CN113421004B - Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method - Google Patents

Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method Download PDF

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CN113421004B
CN113421004B CN202110741272.7A CN202110741272A CN113421004B CN 113421004 B CN113421004 B CN 113421004B CN 202110741272 A CN202110741272 A CN 202110741272A CN 113421004 B CN113421004 B CN 113421004B
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distribution network
power distribution
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scene
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CN113421004A (en
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朱海南
孙华忠
李玉志
李丰硕
王娟娟
张锴
薛云霞
李宗璇
宋静
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State Grid Corp of China SGCC
Weifang Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Weifang Power Supply Co of State Grid Shandong Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a distributed robust extension planning system and a distributed robust extension planning method for an active power distribution network with cooperative transmission and distribution. The method comprises the following steps: based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set; the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model; and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.

Description

Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method
Technical Field
The invention belongs to the field of active power distribution network system expansion planning, and particularly relates to a transmission and distribution collaborative active power distribution network distributed robust expansion planning system and method.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Energy is an important material basis for economic and social development, but the consumption of traditional fossil energy resources also causes serious ecological environment problems, and the structural reform of energy is imperative. The development of the electric power industry as a basic energy industry for the relationship of national and folk life has also revealed many new characteristics in recent years. On the supply side, new energy power generation such as wind power and photovoltaic has been rapidly developed in recent years, and the installed capacity has been continuously increased. On the demand side, the electric automobile is continuously increased in conservation amount due to the characteristics of cleanness, green and environmental protection. The changes of the supply and demand sides make the influence factors of the original power distribution network planning more difficult to determine, and the power distribution network planning requires new theoretical guidance.
Firstly, the connection of wind power, photovoltaic and other distributed new energy power generation changes the relation between a power distribution network and a power transmission network. The distribution network is no longer just receiving power from the transmission network as a lower network, but has the ability to return power to the transmission network, i.e. the distribution network is converted into an active distribution network. The presence of active distribution networks increases the degree of coupling between the distribution network and the transmission network, and the planning strategy of a single distribution network affects the transmission network connected to it and, in turn, affects other distribution networks connected to it. At present, the power distribution network planning strategy is mainly focused on planning a single power distribution network, and less research is conducted on planning of multiple power distribution networks considering transmission and distribution coordination.
Secondly, the uncertainty of the running condition of the power distribution network is increased due to the flexible load connection of the supply side distributed new energy power generation and the demand side electric automobile. The output of new energy power generation is influenced and restricted by uncontrollable factors such as wind speed, temperature, closing strength and the like, and has randomness, volatility and uncertainty. The charging load of the electric automobile is influenced by factors such as weather, work, trip habits and the like, and the electric automobile has stronger uncertainty. At present, uncertainty modeling is carried out on an uncertainty-considered planning strategy by a scene method, robust optimization, opportunity constraint, probability model and other methods.
Disclosure of Invention
In order to solve the technical problems in the background technology, the invention provides a distributed robust extension planning system and a distributed robust extension planning method for an active power distribution network with cooperative transmission and distribution.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a distributed robust extension planning method for an active power distribution network with cooperative transmission and distribution.
The distributed robust extension planning method for the active power distribution network with cooperative transmission and distribution comprises the following steps:
based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set;
the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model;
and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.
Further, the determining of the typical operation scene under the normal operation condition of the power distribution network includes: and generating a typical operation scene of photovoltaic treatment, wind power output and load when the power distribution network operates normally by adopting a K-means clustering method.
Furthermore, in the two-stage robust optimization model of the power distribution network problem, a first-stage decision variable is a construction variable of each alternative line of the power distribution network, and a second-stage decision variable is an operation variable of the power distribution network in each scene.
Further, the operation variables of the power distribution network in each scene include: node voltage, line power flow, substation output power.
Furthermore, a two-stage robust optimization model of the power distribution network problem is established by taking line investment construction cost, operation cost and penalty items as objective functions and taking line construction logic constraint, line capacity constraint, radial topology constraint, power flow constraint, node voltage upper and lower limit constraint and substation capacity constraint as constraint conditions.
Further, the model solving process includes:
the first step: initializing Lagrangian term coefficients v and w and a target variable t, and transmitting the Lagrangian term coefficients v and w and the target variable t to a power distribution network problem;
and a second step of: solving each power distribution network sub-problem by using a C & CG algorithm, updating a response variable r and transmitting the response variable r to the power transmission network sub-problem;
and a third step of: solving the power transmission network sub-problem, updating the target variable t and transmitting the target variable t to each power distribution network sub-problem;
fourth step: performing internal circulation convergence judgment, if so, performing the next step, otherwise, returning to the second step to solve the power distribution network sub-problem;
fifth step: and (3) performing outer loop judgment, ending iteration and outputting each power distribution network planning scheme if convergence is performed, otherwise, updating Lagrangian coefficients v and w and returning to a second step to solve the power distribution network sub-problem.
Further, the iterative solving process of the distributed framework method of the ATC and C & CG algorithm comprises the following steps:
the first step: setting the outer circulation times m and the inner circulation times n to be zero, namely, making m=0 and n=0; setting initial value of Lagrange coefficient
Figure BDA0003141466190000041
Is +.>
Figure BDA0003141466190000042
And a second step of: setting the internal circulation times n=n+1, and starting the nth internal circulation;
and a third step of: by C&CG algorithm solves two-stage robust optimization model of power distribution network sub-problem in parallel, updates response variable
Figure BDA0003141466190000043
And transmitting the data to an SOCP model of the power transmission network sub-problem;
fourth step: solving the power transmission network sub-problem and updating the target variable
Figure BDA0003141466190000044
And transmitting the problems to a power distribution network;
fifth step: judging whether the inner loop convergence conditional expression (51) is satisfied; if the formula (51) is established, jumping to a sixth step; otherwise, returning to the second step;
Figure BDA0003141466190000045
wherein f n The sum of the power transmission network sub-problem objective function obtained for the nth internal loop calculation and each power distribution network sub-problem objective function, namely
Figure BDA0003141466190000046
ε 1 The gap is converged for internal circulation;
sixth step: device for placing articles
Figure BDA0003141466190000047
Judging whether the outer circulation convergence conditional expression (52) and the expression (53) are simultaneously established; if the two power distribution network planning schemes are simultaneously established, ending iteration and outputting each power distribution network planning scheme; otherwise, jumping to the seventh step.
Figure BDA0003141466190000048
Figure BDA0003141466190000049
Wherein ε 2 、ε 3 Converging the gap for the outer circulation;
seventh step: the number of outer loops m=m+1. Updating the Lagrangian coefficient according to equation (54), equation (55)
Figure BDA0003141466190000051
Figure BDA0003141466190000052
Figure BDA0003141466190000053
Wherein β is a constant greater than 1, and is related to the convergence speed;
eighth step: are sequentially arranged
Figure BDA0003141466190000054
n=0, returning to the second step.
The second aspect of the invention provides a distributed robust extension planning system for an active power distribution network with cooperative transmission and distribution.
The distributed robust extension planning system of the active power distribution network with cooperative transmission and distribution comprises:
a two-stage robust optimization model building module for power distribution network sub-problems, configured to: based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set;
a power grid sub-problem SOCP model building module configured to: the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model;
a multi-distribution network planning scheme acquisition module configured to: and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the transmission and distribution collaborative active power distribution network distributed robust extension planning method according to the first aspect described above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the transmission and distribution collaborative active power distribution network distributed robust extension planning method according to the first aspect described above when the program is executed by the processor.
Compared with the prior art, the invention has the beneficial effects that:
according to the transmission and distribution collaborative active power distribution network distributed robust extension planning method, multiple power distribution networks connected to the same transmission network are planned in a collaborative mode, the obtained power distribution network planning schemes account for coupling relations between the power distribution network and the transmission network, and the power distribution network, and the planning scheme is superior to independent planning of multiple power distribution networks as a whole.
According to the distributed robust extension planning method for the active power distribution network with cooperative transmission and distribution, disclosed by the invention, the transmission network and the power distribution network are decoupled by utilizing the distributed ATC algorithm principle, the transmission network and the power distribution network can be independently calculated in parallel, so that the calculation time is greatly shortened, meanwhile, the transmission network and the power distribution network only need to exchange a target variable t and a response variable r in the iterative process, the privacy of a user can be well protected, and the burden of a communication system and an information processor is reduced.
According to the transmission and distribution cooperative active power distribution network distributed robust extension planning method, a two-stage robust optimization model of a power distribution network sub-problem is built by taking the variation range of wind and light output and load levels in a typical scene of a power distribution network as an uncertainty set, a power distribution network planning scheme is built under the worst condition, and the robustness of the power distribution network planning scheme is guaranteed.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of a distributed robust extension planning method for an active power distribution network with cooperative transmission and distribution provided by the method of the present invention;
FIG. 2 is a schematic diagram of a C & CG algorithm solution flow;
FIG. 3 is a flow chart of a distributed framework method of the present invention for providing ATC and C & CG algorithms;
fig. 4 is a representative system for the application of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
After new energy power generation such as scene is connected into the distribution network, the distribution network has the ability of back-transmitting electric energy to the transmission network, and the degree of coupling of distribution network and transmission network increases, and simultaneously the uncertainty characteristic that new energy power generation possessed has brought new challenges for the planning operation of distribution network. The invention provides a distributed robust expansion planning system and method for an active power distribution network with cooperative transmission and distribution. The invention fully considers the coupling relation between the power distribution network and the power transmission network and the uncertainty in the power distribution network, and ensures that the planning result of each power distribution network is overall optimal. For this purpose, the technical solution of the invention is illustrated from the following several embodiments.
Example 1
The embodiment provides a distributed robust extension planning method for an active power distribution network with cooperative transmission and distribution, which is applied to a server for illustration, and it can be understood that the method can also be applied to a terminal, can also be applied to a system and a terminal, and can be realized through interaction of the terminal and the server. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, which is not limited herein. In this embodiment, the method includes the steps of:
the distributed robust extension planning method for the active power distribution network with cooperative transmission and distribution comprises the following steps:
s101: based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set;
s102: the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model;
s103: and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.
Specifically, as shown in fig. 1, first, a k-means clustering method is adopted to generate a typical operation scene of a power distribution network, and an uncertain collection value range is set. Secondly, establishing a two-stage robust optimization model of each power distribution network sub-problem, and carrying out parallel solving by using a C & CG algorithm. Then, a power transmission network sub-problem model is established, and the model is a deterministic problem and can be directly solved. And finally, judging whether convergence is carried out, if convergence is carried out, ending operation and outputting each power distribution network planning scheme, otherwise, continuing iteration of the power distribution network problem.
Explanation of key problems:
1. typical scene generation
In the invention, a K-means clustering method is adopted to generate typical operation scenes of normal operation of the power distribution network, and an elbow method is adopted to determine the number of the clustering scenes. The elbow method uses the ratio of the intra-class average distance (nSE) to the inter-class average distance (wSE) as an indicator of cluster error. Setting the number of true clusters to k, the elbow model can be expressed as:
Figure BDA0003141466190000091
Figure BDA0003141466190000101
Figure BDA0003141466190000102
wherein: delta i Represents class i, ks represents delta i A sample of (b); m is m i Representing delta i A medium sample mean; kn represents delta i Is the number of samples.
2. Problems with distribution networks
In order to solve the uncertainty related to renewable energy sources and loads in a power distribution network, the invention models the problem of the power distribution network as a two-stage robust optimization model, wherein an uncertainty set in the model is the variation range of wind-light output and power distribution network loads in a typical scene, a decision variable in a first stage is a construction variable of each alternative line of the power distribution network, a decision variable in a second stage is an operation variable of the power distribution network in each scene, and the problems comprise node voltage, line power flow, transformer substation output power and the like, and can be expressed as follows:
min f dis,p ＝C inv,p +C pen,p +C ope,p (4)
Figure BDA0003141466190000103
Figure BDA0003141466190000104
Figure BDA0003141466190000105
Figure BDA0003141466190000106
Figure BDA0003141466190000107
Figure BDA0003141466190000108
s.t.
Figure BDA0003141466190000109
Figure BDA00031414661900001010
Figure BDA00031414661900001011
Figure BDA00031414661900001012
Figure BDA0003141466190000111
Figure BDA0003141466190000112
Figure BDA0003141466190000113
Figure BDA0003141466190000114
Figure BDA0003141466190000115
Figure BDA0003141466190000116
Figure BDA0003141466190000117
Figure BDA0003141466190000118
Figure BDA0003141466190000119
/>
Figure BDA00031414661900001110
Figure BDA00031414661900001111
V min ≤V i,p,s ≤V max (26)
Figure BDA00031414661900001112
Figure BDA00031414661900001113
Figure BDA00031414661900001114
Figure BDA00031414661900001115
Figure BDA00031414661900001116
Figure BDA00031414661900001117
Figure BDA00031414661900001118
Figure BDA00031414661900001119
Figure BDA00031414661900001120
Figure BDA00031414661900001121
in the method, in the process of the invention,
Figure BDA00031414661900001122
be is an alternative line set in the power distribution network; ne is a node set of the power distribution network; di is a power distribution network set; se is scene set; tc is a set of alternative transmission line types; ns is a substation node set in the power distribution network; ndg is a distributed power supply node set in a power distribution network; c (C) inv,p 、C pen,p 、C ope,p Respectively representing the investment cost, penalty term and operation cost of the p-type power distribution network; cn (control unit) c 、Cc c Representing the cost of newly-built and expanded unit length c type feeder lines respectively; v p,s 、w p,s The coefficient vector of the Lagrangian item and the augmented Lagrangian item is continuously updated in the iterative process of the ATC algorithm; r is (r) p,s The system is characterized by being a response variable, and comprising active power, reactive power and voltage amplitude at a transformer substation node connected with a power transmission network; t is t p,s The target variable is obtained by calculating a power transmission network sub-problem, and is a constant in the power distribution network sub-problem; Φ is the uncertainty set in the max problem; n is n s The number of hours scene s appears in one year, which sums to 8760; probability C s The unit electricity purchasing cost; b i 、c i Is a distributed power cost coefficient; kappa represents the annual investment cost conversion coefficient of the equipment; r represents annual rate; t represents the equipment age;
V i,p,s the voltage amplitude of the node i in the power distribution network p under the scene s is represented, and subscripts p and s in the following variables respectively represent the scene of the power distribution network p and the scene of the scene s, and are not repeated; v (V) ref Taking 1 as a reference node voltage value;
Figure BDA0003141466190000121
active power and reactive power flowing through the type-c feeder at the line ij are respectively represented; />
Figure BDA0003141466190000122
Respectively representing active power and reactive power sent by a transformer substation at a node i; />
Figure BDA0003141466190000123
Respectively represent node iActive and reactive power emitted by the distributed power supply; />
Figure BDA0003141466190000124
Respectively representing active power and reactive power emitted by a photovoltaic power station at a node i; />
Figure BDA0003141466190000125
Respectively representing active power and reactive power emitted by the wind turbine generator at the node i; />
Figure BDA0003141466190000126
Respectively representing the active and reactive loads of the node i; />
Figure BDA0003141466190000127
Representing the upper limit of the capacity of the transformer substation; />
Figure BDA0003141466190000128
Representing an upper limit of the capacity of the c-type alternative feeder line; />
Figure BDA0003141466190000129
Representing the upper limit of active power sent by the distributed power supply at the node i; v (V) max 、V min Respectively representing the maximum value and the minimum value of the node voltage; b ij,p,s Is a relaxation variable; />
Figure BDA00031414661900001210
Reference output power of photovoltaic power and wind power in a scene s with unit capacity respectively, +.>
Figure BDA00031414661900001211
The normalized load under the scene s is the result of k-means clustering; />
Figure BDA00031414661900001212
Generated by the normalized load through k-means clustering; />
Figure BDA00031414661900001213
Figure BDA00031414661900001214
Respectively representing the photovoltaic installed capacity, the wind power installed capacity and the load maximum value at the node i; r is (r) c 、x c The resistance value and the reactance value of the unit length of the c-type wire are respectively shown; l (L) ij Representing the length of the line ij; />
Figure BDA0003141466190000131
A variable of 0-1 is used for indicating whether a c-type feeder exists at a planned power distribution network line ij; />
Figure BDA0003141466190000132
The variables are 0-1, and respectively represent whether a new built, expanded and original c-type feeder exists at the ij of the power distribution network line; />
Figure BDA0003141466190000133
For a known constant, when a c-type feeder line value is 1 at the position of the power distribution network line ij before planning, otherwise, the value is zero; />
Figure BDA0003141466190000134
Is a known constant, 1 when there is a substation at node i, or zero; />
Figure BDA0003141466190000135
Is a non-negative continuous auxiliary variable.
The two-stage robust optimization model is solved by adopting a C & CG algorithm, and the solving flow is shown in figure 2.
3. Problems of the Power Transmission network
The second step of optimal configuration of the energy storage system is to perform optimal configuration of the energy storage system under the condition of power distribution network faults, and establish a two-stage robust optimal configuration model of the energy storage configuration in the power distribution network under the condition of uncertain fault of broken lines in a subarea of the power distribution network, so as to obtain an optimal configuration scheme of the energy storage system and ensure continuous power supply of important loads under the condition of worst faults of the power distribution network. In summary, the optimization objective of the optimization configuration of the energy storage system in the second step is to minimize the investment cost and annual comprehensive load loss cost of the energy storage system under the worst fault scenario of the power distribution network.
Figure BDA0003141466190000136
Figure BDA0003141466190000137
Figure BDA0003141466190000138
Figure BDA0003141466190000139
Figure BDA00031414661900001310
Figure BDA00031414661900001311
s.t.
Figure BDA00031414661900001312
Figure BDA00031414661900001313
Figure BDA0003141466190000141
Figure BDA0003141466190000142
/>
Figure BDA0003141466190000143
Figure BDA0003141466190000144
Figure BDA0003141466190000145
Figure BDA0003141466190000146
In the method, in the process of the invention,
Figure BDA0003141466190000147
bt is a power transmission network line set; nt is a power transmission network node set; di is a distribution network sequence number set; />
Figure BDA0003141466190000148
Respectively representing the power generation cost of a generator, the profit obtained by the transmission of a transmission network to a power distribution network and penalty items under a scene s; n is n h The number of hours scene s appears in one year; c (C) s The unit electricity price is the electricity price of the transmission network when selling electricity to the distribution network; a, a i '、b i '、c i ' is the cost factor of the generator at grid node i; v p,s 、w p,s The coefficient vector of the Lagrangian item and the augmented Lagrangian item is continuously updated in the iterative process of the ATC algorithm; r is (r) p,s The response variable is obtained by calculating a power distribution network sub-problem, and is constant in the power transmission network sub-problem; t is t p,s The power transmission network node is used as a target variable and consists of active power, reactive power and voltage square at the power transmission network node connected with the power distribution network p; />
Figure BDA0003141466190000149
Respectively representing active power and reactive power generated by a generator at a power transmission network node i in a scene s;/>
Figure BDA00031414661900001410
respectively representing active power and reactive power which are transmitted to a power distribution network at a power transmission network node i in a scene s; />
Figure BDA00031414661900001411
Representing the square of the voltage amplitude at grid node i in scenario s; r is R ij,s 、T ij,s As auxiliary variable, the relation between the voltage amplitude and the phase angle is R ij,s ＝U i,s U j,s cosδ ij,s 、T ij,s ＝U i,s U j,s sinδ ij,s If the relation is needed, the voltage amplitude and the phase angle can be obtained, and the model does not need to be solved; g ij 、B ij The real part and the imaginary part of the elements in the admittance matrix of the power transmission network; v (V) max 、V min Respectively representing upper and lower limits of node voltage; i ij,max An upper current limit through which the line ij is allowed to flow; />
Figure BDA00031414661900001412
Representing the upper and lower limits of active power generated by the generator at node i; />
Figure BDA00031414661900001413
Representing the upper limit of the capacity of the distribution network transformer connected to the transmission network node i.
4. Distributed framework of ATC and C & CG algorithm
A flow chart of a distributed framework method of ATC and C & CG algorithms is shown in fig. 3. Specifically, the following eight steps are explained:
the first step: setting the outer circulation times m and the inner circulation times n to be zero, namely, making m=0 and n=0; setting initial value of Lagrange coefficient
Figure BDA0003141466190000151
Is +.>
Figure BDA0003141466190000152
And a second step of: the number of inner loops n=n+1 is set, and the nth inner loop is started.
And a third step of: by C&CG algorithm solves two-stage robust optimization model of power distribution network sub-problem in parallel, updates response variable
Figure BDA0003141466190000153
And to the grid problem.
Fourth step: solving the power transmission network sub-problem and updating the target variable
Figure BDA0003141466190000154
And passed to the distribution network problem.
Fifth step: it is determined whether the inner loop convergence conditional expression (51) is satisfied. If the formula (51) is established, jumping to a sixth step; otherwise, returning to the second step.
Figure BDA0003141466190000155
Wherein f n The sum of the power transmission network sub-problem objective function obtained for the nth internal loop calculation and each power distribution network sub-problem objective function, namely
Figure BDA0003141466190000156
ε 1 The gap is converged for the inner loop.
Sixth step: device for placing articles
Figure BDA0003141466190000157
It is determined whether or not the outer loop convergence conditional expression (52) and the expression (53) are simultaneously established. If the two power distribution network planning schemes are simultaneously established, ending iteration and outputting each power distribution network planning scheme; otherwise, jumping to the seventh step.
Figure BDA0003141466190000158
Figure BDA0003141466190000159
Wherein ε 2 、ε 3 The gap is converged for the outer loop.
Seventh step: the number of outer loops m=m+1. Updating the Lagrangian coefficient according to equation (54), equation (55)
Figure BDA00031414661900001510
Figure BDA0003141466190000161
Figure BDA0003141466190000162
Where β is a constant greater than 1, and is related to the convergence rate.
Eighth step: are sequentially arranged
Figure BDA0003141466190000163
n=0, returning to the second step.
Example two
The embodiment provides a distributed robust extension planning system for an active power distribution network with cooperative transmission and distribution.
The distributed robust extension planning system of the active power distribution network with cooperative transmission and distribution comprises:
a two-stage robust optimization model building module for power distribution network sub-problems, configured to: based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set;
a power grid sub-problem SOCP model building module configured to: the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model;
a multi-distribution network planning scheme acquisition module configured to: and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.
A typical system for the application of the invention is shown in fig. 4, which is formed by a six-node three-generator power transmission network connected to three distribution networks, of which only distribution network D3 is specifically developed in the figure. Node 1 in the power distribution network D3 is connected with a power transmission network node N5 and is the same node on a physical model; the solid black lines represent existing lines and the dashed gray lines represent alternative lines to be planned, i.e. lines 1-10, 10-14, 10-19, 10-20 are existing lines and the remaining lines are alternative lines to be planned.
It should be noted that, the two-stage robust optimization model building module for the power distribution network sub-problem, the power transmission network sub-problem SOCP model building module, and the multi-power distribution network planning scheme obtaining module correspond to steps S101 to S103 in the first embodiment, and the modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps in the active distribution network distributed robust extension planning method for transmission and distribution coordination as described in the above embodiment one.
Example IV
The present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps in the active distribution network distributed robust extension planning method with coordinated transmission and distribution according to the above embodiment when executing the program.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in the embodiments may be accomplished by computer programs stored in a computer-readable storage medium, which when executed, may include the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. The distributed robust extension planning method for the active power distribution network with the cooperative transmission and distribution is characterized by comprising the following steps of:
based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set;
the power distribution network problem is modeled as a two-stage robust optimization model, an uncertainty set in the model is a variation range of wind-light output and power distribution network load under a typical scene, a first-stage decision variable is a construction variable of each alternative line of the power distribution network, a second-stage decision variable is an operation variable of the power distribution network under each scene, and the operation variables comprise node voltage, line power flow and transformer substation output power, and the problem can be expressed as follows:
min f dis,p ＝C inv,p +C pen,p +C ope,p (4)
Figure FDA0004150429730000011
Figure FDA0004150429730000012
Figure FDA0004150429730000013
Figure FDA0004150429730000014
Figure FDA0004150429730000015
Figure FDA0004150429730000016
s.t.
Figure FDA0004150429730000017
Figure FDA0004150429730000018
Figure FDA0004150429730000019
Figure FDA00041504297300000110
Figure FDA00041504297300000111
Figure FDA00041504297300000112
Figure FDA00041504297300000113
Figure FDA0004150429730000021
Figure FDA0004150429730000022
Figure FDA0004150429730000023
Figure FDA0004150429730000024
Figure FDA0004150429730000025
/>
Figure FDA0004150429730000026
Figure FDA0004150429730000027
Figure FDA0004150429730000028
V min ≤V i,p,s ≤V max (26)
Figure FDA0004150429730000029
Figure FDA00041504297300000210
Figure FDA00041504297300000211
Figure FDA00041504297300000212
Figure FDA00041504297300000213
Figure FDA00041504297300000214
Figure FDA00041504297300000215
Figure FDA00041504297300000216
Figure FDA00041504297300000217
Figure FDA00041504297300000218
in the method, in the process of the invention,
Figure FDA00041504297300000219
be is an alternative line set in the power distribution network; ne is a node set of the power distribution network; di is a power distribution network set; se is scene set; tc is a set of alternative transmission line types; ns is a substation node set in the power distribution network; ndg is a distributed power supply node set in a power distribution network; c (C) inv,p 、C pen,p 、C ope,p Respectively representing the investment cost, penalty term and operation cost of the p-type power distribution network; cn (control unit) c 、Cc c Representing the cost of newly-built and expanded unit length c type feeder lines respectively; v p,s 、w p,s The coefficient vector of the Lagrangian item and the augmented Lagrangian item is continuously updated in the iterative process of the ATC algorithm; r is (r) p,s The system is characterized by being a response variable, and comprising active power, reactive power and voltage amplitude at a transformer substation node connected with a power transmission network; t is t p,s The target variable is obtained by calculating a power transmission network sub-problem, and is a constant in the power distribution network sub-problem; Φ is the uncertainty set in the max problem; n is n s The number of hours scene s appears in one year, which sums to 8760; probability C s The unit electricity purchasing cost; b i 、c i Is a distributed power cost coefficient; kappa represents the annual investment cost conversion coefficient of the equipment; r represents annual rate; t represents the equipment age;
V i,p,s the voltage amplitude of the node i in the power distribution network p under the scene s is represented, and subscripts p and s in the following variables respectively represent the scene of the power distribution network p and the scene of the scene s, and are not repeated; v (V) ref Taking 1 as a reference node voltage value;
Figure FDA0004150429730000031
active power and reactive power flowing through the type-c feeder at the line ij are respectively represented; />
Figure FDA0004150429730000032
Respectively representing active power and reactive power sent by a transformer substation at a node i; />
Figure FDA0004150429730000033
Active power and reactive power sent by the distributed power supply at the node i are respectively represented; />
Figure FDA0004150429730000034
Respectively representing active power and reactive power emitted by a photovoltaic power station at a node i; />
Figure FDA0004150429730000035
Respectively representing active power and reactive power emitted by the wind turbine generator at the node i; />
Figure FDA0004150429730000036
Respectively representing the active and reactive loads of the node i; />
Figure FDA0004150429730000037
Representing the upper limit of the capacity of the transformer substation; />
Figure FDA0004150429730000038
Representing an upper limit of the capacity of the c-type alternative feeder line; />
Figure FDA0004150429730000039
Representing the upper limit of active power sent by the distributed power supply at the node i; v (V) max 、V min Respectively representing the maximum value and the minimum value of the node voltage; b ij,p,s Is a relaxation variable; />
Figure FDA00041504297300000310
Reference output power of photovoltaic power and wind power in a scene s with unit capacity respectively, +.>
Figure FDA00041504297300000311
The normalized load under the scene s is the result of k-means clustering; />
Figure FDA00041504297300000312
Generated by the normalized load through k-means clustering; />
Figure FDA00041504297300000313
Figure FDA00041504297300000314
Respectively representing the photovoltaic installed capacity, the wind power installed capacity and the load maximum value at the node i; r is (r) c 、x c The resistance value and the reactance value of the unit length of the c-type wire are respectively shown; l (L) ij Representing the length of the line ij; />
Figure FDA00041504297300000315
A variable of 0-1 is used for indicating whether a c-type feeder exists at a planned power distribution network line ij; />
Figure FDA00041504297300000316
The variables are 0-1, and respectively represent whether a new built, expanded and original c-type feeder exists at the ij of the power distribution network line; />
Figure FDA0004150429730000041
For a known constant, when a c-type feeder line value is 1 at the position of the power distribution network line ij before planning, otherwise, the value is zero; />
Figure FDA0004150429730000042
Is a known constant, 1 when there is a substation at node i, or zero; />
Figure FDA0004150429730000043
Is a non-negative continuous auxiliary variable;
the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model;
the optimization goal of the energy storage system optimization configuration is to minimize the investment cost and the annual comprehensive load loss cost of the energy storage system under the worst fault scene of the power distribution network;
Figure FDA0004150429730000044
Figure FDA0004150429730000045
Figure FDA0004150429730000046
Figure FDA0004150429730000047
Figure FDA0004150429730000048
Figure FDA0004150429730000049
s.t.
Figure FDA00041504297300000410
Figure FDA00041504297300000411
Figure FDA00041504297300000412
Figure FDA00041504297300000413
/>
Figure FDA00041504297300000414
Figure FDA00041504297300000415
Figure FDA0004150429730000051
Figure FDA0004150429730000052
in the method, in the process of the invention,
Figure FDA0004150429730000053
bt is a power transmission network line set; nt is a power transmission network node set; di is a distribution network sequence number set; />
Figure FDA0004150429730000054
Respectively representing the power generation cost of a generator, the profit obtained by the transmission of a transmission network to a power distribution network and penalty items under a scene s; n is n h The number of hours scene s appears in one year; c (C) s The unit electricity price is the electricity price of the transmission network when selling electricity to the distribution network; a, a i '、b i '、c i ' is the cost factor of the generator at grid node i; v p,s 、w p,s The coefficient vector of the Lagrangian item and the augmented Lagrangian item is continuously updated in the iterative process of the ATC algorithm; r is (r) p,s In response to the variable, the power distribution network problem is calculated and obtained, and the power is transmittedThe net problem is constant; t is t p,s The power transmission network node is used as a target variable and consists of active power, reactive power and voltage square at the power transmission network node connected with the power distribution network p; />
Figure FDA0004150429730000055
Respectively representing active power and reactive power generated by a generator at a power transmission network node i in a scene s; />
Figure FDA0004150429730000056
Respectively representing active power and reactive power which are transmitted to a power distribution network at a power transmission network node i in a scene s; />
Figure FDA0004150429730000057
Representing the square of the voltage amplitude at grid node i in scenario s; r is R ij,s 、T ij,s As auxiliary variable, the relation between the voltage amplitude and the phase angle is R ij,s ＝U i,s U j,s cosδ ij,s 、T ij,s ＝U i,s U j,s sinδ ij,s If the relation is needed, the voltage amplitude and the phase angle can be obtained, and the model does not need to be solved; g ij 、B ij The real part and the imaginary part of the elements in the admittance matrix of the power transmission network; v (V) max 、V min Respectively representing upper and lower limits of node voltage; i ij,max An upper current limit through which the line ij is allowed to flow; />
Figure FDA0004150429730000058
Representing the upper and lower limits of active power generated by the generator at node i; />
Figure FDA0004150429730000059
Representing an upper limit of the capacity of a power distribution network transformer connected with a power transmission network node i;
and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.
2. The transmission and distribution collaborative active power distribution network distributed robust extension planning method according to claim 1, wherein the determining of a typical operation scenario under normal operation conditions of the power distribution network comprises: and generating a typical operation scene of photovoltaic treatment, wind power output and load when the power distribution network operates normally by adopting a K-means clustering method.
3. The transmission and distribution collaborative active power distribution network distributed robust extension planning method according to claim 1, wherein line investment construction cost, operation cost and penalty term are taken as objective functions, line construction logic constraint, line capacity constraint, radial topology constraint, power flow constraint, node voltage upper and lower limit constraint and substation capacity constraint are taken as constraint conditions, and a two-stage robust optimization model of a power distribution network problem is established.
4. The transmission and distribution collaborative active power distribution network distributed robust extension planning method according to claim 1, wherein the process of model solution comprises:
the first step: initializing Lagrangian term coefficients v and w and a target variable t, and transmitting the Lagrangian term coefficients v and w and the target variable t to a power distribution network problem;
and a second step of: solving each power distribution network sub-problem by using a C & CG algorithm, updating a response variable r and transmitting the response variable r to the power transmission network sub-problem;
and a third step of: solving the power transmission network sub-problem, updating the target variable t and transmitting the target variable t to each power distribution network sub-problem;
fourth step: performing internal circulation convergence judgment, if so, performing the next step, otherwise, returning to the second step to solve the power distribution network sub-problem;
fifth step: and (3) performing outer loop judgment, ending iteration and outputting each power distribution network planning scheme if convergence is performed, otherwise, updating Lagrangian coefficients v and w and returning to a second step to solve the power distribution network sub-problem.
5. The transmission and distribution collaborative active power distribution network distributed robust extension planning method according to claim 1, wherein the process of iterative solution of the distributed framework method of ATC and C & CG algorithms comprises:
the first step: setting the outer circulation times m and the inner circulation times n to be zero, namely, making m=0 and n=0; setting initial value of Lagrange coefficient
Figure FDA0004150429730000061
Is +.>
Figure FDA0004150429730000062
And a second step of: setting the internal circulation times n=n+1, and starting the nth internal circulation;
and a third step of: by C&CG algorithm solves two-stage robust optimization model of power distribution network sub-problem in parallel, updates response variable
Figure FDA0004150429730000071
And transmitting the data to an SOCP model of the power transmission network sub-problem;
fourth step: solving the power transmission network sub-problem and updating the target variable
Figure FDA0004150429730000072
And transmitting the problems to a power distribution network;
fifth step: judging whether the inner loop convergence conditional expression (51) is satisfied; if the formula (51) is established, jumping to a sixth step; otherwise, returning to the second step;
Figure FDA0004150429730000073
wherein f n The sum of the power transmission network sub-problem objective function obtained for the nth internal loop calculation and each power distribution network sub-problem objective function, namely
Figure FDA0004150429730000074
ε 1 The gap is converged for internal circulation;
sixth step: device for placing articles
Figure FDA0004150429730000075
Judging whether the outer circulation convergence conditional expression (52) and the expression (53) are simultaneously established; if the two power distribution network planning schemes are simultaneously established, ending iteration and outputting each power distribution network planning scheme; otherwise, jumping to a seventh step;
Figure FDA0004150429730000076
Figure FDA0004150429730000077
wherein ε 2 、ε 3 Converging the gap for the outer circulation;
seventh step: setting the outer circulation times m=m+1; updating the Lagrangian coefficient according to equation (54), equation (55)
Figure FDA0004150429730000078
Figure FDA0004150429730000079
Figure FDA00041504297300000710
Wherein β is a constant greater than 1, and is related to the convergence speed;
eighth step: are sequentially arranged
Figure FDA00041504297300000711
n=0, returning to the second step.
6. The distributed robust extension planning system of the active power distribution network with cooperative transmission and distribution is characterized by comprising:
a two-stage robust optimization model building module for power distribution network sub-problems, configured to: based on a typical operation scene under the condition of normal operation of a power distribution network, setting the variation range of uncertain concentrated photovoltaic output, wind power output and load according to the confidence level, and establishing a two-stage robust optimization model of the power distribution network sub-problem based on the uncertain set;
the power distribution network problem is modeled as a two-stage robust optimization model, an uncertainty set in the model is a variation range of wind-light output and power distribution network load under a typical scene, a first-stage decision variable is a construction variable of each alternative line of the power distribution network, a second-stage decision variable is an operation variable of the power distribution network under each scene, and the problems comprise node voltage, line power flow, transformer substation output power and the like, and can be expressed as follows:
min f dis,p ＝C inv,p +C pen,p +C ope,p (4)
Figure FDA0004150429730000081
Figure FDA0004150429730000082
Figure FDA0004150429730000083
Figure FDA0004150429730000084
Figure FDA0004150429730000085
Figure FDA0004150429730000086
s.t.
Figure FDA0004150429730000087
Figure FDA0004150429730000088
Figure FDA0004150429730000089
Figure FDA00041504297300000810
Figure FDA00041504297300000811
Figure FDA00041504297300000812
Figure FDA00041504297300000813
Figure FDA0004150429730000091
Figure FDA0004150429730000092
Figure FDA0004150429730000093
Figure FDA0004150429730000094
Figure FDA0004150429730000095
Figure FDA0004150429730000096
Figure FDA0004150429730000097
Figure FDA0004150429730000098
/>
V min ≤V i,p,s ≤V max (26)
Figure FDA0004150429730000099
Figure FDA00041504297300000910
Figure FDA00041504297300000911
Figure FDA00041504297300000912
Figure FDA00041504297300000913
Figure FDA00041504297300000914
Figure FDA00041504297300000915
Figure FDA00041504297300000916
Figure FDA00041504297300000917
Figure FDA00041504297300000918
in the method, in the process of the invention,
Figure FDA00041504297300000919
be is an alternative line set in the power distribution network; ne is a node set of the power distribution network; di is a power distribution network set; se is scene set; tc is a set of alternative transmission line types; ns is a substation node set in the power distribution network; ndg is a distributed power supply node set in a power distribution network; c (C) inv,p 、C pen,p 、C ope,p Respectively representing the investment cost, penalty term and operation cost of the p-type power distribution network; cn (control unit) c 、Cc c Representing the cost of newly-built and expanded unit length c type feeder lines respectively; v p,s 、w p,s The coefficient vector of the Lagrangian item and the augmented Lagrangian item is continuously updated in the iterative process of the ATC algorithm; r is (r) p,s In response to variables, by substations connected to the transmission networkActive power, reactive power and voltage amplitude at the node; t is t p,s The target variable is obtained by calculating a power transmission network sub-problem, and is a constant in the power distribution network sub-problem; Φ is the uncertainty set in the max problem; n is n s The number of hours scene s appears in one year, which sums to 8760; probability C s The unit electricity purchasing cost; b i 、c i Is a distributed power cost coefficient; kappa represents the annual investment cost conversion coefficient of the equipment; r represents annual rate; t represents the equipment age;
V i,p,s the voltage amplitude of the node i in the power distribution network p under the scene s is represented, and subscripts p and s in the following variables respectively represent the scene of the power distribution network p and the scene of the scene s, and are not repeated; v (V) ref Taking 1 as a reference node voltage value;
Figure FDA0004150429730000101
active power and reactive power flowing through the type-c feeder at the line ij are respectively represented; />
Figure FDA0004150429730000102
Respectively representing active power and reactive power sent by a transformer substation at a node i; />
Figure FDA0004150429730000103
Active power and reactive power sent by the distributed power supply at the node i are respectively represented; />
Figure FDA0004150429730000104
Respectively representing active power and reactive power emitted by a photovoltaic power station at a node i; />
Figure FDA0004150429730000105
Respectively representing active power and reactive power emitted by the wind turbine generator at the node i; />
Figure FDA00041504297300001016
Respectively representing the active and reactive loads of the node i; />
Figure FDA0004150429730000106
Representing the upper limit of the capacity of the transformer substation; />
Figure FDA0004150429730000107
Representing an upper limit of the capacity of the c-type alternative feeder line; />
Figure FDA0004150429730000108
Representing the upper limit of active power sent by the distributed power supply at the node i; v (V) max 、V min Respectively representing the maximum value and the minimum value of the node voltage; b ij,p,s Is a relaxation variable; />
Figure FDA0004150429730000109
Reference output power of photovoltaic power and wind power in a scene s with unit capacity respectively, +.>
Figure FDA00041504297300001010
The normalized load under the scene s is the result of k-means clustering; />
Figure FDA00041504297300001011
Generated by the normalized load through k-means clustering; />
Figure FDA00041504297300001012
Figure FDA00041504297300001013
Respectively representing the photovoltaic installed capacity, the wind power installed capacity and the load maximum value at the node i; r is (r) c 、x c The resistance value and the reactance value of the unit length of the c-type wire are respectively shown; l (L) ij Representing the length of the line ij; />
Figure FDA00041504297300001014
A variable of 0-1 is used for indicating whether a c-type feeder exists at a planned power distribution network line ij; />
Figure FDA00041504297300001015
The variables are 0-1, and respectively represent whether a new built, expanded and original c-type feeder exists at the ij of the power distribution network line; />
Figure FDA0004150429730000111
For a known constant, when a c-type feeder line value is 1 at the position of the power distribution network line ij before planning, otherwise, the value is zero; />
Figure FDA0004150429730000112
Is a known constant, 1 when there is a substation at node i, or zero; />
Figure FDA0004150429730000113
Is a non-negative continuous auxiliary variable;
a power grid sub-problem SOCP model building module configured to: the method comprises the steps of taking three parts of the power generation cost of a generator, profit obtained by selling electricity to a power distribution network and punishment term as objective functions, and taking power transmission network tide constraint, node voltage upper and lower limit constraint, power transmission line current constraint and generator capacity constraint as constraint conditions to establish a power transmission network sub-problem SOCP model;
the optimization goal of the energy storage system optimization configuration is to minimize the investment cost and the annual comprehensive load loss cost of the energy storage system under the worst fault scene of the power distribution network;
Figure FDA0004150429730000114
Figure FDA0004150429730000115
Figure FDA0004150429730000116
Figure FDA0004150429730000117
Figure FDA0004150429730000118
Figure FDA0004150429730000119
s.t.
Figure FDA00041504297300001110
Figure FDA00041504297300001111
Figure FDA00041504297300001112
Figure FDA00041504297300001113
Figure FDA00041504297300001114
Figure FDA0004150429730000121
Figure FDA0004150429730000122
/>
Figure FDA0004150429730000123
in the method, in the process of the invention,
Figure FDA0004150429730000124
bt is a power transmission network line set; nt is a power transmission network node set; di is a distribution network sequence number set; />
Figure FDA0004150429730000125
Respectively representing the power generation cost of a generator, the profit obtained by the transmission of a transmission network to a power distribution network and penalty items under a scene s; n is n h The number of hours scene s appears in one year; c (C) s The unit electricity price is the electricity price of the transmission network when selling electricity to the distribution network; a, a i '、b i '、c i ' is the cost factor of the generator at grid node i; v p,s 、w p,s The coefficient vector of the Lagrangian item and the augmented Lagrangian item is continuously updated in the iterative process of the ATC algorithm; r is (r) p,s The response variable is obtained by calculating a power distribution network sub-problem, and is constant in the power transmission network sub-problem; t is t p,s The power transmission network node is used as a target variable and consists of active power, reactive power and voltage square at the power transmission network node connected with the power distribution network p; />
Figure FDA0004150429730000126
Respectively representing active power and reactive power generated by a generator at a power transmission network node i in a scene s; />
Figure FDA0004150429730000127
Respectively representing active power and reactive power which are transmitted to a power distribution network at a power transmission network node i in a scene s; />
Figure FDA0004150429730000128
Representing the square of the voltage amplitude at grid node i in scenario s；R ij,s 、T ij,s As auxiliary variable, the relation between the voltage amplitude and the phase angle is R ij,s ＝U i,s U j,s cosδ ij,s 、T ij,s ＝U i,s U j,s sinδ ij,s If the relation is needed, the voltage amplitude and the phase angle can be obtained, and the model does not need to be solved; g ij 、B ij The real part and the imaginary part of the elements in the admittance matrix of the power transmission network; v (V) max 、V min Respectively representing upper and lower limits of node voltage; i ij,max An upper current limit through which the line ij is allowed to flow; />
Figure FDA0004150429730000129
Representing the upper and lower limits of active power generated by the generator at node i; />
Figure FDA00041504297300001210
Representing an upper limit of the capacity of a power distribution network transformer connected with a power transmission network node i;
a multi-distribution network planning scheme acquisition module configured to: and (3) iteratively solving a two-stage robust optimization model of the power distribution network sub-problem and an SOCP model of the power transmission network sub-problem by using a distributed framework method based on ATC and C & CG algorithms until the models converge to obtain a multi-power distribution network planning scheme.
7. A computer readable storage medium, having stored thereon a computer program, which when executed by a processor, implements the steps of the transmission and distribution collaborative active power distribution network distributed robust extension planning method according to any of claims 1-5.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the transmission and distribution collaborative active power distribution network distributed robust extension planning method according to any of claims 1-5 when the program is executed by the processor.
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* Cited by examiner, † Cited by third party
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CN108054789A (en) * 2017-12-22 2018-05-18 清华大学 A kind of embedded idle and voltage security constraint economic dispatch method
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108054789A (en) * 2017-12-22 2018-05-18 清华大学 A kind of embedded idle and voltage security constraint economic dispatch method
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