CN112884270A - Multi-scene power distribution network planning method and system considering uncertainty factors - Google Patents

Multi-scene power distribution network planning method and system considering uncertainty factors Download PDF

Info

Publication number
CN112884270A
CN112884270A CN202011641023.2A CN202011641023A CN112884270A CN 112884270 A CN112884270 A CN 112884270A CN 202011641023 A CN202011641023 A CN 202011641023A CN 112884270 A CN112884270 A CN 112884270A
Authority
CN
China
Prior art keywords
distribution network
power distribution
planning
scene
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011641023.2A
Other languages
Chinese (zh)
Other versions
CN112884270B (en
Inventor
王春义
罗志杰
王李龑
陈芳
李先栋
许强
刘玉田
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
Shandong University
Liaocheng Power Supply Co of State Grid Shandong Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
Shandong University
Liaocheng Power Supply Co of State Grid Shandong Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, Shandong University, Liaocheng Power Supply Co of State Grid Shandong Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202011641023.2A priority Critical patent/CN112884270B/en
Publication of CN112884270A publication Critical patent/CN112884270A/en
Application granted granted Critical
Publication of CN112884270B publication Critical patent/CN112884270B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Water Supply & Treatment (AREA)
  • Game Theory and Decision Science (AREA)
  • Primary Health Care (AREA)
  • Quality & Reliability (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a multi-scene power distribution network planning method and system considering uncertainty factors. The influence of uncertain factors on the power distribution network planning can be effectively reduced, the planning efficiency is improved, the economical efficiency and the reliability of the power distribution network are improved, and the method has strong practical application background and engineering value.

Description

Multi-scene power distribution network planning method and system considering uncertainty factors
Technical Field
The disclosure relates to the technical field of power distribution network planning, in particular to a multi-scene power distribution network planning method and system considering uncertainty factors.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Along with the development of social economy, the demand of residents for electricity is continuously increased, and the current power distribution network is changed in the aspects of structure and function due to the access of diversified power sources and loads. The access of distributed power supplies such as wind power, photovoltaic and the like, the massive application of electric automobiles, the scale development of energy storage equipment and the use of power electronic devices, so that uncertain factors are tested for the safe and stable operation capacity of a power distribution network. In consideration of the gradual planning of the power distribution network, the construction of wind and light storage and electric vehicle charging stations can have a significant influence on the power distribution network flow. The traditional power distribution network focuses on changes of a transformer substation, a feeder line and loads, and how to plan the power distribution network becomes a center of gravity suitable for future power distribution network development along with the increase of influence of uncertainty factors.
Aiming at power distribution network planning, the planning difficulty is greatly increased by uncertainty factors, and the power distribution network planning can be more consistent with the development of actual conditions by selecting a proper method to describe the uncertainty of the power distribution network. A flexible planning method for the active power distribution network frame expansion based on the uncertainty of source load is disclosed in the literature, namely 'Cai Jia Ming, Zhao, Wang Chengmen, Xiening, Zhu Bin', an electric power automation device, 2019, 39(10) '109 and 115' flexible processing is carried out on the uncertainty of source load, a prediction error is corrected, the flexible planning of the power distribution network frame is realized through the establishment of a mathematical model and the selection of a solving method, and the economy and the reliability are improved. The document' Pengxiangguang, Lin Li Xiang, Liu Yi, Lin Zhu Qiong, electric automobile and renewable energy uncertain factor multi-objective distributed power supply optimal configuration [ J ] power grid technology, 2015,39(08): 2188-. Therefore, in the prior art, the influence of uncertain factors is rarely considered, and a plurality of influences of uncertain factors are comprehensively considered for better analysis of power distribution network planning.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-scene power distribution network planning method and system considering uncertainty factors, which can effectively reduce the influence of the uncertainty factors on power distribution network planning, improve planning efficiency, improve the economical efficiency and reliability of a power distribution network, and have strong practical application background and engineering value.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
one or more embodiments provide a multi-scene power distribution network planning method considering uncertainty factors, wherein the uncertainty influence factors influencing the wind, light and energy storage and the power of an electric vehicle charging station are analyzed by combining a power distribution network progressive planning mode to generate a plurality of scenes, a typical scene set is built through reduction to carry out power distribution network planning, and an optimal planning scheme is obtained to carry out power distribution network construction.
One or more embodiments provide a multi-scenario power distribution network planning system considering uncertainty factors, comprising:
an uncertainty factor determination module: configured for determining uncertainty factors for wind-solar storage and electric vehicle charging station power affecting power distribution grid planning;
the typical scene set generation module: the scene generation and scene reduction device is configured for generating a typical scene set according to uncertainty factors;
a planning scheme generation module: the system is configured to be used for establishing a double-layer optimization mathematical model and obtaining a planning scheme corresponding to each typical scene according to the double-layer optimization mathematical model; the double-layer optimization mathematical model takes the minimum comprehensive investment as an upper-layer objective function and the operational reliability as a lower-layer objective function;
a planning scheme screening module: and the optimal planning scheme is configured to be used for calculating the Euclidean distance between uncertainty factors and each typical scene set when the power distribution network is planned corresponding to the planning scheme, and searching the matched optimal planning scheme by taking the minimum comprehensive distance as a target.
An electronic device comprising a memory and a processor, and computer instructions stored on the memory and executable on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) according to the method, when the uncertainty planning of the power distribution network is researched, a scene generation and reduction method is utilized, a clustering method is adopted for distinguishing scenes with large differences, so that planning and discussion are carried out on each group, a proper method is selected for typical scenes for optimization processing, and the planning method can better meet the requirement of power distribution network development. Aiming at each typical scene planning scheme, the power distribution network is matched with uncertain factor conditions during planning, so that the optimal scheme is selected for construction, time is saved, and efficiency is greatly improved.
(2) According to the method, a multi-scenario model is established, uncertainty factors are subjected to clustering analysis, planning and research are conducted according to different conditions, the economy and the reliability of the power distribution network can be improved, and risk challenges brought by the uncertainty factors can be solved to a greater extent.
(3) The power distribution network is planned by using the economic and reliable double-layer planning mathematical model, and the development requirement of the power distribution network is met.
(4) According to the method and the device, the Euclidean distance from uncertain factors of a typical scene in the planning is calculated, the power distribution network planning scheme with the shortest comprehensive distance is selected, and the real-time planning time is saved by establishing an alternative planning scheme library.
Advantages of additional aspects of the disclosure 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 disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure.
Fig. 1 is a flowchart of a power distribution network planning method according to embodiment 1 of the present disclosure;
FIG. 2 is a schematic diagram of a two-level model planning for example 1 of the present disclosure;
fig. 3 is a flowchart of a typical scene clustering method in embodiment 1 of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. 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 disclosure 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 example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments in the present disclosure may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
Example 1
In the technical solutions disclosed in one or more embodiments, as shown in fig. 1, a multi-scenario power distribution network planning method considering uncertainty factors is combined with a power distribution network progressive planning mode, analyzes uncertainty influence factors influencing wind, photovoltaic storage and electric vehicle charging station power, generates a large number of scenarios, and performs power distribution network planning by reducing and establishing a typical scenario set, so as to obtain an optimal planning scheme for power distribution network construction, and includes the following steps:
step 1, determining uncertainty factors of wind-solar energy storage and electric vehicle charging station power influencing power distribution network planning;
step 2, generating a scene and reducing the scene according to uncertainty factors to obtain a typical scene set;
step 3, establishing an economic and reliability double-layer optimization mathematical model, wherein the double-layer optimization mathematical model takes the minimum comprehensive investment as an upper-layer objective function and the operation reliability as a lower-layer objective function, and a planning scheme corresponding to each typical scene is obtained according to the double-layer optimization mathematical model;
and 4, calculating uncertainty factors and Euclidean distances of each typical scene set during power distribution network planning corresponding to the planning scheme, and searching for a matched optimal planning scheme by taking the minimum comprehensive distance as a target.
In the embodiment, when uncertainty planning of a power distribution network is researched, gradual planning of a wind-solar energy storage station and an electric vehicle charging station is considered, and influences of main uncertainty factors are analyzed.
In the embodiment, a scene generation and reduction method is used, a clustering method is adopted for distinguishing scenes with large differences, so that planning and discussion are performed on each group, and a proper method is selected for optimizing typical scenes, so that the planning method better meets the requirement of power distribution network development. Aiming at each typical scene planning scheme, the power distribution network is matched with uncertain factor conditions in the typical scene during planning so as to select the optimal scheme for construction, thereby saving time and greatly improving efficiency.
In the step 1, by combining a power distribution network progressive planning mode, the uncertainty such as regional economic development level, unit installation cost of wind and light storage, installation capacity and service life, and electric vehicle holding capacity is researched, and the uncertainty factors influencing progressive planning are deeply significant for a power distribution network comprising the wind and light storage and an electric vehicle charging station.
The gradual planning mode of the power distribution network regards the development of the power distribution network as a nonlinear process, the gradual adjustment is realized by continuously and dynamically feeding back the system, the practical problem is solved, and the recent target and the plan are made on the basis of historical analysis and grasping of the change rule, namely the implementation and achievement of the short-term target of the last planning step become the basis of the planning strategy adjustment of the next step.
For wind power photovoltaic and photovoltaic power generation, uncertainty of unit installation cost, installation capacity and service life has a large influence; for the energy storage device, the uncertainty of the cost of energy storage unit kilowatt and the total capacity has great influence; for electric vehicle charging stations, the uncertainty of the level of regional economic growth and the amount of electric vehicle holdings has a large impact.
In the step 2, under the combined action of a plurality of uncertain factors, the complexity of power distribution network planning is greatly improved, and the influence on the power distribution network is difficult to accurately describe. In order to simplify the power distribution network planning, multiple scenes are formed through a mathematical model of uncertain factors to solve problems, the modeling and solving difficulty is reduced, and meanwhile, a large number of scenes are reduced to obtain typical scenes to plan the power distribution network.
The method for generating the scene and reducing the scene according to the uncertain factors to obtain the typical scene set can be a Markov method combined with a Monte Carlo method and a K-means clustering method, and comprises the following specific steps of:
step 21, generating a large number of scenes by combining a Markov method with a Monte Carlo method:
calculating the probability of regional economic growth level, unit installation cost, installation capacity, equipment service life and electric automobile holding capacity according to a Markov method; the Monte Carlo method is utilized to simulate the operation condition of the power distribution network, the power of the wind power generation device, the photovoltaic power generation device, the energy storage device and the electric vehicle charging station is predicted, and the scene construction is carried out by combining the predicted power and the predicted error and taking months as time units.
In the embodiment, a Markov method is combined with a Monte Carlo method to generate a scene with multiple uncertainties, so that the common influence of multiple uncertainties can be comprehensively considered.
And 22, carrying out scene reduction through a K-means clustering method to obtain a typical scene set.
(1) Generation of a large number of scenes using a Markov method in combination with a Monte Carlo method
When wind and light storage and electric vehicle charging stations with different capacities are connected to a power distribution network, the power of the power grid can be changed remarkably. By analyzing the planning data of the power distribution network, the change of uncertain factors such as regional economic growth level, unit installed cost, installation capacity, equipment service life, electric automobile holding capacity and the like is related to the last state, a Markov prediction method is utilized to research the random process, and the state E is usediTransition to state EjConditional probability P (E) of state probability of (2)j|Ei) Namely:
P(Ei→Ej)＝P(Ej|Ei)＝Pij (1)
setting up n possible states of the random process, i.e. E1,E2,…,EnThen the state transition probability matrix is:
Figure BDA0002880664380000071
πj(k) indicating that the event is in the state E at the kth moment after k state transitions under the condition that the initial state is knownjThe formula of the calculation is:
Figure BDA0002880664380000072
since pi (k) ═ pi (k-1) P ═ pi (0) PkAfter k times of state transition, the probability of the prediction object in different states tends to be stable, the regional economic growth level, the unit installation cost, the installation capacity, the equipment service life and the electric steamThe state probabilities of uncertain factors such as vehicle holding quantity and the like can be obtained by a Markov prediction method.
And simulating the running condition of the power distribution network by using a Monte Carlo method, and predicting the power of the wind power generation device, the photovoltaic power generation device, the energy storage device and the electric vehicle charging station. Scene construction is carried out by taking months as a time unit in combination with the prediction power and the prediction error, and the expression of the random variable of each scene is as follows:
wind power generation:
Figure BDA0002880664380000081
photovoltaic power generation:
Figure BDA0002880664380000082
electric vehicle charging station:
Figure BDA0002880664380000083
an energy storage device:
Figure BDA0002880664380000084
in the formula: pWT,tThe corrected value of the wind power generation power at the moment t;
Figure BDA0002880664380000085
the predicted value of the wind power generation power at the moment t is obtained; delta PWT,tAnd predicting an error value for the wind power at the time t. PPV,tThe photoelectric power correction value at the time t;
Figure BDA0002880664380000086
the predicted value of the photovoltaic generating electric power at the time t is obtained; delta PPV,tAnd predicting an error value for the wind power at the time t. PEV,tThe power correction value is the electric vehicle charging station power correction value at the moment t;
Figure BDA0002880664380000087
the predicted value is the electric vehicle charging station power at the moment t; delta PEV,tAt time tAnd predicting the power prediction error value of the electric vehicle charging station. PESS,tThe corrected value of the power of the energy storage device at the moment t;
Figure BDA0002880664380000088
the predicted value of the power of the energy storage device at the moment t is obtained; delta PESS,tAnd predicting an error value for the power of the energy storage device at the time t.
Setting a plurality of power intervals aiming at the power of wind power generation, photovoltaic power generation, an energy storage device and an electric vehicle charging station, counting the probability distribution condition of the power intervals, and calculating the power P of the intermediate valueimAs typical values, the calculation formula is as follows:
Pim＝Pi-1+Pi(0<i≤N) (8)
in the formula PiIs the power value at i point, Pi-1Is the power value of i-1 point.
Obtaining N generated by uncertain factors during operation of the power distribution network according to power conditions of wind power generation, photovoltaic power generation, an energy storage device and an electric vehicle charging station in a random number pairing mode5Individual scenes and their corresponding probability distribution.
(2) Scene reduction by K-means clustering
And the K-means clustering method is adopted to reduce the scenes, so that the calculation can be simplified under the condition of losing statistical information as little as possible. As shown in fig. 3, the specific steps may be as follows:
step 22-1: determining the clustering number k, initializing k clustering centers U, and respectively allocating the clustering centers U to the classes represented by the similar clustering centers U according to the maximum similarity of the generated scene and the clustering centers U;
optionally, the similarity may be determined by calculating a euclidean distance between each scene set and the cluster center U, where the euclidean distance is the closest and maximum similarity.
Step 22-2: calculating the mean value of all objects in each category as a new clustering center of the category;
step 22-3: until the cluster center is not changed any more, the new cluster center is used as a typical scene for planning calculation in the following step. And finishing clustering all the scenes, wherein K clustering centers are selected.
Specifically, the calculating step may be:
1. from a set of data in a scene X ═ { X ═ X1,x2,…,xnDividing the scene set into K classes C ═ CkI is 1,2, … K }. Each partition representing a class ckEach class ckWith a class center μi。
2. Calculating scene data xiAnd class center muiThe Euclidean distance of (c), if | | x is satisfiedi-μj||<||xi-μmIf 1,2,3, …, k, m ≠ j, then xi∈cj。
3. Recalculating center points
Figure BDA0002880664380000091
The formula is as follows:
Figure BDA0002880664380000092
4. if it is
Figure BDA0002880664380000093
The program terminates, the algorithm converges, and the result is output. Otherwise, returning to the step 2.
5. And when the iteration times are larger than the maximum iteration times, resetting the iteration times and returning to the step 1.
And according to the K typical scene sets obtained by the K-means scene reduction, planning the power distribution network by taking the category center as typical scene data.
In step 3, the power distribution network is planned according to a typical scene set formed by uncertainty factors: and establishing a double-layer planning mathematical model to form a power distribution network planning scheme. According to the development requirements of the power distribution network, the minimum comprehensive investment of the power distribution network is taken as an upper-layer objective function, and the operational reliability is taken as a lower-layer objective function to obtain a corresponding planning scheme, as shown in fig. 2.
The power distribution network is planned by the aid of the economic and reliable double-layer planning mathematical model, and development requirements of the power distribution network are met.
(1) Upper-layer planning model in double-layer planning mathematical model
The upper-layer planning takes the minimum comprehensive investment of the power distribution network as an objective function, and the comprehensive investment of the power distribution network can comprise wind power generation investment, photovoltaic power generation investment, electric vehicle charging station investment and energy storage device investment. The upper layer objective function may be:
minCtotal＝C1+C2+C3+C4 (9)
wherein, C1Investment for wind power generation, C2Investment for photovoltaic power generation, C3For investment in energy storage devices, C4Investing for electric vehicle charging stations.
The specific calculation formula is
Figure BDA0002880664380000101
Figure BDA0002880664380000102
Figure BDA0002880664380000103
Figure BDA0002880664380000111
Wherein, cwRepresenting unit installation cost of the fan, OwRepresenting annual maintenance cost of wind power generation, NwIndicates the number of wind power generation installations, QwDenotes unit fan capacity, mwRepresenting the running life of the fan; c. CsRepresents the photovoltaic unit installation cost, OsRepresents the annual operation and maintenance cost of photovoltaic power generation, NsRepresents the number of photovoltaic power generation installations, QsDenotes the unit photovoltaic capacity, msRepresenting the photovoltaic operating life; c. CESSRepresenting unit installation cost of the energy storage device, OESSRepresents the annual maintenance cost of the energy storage device, NESSIndicates the number of installed energy storage devices, QESSRepresenting unit energy storage device capacity, mESSRepresenting the operating life of the energy storage device; c. CEVRepresents the unit installation cost of the electric vehicle charging station, OEVRepresents the annual operation and maintenance cost of the electric vehicle charging station, NEVRepresents the number of installed electric vehicle charging stations, QEVRepresents the electric vehicle charging station capacity, mEVAnd d is the discount rate.
The constraints of the upper layer objective function include: a power balance constraint and a node voltage constraint.
The power balance constraints include: the active power balance and the reactive power balance of the nodes are realized;
Figure BDA0002880664380000112
Figure BDA0002880664380000113
wherein phi is a system node set; u and theta are respectively a node voltage and a power angle; G. b is a branch admittance parameter;
Figure BDA0002880664380000114
the load which can be interrupted, the load which can be transferred and the reactive power of the energy storage unit are respectively a node i at the time t;
Figure BDA0002880664380000115
and
Figure BDA0002880664380000116
respectively the active power and the reactive power of a generator connected with a node i at the time t or a power transmission line connected with a superior network,
Figure BDA0002880664380000117
and
Figure BDA0002880664380000118
respectively the active power and the reactive power of the wind turbine generator at a node i at the time t,
Figure BDA0002880664380000119
And
Figure BDA00028806643800001110
respectively the active power and the reactive power of the photovoltaic array at the node i at the time t.
The node voltage constraint is: the voltage at the node is between the upper and lower voltage limits.
Ui,min≤Ui≤Ui,max (16)
Wherein, Ui,min、Ui,maxThe lower and upper voltage limits of the node i are shown.
(2) Lower layer planning model in double-layer planning mathematical model
The lower-layer planning takes the operation reliability of the power distribution network as an objective function, and evaluation indexes of the lower-layer planning comprise average insufficient power ENS [ kWh/(household-year), average system power failure frequency SAIFI [ times/(household-year) ], system annual average power failure time SAIDI [ h/(household-year) ] and average power supply availability ASAI (%).
ENS＝∑PiUi (17)
Figure BDA0002880664380000121
Figure BDA0002880664380000122
Figure BDA0002880664380000123
Wherein, PiIs the active power of node i, UiIs the voltage of node i, ηiIs the failure rate of node i, NiIs the number of users of node i.
The lower layer objective function may be:
minF＝ω1ENS+ω2SAIFI+ω3SAIDI-ω4ASAI (21)
wherein, ω isiFor the corresponding index weights, the corresponding values are determined by expert discussion or empirically.
The constraints of the lower layer objective function include: line current constraints, transformer capacity constraints, and distributed energy permeability constraints.
And (3) line current constraint: the current flowing through the line is less than the maximum current allowed to flow.
Figure BDA0002880664380000124
Wherein,
Figure BDA0002880664380000125
the current and the maximum current allowed to flow through the wires of the line i → j, respectively;
and (3) transformer capacity constraint: the transformer usage capacity of a line is less than the transformer capacity of the line connection.
Figure BDA0002880664380000131
Wherein,
Figure BDA0002880664380000132
the transformer usage capacity of the line i and the transformer capacity of the line i connection are respectively set;
and (3) restricting the permeability of the distributed energy sources: the power supply capacity of the distributed energy sources is not greater than the load demand.
Figure BDA0002880664380000133
Wherein, Fi WT,Fi PV,Fi LAre respectively omega of the distribution network0The wind power capacity of the node i is,photovoltaic capacity and load demand, ω0And limiting the proportion of distributed energy sources of the power distribution network.
In step 4, calculating uncertainty factors and Euclidean distances of each typical scene set when the power distribution network is planned corresponding to the planning scheme, and searching for a matched optimal planning scheme by taking the minimum comprehensive distance as a target, wherein the method specifically comprises the following steps:
and determining uncertain factors of an actual power grid corresponding to the power distribution network planning scheme based on the power distribution network planning scheme obtained by typical scene calculation, calculating Euclidean distances between the uncertain factors of the actual power grid and a typical scene classification center, and selecting the power distribution network planning scheme obtained by the typical scene with the shortest distance to the typical scene as a power distribution network construction scheme.
The calculation formula of the Euclidean distance is as follows:
Figure BDA0002880664380000134
wherein d is Euclidean distance, xnFor the abscissa, y, of the uncertainty factor n during planningnIs a vertical coordinate of the main body of the device,
Figure BDA0002880664380000135
being the abscissa of the uncertainty factor n in a typical scene m,
Figure BDA0002880664380000136
is the ordinate of the uncertainty factor n in a typical scene m.
And selecting a planning scheme of the power distribution network planning obtained by the typical scene with the shortest distance to the typical scene as a power distribution network construction scheme.
According to the embodiment, the Euclidean distance from the uncertain factors of the typical scene in the planning is calculated, the power distribution network planning scheme with the shortest comprehensive distance is selected, and the real-time planning time is saved by establishing the alternative planning scheme library.
Example 2
The embodiment provides a multi-scenario power distribution network planning system considering uncertainty factors, which includes:
an uncertainty factor determination module: configured for determining uncertainty factors for wind-solar storage and electric vehicle charging station power affecting power distribution grid planning;
the typical scene set generation module: the scene generation and scene reduction device is configured for generating a typical scene set according to uncertainty factors;
a planning scheme generation module: the system is configured to be used for establishing a double-layer optimization mathematical model and obtaining a planning scheme corresponding to each typical scene according to the double-layer optimization mathematical model; the double-layer optimization mathematical model takes the minimum comprehensive investment as an upper-layer objective function and the operational reliability as a lower-layer objective function;
a planning scheme screening module: and the optimal planning scheme is configured to be used for calculating the Euclidean distance between uncertainty factors and each typical scene set when the power distribution network is planned corresponding to the planning scheme, and searching the matched optimal planning scheme by taking the minimum comprehensive distance as a target.
Example 3
The present embodiment provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of embodiment 1.
Example 4
The present embodiment provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of embodiment 1.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (10)

1. A multi-scene power distribution network planning method considering uncertainty factors is characterized in that: and analyzing uncertain influence factors influencing wind-solar energy storage and electric vehicle charging station power by combining a power distribution network progressive planning mode to generate a plurality of scenes, and establishing a typical scene set through reduction to plan the power distribution network, so as to obtain an optimal planning scheme to construct the power distribution network.
2. The method for planning the multi-scenario power distribution network considering the uncertainty factors as claimed in claim 1, wherein the method comprises the following steps:
determining uncertainty factors of wind-solar energy storage and electric vehicle charging station power influencing power distribution network planning;
scene generation and scene reduction are carried out according to uncertainty factors to obtain a typical scene set;
establishing a double-layer optimization mathematical model, and obtaining a planning scheme corresponding to each typical scene according to the double-layer optimization mathematical model; the double-layer optimization mathematical model takes the minimum comprehensive investment as an upper-layer objective function and the operational reliability as a lower-layer objective function;
and calculating uncertainty factors corresponding to the planning scheme in the power distribution network planning process and Euclidean distances of each typical scene set, and searching for a matched optimal planning scheme by taking the minimum comprehensive distance as a target.
3. The multi-scenario power distribution network planning method considering uncertainty factors according to claim 2, characterized in that: and (3) performing scene generation and scene reduction according to uncertainty factors to obtain a typical scene set, wherein the method is a Markov method combined with a Monte Carlo method and a K-means clustering method.
4. The multi-scenario power distribution network planning method considering uncertainty factors according to claim 3, characterized in that: the method for generating the scene and reducing the scene according to the uncertainty factors to obtain the typical scene set specifically comprises the following steps:
generating a large number of scenes by combining a Markov method with a Monte Carlo method;
and (5) performing scene subtraction by using a K-means clustering method to obtain a typical scene set.
5. The multi-scenario power distribution network planning method considering uncertainty factors according to claim 4, characterized in that: the method for generating the scene and reducing the scene according to the uncertainty factors to obtain the typical scene set comprises the following steps:
determining the clustering number k, initializing k clustering centers U, and respectively allocating the clustering centers U to the classes represented by the similar clustering centers U according to the maximum similarity of the generated scene and the clustering centers U;
calculating the mean value of all objects in each category as a new clustering center of the category;
and taking the new clustering center as a typical scene until the clustering center does not change any more.
6. The multi-scenario power distribution network planning method considering uncertainty factors according to claim 2, characterized in that: the upper-layer planning of the double-layer optimization mathematical model takes the minimum comprehensive investment of a power distribution network as an objective function, wherein the comprehensive investment of the power distribution network comprises wind power generation investment, photovoltaic power generation investment, electric vehicle charging station investment and energy storage device investment;
or/and
the lower-layer planning of the double-layer optimization mathematical model takes the operation reliability of the power distribution network as an objective function, and evaluation indexes of the double-layer optimization mathematical model comprise average insufficient electric quantity, average system power failure frequency, system annual average power failure time and average power supply availability.
7. The multi-scenario power distribution network planning method considering uncertainty factors according to claim 2, characterized in that: aiming at wind power photovoltaic and photovoltaic power generation, uncertain factors comprise unit installation cost, installation capacity and service life; aiming at the energy storage device, uncertain factors comprise the cost of energy storage unit kilowatt and the total capacity; for electric vehicle charging stations, the uncertainty factors include regional economic growth levels and electric vehicle holdup.
8. A multi-scenario power distribution network planning system considering uncertainty factors is characterized by comprising the following steps:
an uncertainty factor determination module: configured for determining uncertainty factors for wind-solar storage and electric vehicle charging station power affecting power distribution grid planning;
the typical scene set generation module: the scene generation and scene reduction device is configured for generating a typical scene set according to uncertainty factors;
a planning scheme generation module: the system is configured to be used for establishing a double-layer optimization mathematical model and obtaining a planning scheme corresponding to each typical scene according to the double-layer optimization mathematical model; the double-layer optimization mathematical model takes the minimum comprehensive investment as an upper-layer objective function and the operational reliability as a lower-layer objective function;
a planning scheme screening module: and the optimal planning scheme is configured to be used for calculating the Euclidean distance between uncertainty factors and each typical scene set when the power distribution network is planned corresponding to the planning scheme, and searching the matched optimal planning scheme by taking the minimum comprehensive distance as a target.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 7.
CN202011641023.2A 2020-12-31 2020-12-31 Multi-scene power distribution network planning method and system considering uncertainty factors Active CN112884270B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011641023.2A CN112884270B (en) 2020-12-31 2020-12-31 Multi-scene power distribution network planning method and system considering uncertainty factors

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011641023.2A CN112884270B (en) 2020-12-31 2020-12-31 Multi-scene power distribution network planning method and system considering uncertainty factors

Publications (2)

Publication Number Publication Date
CN112884270A true CN112884270A (en) 2021-06-01
CN112884270B CN112884270B (en) 2023-02-17

Family

ID=76046657

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011641023.2A Active CN112884270B (en) 2020-12-31 2020-12-31 Multi-scene power distribution network planning method and system considering uncertainty factors

Country Status (1)

Country Link
CN (1) CN112884270B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113505939A (en) * 2021-07-27 2021-10-15 云南电网有限责任公司电力科学研究院 Energy system random planning method and system considering uncertainty factors
CN113627655A (en) * 2021-07-16 2021-11-09 深圳供电局有限公司 Power distribution network pre-disaster fault scene simulation prediction method and device
CN113723512A (en) * 2021-08-31 2021-11-30 南京邮电大学盐城大数据研究院有限公司 Time-period joint pricing method and system for electric vehicle photovoltaic charging station network
CN113780781A (en) * 2021-08-31 2021-12-10 国网河北省电力有限公司经济技术研究院 Capacity expansion equipment selection method and device of comprehensive energy system and terminal
CN113807019A (en) * 2021-09-24 2021-12-17 清华大学 MCMC wind power simulation method based on improved scene classification and coarse grain removal
CN116054167A (en) * 2023-03-06 2023-05-02 国网山东省电力公司聊城供电公司 Power grid comprehensive dispatching management system and method based on power distribution network flexible controller

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105868499A (en) * 2016-04-20 2016-08-17 电子科技大学 Method for capacity configuration of wind-light storage electric vehicle charging station
CN109508857A (en) * 2018-09-28 2019-03-22 国网浙江省电力有限公司经济技术研究院 A kind of active distribution network multistage programming method
CN109559035A (en) * 2018-11-29 2019-04-02 国网(苏州)城市能源研究院有限责任公司 A kind of urban power distribution network bi-level programming method considering flexibility
CN109978404A (en) * 2019-04-04 2019-07-05 国网经济技术研究院有限公司 Power transmission channel expansion planning method considering uncertainty factors and scene reduction
CN110751368A (en) * 2019-09-18 2020-02-04 清华大学 Electric vehicle storage and charging station planning method considering flexibility of charging load
US20200212681A1 (en) * 2019-01-02 2020-07-02 Tsinghua University Method, apparatus and storage medium for transmission network expansion planning considering extremely large amounts of operation scenarios
CN111401755A (en) * 2020-03-19 2020-07-10 国电南瑞科技股份有限公司 Multi-new-energy output scene generation method, device and system based on Markov chain
CN111639870A (en) * 2020-06-04 2020-09-08 国网山东省电力公司潍坊供电公司 Power transmission network multi-target flexible planning method and system considering source load uncertainty

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105868499A (en) * 2016-04-20 2016-08-17 电子科技大学 Method for capacity configuration of wind-light storage electric vehicle charging station
CN109508857A (en) * 2018-09-28 2019-03-22 国网浙江省电力有限公司经济技术研究院 A kind of active distribution network multistage programming method
CN109559035A (en) * 2018-11-29 2019-04-02 国网(苏州)城市能源研究院有限责任公司 A kind of urban power distribution network bi-level programming method considering flexibility
US20200212681A1 (en) * 2019-01-02 2020-07-02 Tsinghua University Method, apparatus and storage medium for transmission network expansion planning considering extremely large amounts of operation scenarios
CN109978404A (en) * 2019-04-04 2019-07-05 国网经济技术研究院有限公司 Power transmission channel expansion planning method considering uncertainty factors and scene reduction
CN110751368A (en) * 2019-09-18 2020-02-04 清华大学 Electric vehicle storage and charging station planning method considering flexibility of charging load
CN111401755A (en) * 2020-03-19 2020-07-10 国电南瑞科技股份有限公司 Multi-new-energy output scene generation method, device and system based on Markov chain
CN111639870A (en) * 2020-06-04 2020-09-08 国网山东省电力公司潍坊供电公司 Power transmission network multi-target flexible planning method and system considering source load uncertainty

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113627655A (en) * 2021-07-16 2021-11-09 深圳供电局有限公司 Power distribution network pre-disaster fault scene simulation prediction method and device
CN113627655B (en) * 2021-07-16 2024-03-05 深圳供电局有限公司 Method and device for simulating and predicting pre-disaster fault scene of power distribution network
CN113505939A (en) * 2021-07-27 2021-10-15 云南电网有限责任公司电力科学研究院 Energy system random planning method and system considering uncertainty factors
CN113723512A (en) * 2021-08-31 2021-11-30 南京邮电大学盐城大数据研究院有限公司 Time-period joint pricing method and system for electric vehicle photovoltaic charging station network
CN113780781A (en) * 2021-08-31 2021-12-10 国网河北省电力有限公司经济技术研究院 Capacity expansion equipment selection method and device of comprehensive energy system and terminal
CN113807019A (en) * 2021-09-24 2021-12-17 清华大学 MCMC wind power simulation method based on improved scene classification and coarse grain removal
CN116054167A (en) * 2023-03-06 2023-05-02 国网山东省电力公司聊城供电公司 Power grid comprehensive dispatching management system and method based on power distribution network flexible controller

Also Published As

Publication number Publication date
CN112884270B (en) 2023-02-17

Similar Documents

Publication Publication Date Title
CN112884270B (en) Multi-scene power distribution network planning method and system considering uncertainty factors
CN112132427B (en) Power grid multi-layer planning method considering user side multiple resource access
CN107679658B (en) Power transmission network planning method under high-proportion clean energy access
Ghadimi et al. PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives
CN111401604B (en) Power system load power prediction method and energy storage power station power distribution method
Ye et al. Optimization of hybrid energy storage system control strategy for pure electric vehicle based on typical driving cycle
CN113285490B (en) Power system scheduling method, device, computer equipment and storage medium
Zhong et al. Bayesian learning-based multi-objective distribution power network reconfiguration
CN112380694B (en) Power distribution network optimization planning method based on differential reliability requirements
CN113988384A (en) Energy storage capacity optimal configuration method for improving reliability of power distribution network
CN112053035A (en) Power transmission channel and energy storage joint planning method considering economy and flexibility
CN115496273A (en) Renewable energy cluster distribution robustness optimization configuration method and system
Wen et al. Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration
CN113690930B (en) NSGA-III algorithm-based medium and long term locating and sizing method for distributed photovoltaic power supply
CN117557009B (en) Power efficiency monitoring method and system
CN113708418A (en) Micro-grid optimization scheduling method
CN117522014A (en) Storage and distribution network joint planning method considering multiple uncertainties
CN113364043A (en) Micro-grid group optimization method based on condition risk value
CN115425697B (en) Distributed cross-region and cross-province scheduling method and system based on alternate direction multiplier method
CN117972968A (en) Photovoltaic multi-point access-oriented installation capacity feasible region model and observation method
CN114204552A (en) Comprehensive energy scheduling method for intelligent park
Zheng et al. A comprehensive decision-making method for coordinated transmission and distribution network planning
Tian et al. Sensitivity guided genetic algorithm for placement of distributed energy resources
Wei et al. Multi‐objective optimal configuration of stand‐alone microgrids based on Benders decomposition considering power supply reliability
CN117748555B (en) Multi-objective optimal configuration method and device for distributed photovoltaic energy storage system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant