Two-layer Expansion Planning Method for Power-Transportation Coupled Network Considering Temporal Correlation

Abstract

The present invention belongs to the field of power transportation network coupling expansion, and particularly relates to a power-transportation coupling network expansion planning method for the expansion of new energy power stations, specifically a two-layer expansion planning method for power-transportation coupling network considering temporal correlation. First, an expansion planning model is established; then, an upper-layer model with the minimum sum of investment cost and operation cost of the power-transportation coupling network as the optimization objective is established, and a plan for the expansion planning model is formulated and transmitted to the lower-layer model; in the lower-layer model, given the plan of the expansion planning model, according to the traffic flow and power load in each time period of the whole day, with the minimum sum of operation costs of the coupling network in each time period as the optimization objective, the traffic flow distribution is optimized, and the sum of the upper-layer investment cost and the operation costs of each time period of the lower-layer model is recorded; the upper-layer model iterates through each time period of the whole day until the plan of the expansion planning model is optimal. The method of the present invention has a reasonable expansion planning area, reduces the expansion and operation costs, and can improve the new energy consumption rate.

Description

Technical Field

[0001] The present invention relates to a method for expanding the power - traffic coupling network planning with new energy power station expansion, specifically a bi - level expansion planning method for the power - traffic coupling network considering the temporal correlation. Background Art

[0002] Electric vehicles with low - carbon and clean characteristics have been widely promoted in China, and the ownership has increased rapidly. At the same time, China has accelerated the promotion of a new power system dominated by new energy, and the new energy penetration rate has been continuously improved. The coupling and connection between the transportation network mainly composed of electric vehicles and the distribution network mainly composed of new energy are becoming increasingly close. However, the traffic load and charging load generated by the large - scale travel of electric vehicles have a huge impact on the safe operation of the distribution network, causing the "peak - on - peak" of the distribution network load, further expanding the volatility brought by the new energy grid connection; at the same time, it causes regional traffic jams or even paralysis. Reasonable planning and expansion of the power - traffic coupling network contribute to the safe operation of the distribution network, alleviating traffic jams, and improving the new energy consumption rate.

[0003] At present, for the problem of expanding the power - traffic coupling network planning, the impact of the charging load of electric vehicles on the expansion planning of the coupling network is mainly considered, ignoring the travel characteristics of electric vehicle users themselves and the impact of the transportation network on the charging behavior of electric vehicles. Moreover, the current research mainly uses data analysis to sample the peak travel load of electric vehicles and the typical output of new energy to conduct the expansion planning of the coupling network at a single moment, ignoring the temporal correlation of the electric vehicle load itself and the volatility of new energy output, which leads to the planning results being too conservative. Therefore, in the expansion planning of the power - traffic coupling network, it is necessary to consider the travel characteristics of electric vehicle users themselves and the impact of the transportation network on the charging behavior of electric vehicles, as well as the temporal correlation of the electric vehicle load and new energy output itself. Summary of the Invention

[0004] Aiming at the problem that the existing research ignores the connection between the peak - valley changes of each time period of the transportation network and the distribution network load and does not consider the volatility of new energy output, resulting in the expansion planning results being too conservative, the present invention proposes a bi - level expansion planning method for the power - traffic coupling network considering the temporal correlation.

[0005] The present invention is implemented by the following technical solutions: A bi - level expansion planning method for the power - traffic coupling network considering the temporal correlation, specifically including the following steps:

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

[0007] Among them, 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 of the number of newly expanded roads, , and m is the index of the expanded road;

[0008] The transmission line expansion model is , where is the number of newly expanded transmission lines; is the number of transmission lines that can be expanded; is a binary variable of the number of newly expanded transmission lines, ; n is the index of the expanded transmission line; the resistance of the expanded line , the reactance of the expanded line , the maximum active power transmitted by the expanded line , the maximum reactive power transmitted by the expanded line , is the maximum active power transmitted by the initial line, is the maximum reactive power transmitted 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 , is the capacity of a newly built single charging pile, is the capacity of an initial single charging pile;

[0010] The wind power and photovoltaic unit expansion model is , is the number of newly added wind power or photovoltaic units beside the distribution network node i; the wind power and photovoltaic units can be expanded beside any distribution network node;

[0011] Step 2: Consider the expansion planning model of mixed traffic equilibrium, and establish an upper-layer model with the minimum sum of the investment cost and operation cost of the power-traffic coupling network at a certain moment as the optimization goal. The specific upper-layer model is as follows: , F TN = ∑ a ∈ T A [ w ( t a + λ 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 ] + ρ ∑ 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 ijn dl + ∑ i ∈ E N Pr i g k n i g , where is the sum of the investment cost and operation cost of the power-traffic coupling network; is the sum of the investment cost and operation cost of the transportation network; is the sum of the investment cost and the operating cost of the distribution network; is the operating cost of the transportation network; is the time value parameter; is the road traffic flow; is the road travel time, t a = t a 0 [ 1 + 0 . 15 ( x a c a ) 4 ] , is the road normal travel time; is the capacity of road a; is the travel delay time when road a is congested; is the investment cost of the transportation network; is the set of all roads; is the discount factor reflecting the relationship between long-term investment cost and short-term operating cost; is the cost required to build a single new road beside road ; ∑ i ∈ E B [ a i ( p i ) 2 + b i p i ] + ρ ∑ j ∈ π ( 0 ) P 0 j l is the operating cost of the distribution network; , , are the power generation cost coefficients; is the set of generator sets at general nodes; is the set of generator sets at the balancing node; is the active power output by the generator sets at general nodes in the distribution network; is the active power output by the generator sets at the balancing node in the distribution network; is the cost required to build charging piles in the coupled network; is the cost required to build a single charging pile beside road ; is the number of charging piles to be built; is the cost required to build transmission lines in the coupled network; is the cost required to build a single transmission line from node to node in the distribution network; is the set of transmission line routes that can be built; is the cost required to build generators in the coupled network; is the cost required to build a set of wind power or photovoltaic units at node ; is the set of nodes where wind power and photovoltaic units can be installed; is the number of wind power or photovoltaic units added beside node i in the distribution network.

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

[0013]

[0014]

[0015]

[0016]

[0017] wherein, is the linear constraint of the tree - shaped power flow of the distribution network; is the typical constraint of each variable in the distribution network; is the traffic network model constraint; is the power - traffic coupling constraint; and are the node numbers of the distribution network; is the line number of the transmission line; is the sum of the basic power load and the charging load at node ; is the basic power load at node ; 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 of all roads; is the capacity of road ; is the initial capacity of road ; is the expansion capacity of each road; x= [ x a ] is the set of traffic flows of all roads; is the set of all paths, ∧= [ δ ak rs ] , is a binary variable related to road traffic. When the path from the starting node to the destination node passes through road ; otherwise it is 0; is the set of all path flows;​​ is a matrix composed of 0s and 1s, whose values correspond to the relationship between travel demand and path flow; q is the travel demand set from the starting node to the destination node ; λ = [ λ a ] is the set of all road congestion travel times; c = [ c a ] is the set of capacities of all roads; is 's dual variable; is 's dual variable; , , , are respectively the active power and reactive power transmitted on the line from node to node , from node to node ; and are respectively the active power and reactive power generated by the generator at node ; and are respectively the active power and reactive power generated by the generator at node ; , are respectively the active power and reactive power injected at node ; π(j) is the set of end nodes of all branches with the head at j; and are respectively the voltages of nodes and ; , are the resistance and reactance of the expanded line; is the balanced node voltage; , are the additional continuous variables of the bilinear term in the linearized voltage drop equation; , are respectively the upper and lower voltage limits at node ; is the upper voltage limit at node ; , are respectively the active power and reactive power generated by adding a new wind power or photovoltaic unit; , are respectively the upper limits of active power and reactive power of the generator at node ; , are the power flow limits of the transmission line.

[0018] The lower-layer model is established with the objective of minimizing the sum of the operating costs of the coupled network at each time period of the whole day. The lower-layer model is as follows:

[0019] min ∑ t = 1 24 ( ∑ a ∈ T A [ w ( t a , t + λ a , t ) x a , t ] + ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + ρ ∑ j ∈ π ( 0 ) P 0 j , t l ) , where, ∑ a ∈ T A [ w ( t a , t + λ a , t ) x a , t ] is the operating cost of the transportation network at time t, is the traffic flow of road at time t; is the travel time of road at time t, t a , t = t a , t 0 [ 1 + 0 . 15 ( x a , t c a ) 4 ] , is the normal travel time of road at time is the travel delay time when road is congested at time t; ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + ρ ∑ 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 of the general node generator set in the distribution network at time t; is the active power output of the balancing node generator set in the distribution network at time t;

[0020] Both the upper-layer model and the lower-layer model follow the power-transportation coupling constraints, distribution network constraints, and transportation network model constraints. The differences in the constraints followed by the upper-layer model and the lower-layer model are: (1) The variables , , , in the constraints followed by the upper-layer model are constants in the constraints followed by the lower-layer model; (2) The constraints followed by the lower-layer model include variables for time t.

[0021] The specific mathematical model is as follows:

[0022]

[0023]

[0024]

[0025]

[0026] where, is the linear constraint of the tree-shaped power flow of the distribution network; is the typical constraint of each variable in the distribution network; is the transportation network model constraint; is the power-transportation coupling constraint. , is the node number of the distribution network; is the line number of the transmission line; is the node at the sum of the basic power load and the charging load at time is the node at the basic power load at time is the flow - active power conversion coefficient; is the road at the traffic flow at time ; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the road capacity; is the road initial capacity; is the expansion capacity of each road; x t = [ ] is the traffic flow set of all roads at time ; λ t = [ λ a , t ] is the set of congestion travel times of all roads at time ; is the set of all paths, ∧= [ δ ak rs ] , is a binary variable related to road traffic. When the path from the starting node to the destination node passes through the road at time ; otherwise it is 0; is the set of all path flows at time ; is a matrix composed of 0 and 1, and its value corresponds to the relationship between travel demand and path flow; is at time from the starting node to the destination node the travel demand set; is at time the dual variable of ; is at time the dual variable of ; , , , are respectively the active power and reactive power transmitted on the line from node to node , and from node to node ; and are respectively the active power and reactive power generated by the generator at node at time ; and are respectively the active power and reactive power generated by the generator at node at time ; , are respectively the active power and reactive power injected into node at time ; π(j) is the set of end nodes of all branches with the head at j; and are respectively the voltages of nodes and at time , are the resistance and reactance of the expanded line; is the balanced node voltage at time ; , are the additional continuous variables of the bilinear terms in the linearized voltage drop equation at time ; , are respectively the upper and lower voltage limits at node ; is the upper voltage limit at node ; , are respectively the active power and reactive power generated by adding a new wind power or photovoltaic unit; , are respectively the upper limits of the active power and reactive power of the generator at node at time ; , are the power flow limits of the transmission line.

[0027] In the upper - layer model, with the minimum sum of the investment cost and the operation cost of the power - transportation coupling network at a certain moment as the optimization objective, a solution for the expansion planning model is formulated and transmitted to the lower - layer model. In the lower - layer model, given the solution of the expansion planning model, according to the traffic travel demand and power load in each period of the whole day, with the minimum sum of the operation costs of the coupling network in each period as the optimization objective, the traffic flow distribution is optimized, and the investment cost of the upper - layer and the sum of the operation costs of the lower - layer model in each period are recorded. The upper - layer model iterates through different traffic travel demands and power loads in each period of the whole day. Finally, the solution of the expansion planning model with the minimum sum of the investment cost of the upper - layer model and the operation costs of the lower - layer model in each period is selected as the optimal solution.

[0028] In view of the fact that the existing planning research ignores the connection between the peak - valley changes of the traffic network and the distribution network load in each period, as well as the volatility of new - energy output, and only conducts planning during peak periods, over - focuses on short - term benefits, and allocates resources far exceeding the growing charging demand, resulting in overly conservative planning results and low applicability. The present invention provides a two - layer expansion planning method for a power - transportation coupling network considering temporal correlation, which can effectively capture the dynamics and mobility of charging demand in the traffic network in each period of the whole day, can meet the safe operation of the power - transportation coupling network under large - scale electric - vehicle travel, reduce unreasonable regional planning, lower the expansion and operation costs, and improve the new - energy consumption rate. Brief Description of the Drawings

[0029] Figure 1 It is a flow chart for solving the two - layer expansion planning of the power - transportation coupling network. Detailed Embodiments

[0030] A two - layer expansion planning method for a power - transportation coupling network considering temporal correlation first establishes an expansion planning model considering mixed - traffic equilibrium with the minimum sum of the investment cost and the operation cost of the power - transportation coupling network as the optimization objective:

[0031] (1) Expansion planning model

[0032] The newly - built and expanded roads and transmission lines can be expanded 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 - built and expanded roads; is the number of roads that can be expanded; is a binary variable of the number of newly - built and expanded roads, , and m is the index of the expanded road.

[0036] 2) Transmission line expansion model

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Wherein, is the number of newly expanded transmission lines; is the number of expandable transmission lines; is a binary variable of the number of newly expanded transmission lines, ; n is the index of the expanded transmission line; is the maximum active power transmitted by the initial line, is the maximum reactive power transmitted 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; Equation - means that after the transmission line is expanded, the resistance and reactance of the line are reduced to of the original; Equation - means that after the transmission line is expanded, the maximum active power and maximum reactive power flowing through the transmission line are reduced to of the original.

[0043] 3) Charging pile expansion model

[0044]

[0045] is the number of newly built charging piles, and the newly built charging piles have the same capacity as the initial charging piles , is the capacity of a newly built single charging pile, is the capacity of an initial single charging pile,

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

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

[0048] The number of expanded roads and transmission lines can be represented by a logarithmic number of binary variables. For example, if at most 7 additional roads can be invested on a road use binary variables for road expansion.

[0049] (2) Objective function

[0050] Considering the expansion planning model of mixed traffic equilibrium, the optimization objective 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 traffic 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 + λ 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 ] + ρ ∑ 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 ijn dl + ∑ i ∈ E N Pr i g k n i g

[0054] In the formula, is the sum of the investment cost and operation cost of the power-traffic coupling network; is the sum of the investment cost and operation cost of the traffic network; is the sum of the investment cost and operation cost of the distribution network; is the operation cost of the traffic network; is the time value parameter; is the traffic flow of road ; is the travel time of road , t a = t a 0 [ 1 + 0 . 15 ( x a c a ) 4 ] , is the normal travel time of road ; is the road capacity; is the travel delay time during congestion of road a; is the investment cost of the traffic network; is the set of all roads; The discount factor reflecting the relationship between long-term investment costs and short-term operating costs; is the cost required to build a single new road beside the road ; ∑ i ∈ E B [ a i ( p i ) 2 + b i p i ] + ρ ∑ j ∈ π ( 0 ) P 0 j l is the operating cost of the distribution network; , , are the power generation cost coefficients; is the set of generator units at general nodes; is the set of generator units at the balancing node; is the active power output by the generator units at general nodes in the distribution network; is the active power output by the generator units at the balancing node in the distribution network; is the cost required to build new charging piles in the coupled network; is the cost required to build a single new charging pile beside the road ; is the cost required to build new transmission lines in the coupled network; is the cost required to build a single new transmission line in the distribution network from node to node ; is the set of available transmission line routes that can be built; is the cost required to build new generators in the coupled network; is the cost required to build a set of wind power or photovoltaic units at node ; is the set of nodes where wind power and photovoltaic units can be installed.

[0055] For the lower-layer model, an optimization model is established with the goal of minimizing the sum of the operating costs of the coupled network at each time period of the whole day:

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

[0057] In the formula, ∑ a ∈ T A [ w ( t a , t + λ a , t ) x a , t ] is the operating cost of the transportation network at time t, is the traffic flow of road at time t; 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 ] , is the normal travel time of road a at time ; ∑ i ∈ E B [ a i ( p i , t ) 2 + b i p i , t ] + ρ ∑ 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 generator units at general nodes in the distribution network at time t; The active power output by the balanced node generator set in the distribution network at time t.

[0058] In the upper-layer model, with the goal of minimizing the sum of the investment cost and operating cost of the power-transportation coupling network at a certain moment, a plan for the expansion planning model is formulated and transmitted to the lower-layer model; in the lower-layer model, given the plan of the expansion planning model, according to the traffic travel demand and power load in each period of the whole day, with the goal of minimizing the sum of the operating costs of the coupling network in each period, the traffic flow distribution is optimized, and the investment cost of the upper-layer model and the sum of the operating costs of the lower-layer model in each period are recorded; the upper-layer model iterates through different traffic travel demands and power loads in each period of the whole day, and finally selects the plan of the expansion planning model with the minimum sum of the investment cost of the upper-layer model and the operating costs of the lower-layer model in each period as the optimal plan.

[0059] Constraint conditions:

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

[0061] ⑫

[0062] ⑬

[0063] ⑭

[0064] ⑮

[0065] In the formula, is the linear constraint of the tree-like power flow of the distribution network; is the typical constraint of each variable in the distribution network; is the traffic network model constraint; is the power-transportation coupling constraint. 、 are the node numbers; is the transmission line number; is the node The sum of the basic power load and charging load at; is the node The basic power load at; 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 ] The set of normal travel times for all roads; is the capacity of road ; is the initial capacity of road ; is the expansion capacity of each road; x= [ x a ] The set of traffic flows for all roads; is the set of all paths, ∧= [ δ ak rs ] , is a binary variable related to road passage. When the path from the starting node to the destination node passes through road , ; otherwise it is 0; The set of all path flows; is a matrix consisting of 0 and 1, and its value corresponds to the relationship between travel demand and path flow; q is the set of travel demands from the starting node to the destination node ; λ = [ λ a ] The set of congested travel times for all roads; c = [ c a ] The set of capacities of all roads; is the dual variable of ; is the dual variable of ; T represents the transpose operation; , , , are the active power and reactive power transmitted on the line from node to node , from node to node respectively; and are the active power and reactive power generated by the generator at node respectively; and are the active power and reactive power generated by the generator at node respectively; , are the active power and reactive power injected at node respectively; π(j) is the set of end nodes of all branches with the head being j; and are the voltages of nodes and respectively; , are the resistance and reactance of the expanded line; is the voltage of the slack node; , are the additional continuous variables of the bilinear terms in the linearized voltage drop equation; , are the upper and lower limits of the voltage at node respectively; is the upper limit of the voltage at node ; , are the active and reactive powers generated by adding a new wind power or photovoltaic unit respectively; , are the upper limits of the active and reactive powers of the generator at node respectively; , are the power flow limits of the transmission line.

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

[0067] ⑯

[0068] ⑰

[0069] ⑱

[0070] ⑲

[0071] In the formula, is the linear constraint of the tree - shaped power flow of the distribution network; is the typical constraint of each variable in the distribution network; is the traffic network model constraint; is the power - traffic coupling constraint. , are the node numbers of the distribution network; is the line number of the transmission line; is the sum of the basic power load and the charging load at node at time , is the basic power load at node at time ; is the flow - active power conversion coefficient; is the road at Traffic flow at a moment; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the road capacity; is the road initial capacity; is the expansion capacity of each road; x t = [ ] is the traffic flow set of all roads at a moment; λ t = [ λ a , t ] is the set of congestion passing times of all roads at a moment; is the set of all paths, ∧= [ δ ak rs ] , is a binary variable related to road passage. When the path from the starting node to the destination node passes through the road , ; otherwise it is 0; is the traffic flow set of all paths at a moment; is a matrix composed of 0 and 1, and its value corresponds to the relationship between travel demand and path traffic flow; is at the travel demand set from the starting node to the destination node at a moment; is at the dual variable of at a moment; is at the dual variable of at a moment; , , , are respectively the active power and reactive power transmitted on the line from node to node , from node to node at a moment; and are respectively the active power and reactive power generated by the generator at node at a moment; and are respectively Time node The active power and reactive power generated by the generator at the location; 、 Are respectively The active power and reactive power injected into the node at the time; π(j) is the set of end nodes of all branches with the head end being j; The active power and reactive power at the location; π(j) is the set of end nodes of all branches with the head end being j; And Are respectively The voltage of the time node And ; 、 Are the resistance and reactance of the expanded line; Is The voltage of the balancing node at the time; 、 Are the additional continuous variables of the bilinear term in the linearized voltage drop equation at the time; ; 、 Are respectively the upper and lower limits of the voltage at the node ; Is the upper limit of the voltage at the node ; 、 Are respectively the active power and reactive power generated by adding a new wind power or photovoltaic unit; 、 Are respectively The upper limits of the active power and reactive power of the generator at the time node ; 、 Are the power flow limits of the transmission line.

[0072] In view of the fact that the existing planning research ignores the connection between the peak-valley changes of the traffic network and the distribution network load in each time period, as well as the volatility of new energy output, and only conducts planning during peak hours, overly focuses on short-term benefits, and allocates far more resources than the growing charging demand, resulting in a too conservative planning result and low applicability, the present invention thus establishes as Figure 1The double-layer expansion planning solution process shown in the figure is as follows: First, an upper-layer model is established with the objective of minimizing the sum of the investment cost and the operation cost of the power-transportation coupling network at a certain moment. The solution of the expansion planning model is formulated and transmitted to the lower-layer model. Then, in the lower-layer model, given the solution of the expansion planning model, according to the traffic flow and power load in each period of the whole day, with the objective of minimizing the sum of the operation costs of the coupling network in each period, the traffic flow distribution is optimized, and the sum of the investment cost of the upper-layer model and the operation costs of each period of the lower-layer model is recorded. The upper-layer model iterates through different travel demands and power loads in each period of the whole day in turn. Finally, the solution of the expansion planning model with the minimum sum of the investment cost of the upper-layer model and the operation costs of each period of the lower-layer model is selected as the optimal solution.

Claims

1. A two-layer expansion planning method for a power-transportation coupled network considering temporal correlation, characterized in that: Specifically, it includes the following steps: Step 1: Establish an extended planning model considering mixed traffic equilibrium with the minimum sum of investment costs and operating costs of the power-transportation coupling network as the optimization objective. The extended planning model includes a road expansion model, a transmission line expansion model, a charging pile expansion model, and a wind power and photovoltaic unit expansion model; Among them, 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 of the number of newly expanded roads, , and m is the index of the expanded road; The transmission line expansion model is , where is the number of newly expanded transmission lines; is the number of expandable transmission lines; is a binary variable of the number of newly expanded transmission lines, ; n is the index of the expanded transmission line 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 , is the capacity of a single newly built charging pile, is the capacity of a single initial charging pile; The expansion model of wind power and photovoltaic units is , is the number of newly added wind power or photovoltaic units beside the distribution network node i; the wind power and photovoltaic units can be expanded beside any node of the distribution network; Step 2: Consider the extended planning model for mixed traffic equilibrium, and establish an upper-layer model with the minimum sum of the investment cost and the operation cost of the power-traffic coupling network at a certain moment as the optimization objective. The specific upper-layer model is as follows: , , , where, is the sum of the investment cost and the operation cost of the power-traffic coupling network; is the sum of the investment cost and the operation cost of the traffic network; is the sum of the investment cost and the operation cost of the distribution network; is the operation cost of the traffic network; is the time value parameter; is the traffic flow of road ; is the traffic flow of road , , is the traffic flow of road ; is the road capacity; is the traffic delay time when road a is congested; is the investment cost of the traffic network; is the set of all roads; is the discount factor reflecting the relationship between long-term investment cost and short-term operation cost; is the cost required to build a single new road beside road ; is the operation cost of the distribution network; , , are the power generation cost coefficients; is the set of generator sets at general nodes; is the set of generator sets at the balancing node; is the active power output of the generator set at the general node in the distribution network; is the active power output of the generator set at the balancing node in the distribution network; is the cost required to build new charging piles in the coupling network; is the cost required to build a single new charging pile beside road ; is the number of new charging piles; is the cost required to build new transmission lines in the coupling network; is the cost required to build a single new transmission line from node to node in the distribution network; is the set of available new transmission line routes; is the cost required to build new generators in the coupling network; is at node The cost required to build a new group of wind power or photovoltaic units; The set of nodes where wind power and photovoltaic units can be installed; The number of newly added wind power or photovoltaic units beside the distribution network node i; Establish a lower-layer model with the minimum sum of operating costs of the coupling network at each time period of the whole day as the optimization objective. The lower-layer model is as follows: wherein, is the operating cost of the transportation network at time t, is the traffic flow of road at time t; is the travel time of road at time t, , is the normal travel time of road at time is the travel delay time of road when it is congested at time t; is the operating cost of the distribution network at time t, and p i,t is the active power output by the general node generator set in the distribution network at time t; is the active power output by the balancing node generator set in the distribution network at time t; In the upper-layer model, with the minimum sum of investment costs and operating costs of the power-transportation coupling network at a certain moment as the optimization objective, formulate the plan of the extended planning model and transfer it to the lower-layer model; in the lower-layer model, given the plan of the extended planning model, according to the traffic travel demand and power load at each time period of the whole day, with the minimum sum of operating costs of the coupling network at each time period as the optimization objective, optimize the traffic flow distribution, and record the investment cost of the upper-layer model and the sum of operating costs of each time period of the lower-layer model; the upper-layer model iterates successively for different traffic travel demands and power loads at each time period of the whole day, and finally selects the plan of the extended planning model with the minimum sum of the investment cost of the upper-layer model and the operating costs of each time period of the lower-layer model as the optimal one.

2. The method for the two-layer expansion planning of the power-transportation coupled network considering the temporal correlation according to claim 1, wherein: The constraints of the upper-layer model include power-transportation coupling constraints, distribution network constraints, and traffic network model constraints. The specific mathematical model is as follows: In the formula, is the linear constraint of the tree - shaped power flow of the distribution network; is the typical constraint of each variable in the distribution network; is the traffic network model constraint; is the power - traffic coupling constraint; 、 are the node numbers of the distribution network; is the line number of the transmission line; is the sum of the basic power load and the charging load at node ; is the basic power load at node ; 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 of all roads; is the capacity of road ; is the initial capacity of road ; is the expansion capacity of each road; is the set of traffic flows of all roads; is the set of all paths, , is a binary variable related to road traffic. When the path from the starting node to the destination node passes through road , , ; otherwise it is 0; is the set of all path flows; is a matrix composed of 0 and 1, and its value corresponds to the relationship between travel demand and path flow; q is the set of travel demands from the starting node to the destination node ; is the set of congested travel times of all roads; is the set of capacities of all roads; is the dual variable of ; is the dual variable of ; , , , are respectively from node to node , from node to node The active power and reactive power transmitted on the line; and are respectively the active power and reactive power generated by the generator at node ; and are respectively the active power and reactive power generated by the generator at node ; , are respectively the active power and reactive power injected at node ; π(j) is the set of end nodes of all branches with the head at j; and are respectively the voltages of nodes and ; , are the resistance and reactance of the expanded line; is the voltage of the reference node; , are the additional continuous variables of the bilinear terms in the linearized voltage drop equation; , are respectively the upper and lower limits of the voltage at node ; is the upper limit of the voltage at node ; , are the active power and reactive power generated by adding a new wind power or photovoltaic unit respectively; , are respectively the upper limits of the active power and reactive power of the generator at node; , is the power limit of the transmission line current The constraints of the lower-layer model include power-transportation coupling constraints, distribution network constraints, and traffic network model constraints. The specific mathematical model is as follows: In the formula, is the linear constraint of the tree - shaped power flow of the distribution network; is the typical constraint of each variable in the distribution network; is the traffic network model constraint; is the power - traffic coupling constraint; and are the node numbers of the distribution network; is the transmission line number; is the node at time, which is the sum of the basic power load and the charging load; is the basic power load of node at time; is the flow - active power conversion coefficient; is the road at time, and the traffic flow; C(i) is the set of charging lines connected to node i; is the initial number of charging piles; is the road capacity; is the initial capacity of road ; is the expansion capacity of each road; is time, the set of traffic flows of all roads; is time, the set of congestion passing times of all roads; is the set of all paths, , is a binary variable related to road passage. When the path from the starting node to the destination node passes through road , ; otherwise it is 0; is time, the set of all path flows; is a matrix composed of 0 and 1, and its value corresponds to the relationship between travel demand and path flow; is at time, from the starting node to the destination node travel demand set; is at time, dual variable; is at time, dual variable; , , , are respectively the active power and reactive power transmitted on the line from node to node , and the active power and reactive power transmitted on the line from node to node ; and are respectively the active power and reactive power generated by the generator at node at time ; and are respectively the active power and reactive power generated by the generator at node at time , are respectively the active power and reactive power injected into node at time ; π(j) is the set of end nodes of all branches with the head end being j; and are respectively the voltages of nodes and at time ; and are the resistance and reactance of the expanded line; is the balancing node voltage at time ; and are respectively the upper and lower limits of the voltage at node ; is the upper limit of the voltage at node , are respectively the active power and reactive power generated by adding a new wind power or photovoltaic unit; , are respectively the time node the upper limits of the active power and reactive power of the generator at; , is the power flow limit of the transmission line.

3. The double-layer expansion planning method for the power-transportation coupled network considering temporal correlation according to claim 2, 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 expanded line , the maximum reactive power transmitted by the expanded line , is the maximum active power transmitted by the initial line, is the maximum reactive power transmitted by the initial line; 、 are the resistance and reactance of the initial line respectively.

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