraint, continuous reactive power compensation equipment constraint, distributed energy storage constraint and interruptible load constraint.
6. The method for optimizing the voltage of the active power distribution network by considering the characteristics of the distributed photovoltaic clusters according to claim 1, wherein in the step six, the Lagrangian penalty term calculation formula is: Wherein L 1 represents Lagrange penalty term, τλ represents penalty coefficient for ensuring data consistency, λ U 、λ P 、λ Q represents Lagrange multiplier of voltage, active power and reactive power respectively, P ab 、Q ab represents active power and reactive power of line ab, 、 Representing virtual active and reactive power transmitted by downstream virtual line a 1 b, U a and The voltages at the upstream edge node a and the downstream virtual edge node a 1 , respectively, the superscript n indicating the number of iterations.
7. The method for optimizing the voltage of the active power distribution network by considering the characteristics of the distributed photovoltaic clusters according to claim 1, wherein in the step seven, the objective function of adding the Lagrangian penalty term is as follows: 。
8. the method for optimizing the voltage of the active power distribution network by considering the characteristics of the distributed photovoltaic clusters according to claim 1, wherein in the step seven, the inter-cluster coordination optimization step is performed according to an alternate direction multiplier method, and the method is characterized in that: (1) Virtual edge node voltage obtained according to cluster autonomous optimization Virtual line power 、 Each cluster accurately calculates the active power P ab,p and the reactive power Q ab,p transmitted by the inter-cluster line ab, the accurate voltage U a,p of the intra-cluster edge node a, and the highest voltage U max and the lowest voltage U min inside the cluster through Niu Lafa, and updates the voltage compensation parameters delta U max and delta U min : (2) The adjacent clusters exchange edge data and deviation thereof, and the edge data is updated on site based on the interactive information: wherein x a is the voltage update value of the edge node of the cluster to the upstream cluster, y ab 、z ab is the active power update value and the reactive power update value of the line transmission between the cluster and the upstream cluster, x c is the voltage update value of the edge node of the cluster to the downstream cluster, y cl 、z cl is the active power update value and the reactive power update value of the line transmission between the cluster and the downstream cluster, U c , The voltages of the upstream edge node c and the downstream virtual edge node c 1 , respectively, U c,p is the exact voltage of the edge node c, P cl,p and Q cl,p are the active power and reactive power transmitted by the inter-group line cl calculated exactly by the bovine-drawn method, And Representing the virtual active and reactive power transmitted by the downstream virtual line c 1 l, respectively; (3) Based on the received inter-cluster edge data, each cluster updates the lagrangian multiplier of the edge data in situ: Wherein lambda u is the edge node voltage Lagrange multiplier updated value of the cluster for the upstream cluster, lambda p 、λ Q is the active power Lagrange multiplier updated value and the reactive power Lagrange multiplier updated value of the line transmission between the cluster and the upstream cluster, lambda c,u is the edge node voltage Lagrange multiplier updated value of the cluster for the downstream cluster, 、 The method comprises the steps of respectively updating an active power Lagrangian multiplier and a reactive power Lagrangian multiplier of line transmission between a cluster and a downstream cluster, wherein tau represents a penalty coefficient for ensuring data consistency, and n represents iteration times.
9. The method for optimizing voltage of active power distribution network by considering characteristics of distributed photovoltaic clusters according to claim 1, wherein in the step eight, whether the result is converged is determined according to cluster edge node data, and the convergence criterion is that cluster edge data deviation is three times continuously , And All smaller than the threshold sigma d , edge data deviation Is the sum of the absolute value of the edge node voltage deviation and the inter-cluster line power deviation of the nth iteration between cluster K and its neighboring clusters.

Description

Active power distribution network voltage optimization method considering distributed photovoltaic cluster characteristics Technical Field The invention relates to the technical field of power distribution networks, in particular to an active power distribution network voltage optimization method considering the characteristics of a distributed photovoltaic cluster. Background The permeability of renewable energy sources in the power system is continuously improved, and the resources are connected to the tail end of the power distribution network, so that the power distribution network is promoted to be transformed from a traditional single-ended mode to a multi-terminal controllable active power distribution network (Active Distribution Network, ADN). However, the intermittent and fluctuating properties of the distributed resources cause the problems of voltage fluctuation, out-of-limit, three-phase imbalance, increased network loss, power reversal and the like of grid-connected nodes, so that the complexity of system power flow calculation is increased, the stable operation of the power system is threatened, and significant economic loss is possibly caused. Therefore, how to improve the voltage control strategy, and on the premise of ensuring the voltage stability of the system, the method for improving the utilization rate of the distributed power supply and reducing the network loss of the power distribution network becomes an important direction of current research. The traditional power distribution network voltage control architecture mainly comprises three types of local control, centralized control and distributed control. The in-situ control has the advantages of high response speed and low investment cost, but the pressure regulating capability is limited, and the controllable resources of the system can not be fully utilized. The centralized control realizes global optimization by uniformly allocating the voltage-regulating resources, but the problems of large data volume, heavy communication burden and high investment cost are faced. In contrast, the distributed control has good autonomy and adaptability, low investment cost and small communication data volume, can fully exert the autonomy of the distributed photovoltaic, and has stronger robustness. The distributed control divides the power distribution network into a plurality of clusters by dividing the clusters, each cluster performs autonomous control and simultaneously adopts inter-cluster coordination optimization, so that the problem of voltage out-of-limit caused by high-proportion access of the distributed photovoltaic is solved, network loss is reduced, and reactive compensation resources of each cluster can be fully utilized. The current common cluster division algorithm mainly comprises three types, namely (1) a complex network community discovery algorithm which is characterized by abstracting a power distribution network into a complex network and identifying closely connected sub-networks through a modularity maximization principle. (2) And a dynamic partitioning algorithm based on cluster analysis, wherein the algorithm realizes cluster partitioning by mining the electrical association characteristics among nodes. (3) Aiming at the multi-constraint optimization problem in cluster division, the intelligent algorithm shows unique advantages. The clustering algorithms of the power system often form complementary relations in practical application, wherein cluster analysis is suitable for rapid preliminary screening, communities find good structural feature extraction, and intelligent optimization is used for fine decision. In the construction of a cluster division criterion and an index system, the main stream division criterion takes electric coupling as a core, emphasizes strong electric connection (such as voltage sensitivity and power interaction degree) between nodes in a cluster and weak coupling characteristics between the clusters, and simultaneously combines indexes such as source load storage matching degree, power storage degree, communication load and the like to construct a comprehensive index system, but lacks evaluation of voltage regulation potential of a reactive compensation device and energy storage in the cluster. In the conventional distributed control optimization regulation strategy, there are two cases of objective functions in the daily regulation stage. One is to consider a single safety or economic objective function, where the safety objective function is mostly based on voltage offset and the economic objective function is mostly based on net loss. However, taking a single safety objective function may increase reactive power output to increase voltage levels and thus increase net losses, as well as taking a single economic objective function may exacerbate voltage out-of-limit risk to reduce net losses. Secondly, a multi-objective optimization objective function is selected, safety and economical inde