CN113612258A - Power distribution network wind power maximum consumption method based on electric vehicle aggregation model - Google Patents

Power distribution network wind power maximum consumption method based on electric vehicle aggregation model Download PDF

Info

Publication number
CN113612258A
CN113612258A CN202110913138.0A CN202110913138A CN113612258A CN 113612258 A CN113612258 A CN 113612258A CN 202110913138 A CN202110913138 A CN 202110913138A CN 113612258 A CN113612258 A CN 113612258A
Authority
CN
China
Prior art keywords
power
distribution network
scene
electric vehicle
time
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
CN202110913138.0A
Other languages
Chinese (zh)
Other versions
CN113612258B (en
Inventor
李洁
钱科军
刘乙
李亚飞
张晓明
李圆琪
朱丹
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.)
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Original Assignee
Suzhou Power Supply Co of State Grid Jiangsu 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 Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd filed Critical Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Priority to CN202110913138.0A priority Critical patent/CN113612258B/en
Publication of CN113612258A publication Critical patent/CN113612258A/en
Application granted granted Critical
Publication of CN113612258B publication Critical patent/CN113612258B/en
Active 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Landscapes

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

Abstract

A power distribution network wind power maximum consumption method based on an electric automobile aggregation model comprises the following steps: step 1, collecting and calculating parameters required by wind power consumption of a power distribution network; step 2, constructing probability distribution required by the configuration based on the parameters acquired in the step 1; step 3, constructing a polymerization model of the electric automobile based on the probability distribution in the step 2; step 4, constructing a mathematical model of electric vehicle optimized dispatching; and 5, solving the maximum wind power consumption capacity of the power distribution network according to the electric vehicle aggregation model established in the step 3 and the electric vehicle optimized dispatching mathematical model established in the step 4. According to the method, the wind power installation capacity is used as a target function, the load, the wind power output and the electric automobile trip randomness are considered at the same time, the aggregation model of the stage one and the mathematical model of the electric automobile optimized dispatching of the stage two are established, and the wind power consumption capability of the power distribution network can be greatly improved after the aggregation model of the stage one and the mathematical model of the electric automobile optimized dispatching of the stage two are combined and solved.

Description

Power distribution network wind power maximum consumption method based on electric vehicle aggregation model
Technical Field
The invention belongs to the technical field of electric vehicle optimization scheduling, and particularly relates to a power distribution network wind power maximum consumption method based on an electric vehicle aggregation model.
Background
According to the message issued by the ministry of public security: by the end of 2020, the inventory of new energy automobiles in China reaches 497 thousands of automobiles. Under the stimulation of relevant matching policies, the development of electric automobiles in China presents wide market prospects. The electric automobile has the potential of energy storage and can carry out time shifting on energy. If large-scale electric vehicles are gathered together, reasonable charging and discharging scheduling is carried out to exert a large-scale energy storage effect, so that the load peak-valley difference can be effectively relieved, the voltage quality is improved, and more distributed power supplies are consumed. Wind power is connected to a power distribution network side to achieve distributed power generation, so that the network loss can be effectively reduced, the power supply reliability can be improved, the grid connection and the dissipation are difficult due to the limitation of the capacity of the power distribution network, and the phenomenon that wind and light are abandoned is more and more serious. Therefore, the optimal scheduling technology of the electric automobile is continuously proposed to solve the problem of wind power consumption.
Most of the prior related technologies do not directly take the wind power absorption value as an objective function, but start from the point of view of load peak-valley difference. Meanwhile, when the optimization model is solved, the algorithm used in the prior art cannot obtain the optimal solution.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a power distribution network wind power maximum consumption method based on an electric vehicle aggregation model.
The invention adopts the following technical scheme:
step 1, collecting relevant parameters of a fan connected with a power distribution network and the time of each electric vehicle leaving or arriving at a charging station;
step 2, constructing distribution required probability distribution based on the parameters acquired in the step 1, wherein the distribution required probability distribution comprises power grid load deviation probability distribution, wind speed probability distribution of a fan and probability distribution of electric vehicle traveling;
step 3, constructing an electric automobile aggregation model and electric automobile aggregation model constraint conditions based on the probability distribution in the step 2;
step 4, constructing a mathematical model of electric vehicle optimization scheduling, wherein the mathematical model comprises an objective function capable of minimizing network loss and a constraint condition corresponding to the objective function;
and 5, solving the maximum wind power consumption capacity of the power distribution network according to the electric vehicle aggregation model established in the step 3 and the electric vehicle optimized dispatching mathematical model established in the step 4.
The relevant parameters of the fans connected with the power distribution network in the step 1 comprise the installed wind power capacity connected with each node of the power distribution network, the cut-in wind speed, the rated wind speed, the cut-out wind speed, the historical wind speed data and the wind speed of the fans in the T time period of the fans in the installed wind power system, and the T time period is cut into nn times at intervals of tt minutes.
The distribution network load deviation probability distribution satisfies the following relational expression:
Figure BDA0003204441030000021
wherein, mud,tMeans, σ, representing the distribution network load deviation corresponding to time td,tRepresents μd,tThe corresponding standard deviation of the distribution network load deviation,
Figure BDA0003204441030000022
is the load deviation value at the time t,
Figure BDA0003204441030000023
multiplying the historical average value of the load to obtain the load value of the power distribution network, and cutting the T time period into any time of nn times at intervals of tt minutes.
The wind speed probability distribution of the fan meets the following relational expression:
Figure BDA0003204441030000024
fv(vt) Representing a wind velocity distribution, which is a rayleigh distribution; sigmavFor parameter values obtained by fitting historical wind speed data, vtThe wind speed value collected at the moment t.
The probability distribution of the electric vehicle traveling comprises time probability distribution of the electric vehicle leaving the charging station, time probability distribution of the electric vehicle reaching the charging station and daily driving distance probability distribution of the electric vehicle;
the time probability distribution of the electric vehicle leaving the charging station satisfies the following relation:
Figure BDA0003204441030000025
fr(tdep) Represents the time probability distribution of the electric vehicle leaving the charging station, which is a normal distribution,
wherein: t is tdepFor the time when the electric vehicle leaves the charging station, murMean value of time, σ, for all electric vehicles leaving the charging stationrIs murThe variance of (c).
The time probability distribution of the electric vehicle reaching the charging station satisfies the following relation:
Figure BDA0003204441030000031
fe(tarr) Representing the probability distribution of the time when the electric vehicle arrives at the charging station, wherein the probability distribution is normal distribution;
wherein: t is tarrTime of arrival of the electric vehicle at the charging station, mueMean value of time, σ, for all electric vehicles arriving at the charging stationeIs mueThe variance of (c).
The probability distribution of the daily driving distance of the electric automobile meets the following relational expression:
Figure BDA0003204441030000032
fdis(xdis) The distribution of the daily driving distance of the electric automobile is lognormal distribution;
wherein: x is the number ofdisThe daily driving distance mu when the electric vehicle arrives at the charging stationdisThe average value of the daily driving distance, sigma, when all the electric vehicles arrive at the charging stationdisIs mudisThe variance of (c).
In step 3, all electric vehicles which are connected to the j-node charging station of the power distribution network at the moment t are aggregated into an equivalent centralized energy storage, namely an aggregation model, wherein the aggregation model meets the following relational expression:
Figure BDA0003204441030000033
Figure BDA0003204441030000034
Figure BDA0003204441030000035
Figure BDA0003204441030000036
Figure BDA0003204441030000037
wherein: g is a candidate node set for installing wind power in the power distribution network,
s is a set after the scene is reduced; the scene is data of a corresponding scene of the power distribution network wind power generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; the data of a plurality of scenes are generated by repeated simulation, the scene reduction refers to the merging of the scenes through a clustering algorithm,
Mjto access the collection of all electric vehicles charged at j-node of the distribution network,
Figure BDA0003204441030000041
and
Figure BDA0003204441030000042
respectively aggregating the maximum capacity, the maximum charging power, the maximum discharging power, the reaching capacity and the leaving capacity of equivalent centralized energy storage of the j-node electric automobile at the t moment under an S scene, wherein the S scene is any scene in the S set;
Figure BDA0003204441030000043
and
Figure BDA0003204441030000044
the maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of the mth electric vehicle, respectively, fm，t，sFor the state variable of the j node of the power distribution network accessed by the mth electric automobile at the moment t, 1 represents access, 0 represents non-access,
Figure BDA0003204441030000045
and
Figure BDA0003204441030000046
state variables respectively indicating whether the m-th electric vehicle has arrived and departed at time t, 1Indicating arrival and departure, and 0 indicating not arrival and departure.
The constraint conditions of the electric automobile aggregation model satisfy the following relational expression:
Figure BDA0003204441030000047
Figure BDA0003204441030000048
Figure BDA0003204441030000049
Figure BDA00032044410300000410
Figure BDA00032044410300000411
Figure BDA00032044410300000412
wherein: ej，t，sAggregated energy storage capacity value, E, of j-node electric vehicle at t moment under s scenej，t-1，sThe accumulated energy storage capacity value of the electric automobile at the j node at the t-1 moment under the s scene is shown, tau represents the step length of the adjacent moment and is consistent with tt, eta+And η-Respectively the charging efficiency and the discharging efficiency,
Figure BDA00032044410300000413
is the energy storage capacity value aggregated by the electric automobile at the j node at the initial moment under the s scene,
Figure BDA00032044410300000414
the aggregated energy storage capacity value of the electric automobile at the j node at the last moment in the s scene,
Figure BDA00032044410300000415
represents the maximum charging power of equivalent centralized energy storage of j node electric automobile aggregation at t moment under the scene of s,
Figure BDA00032044410300000416
represents the maximum discharge power of equivalent centralized energy storage of j node electric automobile aggregation at t moment under the scene of s,
Figure BDA00032044410300000417
and
Figure BDA00032044410300000418
respectively is a charge-discharge flag bit, is an integer variable of 0 and 1
Figure BDA0003204441030000051
When the value is 1, the charging is performed, and when the value is 1
Figure BDA0003204441030000052
When the value is 1, the discharge is performed, and both 0 values indicate that neither charge nor discharge is performed, and at most, only 1 of the two values can be 0 at the same time.
The objective function of the mathematical model for optimizing and scheduling the electric automobile is as follows:
Figure BDA0003204441030000053
wherein: g is a candidate node set for installing wind power in the power distribution network,
s is a set after the scene is reduced; the scene is data of a corresponding scene of the power distribution network wind power generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; the data of a plurality of scenes are generated by repeated simulation, the scene reduction refers to the merging of the scenes through a clustering algorithm,
l is the set of all the branches of the distribution network,
Figure BDA0003204441030000054
for installation in the j node of the distribution networkijIs the resistance of the branch ij and,
Figure BDA0003204441030000055
the square of the current of the branch ij at the time t under the S scene, wherein the S scene is any scene in the S set, rhosThe probability of S scene occurrence is the number of S scene occurrences divided by the total number of all scenes in the set S; omega1And ω2And the weighting coefficients are respectively wind power installation capacity and system network loss.
Constraint conditions required to be met by the mathematical model of the electric vehicle optimization scheduling comprise system power flow constraint, distribution network voltage constraint, distribution network power piecewise linearization constraint, distribution network current and voltage constraint, distribution network transformer gear constraint and distribution network wind power output constraint.
The power distribution network power piecewise linearization satisfies the following relational expression:
Figure BDA0003204441030000056
Figure BDA0003204441030000057
Figure BDA0003204441030000058
Figure BDA0003204441030000059
Figure BDA00032044410300000510
Figure BDA00032044410300000511
Figure BDA00032044410300000512
wherein: sigmaij，kIs the power piecewise linear slope of the ij branch,
Figure BDA0003204441030000061
Is a power piecewise linear active variable of the ij branch at the t moment in the s scene,
Figure BDA0003204441030000062
Is a piecewise linear reactive variable of the power of the ij branch at the time t under the s scene,
Figure BDA0003204441030000063
Positive parameter of active power of the ij branch at the t moment in s scene,
Figure BDA0003204441030000064
Is a negative parameter of the active power of the ij branch at the t moment in the s scene,
Figure BDA0003204441030000065
Positive parameter of reactive power of the ij branch at t moment in s scene,
Figure BDA0003204441030000066
Is the negative parameter, tau, of the reactive power of the ij branch at the moment t under the s sceneijThe size of each segment, k is the value of the segment,
Figure BDA0003204441030000067
The total number of the segments is the parameter introduced for the power segment linearization.
The gear constraint of the distribution network transformer meets the following relational expression:
Figure BDA0003204441030000068
Figure BDA0003204441030000069
Figure BDA00032044410300000610
Figure BDA00032044410300000611
Figure BDA00032044410300000612
wherein:
Figure BDA00032044410300000613
representing the head end node, voltage Δ tap, of the transformerjFor the step value, tap, of each gear of the transformerj，tIs the gear position value, tap, of the j node at time tj，t+1The gear stage value of the j node at time t +1,
Figure BDA00032044410300000614
and
Figure BDA00032044410300000615
the maximum value and the minimum value of the j node gear respectively,
Figure BDA00032044410300000616
for the maximum change value of two gears at adjacent times,
Figure BDA00032044410300000617
the maximum value of allowed gear changes for node j during the whole period of time T.
The power distribution network wind power output constraint satisfies the following relational expression:
Figure BDA00032044410300000618
Figure BDA00032044410300000619
Figure BDA00032044410300000620
Figure BDA00032044410300000621
Figure BDA0003204441030000071
wherein:
Figure BDA0003204441030000072
the wind power simulation refers to wind power output data of j nodes of the power distribution network at the t moment under the s scene generated by Monte Carlo simulation on the wind speed probability distribution of the fan in the step 2;
Figure BDA0003204441030000073
the abandoned wind power gamma of j node wind power of the power distribution network at t moment under s scenecAnd theta and-theta are respectively an angle of reactive power advance and an angle of reactive power lag.
The system power flow constraint satisfies the following relational expression:
Figure BDA0003204441030000074
Figure BDA0003204441030000075
wherein: pij，t，sAnd Qij，t，sRespectively the active power and reactive power at t moment of the branch ij in s scene, xijBeing the reactance of the branch ij,
Figure BDA0003204441030000076
and
Figure BDA0003204441030000077
respectively a historical active average value and a historical reactive average value of a node j at the moment t,
Figure BDA0003204441030000078
and
Figure BDA0003204441030000079
respectively connecting the active power and the reactive power of the j-th node at the time t under the s scene,
Figure BDA00032044410300000710
and
Figure BDA00032044410300000711
respectively charging power and discharging power of equivalent centralized energy storage of electric automobile polymerization,
Figure BDA00032044410300000712
data representing the time t of scene s in the load deviation distribution, multiplied by
Figure BDA00032044410300000713
And
Figure BDA00032044410300000714
the load actual data at the time t is later.
Compared with the prior art, the method has the beneficial effects that the installation capacity of the wind power is taken as a target function, the load, the wind power output and the randomness of the electric automobile traveling are considered at the same time, and a two-stage random mixed integer linear programming model is established and is a first-stage aggregation model and a second-stage mathematical model for optimizing and scheduling of the electric automobile respectively. In the first stage, a plurality of electric automobile users are aggregated together to be equivalent to a centralized energy storage, and the centralized energy storage is accessed to a corresponding power distribution network node to optimize the installation capacity of wind power and maximize the installation capacity of the wind power; and in the second stage, the system network loss of the power distribution network is optimized, so that the network loss is minimum. The established final model is a mixed integer linear programming model, a commercial solver can be directly adopted to solve the model, the optimal scheduling scheme of the electric automobile in different periods is obtained, and the wind power consumption capability of the power distribution network can be greatly improved.
Drawings
FIG. 1 is a two-phase stochastic programming model proposed by the present invention;
FIG. 2 is a flow chart of a power distribution network wind power maximum consumption method based on an electric vehicle aggregation model.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
FIG. 1 is a schematic diagram of two stages in a model provided by a power distribution network wind power maximum consumption method based on an electric vehicle aggregation model, and the model can be divided into two stages; fig. 2 is a practical flowchart of the method of the present invention, which specifically includes the following contents:
step 1: collecting related parameters of a fan connected with a power distribution network and the time of each electric vehicle leaving or arriving at a charging station;
the relevant parameters of the fans connected with the power distribution network comprise the installed wind power capacity connected with each node of the power distribution network, the cut-in wind speed, the rated wind speed, the cut-out wind speed, the historical wind speed data and the wind speed of the fans in the T time period of the fans in the installed wind power system, and the T time period is cut into nn times at the T moment at intervals of tt minutes.
In this embodiment, the time period of the whole planning model may be 24 hours a day as T, and tt minutes is 30 minutes every two time intervals; .
Step 2: constructing required probability distribution based on the parameters acquired in the step 1, wherein the required probability distribution comprises power grid load deviation probability distribution, wind speed probability distribution of a fan and probability distribution of electric vehicle traveling;
the distribution network load deviation probability distribution satisfies the following relational expression:
Figure BDA0003204441030000081
wherein, mud,tMeans, σ, representing the distribution network load deviation corresponding to time td,tRepresents μd,tThe corresponding standard deviation of the distribution network load deviation,
Figure BDA0003204441030000082
the load deviation value at the time t.
Figure BDA0003204441030000083
And multiplying the historical average value of the load to obtain the load value of the power distribution network.
The wind speed probability distribution of the fan meets the following relational expression:
Figure BDA0003204441030000084
fv(vt) Representing a wind velocity distribution, which is a rayleigh distribution; sigmavFor the parameter values obtained by fitting the historical wind speed data, v is outside the scope of the present discussion since the parameter fitting method is prior arttThe wind speed value collected at the moment t.
The relative value of the wind speed and the power of the fan satisfies the following relation:
Figure BDA0003204441030000091
wherein the content of the first and second substances,
Figure BDA0003204441030000092
representing the relative value of wind speed and power, v, of the wind turbine at time tciFor cutting into the wind speed, vrRated wind speed, vcoTo cut out the wind speed.
The probability distribution of the electric vehicle traveling comprises time probability distribution of the electric vehicle leaving the charging station, time probability distribution of the electric vehicle reaching the charging station and daily driving distance probability distribution of the electric vehicle;
the time probability distribution of the electric vehicle leaving the charging station satisfies the following relation:
Figure BDA0003204441030000093
fr(tdep) The time probability distribution of the electric vehicle leaving the charging station is represented, and the time probability distribution is a normal distribution. Wherein: t is tdepFor the time when the electric vehicle leaves the charging station, murMean value of time, σ, for all electric vehicles leaving the charging stationrIs murThe variance of (c).
The time probability distribution of the electric vehicle reaching the charging station satisfies the following relation:
Figure BDA0003204441030000094
fe(tarr) The time probability distribution of the electric vehicle arriving at the charging station is represented, and the time probability distribution is normal distribution. Wherein: t is tarrTime of arrival of the electric vehicle at the charging station, mueMean value of time, σ, for all electric vehicles arriving at the charging stationeIs mueThe variance of (c).
The probability distribution of the daily driving distance of the electric automobile meets the following relational expression:
Figure BDA0003204441030000101
fdis(xdis) The distribution of the daily driving distance of the electric automobile is lognormal distribution. Wherein: x is the number ofdisThe daily driving distance mu when the electric vehicle arrives at the charging stationdisThe average value of the daily driving distance, sigma, when all the electric vehicles arrive at the charging stationdisIs mudisThe variance of (c).
And step 3: constructing an electric automobile aggregation model and electric automobile aggregation model constraint conditions based on the probability distribution in the step 2;
the electric vehicles which are all connected to the j-node charging station of the power distribution network at the moment t are aggregated into an equivalent concentrated energy storage, namely an aggregation model, and the equivalent concentrated energy storage meets the following relational expression:
Figure BDA0003204441030000102
Figure BDA0003204441030000103
Figure BDA0003204441030000104
Figure BDA0003204441030000105
Figure BDA0003204441030000106
wherein: g is a candidate node set for installing wind power in the power distribution network,
s is a set after the scene is reduced; the scene is data of a corresponding scene of the power distribution network wind power generated by performing monte carlo simulation on the probability distribution in the step 2, in this embodiment, the data of the corresponding scene comprises the load data of the power distribution network in one day and the wind power output data of the power distribution network in one day, the data of the electric vehicle randomly arriving at the charging station in one day, and the data of the electric vehicle randomly leaving the charging station in one day; the data of a plurality of scenes can be generated by repeated simulation for a plurality of times, scene reduction refers to merging the scenes by a clustering algorithm, a common k-means clustering method can be adopted, mature software is directly applied, the specific steps are not the key points of the invention,
Mjto access the collection of all electric vehicles charged at j-node of the distribution network,
Figure BDA0003204441030000111
and
Figure BDA0003204441030000112
respectively aggregating the maximum capacity, the maximum charging power, the maximum discharging power, the reaching capacity and the leaving capacity of equivalent centralized energy storage of the j-node electric automobile at the t moment under an S scene, wherein the S scene is any scene in the S set;
Figure BDA0003204441030000113
and
Figure BDA0003204441030000114
the maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of the mth electric vehicle, respectively, fm,t,sFor the state variable of the j node of the power distribution network accessed by the mth electric automobile at the moment t, 1 represents access, 0 represents non-access,
Figure BDA0003204441030000115
and
Figure BDA0003204441030000116
and the state variables respectively represent whether the mth electric vehicle arrives and departs at the time t, 1 represents that the electric vehicle arrives and departs, and 0 represents that the electric vehicle does not arrive and departs.
The constraint conditions of the electric automobile aggregation model satisfy the following relational expression:
Figure BDA0003204441030000117
Figure BDA0003204441030000118
Figure BDA0003204441030000119
Figure BDA00032044410300001110
Figure BDA00032044410300001111
Figure BDA00032044410300001112
wherein: ej，t,sAggregated energy storage capacity value, E, of j-node electric vehicle at t moment under s scenej,t-1,sFor the aggregated energy storage capacity value of the electric automobile at the j node at the t-1 moment under the s scene, tau is the step length of the adjacent moment and is consistent with tt, which is expressed in 30 minutes in the embodiment, eta+And η-Respectively the charging efficiency and the discharging efficiency,
Figure BDA00032044410300001113
is the energy storage capacity value aggregated by the electric automobile at the j node at the initial moment under the s scene,
Figure BDA00032044410300001114
the aggregated energy storage capacity value of the electric automobile at the j node at the last moment in the s scene,
Figure BDA00032044410300001115
represents the maximum charging power of equivalent centralized energy storage of j node electric automobile aggregation at t moment under the scene of s,
Figure BDA00032044410300001116
denotes sUnder the scene, the maximum discharge power of equivalent centralized energy storage of j-node electric automobile aggregation at the time t,
Figure BDA00032044410300001117
and
Figure BDA00032044410300001118
respectively is a charge-discharge flag bit, is an integer variable of 0 and 1
Figure BDA00032044410300001119
When the value is 1, the charging is performed, and when the value is 1
Figure BDA00032044410300001120
When the value is 1, the discharge is performed, and both 0 values indicate that neither charge nor discharge is performed, and at most, only 1 of the two values can be 0 at the same time.
And 4, step 4: constructing a mathematical model of electric vehicle optimization scheduling, wherein the mathematical model comprises an objective function capable of minimizing network loss and a constraint condition corresponding to the objective function;
the objective function of the mathematical model for optimizing and scheduling the electric automobile is as follows:
Figure BDA0003204441030000121
wherein: g is a candidate node set for installing wind power in the power distribution network,
s is a set after the scene is reduced; the scene is data of a corresponding scene of the wind power of the power distribution network generated by performing monte carlo simulation on the probability distribution in the step 2, in the embodiment, the data of the corresponding scene comprises load deviation data of the power distribution network in one day and wind power output data of the power distribution network in one day, data of an electric vehicle randomly arriving at a charging station in one day, and data of the electric vehicle randomly leaving the charging station in one day; the data of a plurality of scenes can be generated by repeated simulation for many times, scene reduction refers to merging the scenes through a clustering algorithm, a common k-means clustering method can be adopted, mature software is directly applied, and the specific steps are not the key points of the invention.
And L is the set of all branches of the power distribution network.
Figure BDA0003204441030000122
For installation in the j node of the distribution networkijIs the resistance of the branch ij and,
Figure BDA0003204441030000123
the square of the current of the branch ij at the time t under the S scene, wherein the S scene is any scene in the S set, rhosThe probability of S scene occurrence is the number of S scene occurrences divided by the total number of all scenes in the set S; omega1And ω2And the weighting coefficients are respectively wind power installation capacity and system network loss.
Constraint conditions required to be met by the mathematical model of the electric vehicle optimization scheduling comprise system power flow constraint, distribution network voltage constraint, distribution network power piecewise linearization constraint, distribution network current and voltage constraint, distribution network transformer gear constraint and distribution network wind power output constraint.
And (3) system power flow constraint:
Figure BDA0003204441030000124
Figure BDA0003204441030000125
wherein: pij,t,sAnd Qij,t,sRespectively the active power and reactive power at t moment of the branch ij in s scene, xijBeing the reactance of the branch ij,
Figure BDA0003204441030000126
and
Figure BDA0003204441030000127
respectively a historical active average value and a historical reactive average value of a node j at the moment t,
Figure BDA0003204441030000128
and
Figure BDA0003204441030000129
respectively connecting the active power and the reactive power of the j-th node at the time t under the s scene,
Figure BDA0003204441030000131
and
Figure BDA0003204441030000132
respectively charging power and discharging power of equivalent centralized energy storage of electric automobile polymerization,
Figure BDA0003204441030000133
data representing time t in s scene in load deviation distribution, multiplied by
Figure BDA0003204441030000134
And
Figure BDA0003204441030000135
the load actual data at the time t is later.
The voltage constraint of the power distribution network meets the following relational expression:
Figure BDA0003204441030000136
wherein: vj,t,sIs the voltage value, V, of the node j at time t under the scene of si,t,sIs the voltage value, V, of the node i at time t under the scene of snThe rated voltage value of the distribution network.
The power distribution network power piecewise linearization constraint satisfies the following relational expression:
Figure BDA0003204441030000137
Figure BDA0003204441030000138
Figure BDA0003204441030000139
Figure BDA00032044410300001310
Figure BDA00032044410300001311
Figure BDA00032044410300001312
Figure BDA00032044410300001313
wherein: sigmaij,kIs the power piecewise linear slope of the ij branch,
Figure BDA00032044410300001314
Is a power piecewise linear active variable of the ij branch at the t moment in the s scene,
Figure BDA00032044410300001315
Is a piecewise linear reactive variable of the power of the ij branch at the time t under the s scene,
Figure BDA00032044410300001316
Positive parameter of active power of the ij branch at the t moment in s scene,
Figure BDA00032044410300001317
Is a negative parameter of the active power of the ij branch at the t moment in the s scene,
Figure BDA00032044410300001318
Reactive power of the ij branch at t moment in s scenePositive parameters of power,
Figure BDA00032044410300001319
Is the negative parameter, tau, of the reactive power of the ij branch at the moment t under the s sceneijThe size of each segment, k is the value of the segment,
Figure BDA00032044410300001320
The total number of the segments is the parameter introduced for the power segment linearization.
The current and voltage constraints of the power distribution network satisfy the following relational expression:
Figure BDA0003204441030000141
Figure BDA0003204441030000142
wherein:
Figure BDA0003204441030000143
maximum current value, V, allowed for branch ij at time s scene tminAnd VmaxMinimum and maximum voltage values, V, allowed for the nodej,t，sThe voltage value of a node j at the moment t under the scene of s;
the gear constraint of the distribution network transformer meets the following relational expression:
Figure BDA0003204441030000144
Figure BDA0003204441030000145
Figure BDA0003204441030000146
Figure BDA0003204441030000147
Figure BDA0003204441030000148
wherein:
Figure BDA0003204441030000149
representing the head end node, voltage Δ tap, of the transformerjFor the step value, tap, of each gear of the transformerj,tIs the gear position value, tap, of the j node at time tj，t+1The gear stage value of the j node at time t +1,
Figure BDA00032044410300001410
and
Figure BDA00032044410300001411
the maximum value and the minimum value of the j node gear respectively,
Figure BDA00032044410300001412
for the maximum change value of two gears at adjacent times,
Figure BDA00032044410300001413
the maximum value of allowed gear changes for node j during the whole period of time T.
The power distribution network wind power output constraint satisfies the following relational expression:
Figure BDA00032044410300001414
Figure BDA00032044410300001415
Figure BDA00032044410300001416
Figure BDA00032044410300001417
Figure BDA00032044410300001418
wherein:
Figure BDA00032044410300001419
the wind power simulation refers to wind power output data of j nodes of the power distribution network at the t moment under the s scene generated by Monte Carlo simulation on the wind speed probability distribution of the fan in the step 2;
Figure BDA0003204441030000151
representing the relative value of wind speed and power of the wind turbine at time t;
Figure BDA0003204441030000152
the abandoned wind power gamma of j node wind power of the power distribution network at t moment under s scenecAnd theta and-theta are respectively an angle of reactive power advance and an angle of reactive power lag.
And 5: solving the maximum wind power consumption capacity of the power distribution network according to the aggregation model of the electric automobile established in the step 3 and the mathematical model of the optimized dispatching of the electric automobile established in the step 4;
the model in the step 4 has integer variables and continuous variables, and the constraint and objective functions only contain linear functions, which belong to the mixed integer linear programming problem, and the models in the step 3 and the step 4 can be directly combined together and solved at the same time to obtain the maximum wind power consumption capacity of the power distribution network. Solvers including Cplex, Mosek, Gurobi may be used in the solution.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (16)

1. The method for maximum wind power consumption of the power distribution network based on the electric automobile aggregation model is characterized by comprising the following steps of:
step 1, collecting relevant parameters of a fan connected with a power distribution network and the time of each electric vehicle leaving or arriving at a charging station;
step 2, constructing distribution required probability distribution based on the parameters acquired in the step 1, wherein the distribution required probability distribution comprises power grid load deviation probability distribution, wind speed probability distribution of a fan and probability distribution of electric vehicle traveling;
step 3, constructing an electric automobile aggregation model and electric automobile aggregation model constraint conditions based on the probability distribution in the step 2;
step 4, constructing a mathematical model of electric vehicle optimization scheduling, wherein the mathematical model comprises an objective function capable of minimizing network loss and a constraint condition corresponding to the objective function;
and 5, solving the maximum wind power consumption capacity of the power distribution network according to the electric vehicle aggregation model established in the step 3 and the electric vehicle optimized dispatching mathematical model established in the step 4.
2. The power distribution network wind power maximum consumption method according to claim 1, characterized in that:
the relevant parameters of the fan connected with the power distribution network in the step 1 comprise the installed wind power capacity connected with each node of the power distribution network, the cut-in wind speed, the rated wind speed, the cut-out wind speed, the historical wind speed data and the wind speed of the fan in the T time period of the fan in the installed wind power system, and the T time period is cut into nn times at intervals of tt minutes.
3. The power distribution network wind power maximum consumption method according to claim 1, characterized in that:
the distribution network load deviation probability distribution satisfies the following relational expression:
Figure FDA0003204441020000011
wherein, mud,tMeans, σ, representing the distribution network load deviation corresponding to time td,tRepresents μd,tThe corresponding standard deviation of the distribution network load deviation,
Figure FDA0003204441020000012
is the load deviation value at the time t,
Figure FDA0003204441020000013
multiplying the historical average value of the load to obtain the load value of the power distribution network, and cutting the T time period into any time of nn times at intervals of tt minutes.
4. The power distribution network wind power maximum consumption method according to claim 1, characterized in that:
the wind speed probability distribution of the fan meets the following relational expression:
Figure FDA0003204441020000014
fv(vt) Representing a wind velocity distribution, which is a rayleigh distribution; sigmavFor parameter values obtained by fitting historical wind speed data, vtThe wind speed value collected at the moment t.
5. The power distribution network wind power maximum consumption method according to claim 1, characterized in that:
the probability distribution of the electric vehicle traveling comprises time probability distribution of the electric vehicle leaving the charging station, time probability distribution of the electric vehicle reaching the charging station and daily driving distance probability distribution of the electric vehicle.
6. The power distribution network wind power maximum absorption method according to claim 5, characterized in that:
the time probability distribution of the electric vehicle leaving the charging station satisfies the following relation:
Figure FDA0003204441020000021
fr(tdep) Represents the time probability distribution of the electric vehicle leaving the charging station, which is a normal distribution,
wherein: t is tdepFor the time when the electric vehicle leaves the charging station, murMean value of time, σ, for all electric vehicles leaving the charging stationrIs murThe variance of (c).
7. The power distribution network wind power maximum absorption method according to claim 5, characterized in that:
the time probability distribution of the electric vehicle reaching the charging station satisfies the following relation:
Figure FDA0003204441020000022
fe(tarr) Representing the probability distribution of the time when the electric vehicle arrives at the charging station, wherein the probability distribution is normal distribution;
wherein: t is tarrTime of arrival of the electric vehicle at the charging station, mueMean value of time, σ, for all electric vehicles arriving at the charging stationeIs mueThe variance of (c).
8. The power distribution network wind power maximum absorption method according to claim 5, characterized in that:
the daily driving distance probability distribution of the electric automobile meets the following relational expression:
Figure FDA0003204441020000023
fdis(xdis) The distribution of the daily driving distance of the electric automobile is lognormal distribution;
wherein: x is the number ofdisThe daily driving distance mu when the electric vehicle arrives at the charging stationdisThe average value of the daily driving distance, sigma, when all the electric vehicles arrive at the charging stationdisIs mudisThe variance of (c).
9. The power distribution network wind power maximum consumption method according to claim 1, characterized in that:
in the step 3, all electric vehicles which are connected to the j-node charging station of the power distribution network at the moment t are aggregated into an equivalent centralized energy storage, namely an aggregation model, wherein the aggregation model meets the following relational expression:
Figure FDA0003204441020000031
Figure FDA0003204441020000032
Figure FDA0003204441020000033
Figure FDA0003204441020000034
Figure FDA0003204441020000035
wherein: g is a candidate node set for installing wind power in the power distribution network,
s is a set after the scene is reduced; the scene is data of a corresponding scene of the power distribution network wind power generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; the data of a plurality of scenes are generated by repeated simulation, the scene reduction refers to the merging of the scenes through a clustering algorithm,
Mjto access the collection of all electric vehicles charged at j-node of the distribution network,
Figure FDA0003204441020000036
and
Figure FDA0003204441020000037
respectively aggregating the maximum capacity, the maximum charging power, the maximum discharging power, the reaching capacity and the leaving capacity of equivalent centralized energy storage of the j-node electric automobile at the t moment under an S scene, wherein the S scene is any scene in the S set;
Figure FDA0003204441020000038
and
Figure FDA0003204441020000039
the maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of the mth electric vehicle, respectively, fm,t,sFor the state variable of the j node of the power distribution network accessed by the mth electric automobile at the moment t, 1 represents access, 0 represents non-access,
Figure FDA00032044410200000310
and
Figure FDA00032044410200000311
and the state variables respectively represent whether the mth electric vehicle arrives and departs at the time t, 1 represents that the electric vehicle arrives and departs, and 0 represents that the electric vehicle does not arrive and departs.
10. The power distribution network wind power maximum absorption method according to claim 9, characterized in that:
the constraint conditions of the electric automobile aggregation model satisfy the following relational expression:
Figure FDA0003204441020000041
Figure FDA0003204441020000042
Figure FDA0003204441020000043
Figure FDA0003204441020000044
Figure FDA0003204441020000045
Figure FDA0003204441020000046
wherein: ej,t,sAggregated energy storage capacity value, E, of j-node electric vehicle at t moment under s scenej,t-1,sThe accumulated energy storage capacity value of the electric automobile at the j node at the t-1 moment under the s scene is shown, tau represents the step length of the adjacent moment and is consistent with tt, eta+And η-Respectively the charging efficiency and the discharging efficiency,
Figure FDA0003204441020000047
is the energy storage capacity value aggregated by the electric automobile at the j node at the initial moment under the s scene,
Figure FDA0003204441020000048
the aggregated energy storage capacity value of the electric automobile at the j node at the last moment in the s scene,
Figure FDA0003204441020000049
represents the maximum charging power of equivalent centralized energy storage of j node electric automobile aggregation at t moment under the scene of s,
Figure FDA00032044410200000410
represents the maximum discharge power of equivalent centralized energy storage of j node electric automobile aggregation at t moment under the scene of s,
Figure FDA00032044410200000411
and
Figure FDA00032044410200000412
respectively is a charge-discharge flag bit, is an integer variable of 0 and 1
Figure FDA00032044410200000413
When the value is 1, the charging is performed, and when the value is 1
Figure FDA00032044410200000414
When the value is 1, the discharge is performed, and both 0 values indicate that neither charge nor discharge is performed, and at most, only 1 of the two values can be 0 at the same time.
11. The method for maximum wind power consumption of a power distribution network according to claim 1 or 10, characterized in that:
the objective function of the mathematical model for optimizing and scheduling the electric automobile is as follows:
Figure FDA00032044410200000415
wherein: g is a candidate node set for installing wind power in the power distribution network,
s is a set after the scene is reduced; the scene is data of a corresponding scene of the power distribution network wind power generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; the data of a plurality of scenes are generated by repeated simulation, the scene reduction refers to the merging of the scenes through a clustering algorithm,
l is the set of all the branches of the distribution network,
Figure FDA00032044410200000416
for installation in the j node of the distribution networkijIs the resistance of the branch ij and,
Figure FDA0003204441020000051
the square of the current of the branch ij at the time t under the S scene, wherein the S scene is any scene in the S set, rhosThe probability of S scene occurrence is the number of S scene occurrences divided by the total number of all scenes in the set S; omega1And ω2And the weighting coefficients are respectively wind power installation capacity and system network loss.
12. The power distribution network wind power maximum absorption method according to claim 11, characterized in that:
the constraint conditions required to be met by the mathematical model of the electric vehicle optimization scheduling comprise system power flow constraint, distribution network voltage constraint, distribution network power piecewise linearization constraint, distribution network current and voltage constraint, distribution network transformer gear constraint and distribution network wind power output constraint.
13. The power distribution network wind power maximum absorption method according to claim 12, characterized in that:
the power distribution network power piecewise linearization satisfies the following relational expression:
Figure FDA0003204441020000052
Figure FDA0003204441020000053
Figure FDA0003204441020000054
Figure FDA0003204441020000055
Figure FDA0003204441020000056
Figure FDA0003204441020000057
Figure FDA0003204441020000058
wherein: sigmaij,kIs the power piecewise linear slope of the ij branch,
Figure FDA0003204441020000059
Is a power piecewise linear active variable of the ij branch at the t moment in the s scene,
Figure FDA00032044410200000510
Is a piecewise linear reactive variable of the power of the ij branch at the time t under the s scene,
Figure FDA00032044410200000511
Positive parameter of active power of the ij branch at the t moment in s scene,
Figure FDA00032044410200000512
Power of ij branch in s fieldNegative parameters of active power at the scene time t,
Figure FDA00032044410200000513
Positive parameter of reactive power of the ij branch at t moment in s scene,
Figure FDA00032044410200000514
Is the negative parameter, tau, of the reactive power of the ij branch at the moment t under the s sceneijThe size of each segment, k is the value of the segment,
Figure FDA00032044410200000515
The total number of the segments is the parameter introduced for the power segment linearization.
14. The power distribution network wind power maximum absorption method according to claim 12, characterized in that:
the gear constraint of the distribution network transformer meets the following relational expression:
Figure FDA0003204441020000061
Figure FDA0003204441020000062
Figure FDA0003204441020000063
Figure FDA0003204441020000064
Figure FDA0003204441020000065
wherein:
Figure FDA0003204441020000066
representing the head end node, voltage Δ tap, of the transformerjFor the step value, tap, of each gear of the transformerj,tIs the gear position value, tap, of the j node at time tj,t+1The gear stage value of the j node at time t +1,
Figure FDA0003204441020000067
and
Figure FDA0003204441020000068
the maximum value and the minimum value of the j node gear respectively,
Figure FDA0003204441020000069
for the maximum change value of two gears at adjacent times,
Figure FDA00032044410200000610
the maximum value of allowed gear changes for node j during the whole period of time T.
15. The power distribution network wind power maximum absorption method according to claim 12, characterized in that:
the power distribution network wind power output constraint satisfies the following relational expression:
Figure FDA00032044410200000611
Figure FDA00032044410200000612
Figure FDA00032044410200000613
Figure FDA00032044410200000614
Figure FDA00032044410200000615
wherein:
Figure FDA00032044410200000616
the wind power simulation refers to wind power output data of j nodes of the power distribution network at the t moment under the s scene generated by Monte Carlo simulation on the wind speed probability distribution of the fan in the step 2;
Figure FDA00032044410200000617
the abandoned wind power gamma of j node wind power of the power distribution network at t moment under s scenecAnd theta and-theta are respectively an angle of reactive power advance and an angle of reactive power lag.
16. The power distribution network wind power maximum absorption method according to claim 12, characterized in that:
the system power flow constraint satisfies the following relational expression:
Figure FDA0003204441020000071
Figure FDA0003204441020000072
wherein: pij，t，sAnd Qij,t,sRespectively the active power and reactive power at t moment of the branch ij in s scene, xijBeing the reactance of the branch ij,
Figure FDA0003204441020000073
and
Figure FDA0003204441020000074
respectively a historical active average value and a historical reactive average value of a node j at the moment t,
Figure FDA0003204441020000075
and
Figure FDA0003204441020000076
respectively connecting the active power and the reactive power of the j-th node at the time t under the s scene,
Figure FDA0003204441020000077
and
Figure FDA0003204441020000078
respectively charging power and discharging power of equivalent centralized energy storage of electric automobile polymerization,
Figure FDA0003204441020000079
data representing the time t of scene s in the load deviation distribution, multiplied by
Figure FDA00032044410200000710
And
Figure FDA00032044410200000711
the load actual data at the time t is later.
CN202110913138.0A 2021-08-10 2021-08-10 Power distribution network wind power maximum absorption method based on electric automobile aggregation model Active CN113612258B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110913138.0A CN113612258B (en) 2021-08-10 2021-08-10 Power distribution network wind power maximum absorption method based on electric automobile aggregation model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110913138.0A CN113612258B (en) 2021-08-10 2021-08-10 Power distribution network wind power maximum absorption method based on electric automobile aggregation model

Publications (2)

Publication Number Publication Date
CN113612258A true CN113612258A (en) 2021-11-05
CN113612258B CN113612258B (en) 2024-04-12

Family

ID=78307885

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110913138.0A Active CN113612258B (en) 2021-08-10 2021-08-10 Power distribution network wind power maximum absorption method based on electric automobile aggregation model

Country Status (1)

Country Link
CN (1) CN113612258B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11994111B2 (en) 2021-03-19 2024-05-28 North China Electric Power University Wind power consumption method of virtual power plant with consideration of comprehensive demand responses of electrical loads and heat loads

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102075014A (en) * 2011-01-06 2011-05-25 清华大学 Large grid real-time scheduling method for accepting access of wind power
CN103840521A (en) * 2014-02-27 2014-06-04 武汉大学 Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow
CN108767906A (en) * 2018-06-01 2018-11-06 深圳供电局有限公司 It is a kind of to consider that the honourable primary frequency modulation spare capacity of cluster electric vehicle determines method
CN111216586A (en) * 2020-03-28 2020-06-02 东南大学 Residential community electric vehicle ordered charging control method considering wind power consumption
US20200298722A1 (en) * 2015-09-11 2020-09-24 Invertedpower Pty Ltd Methods and systems for an integrated charging system for an electric vehicle

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102075014A (en) * 2011-01-06 2011-05-25 清华大学 Large grid real-time scheduling method for accepting access of wind power
CN103840521A (en) * 2014-02-27 2014-06-04 武汉大学 Large-scale electric vehicle optimized charging and discharging system and method based on the optimal power flow
US20200298722A1 (en) * 2015-09-11 2020-09-24 Invertedpower Pty Ltd Methods and systems for an integrated charging system for an electric vehicle
CN108767906A (en) * 2018-06-01 2018-11-06 深圳供电局有限公司 It is a kind of to consider that the honourable primary frequency modulation spare capacity of cluster electric vehicle determines method
CN111216586A (en) * 2020-03-28 2020-06-02 东南大学 Residential community electric vehicle ordered charging control method considering wind power consumption

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11994111B2 (en) 2021-03-19 2024-05-28 North China Electric Power University Wind power consumption method of virtual power plant with consideration of comprehensive demand responses of electrical loads and heat loads

Also Published As

Publication number Publication date
CN113612258B (en) 2024-04-12

Similar Documents

Publication Publication Date Title
CN108599206B (en) Power distribution network hybrid energy storage configuration method under high-proportion uncertain power supply scene
CN110633854A (en) Full life cycle optimization planning method considering energy storage battery multiple segmented services
CN107482690B (en) Power system scheduling optimization method and system for cooperative scheduling of wind power and electric automobile
CN112564152B (en) Energy storage optimization configuration method for charging station operators
CN111626527A (en) Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle
CN111476407A (en) Medium-and-long-term hidden random scheduling method for cascade hydropower station of combined wind power photovoltaic power station
CN112550047B (en) Optimal configuration method and device for light charging and storage integrated charging station
CN102593855B (en) Method for stabilizing fluctuation of output power of renewable energy power supply in power system
CN114398723B (en) Large-scale electric vehicle cluster characteristic analysis method and system based on Minkowski sum
CN105391092A (en) Virtual power plant multi-objective bidding control and optimization method based on dependent chance programming
CN113131529A (en) Renewable energy bearing capacity assessment method considering multiple flexible resources
CN114301089B (en) Energy storage capacity configuration optimization method for wind-solar combined power generation system
CN113612258A (en) Power distribution network wind power maximum consumption method based on electric vehicle aggregation model
Rezaei et al. Optimal stochastic self-scheduling of a water-energy virtual power plant considering data clustering and multiple storage systems
CN115114854A (en) Two-stage self-organizing optimization aggregation method and system for distributed resources of virtual power plant
CN109038655B (en) Method for calculating matched energy storage capacity of large photovoltaic power station under power limiting requirement
CN114118787A (en) Dispatching optimization method for urban distributed source network load storage based on LSTM algorithm
CN113469750A (en) Charging station and power distribution network coordinated planning method and system considering extreme weather
CN110861508B (en) Charging control method and system shared by residential area direct current chargers and storage medium
Verzijlbergh et al. The role of electric vehicles on a green island
Mao et al. Economic Dispatch of Microgrid Considering Fuzzy Control Based Storage Battery Charging and Discharging.
CN116488140A (en) Method, device, equipment and medium for dispatching comprehensive energy management and control system of expressway
CN118036233A (en) Complex power grid investment decision-making method considering source-network-load-storage
CN104112168A (en) Intelligent home economic optimization method based on multi-agent system
CN105608501A (en) Medium and long term power grid planning method for optimizing new energy construction and grid connected time sequence

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