CN113612258B - Power distribution network wind power maximum absorption method based on electric automobile aggregation model - Google Patents

Power distribution network wind power maximum absorption method based on electric automobile aggregation model Download PDF

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CN113612258B
CN113612258B CN202110913138.0A CN202110913138A CN113612258B CN 113612258 B CN113612258 B CN 113612258B CN 202110913138 A CN202110913138 A CN 202110913138A CN 113612258 B CN113612258 B CN 113612258B
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power
distribution network
scene
value
moment
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CN113612258A (en
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李洁
钱科军
刘乙
李亚飞
张晓明
李圆琪
朱丹
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Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—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

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

Abstract

A power distribution network wind power maximum absorption method based on an electric automobile aggregation model comprises the following steps: step 1, acquiring and calculating parameters required by wind power consumption of a power distribution network; step 2, constructing probability distribution required by matching based on the parameters acquired in the step 1; step 3, constructing an aggregation 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 capacity of the wind power of the power distribution network according to the aggregation model of the electric vehicle established in the step 3 and the mathematical model of the electric vehicle optimized scheduling established in the step 4. According to the invention, the installation capacity of wind power is taken as an objective function, and simultaneously, the load, the wind power output and the randomness of the electric vehicle travel are considered, so that a first-stage aggregation model and a second-stage mathematical model for optimal dispatching of the electric vehicle are established, and the wind power absorption capacity of the power distribution network can be greatly improved after the two models are combined and solved.

Description

Power distribution network wind power maximum absorption method based on electric automobile aggregation model
Technical Field
The invention belongs to the technical field of electric automobile optimized dispatching, and particularly relates to a power distribution network wind power maximum absorption method based on an electric automobile aggregation model.
Background
According to the message issued by the public security department: by the year 2020, the storage capacity of new energy automobiles in China reaches 497 ten thousand. Under the stimulation of related matched policies, the development of electric automobiles in China presents a wide market prospect. The electric automobile has the potential of energy storage and can shift the time of energy. If large-scale electric vehicles are aggregated together, reasonable charge and discharge scheduling is performed to exert large-scale energy storage effect, so that load peak-valley difference can be effectively relieved, voltage quality is improved, and more distributed power supplies are consumed. Wind power is connected to the power distribution network side to realize distributed power generation, so that network loss can be effectively reduced, and power supply reliability is improved, but the capacity limitation of the power distribution network is difficult to realize grid connection and absorption, so that the phenomenon of wind and light abandoning is more and more serious. Therefore, an electric automobile optimal scheduling technology is continuously proposed to solve the problem of wind power consumption.
Most of the existing related technologies do not directly take the wind power consumption value as an objective function, but are from the point of view of load peak-valley difference. Meanwhile, when the optimization model is solved, an algorithm used in the prior art cannot obtain the optimal solution.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a power distribution network wind power maximum absorption method based on an electric automobile 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 when each electric automobile leaves or arrives at a charging station;
step 2, constructing a required probability distribution based on the parameters acquired in the step 1, wherein the probability distribution comprises power grid load deviation probability distribution, wind speed probability distribution of a fan and probability distribution of electric vehicle travel;
step 3, constructing an aggregation model of the electric automobile based on the probability distribution in the step 2 and constraint conditions of the aggregation model of the electric automobile;
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 capacity of the wind power of the power distribution network according to the aggregation model of the electric vehicle established in the step 3 and the mathematical model of the electric vehicle optimized scheduling established in the step 4.
The relevant parameters of the fans connected with the power distribution network in the step 1 comprise the installed capacity of wind power 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 of the fans in the wind power installation, the wind speed of the fans in a T time period, and the T time period is cut into nn times T at intervals of tt minutes.
The distribution network load deviation probability distribution satisfies the following relation:
wherein mu d,t Mean value sigma of load deviation of power distribution network corresponding to t moment d,t Representation mu d,t The standard deviation of the corresponding power distribution network load deviation,for the load deviation value at time t +.>Taking the historical average value of the load as the load value of the power distribution network, wherein T is any time of nn times cut by taking tt minutes as intervals in a T time period.
The wind speed probability distribution of the fan meets the following relation:
f v (v t ) Represents wind speed distribution, which is Rayleigh distribution; sigma (sigma) v V is a parameter value obtained by fitting the historical wind speed data t And the wind speed value is acquired at the moment t.
The probability distribution of the electric vehicle traveling comprises the time probability distribution of the electric vehicle leaving the charging station, the time probability distribution of the electric vehicle reaching the charging station and the 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:
f r (t dep ) Representing the time probability distribution of the electric vehicle leaving the charging station, which is a normal distribution,
wherein: t is t dep Mu for the time the electric vehicle leaves the charging station r For the time average of all electric vehicles leaving the charging station, σ r Mu is r Is a variance of (c).
The time probability distribution of the electric vehicle reaching the charging station satisfies the following relationship:
f e (t arr ) The time probability distribution of the electric automobile reaching the charging station is represented, and the time probability distribution is normal distribution;
wherein: t is t arr Mu for the time of arrival of the electric vehicle at the charging station e For the time average of all electric vehicles arriving at the charging station, sigma e Mu is e Is a variance of (c).
The probability distribution of the daily driving distance of the electric automobile satisfies the following relation:
f dis (x dis ) The daily driving distance distribution of the electric automobile is lognormal distribution;
wherein: x is x dis Mu, the daily travel distance when the electric automobile arrives at the charging station dis To a charging station for all electric vehiclesAverage value of time daily travel distance sigma dis Mu is dis Is a variance of (c).
In step 3, electric vehicles which are all connected to j node charging stations of the power distribution network at the time t are aggregated into an equivalent concentrated energy storage, namely an aggregation model, wherein the aggregation model meets the following relational expression:
wherein: g is a node set to be selected for installing wind power in the power distribution network,
s is a scene reduced set; the scene refers to data of a corresponding scene of wind power of the power distribution network generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; repeated simulation is carried out for a plurality of times to generate data of a plurality of scenes, scene reduction means that the scenes are combined through a clustering algorithm,
M j to access the collection of all electric vehicles at the j-node charging station of the distribution network,
and->Respectively obtaining maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of equivalent concentrated energy storage of j-node electric vehicles aggregated at t moment under an S scene, wherein the S scene is any scene in an S set;
and->Maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of the mth electric automobile, respectively, f m，t，s For the state variable of the mth electric automobile which is accessed to the j node of the power distribution network at the moment t, 1 represents access, 0 represents non-access and +.>And->The state variables indicating whether the mth electric automobile arrives and departs at time t are respectively represented by 1 indicating that the mth electric automobile arrives and departs, and 0 indicating that the mth electric automobile does not arrive and does not depart.
The constraint condition of the electric automobile aggregation model meets the following relation:
wherein: e (E) j，t，s Energy storage capacity value E aggregated for j-node electric vehicles at t moment under s scene j，t-1，s For the energy storage capacity value aggregated by the electric vehicle at the j node at the t-1 moment under the s scene, tau represents the step length of the adjacent moment, and is consistent with tt, eta + And eta - The charge efficiency and the discharge efficiency are respectively defined,for the energy storage capacity value aggregated by the j-node electric automobile at the initial moment under the s scene, < + >>Energy storage capacity value aggregated for j-node electric vehicles at last moment in s scene, < + >>Represents the maximum charging power of equivalent concentrated energy storage of j-node electric automobile aggregation at t moment in s scene,/>Represents the maximum discharge power of the equivalent concentrated energy storage of the j-node electric vehicle aggregation at the t moment in the s scene,/>And->Respectively charge and discharge zone bits, which are 0,1 integer variable, when +.>When the value is 1, charging is performed, and when +.>When the value is 1, the discharge is performed, and when the values are 0, the discharge is neither performed, but only 1 at most can be 0 at the same time.
The objective function of the mathematical model of the optimal scheduling of the electric automobile is as follows:
wherein: g is a node set to be selected for installing wind power in the power distribution network,
s is a scene reduced set; the scene refers to data of a corresponding scene of wind power of the power distribution network generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; repeated simulation is carried out for a plurality of times to generate data of a plurality of scenes, scene reduction means that the scenes are combined through a clustering algorithm,
l is the set of all the branches of the distribution network,for the wind power capacity of j nodes of a power distribution network, r ij For the resistance of branch ij +.>The current square of the branch ij at the t moment under the S scene, wherein the S scene is any scene in the S set, and ρ is as follows s The probability of occurrence of S scenes is the number of occurrences of S scenes divided by the total number of occurrences of all scenes in the set S; omega 1 And omega 2 And respectively weighing coefficients of wind power installation capacity and system network loss.
Constraint conditions to be met by the mathematical model of the electric automobile optimization scheduling include system power flow constraint, power distribution network voltage constraint, power distribution network power piecewise linearization constraint, power distribution network current and voltage constraint, gear constraint of a power distribution network transformer and power distribution network wind power output constraint.
The power piecewise linearization of the distribution network satisfies the following relationship:
wherein: sigma (sigma) ij，k A power piecewise linear slope for ij branch,For the power piecewise linear active variable of the ij branch at the moment t under the s scene,/for the moment t>The power of the ij branch is a power piecewise linear reactive variable of t moment in an s scene,Positive parameter of active power at time t under s scene for power of ij branch, +.>Negative parameter of active power at time t under s scene for power of ij branch, +.>Positive parameters of reactive power at time t in s-scenario for the power of ij branch, +.>Negative parameter τ for reactive power of ij branch at time t in s scene ij For the size of each segment, k is the value of the segment,/and->The total number of segments is the parameters introduced for power segment linearization.
The gear constraint of the power distribution network transformer meets the following relation:
wherein:representing the voltage Δtap at the head-end node of the transformer j Tap for step value of each gear of transformer j，t Is the gear value of the j node at the moment t, tap j，t+1 For the gear value of node j at time t+1,/->And->Maximum value and minimum value of j node gear respectively,/->Maximum change value for two gear positions at adjacent times,/->The maximum value of the shift position allowed to change in the whole T time period is provided for the j node.
The wind power output constraint of the power distribution network meets the following relation:
wherein:the method comprises the steps that active power which can be actually generated during wind power simulation is obtained by simulating wind power output data of j nodes of a power distribution network at a moment t under an s scene generated by using Monte Carlo for wind speed probability distribution of a fan in the step 2;
the wind power is gamma, which is the wind power abandoned by j nodes of the power distribution network at the moment t under the s scene c For the maximum wind curtailment index in T time, θ and θ are the angle of reactive lead and the angle of reactive lag respectively.
The system power flow constraint satisfies the following relation:
wherein: p (P) ij，t，s And Q ij，t，s Respectively the active power and reactive power of branch ij at t moment under s scene, x ij For the reactance of the branch ij,and->The historical active average value and the historical reactive average value of the node j at the moment t are respectively>And->Respectively connecting active power and reactive power of a j-th node at t moment in an s scene, < ->And->Charging power and discharging power of equivalent concentrated energy storage aggregated by electric vehicles respectively, +.>Data representing the moment t of the s scene in the load deviation distribution multiplied by +.>And->The actual data of the load at the moment t follows.
Compared with the prior art, the method has the advantages that the method takes the installation capacity of wind power as an objective function, simultaneously considers the load, the wind power output and the trip randomness of the electric automobile, and establishes a two-stage random mixed integer linear programming model which is a mathematical model for optimizing and dispatching the electric automobile in a first stage aggregation model and a second stage respectively. The first stage is to aggregate a plurality of electric automobile users together to be equivalent to a concentrated energy storage, and connect the concentrated energy storage to a corresponding power distribution network node, so as to optimize the installation capacity of wind power and maximize the installable capacity of the wind power; and in the second stage, optimizing the system loss of the power distribution network to minimize the network loss. The final model is a mixed integer linear programming model, the model can be directly solved by adopting a commercial solver, the optimal scheduling schemes of the electric automobile in different time periods can be obtained, and the wind power absorption capacity of the power distribution network can be greatly improved.
Drawings
FIG. 1 is a two-stage stochastic programming model of the present invention;
fig. 2 is a flow chart of a power distribution network wind power maximum absorption method based on an electric automobile aggregation model.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
FIG. 1 is a schematic diagram of two stages in a model proposed by a power distribution network wind power maximum absorption method based on an electric automobile aggregation model, which can be divided into two stages; fig. 2 is a practical flow chart of the method of the present invention, which specifically includes the following:
step 1: collecting relevant parameters of a fan connected with the power distribution network and the time when each electric automobile leaves or arrives at a charging station;
the relevant parameters of the fans connected with the power distribution network comprise the installed capacity of wind power connected with each node of the power distribution network, cut-in wind speed, rated wind speed, cut-out wind speed, historical wind speed data of the fans in the wind power installation, wind speed of the fans in a T time period, and the T time period is cut into nn times T at intervals of tt minutes.
In this embodiment, the time period of the whole planning model may be selected to be T24 hours a day, and the tt minutes between every two time intervals is 30 minutes; .
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 travel;
the distribution network load deviation probability distribution satisfies the following relation:
wherein mu d,t Mean value sigma of load deviation of power distribution network corresponding to t moment d,t Representation mu d,t The standard deviation of the corresponding power distribution network load deviation,the load deviation value at time t. />And taking the historical average value of the load as the load value of the power distribution network.
The wind speed probability distribution of the fan meets the following relation:
f v (v t ) Represents wind speed distribution, which is Rayleigh distribution; sigma (sigma) v For the parameter values obtained by fitting the historical wind speed data, the parameter fitting method is the prior art, and is therefore not within the discussion of the invention, v t And the wind speed value is acquired at the moment t.
The relative values of wind speed and power of the fan satisfy the following relation:
wherein,representing the relative value of wind speed and power of the fan at the moment t, v ci To cut in wind speed v r For rated wind speed v co To cut out wind speed.
The probability distribution of the electric vehicle traveling comprises the time probability distribution of the electric vehicle leaving the charging station, the time probability distribution of the electric vehicle reaching the charging station and the 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:
f r (t dep ) The time probability distribution of the electric vehicle leaving the charging station is represented, which is a normal distribution. Wherein: t is t dep Mu for the time the electric vehicle leaves the charging station r For the time average of all electric vehicles leaving the charging station, σ r Mu is r Is a variance of (c).
The time probability distribution of the electric vehicle reaching the charging station satisfies the following relationship:
f e (t arr ) The time probability distribution of an electric vehicle arriving at a charging station is represented, which is a normal distribution. Wherein: t is t arr Mu for the time of arrival of the electric vehicle at the charging station e For the time average of all electric vehicles arriving at the charging station, sigma e Mu is e Is a variance of (c).
The probability distribution of the daily driving distance of the electric automobile satisfies the following relation:
f dis (x dis ) The daily driving distance distribution of the electric automobile is lognormal distribution. Wherein: x is x dis Mu, the daily travel distance when the electric automobile arrives at the charging station dis For the average value of the daily travel distance sigma of all electric vehicles reaching a charging station dis Mu is dis Is a variance of (c).
Step 3: constructing an aggregation model of the electric automobile based on the probability distribution in the step 2 and constraint conditions of the aggregation model of the electric automobile;
electric vehicles which are all connected to j node charging stations of the power distribution network at the time t are aggregated into an equivalent concentrated energy storage, namely an aggregation model, wherein the equivalent concentrated energy storage meets the following relational expression:
wherein: g is a node set to be selected for installing wind power in the power distribution network,
s is a scene reduced set; the scene refers to data of a corresponding scene of wind power of the power distribution network generated by performing Monte Carlo simulation on the probability distribution in the step 2, and in the embodiment, the data of the corresponding scene comprises power distribution network load data of one day and power distribution network wind power output data of one day, data of electric vehicles randomly arriving at a charging station one day and data of electric vehicles randomly leaving the charging station one day; repeated simulation is carried out for multiple times to generate data of multiple scenes, scene reduction means that the scenes are combined through a clustering algorithm, a common k-means clustering method can be adopted, mature software is directly applied, the specific steps are not the key point of the invention,
M j to access the collection of all electric vehicles at the j-node charging station of the distribution network,
and->Respectively obtaining maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of equivalent concentrated energy storage of j-node electric vehicles aggregated at t moment under an S scene, wherein the S scene is any scene in an S set;
and->Maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of the mth electric automobile, respectively, f m,t,s For the state variable of the mth electric automobile which is accessed to the j node of the power distribution network at the moment t, 1 represents access, 0 represents non-access and +.>And->The state variables indicating whether the mth electric automobile arrives and departs at time t are respectively represented by 1 indicating that the mth electric automobile arrives and departs, and 0 indicating that the mth electric automobile does not arrive and does not depart.
The constraint condition of the electric automobile aggregation model meets the following relation:
wherein: e (E) j，t,s Energy storage capacity value E aggregated for j-node electric vehicles at t moment under s scene j,t-1,s For the energy storage capacity value aggregated by the electric vehicle at the j node at the t-1 moment in the s scene, τ is the step length of the adjacent moment, which is consistent with tt, and is expressed for 30 minutes in the embodiment, η + And eta - The charge efficiency and the discharge efficiency are respectively defined,for the energy storage capacity value aggregated by the j-node electric automobile at the initial moment under the s scene, < + >>The energy storage capacity value aggregated by the electric automobile at the j node at the last moment in the s scene,represents the maximum charging power of equivalent concentrated energy storage of j-node electric automobile aggregation at t moment in s scene,/>Represents the maximum discharge power of the equivalent concentrated energy storage of the j-node electric vehicle aggregation at the t moment in the s scene,/>And->Respectively charge and discharge zone bits, which are 0,1 integer variable, when +.>When the value is 1, charging is performed, and when +.>When the value is 1, the discharge is performed, and when the values are 0, the discharge is neither performed, but only 1 at most can be 0 at the same time.
Step 4: constructing a mathematical model of electric automobile 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 of the optimal scheduling of the electric automobile is as follows:
wherein: g is a node set to be selected for installing wind power in the power distribution network,
s is a scene reduced set; the scene refers to data of a corresponding scene of wind power of the power distribution network generated by performing Monte Carlo simulation on the probability distribution in the step 2, and in the embodiment, the data of the corresponding scene comprises power distribution network load deviation data of one day and power distribution network wind power output data of one day, data of electric vehicles randomly arriving at a charging station one day and data of electric vehicles randomly leaving the charging station one day; repeated simulation is carried out for multiple times to generate data of multiple scenes, scene reduction means that the scenes are combined through a clustering algorithm, a common k-means clustering method can be adopted, mature software is directly applied, and specific steps are not important in the invention.
L is the collection of all branches of the power distribution network.For the wind power capacity of j nodes of a power distribution network, r ij For the resistance of branch ij +.>The current square of the branch ij at the t moment under the S scene, wherein the S scene is any scene in the S set, and ρ is as follows s The probability of occurrence of S scenes is the number of occurrences of S scenes divided by the total number of occurrences of all scenes in the set S; omega 1 And omega 2 And respectively weighing coefficients of wind power installation capacity and system network loss.
Constraint conditions to be met by the mathematical model of the electric automobile optimization scheduling include system power flow constraint, power distribution network voltage constraint, power distribution network power piecewise linearization constraint, power distribution network current and voltage constraint, gear constraint of a power distribution network transformer and power distribution network wind power output constraint.
And (3) constraint of system tide:
wherein: p (P) ij,t,s And Q ij,t,s Respectively the active power and reactive power of branch ij at t moment under s scene, x ij For the reactance of the branch ij,and->The historical active average value and the historical reactive average value of the node j at the moment t are respectively>And->Respectively connecting active power and reactive power of a j-th node at t moment in an s scene, < ->And->Charging power and discharging power of equivalent concentrated energy storage aggregated by electric vehicles respectively, +.>Data representing time t in s scene in load deviation distribution multiplied by +.>And->The actual load data at time t follows.
The power distribution network voltage constraint satisfies the following relation:
wherein: v (V) j,t,s Is the voltage value of the node j at the moment t under the scene s, V i,t,s The voltage value of the node i at the time t under the s scene is V n And the rated voltage value of the power distribution network is obtained.
The power distribution network power piecewise linearization constraint satisfies the following relation:
wherein: sigma (sigma) ij,k A power piecewise linear slope for ij branch,For the power piecewise linear active variable of the ij branch at the moment t under the s scene,/for the moment t>The power of the ij branch is a power piecewise linear reactive variable of t moment in an s scene,Positive parameter of active power at time t under s scene for power of ij branch, +.>Negative parameter of active power at time t under s scene for power of ij branch, +.>Positive parameters of reactive power at time t in s-scenario for the power of ij branch, +.>Negative parameter τ for reactive power of ij branch at time t in s scene ij For the size of each segment, k is the value of the segment,/and->The total number of segments is the parameters introduced for power segment linearization.
The power distribution network current and voltage constraints satisfy the following relation:
wherein:maximum current value V allowed by branch ij at s scene t min And V max Minimum and maximum voltage values allowed for the node, V j,t，s The voltage value of a node j at the moment t under the scene s;
the gear constraint of the power distribution network transformer meets the following relation:
wherein:representing the voltage Δtap at the head-end node of the transformer j Tap for step value of each gear of transformer j,t J node at time tTap of the gear value of (a) j，t+1 For the gear value of node j at time t+1,/->And->Maximum value and minimum value of j node gear respectively,/->Maximum change value for two gear positions at adjacent times,/->The maximum value of the shift position allowed to change in the whole T time period is provided for the j node.
The wind power output constraint of the power distribution network meets the following relation:
wherein:for the active power actually generated during wind power simulationWind power simulation refers to wind power output data of j nodes of the power distribution network at t moment under s scene generated by using Monte Carlo simulation on wind speed probability distribution of the fan in the step 2;the relative value of the wind speed and the power of the fan at the moment t is represented;
the wind power is gamma, which is the wind power abandoned by j nodes of the power distribution network at the moment t under the s scene c For the maximum wind curtailment index in T time, θ and θ are the angle of reactive lead and the angle of reactive lag respectively.
Step 5: solving the maximum absorption capacity of the wind power of the power distribution network according to the aggregation model of the electric vehicle established in the step 3 and the mathematical model of the electric vehicle optimized dispatching established in the step 4;
the model in the step 4 has integer variables and continuous variables, the constraint and objective functions only contain linear functions, the problem of mixed integer linear programming is solved directly by combining the models in the step 3 and the step 4 together, and the maximum absorption capacity of the wind power of the power distribution network is obtained. A solver may be used for solving, and the solver includes Cplex, mosek, gurobi.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only 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 to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.

Claims (7)

1. The power distribution network wind power maximum absorption method 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 when each electric automobile leaves or arrives at a charging station;
the relevant parameters of the fans connected with the power distribution network comprise the installed capacity of the wind power 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 time period T of the wind power installation;
step 2, constructing required probability distribution based on the parameters acquired in the step 1, wherein the required probability distribution comprises distribution network load deviation probability distribution, wind speed probability distribution of a fan and probability distribution of electric automobile travel;
load deviation probability distribution of power distribution networkThe following relationship is satisfied:
wherein mu d,t Mean value sigma of load deviation of power distribution network corresponding to t moment d,t Representation mu d,t The standard deviation of the corresponding power distribution network load deviation,the load deviation value at the moment t; t is any time of nn times cut in a T time period with tt minutes as an interval;
the wind speed probability distribution of the fan meets the following relation:
f v (v t ) Represents wind speed distribution and is Rayleigh distribution; sigma (sigma) v V is a parameter value obtained by fitting the historical wind speed data t The wind speed value acquired at the moment t;
the probability distribution of the electric vehicle traveling comprises the time probability distribution of the electric vehicle leaving the charging station, the time probability distribution of the electric vehicle reaching the charging station and the 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:
f r (t dep ) Representing the time probability distribution of the electric vehicle leaving the charging station, which is a normal distribution,
wherein: t is t dep Mu for the time the electric vehicle leaves the charging station r For the time average of all electric vehicles leaving the charging station, σ r Mu is r Is a variance of (2);
the time probability distribution of the electric automobile reaching the charging station satisfies the following relation:
f e (t arr ) The time probability distribution of the electric automobile reaching the charging station is represented, and the time probability distribution is normal distribution;
wherein: t is t arr Mu for the time of arrival of the electric vehicle at the charging station e For the time average of all electric vehicles arriving at the charging station, sigma e Mu is e Is a variance of (2);
the probability distribution of the daily driving distance of the electric automobile meets the following relation:
f dis (x dis ) The daily driving distance distribution of the electric automobile is lognormal distribution;
wherein: x is x dis Mu, the daily travel distance when the electric automobile arrives at the charging station dis For all electricityAverage value sigma of daily travel distance when the motor vehicle arrives at the charging station dis Mu is dis Is a variance of (2); ln is a natural logarithmic function;
step 3, constructing an aggregation model of the electric automobile based on the probability distribution in the step 2 and constraint conditions of the aggregation model of the electric automobile;
electric vehicles which are all connected to j node charging stations of the power distribution network at the time t are aggregated into an equivalent aggregation model for concentrated energy storage, and the aggregation model meets the following relational expression:
wherein: g is a node set to be selected for installing wind power in the power distribution network,
s is a scene reduced set; the scene refers to data of a corresponding scene of wind power of the power distribution network generated by carrying out Monte Carlo simulation on the probability distribution in the step 2; repeated simulation is carried out for a plurality of times to generate data of a plurality of scenes, scene reduction means that the scenes are combined through a clustering algorithm,
M j to access the collection of all electric vehicles at the j-node charging station of the distribution network,
and->Respectively obtaining maximum capacity, maximum charging power, maximum discharging power, arrival capacity and departure capacity of equivalent concentrated energy storage of j-node electric vehicles aggregated at t moment under an S scene, wherein the S scene is any scene in an S set;
and->The maximum capacity, the maximum charging power, the maximum discharging power, the arrival capacity and the departure capacity of the mth electric automobile are respectively; f (f) m,t,s The method is characterized in that the method is used for connecting an mth electric automobile to a state variable of a j node of a power distribution network at the moment t, and f m,t,s For 1 denotes access, f m,t,s A0 indicates unaccessed->And->State variables respectively representing whether the mth electric vehicle has arrived and has departed at time t,/>1 indicates that ∈1 has been reached>A value of 1 indicates that the device has left,a value of 0 indicates not reached, < >>A value of 0 indicates no departure;
step 4, constructing a mathematical model of electric vehicle optimization scheduling, which comprises an objective function for minimizing network loss and a constraint condition corresponding to the objective function;
the objective function of the mathematical model of the optimal scheduling of the electric automobile is as follows:
wherein: max represents maximum value;
l is the set of all the branches of the distribution network,for the wind power capacity of j nodes of a power distribution network, r ij For the resistance of branch ij +.>The square of the current of the branch ij at the moment t under the scene s; ρ s Dividing the probability of occurrence of S scenes by the total number of occurrence of all scenes in the set S; omega 1 And omega 2 Respectively weighing coefficients of wind power installation capacity and system network loss;
and 5, solving the maximum capacity of the wind power of the power distribution network according to the aggregation model of the electric vehicle established in the step 3 and the mathematical model of the electric vehicle optimized scheduling established in the step 4.
2. The method according to claim 1, wherein:
the constraint condition of the electric automobile aggregation model meets the following relational expression:
wherein: e (E) j,t,s Energy storage capacity value E aggregated for j-node electric vehicles at t moment under s scene j,t-1,s The energy storage capacity value is aggregated for the electric automobile at the j node at the t-1 moment in the s scene; τ represents the step size of adjacent time points to be consistent with the time interval tt; η (eta) + And eta - The charge efficiency and the discharge efficiency are respectively defined,the energy storage capacity value aggregated by the electric automobile at the j node at the initial moment in the s scene,the energy storage capacity value is aggregated for the j-node electric vehicle at the last moment in the s scene; />And->Charging power and discharging power of equivalent concentrated energy storage aggregated by electric vehicles respectively; />And->Respectively charge and discharge zone bit, and is an integer of 0 or 1, when +.>When the value is 1, charging is performed, and when +.>When the value is 1, discharge is indicated, < >>And->All 0's indicating neither charging nor discharging, at the same time +.>And->Only 1 is 0 at most.
3. The method according to claim 2, characterized in that:
constraint conditions to be met by the mathematical model of the electric automobile optimization scheduling include system power flow constraint, power distribution network voltage constraint, power distribution network power piecewise linearization constraint, power distribution network current and voltage constraint, gear constraint of a power distribution network transformer and power distribution network wind power output constraint.
4. A method according to claim 3, characterized in that:
the power distribution network power piecewise linearization constraint satisfies the following relation:
σ ij,k ＝(2k-1)τ ij ，
wherein: v (V) n The rated voltage value of the power distribution network; sigma (sigma) ij,k A power piecewise linear slope for ij branch,For the power piecewise linear active variable of the ij branch at the moment t under the s scene,/for the moment t>The power of the ij branch is t time under the s sceneA carved power piecewise linear reactive variable; p (P) ij,t,s The actual active power of the ij branch at the t moment in the s scene is as follows; q (Q) ij,t,s The actual reactive power of the ij branch at the t moment in the s scene is obtained; />Positive parameter of active power at time t under s scene for power of ij branch, +.>Negative parameter of active power at time t under s scene for power of ij branch, +.>Positive parameters of reactive power at time t in s-scenario for the power of ij branch, +.>The negative parameter of reactive power of the ij branch at the moment t under the s scene is adopted; τ ij A size for each segment; k is the value of the segment; />Is the total number of segments.
5. A method according to claim 3, characterized in that:
the gear constraint of the power distribution network transformer meets the following relation:
wherein: v (V) j,t,s The voltage value of the j node at the moment t under the s scene is;representing the voltage Δtap at the head-end node of the transformer j Tap for step value of each gear of transformer j,t Is the gear value of the j node at the moment t, tap j,t+1 For the gear value of node j at time t+1,/->And->Minimum value and maximum value of j node gear respectively,/->Maximum change value for two gear positions at adjacent times,/->The maximum value of the shift position allowed to change in the whole T time period is provided for the j node.
6. A method according to claim 3, characterized in that:
the wind power output constraint of the power distribution network meets the following relation:
wherein:the method comprises the steps that active power which can be actually generated during wind power simulation is obtained by simulating wind power output data of j nodes of a power distribution network at a moment t under an s scene generated by using Monte Carlo for wind speed probability distribution of a fan in the step 2; />The relative value of the wind speed and the power of the fan at the moment t is represented; />And->Respectively connecting active power and reactive power of a j-th node at a t moment in an s scene;
the wind power is gamma, which is the wind power abandoned by j nodes of the power distribution network at the moment t under the s scene c For the maximum wind curtailment index in T time, θ and θ are the angle of reactive lead and the angle of reactive lag respectively.
7. The method according to claim 4, wherein:
the system power flow constraint satisfies the following relation:
wherein: x is x ij Reactance for branch ij;and->The historical active average value and the historical reactive average value of the node j at the moment t are respectively; />And->Respectively connecting active power and reactive power of a j-th node at a t moment in an s scene; />Data representing the time t of s scene in the load deviation distribution.
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