A joint planning method and system for power-transportation-information network

Abstract

The present invention discloses a joint planning method and system for an electric power-transport-information network. First, considering the large-scale growth of the demand for communication of intelligent electric vehicles, a communication network model considering vehicle flow is established. By coupling the transportation network and the information network, the influence of the node vehicle flow on the communication demand under the user equilibrium condition is simulated. Secondly, the electric power-transport-information network is effectively coupled through the power consumption of the infrastructure in the transportation network and the information network. In order to minimize the overall construction cost and operating cost, a joint planning model is established. Finally, the model is converted into a mixed integer linear programming model by using piecewise linearization and the big M method, and the optimal planning strategy of the electric power-transport-information network is obtained by solving. The method of the present invention simplifies the joint planning model into a mixed integer linear programming model, significantly reduces the difficulty of solving, and realizes the coordinated optimization of the three networks.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to a joint planning method for an electric power-transportation-information network. Background Art

[0002] In order to meet the increasing travel needs of electric vehicles, the number of charging stations has increased significantly, and the dependence on the distribution network and transportation network has increased. At the same time, the intelligent travel of electric vehicles and the dispatch of distributed resources in the distribution network are highly dependent on communication facilities, such as Internet data centers and base stations, which makes the information network and the distribution network highly coupled. Therefore, the coupling relationship between the distribution network, transportation network and information network is becoming increasingly close.

[0003] There are more and more studies devoted to the coordinated planning of power and transportation networks and the coordinated planning of power and information networks, but there are fewer studies on the joint planning of the integration of power, transportation and information networks. Independent planning of power, transportation and information networks will lead to low resource allocation efficiency, duplicate construction of facilities, lack of synergy, and difficulty in achieving efficient network integration and sustainable development, resulting in waste of resources. Therefore, it is necessary to rethink the distribution network planning problem from the perspective of the integration of power, transportation and information networks.

[0004] CN109193644A discloses a modeling and simulation method based on the integration of power grid, information and transportation. It establishes a simulation model of the power grid, information and transportation three-network integration system, and simulates and analyzes the interactive relationship between the power grid, information and transportation attributes as the entry point to reflect the main coupling factors affecting the operating status in the three-network integration system. However, it is only limited to the simulation stage. It is also necessary to further model and solve the power-transportation-information network in detail to obtain the optimal expansion planning scheme for the transportation network, information network and distribution network. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to carry out specific modeling and solution of the power-transportation-information network to obtain the optimal expansion planning scheme of the transportation network, information network and distribution network.

[0006] In order to solve the above technical problems, the present invention provides a joint planning method for a power-transportation-information network, comprising the following steps:

[0007] Step 1: construct a traffic network model using a traffic allocation model that takes user equilibrium into account, and obtain key data of the traffic network, wherein the key data of the traffic network includes path traffic flow, node traffic flow, and electric vehicle travel cost;

[0008] Step 2: Using the node traffic flow information and path traffic flow information in the key data of the traffic network, an information network model considering the communication delay of electric vehicles is constructed to obtain the key information of the information network, including the node data volume, the node data unit delay time and the total delay time;

[0009] Step 3: Use linear power flow to represent the distribution network model, utilize the coupling of the traffic network model and the information network model in terms of vehicle flow and data flow, the coupling of the traffic network model and the power network model in terms of vehicle flow information and charging and discharging loads of charging stations, and the coupling of the information network model and the power network model in terms of data flow and power load, and establish a power-transportation-information network joint planning model with the electric vehicle travel cost, data transmission delay penalty cost, and power generation cost as the targets;

[0010] Step 4: Use piecewise linearization method and big M method to transform the power-transportation-information network joint planning model into a mixed integer linear programming model;

[0011] Step 5: Solve the power-transportation-information network joint planning model to obtain the optimal solution for the expansion planning parameters of the transportation network, information network and distribution network.

[0012] In the aforementioned joint planning method for a power-transportation-information network, in step 1, the transportation network model includes:

[0013] Path traffic flow and node traffic flow data:

[0014] (1-1)

[0015] (1-2)

[0016] (1-3)

[0017] (1-4)

[0018] (1-5)

[0019] (1-6)

[0020] in, and Represents the index and collection of transportation network nodes, and Represents the index and collection of OD, Indicates road segment Traffic flow on Representation Node of traffic, Indicates OD Upper path Traffic volume; and A 0-1 variable representing traffic flow Whether it passes through the road section and nodes ; Indicates that the electric car is passing the road section time; Indicates OD Upper path Travel costs The dual variable of represents the charging cost, Represents a set of road segments. represents a time collection, Indicates road segment The initial travel time on Indicates road segment The capacity, Indicates OD Upper path gather, Indicates OD Upper path travel needs, is an orthogonal symbol;

[0021] Electric vehicle charging and discharging load data and charging pile data:

[0022] (1-7)

[0023] (1-8)

[0024] (1-9)

[0025] (1-10)

[0026] (1-11)

[0027] (1-12)

[0028] in, Representation Node Electric vehicles in The total charging load at the moment, Representation Node Electric vehicles in Total discharge load at the moment; and The charging and discharging power of an electric car; For Node A sign indicating whether a charging station should be built. For Node The number of ordinary charging piles at For Node The number of V2G piles, is the charging power of ordinary charging pile and V2G charging pile, The maximum number of ordinary charging piles and V2G charging piles that can be built.

[0029] In the aforementioned joint planning method for power-transportation-information network, in step 2, the information network model is represented as:

[0030] (1-13)

[0031] (1-14)

[0032] (1-15)

[0033] (1-16)

[0034] (1-17)

[0035] (1-18)

[0036] in, represents the communication node set, Represents a communication node The transmission delay time of the information flow per unit distance at Represents a communication node In time The data traffic generated, Indicates the transmission bandwidth, Indicates time The sum of all node data traffic, represents the sum of data transmission delays of all nodes, represents the charging node set, is a time set; matrix Represents the association matrix between the transportation network and the distribution network, and its elements ;matrix is the association matrix between the distribution network nodes and the communication network nodes, and its elements , formula (1-18) represents the communication node In time The communication data generated, A 0-1 variable indicating whether it is in the communication node Building data centers, represents the set of communication nodes, Represents a communication node With communication node The communication distance between

[0037] (1-19)

[0038] (1-20)

[0039] (1-21)

[0040] Represents a communication node The number of new base stations at Represents the data transmission capacity of a single base station, represents the power of the data center, represents the fixed power consumption of the data center, Indicates the power consumed by a data center to process a unit of data. Indicates the planned capacity of a data center.

[0041] In the aforementioned joint planning method for power-transportation-information network, in step 3, the expression of the objective function is as follows:

[0042] (1-22)

[0043] (1-23)

[0044] (1-24)

[0045] (1-25)

[0046] (1-26)

[0047] Formula (1-22) is the objective function, which uses the weighted cost of typical scenarios to describe the uncertainty of new energy; is the objective function of the joint planning model, , They are indexes and collections of typical scenarios. is the number of typical scenes, , , and Describe the scenes separately The cost of power distribution network, transportation network, communication network and total cost; , and They represent the unit electricity purchase cost, unit load shedding penalty cost and the power generation cost in the distribution network, respectively. Indicates that the substation is at the node exist The effort of the moment, Indicates that the micro gas turbine is at node exist The effort of the moment, represents the set of power grid nodes, Indicates OD Upper path Travel costs The dual variable of Indicates OD exist Total travel demand at the time, A 0-1 variable, indicating that the communication node Whether to build a data center, is the unit delay penalty cost, and State the unit construction costs of distributed energy storage and photovoltaics respectively, , and They represent the construction cost of electric vehicle charging stations, ordinary charging piles and V2G piles respectively. and Represent the construction costs of data centers and base stations respectively, Indicates energy storage The planned capacity, Photovoltaic The planning power, A 0-1 variable representing a node in the transportation network Whether charging stations are planned. Representation Node Number of ordinary charging piles, Representation Node Number of V2G piles, Represents a communication node The number of base stations at

[0048] The constraints include transportation network operation constraints, information network operation constraints, distribution network operation constraints, distributed energy storage system constraints and photovoltaic constraints.

[0049] In the aforementioned joint planning method for a power-transportation-information network, the distributed energy storage system constraints include:

[0050] (1-27)

[0051] (1-28)

[0052] (1-29)

[0053] in, , They are the index and collection of energy storage, It is energy storage exist The capacity of the moment, It is energy storage Rated capacity, It is energy storage Rated power, , Respectively represent energy storage exist The charging and discharging power at the moment, is the time interval;

[0054] The constraints that the energy storage system needs to meet during operation are as follows:

[0055] (1-30)

[0056] (1-31)

[0057] (1-32)

[0058] (1-33)

[0059] (1-34)

[0060] in, and Is a 0-1 variable, for energy storage exist The charging and discharging signs at the moment, and Energy storage The charge and discharge efficiency, , Indicates energy storage exist Moment and +1 moment capacity.

[0061] In the aforementioned joint planning method for a power-transportation-information network, the photovoltaic constraints include:

[0062] (1-35)

[0063] (1-36)

[0064] in, , They are the index and collection of photovoltaics, and Represents photovoltaic exist Active and reactive power output at all times, is the photovoltaic output under different typical days;

[0065] (1-37)

[0066] (1-38)

[0067] (1-39)

[0068] (1-40)

[0069] (1-41)

[0070] (1-42)

[0071] (1-43)

[0072] (1-44)

[0073] (1-45)

[0074] (1-46)

[0075] (1-47)

[0076] in, Represents a distribution network node exist Total active load at time, including nodes Basic active load , the charging and discharging power of distributed energy storage and , load shedding , Electric vehicle charging power 、V2G power and data center active power ; Representation Node exist Reactive power at the moment, is the power factor angle, and Branch The active power flow and reactive power flow, and Branch The maximum active power flow and the maximum reactive power flow, and The nodes are The active injection power and reactive injection power of and They are respectively the active power and reactive power transmitted by the upper power grid through the transformer. Is a node exist The voltage at the moment, and represent the minimum and maximum node voltages, respectively, and Branch The resistance and reactance, Indicates branch exist The meritorious trend of the moment, Indicates branch exist The meritorious trend of the moment, Indicates branch exist The reactive power trend of the moment, Indicates branch exist The reactive power trend of the moment, Is a node exist Voltage at the moment.

[0077] In the aforementioned joint planning method for a power-transportation-information network, in step 4, the mixed integer linear programming model includes:

[0078] (1-48)

[0079] (1-49)

[0080] (1-50)

[0081] (1-51)

[0082] (1-52)

[0083] (1-53)

[0084] (1-54)

[0085] in, is an auxiliary 0-1 variable, and Represents segment index and collection respectively, indicating road segment Traffic flow on Is it within the set range? is an auxiliary variable; express In segmentation The value on and Represents the remainder after segmentation of an integer, where and All are 0-1 variables. is a positive number set. express The vehicle is passing the road section time, express In segmentation The value on for At all times in OD superior Traffic flow on the path, is a binary auxiliary variable, Indicates OD Upper path Travel costs The dual variable of is a binary auxiliary variable, The power required to output a unit of data for a data center.

[0086] In the aforementioned joint planning method of power-transportation-information network, in step 5, the optimal solution of the expanded planning parameters includes the transportation nodes Number of ordinary charging piles , transportation nodes Number of V2G piles , Energy Storage Capacity 、Photovoltaic Power , communication nodes The number of new base stations , Data Center Location , Data Center Power .

[0087] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0088] A computer-readable storage medium stores a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0089] The beneficial effects achieved by the present invention are as follows: the present invention utilizes a traffic distribution model that takes user equilibrium into account to model the traffic network, obtains key data such as path traffic, node traffic, electric vehicle travel costs, and charging and discharging loads, thereby laying a data foundation for subsequent optimization, constructs an information network model that takes into account electric vehicle communication delays, reflects the coupling relationship between traffic and information networks, establishes a joint planning model, comprehensively optimizes electric vehicle travel costs, data delay costs, and power generation costs, simplifies the joint planning model into a mixed integer linear programming model, significantly reduces the difficulty of solving, realizes coordinated optimization of the three networks, and provides important support and application value for the integrated development of intelligent transportation, smart grids, and smart communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is a flow chart of a joint planning method for a power-transportation-information network in Embodiment 1 of the present invention;

[0091] Figure 2 This is a comparison diagram of planning results of the joint planning method for the power-transportation-information network in Example 1 of the present invention;

[0092] Figure 3 This is a comparison chart of operating costs of the joint planning method for the power-transportation-information network in Example 1 of the present invention, wherein (a) is the operating cost of the transportation network, (b) is the operating cost of the distribution network, (c) is the operating cost of the information network, and (d) is the overall operating cost. DETAILED DESCRIPTION

[0093] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0094] like Figure 1 As shown, this embodiment provides a joint planning method for a power-transportation-information network, comprising the following steps:

[0095] Step 1: construct a traffic network model using a traffic allocation model that takes user equilibrium into account, and obtain key data of the traffic network, wherein the key data of the traffic network includes path traffic flow, node traffic flow, electric vehicle travel cost, etc.;

[0096] Step 2: Using the node traffic flow information, path traffic flow and other information in the key data of the traffic network, an information network model considering the communication delay of electric vehicles is constructed to obtain the key information of the information network, including the node data volume, node data unit delay time and total delay time;

[0097] Step 3: Use linear power flow to represent the distribution network model, utilize the coupling of the traffic network model and the information network model in terms of vehicle flow and data flow, the coupling of the traffic network model and the distribution network model in terms of vehicle flow information and charging and discharging loads of charging stations, and the coupling of the information network model and the distribution network model in terms of data flow and power load, and establish a power-transportation-information network joint planning model with the electric vehicle travel cost, data transmission delay penalty cost, and power generation cost as the targets;

[0098] Step 4: Use piecewise linearization method and big M method to transform the power-transportation-information network joint planning model into a mixed integer linear programming model;

[0099] Step 5: Solve the power-transportation-information network joint planning model to obtain the optimal solution for the expansion planning parameters of the transportation network, information network and distribution network.

[0100] In step 1, the traffic network model includes:

[0101] Path traffic flow and node traffic flow data:

[0102] (1-1)

[0103] (1-2)

[0104] (1-3)

[0105] (1-4)

[0106] (1-5)

[0107] (1-6)

[0108] in, and Represents the index and collection of transportation network nodes, and Represents the index and collection of origin-destination (OD), Indicates road segment Traffic flow on Representation Node of traffic, Indicates OD Upper path Traffic volume; and A 0-1 variable representing traffic flow Whether it passes through the road section and nodes ; Indicates that the electric car is passing the road section time; Indicates OD Upper path Travel costs The dual variable of represents the charging cost, Represents a set of road segments. represents a time collection, Indicates road segment The initial travel time on Indicates road segment The capacity, Indicates OD Upper path gather, Indicates OD Upper path travel needs, is an orthogonal symbol;

[0109] Electric vehicle charging and discharging load data and charging pile data:

[0110] (1-7)

[0111] (1-8)

[0112] (1-9)

[0113] (1-10)

[0114] (1-11)

[0115] (1-12)

[0116] in, Representation Node Electric vehicles in The total charging load at the moment, Representation Node Electric vehicles in Total discharge load at the moment; and The charging and discharging power of an electric car; For Node A sign indicating whether a charging station should be built. For Node The number of ordinary charging piles at For Node The number of V2G piles, is the charging power of ordinary charging pile and V2G charging pile, The maximum number of ordinary charging piles and V2G charging piles that can be built.

[0117] In step two, the traffic data in the traffic network is brought into the information network, and an information network model considering the communication delay of electric vehicles is constructed to obtain key information of the information network, including the node data volume, node data unit delay time and total delay time.

[0118] Undirected Graph represents an information network, where represents the communication node set, Represents a collection of information channels. The communication network includes data nodes, transmission branches and topological structures. In the communication network, the functions of key components such as Internet data centers, base stations, measurement units and communication branches are equivalent to independent system operators, power buses, load aggregators and power branches in the power grid.

[0119] To simplify the model, the present invention makes the following assumptions: 1) It is assumed that each node in the communication network directly corresponds to each bus in the distribution network; 2) It is assumed that the distribution network contains a single data center, and the location and capacity of the data center can be set; 3) It is assumed that the communication distance between each node in the communication network is the same and is defined as the unit communication distance; 4) Considering the great progress of intelligence and the rapid growth of the number of electric vehicles, the information network model only considers the communication traffic generated by electric vehicles; 5) The power level of the data center is megawatts and above, and the power level of the base station is usually kilowatts and below. Therefore, the information network model considers that the power load of the base station can be ignored, which is expressed as:

[0120] (1-13)

[0121] (1-14)

[0122] (1-15)

[0123] (1-16)

[0124] (1-17)

[0125] (1-18)

[0126] in, represents the communication node set, Represents a communication node The transmission delay time of the information flow per unit distance at Represents a communication node In time The data traffic generated, Indicates the transmission bandwidth, Indicates time The sum of all node data traffic, represents the sum of data transmission delays of all nodes, represents the charging node set, is a time set; matrix Represents the association matrix between the transportation network and the distribution network, and its elements , if the traffic network node Distribution network nodes Related, then =1, otherwise =0; Similarly, the matrix is the association matrix between the distribution network nodes and the communication network nodes, and its elements ,if =1 indicates a distribution network node Traffic network nodes Related, formula (1-18) represents the communication node In time The amount of communication data generated is only related to the node traffic, where A 0-1 variable indicating whether it is in the communication node Building data centers, represents the communication node set, Represents a communication node With communication node The communication distance between Dijkstra Algorithmic calculation.

[0127] (1-19)

[0128] (1-20)

[0129] (1-21)

[0130] Represents a communication node The number of new base stations at Indicates the data transmission capacity of a single base station. represents the power of the data center, represents the fixed power consumption of the data center, Indicates the power consumed by a data center to process a unit of data. Indicates the planned capacity of a data center.

[0131] In step three, the coupling relationship and interaction between the transportation network, distribution network and information network are utilized, and the distribution network, transportation network and communication network are regarded as different stakeholders. A power-transportation-information network joint planning model is established to obtain key information of the power network, including the planned location and capacity of energy storage, the planned location and power of photovoltaics, etc.

[0132] The joint planning model aims to minimize the overall operating cost of the system, including investment cost and operating cost. The total investment cost of infrastructure construction includes the cost of building electric vehicle charging piles, V2G infrastructure, photovoltaic devices, distributed energy storage, data centers and base stations. The operating cost includes the cost of purchasing electricity for power generation, load shedding penalty, total electric vehicle operation cost and information transmission delay penalty cost. In order to cope with the uncertainty of renewable energy generation, the typical day weighted operating cost is used to capture the uncertainty. The expression of the objective function is as follows:

[0133] (1-22)

[0134] (1-23)

[0135] (1-24)

[0136] (1-25)

[0137] (1-26)

[0138] Formula (1-22) is the objective function, which uses the weighted cost of typical scenarios to describe the uncertainty of new energy; is the objective function of the joint planning model, , They are indexes and collections of typical scenarios. is the number of typical scenes, , , and Describe the scenes separately The cost of power distribution network, transportation network, communication network and total cost; , and They represent the unit electricity purchase cost, unit load shedding penalty cost and the power generation cost in the distribution network, respectively. Indicates that the substation is at the node exist The effort of the moment, Indicates that the micro gas turbine is at node exist The effort of the moment, represents the set of power grid nodes, Indicates OD Upper path Travel costs The dual variable of Indicates OD exist Total travel demand at the time, A 0-1 variable, indicating that the communication node Whether to build a data center, is the unit delay penalty cost, and State the unit construction costs of distributed energy storage and photovoltaics respectively, , and They represent the construction cost of electric vehicle charging stations, ordinary charging piles and V2G piles respectively. and Represent the construction costs of data centers and base stations respectively, Indicates energy storage The planned capacity, Photovoltaic The planning power, A 0-1 variable representing a node in the transportation network Whether charging stations are planned. Representation Node Number of ordinary charging piles, Representation Node Number of V2G piles, Represents a communication node The number of base stations at the location.

[0139] The constraints include transportation network operation constraints, information network operation constraints and distribution network operation constraints, as well as constraints on distributed energy storage and photovoltaics.

[0140] The state of charge of the energy storage system fluctuates within the time step. Therefore, it is necessary not only to ensure that the operating capacity and charging and discharging power of the energy storage system are lower than the rated capacity and rated power, but also to consider the inherent volatility of the system capacity.

[0141] (1-27)

[0142] (1-28)

[0143] (1-29)

[0144] in, , They are the index and collection of energy storage, It is energy storage exist The capacity of the moment, It is energy storage Rated capacity, It is energy storage Rated power, , Respectively represent energy storage exist The charging and discharging power at the moment, is the time interval; constraint (1-28) indicates that the capacity must be less than the planned capacity, and constraints (1-29) and (1-30) indicate that the power must be less than the rated power.

[0145] The constraints that the energy storage system needs to meet during operation are as follows:

[0146] (1-30)

[0147] (1-31)

[0148] (1-32)

[0149] (1-33)

[0150] (1-34)

[0151] in, and Is a 0-1 variable, for energy storage exist The charging and discharging signs at the moment, and Energy storage The charge and discharge efficiency, , Indicates energy storage exist Moment and +1 moment capacity.

[0152] The active power of photovoltaic is constrained by investment and typical daily output, while the reactive power is only related to the investment variable state, as follows:

[0153] (1-35)

[0154] (1-36)

[0155] in, , They are the index and collection of photovoltaics, and Represents photovoltaic exist Active and reactive power output at all times, is the photovoltaic output under different typical days;

[0156] (1-37)

[0157] (1-38)

[0158] (1-39)

[0159] (1-40)

[0160] (1-41)

[0161] (1-42)

[0162] (1-43)

[0163] (1-44)

[0164] (1-45)

[0165] (1-46)

[0166] (1-47)

[0167] in, Represents a distribution network node exist Total active load at time, including nodes Basic active load , the charging and discharging power of distributed energy storage and , load shedding , Electric vehicle charging power 、V2G power and data center active power ; Representation Node exist Reactive power at the moment, is the power factor angle, and Branch The active power flow and reactive power flow, and Branch The maximum active power flow and the maximum reactive power flow, and The nodes are The active injection power and reactive injection power of and They are respectively the active power and reactive power transmitted by the upper power grid through the transformer. Is a node exist The voltage at the moment, and represent the minimum and maximum node voltages, respectively, and Branch The resistance and reactance, Indicates branch exist The meritorious trend of the moment, Indicates branch exist The meritorious trend of the moment, Indicates branch exist The reactive power trend of the moment, Indicates branch exist The reactive power trend of the moment, Is a node exist Voltage at the moment.

[0168] In step 4, the model is converted into a mixed integer linear programming model using piecewise linearization and big-M method to convert the model into a mixed integer linear programming model and reduce the complexity of the model.

[0169] (1-48)

[0170] (1-49)

[0171] (1-50)

[0172] (1-51)

[0173] (1-52)

[0174] (1-53)

[0175] (1-54)

[0176] in, is an auxiliary 0-1 variable, and Represents segment index and collection respectively, indicating road segment Traffic flow on Is it within the set range? is to avoid Auxiliary variables are introduced to avoid nonlinear terms that may be generated by multiplying other variables in order to ensure the linearity of the model and simplify calculations; express In segmentation The value on and Represents the remainder after integer segmentation. There is an approximate linear relationship between them, which helps to ensure the accuracy of the linearized model. These constraints and auxiliary variables together simplify the nonlinear terms of the model and ensure that the model is a convex optimization model. and All are 0-1 variables. is a larger positive number set, and in the present invention, 10 is taken 5 , express The vehicle is passing the road section time, express In segmentation The value on for At all times in OD superior Traffic flow on the path, is a binary auxiliary variable, Indicates OD Upper path Travel costs The dual variable of is a binary auxiliary variable, The power required to output a unit of data for a data center.

[0177] In step 5, the above model is solved based on commercial solvers (such as GURUBI, CPLEX, etc.) to obtain the optimal solution of the expansion planning parameters of the transportation network, information network and distribution network, including the transportation nodes Number of ordinary charging piles , transportation nodes Number of V2G piles , Energy Storage Capacity 、Photovoltaic Power , communication nodes The number of new base stations , Data Center Location , Data Center Power The optimal solution of .

[0178] exist Figure 2 In the paper, the planning costs of joint planning are compared with those of separate planning. It can be seen that in order to meet the charging needs of electric vehicles, separate planning requires significantly higher investment in charging piles and V2G piles, resulting in a 14.5% increase in the investment cost of the transportation network. This is because in separate planning, the focus is on meeting the needs of electric vehicles, resulting in the installation of a large number of charging piles and V2G piles in multiple locations to ensure sufficient coverage. Although the charging needs are met, it also significantly increases the investment cost of the transportation network. In addition, the investment costs of the distribution network and information network have also increased. Compared with the information network, the cost increase of the distribution network is more significant. This is because the information network and power network are planned based on the maximum data flow and the maximum charging load during separate planning, lacking coordination and comprehensive consideration between the networks, resulting in inefficient resource allocation. For example, separate planning may lead to the installation of too many charging piles in areas with high charging demand, which not only increases the load of certain nodes in the distribution network, but also may cause local communication nodes to be overloaded, requiring additional investment to upgrade the communication infrastructure and grid facilities.

[0179] In order to ensure that the results are not due to accidental occurrences of specific travel demands, the travel demands of five different typical days are selected for comparison. The operating costs of the transportation network, communication network, distribution network and the entire system are as follows: Figure 3 It is obvious that compared with the separate sequential planning, the joint planning has lower travel costs, communication costs, grid operation costs and total costs. This is because the joint planning not only meets the needs of electric vehicles, but also takes into account the overall economic efficiency and the interdependence between the three networks. By comprehensively considering the coupling relationship between the transportation network, communication network and distribution network, the operating cost is minimized.

[0180] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0181] A computer-readable storage medium stores a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0182] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0183] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0186] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A joint planning method for power-transportation-information network, characterized in that: The following steps are involved: Step 1: construct a traffic network model using a traffic allocation model that takes user equilibrium into account, and obtain key data of the traffic network, wherein the key data of the traffic network includes path traffic flow, node traffic flow, and electric vehicle travel cost; Step 2: Using the node traffic flow information and path traffic flow information in the key data of the traffic network, an information network model considering the communication delay of electric vehicles is constructed to obtain the key information of the information network, including the node data volume, the node data unit delay time and the total delay time; Step 3: Use linear power flow to represent the distribution network model, utilize the coupling of the traffic network model and the information network model in terms of vehicle flow and data flow, the coupling of the traffic network model and the power network model in terms of vehicle flow information and charging and discharging loads of charging stations, and the coupling of the information network model and the power network model in terms of data flow and power load, and establish a power-transportation-information network joint planning model with the electric vehicle travel cost, data transmission delay penalty cost, and power generation cost as the targets; Step 4: Use piecewise linearization method and big M method to transform the power-transportation-information network joint planning model into a mixed integer linear programming model; Step 5: Solve the power-transportation-information network joint planning model to obtain the optimal solution for the expansion planning parameters of the transportation network, information network and distribution network; In step 1, the traffic network model includes: Path traffic flow and node traffic flow data: Among them, k and K represent the index and set of traffic network nodes, w and W represent the index and set of OD, and x a,t represents the traffic volume on road section a, x k,t represents the traffic flow at node k, f p,w,t represents the traffic flow of path p on OD pair w; δ p,w,a and σ p,w,k A 0-1 variable representing the traffic flow f p,w,t Whether it passes through section a and node k; t a (x a,t ) represents the time it takes for the electric vehicle to travel through section a; represents the travel cost of path p on OD for w The dual variable of represents the charging cost, A represents the road segment set, T represents the time set, t a,0 represents the initial travel time on road section a, c a represents the capacity of road section a, p w represents the set of paths p on OD pair w, represents the travel demand of OD for path p on w, ⊥ is an orthogonal symbol; Electric vehicle charging and discharging load data and charging pile data: in, represents the total charging load of the electric vehicles at node k at time t, represents the total discharge load of the electric vehicle at node k at time t; and The charging and discharging power of an electric car; is the flag of whether to build a charging station at node k, is the number of common charging piles at node k, is the number of V2G piles of node k, E EVC is the charging power of ordinary charging pile and V2G charging pile, N k,max The maximum number of ordinary charging piles and V2G charging piles that can be built; In step 2, the information network model is expressed as: Where M represents the communication node set, Denotes the transmission delay time of information flow per unit distance at communication node m, D m.t represents the data flow generated by communication node m at time t, W BW Indicates the transmission bandwidth, represents the sum of data traffic of all nodes at time t, represents the sum of data transmission delays of all nodes, K represents the charging node set, and T is the time set; the matrix E P Represents the association matrix between the transportation network and the distribution network, and its elements Matrix G C is the association matrix between the distribution network nodes and the communication network nodes, and its elements Formula (1-18) represents the communication data generated by communication node m at time t, is a 0-1 variable, indicating whether to build a data center at communication node m, Ω Com represents the communication node set, d m,n represents the communication distance between communication node m and communication node n; represents the number of new base stations at communication node m, ρ Base Indicates the data transmission capacity of a single base station. Represents the power of the data center, P IDC represents the fixed power consumption of the data center, P bit e Indicates the power consumed by a data center to process unit data, P IDC p Indicates the planned capacity of the data center; In step 4, the mixed integer linear programming model includes: in, is an auxiliary 0-1 variable, q and Q represent the segment index and set respectively, indicating the traffic flow x on road segment a a,t Is it within the set range? is an auxiliary variable; Represents x a,t The value on segment q, and Represents the remainder after integer segmentation, where ε p,w,t and All are 0-1 variables. is a positive number, t a,t represents the time it takes for the vehicle to travel through section a at time t, Indicates t a,t The value on segment q, f p,w,t is the traffic flow of path p on OD pair w at time t, ε p,w,t is a binary auxiliary variable, represents the travel cost of path p on OD for w The dual variable of is a binary auxiliary variable, P bit The power required to output a unit of data for a data center.

2. A method for joint planning of a power-transportation-information network according to claim 1, characterized in that: In step 3, the objective function is expressed as follows: Formula (1-22) is the objective function, which uses the weighted cost of typical scenarios to describe the uncertainty of new energy; obj is the objective function of the joint planning model, α, Ω sce They are the index and collection of typical scenarios, N sce is the number of typical scenes, obj PN,α 、obj TN,α 、obj CN,α and obj CON The distribution network cost, transportation network cost, communication network cost and total cost under scenario α are respectively expressed; and They represent the unit electricity purchase cost, unit load shedding penalty cost and the power generation cost in the distribution network, respectively. represents the output of the substation at node i at time t, represents the output of the micro gas turbine at node i at time t, Ω Bus represents the set of power grid nodes, represents the travel cost of path p on OD for w The dual variable of represents the total travel demand of OD for w at time t, is a 0-1 variable, indicating whether a data center is built at the communication node n, c Td is the unit delay penalty cost, c ES and c PV Describe the unit construction cost of distributed energy storage and photovoltaic respectively, c EVCS 、c EV and c EV2G They represent the construction cost of electric vehicle charging stations, ordinary charging piles and V2G piles respectively, IDC and c Base Represent the construction costs of data centers and base stations respectively, represents the planned capacity of energy storage e, represents the planned power of photovoltaic s, It is a 0-1 variable indicating whether a charging station is planned at the transportation network node k. represents the number of common charging piles at node k, represents the number of V2G piles at node k, represents the number of base stations at communication node m; The constraints include transportation network operation constraints, information network operation constraints, distribution network operation constraints, distributed energy storage system constraints and photovoltaic constraints.

3. A joint planning method for power-transportation-information network according to claim 2, characterized in that: Distributed energy storage system constraints include: Among them, e and E are the index and set of energy storage respectively. is the capacity of energy storage e at time t, is the rated capacity of the energy storage e, is the rated power of the energy storage e, They represent the charging and discharging power of the energy storage e at time t, respectively, and Δt is the time interval; The constraints that the energy storage system needs to meet during operation are as follows: in and is a 0-1 variable, which is the charge and discharge sign of the energy storage e at time t, η e,ch and η e,dch are the charging and discharging efficiency of energy storage e, Represents the capacity of energy storage e at time t and time t+1.

4. A method for joint planning of a power-transportation-information network according to claim 2, characterized in that: PV constraints include: Where s and S are the index and set of photovoltaics respectively, and They represent the active and reactive output of photovoltaic s at time t, is the photovoltaic output under different typical days; -P ij,max ≤P ij,t ≤P ij,max ,ij∈Ω Line (1-41) -Q ij,max ≤Q ij,t ≤Q ij,max ,ij∈Ω Line (1-42) v j,t -v i,t +2(R ij P ij,t +X ij Q ij,t )=0,ij∈Ω Line ,i,j∈Ω Bus ,t∈T (1-45) v 0,t =U ref (1-47) in Represents the total active load of distribution network node i at time t, including the basic active load of node i Charging and discharging power of distributed energy storage and Load shedding Electric vehicle charging power V2G Power and data center active power represents the reactive power of node i at time t, θ j is the power factor angle, P ij,t and Q ij,t are respectively the active power flow and reactive power flow of branch ij, P ij,max and Q ij,max are the maximum active power flow and maximum reactive power flow of branch ij, P j,t and Q j,t are the active injection power and reactive injection power of node j, and They are respectively the active power and reactive power transmitted by the upper power grid through the transformer, v j,t is the voltage of node j at time t, U min and U max Represent the minimum and maximum node voltages, R ij and X ij are the resistance and reactance of branch ij, respectively, P ij,t represents the active power flow of branch ij at time t, P jh,t represents the active power flow of branch jh at time t, Q ij,t represents the reactive power flow of branch ij at time t, Q jh,t represents the reactive power flow of branch jh at time t, v i,t is the voltage at node i at time t.

5. A method for joint planning of a power-transportation-information network according to claim 1, characterized in that: In step 5, the optimal solution of the expanded planning parameters includes the number of ordinary charging piles at traffic node k Number of V2G poles at traffic node k Energy storage capacity Photovoltaic power The number of new base stations for communication node m Data Center Locations Data center power P IDCp .

6. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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