CN114156879A

CN114156879A - Cluster division-based power distribution network optimal scheduling method and system

Info

Publication number: CN114156879A
Application number: CN202111475451.7A
Authority: CN
Inventors: 刘洋; 李立生; 张世栋; 刘合金; 李勇; 苏国强; 于海东; 孙勇; 王峰; 李帅; 张鹏平; 由新红; 黄敏; 李文博; 张林利
Original assignee: State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
Priority date: 2021-12-06
Filing date: 2021-12-06
Publication date: 2022-03-08

Abstract

The invention belongs to the technical field of electricity, and discloses a power distribution network optimal scheduling method and system based on cluster division, wherein the method comprises the following steps: constructing an electrical distance according to the impedance distance and the sensitivity, and carrying out cluster division on an alternating current-direct current power distribution network containing a distributed power supply based on a spectral clustering cluster division algorithm and the electrical distance to obtain a cluster division result; dividing the optimized duration into a first duration scale and a second duration scale based on the cluster division result, wherein the first duration scale is longer than the second duration scale; and in the first time length scale, establishing an optimization model with the lowest cost of the AC/DC power distribution network as a target so as to output a first scheduling plan value through the optimization model, and in the second time length scale, obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization mode. The invention solves the technical problem of poor accuracy of optimal scheduling of the existing AC/DC power distribution network.

Description

Cluster division-based power distribution network optimal scheduling method and system

Technical Field

The invention relates to the technical field of electricity, in particular to a power distribution network optimal scheduling method and system based on cluster division.

Background

Distributed power sources generally refer to power systems that rely on renewable energy sources such as wind energy and solar energy, and clean and efficient renewable energy sources such as natural gas. With the continuous development of distributed energy technology, the utilization of an alternating current-direct current active power distribution network is becoming wide. The alternating current-direct current active power distribution network comprises direct current loads and various distributed energy sources, such as photovoltaic power, wind driven generators and batteries, and the distributed energy sources can be directly connected into the direct current power distribution network, so that network loss is greatly reduced.

Output power fluctuation of a wind driven generator and photovoltaic power generation connected into an alternating current-direct current power distribution network is large, and prediction accuracy is greatly reduced along with the increase of time scale. In order to reduce the impact of distributed generation fluctuations, it is necessary to schedule controllable distributed generation. However, the optimization scheduling accuracy of the alternating current-direct current power distribution network is poor, so that the operation economy of a distributed power system is poor, and the utilization efficiency of the comprehensive power supply is low.

Therefore, providing a power distribution network optimal scheduling method and system based on cluster division to improve the accuracy of optimal scheduling of the ac/dc power distribution network, thereby improving the operation economy of the distributed power system and the utilization efficiency of the integrated power source, which is a problem to be solved by those skilled in the art.

Disclosure of Invention

The embodiment of the invention provides a power distribution network optimal scheduling method and system based on cluster division, and aims to at least partially solve the technical problem that the optimal scheduling accuracy of the existing alternating current/direct current power distribution network is poor. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

According to a first aspect of the embodiments of the present invention, a power distribution network optimal scheduling method based on cluster division is provided, the method includes:

constructing an electrical distance from the impedance distance and the sensitivity;

based on a spectral clustering cluster division algorithm and the electrical distance, carrying out cluster division on an alternating current-direct current power distribution network containing distributed power supplies, and obtaining cluster division results;

dividing the optimized duration into a first duration scale and a second duration scale based on the cluster division result, wherein the first duration scale is longer than the second duration scale;

in the first time length scale, establishing an optimization model with the lowest cost of the AC/DC power distribution network as a target so as to output a first scheduling plan value through the optimization model;

and in the second time length scale, obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization mode.

Further, the electrical distance is constructed using the following formula:

in the formula: i, j belongs to N, and N is the total node number of the distributed power system;

e_Γ(i, j) is the electrical distance from node i to node j;

Z_ij.equis the equivalent impedance distance between node i and node j;

injecting a voltage influence factor matrix of active power to the node j for the node i;

injecting reactive power for node i affects the voltage factor matrix for node j.

Further, the equivalent impedance distance Z between the node i and the node j is calculated using the following formula_ij.equ：

Z_ij.equ＝(Z_ii-Z_ij)-(Z_ij-Z_jj)

In the formula: z_ijIs a system node impedance matrixRow i, column j;

Z_iian ith row and an ith column of elements of the system node impedance matrix;

Z_jjis the jth row and jth column element of the system node impedance matrix.

Further, in the first time length scale, an optimization model with the lowest cost of the alternating current/direct current power distribution network as a target is established, and the optimization model specifically comprises the following steps:

constructing an objective function by taking the minimum total running cost of the AC/DC distribution network as a target;

and optimizing the objective function based on constraint conditions to obtain the optimization model.

Further, the objective function is:

in the formula: t is₁An optimized scheduling period for the first time length scale;

C_grid(t) the electricity purchasing cost from the main network at the moment t;

C_DG(t) is the controllable distributed energy scheduling cost;

C_store(t) scheduling costs for the energy storage device.

Further, the electricity purchasing cost C from the main grid at the time t is calculated by the following formula_grid(t)：

C_grid(t)＝c_grid.tP_grid.t

In the formula: c. C_grid.tPurchasing electricity price from the main grid at the time t;

P_grid.tand injecting the power of the AC/DC distribution network into the main network at the moment t.

Further, the controllable distributed energy scheduling cost C is calculated by using the following formula_DG(t)：

In the formula: n is a radical of_DGThe number of controllable distributed energy sources in the AC/DC distribution network;

a_DG.i、b_DG.i、c_DG.ia controllable distributed energy output cost coefficient;

P_DG.t.iand the active power of the controllable distributed energy source connected with the ith node at the moment t.

Further, the energy storage device scheduling cost C is calculated by using the following formula_store(t)：

In the formula: n is a radical of_storeThe number of energy storage devices in the AC/DC distribution network;

a_store.ischeduling a cost coefficient for the energy storage device;

P_store.t.iand the charging and discharging power of the energy storage device connected with the ith node at the moment t.

Further, the constraints include ac section power balance constraints, and the ac section power balance constraints are calculated using the following formula:

P_ac.j.t＝P_DG.j.t+P_store.j.t+P_v.j.t+P_wt.j.t-P_load.j.t

Q_ac.j.t＝Q_DG.j.t+Q_v.j.t+Q_wt.j.t-Q_load.j.t

Q_DG.j.t＝P_DG.j.ttanθ

in the formula: omega^ijIs a set of branches with an alternating current node j as a tail node;

Ω^jkthe branch is a set of branches taking the alternating current node j as a first node;

P_ac.jk.tthe active power of the alternating current branch jk at the moment t;

Q_ac.jk.tthe reactive power of the alternating current branch jk at the moment t;

P_ac.ij.tthe active power of the alternating current branch ij at the moment t is obtained;

Q_ac.ij.tthe reactive power of the alternating current branch ij at the moment t is obtained;

I_ac.ij.tthe current of the alternating current branch ij at the time t;

R_ac.ij、X_ac.ijimpedance of the ac branch ij;

P_ac.j.tactive power injected into the node of the alternating current node j at the time t;

Q_ac.j.tinjecting reactive power into the node of the alternating current node j at the time t;

U_ac.i.tthe node voltage of the alternating current node i at the time t;

U_ac.j.tthe node voltage of the alternating current node j at the time t;

P_DG.j.tthe active power of the controllable distributed energy connected with the alternating current node j at the moment t;

P_store.j.tthe active power of the energy storage device connected with the alternating current node j at the moment t;

P_v.j.tthe active power of the photovoltaic connected with the alternating current node j at the moment t;

P_wt.j.tthe active power of the wind power connected with the alternating current node j at the moment t;

Q_DG.j.tconnected to AC node j for time tReactive power of the controllable distributed energy source;

Q_v.j.tthe reactive power of the photovoltaic connected with the alternating current node j at the moment t;

Q_wt.j.tthe reactive power of the wind power connected with the alternating current node j at the moment t;

P_load.j.tthe active load of the AC node j at the moment t;

Q_load.j.tis the reactive load of the AC node j at time t;

and theta is the power factor angle of the controllable distributed energy source.

Further, the constraint includes a dc portion power balance constraint, and the dc portion power balance constraint is calculated using the following formula:

P_dc.i.t＝P_DG.i.t+P_store.i.t+P_v.i.t+P_wt.i.t-P_load.i.t

in the formula: omega^ijThe branch is a set of branches taking the direct current node j as a tail node;

Ω^jkthe direct current node j is a set of branches taking the direct current node j as a first node;

P_dc.jk.tthe active power of the direct current branch jk at the moment t;

P_dc.ij.tthe active power of the direct current branch ij at the moment t is obtained;

I_dc.ij.tthe current of the direct current branch ij at the time t is obtained;

R_dc.ijthe resistance of the direct current branch ij;

U_dc.i.tis the node voltage of the direct current node i at the moment t;

U_dc.j.tis the node voltage of the direct current node j at the moment t;

P_dc.i.tinjecting active power into a node of the direct current node i at the moment t;

P_dc.j.tinjecting active power into a node of the direct current node j at the time t;

P_DG.i.tthe active power of the controllable distributed energy connected with the direct current node j at the moment t;

P_store.i.tthe active power of the energy storage device connected with the direct current node j at the moment t;

P_v.i.tthe active power of the photovoltaic connected with the direct current node j at the moment t;

P_wt.i.tthe active power of the wind power connected with the direct current node j at the moment t;

P_load.i.tthe active load of the direct current node j at the moment t.

Further, in the second time scale, obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization manner, specifically including:

establishing a prediction model, solving control variables through rolling optimization of the prediction model, and predicting output values of each controllable distributed energy source and each energy storage device and power injected by a main network in a future period of time;

constructing an objective function;

and solving based on the objective function to obtain a second scheduling plan value within N future moments.

Further, the prediction model is:

in the formula: n represents a prediction step size;

P_DG0(k) measuring an actual value of the controllable distributed energy power at a sampling moment;

P_store0(k) the actual value of the energy storage device power measured at the sampling moment;

P_insert0(k) injecting an actual value of the power measured at the sampling moment for the outside of the cluster;

Δu_DG(k + t | k) predicting the active power increment of the controllable distributed energy in the future time period for the moment k;

Δu_store(k + t | k) predicting the active power output increment of the energy storage device in the future time period for the moment k;

Δu_insert(k + t | k) predicts an increment of cluster external injection power in a future time period for time k;

P_DG(k + i | k) predicting an active output value of the distributed energy at the future k + i moment for the k moment;

P_store(k + i | k) predicting an active output value of the energy storage device at a future k + i moment for a k moment;

P_insert(k + i | k) predicts the cluster external injected power at a future time k + i for time k.

Further, the objective function is:

in the formula: w is a weight coefficient matrix of the distributed energy sources and the energy storage devices;

C_cluster.i(t) is the total scheduling cost of all clusters at time t;

T₁an optimized scheduling period for the first time length scale;

q is a weight coefficient matrix of the main network injection power;

P_insert.prepredicting the power injected outside the cluster at the future k + i moment at the sampling moment;

P_insert.refset of time instants of k + iActual value of power injected outside the cluster;

P_prepredicting the force output values of each controllable distributed energy and the energy storage device at the future k + i moment at the sampling moment;

P_refthe actual values of the output force of each controllable distributed energy source and the energy storage device at the moment k + i.

According to a second aspect of the embodiments of the present invention, a power distribution network optimized scheduling system based on cluster division is provided.

In some embodiments, the system comprises:

an electrical distance construction unit for constructing an electrical distance from the impedance distance and the sensitivity;

the cluster division unit is used for carrying out cluster division on the alternating current-direct current power distribution network containing the distributed power supply based on a spectral clustering cluster division algorithm and the electrical distance and obtaining a cluster division result;

the time length scale dividing unit is used for dividing the optimized time length into a first time length scale and a second time length scale based on the cluster dividing result, wherein the first time length scale is longer than the second time length scale;

the first optimization scheduling unit is used for establishing an optimization model with the lowest cost of the alternating current-direct current power distribution network as a target in the first time length scale so as to output a first scheduling plan value through the optimization model;

and the second optimized scheduling unit is used for obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization mode in the second time length scale.

According to a third aspect of embodiments of the present invention, there is provided a computer apparatus.

In some embodiments, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.

According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium.

In some embodiments, the computer storage medium has embodied therein one or more program instructions for performing the method as described above.

The technical scheme provided by the embodiment of the invention has the following beneficial effects:

according to the power distribution network optimal scheduling method based on cluster division, the electrical distance is constructed according to the impedance distance and the sensitivity, the cluster division is carried out on the alternating current-direct current power distribution network containing the distributed power supply based on the spectral clustering cluster division algorithm and the electrical distance, and the cluster division result is obtained; further dividing the optimized duration into a first duration scale and a second duration scale based on the cluster division result, wherein the first duration scale is longer than the second duration scale; and in the first time scale, establishing an optimization model aiming at the lowest cost of the AC/DC power distribution network so as to output a first scheduling plan value through the optimization model, and in the second time scale, obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization mode. Therefore, the method improves the accuracy of the optimized dispatching of the alternating current and direct current power distribution network based on the spectral clustering division and the model optimization distinguished from the time scale, thereby improving the operation economy of a distributed power system and the utilization efficiency of a comprehensive power supply, and solving the technical problem of poor accuracy of the optimized dispatching of the existing alternating current and direct current power distribution network.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a flowchart of a specific embodiment of a power distribution network optimal scheduling method based on cluster division according to the present invention;

FIG. 2 is a diagram of a model of an exchange station;

FIG. 3 is a diagram of an exemplary system of 50 nodes in one embodiment;

FIG. 4 is a cluster partition diagram of a 50 node computing system in one embodiment;

fig. 5 is a schematic diagram of a first time scale distributed energy active power output planned value in an embodiment;

FIG. 6 is a schematic diagram of the active power output of distributed energy sources over a second timescale in one embodiment;

fig. 7 is a block diagram of a specific embodiment of a power distribution network optimal scheduling system based on cluster partitioning according to the present invention;

FIG. 8 is a block diagram of an embodiment of a computer device.

Detailed Description

The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a structure, device or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on the orientation or positional relationship shown in the drawings, and are used for convenience in describing and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.

Herein, the term "plurality" means two or more, unless otherwise specified.

Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.

Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.

The invention provides an alternating current-direct current power distribution network multi-time scale optimization scheduling method based on cluster division, which can solve the challenge brought by large-scale renewable energy access to alternating current-direct current power distribution network multi-time scale optimization scheduling, and comprises the steps of firstly defining an improved electrical distance by taking an impedance distance and sensitivity as dual standards, comprehensively considering node electrical distance coupling connectivity and voltage sensitivity of a distributed power supply after access respectively, performing cluster division on the alternating current-direct current power distribution network containing the distributed power supply by applying spectral clustering, and then performing multi-time scale optimization scheduling on the basis of cluster division results; then, performing multi-time scale optimization scheduling on the basis of cluster division results, and establishing an optimization model aiming at the lowest cost of the AC/DC power distribution network in a first time scale; and performing rolling optimization on the second time scale to solve the scheduling plan value of the operating unit in each cluster. Compared with the traditional optimization scheduling strategy, the method has the advantages that controllable resources in each level group are respectively optimized in the second time length scale, the search range of optimization calculation can be greatly reduced, the operation difficulty of an optimization scheduling control mode is reduced, the optimization efficiency is improved, and the stable operation of the alternating current-direct current power distribution network is ensured.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for optimal scheduling of a power distribution network based on cluster partitioning according to an embodiment of the present invention.

In a specific embodiment, the power distribution network optimal scheduling method based on cluster division provided by the invention comprises the following steps:

s1: the electrical distance is constructed from the impedance distance and the sensitivity.

Specifically, the electrical distance is constructed using the following formula:

e_Γ(i, j) is the electrical distance from node i to node j;

Z_ij.equis the equivalent impedance distance between node i and node j;

Wherein the equivalent impedance distance Z between the node i and the node j is calculated by the following formula_ij.equ：

Z_ij.equ＝(Z_ii-Z_ij)-(Z_ij-Z_jj) (formula 2)

In the formula: z_ijThe ith row and the jth column of elements of the system node impedance matrix;

Z_jjis the jth row and jth column element of the system node impedance matrix.

S2: and performing cluster division on the AC/DC power distribution network containing the distributed power supply based on a spectral clustering cluster division algorithm and the electrical distance, and obtaining a cluster division result.

In principle, for a cluster division algorithm based on spectral clustering, in recent years, researchers have proposed cluster division based on impedance distance, modularization index and sensitivity matrix for a power system with a distributed power supply. The sensitivity coefficient is considered only, the selected key nodes are concentrated at the tail end of the line or a heavy load area, the principle of reactive power dispersion compensation is not suitable, and the compensation range overlapping is easily caused. The embodiments of the present application combine impedance distance and sensitivity coefficient to define an improved electrical distance. Equivalent impedance Z between nodes i, j of power system_ij.equEqual to the voltage between the nodes i, j after the node i injects into the unit current source

Namely:

the value can be calculated from the node impedance matrix element, and the formula (2) is obtained:

Z_ij.equ＝(Z_ii-Z_ij)-(Z_ij-Z_jj) (formula 2)

In the formula: z_ijIs the ith row and the jth column element of the system node impedance matrix. The equivalent impedance based indicator can electrically identify whether a node is at a critical location in the electrical structure of the system.

After the multiple distributed power supplies are connected, on the basis of considering the impedance distance relationship among the nodes, the influence degree of the distributed power supplies on other nodes when the output power changes needs to be analyzed. According to the influence of the power change on the bus voltage and the phase angle, the voltage sensitivity after the distributed power supply is accessed can be defined:

delta is the voltage phase angle variation, delta V is the voltage amplitude variation, J is the jacobian matrix, delta P is the active power variation, delta Q is the reactive power variation, P is the active power value, delta is the voltage phase angle value, and V is the voltage amplitude.

Voltage active sensitivity matrix of distributed renewable energy system under working condition gamma is constructed by utilizing inverse matrix of Jacobian matrix J

Voltage reactive sensitivity matrix

and

the voltage active and reactive sensitivity coefficients of the node i to the node j under the working condition gamma are respectively; p_iAnd Q_iRespectively injecting active power and reactive power of the node i; v_jThe voltage magnitude at node j.

Assuming that each node has power regulation and the active power and reactive power of the jth node regulate Δ P and Δ Q, respectively, the voltage at node i changes to:

in order to distinguish the voltage sensitivity of different access points to other nodes, a voltage influence factor matrix of the node i to the node j under the working condition gamma is defined

And

in order to comprehensively consider the node distance coupling connectivity and the voltage sensitivity of the distributed power supply after the distributed power supply is respectively connected in the power grid topological structure, the embodiment of the application combines the traditional equivalent impedance and the voltage sensitivity to construct an improved electrical distance, and the formula (1) is obtained.

The cluster division based on a cluster analysis method is a common partitioning strategy, and a spectral clustering algorithm and a K-means clustering algorithm are applied to the problem of cluster division of the power system. The spectral clustering algorithm derives characteristic values and characteristic vectors representing the properties of the clustering objects through a matrix spectral analysis theory, and then clusters the original data by using new data characteristics. The normalized feature vector space is constructed in the algorithm, so that the similarity relation between data space sample groups is more visual while an original data space structure is kept. Compared with other clustering algorithms, the spectral clustering algorithm is not easy to fall into a local optimal solution and has the capability of identifying clusters with non-convex distribution.

And expressing the cluster division problem of the power system with the distributed power supply as a cluster integration problem. Data set V ═ V₁,V₁,…,V_NAnd E is the set of all edges in the data set, and then the power system forms an undirected graph G (V, E) according to graph theory. Using improved electrical distance e (i, j) determinationThe symmetric weight matrix W is defined as follows:

e_ijand e (i, j) is the improved electrical distance from the node i to the node j, and e (j, i) is the improved electrical distance from the node j to the node i.

An N × N type matrix D is constructed as a degree matrix, as follows:

and n is the number of nodes of the power system.

The normalized laplacian matrix L is calculated as:

and (5) decomposing the characteristic value of the L to obtain the characteristic value and the characteristic vector. Sorting the eigenvalues from small to large, taking the top k_iEach eigenvalue and corresponding eigenvector are used to form N × k_iDimensional feature matrix

The nodes of the original power system are mapped into each line of spectrum data points of F, and the introduced Laplace transform effectively realizes data dimension reduction. Is provided with

For the ith column vector of F, the K-means algorithm is used to change L to (L)_i)_i×NGrouped into n sub-communities (clusters) { C₁,C₂,…,C_n}. Subgroup k, subgroup C_kIncluding the number of nodes N_k(ii) a Wherein the key node is selected as the clustering centroid mu_kIs marked as

The remaining N_k-1 node

Is a common node. To achieve the best voltage regulation effect and reduce installation cost, the controllable PV node is addressed to the key node. The node with large improved electrical distance defined by the formula (7) has large impedance distance and high voltage sensitivity, and the key node can be effectively selected by taking the parameter as a measurement parameter of a clustering algorithm.

S3: and dividing the optimized duration into a first duration scale and a second duration scale based on the cluster division result, wherein the first duration scale is longer than the second duration scale.

S4: and in the first time length scale, establishing an optimization model with the lowest cost of the AC/DC power distribution network as a target so as to output a first scheduling plan value through the optimization model.

Step S4 specifically includes:

Wherein the objective function is:

C_DG(t) is the controllable distributed energy scheduling cost;

C_store(t) scheduling costs for the energy storage device.

Wherein, the electricity purchasing cost C from the main network at the time t is calculated by the following formula_grid(t)：

C_grid(t)＝c_grid.tP_grid.t(formula 12)

In the formula:c_grid.tpurchasing electricity price from the main grid at the time t;

Calculating controllable distributed energy scheduling cost C by using the following formula_DG(t)：

Calculating the energy storage device scheduling cost C by using the following formula_store(t)：

a_store.ischeduling a cost coefficient for the energy storage device;

P_ac.j.t＝P_DG.j.t+P_store.j.t+P_v.j.t+P_wt.j.t-P_load.j.t(formula 19)

Q_ac.j.t＝Q_DG.j.t+Q_v.j.t+Q_wt.j.t-Q_load.j.t(formula 20)

Q_DG.j.t＝P_DG.j.ttan theta (equation 21)

P_ac.jk.tthe active power of the alternating current branch jk at the moment t;

I_ac.ij.tthe current of the alternating current branch ij at the time t;

R_ac.ij、X_ac.ijimpedance of the ac branch ij;

U_ac.i.tthe node voltage of the alternating current node i at the time t;

U_ac.j.tnode voltage of AC node j at time t；

Q_DG.j.tthe reactive power of the controllable distributed energy connected with the alternating current node j at the moment t;

P_load.j.tthe active load of the AC node j at the moment t;

Q_load.j.tis the reactive load of the AC node j at time t;

P_dc.i.t＝P_DG.i.t+P_store.i.t+P_v.i.t+P_wt.i.t-P_load.i.t(formula)25)

P_dc.jk.tthe active power of the direct current branch jk at the moment t;

I_dc.ij.tthe current of the direct current branch ij at the time t is obtained;

R_dc.ijthe resistance of the direct current branch ij;

U_dc.i.tis the node voltage of the direct current node i at the moment t;

U_dc.j.tis the node voltage of the direct current node j at the moment t;

P_load.i.tthe active load of the direct current node j at the moment t.

In addition, the invention also comprises the step of constructing the VSC converter station model. The alternating current part and the direct current part are connected through the VSC, when an alternating current-direct current power distribution network model is constructed, the VSC connecting the alternating current node and the tributary node is regarded as a virtual node, as shown in figure 2, P_ac.ij.t、Q_ac.ij.t、P_dc.jk.tThe power source is respectively alternating current active power, alternating current reactive power and direct current power; q_vsc.j.tReactive power output is achieved for VSC; u shape_ac.i.tIs a.c.A side voltage amplitude; u shape_dc.k.tIs the DC voltage amplitude; u shape_vsc.j.tIs the VSC virtual node voltage magnitude.

From the equivalent circuit of fig. 2, we can see:

-Q_vsc.j.max≤Q_vsc.j.t≤Q_vsc.j.max(formula 28)

In the formula: q_vsc.j.max、-Q_vsc.j.maxRespectively are the upper limit and the lower limit of VSC reactive power output; r_vsc.ijIs a branch resistance, X, between an AC node i and a VSC virtual node j_vsc.ijIs the reactance of the branch between the alternating current node i and the VSC virtual node j.

Further, the constraint condition includes an ESS operation constraint, which is obtained by using the following formula:

S_ESS.i.t＝S_ESS.i.t-1(1-δ)-P_store.i.tΔ t (equation 29)

P_store.i.min≤P_store.i.t≤P_store.i.max(equation 30)

S_ESS.i.min≤S_ESS.i.t≤S_ESS.i.max(formula 31)

In the formula: s_ESS.i.tThe electric quantity of the ESS connected with the ith node at the time t; p_store.i.tThe charging and discharging power of the ESS connected with the ith node at the time t; p_store.i.max、P_store.i.minAn ESS output upper and lower limit connected with the ith node; s_ESS.i.max、S_ESS.i.minThe upper and lower capacity limits of the ESS connected with the ith node; delta is the self-discharge rate of the energy storage device.

Further, the constraint condition includes a safe operation constraint, and is obtained by using the following formula:

U_ac.j.min≤U_ac.j.t≤U_ac.j.max(formula 32)

U_dc.j.min≤U_dc.j.t≤U_dc.j.max(formula 33)

P_DG.i.min≤P_DG.i.t≤P_DG.i.max(formula 34)

In the formula: u shape_dc.j.max、U_dc.j.minThe voltage amplitude value of the direct current node j is an upper limit and a lower limit; u shape_ac.j.max、U_ac.j.minThe voltage amplitude upper and lower limits of the AC node j are set; p_DG.i.max、P_DG.i.minThe active output upper and lower limits of the distributed energy connected with the ith node.

S5: and in the second time length scale, obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization mode.

And in the second time length scale, taking the nodes in each cluster as basic units, respectively optimizing the interior of each cluster, for example, sampling every 5min, establishing an output prediction model by taking the actual output values of each controllable distributed energy source and energy storage device in the current optimization level cluster as initial states, performing rolling optimization on a control instruction sequence of 15min in the future according to a scheduling plan value, issuing a control instruction of a first time period, correcting a control variable, and performing a new round of optimization by taking the actually measured value as an initial value of the rolling optimization at the next sampling moment.

Step S5 specifically includes:

and establishing a prediction model, solving the control variable through rolling optimization of the prediction model, and predicting the output value of each controllable distributed energy source and each controllable distributed energy storage device and the power injected by the main network in a period of time in the future.

Specifically, the control variables are solved through rolling optimization of a prediction model, the output value of each controllable distributed energy source and the energy storage device and the power injected by the main network in a future period are predicted, and the prediction is as follows:

in the formula: n represents a prediction step size;

Constructing an objective function, wherein the second time length scale optimization objective function takes the scheduling plan value of the first time length scale as a reference value, and the error between the control instruction sequence issued by the second time length scale and the scheduling plan value of the first time length scale is made as small as possible, and accordingly, the objective function is as follows:

C_cluster.i(t) is the total scheduling cost of all clusters at time t;

T₁an optimized scheduling period for the first time length scale;

q is a weight coefficient matrix of the main network injection power;

P_insert.refthe actual value of the power injected outside the cluster at the moment k + i;

P_insert.refActual value of power injected outside the cluster for time k + i, P_preIn order to predict the output of each controllable distributed energy and energy storage device at the future k + i moment at the sampling moment, the method specifically comprises the following steps:

in the formula: p^T(k + i | k) represents the value of the distributed energy source and energy storage device's successful power at the predicted future time k + i at time k, and can be expressed as:

in the formula: p_insert(k + i | k) represents the power injected outside the cluster at the predicted future time k + i at time k.

P_refFor the actual value of the output of the respective controllable distributed energy source and energy storage device at the time k + i, P_insert.refThe actual value of the power injected outside the cluster at the time k + i is specifically:

in the formula: p_ref(k + i | k) represents the actual value of the respective controllable distributed energy source and energy storage device output at the moment k + i, P_insert.ref(k + i | k) represents the actual value of the cluster external injected power at time k + i.

And according to the second time scale optimization model, the control increment sequence of each controllable distributed energy source and each controllable distributed energy storage device in the next N moments can be obtained:

{Δu^T(k+1|k)Δu^T(k+2|k)…Δu^T(k + N | k) } (equation 40)

In the formula: Δ u (k + i | k) represents the control increment of the respective controllable distributed energy source and energy storage at the predicted future time k + i at time k.

Issuing a first instruction in the obtained control increment sequence, and solving the active output of the controllable distributed energy sources and the energy storage devices of the alternating current and direct current distribution network at the next moment:

P(k+1|k)＝P₀(k)+Δu^T(k +1| k) (equation 41)

In the formula: p (k +1| k) represents the active power output of the controllable distributed energy sources and the energy storage devices of the AC/DC distribution network at the future k + i moment predicted at the k moment, and P₀(k) And the actual active output of the controllable distributed energy sources and the energy storage devices of the AC/DC distribution network at the moment k is shown.

Further, in order to improve the model accuracy, the invention can also comprise a feedback correction step. Certain errors exist between the control instruction values of the controllable distributed energy sources and the energy storage devices issued by the model prediction control under the existing prediction precision and the actually measured values, and feedback correction needs to be carried out after the control instructions are issued. Before next rolling optimization, the current actually measured value is used as the initial value of the new rolling optimization, so that the interference caused by the uncertainty of wind power and photovoltaic power can be avoided to the greatest extent, and the prediction precision of the MPC is higher. The feedback formula is as follows:

P₀(k+1)＝P_real(k +1) + δ (formula 42)

In the formula: p₀(k +1) represents an active initial value at the k +1 moment; p_real(k +1) represents an active output value at the next moment actually measured at the moment k; δ is the measurement error.

The technical effect of the method provided by the invention is verified in a simulation comparison mode by using a specific embodiment.

Based on the method and the system provided by the invention, programming is carried out on MATLAB 2016a, modeling and solving are carried out by utilizing a yalmould toolkit and a cplex solver, and the hardware environment is an Intel Core (TM) i5-3340S CPU @ 2.8GHz processor and an 8GB memory.

Simulation is carried out on an arithmetic example of a 50-node alternating current-direct current power distribution network, the topological structure of the arithmetic example is shown in FIG. 3, and WT represents a wind turbine generator; PV represents a photovoltaic; the distributed power supply represents a micro gas turbine set; ESS denotes an energy storage device. A micro gas turbine with the output active upper limit of 300kW is connected into the

nodes

10, 18, 37 and 46, and the power factor angle is 0.9; energy storage devices with the upper limit of energy storage of 1800kWh and the upper limit of charging and discharging power of 300kW are connected into the

nodes

36 and 49; energy storage devices with the upper limit of energy storage of 1400kWh and the upper limit of charging and discharging power of 240kWh are connected into the

nodes

41 and 45. Based on the cluster division method provided by the present disclosure, a 50-node ac/dc distribution network example is divided into 4 sub-clusters, and the division result is shown in fig. 4.

Fig. 5 shows the planned value of the active power output of the distributed energy sources on the first time scale of 10:00 to 12: 00. Fig. 6 shows the second time scale distributed energy active power output plan value of the distributed power supply during the period from 10:00 to 11: 00. As can be seen from the figure, the optimal value of the first time scale schedule is relatively smooth due to the longer interval time, while the optimal value of the second time scale schedule is relatively smooth due to the further subdivision of the time scale.

The results of three different optimization methods were compared, as shown in table 1, with clusters representing the optimization model shown herein and non-clusters representing the traditional optimization model without cluster partitions. It can be seen that the optimization method based on cluster partitioning is the same as the calculation result of the traditional optimization model, but the calculation time of the proposed model is faster than that of the traditional cluster partitioning model, and the calculation efficiency of the optimization scheduling can be effectively improved.

TABLE 1 results of different optimization models

Therefore, the method provided by the invention utilizes the new concept of improving the electrical distance as the measurement standard of the clustering algorithm, and then utilizes the frequency spectrum clustering method to divide the AC/DC distribution network into a plurality of clusters, which is the basis of multi-period scale optimization. Finally, a multi-time scale optimization is performed, and the scheduling result of the long-term scale optimization is calculated by minimizing the total operation cost. In the short-time scale optimization, the output power of the distributed generation of each node in each cluster is calculated by adopting a rolling optimization method according to the result of the long-time scale optimization. The simulation performed on 50 alternating current and direct current power distribution systems shows that the provided optimization model based on cluster division has faster calculation time than the traditional model, and can effectively improve the optimization calculation efficiency.

In some embodiments, as shown in fig. 7, the system comprises:

an electrical distance construction unit 100 for constructing an electrical distance from the impedance distance and the sensitivity;

the cluster division unit 200 is used for carrying out cluster division on the alternating current-direct current power distribution network containing the distributed power supply based on a spectral clustering cluster division algorithm and the electrical distance, and obtaining a cluster division result;

a duration scale dividing unit 300, configured to divide the optimized duration into a first duration scale and a second duration scale based on the cluster division result, where the first duration scale is longer than the second duration scale;

a first optimization scheduling unit 400, configured to establish, in the first time length scale, an optimization model with the lowest cost of the ac/dc distribution network as a target, so as to output a first scheduling plan value through the optimization model;

and a second optimized scheduling unit 500, configured to obtain a second scheduling plan value of each cluster internal operation unit in a rolling optimization manner in the second time scale.

The working principle of the power distribution network optimal scheduling system based on cluster division provided by the invention is the same as that of the power distribution network optimal scheduling method based on cluster division in the embodiments, and the description is omitted here.

According to the power distribution network optimal scheduling system based on cluster division, the electrical distance is constructed according to the impedance distance and the sensitivity, the cluster division is carried out on the alternating current-direct current power distribution network containing the distributed power supply based on the spectral clustering cluster division algorithm and the electrical distance, and the cluster division result is obtained; further dividing the optimized duration into a first duration scale and a second duration scale based on the cluster division result, wherein the first duration scale is longer than the second duration scale; and in the first time scale, establishing an optimization model aiming at the lowest cost of the AC/DC power distribution network so as to output a first scheduling plan value through the optimization model, and in the second time scale, obtaining a second scheduling plan value of each cluster internal operation unit in a rolling optimization mode. Therefore, the method improves the accuracy of the optimized dispatching of the alternating current and direct current power distribution network based on the spectral clustering division and the model optimization distinguished from the time scale, thereby improving the operation economy of a distributed power system and the utilization efficiency of a comprehensive power supply, and solving the technical problem of poor accuracy of the optimized dispatching of the existing alternating current and direct current power distribution network.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a model prediction. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The model prediction of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program is executed by a processor to carry out the steps in the above-described method embodiments.

Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the inventive arrangements and is not intended to limit the computing devices to which the inventive arrangements may be applied, as a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, model prediction, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims

1. A power distribution network optimal scheduling method based on cluster division is characterized by comprising the following steps:

2. The optimal scheduling method for power distribution network according to claim 1, wherein the electrical distance is constructed by using the following formula:

e_Γ(i, j) is a sectionThe electrical distance from point i to node j;

Z_ij.equis the equivalent impedance distance between node i and node j;

3. The optimal scheduling method for power distribution network according to claim 2, wherein the equivalent impedance distance Z between the node i and the node j is calculated by using the following formula_ij.equ：

Z_ij.equ＝(Z_ii-Z_ij)-(Z_ij-Z_jj)

Z_jjis the jth row and jth column element of the system node impedance matrix.

4. The optimal scheduling method for the power distribution network according to claim 1, wherein in the first time scale, establishing an optimization model aiming at the lowest cost of the ac/dc power distribution network specifically comprises:

5. The optimal scheduling method for the power distribution network according to claim 4, wherein the objective function is:

C_DG(t) is the controllable distributed energy scheduling cost;

C_store(t) scheduling costs for the energy storage device.

6. The optimal scheduling method for power distribution network according to claim 5, wherein the following formula is used to calculate the electricity purchasing cost C from the main network at the time t_grid(t)：

C_grid(t)＝c_grid.tP_grid.t

7. The optimal scheduling method for power distribution network according to claim 5, wherein the controllable distributed energy scheduling cost C is calculated by using the following formula_DG(t)：

8. The optimal dispatcher of the power distribution network of claim 5The method is characterized in that the energy storage device dispatching cost C is calculated by using the following formula_store(t)：

a_store.ischeduling a cost coefficient for the energy storage device;

9. The optimal scheduling method for power distribution network according to claim 4, wherein the constraint condition comprises an AC partial power balance constraint condition, and the AC partial power balance constraint condition is calculated by using the following formula:

P_ac.j.t＝P_DG.j.t+P_store.j.t+P_v.j.t+P_wt.j.t-P_load.j.t

Q_ac.j.t＝Q_DG.j.t+Q_v.j.t+Q_wt.j.t-Q_load.j.t

Q_DG.j.t＝P_DG.j.ttanθ

P_ac.jk.tthe active power of the alternating current branch jk at the moment t;

I_ac.ij.tthe current of the alternating current branch ij at the time t;

R_ac.ij、X_ac.ijimpedance of the ac branch ij;

U_ac.i.tthe node voltage of the alternating current node i at the time t;

U_ac.j.tthe node voltage of the alternating current node j at the time t;

Q_wt.j.tis at t timeCarving the reactive power of wind power connected with the alternating current node j;

P_load.j.tthe active load of the AC node j at the moment t;

Q_load.j.tis the reactive load of the AC node j at time t;

10. The optimal scheduling method for the power distribution network according to claim 4, wherein the constraint condition comprises a DC partial power balance constraint condition, and the DC partial power balance constraint condition is calculated by using the following formula:

P_dc.i.t＝P_DG.i.t+P_store.i.t+P_v.i.t+P_wt.i.t-P_load.i.t

P_dc.jk.tthe active power of the direct current branch jk at the moment t;

I_dc.ij.tthe current of the direct current branch ij at the time t is obtained;

R_dc.ijthe resistance of the direct current branch ij;

U_dc.i.tis a direct current node at the time of tA node voltage at point i;

U_dc.j.tis the node voltage of the direct current node j at the moment t;

P_load.i.tthe active load of the direct current node j at the moment t.

11. The optimal scheduling method for the power distribution network according to claim 1, wherein in the second time scale, a second scheduling plan value of each cluster internal operation unit is obtained in a rolling optimization manner, and specifically includes:

constructing an objective function;

12. The optimal scheduling method for the power distribution network according to claim 11, wherein the prediction model is:

in the formula: n represents a prediction step size;

13. The optimal scheduling method for power distribution network according to claim 11, wherein the objective function is:

C_cluster.i(t) is the total scheduling cost of all clusters at time t;

T₁an optimized scheduling period for the first time length scale;

q is a weight coefficient matrix of the main network injection power;

14. A power distribution network optimal scheduling system based on cluster division is characterized by comprising:

15. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any of claims 1-13 when executing the computer program.

16. A computer-readable storage medium having one or more program instructions embodied therein for performing the method of any of claims 1-13.