CN115473233A - Voltage reactive power autonomous cooperative control method and device and computer equipment - Google Patents
Voltage reactive power autonomous cooperative control method and device and computer equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
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Abstract
The application relates to a voltage reactive power autonomous cooperative control method, a voltage reactive power autonomous cooperative control device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a reactive/voltage sensitivity matrix of a multi-node network, and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix; establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources; based on the reactive power source control space, nodes in the multi-node network, which respond to the reactive power source change trend and have similarity within a preset similarity interval, are classified into a cluster; performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster; and determining an additional reactive power regulating quantity based on the voltage deviation value. By adopting the method, the multi-node network is subjected to partition automatic control, and the access of novel equipment such as a distributed power supply and distributed energy storage can be supported.
Description
Technical Field
The present application relates to the field of electrical engineering technologies, and in particular, to a voltage reactive power autonomous cooperative control method, apparatus, computer device, storage medium, and computer program product.
Background
Energy shortages have become a century-like problem in modern civilization society, against which the use of Distributed Generation (DG) and renewable energy has been more and more widely developed.
In the traditional technology, distributed control is used as a system reactive power optimization method, system reactive power balance can be well completed under limited data exchange, and the problems that pure local control is lack of coordination, pure centralized control has high technical requirements on communication and measurement systems and the like are effectively avoided.
However, in the face of the increasing permeability of the distributed power supply, the traditional distributed control fails to realize the coordinated control of the novel equipment and the traditional compensation device, and fails to promote the system to absorb various novel DGs while improving the power supply quality.
Disclosure of Invention
In view of the above, it is necessary to provide a voltage reactive power autonomous cooperative control method, apparatus, computer device, computer readable storage medium and computer program product for solving the above technical problems.
In a first aspect, the application provides a voltage reactive power autonomous cooperative control method. The method comprises the following steps:
acquiring a reactive/voltage sensitivity matrix of a multi-node network, and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix;
establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources;
based on the reactive power source control space, nodes in the multi-node network, which have response trend similarity to the reactive power source change within a preset similarity interval, are classified into a cluster;
performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster;
and determining an additional reactive power regulating quantity based on the voltage deviation value.
In one embodiment, obtaining a reactive/voltage sensitivity matrix for a multi-node network comprises:
and aiming at the multi-node network, performing Newton-Raphson load flow calculation on the multi-node network under a polar coordinate system to obtain a reactive/voltage sensitivity matrix corresponding to the multi-node network.
In one embodiment, determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix comprises:
and acquiring a node corresponding to the reactive/voltage sensitivity matrix with the absolute value larger than a preset threshold value as a candidate node based on the reactive/voltage sensitivity matrix.
In one embodiment, establishing a reactive source control space based on the candidate node and the plurality of reactive sources comprises:
acquiring the number of reactive power sources in a multi-node network;
establishing a multi-dimensional reactive power source control space based on a plurality of reactive power sources in a multi-node network; the dimensionality of the reactive power source control space is the same as the number of the reactive power sources.
In one embodiment, establishing a multi-dimensional reactive source control space based on a plurality of reactive sources in a multi-node network comprises:
aiming at each reactive power source, taking the reactive power source as a coordinate axis; wherein, different reactive power sources correspond to different coordinate axes;
and establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
In one embodiment, the multi-objective dynamic reactive power optimization of the plurality of nodes in each cluster is implemented by an optimization model trained in advance, and the training mode of the optimization model includes:
acquiring a first training node set, wherein the first training node set comprises a plurality of training nodes;
performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjusting cost as optimization indexes to obtain a second training node set; the minimum network loss is the minimum power loss of the multi-node network in actual operation;
performing second optimization on each training node in the second training node set based on the entropy weight and the multi-level fuzzy comprehensive evaluation index to obtain a third training node set;
inputting the third training node sets into a pre-constructed initial optimization model, and performing multi-target dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value;
determining a model loss of the initial optimization model based on the voltage reference value and the first training node set;
and carrying out iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
In a second aspect, the application further provides a voltage reactive power autonomous cooperative control device. The device comprises:
the candidate node determining module is used for acquiring a reactive/voltage sensitivity matrix of a multi-node network and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix;
the space establishing module is used for establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources;
the partition module is used for grouping nodes with response trend similarity to the reactive power source change in the multi-node network within a preset similarity interval into a cluster based on the reactive power source control space;
the optimization module is used for performing multi-target dynamic reactive power optimization on the plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster;
and the extra reactive power regulating quantity determining module is used for determining the extra reactive power regulating quantity based on the voltage deviation value.
In a third aspect, the application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method according to any of the embodiments described above when executing the computer program.
In a fourth aspect, the present application further provides a computer device readable storage medium. The computer device readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program which, when being executed by a processor, carries out the steps of the method according to any of the embodiments described above.
According to the voltage reactive power autonomous cooperative control method, the voltage reactive power autonomous cooperative control device, the computer equipment, the storage medium and the computer program product, a reactive power/voltage sensitivity matrix of a multi-node network is obtained first, and a plurality of candidate nodes in the multi-node network are determined based on the reactive power/voltage sensitivity matrix; then, establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources; and then, based on the reactive power source control space, nodes in the multi-node network, which have the similarity of the reactive power source change response trend within a preset similarity interval, are classified into a cluster. Further, performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; and obtaining a voltage deviation value based on the voltage reference value and the actual voltage values of the plurality of nodes in each cluster. Finally, an additional reactive power adjustment amount is determined based on the voltage deviation value. According to the method, the candidate nodes are partitioned, and then the voltage in each partition is guided based on the result of multi-objective dynamic reactive power optimization, so that the access of novel equipment such as a distributed power supply and distributed energy storage can be supported.
Drawings
Fig. 1 is an application environment diagram of the voltage reactive power autonomous cooperative control method in an embodiment;
fig. 2 is a schematic flow chart of the voltage reactive power autonomous cooperative control method in one embodiment;
fig. 3 is a schematic flow chart of a voltage reactive power autonomous cooperative control method under a three-phase imbalance condition in an embodiment;
FIG. 4 is a block diagram of an embodiment of a voltage reactive autonomous cooperative control apparatus;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The voltage reactive power autonomous cooperative control method provided by the embodiment of the application can be applied to an application environment as shown in fig. 1. Wherein the power distribution network 102 communicates with the server 104 over a network. The data storage system may store data that the receiver 104 needs to process. The data storage system may be placed on the cloud or other network server. The server 104 may provide an environment for voltage non-autonomous coordinated control of the power distribution grid 102. First, the server 104 obtains a reactive/voltage sensitivity matrix of the power distribution network 102, and determines a plurality of candidate nodes in the power distribution network 102 based on the reactive/voltage sensitivity matrix. The server 104 may then establish a reactive source control space based on the candidate nodes and the plurality of reactive sources; and then, based on the reactive power source control space, nodes in the power distribution network 102, which have the similarity of the reactive power source change response trend within a preset similarity interval, are classified into a cluster. Further, the server 104 may perform multi-objective dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; and obtaining a voltage deviation value based on the voltage reference value and the actual voltage values of the plurality of nodes in each cluster. Finally, the server 104 may determine an additional amount of reactive power regulation based on the voltage deviation value. Wherein the power distribution network 102 may be a multi-node network; the server 104 may be implemented as a stand-alone server or a server cluster comprised of multiple servers.
The voltage reactive power autonomous cooperative control method provided by the embodiment of the application can be applied to a system comprising a power distribution network and a server, and is realized through interaction of the power distribution network and the server.
In one embodiment, as shown in fig. 2, a voltage reactive power autonomous cooperative control method is provided, which is described by taking a system implementation of the method applied to a power distribution network and a server as an example, and includes the following steps 202 to 210.
In this embodiment, in the actual operation of the power distribution network, the system voltage is often unstable due to the fluctuation of the load and the DG output, the power-voltage change relationship of each node in the network is visually represented in the jacobian matrix, and the voltage sensitivity of each node can be obtained by inverting the jacobian matrix.
In this embodiment, the server may extract the reactive/voltage sensitivity matrix S according to the relationship between the voltage amplitude and the reactive strong coupling and the relationship between the voltage amplitude and the active weak coupling in the multi-node network under the medium-high voltage condition.
In this embodiment, the candidate node is a heavy-load node with a high reactive demand.
In the embodiment, the server performs local compensation of the reactive power through the candidate nodes based on the reasonability of the distribution of the node areas and the characteristic that the reactive power is not suitable for long-distance transmission.
And step 204, establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources.
In this embodiment, the server may establish the reactive power source control space according to a relationship between the control capability of the reactive power source such as the generator and the reactive power compensation device and the controlled object such as the candidate node voltage.
In this embodiment, the reactive/voltage relationship of each candidate node may be represented in the reactive source control space by vector coordinates.
And step 206, classifying the nodes with the reactive power source change response trend similarity within a preset similarity interval in the multi-node network into a cluster based on the reactive power source control space.
In the embodiment, in the multi-node network, the similarity of the response trend of the plurality of nodes to the change of the reactive power source is determined by the electrical distance between every two nodes in the plurality of nodes. The electrical distance between the two nodes is obtained by calculating the vector coordinates of the two nodes in the reactive power source control space. And partitioning (clustering) is carried out according to the distance characteristic among the nodes, so that the voltage self-discipline control on the nodes in each partition is facilitated subsequently.
208, performing multi-target dynamic reactive power optimization on the plurality of nodes in each cluster to obtain a voltage reference value; and obtaining a voltage deviation value based on the voltage reference value and the actual voltage values of the plurality of nodes in each cluster.
In this embodiment, the server may perform optimization calculation on a plurality of nodes in each cluster through a fast particle swarm algorithm, and calculate to obtain an optimal operating state and voltage reference values of the nodes in each hour.
In this embodiment, the server may determine the voltage value U of the candidate node in each cluster according to the voltage value U i ( t ) And a voltage reference value
Calculating the system voltage deviation value delta U at the moment i (t); wherein the content of the first and second substances,
and 210, determining an additional reactive power regulating quantity based on the voltage deviation value.
In this embodiment, the additional reactive power adjustment Δ Q i (t) is calculated from equation (1):
wherein, α is a constant greater than zero, and the value of α needs to avoid excessive overshoot after the local controllers operate simultaneously, thereby ensuring the stability of the multi-node network. Preferably, alpha is equal to the reciprocal of the number of nodes in the multi-node network, so that the voltage quality of the multi-node network can be ensured to be stable in a reasonable range.
In the voltage reactive power autonomous cooperative control method, a reactive power/voltage sensitivity matrix of a multi-node network is obtained first, and a plurality of candidate nodes in the multi-node network are determined based on the reactive power/voltage sensitivity matrix; then, establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources; and then, based on the reactive power source control space, nodes in the multi-node network, which have response trend similarity to the reactive power source change within a preset similarity interval, are classified into a cluster. Further, performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; and obtaining a voltage deviation value based on the voltage reference value and the actual voltage values of the plurality of nodes in each cluster. Finally, an additional reactive power adjustment amount is determined based on the voltage deviation value. According to the method, the candidate nodes are partitioned, and then the voltage in each partition is guided based on the result of multi-objective dynamic reactive power optimization, so that the access of novel equipment such as a distributed power supply and distributed energy storage can be supported.
In some embodiments, obtaining a reactive/voltage sensitivity matrix for a multi-node network comprises: aiming at the multi-node network, newton-Raphson load flow calculation is carried out on the multi-node network under a polar coordinate system, and a reactive/voltage sensitivity matrix corresponding to the multi-node network is obtained.
In this embodiment, for an n-node network, nodes 1 to m are assumed to be the active power and reactive power (PQ node) drawn or emitted by the power supply point, m +1 to n-1 are the voltage amplitude and active power (PV node) of a given voltage control point, and the nth node is a balanced node. Under polar coordinates, an equation for performing newton-raphson power flow calculation on the n-node network is shown as formula (2):
wherein: delta P and delta Q are respectively micro-increment column vectors of active power and reactive power injected into the nodes; delta theta and delta U are respectively a node voltage phase angle and an amplitude change column vector; h is
Sub-arrays; n is
Sub-arrays; j is
Sub-arrays; l is
And (5) sub-array.
In this embodiment, the active injection is maintained unchanged, i.e. Δ P =0, and the reactive micro-increment column vector is shown in formula (3):
ΔQ＝(JH -1 N-L)ΔU (3)
further, let (JH) -1 N-L) -1 = S, the amplitude variation column vector is as shown in equation (4):
ΔU＝SΔQ (4)
in some embodiments, determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix comprises: and acquiring nodes corresponding to the reactive/voltage sensitivity matrix with the absolute value larger than a preset threshold value as candidate nodes based on the reactive/voltage sensitivity matrix.
In this embodiment, the server may obtain an absolute value of the matrix S corresponding to each node according to the matrix S corresponding to each node in the multi-node network.
In the embodiment, the server combines the rationality of node area distribution, fully considers the characteristic that the reactive power is not suitable for long-distance transmission, and exerts the local compensation capability thereof, and finally determines candidate nodes, namely weak nodes which need to be accessed by each reactive power source most. Wherein, weak node means: the method has the problems of heavy load and the like, and is suitable for nodes with high reactive power demand.
In some embodiments, establishing a reactive source control space based on the candidate node and the plurality of reactive sources comprises: acquiring the number of reactive power sources in a multi-node network; establishing a multi-dimensional reactive power source control space based on a plurality of reactive power sources in a multi-node network; the dimensionality of the reactive power source control space is the same as the number of the reactive power sources.
In this embodiment, when there are g reactive power sources in the multi-node network, a g-dimensional reactive power source control space is correspondingly established.
In the embodiment, each candidate node corresponds to one vector coordinate in the multidimensional reactive power source control space. For example, in a g-dimensional reactive power source control space, each candidate node can pass through a g-dimensional vector (x) i1 ,x i2 ,…,x ig ) Described as coordinates, the calculation of each component in the vector is as in equation (5) so:
x ij ＝-lg|S ij | (5)
wherein S is ij And controlling the sensitivity of the reactive power injected into the reactive power source node j to the voltage of the candidate node i.
In the present embodiment, the euclidean electrical distance D between any two candidate nodes v, w vw As shown in equation (6):
wherein x is vg The coordinate component of the node v under the g-th reactive source node is obtained; x is the number of wg Is the coordinate component of the node w under the g-th reactive source node.
In some embodiments, establishing a multi-dimensional reactive source control space based on a plurality of reactive sources in a multi-node network includes: aiming at each reactive power source, taking the reactive power source as a coordinate axis; wherein, different reactive sources correspond to different coordinate axes; and establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
In some embodiments, the multi-objective dynamic reactive power optimization of the plurality of nodes in each cluster is implemented by using an optimization model trained in advance, and the training mode of the optimization model includes: acquiring a first training node set, wherein the first training node set comprises a plurality of training nodes; performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjusting cost as optimization indexes to obtain a second training node set; the minimum network loss is the minimum power loss of the multi-node network in actual operation; performing second optimization on each training node in the second training node set based on the entropy weight and the multi-level fuzzy comprehensive evaluation index to obtain a third training node set; inputting the third training node sets into a pre-constructed initial optimization model, and performing multi-target dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value; determining a model loss of the initial optimization model based on the voltage reference value and the first training node set; and performing iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
In this embodiment, the optimal device adjustment cost represents: and (4) the lowest reactive compensation equipment adjusting cost.
In this embodiment, the calculation of the first optimization performed on each training node by using the minimum network loss, the minimum voltage deviation, and the optimal device adjustment cost as optimization indexes is shown in formula (7):
k is a training node set directly connected with a training node i; u shape i 、U j The voltages of training nodes i and j at the moment t are respectively; g ij 、θ ij Respectively the conductance and the voltage phase difference of the line between the training nodes i and j; u shape i (t) training the voltage value of the node i at the time t;
training a node i voltage expected value for t moment; n is Q The total number of the training nodes connected with the reactive compensation equipment; Δ b q (t) is a variable column vector of a discrete control variable of a reactive compensation training node q at the moment t; c UST Investment is switched for compensating equipment units; c ct,max 、C ct,min Switching the upper limit and the lower limit of the group number for each parallel capacitor node at the time t respectively; q DGt,max 、Q DGt,min Respectively injecting an upper limit and a lower limit into each DG node at the time t in a reactive mode; u shape t,max 、U t,min For maximum and minimum voltages of training nodes at time tThe value is obtained.
In this embodiment, the entropy weight and multi-level fuzzy comprehensive evaluation index is constructed in the following manner: aiming at an objective decision method based on an entropy weight theory, for alpha samples, beta indexes and x ij The value of the ith sample under the jth index (i =1, \8230;, α; j =1, \8230;, β) is shown. Because the measurement units of all indexes are different, normalization processing is firstly carried out, wherein the normalization of positive and negative index data is shown as a formula (8):
further, the server calculates the ith sample value x 'under the jth index' ij Specific gravity p of the index ij And calculating the entropy e of the j index j As shown in equation (9):
wherein k = [1/ln (alpha) ]]>0；e j Not less than 0; alpha is a constant larger than zero, and the value of alpha needs to avoid overhigh overshoot after the local controllers run simultaneously, so that the stability of the system is guaranteed. Preferably, the alpha is equal to the reciprocal of the number of nodes of the system, so that the requirement of the system on stability can be ensured.
Further, the server calculates the information entropy redundancy r j And calculating objective weight coefficient a of each index j As shown in equation (10):
in the embodiment, aiming at a subjective decision method based on a multi-level fuzzy comprehensive evaluation theory, firstly, starting from the voltage stability of a power distribution network, selecting a voltage stability margin, voltage fluctuation and a fatigue state of traditional equipment as three evaluation indexes, and describing the relation between state control by adopting a trapezoidal distribution membership function; the voltage stability margin index and the membership function thereof are shown as a formula (11):
wherein, U i Is the voltage value of the load node i;
is the desired value of the voltage at node i; u shape p Is a voltage tolerance;U sm evaluating a good boundary for the voltage stability margin;
evaluating an inferior bound for the voltage stability margin; f (U) sm ) =1 denotes that the state is excellent, F (U) sm ) =0 means the status is bad.
In this embodiment, the voltage fluctuation evaluation index and its membership function are shown in equation (12):
wherein, Δ u is a voltage fluctuation evaluation optimal boundary:
the bad boundary was evaluated for voltage fluctuations.
In this embodiment, in consideration of the mechanical life of the conventional equipment, a fatigue state evaluation index is established, and the time length from the reactive compensation equipment in the system to the last switching is mainly considered, the shorter the distance is, the restart should be avoided, and the longer the distance is, the action tends to be performed. The membership function of the fatigue state evaluation index is shown in formula (13):
wherein, the first and the second end of the pipe are connected with each other,
evaluating a high margin for a fatigue state;tthe bad boundary was evaluated for fatigue state.
Further, the server can substitute each sub-target data into the corresponding membership function respectively to obtain an evaluation matrix R i And calculating the total evaluation by adopting a proper fuzzy synthesis operator to obtain the subjective weight b j As shown in equation (14):
in this embodiment, based on the formulas (10) and (14), the server may calculate the comprehensive weight to obtain the overall optimization objective function, as shown in the formula (15):
wherein, a j 、b j Respectively an objective weight value and a subjective weight value; m is a unit of obj Is the sub-target number.
In this embodiment, the multi-node network is in a balanced state, processing as a single phase.
In an exemplary embodiment, as shown in FIG. 3, in the case where the multi-node network is in an unbalanced state (three-phase imbalance), the processing may be done in phase-splitting.
Under the condition of medium and low voltage, and in a multi-node network with single-phase load access, the problem of three-phase imbalance can occur.
In this embodiment, when the multi-node network is in a three-phase unbalanced state, the server may train with the comprehensive voltage deviation, the negative sequence voltage, and the network loss as optimization indexes, and establish an optimization model for the three-phase unbalanced state in consideration of the access of the inverter DG. Aiming at a three-phase unbalanced system, an optimization model is established by taking comprehensive voltage deviation, negative sequence voltage and network loss as optimization targets and taking a continuously adjustable static reactive power compensation device and DG split-phase reactive power output as decision variables, so that the economy of the three-phase unbalanced system can be well realized in a three-phase unbalanced state, and meanwhile, the three-phase unbalanced system exhibits excellent processing capacity on the aspects of voltage and three-phase unbalanced degree.
In this embodiment, the calculation of the first optimization for each training node by using the comprehensive voltage deviation, the negative sequence voltage, and the network loss as optimization indexes is shown in formula (16):
wherein n is the total number of nodes, V i γ Is the effective value of the gamma phase voltage of the node i; v i γ,exp A gamma phase voltage desired value of a node i; e.g. of the type i,- And f i,- Is node i negative sequence voltage (V) i，- ) The real and imaginary parts of (a). Wherein
In the formula
The voltages of a, b and c at the node i respectively; n is a radical of b Is a set of all branches;
and f i γ The real and imaginary parts of the gamma phase voltage of the node i;
and
real and imaginary parts of corresponding elements of a gamma phase of a node i and a beta of a node j in a node admittance matrix are obtained; and the method assumes that each compensation device can be independently adjusted in three phases, so in the formula, γ = a, b, c; v max 、V min Representing the maximum and minimum values of the node voltage amplitude;
and respectively representing the maximum and minimum reactive outputs of each phase of the inverter DG and the inverter SVG.
In this embodiment, based on equation (12), the server may establish the static voltage stability evaluation index in consideration of the three-phase stability, as shown in equation (17):
L ij is shown in equation (18):
wherein L is ij Evaluating a good boundary for the voltage stability margin;
evaluating an inferior bound for the voltage stability margin; f (L) ij ) =1 denotes the state is excellent, F (L) ij ) =0 indicates that the state is poor.
In this embodiment, the server may substitute each sub-target data into the corresponding membership function to obtain the evaluation matrix R i And substituting into formula (14), calculating to obtain subjective weight b j . Further, the server calculates the comprehensive weight to obtain an overall optimization objective function.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a voltage reactive power autonomous cooperative control device for realizing the voltage reactive power autonomous cooperative control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitations in one or more embodiments of the voltage reactive power autonomous cooperative control device provided below can be referred to the limitations on the voltage reactive power autonomous cooperative control method in the foregoing, and details are not described here.
In one embodiment, as shown in fig. 4, there is provided a voltage reactive power autonomous cooperative control apparatus including: a candidate node determination module 402, a space establishment module 404, a partitioning module 406, an optimization module 408, and an additional reactive power regulation determination module 410, wherein:
the candidate node determining module 402 is configured to obtain a reactive/voltage sensitivity matrix of a multi-node network, and determine a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix.
A space establishment module 404 is configured to establish a reactive source control space based on the candidate nodes and the plurality of reactive sources.
And the partitioning module 406 is configured to group nodes in the multi-node network, which have response trend similarity to the change of the reactive power source within a preset similarity interval, into a cluster based on the reactive power source control space.
The optimization module 408 is configured to perform multi-target dynamic reactive power optimization on the multiple nodes in each cluster to obtain a voltage reference value; and obtaining a voltage deviation value based on the voltage reference value and the actual voltage values of the plurality of nodes in each cluster.
And an additional reactive power regulation amount determination module 410, configured to determine an additional reactive power regulation amount based on the voltage deviation value.
In one embodiment, the candidate node determining module 402 may include:
and the matrix acquisition submodule is used for carrying out Newton-Raphson load flow calculation on the multi-node network under a polar coordinate system aiming at the multi-node network to obtain a reactive/voltage sensitivity matrix corresponding to the multi-node network.
In one embodiment, the candidate node determining module 402 may include:
and the candidate node selection submodule is used for acquiring a node corresponding to the reactive/voltage sensitivity matrix with an absolute value larger than a preset threshold value based on the reactive/voltage sensitivity matrix and taking the node as a candidate node.
In one embodiment, the space creation module 404 may include:
and the reactive power source quantity obtaining submodule is used for obtaining the quantity of the reactive power sources in the multi-node network.
The reactive power source control space building submodule is used for building a multi-dimensional reactive power source control space based on a plurality of reactive power sources in a multi-node network; the dimensionality of the reactive power source control space is the same as the number of the reactive power sources.
In one embodiment, the reactive source control space establishment sub-module may include:
the coordinate axis determining unit is used for regarding each reactive power source and taking the reactive power source as a coordinate axis; wherein, different reactive sources correspond to different coordinate axes.
And the multi-dimensional space establishing unit is used for establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
In one embodiment, the apparatus may further include: and the model training module is used for training the optimization model.
In this embodiment, the model training module may include:
and the first training node set acquisition submodule is used for acquiring a first training node set, and the first training node set comprises a plurality of training nodes.
The first optimization submodule is used for performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjustment cost as optimization indexes to obtain a second training node set; wherein, the minimum network loss is the minimum power loss of the multi-node network in actual operation.
And the second optimization submodule is used for carrying out second optimization on each training node in the second training node set based on the entropy weight and the multi-level fuzzy comprehensive evaluation index to obtain a third training node set.
And the voltage reference value generation submodule is used for inputting the third training node set into a pre-constructed initial optimization model, and performing multi-objective dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value.
And the model loss determining submodule is used for determining the model loss of the initial optimization model based on the voltage reference value and the first training node set.
And the iterative training submodule is used for performing iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
All or part of each module in the voltage reactive power autonomous cooperative control device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 5. 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as voltage reference values, actual voltage values and the like of all nodes. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a voltage reactive autonomous cooperative control method.
It will be appreciated by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that 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 one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring a reactive/voltage sensitivity matrix of a multi-node network, and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix; establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources; based on the reactive power source control space, nodes in the multi-node network, which respond to the reactive power source change trend and have similarity within a preset similarity interval, are classified into a cluster; performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster; and determining an additional reactive power regulating quantity based on the voltage deviation value.
In one embodiment, the processor when executing the computer program further performs obtaining a reactive/voltage sensitivity matrix for a multi-node network, may include: and aiming at the multi-node network, performing Newton-Raphson load flow calculation on the multi-node network under a polar coordinate system to obtain a reactive/voltage sensitivity matrix corresponding to the multi-node network.
In one embodiment, the processor when executing the computer program further implements determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix, which may include: and acquiring a node corresponding to the reactive/voltage sensitivity matrix with the absolute value larger than a preset threshold value as a candidate node based on the reactive/voltage sensitivity matrix.
In one embodiment, the processor when executing the computer program further implements establishing a reactive source control space based on the candidate node and the plurality of reactive sources may include: acquiring the number of reactive power sources in a multi-node network; establishing a multi-dimensional reactive power source control space based on a plurality of reactive power sources in a multi-node network; the dimensionality of the reactive power source control space is the same as the number of the reactive power sources.
In one embodiment, the processor when executing the computer program further enables establishing a multi-dimensional reactive source control space based on a plurality of reactive sources in a multi-node network, may include: aiming at each reactive power source, taking the reactive power source as a coordinate axis; wherein, different reactive power sources correspond to different coordinate axes; and establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
In one embodiment, the processor, when executing the computer program, further performs multi-objective dynamic reactive power optimization on the plurality of nodes in each cluster by using an optimization model trained in advance, where the training mode of the optimization model may include: acquiring a first training node set, wherein the first training node set comprises a plurality of training nodes; performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjusting cost as optimization indexes to obtain a second training node set; the minimum network loss is the minimum power loss of the multi-node network in actual operation; performing second optimization on each training node in the second training node set based on the entropy weight and the multi-level fuzzy comprehensive evaluation index to obtain a third training node set; inputting the third training node sets into a pre-constructed initial optimization model, and performing multi-target dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value; determining a model loss of the initial optimization model based on the voltage reference value and the first training node set; and carrying out iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a reactive/voltage sensitivity matrix of a multi-node network, and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix; establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources; based on the reactive power source control space, nodes in the multi-node network, which have response trend similarity to the reactive power source change within a preset similarity interval, are classified into a cluster; performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster; and determining an additional reactive power regulating quantity based on the voltage deviation value.
In one embodiment, the computer program when executed by the processor further implements obtaining a reactive/voltage sensitivity matrix for a multi-node network, which may include: aiming at the multi-node network, newton-Raphson load flow calculation is carried out on the multi-node network under a polar coordinate system, and a reactive/voltage sensitivity matrix corresponding to the multi-node network is obtained.
In one embodiment, the computer program when executed by the processor further enables determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix, may include: and acquiring a node corresponding to the reactive/voltage sensitivity matrix with the absolute value larger than a preset threshold value as a candidate node based on the reactive/voltage sensitivity matrix.
In one embodiment, the computer program when executed by the processor further enables establishing a reactive source control space based on the candidate node and the plurality of reactive sources, may include: acquiring the number of reactive power sources in a multi-node network; establishing a multi-dimensional reactive power source control space based on a plurality of reactive power sources in a multi-node network; the dimensionality of the reactive power source control space is the same as the number of the reactive power sources.
In one embodiment, the computer program when executed by the processor further enables establishing a multi-dimensional reactive source control space based on a plurality of reactive sources in a multi-node network, may include: aiming at each reactive power source, taking the reactive power source as a coordinate axis; wherein, different reactive sources correspond to different coordinate axes; and establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
In one embodiment, the computer program, when executed by the processor, further implements the multi-objective dynamic reactive power optimization for the plurality of nodes in each cluster by using an optimization model trained in advance, and the training mode of the optimization model may include: acquiring a first training node set, wherein the first training node set comprises a plurality of training nodes; performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjusting cost as optimization indexes to obtain a second training node set; the minimum network loss is the minimum power loss of the multi-node network in actual operation; performing second optimization on each training node in the second training node set based on the entropy weight and the multi-level fuzzy comprehensive evaluation index to obtain a third training node set; inputting the third training node sets into a pre-constructed initial optimization model, and performing multi-target dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value; determining a model loss of the initial optimization model based on the voltage reference value and the first training node set; and performing iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of: acquiring a reactive/voltage sensitivity matrix of a multi-node network, and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix; establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources; based on the reactive power source control space, nodes in the multi-node network, which respond to the reactive power source change trend and have similarity within a preset similarity interval, are classified into a cluster; performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster; and determining an additional reactive power regulating quantity based on the voltage deviation value.
In one embodiment, the computer program when executed by the processor further implements obtaining a reactive/voltage sensitivity matrix for a multi-node network, which may include: and aiming at the multi-node network, performing Newton-Raphson load flow calculation on the multi-node network under a polar coordinate system to obtain a reactive/voltage sensitivity matrix corresponding to the multi-node network.
In one embodiment, the computer program when executed by the processor further enables determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix, which may include: and acquiring nodes corresponding to the reactive/voltage sensitivity matrix with the absolute value larger than a preset threshold value as candidate nodes based on the reactive/voltage sensitivity matrix.
In one embodiment, the computer program when executed by the processor further enables establishing a reactive source control space based on the candidate node and the plurality of reactive sources, may include: acquiring the number of reactive power sources in a multi-node network; establishing a multi-dimensional reactive power source control space based on a plurality of reactive power sources in a multi-node network; the dimensionality of the reactive power source control space is the same as the number of the reactive power sources.
In one embodiment, the computer program when executed by the processor further enables establishing a multi-dimensional reactive source control space based on a plurality of reactive sources in a multi-node network, may include: aiming at each reactive power source, taking the reactive power source as a coordinate axis; wherein, different reactive power sources correspond to different coordinate axes; and establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
In one embodiment, the computer program, when executed by the processor, further implements the multi-objective dynamic reactive power optimization for the plurality of nodes in each cluster by using an optimization model trained in advance, and the training mode of the optimization model may include: acquiring a first training node set, wherein the first training node set comprises a plurality of training nodes; performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjusting cost as optimization indexes to obtain a second training node set; the minimum network loss is the minimum power loss of the multi-node network in actual operation; performing second optimization on each training node in the second training node set based on the entropy weight and the multi-level fuzzy comprehensive evaluation index to obtain a third training node set; inputting the third training node sets into a pre-constructed initial optimization model, and performing multi-target dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value; determining a model loss of the initial optimization model based on the voltage reference value and the first training node set; and performing iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
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, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. 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), for example. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided herein may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A voltage reactive power autonomous cooperative control method is characterized by comprising the following steps:
acquiring a reactive/voltage sensitivity matrix of a multi-node network, and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix;
establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources;
based on the reactive power source control space, nodes in the multi-node network, which have response trend similarity to reactive power source change within a preset similarity interval, are classified into a cluster;
performing multi-target dynamic reactive power optimization on a plurality of nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster;
and determining an additional reactive power regulating quantity based on the voltage deviation value.
2. The method of claim 1, wherein obtaining a reactive/voltage sensitivity matrix for a multi-node network comprises:
and aiming at the multi-node network, carrying out Newton-Raphson load flow calculation on the multi-node network under a polar coordinate system to obtain a reactive/voltage sensitivity matrix corresponding to the multi-node network.
3. The method of claim 1, wherein said determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix comprises:
and acquiring a node corresponding to the reactive/voltage sensitivity matrix with the absolute value larger than a preset threshold value as a candidate node based on the reactive/voltage sensitivity matrix.
4. The method of claim 1, wherein establishing a reactive source control space based on the candidate nodes and a plurality of reactive sources comprises:
acquiring the number of reactive power sources in the multi-node network;
establishing a multi-dimensional reactive power source control space based on a plurality of reactive power sources in the multi-node network; and the dimension of the reactive power source control space is the same as the number of the reactive power sources.
5. The method of claim 1, wherein establishing a multi-dimensional reactive source control space based on a plurality of reactive sources in the multi-node network comprises:
aiming at each reactive power source, taking the reactive power source as a coordinate axis; different reactive power sources correspond to different coordinate axes;
and establishing a multi-dimensional reactive power source control space based on a plurality of coordinate axes.
6. The method according to any one of claims 1 to 5, wherein the multi-objective dynamic reactive power optimization of the plurality of nodes in each cluster is realized by a pre-trained optimization model, and the training mode of the optimization model comprises:
acquiring a first training node set, wherein the first training node set comprises a plurality of training nodes;
performing first optimization on each training node by taking the minimum network loss, the minimum voltage deviation and the optimal equipment adjusting cost as optimization indexes to obtain a second training node set; wherein the minimum network loss is the minimum power loss of the multi-node network in actual operation;
performing second optimization on each training node in the second training node set based on entropy weight and multi-level fuzzy comprehensive evaluation indexes to obtain a third training node set;
inputting the third training node sets into a pre-constructed initial optimization model, and performing multi-objective dynamic reactive power optimization on each node in each third training node set through the initial optimization model to generate a voltage reference value;
determining a model loss for the initial optimization model based on the voltage reference value and the first set of training nodes;
and carrying out iterative training on the initial optimization model according to the model loss to obtain a trained optimization model.
7. A voltage reactive autonomous cooperative control apparatus, characterized in that the apparatus comprises:
the candidate node determining module is used for acquiring a reactive/voltage sensitivity matrix of a multi-node network and determining a plurality of candidate nodes in the multi-node network based on the reactive/voltage sensitivity matrix;
the space establishing module is used for establishing a reactive power source control space based on the candidate nodes and the plurality of reactive power sources;
the partition module is used for grouping nodes with reactive power source change response trend similarity in a preset similarity interval in the multi-node network into a cluster based on the reactive power source control space;
the optimization module is used for carrying out multi-target dynamic reactive power optimization on the nodes in each cluster to obtain a voltage reference value; obtaining a voltage deviation value based on the voltage reference value and actual voltage values of a plurality of nodes in each cluster;
and the extra reactive power regulating quantity determining module is used for determining an extra reactive power regulating quantity based on the voltage deviation value.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| CN116706921B (en) * | 2023-08-09 | 2023-10-03 | 合肥工业大学 | Quantum Newton-Laporton method power flow calculation method and system based on HIL algorithm |
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