Power-traffic network collaborative optimization operation method based on electricity-carbon coupling price

Abstract

The invention discloses a power-traffic network collaborative optimization operation method based on an electricity-carbon coupling price, and belongs to the technical field of low-carbon power-traffic collaborative optimization. The method comprises the following steps: acquiring network parameters and real-time data of a power system and a traffic system; constructing an optimal power flow model of the power distribution network; calculating the carbon emission intensity of each node by adopting a carbon emission flow theory, fusing to form a node electricity-carbon comprehensive price signal, taking the price signal as the charging price of the charging pile, and cooperatively guiding the travel and charging behaviors of the electric vehicle in combination with a dynamic traffic distribution model; outputting an optimal scheduling scheme; according to the invention, the node marginal electricity price and the node carbon emission intensity are fused into a unified electricity-carbon comprehensive price signal, and the signal is utilized to reflect the economic value of electric energy and internalize the environmental cost; electric power-traffic collaborative optimization is established, an electric vehicle and a charging station are key coupling nodes, and two independent physical networks are combined into a two-way interactive organic whole.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon energy-transportation collaborative optimization technology, specifically relating to a method for collaborative optimization operation of power-transportation networks based on electricity-carbon coupling price. Background Technology

[0002] Electric vehicles, through their charging behavior, tightly couple the power system and the transportation system at both the physical and informational levels, forming a novel power-transportation coupled network. This coupling presents different challenges for the independent operation of the two systems: The power system faces increasing load uncertainty and the problem of clean energy consumption: the charging demand of electric vehicles has significant spatiotemporal randomness and volatility, and their disorderly charging behavior can lead to overload of local power distribution facilities, threatening the safe and stable operation of the power grid; in addition, if the charging behavior of electric vehicles is not coordinated with the output characteristics of intermittent renewable energy sources such as wind power and photovoltaics, it will not be able to effectively promote the consumption of clean energy and will be difficult to fully realize its carbon emission reduction potential.

[0003] The transportation system faces challenges related to charging and traffic congestion: electric vehicle users typically choose routes and charging stations based on the shortest distance or time, which easily leads to queues and congestion at popular routes and charging stations in city centers during peak hours. Users' "range anxiety" and long waiting times severely impact user experience and hinder the promotion of electric vehicles.

[0004] Currently, the operation and management of power and transportation systems are largely conducted under an independent planning and separate decision-making model. Electricity price signals from the power system primarily focus on the balance and economics of the grid itself, failing to reflect carbon emission costs and thus unable to accurately guide users to adjust their electricity consumption behavior from a carbon reduction perspective. Meanwhile, traffic navigation systems mostly rely on traditional indicators such as shortest travel time for route planning, neglecting both the economic and efficiency losses caused by charging wait times and the low-carbon guidance from the power system, resulting in difficulties in inter-system coordination. The lack of coordinated interaction between electricity flow and carbon emission flow in the power system and traffic flow in the transportation network leads to low operating efficiency of the entire coupled system and an inability to maximize overall benefits.

[0005] Furthermore, the traditional method of enumerating path combinatorial explosion further hinders the realization of collaborative optimization at the computational level. When attempting to optimize electric-transport networks, researchers often need to pre-generate all possible driving and charging paths for a massive number of vehicles. However, as the network size increases, the number of potential paths grows exponentially, leading to additional computational burdens.

[0006] Therefore, there is an urgent need to design an innovative collaborative optimization operation mechanism. By introducing an electricity-carbon coupled price signal that can simultaneously reflect the economic and environmental value of electricity, it is possible to accurately guide the charging load of electric vehicles while ensuring energy security and smooth traffic flow, reduce the computational burden, and fully tap the scheduling potential and carbon reduction benefits of the coupled system. Summary of the Invention

[0007] The purpose of this invention is to provide a collaborative optimization operation mechanism for power-transportation networks based on electricity-carbon coupling price, so as to solve the problems mentioned in the background art.

[0008] The objective of this invention is achieved as follows: a method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price, characterized by the following steps: Step S1: Obtain network parameters and real-time operating data of the power system and transportation system; Step S2: Construct an optimal power flow model for the distribution network with the goal of minimizing the total operating cost of the distribution network; Step S3: Using the carbon emission flow theory based on the "power point tracking" principle, calculate the carbon emission intensity of each node to obtain the node-based comprehensive carbon price signal that reflects the economic value and environmental cost of electricity. Step S4: Use the calculated comprehensive price signal of electric carbon as the charging price of charging piles in the transportation network to construct a dynamic traffic allocation model, thereby achieving coordinated optimization of electric vehicle travel and charging behavior; Step S5: Feed the charging load distribution calculated by the dynamic traffic assignment model back to the optimal power flow model of the distribution network as a new boundary condition to update the load of the grid nodes, recalculate the comprehensive price of electricity carbon, and finally output the best scheduling scheme that takes into account economy, network topology security and environmental friendliness.

[0009] Preferably, in step S1, obtaining the network parameters and real-time operating data of the power system and transportation system specifically involves: Power network data includes topology, line parameters, generator operating costs and carbon emission intensity, node loads, and distributed energy output; Traffic network data includes road network topology, road segment capacity, and electric vehicle penetration rate; Establish a mapping relationship between power network data and transportation network data, and clarify the common coupling point corresponding to each charging station in the transportation network in the power network.

[0010] Preferably, in the distribution network, the cost related to electricity is represented as network loss cost or the cost of purchasing electricity from the upstream grid; the objective function of the optimal distribution network power flow model should be to minimize the total operating cost of the DSO, which includes the cost of purchasing the shortfall electricity from the upstream grid and the cost of generating electricity from internal thermal power generators. The optimal distribution network power flow model is as follows: ; in, and These represent the two cost coefficients for thermal power generators. and Let N and T represent the electricity purchase price and the amount of electricity purchased from the upstream power grid at time t, respectively, and let N and T represent the set of distribution network nodes and time periods, respectively. Based on second-order cone programming, the power flow equations of the distribution network are first treated with phase angle relaxation and second-order cone relaxation, resulting in the following constraints: ; ; ; ; ; ; ; ; ; in, and These represent the active and reactive power flows flowing through line l at time t, respectively. and These represent the resistance and reactance on line l, respectively. This represents the square of the current in line l. and These represent the conductance and susceptance of node n, respectively. Represents complex power. This represents the voltage at node n. and These represent the upper and lower limits of the voltage at node n, respectively. This represents the electric vehicle charging load at node n. This represents the power generation of the thermal power generator at node n. This indicates the projected amount of new energy power generation; and These are the indices of the start and end nodes of the power distribution line l; and This is a set of indices representing the starting and ending nodes of power distribution line l, respectively.

[0011] Preferably, in step S3, a carbon emission flow theory based on the "power point tracking" principle is used to calculate the carbon emission intensity of each node, thereby obtaining a node-based carbon emission price signal that reflects the economic value and environmental cost of electricity. Specifically: Step S3-1: Calculate the carbon emission intensity at each node: The nodal carbon emission intensity of each node n is calculated as follows: ; In the above formula, This represents the carbon flow into node n. This represents the electrical energy flowing into node n. and Representing the lines respectively The amount of carbon emissions carried by the power flow in the middle and the accompanying power flow in the line; Representative Line The resistor on Representative Line The square of the current on it, and These represent distributed thermal power units connected to node n. The output and carbon flow, Indicates the carbon emission intensity of distributed thermal power units. Represents a collection of distributed thermal power units. This represents the output of distributed new energy source i connected to the node. A collection representing distributed new energy sources; The carbon emission intensity of the line is expressed as: ; Step S3-2: Calculate the comprehensive carbon price signal for distribution network nodes.

[0012] Preferably, the calculation of the comprehensive carbon price signal for distribution network nodes in step S3-2 specifically involves: The comprehensive electricity carbon price signal is composed of both the nodal marginal electricity price, which reflects the intensity of electricity supply and demand, and the carbon price signal, which reflects the intensity of carbon emissions. The expression is as follows: ; in, This indicates the overall price signal for electricity carbon. The market price of carbon tax.

[0013] Preferably, in step S4, the calculated comprehensive price signal of electric vehicle carbon dioxide is used as the charging price of charging piles in the transportation network to construct a dynamic traffic allocation model, thereby achieving coordinated optimization of electric vehicle travel and charging behavior. Specifically: Step S4-1: Construct an effective path generation model to pre-generate a set of feasible paths and charging station selections for vehicles in the road network, specifically: The effective path generation models include effective path generation models for gasoline-powered vehicles and effective path generation models for electric vehicles. The effective path generation model for gasoline-powered vehicles is as follows: ; in, Indicates OD pair Inter-fuel vehicle choice path The cost of driving; Indicates the route of fuel-powered vehicles and road sections The coupling relationship, when the path The value is 1 when passing through a road segment, and 0 otherwise. A relational matrix representing the nodes and road segments of a traffic network; This represents the travel demand vector for inter-fuel vehicles (OD). A single gasoline-powered vehicle finds the route with the lowest current travel cost while meeting road congestion constraints. Step S4-2: Construct a dynamic traffic assignment model to transform the operating status of the power system into adjustment signals that guide traffic flow and to coordinate the optimization of electric vehicle travel and charging behavior.

[0014] Preferably, the effective path generation model for electric vehicles is: ; in, Indicates OD pair The driving cost of choosing route ke for an electric vehicle; This represents the coupling relationship between the electric vehicle path ke and road segment a. It is 1 when path ke passes through road segment a, and 0 otherwise. This represents the travel demand vector for electric vehicles between different destinations (ODs). This represents the vector of charging segments. When there is a charging station on segment 'a', The value is 1 if it is not 1, otherwise it is 0. Under the constraints of road congestion and charging station congestion, a single electric vehicle finds the route with the lowest overall cost for travel and charging at present, and each electric vehicle can only choose one charging station to charge.

[0015] Preferably, the construction of the dynamic traffic assignment model in step S4-2 specifically includes: Step S4-2-1: Determine the road topology constraints for traffic flow and construct the objective function of the dynamic traffic assignment model, specifically: Traffic flow road topology constraints: ; ; Where w represents the origin-destination (OD) pair, and These represent the traffic flow of gasoline-powered vehicles and electric vehicles on paths kg and ke, respectively. and Representing OD pairs Total commuting demand for gasoline-powered and electric vehicles; According to the law of conservation of traffic flow, the traffic flow on any road segment is equal to the sum of the traffic flows of all gasoline-powered vehicles and electric vehicles passing through that road segment: ; ; ; and These represent the traffic flow of gasoline-powered vehicles and electric vehicles on road segment a, respectively. This represents the total traffic flow on road segment a. Denotes the set of OD pairs with w; and These represent the sets of paths kg for gasoline-powered vehicles and paths ke for electric vehicles, respectively. Traffic flows on all paths must satisfy the nonnegativity constraint: ; The objective function of the dynamic traffic assignment model is: ; Step S4-2-2: Determine the traffic flow cost model.

[0016] Preferably, the determination of the traffic flow cost model in step S4-2-2 specifically involves: Travel time on roads is positively correlated with traffic volume; that is, travel time increases as traffic volume on the road increases. The traffic flow cost model is as follows: ; in, This indicates the actual travel time of the car in road segment a. This represents the theoretical commute time under zero traffic flow. This indicates the maximum traffic flow on road segment a. The coefficient representing the increasing cost of travel is derived by fitting actual road conditions; When passing through sections of road with charging stations, the travel time is considered to be equivalent to the charging waiting time. The charging waiting time includes the actual charging time and the queuing time. The actual charging time is usually limited by the output power of the charging facilities. In fast charging scenarios, the actual charging time for each user is relatively constant and can be approximated as a constant. The queuing time depends on the service capacity of the charging station and increases significantly as the number of users requesting charging at the same time increases. The traffic flow cost model is converted to: ; in, This indicates the charging time in charging segment a. This indicates the maximum traffic flow on charging segment 'a', which is also the maximum capacity of the charging station. This represents the rate of increase in charging queue time costs. Let a be the set representing charging segment a; Based on road travel time costs and charging station waiting time costs, the commuting costs for each OD pair for gasoline and electric vehicles. and They are shown below: ; ; in, This represents the time delay cost associated with each unit of commuting time. and These represent the coupling relationship between OD and the path and road segment a for fuel-powered vehicles and electric vehicles, respectively. The value is 1 when the path passes through road segment a, and 0 otherwise. This indicates the amount of electricity charged for an electric vehicle. This represents the comprehensive price of electricity carbon at node n of the distribution network coupled to segment a.

[0017] Compared with the prior art, the present invention has the following improvements and advantages: 1. By integrating the marginal electricity price at each node with the carbon emission intensity at each node into a unified comprehensive electricity carbon price signal, this signal not only reflects the economic value of electricity but also internalizes environmental costs. At the same time, when this price is transmitted to the transportation side as a charging service fee, it effectively incentivizes electric vehicle users to spontaneously switch to "low-cost, low-emission" charging stations, thereby smoothing the grid load, alleviating line congestion, and achieving a synergistic effect of economic, safety, and environmental protection goals.

[0018] 2. By establishing a power-transportation collaborative optimization, with electric vehicles and charging stations as key coupling nodes, two independent physical networks are combined into a two-way interactive organic whole.

[0019] 3. This invention innovatively proposes an effective path generation model for electric vehicles. By dynamically considering actual travel costs and charging needs, it generates a limited set of truly selectable effective paths for different types of vehicles, eliminating a large number of redundant paths from the root, reducing the computational burden of subsequent dynamic traffic assignment models, and making it possible to solve large-scale electric-traffic coupled systems quickly and efficiently. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the method of the present invention.

[0021] Figure 2 This is a coupled topology diagram of the power distribution network and the transportation network.

[0022] Figure 3 This is a schematic diagram showing the comparison results of dynamic response in the safe zone.

[0023] Figure 4 This is a schematic diagram showing the comparison results of electric field-point cloud coupling compensation accuracy. Detailed Implementation

[0024] The invention will be further summarized below with reference to the accompanying drawings.

[0025] like Figure 1 As shown, a method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price is proposed. This method includes the following steps: Step S1: Obtain network parameters and real-time operating data of the power system and transportation system; Power network data includes topology, line parameters, generator operating costs and carbon emission intensity, node loads, and distributed energy output; Traffic network data includes road network topology, road segment capacity, and electric vehicle penetration rate; Establish a mapping relationship between power network data and transportation network data, and clarify the common coupling point corresponding to each charging station in the transportation network in the power network.

[0026] The mapping relationship between the IEEE-33 node distribution network and the 12-node ring transportation network is established by using spatial association and physical connection to map the power sources of charging stations in the transportation network to the load nodes of the power network. Specifically, CS1 in the transportation network corresponds to distribution network node 2, CS2 to distribution network node 7, CS3 to distribution network node 11, CS4 to distribution network node 14, CS5 to distribution network node 24, and CS6 to distribution network node 30.

[0027] In addition, the distributed resources of the power grid are the power source end of the power system. In the distribution network, wind power is connected to nodes 2, 27, and 30, photovoltaic power is connected to nodes 3, 17, and 16, and distributed thermal power is connected to nodes 7, 11, 15, and 25. These distributed resources, as power sources, inject electrical energy into the distribution network nodes where they are located, and these distribution network nodes happen to be associated with the common coupling points of the transportation network charging stations.

[0028] In step S2, with the goal of minimizing the total operating cost of the distribution network, an optimal distribution network power flow model is constructed, specifically as follows:

[0029] With the goal of minimizing the total operating cost of the distribution network, optimization calculations are performed under strict constraints such as power balance, line transmission capacity, and upper and lower limits of generator output to obtain the optimal generation dispatch scheme and the nodal marginal electricity price of each node.

[0030] In a distribution network, electricity-related costs are represented as network loss costs or the cost of purchasing electricity from the upstream grid. The objective function of the optimal distribution network power flow model should be to minimize the total operating cost of the Distribution System Occupation (DSO). The total operating cost includes the cost of purchasing the deficit electricity from the upstream grid and the cost of generating electricity from internal thermal power generators. The optimal distribution network power flow model is as follows:

[0031] ;

[0032] in, and These represent the two cost coefficients for thermal power generators. and Let N and T represent the electricity purchase price and the amount of electricity purchased from the upstream power grid at time t, respectively, and let N and T represent the set of distribution network nodes and time periods, respectively.

[0033] Based on second-order cone programming, the power flow equations of the distribution network are first treated with phase angle relaxation and second-order cone relaxation, resulting in the following constraints:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ; in, and They represent the lines at time t respectively. l The active and reactive power flow passing through the middle; and Representing the lines respectively l The resistance and reactance on it, Representative Line l The square of the current on it, and These represent the conductance and susceptance of node n, respectively. Represents complex power. Representative node n The voltage on, and These represent the upper and lower limits of the node voltage, respectively. Represents a node n Electric vehicle charging load, Represents a node n Power generation of thermal power generators This indicates the projected amount of new energy power generation; and It is a power distribution line l The indices of the start and end nodes; and This is a set of indices representing the starting and ending nodes of power distribution line l, respectively.

[0043] The nodal electricity price in the distribution network is the nodal marginal electricity price. By solving the optimized distribution network power flow model, and using the Lagrange multiplier method or duality theory in optimization theory, the nodal marginal electricity price of each node at each time step is obtained. Its value is the dual variable of the active power balance constraint of the distribution network. The marginal electricity price at a node is essentially a reflection of the spatiotemporal value of electrical energy. Its value equals the marginal system cost incurred to meet the increased unit electricity demand at a given moment at that node, while satisfying grid operation constraints. It can be decomposed into the external grid purchase price, congestion price, and reactive power support price.

[0044] Step S3 employs a carbon emission flow theory based on the "power point tracking" principle to calculate the carbon emission intensity at each node, thereby obtaining a node-specific carbon price signal that reflects the economic value and environmental cost of electricity. Specifically:

[0045] Calculate the carbon emission intensity at each node:

[0046] The nodal carbon emission intensity of each node n is calculated as follows:

[0047] ;

[0048] ;

[0049] In the above formula, This represents the carbon flow into node n. This represents the electrical energy flowing into node n. and These represent the electrical flow through line l and the carbon emissions accompanying the electrical flow through line l, respectively. This represents the resistance on line l. This represents the square of the current in line l. and These represent the power output and carbon flow rate of the distributed thermal power unit g connected to node n, respectively. This represents the carbon emission intensity of a distributed thermal power unit g. Represents a collection of distributed thermal power units. This represents the output of the distributed new energy source i connected to node n. A collection representing distributed new energy sources; The carbon emission intensity of the line is expressed as:

[0050] ;

[0051] Calculate the comprehensive carbon price signal for distribution network nodes:

[0052] The comprehensive electricity carbon price signal is composed of both the nodal marginal electricity price, which reflects the intensity of electricity supply and demand, and the carbon price signal, which reflects the intensity of carbon emissions. The expression is as follows:

[0053] ;

[0054] in, This indicates the overall price signal for electricity carbon. The market price of carbon tax.

[0055] In step S4, the calculated comprehensive price signal of electric vehicle carbon dioxide is used as the charging price of charging piles in the transportation network to construct a dynamic traffic allocation model, thereby achieving coordinated optimization of electric vehicle travel and charging behavior. Specifically:

[0056] An effective path generation model is constructed to pre-generate feasible paths and charging station selection sets for vehicles in the road network. The effective path generation model includes a gasoline-powered vehicle effective path generation model and an electric vehicle effective path generation model. The gasoline-powered vehicle effective path generation model is as follows:

[0057] ;

[0058] ;

[0059] in, This represents the driving cost (kg) of a fuel-powered vehicle choosing a route between w locations using the OD (Optical Distance) method. This indicates the coupling relationship between the path kg of a fuel-powered vehicle and the road segment a. It is 1 when the path kg passes through the road segment a, and 0 otherwise. A relational matrix representing the nodes and road segments of a traffic network; This represents the travel demand vector (OD) for fuel-powered vehicles between w locations.

[0060] A single gasoline-powered vehicle finds the route with the lowest current travel cost while meeting road congestion constraints.

[0061] The effective path generation model for electric vehicles is as follows:

[0062] ;

[0063] ;

[0064] ;

[0065] in, This represents the driving cost of the electric vehicle choosing the path ke between w and OD; This represents the coupling relationship between the electric vehicle path ke and road segment a. It is 1 when path ke passes through road segment a, and 0 otherwise. Let represent the travel demand vector (OD) for electric vehicles between w locations. This represents the vector of charging segments. When there is a charging station on segment 'a', The value is 1 if it is not 1, otherwise it is 0.

[0066] Under the constraints of road congestion and charging station congestion, a single electric vehicle finds the route with the lowest overall cost for travel and charging at present, and each electric vehicle can only choose one charging station to charge.

[0067] A dynamic traffic assignment model is constructed to transform the operating status of the power system into adjustment signals that guide traffic flow, thereby coordinating the optimization of electric vehicle travel and charging behavior.

[0068] Determine the road topology constraints for traffic flow and construct the objective function for the dynamic traffic assignment model:

[0069] Traffic flow road topology constraints:

[0070] ;

[0071] ;

[0072] Where w represents the origin-destination (OD) pair for travel. and These represent the traffic flow of gasoline-powered vehicles and electric vehicles on paths kg and ke, respectively. and Representing OD pairs Total commuting demand for gasoline-powered and electric vehicles;

[0073] According to the law of conservation of traffic flow, the traffic flow on any road segment is equal to the sum of the traffic flows of all gasoline-powered vehicles and electric vehicles passing through that road segment:

[0074] ;

[0075] ;

[0076] ;

[0077] and These represent the traffic flow of gasoline-powered vehicles and electric vehicles on road segment a, respectively. This represents the total traffic flow on road segment a. Denotes the set of OD pairs with w; and These represent the sets of paths kg for gasoline-powered vehicles and paths ke for electric vehicles, respectively.

[0078] Traffic flows on all paths must satisfy the nonnegativity constraint:

[0079] ;

[0080] ;

[0081] The objective function of the dynamic traffic assignment model is:

[0082] ;

[0083] Determine the traffic flow cost model:

[0084] Generally, travel time on a road is positively correlated with traffic volume; that is, travel time increases as traffic volume on the road increases.

[0085] ;

[0086] in, This indicates the actual travel time of the car in road segment a. This represents the theoretical commute time under zero traffic flow. This indicates the maximum traffic flow on road segment a. The coefficient representing the increasing cost of travel is derived by fitting actual road conditions;

[0087] When passing through sections of road with charging stations, the travel time can be considered roughly equivalent to the charging waiting time. The charging waiting time mainly consists of two parts: the actual charging time and the queuing time. The actual charging time is usually limited by the output power of the charging facility, and in fast charging scenarios, the actual charging time for each user is relatively constant and can be approximated as a constant. The queuing time, on the other hand, depends on the service capacity of the charging station and increases significantly as the number of users requesting charging at the same time increases.

[0088] The traffic flow cost model is converted to:

[0089] ;

[0090] ;

[0091] in, This indicates the charging time in charging segment a. This indicates the maximum traffic flow on charging segment 'a', which is also the maximum capacity of the charging station. This represents the rate of increase in charging queue time costs. Let a be the set representing charging segment a;

[0092] Based on road travel time costs and charging station waiting time costs, the commuting costs for each OD pair for gasoline and electric vehicles. and They are shown below:

[0093] ;

[0094] ;

[0095] in, This represents the time delay cost associated with each unit of commuting time. and These represent the coupling relationship between OD and the path and road segment a for fuel-powered vehicles and electric vehicles, respectively. The value is 1 when the path passes through road segment a, and 0 otherwise. This indicates the amount of electricity charged for an electric vehicle. This represents the comprehensive price of electricity carbon at node n of the distribution network coupled to segment a.

[0096] To verify the effectiveness of the method of the present invention, the following calculation example is used:

[0097] This invention selects an improved IEEE 33-node power distribution network and a typical 12-node ring-structured transportation network for simulation. The coupling positions of distributed resources, electric vehicle charging stations, and the power distribution network are as follows: Figure 2As shown in the figure. Based on the power structure of the charging pile access points, they are divided into two categories: low-carbon and high-carbon. Charging piles 1 and 6 are located near a large number of distributed photovoltaic and wind power sources and are defined as low-carbon charging stations. Charging piles 2-5 are located near distributed thermal power units and are therefore considered as high-carbon emission charging stations.

[0098] Basic parameter settings: Cost coefficient of distributed thermal power generators and Set to 0.005 yuan / kWh respectively 2 And 0.3 yuan / kWh, carbon emission intensity The external carbon emission tax is set at 0.8 kg / kWh, and the external carbon emission tax price is set at 0.2 yuan / kg. The power base value for the distribution network is 10 MWh, and the upper and lower limits of the voltage amplitude at each node are 1.05 pu and 0.9 pu, respectively. Electric vehicle charging time. The maximum commuter traffic flow and maximum charging traffic flow on road a are 0.4h. and The latency cost per unit commute time is set to 1000 and 70 respectively. The charging demand for electric vehicles is set at 20 yuan per hour, which is the amount needed for charging within one hour. It is 30kWh.

[0099] To verify the superiority of the method proposed in this invention, three cases were compared:

[0100] Case 1: Ignoring carbon emission tax costs, only considering the commuting costs of electric vehicles and the operating costs of the power distribution network;

[0101] Case 2: Considering carbon emission tax costs, but without using an electricity-carbon coupling pricing mechanism, the carbon emission tax costs are directly included in the costs of distributed thermal power units. In this case, the cost coefficient of distributed thermal power units... Set to (0.3 + 0.16) yuan / kWh;

[0102] Case 3: Using the electricity-carbon coupling pricing mechanism proposed in this invention to achieve coordinated optimization of electricity and transportation.

[0103] like Figure 3 As shown, simulation results demonstrate that the proposed method is highly effective in reducing emissions and guiding low-carbon charging behavior: Regarding carbon emissions, the total emissions in Case 3 are reduced by 8.9% and 1.2% compared to Case 1 and Case 2, respectively. Regarding charging prices, the charging prices at low-carbon charging stations 1 and 6 in Case 3 are reduced by 11.9% and 7.9% compared to Case 1 and Case 2, respectively.

[0104] like Figure 4 As shown, in terms of charging behavior, the charging volume of the low-carbon charging station in Case 3 increased by 7.3% and 6.9% compared with Case 1 and Case 2, respectively.

[0105] Furthermore, the simulation results from the three cases show a typical market supply and demand relationship between charging prices and charging volume: when Case 3 lowers the price of low-carbon charging piles through an electro-carbon coupling mechanism, its charging demand increases significantly. This phenomenon indicates that the electro-carbon coupling price signal proposed in this paper can effectively guide the charging behavior of electric vehicle users, creating favorable conditions for the distribution side to absorb clean energy through demand-side response, and ultimately reducing the total carbon emissions of the system.

[0106] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price, characterized in that: The method includes the following steps: Step S1: Obtain network parameters and real-time operating data of the power system and transportation system; Step S2: Construct an optimal power flow model for the distribution network with the goal of minimizing the total operating cost of the distribution network; Step S3: Using the carbon emission flow theory based on the "power point tracking" principle, calculate the carbon emission intensity of each node to obtain the node-based comprehensive carbon price signal that reflects the economic value and environmental cost of electricity. Step S4: Use the calculated comprehensive price signal of electric carbon as the charging price of charging piles in the transportation network to construct a dynamic traffic allocation model, thereby achieving coordinated optimization of electric vehicle travel and charging behavior; Step S5: Feed the charging load distribution calculated by the dynamic traffic assignment model back to the optimal power flow model of the distribution network as a new boundary condition to update the load of the grid nodes, recalculate the comprehensive price of electricity carbon, and finally output the best scheduling scheme that takes into account economy, network topology security and environmental friendliness.

2. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 1, characterized in that: The acquisition of network parameters and real-time operating data of the power system and transportation system in step S1 specifically includes: Power network data includes topology, line parameters, generator operating costs and carbon emission intensity, node loads, and distributed energy output; Traffic network data includes road network topology, road segment capacity, and electric vehicle penetration rate; Establish a mapping relationship between power network data and transportation network data, and clarify the common coupling point corresponding to each charging station in the transportation network in the power network.

3. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 1, characterized in that: In step S2, with the goal of minimizing the total operating cost of the distribution network, an optimal distribution network power flow model is constructed, specifically as follows: In a distribution network, electricity-related costs are represented as network loss costs or the cost of purchasing electricity from the upstream grid. The objective function of the optimal distribution network power flow model should be to minimize the total operating cost of the Distribution System Occupation (DSO). The total operating cost includes the cost of purchasing the deficit electricity from the upstream grid and the cost of generating electricity from internal thermal power generators. The optimal distribution network power flow model is as follows: ； in, and These represent the two cost coefficients for thermal power generators. and Let N and T represent the electricity purchase price and the amount of electricity purchased from the upstream power grid at time t, respectively, and let N and T represent the set of distribution network nodes and time periods, respectively. Based on second-order cone programming, the power flow equations of the distribution network are first treated with phase angle relaxation and second-order cone relaxation, resulting in the following constraints: ； ； ； ； ； ； ； ； ； in, and These represent the active and reactive power flows flowing through line l at time t, respectively. and These represent the resistance and reactance on line l, respectively. This represents the square of the current in line l. and These represent the conductance and susceptance of node n, respectively. Represents complex power. This represents the voltage at node n. and These represent the upper and lower limits of the voltage at node n, respectively. This represents the electric vehicle charging load at node n. This represents the power generation of the thermal power generator at node n. This indicates the projected amount of new energy power generation; and These are the indices of the start and end nodes of the power distribution line l; and This is a set of indices representing the starting and ending nodes of power distribution line l, respectively.

4. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 1, characterized in that: In step S3, the carbon emission flow theory based on the "power point tracking" principle is used to calculate the carbon emission intensity of each node, thereby obtaining a node-specific carbon price signal that reflects the economic value and environmental cost of electricity. Specifically: Step S3-1: Calculate the carbon emission intensity at each node: The nodal carbon emission intensity of each node n is calculated as follows: ； In the above formula, This represents the carbon flow into node n. This represents the electrical energy flowing into node n. and Representing the lines respectively The amount of carbon emissions carried by the power flow in the middle and the accompanying power flow in the line; Representative Line The resistor on Representative Line The square of the current on it, and These represent distributed thermal power units connected to node n. The output and carbon flow, Indicates the carbon emission intensity of distributed thermal power units. Represents a collection of distributed thermal power units. This represents the output of distributed new energy source i connected to the node. A collection representing distributed new energy sources; The carbon emission intensity of the line is expressed as: ； Step S3-2: Calculate the comprehensive carbon price signal for distribution network nodes.

5. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 4, characterized in that: The calculation of the comprehensive carbon price signal for the distribution network nodes in step S3-2 is specifically as follows: The comprehensive electricity carbon price signal is composed of both the nodal marginal electricity price, which reflects the intensity of electricity supply and demand, and the carbon price signal, which reflects the intensity of carbon emissions. The expression is as follows: ； in, This indicates the overall price signal for electricity carbon. The market price of carbon tax.

6. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 1, characterized in that: In step S4, the calculated comprehensive price signal of electric vehicle carbon dioxide is used as the charging price of charging piles in the transportation network to construct a dynamic traffic allocation model, thereby achieving coordinated optimization of electric vehicle travel and charging behavior. Specifically: Step S4-1: Construct an effective path generation model to pre-generate a set of feasible paths and charging station selections for vehicles in the road network, specifically: The effective path generation models include effective path generation models for gasoline-powered vehicles and effective path generation models for electric vehicles. The effective path generation model for gasoline-powered vehicles is as follows: ； in, Indicates OD pair Inter-fuel vehicle choice path The cost of driving; Indicates the route of fuel-powered vehicles and road sections The coupling relationship, when the path The value is 1 when passing through a road segment, and 0 otherwise. A relational matrix representing the nodes and road segments of a traffic network; This represents the travel demand vector for inter-fuel vehicles (OD). A single gasoline-powered vehicle finds the route with the lowest current travel cost while meeting road congestion constraints. Step S4-2: Construct a dynamic traffic assignment model to transform the operating status of the power system into adjustment signals that guide traffic flow and to coordinate the optimization of electric vehicle travel and charging behavior.

7. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 6, characterized in that: The effective path generation model for electric vehicles is as follows: ； in, Indicates OD pair The driving cost of choosing route ke for an electric vehicle; This represents the coupling relationship between the electric vehicle path ke and road segment a. It is 1 when path ke passes through road segment a, and 0 otherwise. This represents the travel demand vector for electric vehicles between different destinations (ODs). This represents the vector of charging segments. When there is a charging station on segment 'a', The value is 1 if it is not 1, otherwise it is 0. Under the constraints of road congestion and charging station congestion, a single electric vehicle finds the route with the lowest overall cost for travel and charging at present, and each electric vehicle can only choose one charging station to charge.

8. The method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 6, characterized in that: The construction of the dynamic traffic assignment model in step S4-2 is specifically as follows: Step S4-2-1: Determine the road topology constraints for traffic flow and construct the objective function of the dynamic traffic assignment model, specifically: Traffic flow road topology constraints: ； ； Where w represents the origin-destination (OD) pair, and These represent the traffic flow of gasoline-powered vehicles and electric vehicles on paths kg and ke, respectively. and Representing OD pairs Total commuting demand for gasoline-powered and electric vehicles; According to the law of conservation of traffic flow, the traffic flow on any road segment is equal to the sum of the traffic flows of all gasoline-powered vehicles and electric vehicles passing through that road segment: ； ； ； and These represent the traffic flow of gasoline-powered vehicles and electric vehicles on road segment a, respectively. This represents the total traffic flow on road segment a. Denotes the set of OD pairs with w; and These represent the sets of paths kg for gasoline-powered vehicles and paths ke for electric vehicles, respectively. Traffic flows on all paths must satisfy the nonnegativity constraint: ； The objective function of the dynamic traffic assignment model is: ； Step S4-2-2: Determine the traffic flow cost model.

9. A method for coordinated optimization of power-transportation network operation based on electricity-carbon coupling price as described in claim 8, characterized in that: The determination of the traffic flow cost model in step S4-2-2 is specifically as follows: Travel time on roads is positively correlated with traffic volume; that is, travel time increases as traffic volume on the road increases. The traffic flow cost model is as follows: ； in, This indicates the actual travel time of the car in road segment a. This represents the theoretical commute time under zero traffic flow. This indicates the maximum traffic flow on road segment a. The coefficient representing the increasing cost of travel is derived by fitting actual road conditions; When passing through sections of road with charging stations, the travel time is considered to be equivalent to the charging waiting time. The charging waiting time includes the actual charging time and the queuing time. The actual charging time is usually limited by the output power of the charging facilities. In fast charging scenarios, the actual charging time for each user is relatively constant and can be approximated as a constant. The queuing time depends on the service capacity of the charging station and increases significantly as the number of users requesting charging at the same time increases. The traffic flow cost model is converted to: ； in, This indicates the charging time in charging segment a. This indicates the maximum traffic flow on charging segment 'a', which is also the maximum capacity of the charging station. This represents the rate of increase in charging queue time costs. Let a be the set representing charging segment a; Based on road travel time costs and charging station waiting time costs, the commuting costs for each OD pair for gasoline and electric vehicles. and They are shown below: ； ； in, This represents the time delay cost associated with each unit of commuting time. and These represent the coupling relationship between OD and the path and road segment a for fuel-powered vehicles and electric vehicles, respectively. The value is 1 when the path passes through road segment a, and 0 otherwise. This indicates the amount of electricity charged for an electric vehicle. This represents the comprehensive price of electricity carbon at node n of the distribution network coupled to segment a.

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