CN108964037B - Construction method based on high-voltage distribution network reconstructability model - Google Patents

Construction method based on high-voltage distribution network reconstructability model Download PDF

Info

Publication number
CN108964037B
CN108964037B CN201810798107.3A CN201810798107A CN108964037B CN 108964037 B CN108964037 B CN 108964037B CN 201810798107 A CN201810798107 A CN 201810798107A CN 108964037 B CN108964037 B CN 108964037B
Authority
CN
China
Prior art keywords
model
photovoltaic
network
power
load
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.)
Expired - Fee Related
Application number
CN201810798107.3A
Other languages
Chinese (zh)
Other versions
CN108964037A (en
Inventor
吕林
刘芳芳
刘友波
张曦
刘俊勇
姚一杨
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.)
Sichuan University
State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Sichuan University
State Grid Zhejiang 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 Sichuan University, State Grid Zhejiang Electric Power Co Ltd filed Critical Sichuan University
Priority to CN201810798107.3A priority Critical patent/CN108964037B/en
Publication of CN108964037A publication Critical patent/CN108964037A/en
Application granted granted Critical
Publication of CN108964037B publication Critical patent/CN108964037B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a high-voltage distribution network reconstructability model, and a model construction method comprises the following steps: step S1: constructing a dynamic load model; step S2: calculating a network blocking risk index NCRI according to random factors such as gap performance source output, electric vehicle charging/discharging, system load removal and the like of a current urban power grid; step S3: calculating an HVDN (high voltage distribution network) topological network according to the dynamic load model; step S4, evaluating the HVDN topology network of the high-voltage distribution network according to the calculated NCRI; the problem of system load unbalance degree, lead to existing model practicality and stability relatively poor, be difficult to adapt to the development of current science and technology is solved.

Description

Construction method based on high-voltage distribution network reconstructability model
Technical Field
The invention relates to the field of high-voltage distribution network reconstruction, in particular to a construction method based on a high-voltage distribution network reconstruction model.
Background
In the process of rapid development of urban novel energy loads, the high-voltage distribution network topology reconstruction technology has obvious defects in the treatment of the problem of large-scale photovoltaic power station grid connection consumption and the problem of local blocking aggravation of an urban power grid caused by random charge and discharge of a high-permeability electric vehicle, and the like, the current technology only aims at a single-section static model constructed by uneven regional load distribution, and the problems of increasing randomness of system tide in time sequence, increasing urban power grid topology risk evaluation difficulty and solving frequent switching operation caused by poor convergence by a non-convex model intelligent algorithm due to the fact that disordered charge and discharge behaviors of the electric vehicle and the random change characteristics of a photovoltaic power generation system are not considered; if the system safety is still inspected by adopting an extreme static deterministic assessment method, the scheme is too conservative, the uncertain fluctuation caused by the novel distributed energy grid-connected system cannot be quantitatively estimated and analyzed, the system is poor in economical efficiency, and the system is difficult to adapt to the power grid safety analysis of the new energy; in addition, in the current stage, research based on a topology reconstruction technology of a high-voltage distribution network aims to solve the problem of local network blocking caused by uneven distribution of conventional loads, and the strong fluctuation of a photovoltaic power supply and an electric vehicle after large-scale grid connection is not strictly studied to enhance the system load imbalance, so that the existing model is poor in practicability and stability and is difficult to adapt to the development of the current technology.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a construction method based on a high-voltage distribution network reconfigurable model, and solves the problems that the existing model is poor in practicability and stability and is difficult to adapt to the development of the current technology due to the unbalanced degree of system load.
The technical scheme adopted by the invention is as follows: the construction method based on the high-voltage distribution network reconstructability model comprises the following steps:
step S1: constructing a dynamic load model;
step S2: calculating a network risk evaluation index NCRI according to the randomness factors of the clearance performance source output, the electric vehicle charging/discharging and the system load removal of the current urban power grid;
step S3: calculating an HVDN (high voltage distribution network) topological network according to the dynamic load model;
step S4: evaluating an HVDN topological network of the high-voltage distribution network according to the calculated NCRI;
step S5: when the NCRI does not exceed the confidence range, the photovoltaic consumption degree of the network and the blocking condition of the system are considered to be in the allowable range at the moment, and no operation action is performed; and triggering HVDN reconstruction when the NCRI exceeds the confidence range, and obtaining the optimal topological state of the network at the moment based on a double-layer optimization model under the comprehensive targets of maximum photovoltaic consumption, maximum system blockage relieving degree and minimum load shedding amount.
The construction method based on the high-voltage distribution network reconstructability model has the following beneficial effects:
1. the multi-objective optimization strategies of novel energy consumption, environmental benefits and user satisfaction based on the probabilistic power flow network topology dynamic risk assessment strategy and HVDN reconfigurability are mutually coordinated, namely, the NCRI index of the network topology risk assessment is fully utilized to evaluate the blocking and consumption degree of the network topology, the real-time monitoring of the network state is realized, the multi-ring non-deep characteristic of the network of the HVDN is fully utilized, the comprehensive load containing the electric automobile and the load containing the photovoltaic power supply are reasonably transferred, and the problem of local blocking aggravation of the system caused by unbalanced load distribution of the urban power grid is solved.
2. The urban power grid absorption and blockage management and control strategy provided by the invention aims at maximizing photovoltaic absorption under the condition of eliminating local blockage, considers the problem of insufficient absorption caused by aggravation of local blockage due to space-time difference of clean energy photovoltaic and electric vehicle load, fully utilizes NCRI indexes containing absorption and blockage factors to carry out prejudgment, and has high practicability.
Drawings
Fig. 1 is a flow chart of a method for constructing a high-voltage distribution network-based reconstructability model according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the construction method based on the high-voltage distribution network reconstructability model includes the following steps:
step S1: constructing a dynamic load model;
step S2: calculating a network risk evaluation index NCRI according to the randomness factors of the clearance performance source output, the electric vehicle charging/discharging and the system load removal of the current urban power grid;
step S3: calculating an HVDN (high voltage distribution network) topological network according to the dynamic load model;
step S4: evaluating an HVDN topological network of the high-voltage distribution network according to the calculated NCRI;
step S5: when the NCRI does not exceed the confidence range, the photovoltaic consumption degree of the network and the blocking condition of the system are considered to be in the allowable range at the moment, and no operation action is performed; and triggering HVDN reconstruction when the NCRI exceeds the confidence range, and obtaining the optimal topological state of the network at the moment based on a double-layer optimization model under the comprehensive targets of maximum photovoltaic consumption, maximum system blockage relieving degree and minimum load shedding amount.
The dynamic load model in step S1 of the present solution includes an electric vehicle charge/discharge load simulation model, an electric vehicle charge/discharge load comprehensive load model, a number distribution model accessed to the electric vehicle in a period of time, a number model accessed to the electric vehicle in a period of time, a predicted output model of the photovoltaic generator, a probability density function of the photovoltaic output active power, and an expected value of the photovoltaic power generation system output power.
The electric vehicle charging/discharging load simulation model comprises the following steps:
Figure GDA0002382292910000041
the comprehensive load model of the electric vehicle charging/discharging load comprises the following steps:
Figure GDA0002382292910000042
in the formula, λEVFor a desired value, n, of the electric vehicle within the time intervalEVIf the probability density function of the number of the electric automobiles which are possibly accessed into the power grid is known, the mean value, the standard deviation and the high-order central moment of the charging/discharging power of the electric automobiles in each period are obtained;
the number distribution model accessed to the electric automobile in a period of time is as follows:
Figure GDA0002382292910000043
the number model of the electric vehicles accessed to the power grid in a period of time is as follows:
Figure GDA0002382292910000044
wherein n is the total number of electric vehicles in the system, mut01 represents the expected time of accessing the electric system by the electric vehicle, σtThe standard deviation indicates the charge/discharge time range, and the charge/discharge time distribution ranges are all sigmat2; if the expected time of charging is mut01, refers to period 1, i.e. 00: 00-01: 00, with a discharge time of μt0Segment 13, i.e., 12: 00-13: 00; electric automobile number lambda connected to power grid in certain time periodEVIs nEV(iii) a desire;
the predicted output model of the photovoltaic generator is as follows:
Figure GDA0002382292910000045
in the formula, the basic function
Figure GDA0002382292910000046
For the output of the photovoltaic under the condition of an expected value, a random variable theta (t) represents the blocking effect of the atmosphere on the solar illumination;
the probability density function of the photovoltaic output active power is as follows:
Figure GDA0002382292910000051
wherein Г is Gamma function, α and β are shape parameters of beta distribution, PpvIs the output power of the photovoltaic generator;
Figure GDA0002382292910000052
is the maximum output power of the photovoltaic array.
The expected value of the output power of the photovoltaic power generation system is as follows:
Figure GDA0002382292910000053
the network risk assessment index NCRI of step S2 of the scheme is GZNAnd (3) characterization:
Figure GDA0002382292910000054
wherein α, gamma is the weight coefficient of the global and local blocking and photovoltaic absorption degree respectively,
Figure GDA0002382292910000055
and
Figure GDA0002382292910000056
respectively representing a network overload risk vector and a photovoltaic permeability vector;
risk overload vector
Figure GDA0002382292910000057
Figure GDA0002382292910000058
Wherein M is the total number of branches of the system,
Figure GDA0002382292910000059
is a column vector, giRepresenting the overload risk of the ith branch;
branch overload risk gi：
Figure GDA00023822929100000510
In the formula, wxi,kIs a probability weight, a is an integer,
Figure GDA00023822929100000511
is the overload of branch i;
branch overload capacity
Figure GDA00023822929100000512
Figure GDA00023822929100000513
In the formula, LiIs the ratio of the actual transmission power of branch i to the power limit, L0M is 1,2,. M for setting a branch safety threshold;
photovoltaic penetration degree vector
Figure GDA0002382292910000061
In the formula, r is the number of transformer substations containing photovoltaic power supplies, ytRepresenting the photovoltaic consumption degree of the t-th substation containing the photovoltaic power supply;
Figure GDA0002382292910000062
in the formula, PactFor actually delivering power, P, to the photovoltaic power supplyplanPower is emitted for the plan.
In the present embodiment, the specific step of information transmission between the upper and lower optimization layer decision variables and the state variables in the two-layer optimization process of step S5 is
Step A1: initializing PSO algorithm parameters and particle initial positions, inputting network parameters, and initializing a sample and a random variable dimension m;
step A2: transforming a sample matrix Z in a standard normal space into a sample matrix X in an input variable through Nataf inverse transformation, wherein the sample matrix X is in an upper-layer optimal topological state TPiAnd then, with the photovoltaic absorption as a target, performing deterministic load flow calculation by using the kth column of the matrix X, and determining the weight of the load to find a load combination zeta with optimal photovoltaic absorptioniAnd transmitting the optimization result to an upper-layer topology optimization model in the form of a fitness function;
step A3: after the lower-layer photovoltaic absorption optimal target result is obtained through calculation, the optimal topological state TP of the network is searched by taking the economic and environmental benefit maximization as a model optimization targeti；
Step A4: updating the historical optimal positions of the particles and the optimal positions of the population;
step A5: judging whether convergence or the maximum iteration number is reached, and if the convergence or the maximum iteration number is reached, entering the step A6; if not, returning to the step A2;
step A6: calculating whether the NCRI is within the confidence range, if the NCRI is beyond the confidence range, returning to the step A1, otherwise, entering the step A7;
step A7: and outputting the optimal topological state of the network.
When the embodiment is implemented, the method for constructing the reconfigurable model based on the high-voltage distribution network comprises the following steps: constructing a dynamic load model: aiming at the condition that the charging/discharging loads of the electric automobile are subjected to centralized charging/discharging in charging piles of different functional areas, decorrelation processing is carried out on the electric automobile load and the area inherent load on the basis of a random correlation principle of Nataf conversion processing, and a semi-invariant method is utilized to carry out superposition processing on two random variables to form a comprehensive load model considering novel energy; modeling the photovoltaic power output in unit time based on seasonal climate states, simulating sunshine hours in different seasons by using real meteorological daily value data, and simulating the ratio of the radiant quantity in the total radiant quantity from sunrise to sunset in each time period to represent strong fluctuation and randomness of the photovoltaic power output in different time periods.
Step two: network topology dynamic risk assessment strategy based on probability trend: aiming at random factors such as intermittent energy output, electric vehicle charging/discharging, system load removal and the like of a current urban power grid, a network blocking risk index (NCRI) is provided for evaluating the operation risk of an urban power transmission system, the system risk is quantitatively considered from local to whole, and the NCRI serves as a quantitative criterion for triggering HVDN reconstruction and provides a theoretical basis for an action strategy of a power transmission network topology reconstruction technology.
Step three, the urban power grid consumption and blocking management and control strategy:
when the NCRI exceeds the confidence range, HVDN reconstruction is triggered; the upper layer model takes the economic and environmental benefits maximization as a model optimization target, and improves the photovoltaic consumption and the bearing capacity of the system under the high permeability of the electric automobile by optimizing the HVDN topological structure;
the lower-layer optimization model is established on the basis of the initial optimal topological state of the upper-layer optimization target, the photovoltaic consumption degree of the system is improved to the maximum extent to find a load combination with optimal photovoltaic consumption, the control cost generated by network constraint conditions such as power flow constraint and the like in a certain uncertain scene is evaluated, and the control cost is fed back to the upper-layer optimization model in the form of a fitness value.
Secondly, constructing a network topology risk assessment method guided by a Network Congestion Risk Index (NCRI) according to the requirement of the strong volatility of the novel system with higher energy permeability on the network state assessment rapidness and stability by the network topology dynamic risk assessment strategy; random power flow is used as an auxiliary tool to represent the volatility of PVs and EVs, and a local to overall evaluation strategy is quickly carried out on the topological state of the system by directly reflecting the risk probability distribution information of each state quantity of the system; network overload risk vector
Figure GDA0002382292910000081
Infinite norm and two norms respectively represent the overall risk condition and the local maximum risk condition of the network topology, and the linear combination of the norms forms the network risk evaluation index NCRI;
the network risk assessment index NCRI is GZNAnd (3) characterization:
Figure GDA0002382292910000082
wherein α, gamma is the weight coefficient of the global and local blocking and photovoltaic absorption degree respectively,
Figure GDA0002382292910000083
and
Figure GDA0002382292910000084
respectively representing a network overload risk vector and a photovoltaic permeability vector;
risk overload vector
Figure GDA0002382292910000085
Figure GDA0002382292910000086
Wherein M is the total number of branches of the system,
Figure GDA0002382292910000087
is a column vector, giRepresenting the overload risk of the ith branch;
branch overload risk gi：
Figure GDA0002382292910000088
In the formula, wxi,kIs a probability weight, a is an integer,
Figure GDA0002382292910000089
is the overload of branch i;
branch overload capacity
Figure GDA00023822929100000810
Figure GDA00023822929100000811
In the formula, LiIs the ratio of the actual transmission power of branch m to the power limit, L0To set a branch safety threshold, M is 1, 2.., M;
photovoltaic penetration degree vector
Figure GDA00023822929100000812
In the formula, r is the number of transformer substations containing photovoltaic power supplies, ytRepresenting the photovoltaic consumption degree of the t-th substation containing the photovoltaic power supply;
Figure GDA0002382292910000091
in the formula, PactFor actually delivering power, P, to the photovoltaic power supplyplanPower is emitted for the plan.
The network dynamic risk evaluation based on the network overload risk index provides a theoretical basis for an action strategy of a power transmission network topology reconstruction technology, and can perform quantitative estimation and analysis on uncertain fluctuation caused by a system after novel distributed energy is connected to the power grid.
Taking the urban power grid absorption and blocking management and control strategy to improve the absorption of the system to the photovoltaic and the bearing capacity of the system under the high permeability of the electric automobile as optimization targets, considering HVDN power transformation unit group topological constraint, tide equality and inequality constraint, node voltage constraint, branch power constraint and second-order cone relaxation conversion condition constraint, and formulating a double-layer optimization strategy; the upper layer optimization model of the double-layer optimization strategy takes the economic environmental benefit maximization as a model optimization target to search for the optimal topological state of the network; the lower-layer optimization model of the double-layer optimization strategy is used for searching a load combination with optimal photovoltaic absorption by taking the photovoltaic absorption as a target under the upper-layer optimal topological state, so that the aim of extinction slow resistance is fulfilled;
the objective function with the optimal economic benefit of the upper layer is as follows:
Figure GDA0002382292910000092
in the formula (I), the compound is shown in the specification,
Figure GDA0002382292910000093
in order to optimize the objective for the lower layer,
Figure GDA0002382292910000094
for the cost of switching action, M represents the number of random variables, δiRepresenting the probability weight corresponding to the ith load combination;
cost of switching operation:
Figure GDA0002382292910000095
wherein τ represents the economic cost per switching operation, χ represents the number of the transforming unit groups,
Figure GDA0002382292910000096
representing the number of times of switching actions of the power transformation unit group in a t period;
the constraint of the upper-layer economic benefit maximum optimization model is mainly the topological constraint of an HVDN (high voltage digital network) power transformation unit group:
a) the topological structure in the unit group needs to meet radial constraint, namely, any power transformation unit in the unit group is provided with one or more passages communicated with a power supply point;
b) the change of the switch states in the unit groups changes the connection relation between the power supply point and the power transformation unit, so that the load rate of the power supply point is changed, and the influence of the change of the switch states of different unit groups and the load rate of the power supply point are independent.
The lower-layer photovoltaic absorption maximum objective function:
Figure GDA0002382292910000101
in the formula, Pwg,i t，Pg,i tRespectively representing an expected value of photovoltaic output of an i node and photovoltaic absorption power in a t period, wherein n refers to the total number of 110kV transformer substation nodes, and omega represents environmental benefit influence generated by light abandoning behaviors;
and (3) carrying out cone conversion on the lower-layer optimization model flow constraint:
Figure GDA0002382292910000103
Figure GDA0002382292910000104
Figure GDA0002382292910000105
Figure GDA0002382292910000106
Figure GDA0002382292910000107
in the formula, the power flow equation constraint after cone conversion, the power flow equation constraint of a direct current method of the high-voltage transmission line, the voltage constraint of a node and the power constraint condition of a branch are respectively,
Figure GDA0002382292910000108
the node set is a 110kV substation node set connected with a 110kV substation node i;
when the feasible region is relaxed into a second-order cone to form a convex feasible region, the constraint after conversion is relaxed:
Figure GDA0002382292910000109
the method comprises the steps of constructing a dynamic load model, wherein the dynamic load model comprises a comprehensive load model of electric vehicle charging/discharging and a photovoltaic power supply fluctuation model, the network topology dynamic risk assessment strategy based on the probability power flow takes NCRI as an index of risk assessment, and the urban power grid consumption and blocking management and control strategy makes a double-layer optimization strategy based on the condition constraint of the second-order cone relaxation conversion non-convex power flow.
And (3) information transmission between decision variables and state variables of upper and lower optimization layers in the double-layer optimization process:
the method comprises the following steps: initializing PSO algorithm parameters and particle initial positions, inputting network parameters, and initializing a sample and a random variable dimension m;
step two: transforming a sample matrix Z in a standard normal space into a sample matrix X in an input variable through Nataf inverse transformation, wherein the sample matrix X is in an upper-layer optimal topological state TPiAnd then, with the photovoltaic absorption as a target, performing deterministic load flow calculation by using the kth column of the matrix X, and determining the weight of the load to find a load combination zeta with optimal photovoltaic absorptioniAnd transmitting the optimization result to an upper-layer topology optimization model in the form of a fitness function;
step three: after the lower-layer photovoltaic absorption optimal target result is obtained through calculation, the optimal topological state TP of the network is searched by taking the economic and environmental benefit maximization as a model optimization targeti；
Step four: updating the historical optimal positions of the particles and the optimal positions of the population;
step five: judging whether convergence or the maximum iteration number is reached, if the convergence or the maximum iteration number is reached, performing a sixth step, and if the convergence or the maximum iteration number is not reached, performing a second step;
step six: and (4) calculating whether the NCRI is in a confidence range, if the NCRI is out of the confidence range, entering the step I, and otherwise, outputting the optimal topological state of the network.

Claims (4)

1. The construction method based on the high-voltage distribution network reconstructability model is characterized by comprising the following steps:
step S1: constructing a dynamic load model;
step S2: calculating a network risk evaluation index NCRI according to the randomness factors of the clearance performance source output, the electric vehicle charging/discharging and the system load removal of the current urban power grid;
the network risk assessment index NCRI of the step S2 is GZNAnd (3) characterization:
Figure FDA0002382292900000011
wherein α, gamma is the weight coefficient of the global and local blocking and photovoltaic absorption degree respectively,
Figure FDA0002382292900000012
and
Figure FDA0002382292900000013
respectively representing a network overload risk vector and a photovoltaic permeability vector;
risk overload vector
Figure FDA0002382292900000014
Figure FDA0002382292900000015
Wherein M is the total number of branches of the system,
Figure FDA0002382292900000016
is a column vector, giRepresenting the overload risk of the ith branch;
branch overload risk gi：
Figure FDA0002382292900000017
In the formula, wxi,kIs a probability weight, a is an integer,
Figure DEST_PATH_GDA00023822929100000511
is the overload of branch i;
branch overload capacity
Figure FDA0002382292900000019
Figure FDA00023822929000000110
In the formula, LiIs the ratio of the actual transmission power of branch i to the power limit, L0To set a branch safety threshold, i ═ 1, 2.., M;
photovoltaic penetration degree vector
Figure FDA00023822929000000111
In the formula, r is the number of transformer substations containing photovoltaic power supplies, ytRepresenting the photovoltaic consumption degree of the t-th substation containing the photovoltaic power supply;
Figure FDA0002382292900000021
in the formula, PactFor actually delivering power, P, to the photovoltaic power supplyplanIssuing power for the plan;
step S3: calculating an HVDN (high voltage distribution network) topological network according to the dynamic load model;
step S4, evaluating the HVDN topology network of the high-voltage distribution network according to the calculated NCRI;
step S5: when the NCRI does not exceed the confidence range, the photovoltaic consumption degree of the network and the blocking condition of the system are considered to be in the allowable range at the moment, and no operation action is performed; and triggering HVDN reconstruction when the NCRI exceeds the confidence range, and obtaining the optimal topological state of the network at the moment based on a double-layer optimization model under the comprehensive targets of maximum photovoltaic consumption, maximum system blockage relieving degree and minimum load shedding amount.
2. The method according to claim 1, wherein the dynamic load model in step S1 includes a model of electric vehicle charging/discharging load simulation, a model of electric vehicle charging/discharging load integration, a model of electric vehicle charging/discharging load distribution, a model of electric vehicle power grid power.
3. The construction method based on the high-voltage distribution network reconfigurable model according to claim 2, characterized in that the electric vehicle charging/discharging load simulation model is:
Figure FDA0002382292900000022
the comprehensive load model of the electric vehicle charging/discharging load comprises the following steps:
Figure FDA0002382292900000023
in the formula, λEVFor a desired value, n, of the electric vehicle within the time intervalEVIf the probability density function of the number of the electric automobiles which are possibly accessed into the power grid is known, the mean value, the standard deviation and the high-order central moment of the charging/discharging power of the electric automobiles in each period are obtained;
the number distribution model accessed to the electric automobile in a period of time is as follows:
Figure FDA0002382292900000031
the number model of the electric vehicles accessed to the power grid in one period of time is as follows:
Figure FDA0002382292900000032
wherein n is the total number of electric vehicles in the system, mut01 represents the expected time of accessing the electric system by the electric vehicle, σtThe standard deviation indicates the charge/discharge time range, and the charge/discharge time distribution ranges are all sigmat2; if the expected time of charging is mut01, refers to period 1, i.e. 00: 00-01: 00, with a discharge time of μt0Segment 13, i.e., 12: 00-13: 00; electric automobile number lambda connected to power grid in certain time periodEVIs nEV(iii) a desire;
the predicted output model of the photovoltaic generator is as follows:
Figure FDA0002382292900000033
in the formula, the basic function
Figure FDA0002382292900000034
For the output of the photovoltaic under the condition of an expected value, a random variable theta (t) represents the blocking effect of the atmosphere on the solar illumination;
the probability density function of the photovoltaic output active power is as follows:
Figure FDA0002382292900000035
wherein Г is Gamma function, α and β are shape parameters of beta distribution, PPVIs the output power of the photovoltaic generator;
Figure FDA0002382292900000036
is the maximum output power of the photovoltaic array;
the expected value of the output power of the photovoltaic power generation system is as follows:
Figure FDA0002382292900000037
4. the construction method based on the high-voltage distribution network reconstructability model according to claim 1, wherein the information transmission between the upper and lower optimization layer decision variables and the state variables in the double-layer optimization process of step S5 comprises the following specific steps:
step A1: initializing PSO algorithm parameters and particle initial positions, inputting network parameters, and initializing a sample and a random variable dimension m;
step A2: transforming a sample matrix Z in a standard normal space into a sample matrix X in an input variable through Nataf inverse transformation, wherein the sample matrix X is in an upper-layer optimal topological state TPiAnd then, with the photovoltaic absorption as a target, performing deterministic load flow calculation by using the kth column of the matrix X, and determining the weight of the load to find a load combination zeta with optimal photovoltaic absorptioniAnd transmitting the optimization result to an upper-layer topology optimization model in the form of a fitness function;
step A3: after the lower-layer photovoltaic absorption optimal target result is obtained through calculation, the optimal topological state TP of the network is searched by taking the economic and environmental benefit maximization as a model optimization targeti；
Step A4: updating the historical optimal positions of the particles and the optimal positions of the population;
step A5: judging whether convergence or the maximum iteration number is reached, and if the convergence or the maximum iteration number is reached, entering the step A6; if not, returning to the step A2;
step A6: calculating whether the NCRI is within the confidence range, if the NCRI is beyond the confidence range, returning to the step A1, otherwise, entering the step A7;
step A7: and outputting the optimal topological state of the network.
CN201810798107.3A 2018-07-19 2018-07-19 Construction method based on high-voltage distribution network reconstructability model Expired - Fee Related CN108964037B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810798107.3A CN108964037B (en) 2018-07-19 2018-07-19 Construction method based on high-voltage distribution network reconstructability model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810798107.3A CN108964037B (en) 2018-07-19 2018-07-19 Construction method based on high-voltage distribution network reconstructability model

Publications (2)

Publication Number Publication Date
CN108964037A CN108964037A (en) 2018-12-07
CN108964037B true CN108964037B (en) 2020-04-07

Family

ID=64481828

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810798107.3A Expired - Fee Related CN108964037B (en) 2018-07-19 2018-07-19 Construction method based on high-voltage distribution network reconstructability model

Country Status (1)

Country Link
CN (1) CN108964037B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111105025A (en) * 2019-12-06 2020-05-05 国网四川省电力公司电力科学研究院 Urban high-voltage distribution network blocking management method based on data-driven heuristic optimization
CN112491037B (en) * 2020-11-09 2023-04-25 四川大学 Multi-target multi-stage dynamic reconstruction method and system for urban power distribution network
CN112688365B (en) * 2020-12-26 2023-07-04 四川大川云能科技有限公司 Mutual information-Bayesian network-based power distribution network topology robust identification method
CN115597872B (en) * 2022-11-25 2023-03-07 南方电网调峰调频发电有限公司检修试验分公司 Load shedding test method, device, equipment and medium for pumped storage unit

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106451429A (en) * 2016-10-19 2017-02-22 合肥工业大学 Power distribution network reconstruction method containing electric automobile network access based on game theory
CN106712037A (en) * 2016-11-28 2017-05-24 武汉大学 Electric power system static voltage stability assessment method considering electric automobile charging characteristic and load fluctuation limit
CN107808256A (en) * 2017-11-20 2018-03-16 四川大学 A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106451429A (en) * 2016-10-19 2017-02-22 合肥工业大学 Power distribution network reconstruction method containing electric automobile network access based on game theory
CN106712037A (en) * 2016-11-28 2017-05-24 武汉大学 Electric power system static voltage stability assessment method considering electric automobile charging characteristic and load fluctuation limit
CN107808256A (en) * 2017-11-20 2018-03-16 四川大学 A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method

Also Published As

Publication number Publication date
CN108964037A (en) 2018-12-07

Similar Documents

Publication Publication Date Title
CN108964037B (en) Construction method based on high-voltage distribution network reconstructability model
Wang et al. A chaos disturbed beetle antennae search algorithm for a multiobjective distribution network reconfiguration considering the variation of load and DG
CN104578157B (en) Load flow calculation method of distributed power supply connection power grid
CN104319768B (en) A kind of micro-capacitance sensor is powered and method for supervising
CN104484833A (en) Photovoltaic power generation output power tracking algorithm based on genetics algorithm improved RBF-BP neural network
CN105741193A (en) Multi-target distribution network reconstruction method considering distributed generation and load uncertainty
CN104866919A (en) Multi-target planning method for power grid of wind farms based on improved NSGA-II
CN107453396A (en) A kind of Multiobjective Optimal Operation method that distributed photovoltaic power is contributed
Rosas et al. Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico
Yang et al. Deep learning-based distributed optimal control for wide area energy Internet
CN112508279A (en) Regional distributed photovoltaic prediction method and system based on spatial correlation
Xin Forecast of photovoltaic generated power based on WOA-LSTM
CN117748444A (en) Operation simulation method of power distribution system
Song et al. Medium and long term load forecasting considering the uncertainty of distributed installed capacity of photovoltaic generation
Zehra et al. Neuro-fuzzy based energy management of PV-FC based grid-connected microgrid for e-mobility
Zahedi et al. Environment Compatible Management Strategy of Distributed Generation Based on Neural Network with a Power Capacity Index
CN111401696B (en) Power distribution system coordination planning method considering uncertainty of renewable resources
Wen et al. A reconfiguration method of distribution network considering time variations for load and renewable distributed generation
Li et al. A gray rbf model improved by genetic algorithm for electrical power forecasting
Liu et al. Short-term photovoltaic power prediction based on bayesian regularized bp neural networks
Chinnadurrai et al. Energy Management of a Microgrid based on LSTM Deep Learning Prediction Model
Syahputra et al. An Optimization of Power Distribution Network Configuration with Distributed Generator Integration Using Genetic Algorithm
Sasaki et al. Solar Power Prediction Using Iterative Network Pruning Technique for Microgrid Operation
Hamidreza et al. Solar Irradiance Forecasting Based on the Combination of Radial Basis Function Artificial Neural Network and Genetic Algorithm
Zhang et al. Line Loss Prediction of Low Voltage Distributions Considering Mass PV and Electric Heating

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
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20200407

Termination date: 20210719