Bi-level expansion planning method for power-transportation coupled network taking temporal correlation into consideration

Abstract

The present invention belongs to the field of power-transportation coupled network expansion, particularly relates to an expansion planning method for a power-transportation coupled network involving the expansion of renewable energy stations, and specifically relates to a bi-level expansion planning method for a power-transportation coupled network taking temporal correlation into consideration. The method comprises: first establishing an expansion planning model; then establishing an upper-level model having an optimization objective of minimizing the sum of the investment cost and operating cost of a power-transportation coupled network, formulating a scheme for the expansion planning model, and transmitting the scheme to a lower-level model; in the lower-level model, with the scheme for the expansion planning model being given, optimizing traffic flow distribution on the basis of the traffic flow and power load in each time period throughout the day and by means of using the minimization of the sum of operating costs of the coupled network across all the time periods as an optimization objective, and recording the sum of an upper-level investment cost and the operating costs of the lower-level model across all the time periods; and the upper-level model sequentially performing iteration across all the time periods throughout the day until the scheme for the expansion planning model is optimal. The method in the present invention achieves rational regional planning for expansion planning, reduces the expansion and operating costs, and can improve the renewable energy accommodation rate.

Description

A Two-Level Extended Programming Method for Power-Transport Coupled Networks that Considers Time-Series Correlation Technical Field

[0001] This invention relates to a power-transportation coupled network expansion planning method for the expansion of new energy power plants, specifically a two-layer expansion planning method for power-transportation coupled networks that takes into account time-series correlation. Background Technology

[0002] Electric vehicles, characterized by low carbon emissions and clean energy, have been widely adopted in my country, leading to a rapid increase in their ownership. Simultaneously, my country is accelerating the development of a new power system primarily based on renewable energy sources, resulting in a continuously increasing penetration rate of renewable energy. The coupling and connection between the electric vehicle-centric transportation network and the renewable energy-centric distribution network are becoming increasingly close. However, the traffic and charging loads generated by large-scale electric vehicle travel have a significant impact on the safe operation of the distribution network, causing peak-to-peak load expansion and further amplifying the volatility brought about by renewable energy grid integration; at the same time, it can cause regional traffic congestion or even paralysis. Rational planning and expansion of the power-transportation coupling network can help ensure the safe operation of the distribution network, alleviate traffic congestion, and improve the renewable energy absorption rate.

[0003] Currently, research on the expansion planning of power-transportation coupled networks primarily considers the impact of electric vehicle (EV) charging load on the network expansion plan, neglecting the travel characteristics of EV users and the influence of the transportation network on EV charging behavior. Furthermore, current studies mainly use data analysis to sample peak EV travel load and typical renewable energy output for single-time-based network expansion planning, ignoring the time-series correlation of EV load and the volatility of renewable energy output, leading to overly conservative planning results. Therefore, in power-transportation coupled network expansion planning, it is necessary to consider the travel characteristics of EV users, the influence of the transportation network on EV charging behavior, and the time-series correlation between EV load and renewable energy output. Summary of the Invention

[0004] This invention addresses the problem that existing studies neglect the connection between peak and valley load changes in transportation and distribution networks at different times, and fail to consider the volatility of renewable energy output, leading to overly conservative extended planning results. It proposes a two-layer extended planning method for power-transportation coupled networks that takes into account time-series correlation.

[0005] This invention is achieved using the following technical solution: a two-layer extended planning method for power-transportation coupled networks that considers time-series correlations, specifically including the following steps:

[0006] Step 1: Establish an extended planning model that considers the equilibrium of mixed traffic, with the optimization objective of minimizing the sum of investment and operating costs of the power-transportation coupled network. 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.

[0007] Among them, the road expansion model is In the formula, This refers to the number of newly constructed or expanded roads; This refers to the number of roads that can be expanded. It is a binary variable representing the number of newly constructed or expanded roads. m is the index of the expanded road;

[0008] The model for power transmission line expansion is In the formula, This refers to the number of newly constructed or expanded power transmission lines; The number of transmission lines that can be expanded; It is a binary variable representing the number of newly constructed or expanded power transmission lines. ; n is the index of the expanded transmission line; the resistance of the expanded line. Reactance of the expanded line The maximum active power transmitted by the expanded line The maximum reactive power transmitted by the expanded line , This represents the maximum active power initially transmitted by the line. This represents the maximum reactive power initially transmitted by the line. , These are the resistance and reactance of the initial circuit, respectively.

[0009] The charging pile expansion model is , This refers to the number of newly built charging stations; furthermore, the newly built charging stations have the same capacity as the existing charging stations. , To build a new single charging station capacity, This represents the initial capacity of a single charging station.

[0010] Expansion models for wind and solar power units are , To increase the number of wind or solar power units next to node i in the distribution network; wind and solar power units can be expanded next to any node in the distribution network.

[0011] Step 2: Consider the extended planning model of hybrid traffic equilibrium, and establish a higher-level model with the optimization objective of minimizing the sum of investment cost and operating cost of the power-transportation coupled network at a certain time. The higher-level model is as follows: , , In the formula, This is the sum of the investment cost and operating cost of the power-transportation coupled network; This is the sum of the investment cost and operating cost of the transportation network; This is the sum of the investment cost and operating cost of the power distribution network; The operating cost of the transportation network; For time value parameters; For roads Traffic flow; For roads Travel time , For roads Normal passage time; Let road a have a capacity; Let be the travel delay time when road a is congested; The investment cost of the transportation network; The set of all roads; A discount factor used to reflect the relationship between long-term investment costs and short-term operating costs; For on the road The cost of constructing a new single road alongside it; The operating cost of the power distribution network; , , This is the power generation cost coefficient; This is a set of generator sets for general nodes; The set of generator sets for the balancing node; This refers to the active power output of generator sets at general nodes in a power distribution network. The active power output of the generator set at the balancing node in the distribution network; The cost required to build new charging stations for the coupled network; For on the road The cost of building a single charging station next to the existing one; This refers to the number of newly built charging stations; The cost of constructing new transmission lines for the coupled network; For nodes in the distribution network To the node The cost of constructing a new single power transmission line; This is a collection of power transmission lines that can be newly constructed; The cost of building a new generator for the coupled network; For the node The cost required to build a new wind power or solar power unit; A collection of nodes that can be installed with wind and solar power units; To determine the number of new wind or solar power units to be added next to node i in the distribution network.

[0012] The upper-level model constraints include power-transportation coupling constraints, distribution network constraints, and transportation network model constraints, with the specific mathematical models as follows:

[0013]

[0014]

[0015]

[0016]

[0017] In the formula, Linear constraints for the tree-like power flow in the distribution network; These are typical constraints for various variables in a power distribution network; Constraints for the transportation network model; For power-transportation coupling constraints; , Numbering of distribution network nodes; Numbering of power transmission lines; For nodes The sum of the basic power load and charging load at the location, For nodes The basic electrical load at the location; The flow-to-active-power conversion factor; Let C be the traffic flow of road a; C(i) be the set of charging lines connected to node i. This represents the initial number of charging stations; This is the set of normal travel times for all roads; For roads The capacity; For roads The initial capacity; Expand the capacity of each road; For the traffic flow set of all roads; For all path sets, , It is a binary variable related to road traffic, starting from the starting node. to destination node path Passing the road hour, Otherwise, it is 0; For the set of traffic from all paths; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and path flow; q represents the value from the starting node. to destination node The travel demand set; Set of all road congestion travel times; The capacity set for all roads; for The dual variable; for The dual variable; T represents the transpose operation; , , , They are slave nodes To the node From node To the node Active power and reactive power transmitted on the line; and They are nodes The active and reactive power generated by the generator; and They are nodes The active and reactive power generated by the generator; , Injection nodes Active power and reactive power at point j; π(j) is the set of terminal nodes of all branches with starting point j; and They are nodes and The voltage; , For the resistance and reactance of the expanded line; To balance the node voltage; , This is an additional continuous variable for the bilinear term in the linearized voltage drop equation; , They are nodes The upper and lower limits of the voltage; For nodes Upper voltage limit; , These refer to the active power and reactive power generated by adding a new wind power or photovoltaic unit, respectively. , They are nodes The upper limits of active power and reactive power of the generator; , For power flow limitation of transmission lines.

[0018] The lower-level model is established with the optimization objective of minimizing the sum of the operating costs of the coupled network at all times of the day. The lower-level model is as follows:

[0019] In the formula, Let t be the operating cost of the transportation network. For the road at time t Traffic flow; For the road at time t The passage time, , for Road at all times Normal passage time; For the road at time t Traffic delay time during congestion; Let p be the operating cost of the distribution network at time t. i,t Let t be the active power output of the generator sets at a general node in the distribution network at time t; Let t be the active power output of the generator set at the balancing node in the distribution network at time t;

[0020] Both the upper-level and lower-level models follow power-transportation coupling constraints, distribution network constraints, and transportation network model constraints. The constraints followed by the upper-level and lower-level models differ in that: (1) the variables in the constraints followed by the upper-level model... , , , (2) The constraints followed by the lower model are constants; (3) 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 the tree-like power flow in the distribution network; These are typical constraints for various variables in a power distribution network; Constraints for the transportation network model; For power-transportation coupling constraints; , Numbering of distribution network nodes; Numbering of power transmission lines; For nodes In The sum of the base power load and the charging load at any given time. For nodes In Basic power load at any given time; The flow-to-active-power conversion factor; For roads exist Traffic flow at any given time; C(i) is the set of charging lines connected to node i; This represents the initial number of charging stations; This is the set of normal travel times for all roads; For road capacity; For roads The initial capacity; Expand the capacity of each road; x t =[x a,t ]for The set of traffic flows for all roads at any given time; for A set of all road congestion and traffic flow times at any given moment; For all path sets, , It is a binary variable related to road traffic, starting from the starting node. to destination node path Passing the road hour, Otherwise, it is 0; for The set of traffic for all paths at any given moment; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and route flow; In order to be in Time from the starting node to destination node The travel demand set; In order to be in At any time The dual variable; In order to be in At any time The dual variable; , , , They are respectively From the node To the node From node To the node Active power and reactive power transmitted on the line; and They are respectively Time Node The active and reactive power generated by the generator; and They are respectively Time Node The active and reactive power generated by the generator; , They are respectively Injecting nodes at all times Active power and reactive power at point j; π(j) is the set of terminal nodes of all branches with starting point j; and They are respectively Time Node and The voltage; , For the resistance and reactance of the expanded line; for Constantly balance node voltages; , In order to be in Additional continuous variables in the bilinear terms of the time-linearized voltage drop equation; , They are nodes The upper and lower limits of the voltage; For nodes Upper voltage limit; , These refer to the active power and reactive power generated by adding a new wind power or photovoltaic unit, respectively. , They are respectively Time Node The upper limits of active power and reactive power of the generator; , For power flow limitation of transmission lines.

[0027] In the upper-level model, the optimization objective is to minimize the sum of investment cost and operating cost of the power-transportation coupled network at a certain moment. An extended planning model scheme is then developed and passed to the lower-level model. In the lower-level model, given the extended planning model scheme, traffic flow distribution is optimized based on traffic demand and power load throughout the day, with the optimization objective being to minimize the sum of operating costs of the coupled network in each time period. The sum of the upper-level investment cost and the lower-level model's operating costs for each time period is recorded. The upper-level model iterates sequentially through different traffic demand and power loads throughout the day, ultimately selecting the extended planning model scheme that minimizes the sum of the upper-level model's investment cost and the lower-level model's operating costs for each time period as the optimal scheme.

[0028] This invention addresses the shortcomings of existing planning studies, which neglect the relationship between peak and valley load variations in transportation and distribution networks across different time periods, as well as the volatility of renewable energy output. These studies often only plan during peak hours, overemphasize short-term gains, and allocate resources far exceeding the growing charging demand, resulting in overly conservative planning outcomes with limited applicability. The invention provides a two-layer extended planning method for power-transportation coupled networks that considers temporal correlations. This method effectively captures the dynamics and mobility of charging demand in the transportation network throughout the day, ensuring the safe operation of power-transportation coupled networks under large-scale electric vehicle travel, reducing unreasonable regional planning, lowering expansion and operating costs, and improving renewable energy absorption rates. Attached Figure Description

[0029] Figure 1 is a flowchart of the solution process for the two-layer extended programming of the power-transportation coupled network. Detailed Implementation

[0030] A two-level extended planning method for power-transport coupled networks, considering time-series correlations, first establishes an extended planning model that takes into account the equilibrium of mixed traffic, with the optimization objective of minimizing the sum of investment and operating costs of the power-transport coupled network:

[0031] (1) Extended programming model;

[0032] Newly expanded roads and power lines can be constructed in parallel with the original lines, possessing the same capacity and parameters. The power-transportation network expansion planning model is as follows:

[0033] 1) Road expansion model;

[0034] ①;

[0035] In the formula, This refers to the number of newly constructed or expanded roads; This refers to the number of roads that can be expanded. It is a binary variable representing the number of newly constructed or expanded roads. m is the index of the expanded road;

[0036] 2) Transmission line expansion model;

[0037] ②;

[0038] ③;

[0039] ④;

[0040] ⑤;

[0041] ⑥;

[0042] In the formula, This refers to the number of newly constructed or expanded power transmission lines; The number of transmission lines that can be expanded; It is a binary variable representing the number of newly constructed or expanded power transmission lines. ; n is the index of the expanded power transmission line; This represents the maximum active power initially transmitted by the line. This represents the maximum reactive power initially transmitted by the line. , These are the resistance and reactance of the initial circuit, respectively. This represents the maximum active power transmitted by the expanded line. This represents the maximum reactive power transmitted by the expanded line. , These represent the resistance and reactance of the initial circuit, respectively; Equation - This means that after the transmission line is expanded, the resistance and reactance of the line are reduced to their original values. ;Mode - This means that after the transmission line is expanded, the maximum active power and maximum reactive power flowing through the transmission line are reduced to their original values. .

[0043] 3) Charging pile expansion model

[0044] ⑦;

[0045] This refers to the number of newly built charging stations, which have the same capacity as the existing charging stations. , To build a new single charging station capacity, This represents the initial capacity of a single charging station.

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

[0047] Wind and solar power units can be added next to any node in the distribution network (PDN). To determine the number of new wind or solar power units to be added next to node i in the distribution network.

[0048] The number of logarithmic binary variables can be used to represent the number of roads and power lines being expanded, for example, if in roads You can invest in up to 7 additional roads, using Road expansion is performed using binary variables.

[0049] (2) Objective function

[0050] Considering the extended planning model for hybrid traffic equilibrium, the optimization objective is to minimize the sum of investment and operating costs of the power-transportation coupled network at a certain time. The investment and operating costs of the power-transportation coupled network include the investment and operating costs of the transportation network and the distribution network. The specific mathematical model is as follows:

[0051] ⑧;

[0052] ⑨;

[0053] ⑩;

[0054] In the formula, This is the sum of the investment cost and operating cost of the power-transportation coupled network; This is the sum of the investment cost and operating cost of the transportation network; This is the sum of the investment cost and operating cost of the power distribution network; The operating cost of the transportation network; For time value parameters; For roads Traffic flow; For roads Travel time , For roads Normal passage time; Let road a have a capacity; Let be the travel delay time when road a is congested; The investment cost of the transportation network; The set of all roads; A discount factor used to reflect the relationship between long-term investment costs and short-term operating costs; For on the road The cost of constructing a new single road alongside it; The operating cost of the power distribution network; , , This is the power generation cost coefficient; This is a set of generator sets for general nodes; The set of generator sets for the balancing node; This refers to the active power output of generator sets at general nodes in a power distribution network. The active power output of the generator set at the balancing node in the distribution network; The cost required to build new charging stations for the coupled network; For on the road The cost of building a single charging station next to the existing one; This refers to the number of newly built charging stations; The cost of constructing new transmission lines for the coupled network; For nodes in the distribution network To the node The cost of constructing a new single power transmission line; This is a collection of power transmission lines that can be newly constructed; The cost of building a new generator for the coupled network; For the node The cost required to build a new wind power or solar power unit; A collection of nodes that can be installed with wind and solar power units; To increase the number of wind or solar power units next to node i in the distribution network;

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

[0056] ⑪;

[0057] In the formula, Let t be the operating cost of the transportation network. For the road at time t Traffic flow; For the road at time t The passage time, , for Road at all times Normal passage time; For the road at time t Traffic delay time during congestion; Let p be the operating cost of the distribution network at time t. i,t Let t be the active power output of the generator sets at a general node in the distribution network at time t; Let t be the active power output of the generator set at the balancing node in the distribution network at time t.

[0058] In the upper-level model, the optimization objective is to minimize the sum of investment cost and operating cost of the power-transportation coupled network at a certain moment. An extended planning model scheme is then developed and passed to the lower-level model. In the lower-level model, given the extended planning model scheme, traffic flow distribution is optimized based on traffic demand and power load throughout the day, with the optimization objective being to minimize the sum of operating costs of the coupled network in each time period. The sum of investment cost of the upper-level model and operating costs of the lower-level model for each time period is recorded. The upper-level model iterates sequentially for different traffic demand and power load throughout the day, ultimately selecting the extended planning model scheme with the minimum sum of investment cost of the upper-level model and operating costs of the lower-level model for each time period as the optimal scheme.

[0059] Constraints:

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

[0061] ⑫;

[0062] ⑬;

[0063] ⑭;

[0064] ⑮;

[0065] In the formula, Linear constraints for the tree-like power flow in the distribution network; These are typical constraints for various variables in a power distribution network; Constraints for the transportation network model; For power-transportation coupling constraints; , Numbering of distribution network nodes; Numbering of power transmission lines; For nodes The sum of the basic power load and charging load at the location, For nodes The basic electrical load at the location; The flow-to-active-power conversion factor; Let C be the traffic flow of road a; C(i) be the set of charging lines connected to node i. This represents the initial number of charging stations; This is the set of normal travel times for all roads; For roads The capacity; For roads The initial capacity; Expand the capacity of each road; For the traffic flow set of all roads; For all path sets, , It is a binary variable related to road traffic, starting from the starting node. to destination node path Passing the road hour, Otherwise, it is 0; For the set of traffic from all paths; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and path flow; q represents the value from the starting node. to destination node The travel demand set; Set of all road congestion travel times; The capacity set for all roads; for The dual variable; for The dual variable; T represents the transpose operation; , , , They are slave nodes To the node From node To the node Active power and reactive power transmitted on the line; and They are nodes The active and reactive power generated by the generator; and They are nodes The active and reactive power generated by the generator; , Injection nodes Active power and reactive power at point j; π(j) is the set of terminal nodes of all branches with starting point j; and They are nodes and The voltage; , For the resistance and reactance of the expanded line; To balance the node voltage; , This is an additional continuous variable for the bilinear term in the linearized voltage drop equation; , They are nodes The upper and lower limits of the voltage; For nodes Upper voltage limit; , These refer to the active power and reactive power generated by adding a new wind power or photovoltaic unit, respectively. , They are nodes The upper limits of active power and reactive power of the generator; , For power flow limitation of transmission lines.

[0066] The specific mathematical model for the lower-level model constraints is as follows:

[0067] 16;

[0068] ⑰;

[0069] 18;

[0070] 19;

[0071] In the formula, Linear constraints for the tree-like power flow in the distribution network; These are typical constraints for various variables in a power distribution network; Constraints for the transportation network model; For power-transportation coupling constraints; , Numbering of distribution network nodes; Numbering of power transmission lines; For nodes In The sum of the base power load and the charging load at any given time. For nodes In Basic power load at any given time; The flow-to-active-power conversion factor; For roads exist Traffic flow at any given time; C(i) is the set of charging lines connected to node i; This represents the initial number of charging stations; This is the set of normal travel times for all roads; For road capacity; For roads The initial capacity; Expand the capacity of each road; x t =[x a,t ]for The set of traffic flows for all roads at any given time; for A set of all road congestion and traffic flow times at any given moment; For all path sets, , It is a binary variable related to road traffic, starting from the starting node. to destination node path Passing the road hour, Otherwise, it is 0; for The set of traffic for all paths at any given moment; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and route flow; In order to be in Time from the starting node to destination node The travel demand set; In order to be in At any time The dual variable; In order to be in At any time The dual variable; , , , They are respectively From the node To the node From node To the node Active power and reactive power transmitted on the line; and They are respectively Time Node The active and reactive power generated by the generator; and They are respectively Time Node The active and reactive power generated by the generator; , They are respectively Injecting nodes at all times Active power and reactive power at point j; π(j) is the set of terminal nodes of all branches with starting point j; and They are respectively Time Node and The voltage; , For the resistance and reactance of the expanded line; for Constantly balance node voltages; , In order to be in Additional continuous variables in the bilinear terms of the time-linearized voltage drop equation; , They are nodes The upper and lower limits of the voltage; For nodes Upper voltage limit; , These refer to the active power and reactive power generated by adding a new wind power or photovoltaic unit, respectively. , They are respectively Time Node The upper limits of active power and reactive power of the generator; , For power flow limitation of transmission lines.

[0072] This invention addresses the shortcomings of existing planning studies, which neglect the relationship between peak and valley load variations in transportation and distribution networks across different time periods, as well as the volatility of renewable energy output. These studies often only plan during peak hours, overemphasize short-term gains, and allocate resources far exceeding the growing charging demand, resulting in overly conservative planning outcomes with limited applicability. Therefore, a two-layer extended planning solution process, as shown in Figure 1, is established. The specific process is as follows: First, an upper-level model is established with the optimization objective of minimizing the sum of investment and operating costs of the power-transportation coupled network at a given moment. An extended planning model scheme is then formulated and passed to the lower-level model. Next, in the lower-level model, given the extended planning model scheme, the traffic flow distribution is optimized based on traffic flow and power load throughout the day, with the optimization objective of minimizing the sum of operating costs of the coupled network across different time periods. The sum of investment costs in the upper-level model and operating costs in the lower-level model for each time period is recorded. The upper-level model iterates through different travel demands and power loads throughout the day, ultimately selecting the extended planning model scheme with the minimum sum of investment costs in the upper-level model and operating costs in the lower-level model for each time period as the optimal solution.

Claims

1. A two-level extended planning method for power-transport coupled networks that considers time-series correlations, characterized in that: Specifically, the following steps are included: Step 1: Establish an extended planning model that considers the equilibrium of mixed traffic, with the optimization objective of minimizing the sum of investment and operating costs of the power-transportation coupled network. 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 In the formula, This refers to the number of newly constructed or expanded roads; This refers to the number of roads that can be expanded. It is a binary variable representing the number of newly constructed or expanded roads. m is the index of the expanded road; The model for power transmission line expansion is In the formula, This refers to the number of newly constructed or expanded power transmission lines; The number of transmission lines that can be expanded; It is a binary variable representing the number of newly constructed or expanded power transmission lines. ; n is the index of the expanded power transmission line; The charging pile expansion model is ， This refers to the number of newly built charging stations; furthermore, the newly built charging stations have the same capacity as the existing charging stations. ， To build a new single charging station capacity, This represents the initial capacity of a single charging station. Expansion models for wind and solar power units are ， To add wind or solar power units next to node i in the distribution network; wind and solar power units can be added next to any node in the distribution network. Step 2: Consider the extended planning model of hybrid traffic equilibrium, and establish a higher-level model with the optimization objective of minimizing the sum of investment cost and operating cost of the power-transportation coupled network at a certain time. The higher-level model is as follows: ， ， In the formula, This is the sum of the investment cost and operating cost of the power-transportation coupled network; This is the sum of the investment cost and operating cost of the transportation network; This is the sum of the investment cost and operating cost of the power distribution network; The operating cost of the transportation network; For time value parameters; For roads Traffic flow; For roads Travel time ， For roads Normal passage time; Let road a have capacity; Let be the travel delay time when road a is congested; The investment cost of the transportation network; The set of all roads; A discount factor used to reflect the relationship between long-term investment costs and short-term operating costs; For on the road The cost of constructing a new single road alongside it; The operating cost of the power distribution network; 、 、 This is the power generation cost coefficient; This is a set of generator sets for general nodes; The set of generator sets for the balancing node; This refers to the active power output of generator sets at general nodes in a power distribution network. The active power output of the generator set at the balancing node in the distribution network; The cost required to build new charging stations for the coupled network; For on the road The cost of building a single charging station next to the existing one; This refers to the number of newly built charging stations; The cost of constructing new transmission lines for the coupled network; For nodes in the distribution network To the node The cost of constructing a new single power transmission line; This is a collection of power transmission lines that can be newly constructed; The cost of building a new generator for the coupled network; For the node The cost required to build a new wind power or solar power unit; A collection of nodes that can be installed with wind and solar power units; To increase the number of wind or solar power units next to node i in the distribution network; The lower-level model is established with the optimization objective of minimizing the sum of the operating costs of the coupled network at all times of the day. The lower-level model is as follows: In the formula, Let t be the operating cost of the transportation network. For the road at time t Traffic flow; For the road at time t The passage time, ， for Road at all times Normal passage time; For the road at time t Traffic delay time during congestion; Let p be the operating cost of the distribution network at time t. i,t Let t be the active power output of the generator sets at a general node in the distribution network at time t; Let t be the active power output of the generator set at the balancing node in the distribution network at time t; In the upper-level model, the optimization objective is to minimize the sum of investment cost and operating cost of the power-transportation coupled network at a certain moment. The scheme of the extended planning model is formulated and passed to the lower-level model. In the lower-level model, given the scheme of the extended planning model, the traffic flow distribution is optimized based on the traffic demand and power load at each time period of the day, with the optimization objective being to minimize the sum of the operating costs of the coupled network at each time period. The investment cost of the upper-level model and the sum of the operating costs of the lower-level model at each time period are recorded. The upper-level model iterates through different traffic demands and power loads at different times of the day, and finally selects the optimal solution from the extended planning model whose sum of investment cost of the upper-level model and operating cost of the lower-level model at each time period is the smallest.

2. The two-layer extended planning method for power-transport coupled networks based on time-series correlation as described in claim 1, characterized in that: The upper-level model constraints include power-transportation coupling constraints, distribution network constraints, and transportation network model constraints, with the specific mathematical models as follows: ； ； ； ； In the formula, Linear constraints for the tree-like power flow in the distribution network; These are typical constraints for various variables in a power distribution network; Constraints for the transportation network model; For power-transportation coupling constraints; 、 Numbering of distribution network nodes; Numbering of power transmission lines; For nodes The sum of the basic power load and charging load at the location, For nodes The basic power load at the location; The flow-to-active-power conversion factor; Let C be the traffic flow of road a; C(i) be the set of charging lines connected to node i. This represents the initial number of charging stations; This is the set of normal travel times for all roads; For roads The capacity; For roads The initial capacity; Expand the capacity of each road; For the traffic flow set of all roads; For all path sets, ， It is a binary variable related to road traffic, starting from the starting node. to destination node path Passing the road hour, Otherwise, it is 0; For the set of traffic from all paths; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and path flow; q represents the value from the starting node. to destination node The travel demand set; Set of all road congestion travel times; The capacity set for all roads; for The dual variable; for The dual variable; T represents the transpose operation; ， ， ， They are slave nodes To the node From node To the node Active power and reactive power transmitted on the line; and They are nodes The active and reactive power generated by the generator; and They are nodes The active and reactive power generated by the generator; 、 Injection nodes Active power and reactive power at point j; π(j) is the set of terminal nodes of all branches with starting point j; and They are nodes and The voltage; 、 For the resistance and reactance of the expanded line; To balance the node voltage; 、 This is an additional continuous variable for the bilinear term in the linearized voltage drop equation; 、 They are nodes Upper and lower voltage limits; For nodes Upper voltage limit; 、 These refer to the active power and reactive power generated by adding a new wind power or photovoltaic unit, respectively. 、 They are nodes The upper limits of active power and reactive power of the generator; 、 For power flow limitation of transmission lines; The lower-level model constraints include power-transportation coupling constraints, distribution network constraints, and transportation network model constraints, with the specific mathematical models as follows: ； ； ； ； In the formula, Linear constraints for the tree-like power flow in the distribution network; These are typical constraints for various variables in a power distribution network; Constraints for the transportation network model; For power-transportation coupling constraints; 、 Numbering of distribution network nodes; Numbering of power transmission lines; For nodes In The sum of the base power load and the charging load at any given time. For nodes In Basic power load at any given time; The flow-to-active-power conversion factor; For roads exist Traffic flow at any given time; C(i) is the set of charging lines connected to node i; This represents the initial number of charging stations; This is the set of normal travel times for all roads; For road capacity; For roads The initial capacity; Increase capacity for each road; x t =[x a,t ]for The set of traffic flows for all roads at any given time; for A real-time set of all road congestion and traffic flow times; For all path sets, ， It is a binary variable related to road traffic, starting from the starting node. to destination node path Passing the road hour, Otherwise, it is 0; for The set of traffic for all paths at any given moment; It is a matrix composed of 0s and 1s, whose values ​​correspond to the relationship between travel demand and route flow; In order to be in Time from the starting node to destination node The travel demand set; In order to be in At any time The dual variable; In order to be in At any time The dual variable; ， ， ， They are respectively From the node To the node From node To the node Active power and reactive power transmitted on the line; and They are respectively Time Node The active and reactive power generated by the generator; and They are respectively Time Node The active and reactive power generated by the generator; 、 They are respectively Injecting nodes at all times Active power and reactive power at point j; π(j) is the set of terminal nodes of all branches with starting point j; and They are respectively Time Node and The voltage; 、 For the resistance and reactance of the expanded line; for Constantly balance node voltages; 、 In order to be in Additional continuous variables in the bilinear terms of the time-linearized voltage drop equation; 、 They are nodes Upper and lower voltage limits; For nodes Upper voltage limit; 、 These refer to the active power and reactive power generated by adding a new wind power or photovoltaic unit, respectively. 、 They are respectively Time Node The upper limits of active power and reactive power of the generator; 、 For power flow limitation of transmission lines.

3. The two-layer extended planning method for power-transport coupled networks based on time-series correlation as described in claim 2, characterized in that: After the power transmission line is expanded, the resistance of the expanded line... Reactance of the expanded line The maximum active power transmitted by the expanded line The maximum reactive power transmitted by the expanded line , This represents the maximum active power initially transmitted by the line. This represents the maximum reactive power initially transmitted by the line. , These are the resistance and reactance of the initial circuit, respectively.

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