Power-traffic coupling network double-layer extension planning method considering time sequence correlation

Abstract

The invention belongs to the field of electric power traffic network coupling expansion, and particularly relates to an electric power-traffic coupling network expansion planning method containing new energy station expansion, in particular to an electric power-traffic coupling network double-layer expansion planning method considering time sequence correlation. Firstly, an expansion planning model is established; establishing an upper-layer model with the minimum sum of the investment cost and the operation cost of the power-traffic coupling network as an optimization target, formulating a scheme of an expansion planning model, and transmitting the scheme to a lower-layer model; in the lower-layer model, a scheme of an extension planning model is known, traffic flow distribution is optimized by taking the minimum sum of the operation cost of the coupling network in each time period as an optimization target according to the traffic flow and the power load in each time period of the whole day, and the sum of the upper-layer investment cost and the operation cost of each time period of the lower-layer model is recorded; and the upper-layer model iterates all time periods of the whole day in sequence until the scheme of the expansion planning model is optimal. According to the method, the expansion planning area is reasonably planned, the expansion and operation cost is reduced, and the new energy consumption rate can be improved.

Description

Technical Field

[0001] The present invention relates to a method for planning the expansion of an electric power-transportation coupling network including the expansion of a new energy station, and specifically to a method for planning the expansion of an electric power-transportation coupling network in a double-layer manner taking into account time sequence correlation. Background Art

[0002] Electric vehicles with low-carbon and clean characteristics have been widely promoted in my country, and the number of vehicles in use has increased rapidly. At the same time, my country is accelerating the promotion of a new power system dominated by new energy, and the penetration rate of new energy is constantly increasing. The coupling and connection between the transportation network dominated by electric vehicles and the distribution network dominated by new energy are becoming increasingly close. The traffic load and charging load generated by large-scale electric vehicle travel have a huge impact on the safe operation of the distribution network, causing the load of the distribution network to "peak on peak", further expanding the volatility brought by the connection of new energy to the grid; at the same time, it causes regional traffic jams and even paralysis. By rationally planning and expanding the power-transportation coupling network, it will help the safe operation of the distribution network, alleviate traffic jams, and increase the absorption rate of new energy.

[0003] At present, for the expansion planning of the power-transportation coupling network, the impact of electric vehicle charging load on the expansion planning of the coupling network is mainly considered, ignoring the travel characteristics of electric vehicle users and the impact of the transportation network on electric vehicle charging behavior. In addition, current research mainly uses data analysis to sample electric vehicle peak travel load and typical output of new energy to carry out the expansion planning of the coupling network at a single moment, ignoring the time-series correlation of electric vehicle load itself and the volatility of new energy output, which leads to overly conservative planning results. Therefore, in the expansion planning of the power-transportation coupling network, it is necessary to consider the impact of the travel characteristics of electric vehicle users themselves and the transportation network on electric vehicle charging behavior, as well as the time-series correlation between electric vehicle load and new energy output itself. Summary of the invention

[0004] In view of the problem that existing research ignores the connection between the peak and valley changes of the traffic network and the distribution network load in different time periods and does not consider the volatility of new energy output, resulting in overly conservative expansion planning results, the present invention proposes a two-layer expansion planning method for the power-traffic coupling network taking into account time series correlation.

[0005] The present invention is implemented by adopting the following technical solution: a two-layer expansion planning method for a power-transportation coupling network taking into account time sequence correlation, specifically comprising the following steps:

[0006] Step 1: Taking the minimum sum of investment cost and operation cost of the power-transportation coupling network as the optimization goal, an expansion planning model considering mixed traffic equilibrium is established. The expansion planning model includes road expansion model, transmission line expansion model, charging pile expansion model, and wind power and photovoltaic unit expansion model;

[0007] The road expansion model is , where is the number of newly expanded roads; is the number of roads that can be expanded; is a binary variable for the number of newly expanded roads, , m is the index of the expanded road;

[0008] The transmission line expansion model is , where is the number of newly expanded transmission lines; The number of transmission lines that can be expanded; is a binary variable for the number of newly expanded transmission lines, ; n is the index of the extended transmission line; the resistance of the extended line , the reactance of the expanded line , the maximum active power transmitted by the line after expansion , the maximum reactive power transmitted by the line after expansion , is the maximum active power transmitted by the initial line, is the maximum reactive power delivered by the initial line; , are the resistance and reactance of the initial line respectively.

[0009] The charging pile expansion model is , is the number of newly built charging piles; in addition, the newly built charging piles have the same capacity as the initial charging piles , To build a new single charging pile capacity, is the initial capacity of a single charging pile;

[0010] The expansion model of wind power and photovoltaic units is , The number of new wind power or photovoltaic units next to the distribution network node i; wind power and photovoltaic units can be expanded next to any node of the distribution network;

[0011] Step 2: Considering the extended planning model of mixed traffic equilibrium, an upper-level model is established with the minimum sum of investment cost and operation cost of the power-traffic coupling network at a certain moment as the optimization goal. The upper-level model is as follows: , F TN = ∑ a ∈ T A [ w ( t a + l a ) x a + ∑ m = 0 N R Pr a r k 2 m z am ] , F PDN = ∑ i ∈ E B [ a i ( p i ) 2 + b i p i ] + r ∑ j ∈ π ( 0 ) P 0 j l + ∑ a ∈ T A Pr a c k n a c + ∑ l ∈ E L ∑ n = 0 N L Pr ij l k 2 n z in dl + ∑ i ∈ E N Pr i g k n i g , In the formula, It is the sum of investment cost and operation cost of the power-transportation coupling network; It is the sum of the investment cost and operation cost of the transportation network; It is the sum of investment cost and operation cost of distribution network; The operating costs of the transport network; is the time value parameter; For the road Traffic flow; For the road Passing time, t a = t a 0 [ 1 + 0 . 15 ( x a c a ) 4 ] , For the road Normal travel time; is the capacity of road a; is the travel delay time of road a during congestion; The investment cost for the transport network; is the set of all roads; A discount factor to reflect the relationship between long-term investment costs and short-term operating costs; For the road Cost of building a new single road next to the ∑ i ∈ E B [ a i ( p i ) 2 + b i p i ] + r ∑ j ∈ π ( 0 ) P 0 j l The operating cost of the distribution network; , , is the power generation cost coefficient; is the set of generators for general nodes; is the set of generators at the balancing node; It is the active power output by the general node generator in the distribution network; It is the active power output by the generator set at the balancing node in the distribution network; Costs of building new charging stations for the coupled network; For the road The cost of building a new charging station nearby; The number of newly built charging piles; Costs of building new transmission lines for the coupling network; For the slave nodes in the distribution network To Node The cost of building a single new transmission line; A collection of transmission lines that can be newly built; Costs of building new generators for the coupled network; For the node The cost of building a new wind or photovoltaic unit; A collection of nodes where wind power and photovoltaic units can be installed; is the number of new wind power or photovoltaic units added next to the distribution network node i.

[0012] The upper model constraints include power-traffic coupling constraints, distribution network constraints and traffic network model constraints. The specific mathematical model is as follows:

[0013]

[0014]

[0015]

[0016]

[0017] In the formula, Linear constraints for tree-like power flows in distribution networks; are typical constraints on the variables in the distribution network; Constraints for the transportation network model; It is the power-transportation coupling constraint; , Number the nodes of the distribution network; Numbering of transmission line routes; For Node The sum of the basic power load and charging load at For Node The basic power load at the location; is the flow-active power conversion coefficient; is the traffic flow of road a; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; t 0 = [ t a 0 ] is the set of normal travel times for all roads; For the road Capacity; For the road The initial capacity of building capacity for every road; x= [ x a ] is the traffic flow set of all roads; For all path sets, ∧= [ d ak rs ] , is a binary variable related to road traffic. To the destination node Path Passing Road hour, ; otherwise 0; is the set of all path flows; It is a matrix composed of 0 and 1, whose value corresponds to the relationship between travel demand and path flow; q is the distance from the starting node To the destination node The travel demand set; l = [ l a ] is the set of congestion travel time for all roads; c = [ c a ] is the capacity set of all roads; for The dual variable of for The dual variable of , , , Slave nodes To Node , from the node To Node Active power and reactive power transmitted on the line; and Node The active power and reactive power generated by the generator; and Node The active power and reactive power generated by the generator; , Injection nodes The active power and reactive power at ; π(j) is the set of end nodes of all branches with the head end being j; and Node and Voltage; , is the resistance and reactance of the line after expansion; To balance the node voltage; , is the additional continuous variable of the bilinear term in the linearized voltage drop equation; , Node The upper and lower voltage limits; For Node At the upper voltage limit; , They are respectively the active power and reactive power generated by a newly added wind power or photovoltaic unit; , Node The upper limit of active power and reactive power of the generator; , It is the power limit of transmission line flow.

[0018] The lower-level model is established with the minimum sum of the coupling network operation costs at all time periods throughout the day as the optimization goal. The lower-level model is:

[0019] minutes ∑ t = 1 24 ( ∑ a ∈ T A [ w ( t a , t + l a , t ) x a , t ] + ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + r ∑ j ∈ π ( 0 ) P 0 j , t l ) , where ∑ a ∈ T A [ w ( t a , t + l a , t ) x a , t ] is the operating cost of the transportation network at time t, is the road at time t Traffic flow; is the road at time t The travel time, t a , t = t a , t 0 [ 1 + 0 . 15 ( x a , t c a ) 4 ] , for Time Road Normal travel time; is the road at time t Traffic delay time during congestion; ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + r ∑ j ∈ π ( 0 ) P 0 j , t l is the operating cost of the distribution network at time t, p i,t is the active power output by the general node generator in the distribution network at time t; is the active power output by the generator set at the balancing node in the distribution network at time t;

[0020] Both the upper model and the lower model follow the power-traffic coupling constraints, distribution network constraints and traffic network model constraints. The constraints followed by the upper model and the lower model are different in that: (1) The variables in the constraints followed by the upper model , , , It is a constant in the constraints followed by the lower model; (2) the constraints followed by the lower model include variables for time t.

[0021] The specific mathematical model is as follows:

[0022]

[0023]

[0024]

[0025]

[0026] In the formula, Linear constraints for tree-like power flows in distribution networks; are typical constraints on the variables in the distribution network; Constraints for the transportation network model; is the power-transportation coupling constraint. , Number the nodes of the distribution network; Numbering of transmission line routes; For Node In The sum of the basic power load and charging load at the moment, For Node In The basic power load at the moment; is the flow-active power conversion coefficient; For the road exist Traffic flow at the time; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the road capacity; For the road The initial capacity of building capacity for every road; x t = [ ] for The traffic flow set of all roads at the time; l t = [ l a , t ] for The set of congestion travel time of all roads at the moment; For all path sets, ∧= [ d ak rs ] , is a binary variable related to road traffic. To the destination node Path Passing Road hour, ; otherwise 0; for The flow set of all paths at the moment; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and path flow; For Time from the starting node To the destination node The travel demand set; For Time The dual variable of For Time The dual variable of , , , They are Time from node To Node , from the node To Node Active power and reactive power transmitted on the line; and They are Time Node The active power and reactive power generated by the generator; and They are Time Node The active power and reactive power generated by the generator; , They are Inject nodes at all times The active power and reactive power at ; π(j) is the set of end nodes of all branches with the head end being j; and They are Time Node and Voltage; , is the resistance and reactance of the line after expansion; for Balance node voltages at all times; , For Additional continuous variable for the bilinear term in the time linearized voltage drop equation; , Node The upper and lower voltage limits; For Node At the upper voltage limit; , They are respectively the active power and reactive power generated by a newly added wind power or photovoltaic unit; , They are Time Node The upper limit of active power and reactive power of the generator; , It is the power limit of transmission line flow.

[0027] In the upper-level model, the optimization objective is to minimize the sum of the investment cost and the operating cost of the power-transportation coupling network at a certain moment, and a plan for the extended planning model is formulated and passed to the lower-level model; in the lower-level model, the plan of the extended planning model is known, and according to the traffic travel demand and power load in each period of the day, the optimization objective is to minimize the sum of the operating costs of the coupling network in each period, optimize the traffic flow distribution, and record the sum of the upper-level investment cost and the operating cost of the lower-level model in each period; the upper-level model iterates the different traffic travel demands and power loads in each period of the day in turn, and finally selects the plan of the extended planning model with the smallest sum of the investment cost of the upper-level model and the operating cost of the lower-level model in each period as the optimal plan.

[0028] The present invention aims at the fact that existing planning studies ignore the connection between the peak and valley changes of the traffic network and the distribution network load in different time periods, as well as the volatility of new energy output, and only plan during peak hours, over-focus on short-term benefits, and allocate resources far exceeding the growing charging demand, resulting in planning results that are too conservative and have low applicability; and provides a two-layer expansion planning method for an electric power-traffic coupling network that considers time series correlation, which effectively captures the dynamics and mobility of charging demand in the traffic network at all times of the day, can meet the safe operation of the electric power-traffic coupling network under large-scale electric vehicle travel, reduce unreasonable regional planning, reduce expansion and operation costs, and improve the new energy consumption rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Solve the flow chart for the two-layer expansion planning of the power-transportation coupling network. DETAILED DESCRIPTION

[0030] A two-level expansion planning method for the power-transportation coupling network considering time series correlation is proposed. First, the expansion planning model considering mixed traffic equilibrium is established with the minimum sum of the investment cost and the operating cost of the power-transportation coupling network as the optimization goal:

[0031] (1) Extended planning model

[0032] Newly expanded roads and transmission lines can be built in parallel with the initial lines and have the same capacity and parameters. The power-transportation network expansion planning model is as follows:

[0033] 1) Road expansion model

[0034]

[0035] In the formula, is the number of newly expanded roads; is the number of roads that can be expanded; is a binary variable for the number of newly expanded roads, , m is the index of the expanded road.

[0036] 2) Transmission Line Expansion Model

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] In the formula, is the number of newly expanded transmission lines; The number of transmission lines that can be expanded; is a binary variable for the number of newly expanded transmission lines, ; n is the index of the transmission line to be expanded; is the maximum active power transmitted by the initial line, is the maximum reactive power delivered by the initial line; , are the resistance and reactance of the line after expansion respectively; is the maximum active power transmitted by the line after expansion, is the maximum reactive power transmitted by the line after expansion; , are the resistance and reactance of the initial line respectively; - The meaning is that after the transmission line is expanded, the resistance and reactance of the line are reduced to the original ;Mode - It means that after the expansion of the transmission line, the maximum active power and maximum reactive power flowing through the transmission line are reduced to the original .

[0043] 3) Charging pile expansion model

[0044]

[0045] is the number of newly built charging piles. The newly built charging piles have the same capacity as the initial charging piles. , To build a new single charging pile capacity, is the initial capacity of a single charging pile,

[0046] 4) Wind power and photovoltaic unit expansion model

[0047] Wind power and photovoltaic units can be expanded near any node of the power distribution network (PDN). is the number of new wind power or photovoltaic units added next to the distribution network node i.

[0048] A logarithmic number of binary variables could be used to represent the number of roads and power lines that were expanded, e.g. Up to 7 additional roads can be invested in using A binary variable is used for road expansion.

[0049] (2) Objective function

[0050] Considering the extended planning model of mixed traffic equilibrium, the optimization goal is to minimize the sum of the investment cost and operation cost of the power-traffic coupling network at a certain moment. The investment cost and operation cost of the power-traffic coupling network include the investment cost and operation cost of the transportation network and the investment cost and operation cost of the distribution network. The specific mathematical model is as follows:

[0051]

[0052] F TN = ∑ a ∈ T A [ w ( t a + l a ) x a + ∑ m = 0 N R Pr a r k 2 m z am ]

[0053] F PDN = ∑ i ∈ E B [ a i ( p i ) 2 + b i p i ] + r ∑ j ∈ π ( 0 ) P 0 j l + ∑ a ∈ T A Pr a c k n a c + ∑ l ∈ E L ∑ n = 0 N L Pr ij l k 2 n z in dl + ∑ i ∈ E N Pr i g k n i g

[0054] In the formula, It is the sum of investment cost and operation cost of the power-transportation coupling network; It is the sum of the investment cost and operation cost of the transportation network; It is the sum of investment cost and operation cost of distribution network; The operating costs of the transport network; is the time value parameter; For the road Traffic flow; For the road Passing time, t a = t a 0 [ 1 + 0 . 15 ( x a c a ) 4 ] , For the road Normal travel time; is the road capacity; is the travel delay time of road a during congestion; The investment cost for the transport network; is the set of all roads; A discount factor to reflect the relationship between long-term investment costs and short-term operating costs; For the road Cost of building a new single road next to the ∑ i ∈ E B [ a i ( p i ) 2 + b i p i ] + r ∑ j ∈ π ( 0 ) P 0 j l The operating cost of the distribution network; , , is the power generation cost coefficient; is the set of generators for general nodes; is the set of generators at the balancing node; It is the active power output by the general node generator in the distribution network; It is the active power output by the generator set at the balancing node in the distribution network; Costs of building new charging stations for the coupled network; For the road The cost of building a new charging station nearby; Costs of building new transmission lines for the coupling network; For the slave nodes in the distribution network To Node The cost of building a single new transmission line; A collection of transmission lines that can be newly built; Costs of building new generators for the coupled network; For the node The cost of building a new wind or photovoltaic unit; It is a collection of nodes where wind power and photovoltaic units can be installed.

[0055] For the lower-level model, an optimization model is established with the goal of minimizing the sum of the coupling network operation costs at all times of the day:

[0056] minutes ∑ t = 1 24 ( ∑ a ∈ T A [ w ( t a , t + l a , t ) x a , t ] + ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + r ∑ j ∈ π ( 0 ) P 0 j , t l ) ⑪

[0057] In the formula, ∑ a ∈ T A [ w ( t a , t + l a , t ) x a , t ] is the operating cost of the transportation network at time t, is the road at time t Traffic flow; is the travel time of road a at time t, t a , t = t a , t 0 [ 1 + 0 . 15 ( x a , t c a ) 4 ] , for Normal travel time of road a at time; is the travel delay time when road a is congested at time t; ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + r ∑ j ∈ π ( 0 ) P 0 j , t l is the operating cost of the distribution network at time t, is the active power output by the general node generator in the distribution network at time t; It is the active power output by the generator set at the balancing node in the distribution network at time t.

[0058] In the upper model, the scheme of the extended planning model is formulated with the minimum sum of the investment cost and the operating cost of the power-transportation coupling network at a certain moment as the optimization goal, and the scheme is passed to the lower model; in the lower model, the scheme of the extended planning model is known, and according to the traffic travel demand and power load in each period of the day, the sum of the coupling network operating costs in each period is minimized as the optimization goal, the traffic flow distribution is optimized, and the sum of the investment cost of the upper model and the operating cost of the lower model in each period is recorded; the upper model iterates the different traffic travel demands and power loads in each period of the day in turn, and finally selects the scheme of the extended planning model with the minimum sum of the investment cost of the upper model and the operating cost of the lower model in each period as the optimal scheme.

[0059] Constraints:

[0060] The constraints of the power-transportation coupling network expansion planning method include power-transportation coupling constraints, distribution network constraints and transportation network model constraints. The specific mathematical model of the upper model constraint is as follows:

[0061] ⑫

[0062] ⑬

[0063] ⑭

[0064] ⑮

[0065] In the formula, Linear constraints for tree-like power flows in distribution networks; are typical constraints on the variables in the distribution network; Constraints for the transportation network model; It is the power-transportation coupling constraint. , Number the node; Numbering of transmission line routes; For Node The sum of the basic power load and charging load at For Node The basic power load at the location; is the flow-active power conversion coefficient; is the traffic flow of road a; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; t 0 = [ t a 0 ] is the set of normal travel times for all roads; For the road Capacity; For the road The initial capacity of building capacity for every road; x= [ x a ] is the traffic flow set of all roads; For all path sets, ∧= [ d ak rs ] , is a binary variable related to road traffic. To the destination node Path Passing Road hour, ; otherwise 0; is the set of all path flows; It is a matrix composed of 0 and 1, whose value corresponds to the relationship between travel demand and path flow; q is the distance from the starting node To the destination node The travel demand set; l = [ l a ] is the set of congestion travel time for all roads; c = [ c a ] is the capacity set of all roads; for The dual variable of for The dual variable of ; T represents the transpose operation; , , , Slave nodes To Node , from the node To Node Active power and reactive power transmitted on the line; and Node The active power and reactive power generated by the generator; and Node The active power and reactive power generated by the generator; , Injection nodes The active power and reactive power at ; π(j) is the set of end nodes of all branches with the head end being j; and Node and Voltage; , is the resistance and reactance of the line after expansion; To balance the node voltage; , is the additional continuous variable of the bilinear term in the linearized voltage drop equation; , Node The upper and lower voltage limits; For Node At the upper voltage limit; , They are respectively the active power and reactive power generated by a newly added wind power or photovoltaic unit; , Node The upper limit of active and reactive power of the generator; , It is the power limit of transmission line flow.

[0066] The specific mathematical model of the lower model constraints is as follows:

[0067] ⑯

[0068] ⑰

[0069] ⑱

[0070] ⑲

[0071] In the formula, Linear constraints for tree-like power flows in distribution networks; are typical constraints on the variables in the distribution network; Constraints for the transportation network model; It is the power-transportation coupling constraint. , Number the nodes of the distribution network; Numbering of transmission line routes; For Node In The sum of the basic power load and charging load at the moment, For Node In The basic power load at the moment; is the flow-active power conversion coefficient; For the road exist Traffic flow at the time; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the road capacity; For the road The initial capacity of building capacity for every road; x t = [ ] for The traffic flow set of all roads at the time; l t = [ l a , t ] for The set of congestion travel time of all roads at the moment; For all path sets, ∧= [ d ak rs ] , is a binary variable related to road traffic. To the destination node Path Passing Road hour, ; otherwise 0; for The flow set of all paths at the moment; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and path flow; For Time from the starting node To the destination node The travel demand set; For Time The dual variable of For Time The dual variable of , , , They are Time from node To Node , from the node To Node Active power and reactive power transmitted on the line; and They are Time Node The active power and reactive power generated by the generator; and They are Time Node The active power and reactive power generated by the generator; , They are Inject nodes at all times The active power and reactive power at ; π(j) is the set of end nodes of all branches with the head end being j; and They are Time Node and Voltage; , is the resistance and reactance of the line after expansion; for Balance node voltages at all times; , For Additional continuous variable for the bilinear term in the time linearized voltage drop equation; , Node The upper and lower voltage limits; For Node At the upper voltage limit; , They are respectively the active power and reactive power generated by a newly added wind power or photovoltaic unit; , They are Time Node The upper limit of active power and reactive power of the generator; , It is the power limit of transmission line flow.

[0072] The present invention aims to solve the problem that existing planning studies ignore the connection between the peak and valley changes of the traffic network and the distribution network load in different periods, as well as the volatility of the output of new energy, and only plan during peak periods, pay too much attention to short-term benefits, and allocate resources far exceeding the growing charging demand, resulting in overly conservative planning results and low applicability. Therefore, the present invention establishes a Figure 1The two-layer extended planning solution process shown in the figure is as follows: first, an upper-level model is established with the minimum sum of the investment cost and the operating cost of the power-traffic coupling network at a certain moment as the optimization goal, and the plan of the extended planning model is formulated and passed to the lower-level model; then, in the lower-level model, the plan of the extended planning model is known, and according to the traffic flow and power load in each period of the day, the sum of the coupling network operating cost in each period is minimized as the optimization goal, the traffic flow distribution is optimized, and the sum of the investment cost of the upper-level model and the operating cost of the lower-level model in each period is recorded; the upper-level model iterates the different travel demands and power loads in each period of the day in turn, and finally selects the extended planning model plan with the minimum sum of the investment cost of the upper-level model and the operating cost of the lower-level model in each period as the optimal plan.

Claims

1. A two-layer expansion planning method for power-transportation coupling network considering time sequence correlation, characterized by: The specific steps include: Step 1: Taking the minimum sum of investment cost and operation cost of the power-transportation coupling network as the optimization goal, an expansion planning model considering mixed traffic equilibrium is established. The expansion planning model includes road expansion model, transmission line expansion model, charging pile expansion model, and wind power and photovoltaic unit expansion model; The road expansion model is , where is the number of newly expanded roads; is the number of roads that can be expanded; is a binary variable for the number of newly expanded roads, , m is the index of the expanded road; The transmission line expansion model is , where is the number of newly expanded transmission lines; The number of transmission lines that can be expanded; is a binary variable for the number of newly expanded transmission lines, ; n is the index of the transmission line to be expanded; The charging pile expansion model is , is the number of newly built charging piles; in addition, the newly built charging piles have the same capacity as the initial charging piles , To build a new single charging pile capacity, is the initial capacity of a single charging pile; The expansion model of wind power and photovoltaic units is , The number of new wind power or photovoltaic units next to the distribution network node i; wind power and photovoltaic units can be expanded next to any node of the distribution network; Step 2: Considering the extended planning model of mixed traffic equilibrium, an upper-level model is established with the minimum sum of investment cost and operation cost of the power-traffic coupling network at a certain moment as the optimization goal. The upper-level model is as follows: ， ， ， In the formula, It is the sum of investment cost and operation cost of the electricity-transportation coupling network; It is the sum of the investment cost and operation cost of the transportation network; It is the sum of investment cost and operation cost of distribution network; The operating costs of the transport network; is the time value parameter; For the road Traffic flow; For the road Passing time, , For the road Normal travel time; is the road capacity; is the travel delay time of road a during congestion; The investment cost for the transport network; is the set of all roads; A discount factor to reflect the relationship between long-term investment costs and short-term operating costs; For the road Cost of building a single new road next to the The operating cost of the distribution network; , , is the power generation cost coefficient; is the set of generators for general nodes; is the set of generator sets at the balancing node; It is the active power output by the general node generator in the distribution network; It is the active power output by the generator set at the balancing node in the distribution network; Costs of building new charging stations for the coupled network; For the road The cost of building a new charging station nearby; The number of newly built charging piles; Costs of building new transmission lines for the coupling network; For the slave nodes in the distribution network To Node The cost of building a single new transmission line; A collection of transmission lines that can be newly built; Costs of building new generators for the coupled network; For the node The cost of building a new wind or photovoltaic unit; A collection of nodes where wind power and photovoltaic units can be installed; is the number of new wind power or photovoltaic units added next to the distribution network node i; The lower-level model is established with the minimum sum of the coupling network operation costs at all time periods throughout the day as the optimization goal. The lower-level model is: , where is the operating cost of the transportation network at time t, is the road at time t Traffic flow; is the road at time t The travel time, , for Time Road Normal travel time; is the road at time t Traffic delay time during congestion; is the operating cost of the distribution network at time t, p i,t is the active power output by the general node generator in the distribution network at time t; is the active power output by the generator set at the balancing node in the distribution network at time t; In the upper model, the scheme of the extended planning model is formulated with the minimum sum of the investment cost and the operating cost of the power-transportation coupling network at a certain moment as the optimization goal, and the scheme is passed to the lower model; in the lower model, the scheme of the extended planning model is known, and according to the traffic travel demand and power load in each period of the day, the sum of the coupling network operating cost in each period is minimized as the optimization goal, the traffic flow distribution is optimized, and the sum of the investment cost of the upper model and the operating cost of the lower model in each period is recorded; the upper model iterates the different traffic travel demands and power loads in each period of the day in turn, and finally selects the scheme of the extended planning model with the minimum sum of the investment cost of the upper model and the operating cost of the lower model in each period as the optimal one.

2. The method for planning a two-layer expansion of a power-transportation coupling network taking into account time sequence correlation according to claim 1 is characterized in that: The upper model constraints include power-traffic coupling constraints, distribution network constraints and traffic network model constraints. The specific mathematical model is as follows: In the formula, Linear constraints for tree-like power flows in distribution networks; are typical constraints on the variables in the distribution network; Constraints for the transportation network model; It is the power-transportation coupling constraint; , Number the nodes of the distribution network; Numbering of transmission line routes; For Node The sum of the basic power load and charging load at For Node The basic power load at the location; is the flow-active power conversion coefficient; is the traffic flow of road a; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the set of normal travel times for all roads; For the road Capacity; For the road The initial capacity of building capacity for every road; is the traffic flow set of all roads; For all path sets, , is a binary variable related to road traffic. To the destination node Path Passing Road hour, ; otherwise 0; is the set of all path flows; It is a matrix composed of 0 and 1, whose value corresponds to the relationship between travel demand and path flow; q is the distance from the starting node To the destination node The travel demand set; is the set of congestion travel time for all roads; is the capacity set of all roads; for The dual variable of for The dual variable of , , , Slave nodes To Node , from the node To Node Active power and reactive power transmitted on the line; and Node The active power and reactive power generated by the generator; and Node The active power and reactive power generated by the generator; , Injection nodes The active power and reactive power at ; π(j) is the set of end nodes of all branches with the head end being j; and Node and Voltage; , is the resistance and reactance of the line after expansion; To balance the node voltage; , is the additional continuous variable of the bilinear term in the linearized voltage drop equation; , Node The upper and lower voltage limits; For Node At the upper voltage limit; , They are respectively the active power and reactive power generated by a newly added wind power or photovoltaic unit; , Node The upper limit of active power and reactive power of the generator; , It is the power limit of power flow of transmission line; The lower-level model constraints include power-traffic coupling constraints, distribution network constraints, and traffic network model constraints. The specific mathematical model is as follows: In the formula, Linear constraints for tree-like power flows in distribution networks; are typical constraints on the variables in the distribution network; Constraints for the transportation network model; It is the power-transportation coupling constraint; , Number the nodes of the distribution network; Numbering of transmission line routes; For Node In The sum of the basic power load and charging load at the moment, For Node In The basic power load at the moment; is the flow-active power conversion coefficient; For the road exist Traffic flow at the time; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the road capacity; For the road The initial capacity of building capacity for every road; for The traffic flow set of all roads at the time; for The set of congestion travel time of all roads at the moment; For all path sets, , is a binary variable related to road traffic. To the destination node Path Passing Road hour, ; otherwise 0; for The flow set of all paths at the moment; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and path flow; For Time from the starting node To the destination node The travel demand set; For Time The dual variable of For Time The dual variable of , , , They are Time from node To Node , from the node To Node Active power and reactive power transmitted on the line; and They are Time Node The active power and reactive power generated by the generator; and They are Time Node The active power and reactive power generated by the generator; , They are Inject nodes at all times The active power and reactive power at ; π(j) is the set of end nodes of all branches with the head end being j; and They are Time Node and Voltage; , is the resistance and reactance of the line after expansion; for Always balance node voltages; , For Additional continuous variable for the bilinear term in the time linearized voltage drop equation; , Node The voltage upper and lower limits; For Node At the upper voltage limit; , They are respectively the active power and reactive power generated by a newly added wind power or photovoltaic unit; , They are Time Node The upper limit of active power and reactive power of the generator; , It is the power limit of transmission line flow.

3. The method for planning a two-layer expansion of a power-transportation coupling network taking into account time sequence correlation according to claim 2 is characterized in that: After the transmission line is expanded, the resistance of the expanded line , the reactance of the expanded line , the maximum active power transmitted by the line after expansion , the maximum reactive power transmitted by the line after expansion , is the maximum active power transmitted by the initial line, is the maximum reactive power delivered by the initial line; , are the resistance and reactance of the initial line respectively.

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