CN113381406B - Electric vehicle charging and discharging control method, device, equipment and storage medium - Google Patents

Electric vehicle charging and discharging control method, device, equipment and storage medium Download PDF

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CN113381406B
CN113381406B CN202110737652.3A CN202110737652A CN113381406B CN 113381406 B CN113381406 B CN 113381406B CN 202110737652 A CN202110737652 A CN 202110737652A CN 113381406 B CN113381406 B CN 113381406B
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CN113381406A (en
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王蓓蓓
倪萌
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Southeast University
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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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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
    • 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]
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • 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
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • 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
    • Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

The invention discloses a method, a device, equipment and a storage medium for controlling the charge and discharge of an electric automobile, wherein the method comprises the following steps: (1) dividing a plurality of functional areas according to a topological structure of a power distribution network, and determining a trip chain structure of the electric automobile; (2) calculating a departure time distribution function and an arrival time distribution function of the electric automobile in each section of trip travel according to the trip chain structure, and obtaining the distribution condition of the electric automobile; (3) determining the starting, arrival and distribution conditions of the electric vehicles in each functional area according to different travel chain structures of different types of cities; (4) establishing an energy consumption model of each node cluster electric automobile; (5) the method comprises the steps of obtaining measured state parameters and measured resource parameters of target resources, inputting the measured state parameters and the measured resource parameters into a cluster electric vehicle charging and discharging model aiming at minimizing the cost of a distribution network to obtain an optimal charging and discharging strategy, providing reference for charging and discharging scheduling of electric vehicles in different functional areas of different cities, ensuring the voltage safety of the distribution network and improving the economical efficiency of the operation of the distribution network.

Description

Electric vehicle charging and discharging control method, device, equipment and storage medium
Technical Field
The invention relates to a method, a device, equipment and a storage medium for controlling charging and discharging of an electric automobile, and belongs to the technical field of charging and discharging scheduling of electric automobiles.
Background
In order to reduce the combustion of fossil fuels and reduce the emission of greenhouse gases such as carbon dioxide or harmful gases, Electric Vehicles (EVs) are popularized by governments of various countries as alternatives to conventional fossil fuel vehicles. On one hand, considering that charging behaviors of EV users have certain uncertainty, and the problems of load increase, power grid voltage fluctuation and the like caused by the fact that a large number of charging loads of electric vehicles are connected into a power grid possibly to cause peaks, the adverse effect of the EV on the power grid needs to be reduced as much as possible through a proper charging strategy; on the other hand, with the development of the V2G (vehicle-to-grid) technology, the electric vehicle battery as a mobile energy storage can participate in demand response by coordinating the charging and discharging strategies thereof, so as to promote the economical efficiency of the power grid operation and support the power grid operation.
The current research on the charge and discharge scheduling of the electric automobile is mainly based on a Monte Carlo simulation method to obtain the space-time distribution condition of the electric automobile, and along with the continuous increase of the current holding capacity of the electric automobile in a city, the time consumption under the traditional Monte Carlo sampling method is gradually increased. Considering that the cluster EV gradually presents a uniform distribution rule along with the increase of the number, the travel mode of the electric automobile can be predicted by utilizing a travel chain, and a probability distribution formula is obtained by adopting mathematical derivation. Based on the method, the electric automobile can be subjected to charging and discharging scheduling so as to be matched with the power grid to run economically and safely.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a storage medium for optimal scheduling of electric vehicle charging and discharging. Firstly, based on a travel chain rule of the electric automobile, taking the travel time, the congestion condition and the distance energy consumption into consideration, deducing a space-time distribution probability density function of the travel of the electric automobile, and obtaining the distribution rules of the electric automobile in different cities, and secondly, based on the space-time distribution model, the invention combines the power flow and the voltage constraint of a distribution network, and optimally schedules the electric automobile under each node in each area by taking the optimal economy as a target.
The purpose of the invention can be realized by the following technical scheme: a charging and discharging control method for an electric vehicle considering a user trip chain structure comprises the following steps:
s1, dividing each functional area of the power distribution network according to the topological structure of the power distribution network, and determining a back-and-forth trip chain structure of the electric automobile among the functional areas;
s2, calculating a distribution function of the departure time and the arrival time of the electric automobile in each section of travel under each trip chain according to the trip chain structure, and obtaining the distribution condition of the electric automobile under each functional area;
s3, determining the starting, arrival and distribution conditions of the electric vehicles in each functional area according to the proportion conditions of different trip chain structures of different types of cities;
s4, obtaining the departure, arrival and distribution conditions of the electric automobile under each node according to the departure, arrival and distribution conditions of the electric automobile in each functional area, and determining the total running energy consumption of the electric automobile reaching a certain node in the functional area at each moment;
and S5, considering power flow and voltage safety constraints of the power distribution network, combining charge and discharge constraints of the electric automobile of each node cluster, and establishing a charge and discharge optimization model of the electric automobile with the aim of minimizing the cost of the power distribution network.
Further, S1 includes the steps of:
s11, determining functional area classification under the distribution network area;
s12, dividing functional areas to which various nodes under the power distribution network belong according to the topological structure of the power distribution network;
and S13, determining the back-and-forth travel chain structure of the electric automobile among the functional areas according to the travel rule of the residents.
The trip chain refers to the trip time, the trip destination and the sequence of each trip of the electric vehicle user.
The trip chain structure is divided into 3 modes of a simple working chain A (H-W-H), a complex working chain B (H-W-H-W-H) and a working-entertainment chain C (H-W-E-H). Wherein, english letters H, W, E are used to represent the stroke: go home, work, and entertainment.
Further, S2 includes the steps of:
s21, determining a distribution function of the departure time of the electric automobile in each section of travel under each trip chain;
and S22, calculating the electric automobile arrival time cumulative distribution function in each journey according to the distance congestion time.
The departure time distribution function of S21 includes:
s211, for the first H-W journey of the simple work chain A, the complex work chain B and the work-entertainment chain C, the single-user starting function meets the following conditions:
Figure GDA0003655048190000031
in the formula: t is the starting trip time or the return trip time of the electric automobile, mu is the mean value of the starting trip time or the return trip time of the electric automobile, and sigma is the standard deviation. For clustered users, μ follows a gamma distribution and σ follows a standard normal distribution. Solving the marginal probability distribution of the variable t to obtain an H-W travel departure time probability distribution function:
Figure GDA0003655048190000032
in the formula, alpha and theta are shape parameters and inverse scale parameters of gamma distribution; sigma2Standard normal distribution standard deviation.
S212, for the second H-W journey of the complex work chain B and the E-H journey of the work-entertainment chain C, the time of the functional area staying in the upper journey meets random distribution, so the cumulative distribution function calculation method of the departure time is as follows:
Figure GDA0003655048190000033
in the formula, the residence time is Deltat-U (t)1,t2)；farr(t)、FarrAnd (t) respectively representing the probability distribution function and the cumulative distribution function of the time when the previous journey reaches the functional area.
S213, for all W-H strokes of the simple working chain A and the complex working chain B and the W-E strokes of the working-entertainment chain C, the starting function of a single user meets normal distribution, the return time mean value mu adopts Weibull distribution, the standard deviation sigma adopts normal distribution, and the probability distribution function of the return starting time is obtained:
Figure GDA0003655048190000041
in the formula, k and lambda are shape parameters and proportion parameters of Weibull distribution; mu.s2And σ3The standard deviation and mean of normal distribution.
The journey time of S22 is:
considering the existence of distance congestion, the congestion starting time is
Figure GDA0003655048190000042
End time of congestion
Figure GDA0003655048190000043
The congestion parameter epsilon is 1.5, which indicates that the vehicle speed is reduced to 1/epsilon time within the congestion time, and then the journey time is as follows:
Figure GDA0003655048190000044
in the formula,. DELTA.tij,tThe travel time of the electric automobile from the i functional area to the j area and the time t is reached, v is the speed of the electric automobile,
Figure GDA0003655048190000045
is the arrival time of the trip from the i area to the j area.
S22, the method for calculating the cumulative distribution function of the arrival time of the electric automobile in each section of journey comprises the following steps:
Figure GDA0003655048190000046
in the formula (I), the compound is shown in the specification,
Figure GDA0003655048190000047
the cumulative distribution function of the electric automobile with the journey from the i area to the j area and the departure time t,
Figure GDA0003655048190000048
the cumulative distribution function of the electric vehicle with the travel from the i area to the j area and arriving at the t moment.
Further, the occupation ratio of different trip chain structures in different cities as described in S3 is the occupation ratio β of the electric vehicle adopting the trip chain A, B, C in different citiesA、βB、βCThe situation is. Determining the arrival and departure probabilities of the electric automobile at the moment t of the functional area i according to the duty ratio of the trip chains and the departure and arrival probabilities of different trip chains in each functional area
Figure GDA0003655048190000049
Subtracting departure correspondences from cumulative probability functions of arrival for each regionThe cumulative probability function of the region determines the distribution probability eta of the electric vehicle at the moment t of the region ii,t。
Further, in S4, the electric vehicles at the nodes with the charging stations are equally distributed in the function area, and the total driving energy consumption calculation method of the electric vehicle reaching a certain node in the function area at each time is as follows:
from residential area (area 1), total energy delta E consumed by all electric vehicles arriving at certain node of working area (area 2) at time t2,tComprises the following steps:
Figure GDA0003655048190000051
in the formula, NallFor electric vehicle holdings, NiThe number of nodes that own an electric vehicle charging station for region i]Is a rounding function.
From the working area, the total energy consumed by all electric vehicles arriving at a certain node of the business area (area 3) at the time t is as follows:
Figure GDA0003655048190000052
starting from a working area or a business area, the total energy consumed by all electric vehicles reaching a certain node of a residential area at the moment t is as follows:
Figure GDA0003655048190000053
further, in the charge and discharge optimization model of the electric vehicle described in S5, the objective function is:
Figure GDA0003655048190000054
wherein, pitElectricity price at time t, P0tAnd (4) minimizing the injection power of the root node of the power distribution network at the time t, wherein the meaning of the objective function is the minimization of the electricity purchasing cost of the root node.
The constraints that the objective function of S5 needs to satisfy in optimization include:
node power balance constraints
Pk,t＝Lk,t+uk,t k≠1，t∈T (11)
T is the set of unit time points. Pk,tIs the net injected power, L, of node k at time tk,tIs the load of node k at time t.
Load flow equation and each node voltage constraint
Figure GDA0003655048190000061
Figure GDA0003655048190000062
Figure GDA0003655048190000063
Figure GDA0003655048190000064
In the formula, Z is a set of all nodes. Skm,tFor the branch k-m apparent power, gkmAnd bkmConductance, susceptance, V, of branch k-m, respectivelyk,tVoltage amplitude at time t, θkm,tIs the voltage phase angle difference between nodes k and m.
Charge-discharge constraint of cluster electric automobile
S5, the charge and discharge constraint of each node cluster electric automobile comprises the following steps:
charge and discharge power constraint
The charging and discharging constraint under the node where the region i is connected with the charging station is as follows:
Figure GDA0003655048190000065
in the formula (I), the compound is shown in the specification,
Figure GDA0003655048190000066
the maximum discharge power and the maximum charge power of each automobile are respectively; u. ofk,tThe node k charges and discharges power at time t, the charging is positive and the discharging is negative.
Capacity constraints
Requiring node k capacity Q at time tk,tThe sum of the SOC value required by the electric automobile leaving at the next moment and the minimum required SOC value of the rest electric automobiles can be ensured, namely the formula (17). Further, Q is requiredk,tThe maximum allowable SOC value under the node, equation (18), cannot be exceeded.
Figure GDA0003655048190000067
Figure GDA0003655048190000068
Wherein i is the area i, gamma to which the node k belongsdwThe minimum required SOC of each electric automobile,
Figure GDA0003655048190000069
and (3) requiring SOC for each electric automobile leaving the area i, wherein C is the battery capacity of the electric automobile.
SOC continuity constraints
Figure GDA00036550481900000610
Wherein chi is the charge-discharge efficiency of the electric automobile, deltaminIs the minimum unit of time.
The embodiment of the invention also provides a device for determining the charge and discharge control strategy of the electric automobile, which is characterized by comprising the following components:
the system comprises a tested resource parameter acquisition module, a tested resource parameter acquisition module and a tested resource parameter acquisition module, wherein the tested resource parameter acquisition module is used for acquiring a tested state parameter and a tested resource parameter of a cluster electric vehicle, the tested resource parameter comprises an electric vehicle starting function parameter, and the tested state parameter comprises a battery state of the electric vehicle;
and the target cluster electric vehicle charge-discharge power output module is used for inputting the measured state parameters and the measured resource parameters into a target electric vehicle charge-discharge optimization model to obtain the charge-discharge power of the cluster electric vehicles of all nodes in different functional areas.
The embodiment of the invention also provides a device for controlling the charging and discharging strategies of the electric automobile, which comprises:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the charging and discharging power of the electric vehicle.
In addition, the embodiment of the present invention also provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used for executing the method for determining the charging and discharging power of the electric vehicle as described above when executed by a computer processor.
Has the advantages that: under the condition of fully considering various urban user trip chain structures, the invention provides a cluster electric vehicle charging and discharging scheduling strategy for each functional area, ensures the voltage safety of the distribution network and improves the economical efficiency of the operation of the distribution network.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings.
In the drawings:
fig. 1A is a schematic flow chart illustrating a charge and discharge control method of an electric vehicle in consideration of a user trip chain structure in an embodiment of the present invention;
FIG. 1B is a schematic diagram of an IEEE14 node power distribution system and functional areas to which the nodes belong according to an embodiment of the present invention;
FIG. 1C is a schematic diagram of probability distributions of electric vehicles in different urban residential areas according to an embodiment of the present invention;
FIG. 1D is a schematic diagram of probability distributions of different types of electric vehicles in urban work areas in an embodiment of the invention;
FIG. 1E is a schematic illustration of probability distributions of different types of electric vehicles in urban commercial districts in an embodiment of the invention;
FIG. 1F is a schematic diagram of different types of charging and discharging strategies for electric vehicles in urban residential areas according to an embodiment of the present invention;
FIG. 1G is a schematic diagram of different types of charging and discharging strategies for electric vehicles in urban work areas according to an embodiment of the invention;
FIG. 1H is a schematic illustration of different types of electric vehicle charging and discharging strategies in an urban area of commerce according to an embodiment of the invention;
fig. 2 is a schematic diagram of a device for determining a charging and discharging strategy of an electric vehicle according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an apparatus according to a third embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The invention provides a charge and discharge control method of an electric automobile with consideration of a user trip chain structure. Firstly, based on the travel chain rule of the electric automobile, the travel time, the congestion condition and the distance energy consumption are considered, the space-time distribution probability density function of the travel of the electric automobile is deduced, and the distribution rule of the electric automobile in different cities (different travel chain occupation ratios) is obtained. Secondly, based on the space-time distribution model, the method combines the power flow and the voltage constraint of the distribution network, and optimally schedules the electric automobile under each node in each region by taking the optimal economy as a target.
The following describes in detail a charge and discharge control method for an electric vehicle in consideration of a user trip chain structure according to an embodiment of the present invention. Fig. 1A is a schematic flow chart of a method for controlling charging and discharging of an electric vehicle in consideration of a user trip chain structure, and referring to fig. 1A, the method may include:
s1: dividing each functional area of the power distribution network according to the topological structure of the power distribution network, and determining a trip chain structure of the electric automobile back and forth between the functional areas;
the functional area is an area that divides a distribution network area into various functions including a residential area, a working area, a business area, and the like. And after different functional areas are determined, dividing each node under the distribution network topology into each functional area.
The trip chain refers to the trip time, the trip destination and the sequence of each trip of the electric vehicle user.
Here, the trip chain structure is divided into 3 modes of a simple working chain A (H-W-H), a complex working chain B (H-W-H-W-H), and a working-entertainment chain C (H-W-E-H). Wherein, english letters H, W, E are used to represent the stroke: go home, work, and entertainment.
S2: according to the trip chain structure, calculating a distribution function of departure time and arrival time of the electric automobile in each section of travel under each trip chain, and obtaining the distribution condition of the electric automobile under each functional area;
specifically, for the first H-W journey of the simple work chain a, the complex work chain B, and the work-entertainment chain C, the single-user departure function satisfies:
Figure GDA0003655048190000091
in the formula: t is the starting trip time or the return trip time of the electric automobile, mu is the mean value of the starting trip time or the return trip time of the electric automobile, and sigma is the standard deviation. For clustered users, μ follows a gamma distribution and σ follows a standard normal distribution. Solving marginal probability distribution for the variable t, wherein the H-W travel departure time probability distribution function calculation method comprises the following steps:
Figure GDA0003655048190000092
in the formula, alpha and theta are shape parameters and inverse scale parameters of gamma distribution; sigma2Standard normal distribution standard deviation.
For the second H-W journey of the complex work chain B and the E-H journey of the work-entertainment chain C, the time staying in the functional area of the upper journey meets the random distribution, so the cumulative distribution function calculation method of the departure time is as follows:
Figure GDA0003655048190000101
in the formula, the residence time is Deltat-U (t)1,t2)；farr(t)、FarrAnd (t) respectively representing the probability distribution function and the cumulative distribution function of the time when the previous journey reaches the functional area.
For all W-H strokes of a simple working chain A and a complex working chain B and W-E strokes of a working-entertainment chain C, a single user departure function meets normal distribution, a return time mean value mu adopts Weibull distribution, and a standard deviation sigma adopts normal distribution, so that a return departure time probability distribution function is obtained:
Figure GDA0003655048190000102
in the formula, k and lambda are shape parameters and proportion parameters of Weibull distribution; mu.s2And σ3The standard deviation and mean of normal distribution.
Considering the existence of distance congestion, the congestion starting time is
Figure GDA0003655048190000103
End time of congestion
Figure GDA0003655048190000104
The congestion parameter ∈ is 1.5, which indicates that the vehicle speed decreases by 1/epsilon within the congestion time, and specifically, the travel time calculation method is as follows:
Figure GDA0003655048190000105
in the formula,. DELTA.tij,tThe travel time of the electric automobile from the i functional area to the j area and the time t is reached, v is the speed of the electric automobile,
Figure GDA0003655048190000106
is the arrival time of the trip from the i area to the j area.
Specifically, the method for calculating the cumulative distribution function of the arrival time of the electric vehicle in each section of journey comprises the following steps:
Figure GDA0003655048190000107
in the formula (I), the compound is shown in the specification,
Figure GDA0003655048190000111
the cumulative distribution function of the electric automobile with the journey from the i area to the j area and the departure time t,
Figure GDA0003655048190000112
the cumulative distribution function of the electric automobile arriving at the time t from the i area to the j area is shown.
S3: determining the starting, arrival and distribution conditions of the electric automobiles in each functional area according to the occupation ratio conditions of different trip chain structures of different types of cities;
the occupation ratio of different trip chain structures of different types of cities is the occupation ratio beta of the electric automobile adopting the trip chain A, B, C under different citiesA、βB、βCThe situation is. Determining the arrival of the electric automobile at the moment t of the functional area i according to the ratio of the trip chains and the departure and arrival probabilities of different trip chains in each functional areaAnd probability of departure
Figure GDA0003655048190000113
According to the cumulative probability function of the arrival of each region, subtracting the cumulative probability function of leaving the corresponding region, and determining the distribution probability eta of the electric vehicle at the time t of the region ii,t。
S4, obtaining the starting, arriving and distributing conditions of the electric automobile under each node with the charging station according to the starting, arriving and distributing conditions of the electric automobile in each functional area, and determining the total running energy consumption of the electric automobile reaching a certain node in the functional area at each moment;
the method for calculating the total running energy consumption of the electric vehicle reaching a certain node of the functional area at each moment comprises the following steps:
from the residential area (1 area), the total energy delta E consumed by all electric vehicles arriving at a certain node of the working area (2 area) at the time t2,tComprises the following steps:
Figure GDA0003655048190000114
in the formula, NallFor electric vehicle holdings, NiThe number of nodes that own an electric vehicle charging station for region i]Is a rounding function.
From the working area, the total energy consumed by all electric vehicles arriving at a certain node of the business area (area 3) at the time t is as follows:
Figure GDA0003655048190000115
starting from a working area or a business area, the total energy consumed by all electric vehicles reaching a certain node of a residential area at the moment t is as follows:
Figure GDA0003655048190000121
further, in the charge and discharge optimization model of the electric vehicle described in S5, the objective function is:
Figure GDA0003655048190000122
wherein, pitElectricity price at time t, P0tAnd (4) minimizing the injection power of the root node of the power distribution network at the time t, wherein the meaning of the objective function is the minimization of the electricity purchasing cost of the root node.
And S5, considering power flow and voltage safety constraints of the power distribution network, combining charge and discharge constraints of the electric automobile of each node cluster, and establishing a charge and discharge optimization model of the electric automobile with the aim of minimizing the cost of the power distribution network.
Specifically, the objective function in the electric vehicle charge-discharge optimization model is as follows:
Figure GDA0003655048190000123
wherein, pitElectricity price at time t, P0tAnd (4) minimizing the injection power of the root node of the power distribution network at the time t, wherein the meaning of the objective function is the minimization of the electricity purchasing cost of the root node.
The constraint conditions include:
node power balance constraints
Pk,t＝Lk,t+uk,t k≠1，t∈T (11)
T is the set of unit time points. Pk,tIs the net injected power, L, of node k at time tk,tIs the load of node k at time t.
Load flow equation and each node voltage constraint
Figure GDA0003655048190000124
Figure GDA0003655048190000125
Figure GDA0003655048190000126
Figure GDA0003655048190000127
In the formula, Z is a set of all nodes. Skm,tFor branch k-m apparent power, gkmAnd bkmConductance, susceptance, V, of branch k-m, respectivelyk,tVoltage amplitude at time t, θkm,tIs the voltage phase angle difference between nodes k and m.
Charge-discharge constraint of cluster electric automobile
Specifically, the charge-discharge constraint of the electric vehicle of each node cluster comprises the following steps:
charge and discharge power constraint
The charging and discharging constraint under the node where the region i is connected with the charging station is as follows:
Figure GDA0003655048190000131
in the formula (I), the compound is shown in the specification,
Figure GDA0003655048190000132
the maximum discharge power and the maximum charge power of each automobile are respectively; u. ofk,tThe node k charges and discharges power at time t, the charging is positive and the discharging is negative.
Capacity constraints
Requiring node k capacity Q at time tk,tThe sum of the SOC value required by the electric automobile leaving at the next moment and the minimum required SOC value of the rest electric automobiles can be ensured, namely the formula (17). Further, Q is requiredk,tThe maximum allowable SOC value under the node, equation (18), cannot be exceeded.
Figure GDA0003655048190000133
Figure GDA0003655048190000134
Wherein i is the area i, gamma to which the node k belongsdwThe minimum required SOC of each electric automobile,
Figure GDA0003655048190000136
and (3) requiring SOC for each electric automobile leaving the area i, wherein C is the battery capacity of the electric automobile.
SOC continuity constraints
Figure GDA0003655048190000135
Wherein chi is the charge-discharge efficiency of the electric automobile, deltaminIs the minimum unit of time.
Example one
The following describes an implementation process and an advantageous effect of the method for controlling charging and discharging of an electric vehicle considering a user trip chain structure according to an embodiment of the present invention. Fig. 1B is a schematic diagram of an IEEE14 node power distribution system and functional areas to which nodes belong according to an embodiment of the present invention, where there are 14 nodes and 13 branches in the IEEE14 node power distribution system. The basic parameters of an IEEE14 node power distribution system can be seen in table 1:
TABLE 1
Figure GDA0003655048190000141
Setting the power factor of each node to be 0.85, and determining the reactive power of the node according to the active power injected by the node;
except the nodes 10 and 11, electric vehicle charging stations are configured, the total number of electric vehicles is 2400, and L12＝15km，L23＝12km，L13The energy consumption factor E is 0.25, and assuming that the electric vehicles are uniformly distributed in the same functional area, specific parameters of the electric vehicles are as follows:
TABLE 2 electric vehicle parameter settings
Tab.2 Parameters of EVs
Figure GDA0003655048190000142
Figure GDA0003655048190000151
The electric power is divided into three periods of peak-valley average to be charged, and the specific electricity price data is as follows:
TABLE 3 Power distribution network electricity price data
Tab.3 The parameters of electricity price
Figure GDA0003655048190000152
The method mainly carries out comparative analysis on the following three types of urban trip chain models:
(1) small-sized city: considering simple working chain A and complex working chain B, not considering working-recreation chain C, betaA＝30％、βB＝70％、βC＝0％；
(2) Industrial type metropolitan: consider the simple working chain A and the working-amusement chain C, betaA＝70％、βB＝0％、βC＝30％；
(3) Commercial metropolitan: consider the simple working chain A and the working-amusement chain C, betaA＝30％、βB＝0％、βC＝70％。
Fig. 1C to 1E illustrate the distribution of electric vehicles obtained under the present invention: in the small city, the electric automobiles are only distributed in a working area and a residential area due to the absence of a work-entertainment chain C. In addition, since a part of electric vehicle users in a small city have a trip to go home to have a meal in the noon, a part of electric vehicles in the working area during the noon time period is transferred to the residential area. Large cities have W-E entertainment trips and therefore some electric vehicles will be transferred to commercial areas at night. As the H-W-E-H stroke fraction increases, i.e., the degree of commercialization increases, 12: 00-22: in the time period of about 00 hours, compared with a small city, the proportion of electric automobiles in a residential area is reduced, and the proportion of electric automobiles in a commercial area is obviously increased in the time period; 17: 00-22: between the time period of 00, electric vehicles in the workplace are also more shifted to the business district as the degree of commercialization of the city increases.
Fig. 1F to 1H illustrate the charging and discharging control strategies of electric vehicles in various functional areas of various cities obtained by the present invention, and it can be seen that the distribution rules of electric vehicles are fully considered in each area to coordinate the charging and discharging strategies: the charging and discharging strategy of the electric automobile in the residential area fully utilizes peak-valley electricity price, the electric automobile is charged at night to fill the valley, and the electric automobile is discharged at the evening (17: 00-21: 00) of the peak of the load to relieve the load at the peak; the electric automobile in the working area is not selected to be immediately charged during the peak load period, but is selected to be discharged during the peak period, and the electric automobile is charged at the ordinary period to meet the follow-up travel; the electric automobile in the commercial district has more random residence time and shorter time, so the charge and discharge power fluctuation is larger.
In addition, because the electric automobile stays in a residential area and a working area for a long time, the power grid fully utilizes the capacity of the electric automobile V2G to transmit electric energy to the power grid in a peak period; because the electric automobile has short residence time in the commercial district, except for small cities, the electric automobile is only in a 20: around 00 a brief period releases a portion of the charge to take up the peak load.
For different cities, it can be observed that under the nodes 2 and 4, the small-sized cities have higher EV proportion in residential areas and working areas, so that the charging and discharging limits are larger, and the release of electric quantity can be maintained for a longer time (the residential areas: 20: 00-22: 00; the working areas: 14: 00-15: 00), and as the H-W-E-H stroke ratio increases, the charging and discharging ranges of the residential areas and the working areas become smaller, but the charging and discharging power range of the commercial areas becomes larger. It should be noted that, although in the same functional area, due to consideration of distribution network load flow, different node scheduling strategies are different.
Example two
Fig. 2 is a schematic diagram of a charging and discharging control device for an electric vehicle according to a first embodiment of the present invention. The present embodiment may be applicable to the case of performing charging and discharging scheduling simulation on a target resource, and the apparatus may be implemented in a software and/or hardware manner, and may be configured in a terminal device. The determination device includes: a measured resource parameter acquisition module 610 and an electric vehicle charging and discharging power output module 620.
The measured resource parameter acquiring module 610 is configured to acquire a measured state parameter and a measured resource parameter of a target resource, where the measured resource parameter includes a starting function parameter of an electric vehicle, and the measured state parameter includes a battery state of the electric vehicle;
and the target cluster electric vehicle charging and discharging power output module 620 is used for inputting the measured state parameters and the measured resource parameters into a target electric vehicle charging and discharging optimization model to obtain the charging and discharging power of the cluster electric vehicles of each node in different functional areas.
On the basis of the technical scheme, optionally, the target resource is a cluster electric vehicle, and the measured state parameter of the cluster electric vehicle is a starting time probability function parameter of the electric vehicle and a distance congestion coefficient of the electric vehicle.
On the basis of the above technical solution, optionally, the device further includes an electric vehicle charging and discharging power module, and the electric vehicle charging and discharging power module includes:
the state parameter storage unit is used for storing the electric automobile departure time probability function parameters and the electric automobile distance congestion coefficients in an experience playback pool;
the distribution condition calculation unit is used for determining the distribution condition of the cluster electric vehicles in the distribution network region at each moment based on the data stored in the experience playback pool;
and the power output unit for the charge and discharge control of the target cluster electric automobile is used for carrying out optimization calculation according to a preset target function and a constraint condition and based on data stored in the experience playback pool until power of the charge and discharge optimization scheduling of the target cluster electric automobile is obtained.
The device for determining the charging and discharging control strategy of the cluster electric vehicle provided by the embodiment of the invention can be used for executing the method for determining the charging and discharging control strategy of the cluster electric vehicle provided by the embodiment of the invention, and has corresponding functions and beneficial effects of the executing method.
It should be noted that, in the embodiment of the determining apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus provided in the fifth embodiment of the present invention, where the fifth embodiment of the present invention provides a service for implementing the method for determining the charging and discharging control policy of the electric vehicle cluster according to the foregoing embodiment of the present invention, and may configure the determining device of the charging and discharging control policy of the electric vehicle cluster in the foregoing embodiment. Fig. 3 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 3 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.
As shown in FIG. 3, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with device 12, and/or with any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown in FIG. 3, the network adapter 20 communicates with the other modules of the device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing the method for determining the charging and discharging control strategy of the cluster electric vehicle provided by the embodiment of the present invention.
Through the equipment, under the condition that various urban user trip chain structures are fully considered, a cluster electric vehicle charging and discharging scheduling strategy is provided for each functional area, the voltage safety of a distribution network is guaranteed, and the economical efficiency of distribution network operation is improved.
Example four
The fourth embodiment of the present invention further provides a storage medium containing computer executable instructions, where the computer executable instructions are executed by a computer processor to perform a method for determining a charging and discharging control strategy of a cluster electric vehicle, and the method includes:
acquiring a measured state parameter and a measured resource parameter of a target resource, wherein the measured resource parameter comprises a starting function parameter of the electric automobile, and the measured state parameter comprises a battery state of the electric automobile;
and inputting the measured state parameters and the measured resource parameters into a preset target electric vehicle charge-discharge optimization model to obtain the charge-discharge power of the electric vehicle of each node cluster in different functional areas.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium provided by the embodiments of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the determination method of the charging and discharging control policy of the cluster electric vehicle provided by any embodiment of the present invention.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (8)

1. A charge and discharge control method for an electric vehicle is characterized by comprising the following steps:
s1, dividing each functional area of the power distribution network according to the topological structure of the power distribution network, and determining a back-and-forth trip chain structure of the electric automobile among the functional areas;
s2, calculating distribution functions of departure time and arrival time of the electric vehicles in each journey under each trip chain in the step S1, and obtaining the distribution conditions of the electric vehicles in each functional area;
s3, determining the starting, arrival and distribution conditions of the electric vehicles in each functional area according to the proportion conditions of different trip chain structures of different types of cities;
s4, obtaining the departure, arrival and distribution conditions of the electric automobile under each node according to the departure, arrival and distribution conditions of the electric automobile in each functional area, and determining the total running energy consumption of the electric automobile reaching a certain node in the functional area at each moment;
s5, considering power flow and voltage safety constraints of the power distribution network, combining charge and discharge constraints of the electric automobile of each node cluster, and establishing a charge and discharge optimization model of the electric automobile with the aim of minimizing the cost of the power distribution network;
s1 includes the steps of:
s11, determining functional area classification under the distribution network area;
s12, dividing functional areas to which various nodes under the power distribution network belong according to the topological structure of the power distribution network;
s13, determining the travel chain structure of the electric automobile back and forth between each functional area according to the resident travel rule, wherein the electric automobile charge and discharge control travel chain refers to the travel time, the travel destination and the sequence of each journey of the electric automobile user,
the trip chain structure is divided into 3 modes of a simple working chain A (H-W-H), a complex working chain B (H-W-H-W-H) and a working-entertainment chain C (H-W-E-H), wherein an English letter H, W, E is used for representing a journey: going home, working and entertaining;
s2 includes the steps of:
s21, determining the distribution function of the departure time of the electric automobile in each section of travel under each trip chain, including:
s211, for the first H-W journey of the simple work chain A, the complex work chain B and the work-entertainment chain C, the single-user starting function meets the following conditions:
Figure FDA0003630614600000021
in the formula: t is the starting trip time or the return trip time of the electric automobile, mu is the mean value of the starting trip time or the return trip time of the electric automobile, and sigma is a standard deviation; for clustered users, μ obeys a gamma distribution and σ obeys a standard normal distribution; solving the marginal probability distribution of the variable t to obtain an H-W travel departure time probability distribution function:
Figure FDA0003630614600000022
in the formula, alpha and theta are shape parameters and inverse scale parameters of gamma distribution; sigma2Standard normal distribution standard deviation;
s212, for the second H-W journey of the complex working chain B and the E-H journey of the working-entertainment chain C, the starting functions meet random distribution;
s213, for all W-H strokes of the simple working chain A and the complex working chain B and the W-E strokes of the working-entertainment chain C, the starting function of a single user meets normal distribution, the return time mean value mu adopts Weibull distribution, the standard deviation sigma adopts normal distribution, and the probability distribution function of the return starting time is obtained:
Figure FDA0003630614600000023
in the formula, k and lambda are shape parameters and proportion parameters of Weibull distribution; mu.s2And σ3Normal distribution standard deviation and mean;
and S22, calculating the electric automobile arrival time cumulative distribution function in each journey according to the distance congestion time.
2. The electric vehicle charge and discharge control method according to claim 1, wherein the journey time of S22 is as follows:
considering the existence of distance congestion, the congestion starting time is
Figure FDA0003630614600000024
End time of congestion
Figure FDA0003630614600000025
And the congestion parameter epsilon is 1.5, which indicates that the vehicle speed is reduced to 1/epsilon within the congestion time, and the journey time is as follows:
Figure FDA0003630614600000031
in the formula,. DELTA.tij,tThe travel time of the electric automobile from the i functional area to the j area and the time t is reached, v is the speed of the electric automobile,
Figure FDA0003630614600000032
the arrival time of the travel from the i area to the j area;
in step S22, the calculation formula of the cumulative distribution function of the arrival time of the electric vehicle in each trip is as follows:
Figure FDA0003630614600000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003630614600000034
the cumulative distribution function of the electric automobile with the journey from the i area to the j area and the departure time t,
Figure FDA0003630614600000035
the cumulative distribution function of the electric automobile arriving at the time t from the i area to the j area is shown.
3. The method of claim 1, wherein the proportion of different trip chain structures in different cities in S3 is β, which is the proportion of electric vehicles using a trip chain A, B, C in different citiesA、βB、βCDetermining the leaving and arriving probabilities of the electric automobile at the time t of the functional area i according to the trip chain occupation ratio and the departure and arrival probabilities of different trip chains in each functional area
Figure FDA0003630614600000036
fi arr(t) determining the distribution probability eta of the electric vehicle at the moment t of the region i by subtracting the cumulative probability function of leaving the corresponding region from the cumulative probability function of the arrival of each regioni,t。
4. The electric vehicle charge and discharge control method according to claim 3, wherein in step S4, electric vehicles at nodes having charging stations are equally distributed in the functional area, and the total driving energy consumption of the electric vehicle reaching a certain node in the functional area at each time is calculated as follows:
from residential area, total energy delta E consumed by all electric vehicles arriving at a certain node of working area at time t2,tComprises the following steps:
Figure FDA0003630614600000037
in the formula, NallFor electric vehicle holdings, NiThe number of nodes that own an electric vehicle charging station for region i]As a rounding function, fi arr(t) is the arrival probability of the electric vehicle at the moment t of the functional area i;
from the working area, the total energy consumed by all electric vehicles arriving at a certain node of the commercial district at the time t is as follows:
Figure FDA0003630614600000041
starting from a working area or a business area, the total energy consumed by all electric vehicles reaching a certain node of a residential area at the moment t is as follows:
Figure FDA0003630614600000042
in the formula (I), the compound is shown in the specification,
Figure FDA0003630614600000043
at time t, the electric vehicle starts from the functional zone i and arrives at the functional zone j with the arrival probability, betaCThe electric automobile adopting the trip chain C accounts for the ratio.
5. The electric vehicle charge and discharge control method according to claim 1, wherein in S5, the electric vehicle charge and discharge optimization model is as follows:
the model objective function is:
Figure FDA0003630614600000044
wherein, pitElectricity price at time t, P0tMinimizing the target function meaning, namely the root node electricity purchasing cost, of the injection power of the root node of the power distribution network at the time t;
the constraint conditions that the objective function needs to satisfy during optimization comprise:
node power balance constraints
Pk,t＝Lk,t+uk,t k≠1，t∈T (10)
T is a set of unit time points; pk,tIs the net injected power, L, of node k at time tk,tIs the load of node k at time t;
load flow equation and each node voltage constraint
Figure FDA0003630614600000045
Figure FDA0003630614600000046
Figure FDA0003630614600000047
Figure FDA0003630614600000051
In the formula, Z is the set of all nodes; skm,tFor the branch k-m apparent power, gkmAnd bkmConductance, susceptance, V, of branch k-m, respectivelyk,tVoltage amplitude at time t, θkm,tIs the voltage phase angle difference between nodes k and m;
cluster electric automobile charge-discharge restraint includes:
charge and discharge constraints
The charging and discharging constraint under the node where the region i is connected with the charging station is as follows:
Figure FDA0003630614600000052
in the formula (I), the compound is shown in the specification,
Figure FDA0003630614600000053
the maximum discharge power and the maximum charge power of each automobile are respectively; u. ofk,tCharging and discharging power for the node k at the moment t, wherein the charging is positive and the discharging is negative;
capacity constraints
Requiring node k to have capacity Q at time tk,tThe sum of the SOC value required by the electric automobile leaving at the next moment and the lowest required SOC value of the rest electric automobiles can be ensured, namely a formula (16); further, Q is requiredk,tThe maximum allowable SOC value under the node cannot be exceeded, namely the formula (17);
Figure FDA0003630614600000054
Figure FDA0003630614600000055
wherein i is the area i, gamma to which the node k belongsdwThe minimum required SOC of each electric automobile,
Figure FDA0003630614600000056
requiring SOC for each electric automobile leaving the area i, wherein C is the battery capacity of the electric automobile;
SOC continuity constraints
Figure FDA0003630614600000057
Wherein chi is the charge-discharge efficiency of the electric automobile, deltaminIs the minimum unit of time.
6. The device for determining the electric vehicle charge-discharge control method according to any one of claims 1 to 5, comprising:
the system comprises a measured resource parameter acquisition module, a measured resource parameter acquisition module and a measured resource parameter acquisition module, wherein the measured resource parameter acquisition module is used for acquiring a measured state parameter and a measured resource parameter of a cluster electric vehicle, the measured resource parameter comprises a starting function parameter of the electric vehicle, and the measured state parameter comprises a battery state of the electric vehicle;
and the target cluster electric vehicle charge-discharge power output module is used for inputting the measured state parameters and the measured resource parameters into a target electric vehicle charge-discharge optimization model to obtain the charge-discharge power of the cluster electric vehicles of all nodes in different functional areas.
7. An apparatus for electric vehicle charge and discharge control strategy, comprising:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the electric vehicle charging and discharging control method according to any one of claims 1 to 5.
8. A storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are configured to perform the electric vehicle charging and discharging control method according to any one of claims 1 to 5.
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