CN114722714A - Electric power-traffic coupling network expansion planning method considering traffic balance - Google Patents

Electric power-traffic coupling network expansion planning method considering traffic balance Download PDF

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CN114722714A
CN114722714A CN202210392210.4A CN202210392210A CN114722714A CN 114722714 A CN114722714 A CN 114722714A CN 202210392210 A CN202210392210 A CN 202210392210A CN 114722714 A CN114722714 A CN 114722714A
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traffic
road
power
network
charging
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何俊
朱理文
黄文涛
于华
何立勋
邓明辉
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Hubei University of Technology
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F30/20—Design optimisation, verification or simulation
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    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides an electric power-traffic coupling network expansion planning method considering traffic balance, which specifically comprises the following steps: the invention provides an electric power-traffic coupling network expansion planning method considering traffic balance based on a user balance principle, which comprehensively considers the safety operation constraint conditions of a traffic network and a power distribution network, takes the minimum sum of the construction cost of expansion facilities as a target, and solves through CPLEX to determine the optimal expansion scheme of an electric automobile under different travel scenes. Compared with shortest path travel, the traffic balance method has the advantages that the traffic balance model is adopted, the actual road traffic condition and the interaction influence among vehicles can be reflected, and the operation of a traffic network system and the travel time of vehicle owners are improved.

Description

Electric power-traffic coupling network expansion planning method considering traffic balance
Technical Field
The invention relates to a method for expanding and planning an electric power-traffic coupling network, in particular to a method for expanding and planning an electric power-traffic coupling network by considering traffic balance.
Background
The electric automobile has the characteristics of low power consumption and no pollution and conforms to the low-carbon travel concept, so that the electric automobile is gradually pursued by people in recent years, and the holding amount of the electric automobile is rapidly increased. And the coupling between the traffic network and the power distribution network is further strengthened by the traffic load and the charging load generated when large-scale electric automobiles travel in the traffic network. On one hand, the large-scale charging demand aggregation can cause the increase of traffic road congestion and even paralysis risk; on the other hand, if these charging loads are not controlled, the load peaks of the urban power grid are caused, the voltage is out of limit, and the like. Therefore, the travel demands of more electric vehicles can be met under the condition that the traffic network and the power distribution network run safely, and the method has important significance for the expansion planning construction of the power-traffic coupling network.
The increase of the holding capacity of the electric vehicles will make the coupling effect between the traffic network and the power distribution network more and more obvious, for the aspect of the power distribution network, the increase of the holding capacity of the electric vehicles means that more electric power facilities need to be invested to meet the charging requirement of the electric power facilities, and the planning of the electric power facilities usually considers the economic and safe operation constraints of the power distribution network to reduce the investment cost caused by the upgrading of the power system. For the aspect of the transportation network, the large-scale travel of the electric vehicles will increase the risk of traffic jam and paralysis, and therefore, it is necessary to research the expansion planning of the electric power facilities and the transportation facilities under the coupling of the transportation network and the power distribution network.
Disclosure of Invention
The invention mainly solves the technical problems existing in the prior art; the power-traffic coupling network expansion planning method considering traffic balance is firstly provided. Aiming at the increase of traffic paralysis risks and power grid safe operation risks caused by the travel of large-scale electric vehicles, a traffic balance model of a hybrid electric vehicle and a common vehicle is provided based on a user balance principle and considering road expansion and charging station congestion effects. Secondly, an optimal expansion planning model of the power-traffic coupling network is provided under traffic balance, the model takes the minimum cost of road traffic time, road capacity expansion, newly-built charging piles, transformer substation capacity expansion and newly-built power distribution network lines as the target, and the model is optimally configured by comprehensively considering traffic balance and power grid safe operation constraint conditions.
The technical problem of the invention is mainly solved by the following technical scheme:
a power-traffic coupling network expansion planning method considering traffic balance is characterized in that,
constructing a coupling network by using a traffic network and a power distribution network, wherein the power distribution network flow form adopts a Distflow flow form;
and solving the optimal solution of the model to obtain the optimal extension plan by taking the minimum sum of the extension plan costs in the established extension plan model of the power-traffic coupling network as an optimization target and taking the traffic balance constraint, the power grid safe operation constraint, the road capacity expansion constraint, the line capacity expansion constraint, the newly added charging pile constraint and the linear and nonlinear constraints of the extension plan model as constraint conditions.
In the above power-traffic coupling network extension planning method considering traffic balance, the establishment of the extension planning model is based on solving a traffic balance model, and specifically, the method comprises the following steps:
constructing a travel path selection network and establishing an optimal travel path generation model considering the satisfaction degree of the vehicle owner;
based on the user balance principle of the hybrid electric vehicle and the common vehicle, considering the road congestion effect, the charging station congestion effect and the road capacity expansion model, and constructing a traffic balance model of the hybrid electric vehicle and the common vehicle;
solving traffic balance under the optimal path to obtain steady traffic flow distribution;
recording the paths, the road traffic flow and charging stations selected by the electric vehicle on each travel path, and acquiring the charging load on the coupling node according to the charging electric quantity; constructing an extension planning model of the power-traffic coupling network; the model comprises road traffic time cost, road capacity expansion cost, new-built charging pile cost, transformer substation capacity expansion cost and new-built distribution network line cost.
In the above power-traffic coupling network expansion planning method considering traffic balance, the charging station congestion effect is as follows:
Figure BDA0003596138510000031
in the formula:
Figure BDA0003596138510000032
average queuing waiting time for the charging stations;
Figure BDA0003596138510000033
for binary variables, when the electric vehicle on path p chooses to charge at charging station s,
Figure BDA0003596138510000034
otherwise, the value is 0; x is the number ofsThe power required by charging the current electric automobile to a charging station s; c. CsIs the capacity of s of the charging station.
In the above power-traffic coupling network expansion planning method considering traffic balance, the road capacity expansion model is as follows:
Figure BDA0003596138510000035
in the formula: t is taRepresents the time when the vehicle passes through the road a;
Figure BDA0003596138510000036
free passage time for road a (free passage time means traffic flow of vehicle on current roadThe amount is not more than the passing time when the passing capacity is limited by the upper limit); x is the number ofaThe traffic flow of the road a; c. CaThe original traffic capacity upper limit of the road a is set; sigmaaRepresenting the number of road extensions, σaNot less than 0; Δ c represents the road traffic capacity that can be increased by road a to build a road.
In the above power-traffic coupling network expansion planning method considering traffic balance, the principle of user balance of the hybrid electric vehicle and the ordinary vehicle is as follows:
Figure BDA0003596138510000037
Figure BDA0003596138510000038
in the formula:
Figure BDA0003596138510000039
and
Figure BDA00035961385100000310
respectively the travel cost of the OD to the w upper paths p and q;
Figure BDA00035961385100000311
and
Figure BDA00035961385100000312
respectively the travel cost of the electric automobile on the OD pair w and the ordinary automobile in a balanced state;
Figure BDA00035961385100000313
and
Figure BDA00035961385100000314
traffic on path p and path q, respectively;
Figure BDA00035961385100000315
available path for electric vehicle, i.e. electric vehicle with charging demandAs available paths;
Figure BDA00035961385100000316
is an available path of a common automobile; w is the set of all OD pairs in the traffic network, and W represents one of the OD pairs.
In the above power-traffic coupling network expansion planning method considering traffic balance, the traffic balance model of the hybrid electric vehicle and the common vehicle is as follows:
Figure BDA0003596138510000041
Figure BDA0003596138510000042
Figure BDA0003596138510000043
Figure BDA0003596138510000044
xa≤ca+Δc·σa,
Figure BDA0003596138510000045
in the formula: omega is the unit time cost; a represents a road in traffic; t isaRepresenting a collection of roads in a traffic network; s is a coupling node set of a traffic network and a power distribution network;
Figure BDA0003596138510000046
charging capacity of the electric vehicle on a charging station s on a p path; theta is the electricity price of the charging station s; gwAnd hwThe travel demands of the electric automobile and the common automobile on the OD pair w are respectively met;
Figure BDA0003596138510000047
and
Figure BDA0003596138510000048
are binary variables for which the path is associated with a road, i.e. paths p and q are 1 when they pass road a, and 0 otherwise.
In the power-traffic coupling network expansion planning method considering traffic balance, the travel path selection network model is
Figure BDA0003596138510000049
In the formula: SOCOThe initial electric quantity of the electric automobile at the initial node is obtained;
Figure BDA00035961385100000410
is the distance from the origin node to the charging station s on the path p; l ispIs the total distance of path p; q is the power consumption of the electric automobile per kilometer; SOC (system on chip)φAnd the minimum electric quantity threshold value of the battery can be accepted by the vehicle owner.
In the above power-traffic coupling network expansion planning method considering traffic balance, the optimal path models considering the owner satisfaction degrees of the electric vehicle and the ordinary vehicle are respectively as follows:
Figure BDA00035961385100000411
Figure BDA0003596138510000051
in the formula:
Figure BDA0003596138510000052
the traffic flow of the current road; ps is the charging power of the charging station s;
Figure BDA0003596138510000053
which is a binary variable, when the path p passes the road a,
Figure BDA0003596138510000054
otherwise it is 0.
In the foregoing power-traffic coupling network extension planning method considering traffic balance, an extension planning model of the power-traffic coupling network is as follows:
minF＝CTN+CPDN
in the formula: f is the total cost of the extension plan; cTNPlanning cost for the traffic network expansion; cPDNCost is expanded and planned for the power distribution network;
the cost expression of the traffic network expansion planning is
Figure BDA0003596138510000055
Figure BDA0003596138510000056
In the formula: kappa is the mark out coefficient; c. C1Unit cost for road expansion; c. C2Unit cost for newly adding a charging pile; h issThe number of the charging piles is increased for the charging station s; r is the discount rate; y is depreciation age;
the cost expression of the power distribution network expansion planning is
Figure BDA0003596138510000057
Figure BDA0003596138510000058
In the formula: c. C3Unit cost for newly building a line; n isijEstablishing the number of new lines between the nodes i and j, wherein the number of the new lines is parallel to the number of the existing power distribution network lines and the line parameters are the same as those of the existing lines; c. C4Expanding unit cost for the transformer substation;
Figure BDA0003596138510000059
capacity expansion capacity of the transformer substation; ksIs a transformer substation set; pk,eThe power of a newly-added charging pile in a charging station serving the transformer substation k;
Figure BDA00035961385100000510
residual capacity of a transformer substation k;
the system constraint conditions are as follows:
the traffic balance model is constrained to
Figure BDA00035961385100000511
Figure BDA0003596138510000061
Figure BDA0003596138510000062
xa≤ca+Δc·σa,
Figure BDA0003596138510000063
In the above formula: the 1 st behavior is a vehicle travel flow conservation constraint; the conservation of road flow and path flow in the 2 nd traffic network is restricted; behavior 3 road capacity constraint;
road expansion and newly-built distribution network line quantity constraint
0≤σa≤σa,max
0≤nij≤nij,max
In the formula: sigmaa,maxExpanding the upper limit of the quantity of the road a; n isij,maxEstablishing an upper limit of the number of the power distribution network lines between the nodes i and j;
the newly added charging pile is constrained to
xs≤(hs+hs,0)ps
In the formula: h iss,0The number of the original charging piles in the charging station is set;
the branch power flow of the power distribution network adopts a Distflow power flow form, the line loss is not considered, and the simplified safe operation constraint of the power distribution network is
Figure BDA0003596138510000064
In the formula:v iand
Figure BDA0003596138510000065
respectively representing the upper limit and the lower limit of the voltage amplitude of the node i; pijIs the active power capacity between nodes i and j;
Figure BDA0003596138510000066
is the upper limit of the active power capacity of the line between the nodes i and j; r is a radical of hydrogenijAnd xijRespectively a resistance and a reactance on the circuit; pjAnd QjActive and reactive power requirements at node j, respectively; v. ofiAnd vjThe voltage amplitudes for nodes i and j; p is a radical ofijAnd q isijThe active and reactive power flows between the nodes i and j; dNA node set of the power distribution network; dAA line set which is a power distribution network; (i, j) is the line between nodes i and j; v. of1Balancing the node voltage amplitude; pi (j) is a branch line set flowing out from the node j; p is a radical of formulajmAnd q isjmActive and reactive power on the branch (j, m), respectively; piAnd
Figure BDA0003596138510000071
the total active power demand and the conventional load active power demand on the node i of the power distribution network are respectively.
In the above power-traffic coupling network extension planning method considering traffic balance, the nonlinear constraint and nonlinear model are linearized as follows:
Figure BDA0003596138510000072
in the formula: Δ xaAnd τa(m, n) is an auxiliary variable, where τa(m, n) is a binary variable representing the position of the segment under the two-dimensional plane; m and n are each xaAnd σaThe current segment number;
voltage drop constrained linearization
Figure BDA0003596138510000073
In the formula:
Figure BDA0003596138510000074
is a binary variable, which satisfies
Figure BDA0003596138510000075
Figure BDA0003596138510000076
And
Figure BDA0003596138510000077
is a continuous variable, it satisfies
Figure BDA0003596138510000078
Transformer substation capacity expansion function linearization
Figure BDA0003596138510000081
In the formula: δ is a binary variable.
The invention provides an electric power-traffic coupling network expansion planning method considering traffic balance based on a user balance principle, which comprehensively considers the safety operation constraint conditions of a traffic network and a power distribution network, takes the minimum sum of the construction cost of expansion facilities as a target, solves through CPLEX, and determines the optimal expansion scheme of an electric automobile under different travel scenes, and has the following advantages: 1) an increase in travel demand by the OD may cause some roads to pass beyond their limits, thereby resulting in a substantial increase in transit time. The road capacity expansion scheme is considered, the passing time can be effectively improved, convenience is brought to the travel of the car owner, and the effectiveness of the expanded planning model for solving the road congestion problem is verified. 2) The charging demand on the road can introduce new loads to the power distribution network nodes under the coupling network, and the active power of some power distribution network lines can exceed the capacity limit of the power distribution network lines due to large-scale charging load access. 3) Compared with shortest path travel, the traffic balance method has the advantages that the traffic balance model is adopted, the actual road traffic condition and the interaction influence among vehicles can be reflected, and the operation of a traffic network system and the travel time of vehicle owners are improved.
Drawings
Fig. 1 is a schematic diagram of an electric vehicle routing network in the embodiment.
Fig. 2 is a topology of a radial distribution network in the present embodiment.
Fig. 3 is a coupling topology structure of the traffic network and the distribution network in the embodiment.
Fig. 4 shows the extended planning cost in different scenarios in this embodiment.
Fig. 5 shows the number of the facility extension plans in different scenarios in the present embodiment.
Fig. 6 shows the traffic flow of each road when the travel demand of the electric vehicle increases by 0% in the embodiment.
Fig. 7 shows the traffic flow of each road when the travel demand of the electric vehicle increases by 100% in the embodiment.
Fig. 8 shows the partial road passing time of the electric vehicle in the embodiment, which is increased by 0%.
Fig. 9 shows the partial road passing time of the electric vehicle in the embodiment, in which the traveling demand of the electric vehicle is increased by 100%.
Fig. 10 shows the charging queue time of the charging station No.1 in the present embodiment in different scenarios.
Detailed Description
In order to facilitate understanding and implementation of the present invention for persons of ordinary skill in the art, the present invention is further described in detail with reference to the drawings and examples, it is to be understood that the implementation examples described herein are only for illustration and explanation of the present invention and are not to be construed as limiting the present invention.
The technical solution of the present invention is further specifically described by the following embodiments in conjunction with the accompanying fig. 1-10.
First, the principle flow of the method of the present invention is introduced.
Step 1: based on a user balance principle, taking a road congestion effect, a charging station congestion effect and a road expansion model into consideration, and providing a traffic balance model of the hybrid electric vehicle and the common vehicle;
the congestion effect of the charging station in the step 1 is as follows:
Figure BDA0003596138510000091
in the formula:
Figure BDA0003596138510000092
average queuing waiting time for the charging stations;
Figure BDA0003596138510000093
for binary variables, when an electric vehicle on path p chooses to charge at charging station s,
Figure BDA0003596138510000094
otherwise, the value is 0; x is the number ofsThe power required by charging the current electric automobile to a charging station s; c. CsThe capacity of s as a charging station;
the road capacity expansion model in the step 1 is as follows:
Figure BDA0003596138510000095
in the formula: t is taRepresents the time when the vehicle passes through the road a; x is the number ofaThe traffic flow of the road a; c. CaThe original traffic capacity upper limit of the road a is set; sigmaaIndicating a road aExtension quantity, σaNot less than 0; Δ c represents the road traffic capacity that can be increased by road a to build a road.
The user balance principle of the hybrid electric vehicle and the common vehicle in the step 1 is as follows:
Figure BDA0003596138510000096
Figure BDA0003596138510000101
in the formula:
Figure BDA0003596138510000102
and
Figure BDA0003596138510000103
respectively the travel cost of the OD to the w upper paths p and q;
Figure BDA0003596138510000104
and
Figure BDA0003596138510000105
respectively the travel cost of the electric automobile on the OD pair w and the ordinary automobile in a balanced state;
Figure BDA0003596138510000106
and
Figure BDA0003596138510000107
traffic on path p and path q, respectively;
Figure BDA0003596138510000108
taking the path of the electric automobile with the charging requirement as an available path;
Figure BDA0003596138510000109
is an available path of a common automobile;
the traffic balance model of the hybrid electric vehicle and the common vehicle in the step 1 is as follows:
Figure BDA00035961385100001010
Figure BDA00035961385100001011
Figure BDA00035961385100001012
Figure BDA00035961385100001013
xa≤ca+Δc·σa,
Figure BDA00035961385100001014
in the formula: omega is the unit time cost; s is a coupling node set of a traffic network and a power distribution network;
Figure BDA00035961385100001015
charging capacity of the electric vehicle on a charging station s on a p path; theta is the electricity price of the charging station s; gwAnd hwThe travel demands of the electric automobile and the common automobile on the OD pair w are respectively met;
Figure BDA00035961385100001016
and
Figure BDA00035961385100001017
is a binary variable for which the path is associated with a road, i.e. paths p and q are 1 when they pass road a, and 0 otherwise;
step 2: in order to solve the traffic balance in the step 1, a travel path selection network is constructed, an optimal travel path generation model considering the satisfaction degree of an owner is provided, and the traffic balance is solved under the optimal path to obtain the steady-state traffic flow distribution;
the travel path selection network model in step 2 is shown in fig. 1 as a schematic diagram
Figure BDA00035961385100001018
In the formula: SOCOThe initial electric quantity of the electric automobile at the initial node is obtained;
Figure BDA00035961385100001019
is the distance from the origin node to the charging station s on the path p; l ispIs the total distance of path p; q is the power consumption of the electric automobile per kilometer; SOCφA minimum electric quantity threshold value of the battery which can be accepted by the vehicle owner;
the optimal path considering the owner satisfaction of the electric automobile and the ordinary automobile is as follows:
Figure BDA0003596138510000111
Figure BDA0003596138510000112
in the formula:
Figure BDA0003596138510000113
the traffic flow of the current road; p is a radical ofsCharging power for a charging station s;
Figure BDA0003596138510000114
which is a binary variable, when the path p passes the road a,
Figure BDA0003596138510000115
otherwise, the value is 0;
and step 3: and after the traffic balance is obtained, recording the paths, the road traffic flow and charging stations selected by the electric vehicle on each travel path, and obtaining the charging load on the coupling node according to the charging electric quantity. An extended planning model of the power-traffic coupling network is provided. The model comprises road traffic time cost, road capacity expansion cost, newly-built charging pile cost, substation capacity expansion cost and newly-built distribution network line cost;
the extended planning model of the power-traffic coupling network in the step 3 is as follows:
minF＝CTN+CPDN
in the formula: f is the total cost of the extension plan; cTNPlanning cost for the traffic network expansion; cPDNCost is expanded and planned for the power distribution network;
the cost expression of the traffic network expansion planning is
Figure BDA0003596138510000116
Figure BDA0003596138510000117
In the formula: kappa is the mark out coefficient; c. C1Unit cost for road expansion; c. C2Unit cost for newly adding a charging pile; h issThe number of the charging piles is increased for the charging station s; r is the discount rate; y is depreciation age;
the cost expression of the power distribution network expansion planning is
Figure BDA0003596138510000118
Figure BDA0003596138510000119
In the formula: c. C3Unit cost for newly building a line; n is a radical of an alkyl radicalijThe number of newly-built lines between the nodes i and j is the same as that of the existing power distribution network lines in parallel extension, and the line parameters are the same as those of the existing lines; c. C4Expanding unit cost for the transformer substation;
Figure BDA0003596138510000121
capacity expansion capacity of the transformer substation; ksIs a transformer substation set; pk,eThe power of a newly added charging pile in a charging station serving the transformer substation k;
Figure BDA0003596138510000122
residual capacity of a transformer substation k;
and 4, step 4: constructing a system constraint condition by taking the minimum sum of the expansion planning cost of the power-traffic coupling network as an optimization target and through traffic balance constraint, power grid safe operation constraint, road expansion, line expansion and newly-added charging pile constraint;
the traffic balance model is constrained to
Figure BDA0003596138510000123
Figure BDA0003596138510000124
Figure BDA0003596138510000125
xa≤ca+Δc·σa,
Figure BDA0003596138510000126
In the above formula: the 1 st behavior is a vehicle travel flow conservation constraint; the conservation of road flow and path flow in the 2 nd traffic network is restricted; behavior 3 road capacity constraint;
road expansion and newly-built distribution network line quantity restraint
0≤σa≤σa,max
0≤nij≤nij,max
In the formula: sigmaa,maxExpanding the upper limit of the quantity of the road a; n isij,maxTo be new between nodes i and jEstablishing an upper limit of the number of the lines of the power distribution network;
the newly added charging pile is constrained to
xs≤(hs+hs,0)ps
In the formula: h iss,0The quantity of original electric pile that fills in for the charging station.
The branch power flow of the power distribution network adopts a Distflow power flow form, the topological structure of the branch power flow is shown in figure 2, the line loss is not considered, and the simplified safe operation constraint of the power distribution network is
Figure BDA0003596138510000131
In the formula:v iand
Figure BDA0003596138510000132
respectively representing the upper limit and the lower limit of the voltage amplitude of the node i;
Figure BDA0003596138510000133
the active power flow capacity of the distribution line between the nodes i and j is obtained; r isijAnd xijRespectively a resistance and a reactance on the circuit; pjAnd QjActive and reactive power requirements at node j, respectively; v. ofiAnd vjThe voltage amplitudes for nodes i and j; pi (j) is a branch line set flowing out from the node j; p is a radical ofjmAnd q isjmActive and reactive power on the branch (j, m), respectively; piAnd
Figure BDA0003596138510000134
the total active power demand and the conventional load active power demand on the power distribution network node i are respectively.
And 5: carrying out linearization processing on the nonlinear constraint and the nonlinear model in the extended programming model;
and (5) linearizing a road capacity expansion equation. The road capacity expansion equation is a non-convex and non-linear function, the solution is difficult in the expansion planning process, and the road capacity expansion equation can be linearized by using a three-dimensional increment segmented relaxation method
Figure BDA0003596138510000135
In the formula: Δ xaAnd τa(m, n) is an auxiliary variable, where τa(m, n) is a binary variable representing the position of the segment under the two-dimensional plane; m and n are each xaAnd σaThe current segment number.
Voltage drop constrained linearization
Figure BDA0003596138510000141
In the formula:
Figure BDA0003596138510000142
is a binary variable, which satisfies
Figure BDA0003596138510000143
Figure BDA0003596138510000144
And
Figure BDA0003596138510000145
is a continuous variable, it satisfies
Figure BDA0003596138510000146
Transformer substation capacity expansion function linearization
Figure BDA0003596138510000147
In the formula: δ is a binary variable.
Step 6: and (2) constructing a coupling network by using an Nguyen transportation network and a 33-node power distribution network, wherein the power distribution network power flow form adopts a Distflow power flow form, the minimum sum of the extension planning costs in the step (3) is used as an optimization target, the constraint in the step (4) and the constraint after linearization in the step (5) are used as system constraint conditions, and a Cplex software package is called on Matlab to solve the model, so that the optimal extension planning strategy of the travel demand of the electric automobile under 5 scenes of increasing 0%, 25%, 50%, 75% and 100% is obtained.
The Cplex solution process is as follows:
step 6.1: basic parameter inputs and variable definitions. The basic parameters comprise parameters such as a traffic network, a power distribution network, investment cost, a charging station, a coupling node, travel demands of electric vehicles and ordinary vehicles and the like. The defined variables include integer variables, auxiliary variables, and real variables.
Step 6.2: and constructing an initial travel path set. The path set can be constructed by a particle swarm algorithm, and the initial traffic flow of the current road is firstly set to be
Figure BDA0003596138510000148
And constructing initial travel path sets of the electric automobile and the ordinary automobile on each OD pair w by considering the satisfaction degree of the automobile owner under the traffic flow
Figure BDA0003596138510000149
And
Figure BDA00035961385100001410
step 6.3: and (5) solving traffic balance. At the initial travel route set
Figure BDA00035961385100001411
And
Figure BDA00035961385100001412
solving the traffic balance to obtain the traffic flow on each road under the traffic balance
Figure BDA00035961385100001413
And travel cost of electric vehicles and ordinary vehicles on paths
Figure BDA0003596138510000151
And
Figure BDA0003596138510000152
step 6.4: the current road traffic flow under the traffic balance state is
Figure BDA0003596138510000153
In time, the optimal paths p and q of the electric automobile and the common automobile are solved by considering the satisfaction degree of the automobile owner on the OD pair w, and the travel cost of the optimal path is obtained
Figure BDA0003596138510000154
And
Figure BDA0003596138510000155
if it satisfies
Figure BDA0003596138510000156
And
Figure BDA0003596138510000157
adding paths p and q to
Figure BDA0003596138510000158
And
Figure BDA0003596138510000159
the condition of iteration termination is that all OD pairs in the traffic network do not meet the condition; otherwise, returning to the third step.
Step 6.5: and (5) charging load statistics. And recording the paths, the road traffic flow and the charging stations selected by the electric automobile on each travel path according to the traffic balance state at the moment, and acquiring the charging load of the electric automobile on the coupling node according to the charging electric quantity.
Step 6.6: constraint column writes. After the constraint conditions are subjected to linearization processing, traffic balance constraint, power grid safe operation constraint, road expansion, line expansion and newly-added charging pile constraint are written. If the actual traffic flow of the road exceeds the upper limit of the capacity, the step 6.2 is returned
Step 6.7: and outputting the result. And calling a Cplex software package on the Matlab to solve and record an optimal extension planning result according to the extension planning model target function and the constraint condition.
Secondly, in order to verify the beneficial effect of the method, the following simulation experiment is carried out:
the invention considers that a coupling network is constructed by an Nguyen-Dupuis traffic network of 13 network nodes and an IEEE 33 node power distribution network to carry out simulation analysis on the planning method, as shown in figure 3. The planning year y is 10 years, and the discount rate r is 0.08.
Road capacity expansion cost c in traffic network1＝7×105Element/bar; charging pile unit cost c2＝3×104Element/station; the unit time cost ω of road passage is 80 yuan/hour. Maximum capacity-enlarging quantity sigma for single road a,max3; the battery capacity of the electric automobile is 30kWh, the power consumption is 0.2kWh/km, and the initial SOC is assumed to follow a normal distribution (0.6,0.12) and a minimum charge threshold SOCφObey normal distribution (0.25,0.12)
In a power distribution network, an upper limit of node voltage amplitude
Figure BDA00035961385100001510
And lower limitv i0.95; voltage amplitude v of the balancing node11.05; new line cost c of power distribution network3＝1.8×106Maximum newly-built line number n between two nodes ij,max3; capacity expansion cost c of transformer substation4The ratio of 800 yuan/kVA,
Figure BDA00035961385100001511
equal to the constant load requirement when the charging pile is not newly added. The active power flow limit for lines D1-D2, D2-D3, D3-D4, D4-D5 and D5-D6 is set to 8pu, and the remaining line active power flow limit is set to 1.8 pu.
The basic parameters of the road are shown in the following table:
TABLE 1 basic parameters of the road
Figure BDA0003596138510000161
Figure BDA0003596138510000171
The travel demands of the electric vehicle and the ordinary vehicle are shown in the following table:
TABLE 2 travel demands of electric and general cars
Figure BDA0003596138510000172
The charging station basic parameters are shown in the following table
TABLE 3 charging station basic parameters
Figure BDA0003596138510000173
The coupling node information is shown in the following table
Table 4 coupling node information
Figure BDA0003596138510000181
According to the method, 5 different traffic travel scenes under traffic balance are considered, namely under the condition that the travel demand of an OD (origin-destination) pair common automobile is not changed, the travel demand of the electric automobile is increased by 0%, 25%, 50%, 75% and 100% respectively, so that the expansion planning strategy of the electric automobile under different travel permeabilities under a power-traffic coupling network is simulated. The investment cost result of the extended planning model is shown in fig. 4, and the corresponding road expansion, the newly added charging pile and the newly built distribution network line number are shown in fig. 5
In fig. 4, as the travel demand of the electric vehicle increases, the investment costs in the extended planning model show an increasing trend. The road expansion cost and the power distribution network new line cost are the main parts of the whole investment cost, and the expansion planning strategy shows that the investment in the two aspects needs to be increased to meet the increase of the travel demand of the electric automobile and the increase of the traffic load and the charging load brought by the increase of the travel demand of the electric automobile. In fig. 5, the number of various facilities is increased as the travel demand of the electric vehicle increases, and it is illustrated that the increased charging demand and traffic load need to be dealt with by expanding the facilities.
The specific positions and quantities of the traffic roads expanded under different travel scenes of the electric automobile are shown in the following table
TABLE 5 road expansion planning strategy
Figure BDA0003596138510000182
Figure BDA0003596138510000191
It can be seen from the table that each road needing capacity expansion can meet the scene that the travel demand of the electric vehicle is increased by 100% after one road is expanded. Moreover, the expansion quantity of roads in the traffic network does not monotonically increase with the increase of the travel traffic of the electric vehicle, such as in scenes in which the travel demand of the electric vehicle increases by 25%, 50% and 75%. After the road is expanded, the original road capacity is increased, but the traffic flow passing through the road under the traffic balance cannot be increased by times so as to exceed the road traffic capacity after the expansion.
Fig. 6 and 7 show the actual traffic flow of each road under the scene that the travel demand of the electric automobile is increased by 0% and 100%. With the increase of the travel demand of the electric automobile, the traffic flow of each road section is increased, so that the actual traffic flow of the road exceeds the upper limit of the traffic capacity. Therefore, the expansion planning strategy needs to expand the roads, 8 roads need to expand under the scene that the travel of the electric vehicle increases by 0%, and 11 roads need to expand under the scene that the travel of the electric vehicle increases by 100%. The passage time before and after the road expansion in the two scenes is as shown in fig. 8 and 9.
In fig. 8 and 9, the traffic time of the road without capacity expansion increases with the increase of the travel demand of the electric vehicle in traffic balance, for example, the traffic time of the road 3 is 15.3min when the travel of the electric vehicle increases by 0%, and the traffic time reaches 24.5min when the travel demand of the electric vehicle increases by 100%. The passing time of the road under the two scenes is reduced after the road is subjected to capacity expansion, so that the convenience of the vehicle owner in traveling is improved, and the effectiveness of the road capacity expansion in the expanded planning model is explained.
Under 5 kinds of scenes, the quantity of newly-increased electric pile that fills in each charging station is as shown in the following table
TABLE 6 charging pile extension planning strategy
Figure BDA0003596138510000201
It can be seen from the table that, as the travel demand of the electric vehicle increases, the number of the charging piles in the charging stations No.1, No.2, No.3 and No.5 increases correspondingly, and the number of the newly added charging piles in the charging station No.1 is the largest because the traffic network node where the new charging pile is located has a plurality of paths and more charging demands pass through. Fig. 10 shows charging queue time before and after a charging pile is newly added in different scenes. And charging station No.4 originally fills electric pile enough can satisfy the charging demand that increases, therefore it need not newly-increased.
In fig. 10, when the charging pile is not added to the charging station No.1, there is a longer charging queuing time in each scene, and the queuing waiting time of the charging station after addition is greatly reduced, which illustrates that the addition of the charging pile in the extended planning strategy can effectively improve the charging experience of the vehicle owner and improve the charging convenience.
The specific positions and the number of the newly-built lines of the power distribution network under different scenes are shown in the following table
TABLE 7 Power distribution network line extension planning strategy
Figure BDA0003596138510000211
In the table, when the travel demand of the electric vehicle increases by 25% and 75%, no additional distribution network line needs to be newly built to meet the newly increased charging load, because the distribution network extension planning strategy can respectively meet the two scenes under the scenes that the travel demand of the electric vehicle increases by 0% and 50%. Under the coupling network, new loads are brought to power nodes by charging demands, and with the increase of 100% of the travel demands of the electric automobile, a large number of charging loads can be generated at the moment, the power distribution network power flow is influenced, and the power distribution network power flow exceeds the line active power flow limit value. Therefore, new construction is required for the lines D6-D7, D7-D8, D6-D26, D26-D27, D27-D28 and D28-D29 to ensure that power can be continuously delivered to the charging stations.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments, or alternatives may be employed, by those skilled in the art, without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (10)

1. A power-traffic coupling network expansion planning method considering traffic balance is characterized in that,
constructing a coupling network by using a traffic network and a power distribution network, wherein the power distribution network flow form adopts a Distflow flow form;
and solving the optimal solution of the model to obtain the optimal extension plan by taking the minimum sum of the extension plan costs in the established extension plan model of the power-traffic coupling network as an optimization target and taking the traffic balance constraint, the power grid safe operation constraint, the road capacity expansion constraint, the line capacity expansion constraint, the newly added charging pile constraint and the linear and nonlinear constraints of the extension plan model as constraint conditions.
2. The power-traffic coupled network extension planning method considering traffic balancing according to claim 1, wherein the extension planning model is established based on solving a traffic balancing model, specifically:
constructing a travel path selection network and establishing an optimal travel path generation model considering the satisfaction degree of the vehicle owner;
based on the user balance principle of the hybrid electric vehicle and the common vehicle, considering the road congestion effect, the charging station congestion effect and the road capacity expansion model, and constructing a traffic balance model of the hybrid electric vehicle and the common vehicle;
solving traffic balance under the optimal path to obtain steady traffic flow distribution;
recording the paths, the road traffic flow and charging stations selected by the electric vehicle on each travel path, and acquiring the charging load on the coupling node according to the charging electric quantity; constructing an extension planning model of the power-traffic coupling network; the model comprises road traffic time cost, road capacity expansion cost, new-built charging pile cost, transformer substation capacity expansion cost and new-built distribution network line cost.
3. The power-traffic coupled network expansion planning method considering traffic balance as claimed in claim 1, wherein the charging station congestion effect is:
Figure FDA0003596138500000021
in the formula:
Figure FDA0003596138500000022
average queuing waiting time for the charging stations;
Figure FDA0003596138500000023
for binary variables, when the electric vehicle on path p chooses to charge at charging station s,
Figure FDA0003596138500000024
otherwise, the value is 0; x is the number ofsThe power required by charging the current electric automobile to a charging station s; c. CsIs the capacity of s of the charging station.
4. The power-traffic coupled network expansion planning method considering traffic balance according to claim 2, wherein the road capacity expansion model is:
Figure FDA0003596138500000025
in the formula: t is taRepresents the time when the vehicle passes through the road a;
Figure FDA0003596138500000026
the free passing time of the road a is set (the free passing time refers to the time when the traffic flow of the current road is not more than the upper limit of the passing capacity); x is the number ofaThe traffic flow of the road a; c. CaThe original traffic capacity upper limit of the road a is set; sigmaaRepresenting the number of road extensions, σaNot less than 0; Δ c represents the road traffic capacity that can be increased by road a to build a road.
5. The power-traffic coupling network expansion planning method considering traffic balance according to claim 2, wherein the principle of user balance of the hybrid electric vehicle and the ordinary vehicle is as follows:
Figure FDA0003596138500000027
Figure FDA0003596138500000028
in the formula:
Figure FDA0003596138500000029
and
Figure FDA00035961385000000210
respectively the travel cost of the OD to the w upper paths p and q;
Figure FDA00035961385000000211
and
Figure FDA00035961385000000212
respectively the travel cost of the electric automobile on the OD pair w and the ordinary automobile in a balanced state;
Figure FDA00035961385000000213
and
Figure FDA00035961385000000214
traffic on path p and path q, respectively;
Figure FDA00035961385000000215
taking the path of the electric automobile with the charging requirement as an available path;
Figure FDA00035961385000000216
is an available path of a common automobile; w is the set of all OD pairs in the traffic network, and W represents one of the OD pairs.
6. The power-traffic coupling network expansion planning method considering traffic balance according to claim 2, wherein the traffic balance model of the hybrid electric vehicle and the ordinary vehicle is as follows:
Figure FDA0003596138500000031
Figure FDA0003596138500000032
Figure FDA0003596138500000033
Figure FDA0003596138500000034
xa≤ca+Δc·σa,
Figure FDA0003596138500000035
in the formula: omega is the unit time cost; a represents a road in traffic; t isaRepresenting a collection of roads in a traffic network; s is a coupling node set of a traffic network and a power distribution network;
Figure FDA0003596138500000036
charging capacity of the electric vehicle on a charging station s on a p path; theta is the electricity price of the charging station s; gwAnd hwThe travel demands of the electric automobile and the common automobile on the OD pair w are respectively met;
Figure FDA0003596138500000037
and
Figure FDA0003596138500000038
are binary variables of the path associated with the road, i.e. paths p and q are 1 when they pass road a, and 0 otherwise.
7. The power-traffic coupled network expansion planning method considering traffic balance as claimed in claim 2, wherein the travel path selection network model is
Figure FDA0003596138500000039
In the formula: SOCOThe initial electric quantity of the electric automobile at the initial node is obtained;
Figure FDA00035961385000000310
is the distance from the origin node to the charging station s on the path p; l ispIs the total distance of path p; q is the power consumption of the electric automobile per kilometer; SOCφThe lowest charge threshold of the battery can be accepted by the vehicle owner.
8. The power-traffic coupling network expansion planning method considering traffic balance according to claim 2, wherein the optimal path models considering the owner satisfaction degrees of the electric vehicle and the ordinary vehicle are respectively as follows:
Figure FDA00035961385000000311
Figure FDA00035961385000000312
in the formula:
Figure FDA0003596138500000041
the traffic flow of the current road; p is a radical ofsCharging power for a charging station s;
Figure FDA0003596138500000042
which is a binary variable, when the path p passes the road a,
Figure FDA0003596138500000043
otherwise it is 0.
9. The power-traffic coupled network extension planning method considering traffic balance according to claim 2, wherein the extension planning model of the power-traffic coupled network is:
min F＝CTN+CPDN
in the formula: f is the total cost of the extension plan; cTNPlanning cost for the traffic network expansion; cPDNCost is expanded and planned for the power distribution network;
the cost expression of the traffic network expansion planning is
Figure FDA0003596138500000044
Figure FDA0003596138500000045
In the formula: kappa is a cash-out coefficient; c. C1Unit cost for road expansion; c. C2Unit cost for newly adding a charging pile; h issThe number of the charging piles is increased for the charging station s; r is the discount rate; y is depreciation age;
the cost expression of the power distribution network expansion planning is
Figure FDA0003596138500000046
Figure FDA0003596138500000047
In the formula: c. C3Unit cost for newly building a line; n isijThe number of newly-built lines between the nodes i and j is the same as that of the existing power distribution network lines in parallel extension, and the line parameters are the same as those of the existing lines; c. C4Expanding unit cost for the transformer substation;
Figure FDA0003596138500000048
capacity expansion capacity of the transformer substation; ksIs a transformer substation set; pk,eThe power of a newly added charging pile in a charging station serving the transformer substation k;
Figure FDA0003596138500000049
residual capacity of a transformer substation k;
the system constraint conditions are as follows:
the traffic balance model is constrained to
Figure FDA00035961385000000410
Figure FDA00035961385000000411
Figure FDA0003596138500000051
xa≤ca+Δc·σa,
Figure FDA0003596138500000052
In the above formula: the 1 st behavior is a vehicle travel flow conservation constraint; the conservation of road flow and path flow in the 2 nd traffic network is restricted; behavior 3 road capacity constraint;
road expansion and newly-built distribution network line quantity restraint
0≤σa≤σa,max
0≤nij≤nij,max
In the formula: sigmaa,maxExpanding the upper limit of the quantity of the road a; n isij,maxEstablishing an upper limit of the number of the power distribution network lines between the nodes i and j;
the newly added charging pile is constrained to
xs≤(hs+hs,0)ps
In the formula: h iss,0The number of the original charging piles in the charging station is set;
the branch power flow of the power distribution network adopts a Distflow power flow form, the line loss is not considered, and the simplified safe operation constraint of the power distribution network is
Figure FDA0003596138500000053
In the formula: v. ofiAnd
Figure FDA0003596138500000054
respectively representing the upper limit and the lower limit of the voltage amplitude of the node i; pijIs a nodeActive power capacity between i and j;
Figure FDA0003596138500000055
is the upper limit of the active power capacity of the line between the nodes i and j; r is a radical of hydrogenijAnd xijRespectively a resistance and a reactance on the line; pjAnd QjActive and reactive power requirements at node j, respectively; v. ofiAnd vjThe voltage amplitudes for nodes i and j; p is a radical ofijAnd q isijThe active and reactive power flows between the nodes i and j; dNA node set of the power distribution network; dAA line set which is a power distribution network; (i, j) is a line between nodes i and j; v. of1To balance the node voltage amplitude; pi (j) is a branch line set flowing out from the node j; p is a radical ofjmAnd q isjmActive and reactive power on the branch (j, m), respectively; piAnd
Figure FDA0003596138500000061
the total active power demand and the conventional load active power demand on the power distribution network node i are respectively.
10. The power-traffic coupled network expansion planning method considering traffic balance as claimed in claim 2, wherein the nonlinear constraint and nonlinear model are linearized by:
Figure FDA0003596138500000062
in the formula: Δ xaAnd τa(m, n) is an auxiliary variable, where τa(m, n) is a binary variable representing the position of the segment under the two-dimensional plane; m and n are each xaAnd σaThe current segment number;
voltage drop constrained linearization
Figure FDA0003596138500000063
In the formula:
Figure FDA0003596138500000064
is a binary variable, which satisfies
Figure FDA0003596138500000065
Figure FDA0003596138500000066
And
Figure FDA0003596138500000067
is a continuous variable, it satisfies
Figure FDA0003596138500000068
Transformer substation capacity expansion function linearization
Figure FDA0003596138500000069
In the formula: δ is a binary variable.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116452074A (en) * 2023-03-13 2023-07-18 浙江大学 Dynamic equilibrium modeling simulation method for electric power traffic coupling network
CN116470550A (en) * 2023-03-13 2023-07-21 浙江大学 Collaborative capacity expansion method for dynamic balance electric power traffic coupling network

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116452074A (en) * 2023-03-13 2023-07-18 浙江大学 Dynamic equilibrium modeling simulation method for electric power traffic coupling network
CN116470550A (en) * 2023-03-13 2023-07-21 浙江大学 Collaborative capacity expansion method for dynamic balance electric power traffic coupling network
CN116452074B (en) * 2023-03-13 2023-11-07 浙江大学 Dynamic equilibrium modeling simulation method for electric power traffic coupling network
CN116470550B (en) * 2023-03-13 2023-11-07 浙江大学 Collaborative capacity expansion method for dynamic balance electric power traffic coupling network

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