CN111768119A - Virtual power plant-based vehicle network fusion scheduling method and device - Google Patents

Virtual power plant-based vehicle network fusion scheduling method and device Download PDF

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CN111768119A
CN111768119A CN202010645683.1A CN202010645683A CN111768119A CN 111768119 A CN111768119 A CN 111768119A CN 202010645683 A CN202010645683 A CN 202010645683A CN 111768119 A CN111768119 A CN 111768119A
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李力
郑建平
陆秋瑜
杨银国
刘文涛
伍双喜
谭嫣
朱誉
林英明
李博
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Abstract

The application discloses a vehicle network fusion scheduling method and device based on a virtual power plant, and the method comprises the following steps: establishing a traffic flow balanced distribution model with the minimum sum of all road section travel cost integrals in the traffic network; establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation; establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model; and solving the traffic network-power distribution network coordination optimization model by adopting an iteration method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicles. The method and the device solve the problems that the existing scheduling scheme does not pay attention to the traffic travel constraint of an electric vehicle owner, charging load modeling is not accurate, and the implementation effect of scheduling signals is poor.

Description

Virtual power plant-based vehicle network fusion scheduling method and device
Technical Field
The application relates to the technical field of power grid dispatching, in particular to a vehicle and network fusion dispatching method and device based on a virtual power plant.
Background
In recent years, electric automobiles gradually replace traditional fuel automobiles by virtue of the advantages of zero pollution, high energy efficiency and the like, and are one of important means for solving the problems of environmental pollution, energy crisis and traffic. However, the charging behavior of large-scale electric vehicles will have a non-negligible impact on the safe operation of the power grid and on the traffic system. On the one hand, the disordered charging of a large number of electric vehicles brings many technical problems for the stable operation of the power grid, resulting in local load overload, increased power grid loss and deteriorated power quality. On the other hand, the charging behavior will change the travel plan and routing of the vehicle owner. The charging station is easily blocked due to the limitation of longer charging time and the capacity of the charging station, and the travel experience of the vehicle owner and the operation efficiency of a traffic network are further influenced. The virtual power plant is used as a management system of distributed energy, and a new solution is brought to the fusion scheduling of a vehicle-power distribution network-traffic network. The virtual power plant depends on advanced information communication and software systems, can aggregate and coordinate charging loads and traditional loads to participate in power grid dispatching and power market trading, and guides the charging and path selection of an electric vehicle owner through information such as electricity price, so that the influence of the charging loads on the system is reduced, and the economic benefits, social benefits and environmental benefits of distributed energy sources such as electric vehicles are fully exerted.
At present, most modeling modes including optimal scheduling of the electric automobile virtual power plant only consider slow charging load, and cannot describe space movement characteristics of fast charging electric automobile vehicles. On the other hand, the modeling mode ignores the reaction of the user to the charging information (such as price and waiting time), so that the influence of the excitation signal on the charging behavior cannot be described, and the research on effective excitation strategy formulation is hindered.
Disclosure of Invention
The application provides a virtual power plant-based vehicle network fusion scheduling method and device, and solves the technical problems that the existing virtual power plant optimization scheduling containing electric vehicles only considers the time characteristic of electric vehicle loads under the condition of slow charging, but does not pay attention to traffic travel constraints of electric vehicle owners, the charging load modeling is not accurate, and the scheduling signal implementation effect is poor.
In view of this, a first aspect of the present application provides a virtual power plant-based vehicle network fusion scheduling method, where the method includes:
establishing a traffic flow balanced distribution model with the minimum sum of all road section travel cost integrals in the traffic network;
establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation;
establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model;
and solving the traffic network-power distribution network coordination optimization model by adopting an iterative method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicle.
Optionally, the establishing a traffic flow equilibrium distribution model with the minimum sum of all the road section travel cost integrals in the traffic network includes:
taking the minimum sum of all road section travel cost integrals in the traffic network as an objective function:
Figure BDA0002573007590000021
in the formula, vαRepresenting traffic traveling on road segment α, set A being a set of traffic network segments, λjIs the electricity price of node j, cα(ω,λj) Representing the cost of passage at traffic network segment α,
Figure BDA0002573007590000022
representing the sum of the travel cost integrals of all the road sections in the traffic network.
Optionally, the constraint conditions of the traffic flow equilibrium distribution model include a flow conservation constraint, a flow association constraint and a flow variable non-negative constraint;
the flow conservation constraint is as follows:
Figure BDA0002573007590000023
the traffic association constraint is:
Figure BDA0002573007590000024
the flow variable non-negative constraint is:
fr，w≥0
where W is a set of origin-destination pairs, W ∈ W is one origin-destination pair, R is a path connecting OD pairs, and R iswIs a set of paths connecting OD to w, fr,wIs the flow on path r connecting OD to w, dwIs the trip demand connecting the OD to w; r represents the set of all paths in the traffic network.
Optionally, the establishing of the power distribution network power flow model based on the Distflow power flow equation with second-order cone relaxation includes:
and taking the minimum power generation cost of the single distribution network as an objective function:
Figure BDA0002573007590000031
wherein M is a node set of the distribution network, GjAs a function of the cost of power generation of the generator at node j,
Figure BDA00025730075900000312
planning the active Power output, λ, for the Generator of node j0Contract price of electricity, P, for electricity purchase from slack node 0 to the main grid0kRepresenting power purchased from the main grid, B is the set of branches of the distribution grid, and branch (0, k) ∈ B represents any branch from relaxation node 0.
Optionally, the constraint conditions of the power distribution network power flow model include power flow constraints and safe operation constraints;
the power flow constraint is as follows:
Figure BDA0002573007590000032
Figure BDA0002573007590000033
Figure BDA0002573007590000034
Figure BDA0002573007590000035
Figure BDA0002573007590000036
in the formula, pjAnd q isjRespectively representing active and reactive power injection, V, at node jjRepresents the square of the voltage magnitude of node j, PijAnd QijRespectively representing the active and reactive power, R, flowing through the branch (i, j)ijAnd XijRespectively representing the resistance and reactance values, L, of the branches (i, j)ijRepresents the square of the current amplitude of the branch (i, j),
Figure BDA00025730075900000313
is the conventional active power load of node j;
the safe operation constraints are:
Figure BDA0002573007590000037
Figure BDA0002573007590000038
Figure BDA0002573007590000039
in the formula (I), the compound is shown in the specification,
Figure BDA00025730075900000310
and
Figure BDA00025730075900000311
respectively representing the lower limit and the upper limit of the generator active power output of the node j,
Figure BDA0002573007590000041
and
Figure BDA0002573007590000042
respectively representing the lower and upper limits of the generator reactive power output of node j, jVand
Figure BDA0002573007590000043
representing the lower and upper limits, respectively, of the square of the voltage magnitude at node j.
Optionally, the establishing a traffic network-power distribution network coordination optimization model according to the traffic flow equilibrium distribution model and the power distribution network power flow model includes:
taking the minimum sum of the travel cost integral of all road sections in the traffic network and the power generation cost of the power distribution network as a target function:
Figure BDA0002573007590000044
in the formula (I), the compound is shown in the specification,
Figure BDA0002573007590000045
representing the charging cost in all electric vehicle owner trips.
Optionally, the constraint conditions of the transportation network-distribution network coordination optimization model include: flow conservation constraints, flow association constraints, flow variable non-negative constraints, power flow constraints, and safe operation constraints.
Optionally, the solving of the traffic network-power distribution network coordination optimization model is performed until a difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold, and the obtaining of the electricity price of the charging station and the flow rate of the charging vehicle specifically includes:
setting initial electricity price of the charging station of a virtual power plant and an iteration zone bit z;
calculating the flow of each path of the traffic network according to the traffic flow balanced distribution model, wherein the traffic network is communicated with a power distribution network through a virtual power plant at a charging station;
solving the power flow model of the power distribution network to obtain power flow parameters of the power distribution network, and solving active power balance constraints in the power flow constraints according to the power flow parameters to obtain the electricity price of the charging station;
calculating the electricity price of the charging station obtained by the z-th iteration
Figure BDA0002573007590000046
And obtaining the electricity price of the charging station by z-1 times of iteration
Figure BDA0002573007590000047
Making a comparison when
Figure BDA0002573007590000048
Ending iteration and representing a preset threshold value; and obtaining the electricity price of the charging station and the flow of the charging station of the virtual power plant.
The trend applies for the second aspect and provides a car network fuses scheduling device based on virtual power plant, the device includes: the traffic flow balanced distribution model establishing unit is used for establishing a traffic flow balanced distribution model according to the minimum sum of all road section travel cost integrals in the traffic network;
the power distribution network power flow model establishing unit is used for establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation;
the traffic network-power distribution network coordination optimization model establishing unit is used for establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model;
and the traffic network-power distribution network coordination optimization model solving unit is used for solving the traffic network-power distribution network coordination optimization model by adopting an iteration method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicles.
Optionally, the solution unit of the traffic network-distribution network coordination optimization model includes:
the initialization unit is used for setting the initial charging station electricity price of the virtual power plant and an iteration zone bit z;
the flow calculation unit is used for calculating the flow of the flow charging stations of each path of the traffic network according to the traffic flow balanced distribution model, and the traffic network and the power distribution network are communicated through virtual power plants at the charging stations;
the power flow calculation unit is used for solving the power flow model of the power distribution network to obtain power flow parameters of the power distribution network, solving active power balance constraints in the power flow constraints according to the power flow parameters to obtain the electricity price of the charging station;
an iteration unit for calculating the electricity price of the charging station obtained by the z-th iteration
Figure BDA0002573007590000051
And obtaining the electricity price of the charging station by z-1 times of iteration
Figure BDA0002573007590000052
Making a comparison when
Figure BDA0002573007590000053
Ending iteration and representing a preset threshold value; and obtaining the electricity price of the charging station and the flow of the charging station of the virtual power plant. Trend of tide
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a virtual power plant-based vehicle network fusion scheduling method and device, and the method comprises the steps of establishing a traffic flow balanced distribution model with the minimum sum of travel cost integrals of all road sections in a traffic network; establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation; establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model; and solving the traffic network-power distribution network coordination optimization model by adopting an iteration method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicles. The virtual power plant-based vehicle-network fusion scheduling method is established, the space movement rule of the electric vehicle is described in detail, the path and charging station selection based on personal interest maximization of a user is fully considered, and the safe and efficient operation of a power distribution network and a traffic network is guaranteed. Meanwhile, a fully-distributed optimization strategy is provided, the virtual power plant can realize coordinated optimization regulation and control of the power distribution network and the traffic network only by exchanging the electricity price and the traffic flow of the charging station node, the coordinated optimization efficiency of the virtual power plant is considered, and the information privacy of the power distribution network and the traffic network is protected.
Drawings
Fig. 1 is a flowchart of a method according to an embodiment of a virtual power plant-based vehicle network fusion scheduling method of the present application;
fig. 2 is a schematic device diagram of an embodiment of a virtual power plant-based vehicle network fusion scheduling device according to the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a flowchart of a method of an embodiment of a virtual power plant-based vehicle network fusion scheduling method according to the present application, as shown in fig. 1, where fig. 1 includes:
101. and establishing a traffic flow balanced distribution model according to the minimum sum of all road section travel cost integrals in the traffic network.
It should be noted that, in the present application, the minimum sum of the travel cost integrals of all the road segments in the traffic network is used as an objective function, and specifically, the objective function may be:
Figure BDA0002573007590000061
in the formula, vαRepresenting traffic traveling on road segment α, set A being a set of traffic network segments, λjIs the electricity price of node j, cα(ω,λj) Representing the cost of passage at traffic network segment α,
Figure BDA0002573007590000062
representing the sum of the travel cost integrals of all the road sections in the traffic network. When the constraint condition of the traffic network is satisfied, the traffic flow distribution satisfying the first balance principle of the Wardrop can be obtained by minimizing the objective function.
Wherein the traffic cost in the traffic network comprises two parts: 1) if the traffic node is not connected with a charging station, the traffic cost is the time cost; 2) if the traffic node is connected with a charging station, the traffic cost is the time cost plus the charging cost. Then the cost function cα(vα,λj) The following were used:
Figure BDA0002573007590000063
wherein β represents the economic value per unit time, tα(vα) As a function of time for road section α, pevRepresenting the average charging power per electric vehicle flow, AttRepresenting a set of road sections without charging stations, AcsRepresenting charging stationsAnd satisfies a ═ att∪Acs，McsRepresents the set of grid nodes to which the charging station is connected.
In addition, in a specific embodiment, the constraint conditions of the traffic flow equilibrium distribution model comprise a flow conservation constraint, a flow association constraint and a flow variable non-negative constraint.
Wherein the flow conservation constraint is:
Figure BDA0002573007590000071
wherein W is an Origin-Destination (OD) set, W ∈ W is an Origin-Destination, R is a path connecting OD pairs, and R iswIs a set of paths connecting OD to w, fr,wIs the flow on path r connecting OD to w, dwIs the trip demand connecting the OD to w; the flow conservation constraint dictates that for any OD pair, the traffic flow through all feasible paths be equal to the demand between that OD pair.
The traffic association constraint is:
Figure BDA0002573007590000072
where R represents the set of all paths in the traffic network. The traffic association constraint requires that the sum of the traffic of all paths r using the road segment α be equal to the traffic of all users on the road segment α.
The flow variable non-negative constraint is:
fr，w≥0
the flow variable non-negative constraint specifies that all flow variables are non-negative.
102. And establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation.
It should be noted that the power distribution network power flow model is established based on a Distflow power flow equation with second-order cone relaxation, the power distribution network power flow model takes the minimum power generation cost as a target function, and the specific target function is as follows:
Figure BDA0002573007590000073
wherein M is a node set of the distribution network, GjAs a function of the cost of power generation of the generator at node j,
Figure BDA0002573007590000074
planning the active Power output, λ, for the Generator of node j0Contract price of electricity, P, for electricity purchase from slack node 0 to the main grid0kRepresenting the power purchased from the main grid, B is the set of branches of the distribution grid, and branch (0, k) ∈ B represents any branch emanating from relaxation node 0.
In a specific embodiment, the constraint conditions of the power distribution network power flow model comprise power flow constraints and safe operation constraints.
The power flow constraint comprises the following steps:
Figure BDA00025730075900000812
Figure BDA0002573007590000081
Figure BDA0002573007590000082
Figure BDA0002573007590000083
Figure BDA0002573007590000084
in the formula, pjAnd q isjRespectively representing active and reactive power injection, V, at node jjRepresents the square of the voltage magnitude of node j, PijAnd QijRespectively representing the active and reactive power, R, flowing through the branch (i, j)ijAnd XijRespectively representing the resistance and reactance values, L, of the branches (i, j)ijRepresents the square of the current amplitude of the branch (i, j),
Figure BDA0002573007590000085
is the conventional active power load of node j. Wherein the constraint of the second-order conic convex relaxation is utilized
Figure BDA0002573007590000086
The power grid power flow model can be converted into a convex problem to be solved, and the efficiency of model solving is greatly improved within the allowable range of solving errors.
Constraining
Figure BDA0002573007590000087
Indicating that it is located in traffic network segment α∈ AcsIs connected to the distribution network node j ∈ McsIs implanted with pevvαThe charging power of the power grid influences the active power balance of the power grid, and further influences the operation of the power distribution network and the numerical value of the electricity price of the marginal node. Assuming that the marginal node electricity price is the charging electricity price provided for the owner of the electric automobile, namely the active power balance constraint
Figure BDA0002573007590000088
Corresponding lagrange multiplier lambdajIs the charging electricity price.
Safe operating constraints include:
Figure BDA0002573007590000089
Figure BDA00025730075900000810
Figure BDA00025730075900000811
in the formula (I), the compound is shown in the specification,
Figure BDA0002573007590000091
and
Figure BDA0002573007590000092
respectively representing the lower limit and the upper limit of the generator active power output of the node j,
Figure BDA0002573007590000093
and
Figure BDA0002573007590000094
respectively representing the lower and upper limits of the generator reactive power output of node j, jVand
Figure BDA0002573007590000095
representing the lower and upper limits, respectively, of the square of the voltage magnitude at node j.
103. And establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model.
It should be noted that, in the present application, a traffic network-distribution network coordination optimization model is established according to a traffic flow equilibrium distribution model and a distribution network power flow model, and the traffic network-distribution network coordination optimization model takes the total cost of minimizing a distribution network and a traffic network as a target function, and specifically includes:
Figure BDA0002573007590000096
in the formula (I), the compound is shown in the specification,
Figure BDA0002573007590000097
representing the charging cost in all electric vehicle owner trips. Since the charging cost is already considered in the power generation cost of the distribution network, this term is subtracted from the objective functions of the traffic network and the distribution network in order to avoid repeated accumulation in the objective functions.
The constraint conditions of the traffic network-power distribution network coordination optimization model comprise: the method comprises the following steps of flow conservation constraint, flow association constraint, flow variable non-negative constraint, power flow constraint and safe operation constraint, and specifically comprises the following steps:
the flow conservation constraint is:
Figure BDA0002573007590000098
the traffic association constraint is:
Figure BDA0002573007590000099
the flow variable non-negative constraint is:
fr，w≥0
the power flow constraint comprises the following steps:
Figure BDA00025730075900000910
Figure BDA00025730075900000911
Figure BDA0002573007590000101
Figure BDA0002573007590000102
Figure BDA0002573007590000103
safe operating constraints include:
Figure BDA0002573007590000104
Figure BDA0002573007590000105
Figure BDA0002573007590000106
104. and solving the traffic network-power distribution network coordination optimization model by adopting an iteration method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicles.
It should be noted that, the method for solving the traffic network-distribution network coordination optimization model in the present application specifically includes setting an initial charging station electricity price of the virtual power plant and an iteration zone z.
The initial iteration flag bit may be set to 0, and one is added to the iteration flag bit every iteration. Specifically, the electricity price of the initial charging station
Figure BDA0002573007590000107
The flow f on each path in the traffic network can be obtained by inputting the flow f into a traffic flow balanced distribution modelr，wFrom calculated fr，wSolving a power distribution network power flow model to obtain power flow parameters of the power distribution network, wherein the power flow parameters comprise Pij，Qij，Lij,VjWhile obtaining active balance constraint
Figure BDA0002573007590000108
The corresponding lagrange multiplier. The Lagrange multiplier is the electricity price of the charging station for the next iteration
Figure BDA0002573007590000109
And calculating the flow of each path of the traffic network according to the traffic flow balanced distribution model, wherein the traffic network is communicated with the power distribution network through a virtual power plant at a charging station.
It should be noted that the flow of each path obtained by solving the traffic flow equilibrium distribution model is transmitted to the power distribution network through virtual power plant communication.
And solving the power distribution network power flow model to obtain the power flow parameters of the power distribution network. And solving the active power balance constraint in the power flow constraint according to the power flow parameters to obtain the electricity price of the charging station.
It should be noted that, for the power grid, the parameters of the traffic network are the charging load of the electric vehicle, and the distribution of the traffic flow is known, and the product of the charging power of the electric vehicle is the spatial distribution of the charging load of the electric vehicle, so that the power grid flow can be solved.
Calculating the electricity price of the charging station obtained by the z-th iteration
Figure BDA0002573007590000111
And obtaining the electricity price of the charging station by z-1 times of iteration
Figure BDA0002573007590000112
Making a comparison when
Figure BDA0002573007590000113
Ending iteration and representing a preset threshold value; and obtaining the electricity price of the charging station and the flow of the charging station of the virtual power plant.
The virtual power plant-based vehicle-network fusion scheduling method is established, the space movement rule of the electric vehicle is described in detail, the path and charging station selection based on personal interest maximization of a user is fully considered, and the safe and efficient operation of a power distribution network and a traffic network is guaranteed. Meanwhile, a fully-distributed optimization strategy is provided, the virtual power plant can realize coordinated optimization regulation and control of the power distribution network and the traffic network only by exchanging the electricity price and the traffic flow of the charging station node, the coordinated optimization efficiency of the virtual power plant is considered, and the information privacy of the power distribution network and the traffic network is protected.
The foregoing is an embodiment of the method of the present application, and the present application further provides an embodiment of a virtual power plant-based vehicle network fusion scheduling apparatus, as shown in fig. 2, where fig. 2 includes:
a traffic flow equilibrium distribution model establishing unit 201, configured to establish a traffic flow equilibrium distribution model with a minimum sum of all road section travel cost integrals in a traffic network;
the power distribution network power flow model establishing unit 202 is used for establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation;
the traffic network-power distribution network coordination optimization model establishing unit 203 is used for establishing a traffic network-power distribution network coordination optimization model according to the traffic flow equilibrium distribution model and the power distribution network flow model;
and the traffic network-power distribution network coordination optimization model solving unit 204 is configured to solve the traffic network-power distribution network coordination optimization model by using an iterative method, and stop iteration until a difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value, so as to obtain the electricity price of the charging station and the flow rate of the charging vehicle.
The traffic network-distribution network coordination optimization model solving unit 204 includes:
the initialization unit 2041 is configured to set an initial charging station electricity price of the virtual power plant and an iteration flag z;
the flow calculation unit 2042 is configured to calculate the charging station flow according to the traffic flow balanced distribution model, where the traffic network and the power distribution network communicate with each other through a virtual power plant at a charging station;
the power flow calculation unit 2043 is used for solving a power flow model of the power distribution network to obtain power flow parameters of the power distribution network;
an iteration unit 2045 for calculating the charging station electricity price obtained by the z-th iteration
Figure BDA0002573007590000114
And obtaining the electricity price of the charging station by z-1 times of iteration
Figure BDA0002573007590000115
Making a comparison when
Figure BDA0002573007590000116
Figure BDA0002573007590000117
Ending iteration and representing a preset threshold value; and obtaining the electricity price of the charging station and the flow of the charging station of the virtual power plant.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In this application, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A vehicle network fusion scheduling method based on a virtual power plant is characterized by comprising the following steps:
establishing a traffic flow balanced distribution model with the minimum sum of all road section travel cost integrals in the traffic network;
establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation;
establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model;
and solving the traffic network-power distribution network coordination optimization model by adopting an iterative method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicle.
2. The virtual power plant-based vehicle network fusion scheduling method according to claim 1, wherein the establishing of the traffic flow balanced distribution model with the minimum sum of all road section travel cost points in the traffic network comprises:
taking the minimum sum of all road section travel cost integrals in the traffic network as an objective function:
Figure FDA0002573007580000011
in the formula, vαRepresenting traffic traveling on road segment α, set A being a set of traffic network segments, λjIs the electricity price of node j, cα(ω,λj) Representing the cost of passage at traffic network segment α,
Figure FDA0002573007580000012
representing the sum of the travel cost integrals of all the road sections in the traffic network.
3. The virtual power plant based vehicle network fusion scheduling method according to claim 2, wherein the constraint conditions of the traffic flow equilibrium distribution model include a flow conservation constraint, a flow association constraint and a flow variable non-negative constraint;
the flow conservation constraint is as follows:
Figure FDA0002573007580000013
the traffic association constraint is:
Figure FDA0002573007580000014
the flow variable non-negative constraint is:
fr，w≥0
where W is a set of origin-destination pairs, W ∈ W is one origin-destination pair, R is a path connecting OD pairs, and R iswIs a set of paths connecting OD to w, fr,wIs the flow on path r connecting OD to w, dwIs the trip demand connecting the OD to w; rRepresenting the set of all paths in the traffic network.
4. The virtual power plant-based vehicle network fusion scheduling method of claim 1, wherein the establishing of the power distribution network power flow model based on the Distflow power flow equation with second-order cone relaxation comprises:
the minimum power generation cost of the power distribution network is taken as a target function:
Figure FDA0002573007580000021
wherein M is a node set of the distribution network, GjAs a function of the cost of power generation of the generator at node j,
Figure FDA0002573007580000022
planning the active Power output, λ, for the Generator of node j0Contract price of electricity, P, for electricity purchase from slack node 0 to the main grid0kRepresenting power purchased from the main grid, B is the set of branches of the distribution grid, and branch (0, k) ∈ B represents any branch from relaxation node 0.
5. The virtual power plant based vehicle network fusion scheduling method according to claim 4, wherein the constraint conditions of the power distribution network power flow model comprise power flow constraints and safe operation constraints;
the power flow constraint is as follows:
Figure FDA0002573007580000023
Figure FDA0002573007580000024
Figure FDA0002573007580000025
Figure FDA0002573007580000026
Figure FDA0002573007580000027
in the formula, pjAnd q isjRespectively representing active and reactive power injection, V, at node jjRepresents the square of the voltage magnitude of node j, PijAnd QijRespectively representing the active and reactive power, R, flowing through the branch (i, j)ijAnd XijRespectively representing the resistance and reactance values, L, of the branches (i, j)ijRepresents the square of the current amplitude of the branch (i, j),
Figure FDA0002573007580000028
is the conventional active power load of node j;
the safe operation constraints are:
Figure FDA0002573007580000029
Figure FDA00025730075800000210
Figure FDA0002573007580000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002573007580000032
and
Figure FDA0002573007580000033
respectively representing the lower limit and the upper limit of the generator active power output of the node j,
Figure FDA0002573007580000034
and
Figure FDA0002573007580000035
respectively representing the lower and upper limits of the generator reactive power output of node j, jVand
Figure FDA0002573007580000036
representing the lower and upper limits, respectively, of the square of the voltage magnitude at node j.
6. The virtual power plant-based vehicle network fusion scheduling method according to claim 1, wherein the establishing of a traffic network-power distribution network coordination optimization model according to the traffic flow equilibrium distribution model and the power distribution network power flow model comprises:
taking the minimum sum of the travel cost integral of all road sections in the traffic network and the power generation cost of the power distribution network as a target function:
Figure FDA0002573007580000037
in the formula (I), the compound is shown in the specification,
Figure FDA0002573007580000038
representing the charging cost in all electric vehicle owner trips.
7. The virtual power plant-based vehicle network fusion scheduling method according to claim 1, wherein the constraint conditions of the transportation network-distribution network coordination optimization model comprise: flow conservation constraints, flow association constraints, flow variable non-negative constraints, power flow constraints, and safe operation constraints.
8. The virtual power plant-based vehicle network fusion scheduling method according to claim 1, wherein the solving of the transportation network-power distribution network coordination optimization model is performed until iteration is stopped when a difference between electricity prices of charging stations obtained by two adjacent iterations is smaller than a preset threshold value, so as to obtain the electricity price of the charging station and the flow rate of the charging vehicles, and specifically includes:
setting initial electricity price of the charging station of a virtual power plant and an iteration zone bit z;
calculating the flow of each path of the traffic network according to the traffic flow balanced distribution model, wherein the traffic network is communicated with a power distribution network through a virtual power plant at a charging station;
solving the power flow model of the power distribution network to obtain power flow parameters of the power distribution network, and solving active power balance constraints in the power flow constraints according to the power flow parameters to obtain the electricity price of the charging station;
calculating the electricity price of the charging station obtained by z-th iteration
Figure FDA0002573007580000039
j∈McsAnd obtaining the electricity price of the charging station by z-1 times of iteration
Figure FDA00025730075800000310
j∈McsMaking a comparison when
Figure FDA00025730075800000311
Ending iteration and representing a preset threshold value; and obtaining the electricity price of the charging station and the flow of the charging station of the virtual power plant.
9. The utility model provides a car net fuses scheduling device based on virtual power plant which characterized in that includes:
the traffic flow balanced distribution model establishing unit is used for establishing a traffic flow balanced distribution model according to the minimum sum of all road section travel cost integrals in the traffic network;
the power distribution network power flow model establishing unit is used for establishing a power distribution network power flow model based on a Distflow power flow equation with second-order cone relaxation;
the traffic network-power distribution network coordination optimization model establishing unit is used for establishing a traffic network-power distribution network coordination optimization model according to the traffic flow balance distribution model and the power distribution network flow model;
and the traffic network-power distribution network coordination optimization model solving unit is used for solving the traffic network-power distribution network coordination optimization model by adopting an iteration method, and stopping iteration until the difference between the electricity prices of the charging stations obtained by two adjacent iterations is smaller than a preset threshold value to obtain the electricity price of the charging station and the flow of the charging vehicles.
10. The virtual power plant-based vehicle network fusion scheduling device of claim 9, wherein the traffic network-distribution network coordination optimization model solving unit comprises:
the initialization unit is used for setting the initial charging station electricity price of the virtual power plant and an iteration zone bit z;
the flow calculation unit is used for calculating the flow of the flow charging stations of each path of the traffic network according to the traffic flow balanced distribution model, and the traffic network and the power distribution network are communicated through virtual power plants at the charging stations;
the power flow calculation unit is used for solving the power flow model of the power distribution network to obtain power flow parameters of the power distribution network, solving active power balance constraints in the power flow constraints according to the power flow parameters to obtain the electricity price of the charging station;
an iteration unit for calculating the electricity price of the charging station obtained by the z-th iteration
Figure FDA0002573007580000041
j∈McsAnd obtaining the electricity price of the charging station by z-1 times of iteration
Figure FDA0002573007580000042
j∈McsMaking a comparison when
Figure FDA0002573007580000043
Ending iteration and representing a preset threshold value; and obtaining the electricity price of the charging station and the flow of the charging station of the virtual power plant.
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