Traffic network-power grid collaborative optimization operation method based on two-way different electricity prices

Abstract

The invention discloses a traffic network-power grid collaborative optimization operation method based on two-way different electricity prices. The object of the invention is collaborative optimization operation of a traffic network-power distribution network coupling network containing a fuel vehicle and an electric vehicle at the same time. According to the method, the supervision efficiency of electric vehicles running in different directions is improved by introducing a differential pricing mechanism, the total social cost is reduced, the complex nonlinear complementary problem of mixed user equalization is effectively solved based on the Frank-Wolfe algorithm, and collaborative optimization operation of a traffic network and power grid coupling system is achieved. The problem that in an existing coupling system, unified charging electricity price is carried out on electric vehicles running bidirectionally on the same road section, and misguidance is generated on driver route selection is solved, the accuracy of electricity price guiding traffic flow is guaranteed, and the hybrid user balance optimization control problem is constructed in combination with a convex combination algorithm; and high-efficiency solving of the problem can be ensured under the condition that the scale of the traffic network-power grid coupling network is relatively large.

Description

Technical Field

[0001] The present invention belongs to a method for collaborative optimal operation of a transportation network - power grid coupling network in the cross - field of transportation and power, and specifically relates to a method and system for collaborative optimal operation of a transportation network - power grid based on hybrid user equilibrium and two - way different charging electricity prices. Background Technique

[0002] The popularity of electric vehicles is growing rapidly. Although fuel - powered vehicles still dominate, electric vehicles are quickly capturing market share. There are fundamental differences in driving characteristics and energy requirements between fuel - powered vehicles and electric vehicles: fuel - powered vehicles rely on fossil fuels, while electric vehicles rely on grid power supply. Therefore, the widespread use of electric vehicles not only poses higher requirements for the transportation network, but also exerts great pressure on the distribution network due to the particularity of the charging behavior. The charging demand of electric vehicles, especially during peak hours and at specific locations, may lead to peak loads in the distribution network, thus affecting the safe operation and economic efficiency of the power grid.

[0003] The mixed use of fuel - powered vehicles and electric vehicles generates complex coupling effects between the transportation network and the distribution network, resulting in a highly coupled operational relationship between the two. This coupling poses challenges at multiple levels: on the one hand, the charging demand of electric vehicles is concentrated in specific time periods and locations, which may form load peaks in the distribution network, thus affecting the stability and economic benefits of the power grid; on the other hand, the existence of traffic congestion affects the spatial distribution of the charging demand of electric vehicles, thereby changing the overall energy consumption pattern. To address these challenges, it is urgent to optimize the coordinated operation of the transportation network and the power grid to ensure their effective integration, taking into account the differences in characteristics between fuel - powered vehicles and electric vehicles.

[0004] Currently, the research on the collaborative optimization of the transportation network - power grid coupling network can be mainly divided into two categories: one is the coupling network based on fixed charging stations, and the other is the coupling network based on dynamic wireless charging. Fixed charging stations often cannot meet the charging needs of a large number of electric vehicles due to their large floor area and limited number of charging piles, which also causes users to have range anxiety. In contrast, dynamic wireless charging technology allows electric vehicles to charge while driving. The rapid development of this technology not only eliminates range anxiety but also reduces the dependence on large - capacity batteries and helps extend battery life.

[0005] In addition, current research on transportation networks usually relies on piecewise linearization methods and the big-M method to handle non-linear constraints. Although solvers such as Cplex and Gurobi can be used for solving, in the application of large-scale coupled networks, there are still certain limitations in the calculation speed and accuracy of existing methods. On the distribution network side, currently the same charging electricity price is implemented for electric vehicles traveling in both directions on the same section of road. This approach that does not distinguish the charging demand differences between two-way traffic flows may mislead the route selection of drivers, resulting in a deviation of the mixed user equilibrium value and affecting the accuracy of the optimization result. Summary of the Invention

[0006] In view of this, to solve the problems existing in the background technology, the present invention proposes a traffic network-power grid collaborative optimization operation method and system based on mixed user equilibrium and different charging electricity prices for two-way traffic. The present invention can accurately distinguish the charging demand differences between two-way traffic flows on the same section of road and generate different charging electricity prices to guide drivers to select reasonable driving routes; at the same time, the use of the Frank-Wolfe algorithm improves the calculation speed of the mixed user equilibrium on the traffic network side.

[0007] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0008] I. A traffic network-power grid collaborative optimization operation method based on different electricity prices for two-way traffic

[0009] Step 1: Determine the structure and connection mode of the distribution network according to the topological information of the traffic network, and obtain the differential connection structure of the distribution network;

[0010] Step 2: Construct a traffic network model based on mixed user equilibrium, and construct a distribution network model based on the branch flow model and the alternating current optimal power flow;

[0011] Step 3: After coupling and optimizing the solution of the traffic network model and the distribution network model according to the topological information of the traffic network, the overall traffic demand, the proportion of fuel vehicles and electric vehicles, the differential connection structure and the physical constraints of the state variables of the distribution network, obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the grid charging data, and realize the collaborative optimization operation of the traffic network-power grid coupled network.

[0012] The specific content of the said Step 1 is as follows:

[0013] For each origin-destination pair of the traffic network, record the traffic demand from the origin to the destination as the positive direction of the link, and record the traffic demand from the destination to the origin as the reverse direction of the link; after traversing and processing all origin-destination pairs, obtain the positive and reverse directions of all links in the traffic network and the distribution network;

[0014] Starting from the slack bus E0, connect the buses for all the charging links in the positive direction in the distribution network according to the radial distribution network topology; starting from the slack bus E0, according to the radial distribution network topology, connect the buses for the corresponding reverse charging links in the same wiring mode as the charging links in the positive direction in the distribution network, so as to obtain the differential connection structure of the distribution network.

[0015] In step 2, the traffic network model based on the mixed user equilibrium satisfies the following formula:

[0016] minF TAP

[0017]

[0018]

[0019] where F TAP represents the total travel time cost and charging cost of fuel vehicles and electric vehicles, T A is the set of links in the traffic network, and respectively represent the flow of fuel vehicles and electric vehicles on link a; x a is the total flow on link a; ω is the unit time cost, t a is the travel time on link a, x is the flow of the vehicle type to be solved on each link, is the charging electricity price of the electric vehicle on link a (determined by the marginal electricity price of the distribution network bus j connected thereto), E C is the charging demand of a single electric vehicle, is the flow of fuel vehicles on the k-th path for the origin-destination pair w, is the path-link incidence variable, r is the starting point of the origin-destination pair w, s is the destination point of the origin-destination pair w, k is the path set or the path set within, is the flow of electric vehicles on the k-th path for the origin-destination pair w, is the travel demand of fuel vehicles in the origin-destination pair w, W is the set of all origin-destination pairs, is the travel demand of electric vehicles in the origin-destination pair w, q w is the total travel demand in the origin-destination pair w; c a is the capacity of link a, ζ e is the proportion of electric vehicles in the travel demand; ζ g is the proportion of fuel vehicles in the travel demand; is the free travel time of link a, is the total travel time of all fuel vehicles, is the path-link association variable for fuel vehicles, is the driving cost of all fuel vehicles, is the set of available paths for fuel vehicles between the origin-destination pair w, is the total driving time of all electric vehicles, is the path-link association variable for electric vehicles, is the driving cost of all electric vehicles, is the set of available paths for electric vehicles between the origin-destination pair w.

[0020] In the said step 2, the distribution network model based on the branch flow model and the AC optimal power flow satisfies the following formula:

[0021]

[0022] where, E N is the set of distribution network buses, a j and b j are two power generation cost coefficients of the distributed generator at bus j, is the active power output of the distributed generator at bus j, ρ is the power purchase cost coefficient from the main grid, P0 is the amount of electricity purchased from the main grid, is the active power load of bus j, P jk is the active power of the branch (j,k) in the distribution network, P ij is the active power of the branch (i,j), μ(j) is the set of all branches starting from bus j, ν(j) is the set of all branches ending at bus j, l ij is the square of the current of the branch (i,j), I ij is the current of the branch (i,j), r ij is the resistance of the branch (i,j), is the reactive power output of the distributed generator at bus j, is the reactive power load of bus j, Q jk is the reactive power of the branch (j,k) in the distribution network, Q ij is the reactive power of the branch (i,j), ν j is the square of the voltage of bus j, ν i is the square of the voltage of bus i, x ij is the reactance of the branch (i,j), is the upper limit value of the current of the branch (i,j), and are respectively the lower and upper limit values of the active power of the generator at bus i, is the active power of the generator at bus i, and They are the lower and upper limit values of the reactive power of the generator at bus i, respectively. is the reactive power of the generator at bus i, is the maximum value of the bus voltage, is the minimum value of the bus voltage; is the inherent load of bus j, T A is the set of all links in the road network, represents the flow of electric vehicles on link a, E C is the charging demand of a single electric vehicle, x a is the total flow on link a, t a is the travel time on the link, ψ is the charging power of the wireless charging device, and η is the charging efficiency.

[0023] Specifically, step 3 is as follows:

[0024] Step 3.1: Solve the traffic network model based on the mixed user equilibrium according to the topological information of the traffic network, the overall traffic demand, and the ratio of fuel vehicles and electric vehicles, so as to minimize the total travel cost of fuel vehicles and electric vehicles, and thus obtain the traffic flow of fuel vehicles and electric vehicles on each link.

[0025] Step 3.2: Combine the differential connection structure and the physical constraints of the state variables of the distribution network and the traffic flow of electric vehicles on each link, solve the distribution network model, and obtain the marginal electricity price of each bus.

[0026] Step 3.3: If the grid cost corresponding to the marginal electricity price of each current bus is the lowest, obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the grid charging data. Otherwise, based on the traffic flow of fuel vehicles and electric vehicles on each current link, repeat steps 3.1 and 3.2, continuously solve the traffic network model and the distribution network model until the grid cost corresponding to the latest marginal electricity price of each bus is the lowest, obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the grid charging data, and realize the coordinated optimal operation of the traffic network-grid coupled network.

[0027] II. A traffic network-grid coordinated optimal operation system based on two-way different electricity prices

[0028] The distribution network differential connection structure generation unit is used to determine the structure and connection mode of the distribution network according to the topological information of the traffic network;

[0029] The traffic network topological information acquisition unit is used to acquire the topological information of the traffic network;

[0030] The overall traffic demand acquisition unit is used to acquire the overall traffic demand;

[0031] A solver, which is used to perform coupled optimization on a traffic network model and a power distribution network model according to the topological information of the traffic network, the overall traffic demand, the ratio of fuel vehicles and electric vehicles, the differential connection structure of the power distribution network, and the physical constraints of state variables, and then obtain the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data;

[0032] An output display unit, which is used to output the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data.

[0033] III. A computer device

[0034] The device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the traffic network-power grid collaborative optimization operation method based on two-way different electricity prices are implemented.

[0035] IV. A computer-readable storage medium

[0036] The medium stores a computer program. When the computer program is executed by a processor, the steps of the traffic network-power grid collaborative optimization operation method based on two-way different electricity prices are implemented.

[0037] V. A computer program product

[0038] The product includes a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the traffic network-power grid collaborative optimization operation method based on two-way different electricity prices are implemented.

[0039] The beneficial effects of the present invention are as follows:

[0040] (1) By designing the wiring mode of the power distribution network coupled with the traffic network, the present invention realizes a differential charging electricity price mechanism for different driving directions on the same dynamic wireless charging section. The charging electricity price for each driving direction is only related to the traffic flow in this direction, which improves the accuracy of guiding drivers' path selection through the charging electricity price;

[0041] (2) The present invention constructs a hybrid equilibrium traffic network model and a power distribution network model based on dynamic wireless charging. The charging demand of electric vehicles is related to the path travel time, the charging power and efficiency of wireless charging devices, and the model is more accurate.

[0042] (3) By using the convex combination algorithm to solve the hybrid user equilibrium, the present invention improves the solution speed and accuracy, which is beneficial to the application in a large-scale traffic network-power distribution network coupled network. Description of the Drawings

[0043] Figure 1 It is a collaborative operation framework diagram of a traffic network-power grid coupling system.

[0044] Figure 2 This is the flow chart for optimizing the coupling solution of the present invention.

[0045] Figure 3 This is the schematic diagram of the distribution network structure with two-way differential charging electricity prices of the present invention.

[0046] Figure 4 This is the topological diagram of the transportation network - distribution network coupling network of the present invention.

[0047] Figure 5 This is the effect diagram of the optimized collaborative operation of the transportation network - distribution network coupling network of the present invention.

[0048] Figure 6 This is the comparison diagram of the experimental results of the present invention. Detailed implementation manners

[0049] The present invention will be further described below in conjunction with the accompanying drawings and specific examples, but the protection scope of the present invention is not limited thereto.

[0050] In this embodiment, wireless charging devices are installed on all main roads in the transportation network, and all drivers can obtain accurate information on traffic congestion and charging electricity prices in the transportation network. It is assumed that all drivers can rationally choose the path with the lowest total driving cost.

[0051] The present invention proposes a method for optimizing the collaborative operation of a transportation network - power grid based on two-way different electricity prices, as Figure 1 and Figure 2 shown. The method includes the following steps:

[0052] Step 1: Collect the topological information and overall traffic demand of the transportation network; and determine the proportions of fuel vehicles and electric vehicles; according to the topological information of the transportation network, determine the structure and connection mode of the corresponding distribution network of the transportation network, and obtain the differential connection structure of the distribution network, as Figure 3 shown in (c) of Figure 3 to achieve a differential pricing mechanism in different directions on the same road section.

[0053] The specific content of Step 1 is as follows:

[0054] For each origin - destination pair in the transportation network, record the traffic demand from the origin to the destination as the positive direction of the link, and record the traffic demand from the destination to the origin as the negative direction of the link; after traversing and processing all origin - destination pairs, obtain the positive and negative directions of all links in the transportation network and the distribution network;

[0055] Starting from the slack bus E0, connect the buses for all the charging links in the positive direction in the distribution network according to the radial distribution network topology to supply power to them; starting from the slack bus E0, according to the radial distribution network topology, connect the buses for the corresponding charging links in the reverse direction in the same wiring manner as the charging links in the positive direction in the distribution network to supply power to them, so as to obtain the differential connection structure of the distribution network.

[0056] Step 2: Construct a traffic network model based on mixed user equilibrium, and construct a distribution network model based on the branch flow model (BFM) and the alternating current optimal power flow (ACOPF), as Figure 3 shown;

[0057] Among them, the traffic network model based on mixed user equilibrium satisfies the following formula:

[0058] minF TAP

[0059]

[0060] Among them, F TAP represents the total travel time cost and charging cost of fuel vehicles and electric vehicles, T A is the set of links in the traffic network, and respectively represent the traffic volumes of fuel vehicles and electric vehicles on link a, and the two constitute the first control variable; x a is the total traffic volume on link a, and the total traffic volume x a on link a is the optimization variable; ω is the unit time cost, t a is the travel time on link a, x is the traffic volume of the vehicle type to be solved on each link, is the charging electricity price of electric vehicles on link a (determined by the marginal electricity price of the distribution network bus j connected to it), E C is the charging demand of a single electric vehicle, is the traffic volume of fuel vehicles on the k-th path for the origin-destination (O-D) pair w, is the path-link incidence variable, indicating whether path k uses link a; if so, take 1, otherwise take 0, r is the starting point of the origin-destination pair w, s is the destination point of the origin-destination pair w, and k is the path set or the path set within, is the traffic volume of electric vehicles on the k-th path for the origin-destination pair w, is the travel demand of fuel vehicles for the origin-destination pair w in W, where W is the set of all origin-destination pairs. is the travel demand of electric vehicles for the origin-destination pair w, q w is the total travel demand for the origin-destination pair w; c a is the capacity of link a, ζ e is the proportion of electric vehicles in the travel demand; ζ g is the proportion of fuel vehicles in the travel demand; is the free-flow travel time of link a, is the total travel time of all fuel vehicles, is the fuel vehicle path-link incidence variable, is the travel cost of all fuel vehicles, is the set of available paths for fuel vehicles between the origin-destination pair w, is the total travel time of all electric vehicles, is the electric vehicle path-link incidence variable, is the travel cost of all electric vehicles, is the set of available paths for electric vehicles between the origin-destination pair w.

[0061] The distribution network model based on the branch flow model (BFM) and the alternating current optimal power flow (ACOPF) satisfies the following formula:

[0062]

[0063] where E N is the set of distribution network buses, a j and b j are the two generation cost coefficients of the distributed generator at bus j, is the active power output of the distributed generator at bus j, ρ is the cost coefficient of purchasing electricity from the main grid, P0 is the amount of electricity purchased from the main grid, is the active power load of bus j, P jk is the active power of the branch (j,k) in the distribution network, P ij is the active power of the branch (i,j), μ(j) is the set of all branches starting from bus j, ν(j) is the set of all branches ending at bus j, l ij is the square of the current in the branch (i,j), I ij is the current in the branch (i,j), r ij is the resistance of the branch (i,j), is the reactive power output of the distributed generator at bus j, Reactive load of bus bar j, Q jk Reactive power of branch (j,k) in the distribution network, Q ij Reactive power of branch (i,j), ν j Square of the voltage of bus bar j, ν j = |V j | 2 , V j Voltage of line j, ν i Square of the voltage of bus bar i, x ij Reactance of branch (i,j), Upper limit value of the current of branch (i,j), and Lower and upper limit values of the active power of the generator at bus bar i respectively, Active power of the generator at bus bar i, and Lower and upper limit values of the reactive power of the generator at bus bar i respectively, Reactive power of the generator at bus bar i, Maximum value of the bus bar voltage, Minimum value of the bus bar voltage; Natural load of bus bar j, T A Set of all links in the road network, Indicates the flow of electric vehicles on link a, E C Charging demand of a single electric vehicle, x a Total flow on link a, t a Travel time on the link, ψ is the charging power of the wireless charging device, η is the charging efficiency. l ij and ν i constitute the second control variable.

[0064] Step 3: Set the initial charging electricity price for each link of the coupled network, set the maximum number of upper and lower layer iterations during the entire simulation process of the coupled network, and the maximum number of its own iterations of the upper layer; according to the topological information of the transportation network, the overall transportation demand, the ratio of fuel vehicles and electric vehicles, the differential connection structure of the distribution network, and the physical constraints of the state variables, perform coupled optimization on the transportation network model and the distribution network model, and after solving, obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the grid charging data, so as to realize the coordinated optimization operation of the transportation network-grid coupled network.

[0065] As Figure 1 shown, the specific content of the said Step 3 is:

[0066] Step 3.1: Solve the traffic network model based on the mixed user equilibrium according to the topological information of the traffic network, the overall traffic demand, and the ratio of fuel vehicles and electric vehicles, so as to minimize the total form cost of fuel vehicles and electric vehicles, and thus obtain the traffic flows of fuel vehicles and electric vehicles on each link;

[0067] In this embodiment, the Frank-Wolfe algorithm is used to solve the traffic network model based on the mixed user equilibrium, specifically as follows:

[0068] The feasible descent direction of the fuel vehicle flow can be obtained by solving the following linear programming:

[0069]

[0070] The feasible descent direction of the electric vehicle flow can be obtained by solving the following linear programming:

[0071]

[0072] Where, x n is the vehicle flow on each link at the nth iteration, F TAP (x n ) is the total travel cost at the nth iteration point x n , is the gradient of the total travel cost with respect to the link flow, is the linearization of F TAP (x n ). T is the transpose, y is the auxiliary link flow, is the auxiliary link flow of fuel vehicles, is the auxiliary path flow of fuel vehicles, is the auxiliary link flow of electric vehicles, is the auxiliary path flow of electric vehicles.

[0073] Perform all-or-nothing network loading for all origin-destination pairs to obtain and and then obtain the feasible descent direction y n -x n .

[0074] The optimal step size can be obtained by solving the following minimization problem:

[0075]

[0076] Where, x n is the vehicle flow on each link at the nth iteration, θ(y n -x n ) is the feasible descent direction of the total travel cost of gasoline vehicles at the nth iteration, κ(y n -xn ) is the feasible descent direction of the total driving cost of the tram at the nth iteration, is the flow of auxiliary gasoline vehicles on each link at the nth iteration, is the flow of gasoline vehicles on each link at the nth iteration, is the flow of auxiliary trams on each link at the nth iteration, is the flow of trams on each link at the nth iteration, θ and κ are two optimal step sizes, and are the stationary points of the above minimization problem.

[0077] Finally, the electric vehicle flow of each link is calculated, and then the charging load is obtained and transmitted to the lower-level distribution network.

[0078] Step 3.2: Combine the differential connection structure of the distribution network, the physical constraints of the state variables, and the electric vehicle flow of each link. Among them, the charging load can be obtained according to the electric vehicle flow of each link, and the distribution network model is solved to obtain the marginal electricity price of each bus;

[0079] In this embodiment, the distribution network model is solved by the Mosek solver, and the marginal electricity price at each bus is obtained by extracting the dual value of the active power constraint to obtain the charging electricity price of each dynamic wireless charging link and transmit it to the upper-level transportation network.

[0080] Step 3.3: If the grid cost corresponding to the marginal electricity price of each current bus is the lowest, the optimal traffic distribution of fuel vehicles and electric vehicles and the grid charging data are obtained; otherwise, based on the flow of fuel vehicles and electric vehicles on each current link, repeat Step 3.1 and Step 3.2, continuously solve the transportation network model and the distribution network model until the grid cost corresponding to the marginal electricity price of the latest each bus is the lowest, and obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the grid charging data to realize the coordinated optimal operation of the transportation network-grid coupling network.

[0081] The present invention has been verified through simulation experiments: the connection mode between the distribution network bus and the transportation network is as Figure 3 shown, where Figure 3 (a) of it represents the two-way traffic flow on the road section, Figure 3 (b) of it is the traditional connection mode between the distribution network bus and the transportation network, Figure 3 (c) of it is the connection mode of the present invention. The experimental platform topology diagram is as Figure 4As shown in the figure, the total traffic flow demand is shown in Table 1. The main parameter settings are as follows: the proportion of electric vehicles is taken as 0.5. The dynamic charging power is ψ = 0.01 MW, and the charging efficiency η = 0.9. For the distribution network, the fixed reactive power load of bus 18 is 2 mw, and the negative load is 1 mw. The fixed load of other buses is 0 MW. The distributed generator coefficient ai = 0.25 $ / MW2h, bi = 150 $ / MWh. The main grid electricity price ρ = 200 $ / MWh, and the travel time ω = 3 $ / h. The convergence tolerance is set to 1×10 -6 . The maximum number of iterations is Kmax = 30. The initial EV load is given by the UE result, and the initial charging price is 90 US dollars.

[0082] Table 1 shows the traffic flow demand in the transportation network

[0083]

[0084] The simulation results are as Figure 5 and Figure 6 shown. Among them, Figure 5 is the overall collaborative optimization result of the transportation network - distribution network coupling network. It can be seen from Figure 5 that since the transportation network and the distribution network are coupled using the topology shown in Figure 4 , the two-way charging prices of electric vehicles on the same dynamic wireless charging section are not the same. Specifically, the charging price of electric vehicles traveling from the upper left to the lower right is higher than that of electric vehicles traveling in the reverse direction. This is because the number of electric vehicles traveling in the forward direction is greater than the number of electric vehicles traveling in the reverse direction. The charging price of electric vehicles is only related to the number of vehicles traveling in that direction, making the path guidance for drivers more accurate. Figure 6 shows the charging prices of the coupling network under different electric vehicle charging power ψ values. Obviously, as the electric vehicle charging power ψ value increases, the charging price in the charging link also increases accordingly. In addition, the overall trend of the curve becomes steeper, indicating that as the electric vehicle charging power ψ increases, the gap between the highest and lowest charging prices also expands. This is mainly because a higher electric vehicle charging power ψ value will lead to an increase in the charging load. By presenting the simulation results, the effectiveness of the present invention is demonstrated.

[0085] The present invention also proposes a transportation network - power grid collaborative optimization operation system based on two-way different electricity prices, including:

[0086] A distribution network differential connection structure generation unit for determining the structure and connection mode of the distribution network according to the topology information of the transportation network;

[0087] A transportation network topology information acquisition unit for acquiring the topology information of the transportation network;

[0088] An overall traffic demand acquisition unit for acquiring the overall traffic demand;

[0089] A solver, which is used to perform coupled optimization on a traffic network model and a distribution network model according to the topological information of the traffic network, the overall traffic demand, the ratio of fuel vehicles and electric vehicles, the differential connection structure of the distribution network, and the physical constraints of state variables, and then obtain the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data;

[0090] An output display unit, which is used to output the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data.

[0091] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the traffic network-grid collaborative optimization operation method based on two-way different electricity prices are implemented.

[0092] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the traffic network-grid collaborative optimization operation method based on two-way different electricity prices are implemented.

[0093] The present invention also provides a computer program product, including computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the traffic network-grid collaborative optimization operation method based on two-way different electricity prices are implemented.

[0094] Finally, it should be noted that the above embodiments and descriptions are only used to illustrate the technical solutions of the present invention and not to limit them. Those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the disclosure of the technical solutions of the present invention, and they should all be covered by the protection scope of the claims of the present invention.

Claims

1. A transportation network-power grid coordinated optimization operation method based on two-way different electricity prices, characterized in that: The following steps are involved: Step 1: Determine the structure and connection mode of the distribution network according to the topological information of the transportation network, and obtain the differentiated connection structure of the distribution network; Step 2: Construct a transportation network model based on mixed user equilibrium, and a distribution network model based on branch flow model and AC optimal power flow; Step 3: Based on the topological information of the transportation network, the overall traffic demand, the ratio of fuel vehicles and electric vehicles, the differentiated connection structure of the distribution network, and the physical constraints of the state quantity, the transportation network model and the distribution network model are coupled and optimized to obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the charging data of the power grid, thus realizing the coordinated optimization operation of the transportation network-power grid coupling network.

2. The method for coordinated optimization operation of a transportation network and a power grid based on two-way different electricity prices according to claim 1 is characterized in that: The step 1 is specifically as follows: For each origin-destination pair in the transportation network, the traffic demand from the origin to the destination is recorded as the positive direction of the link, and the traffic demand from the destination to the origin is recorded as the reverse direction of the link; after traversing and processing all the origin-destination pairs, the positive and reverse directions of all links in the transportation network and the distribution network are obtained; Starting from the relaxed bus E0, all the charging links in the positive direction of the distribution network are connected to the bus according to the radial distribution network topology; starting from the relaxed bus E0, according to the radial distribution network topology, the corresponding reverse charging links are connected to the bus in the same way as the positive charging links in the distribution network, thereby obtaining a differentiated connection structure of the distribution network.

3. The method for coordinated optimization operation of a transportation network and a power grid based on two-way different electricity prices according to claim 1 is characterized in that: In step 2, the traffic network model based on mixed user equilibrium satisfies the following formula: Among them, F TAP represents the total travel time cost and charging cost of fuel vehicles and electric vehicles, T A is the set of links in the transportation network, and represent the traffic of fuel vehicles and electric vehicles on link a respectively; x a is the total traffic on link a; ω is the unit time cost, t a is the travel time on link a, x is the flow of the model to be found on each link, is the charging price of the electric vehicle on link a, E C For the charging needs of a single electric vehicle, is the fuel vehicle flow of the origin-destination pair w on the kth path, is the path-link association variable, r is the starting point of the origin-destination pair w, s is the destination point of the origin-destination pair w, and k is the path set or path set A path within is the electric vehicle flow of the origin-destination pair w on the kth path, is the travel demand of fuel vehicles in the departure-destination pair w, W is the set of all departure-destination pairs, is the travel demand of electric vehicles in the departure-destination pair w, q w is the total travel demand of the origin-destination pair w; c a is the capacity of link a, ζ e is the proportion of trams in travel demand; g is the proportion of gasoline vehicles in travel demand; is the free travel time of link a, is the total driving time of all fuel vehicles, is the fuel vehicle path-link association variable, For the driving cost of all fuel vehicles, is the set of paths available for a fuel vehicle between the departure and destination pair w, is the total driving time of all electric vehicles, is the electric vehicle path-link association variable, For all electric vehicle operating costs, is the set of paths available to the electric vehicle between the origin-destination pair w.

4. The method for coordinated optimization operation of a transportation network and a power grid based on two-way different electricity prices according to claim 1 is characterized in that: In step 2, the distribution network model based on the branch flow model and the AC optimal power flow satisfies the following formula: Among them, E N is the busbar set of the distribution network, a j and b j are the two generation cost coefficients of distributed generators at bus j, is the active power generated by the distributed generator at bus j, ρ is the cost coefficient of purchasing electricity from the main grid, P0 is the amount of electricity purchased from the main grid, is the active load of bus j, P jk is the active power of branch (j, k) in the distribution network, P ij is the active power of branch (i, j), μ(j) is the set of all branches starting from bus j, ν(j) is the set of all branches ending at bus j, l ij is the square of the branch (i, j) current, I ij is the current of branch (i, j), r ij is the resistance of branch (i,j), is the reactive power generated by the distributed generator at bus j, is the reactive load of bus j, Q jk is the reactive power of branch (j, k) in the distribution network, Q ij is the reactive power of branch (i, j), ν j is the square of bus voltage j, ν i is the square of bus voltage i, x ij is the reactance of branch (i,j), is the upper limit of the branch (i, j) current, and are the lower and upper limits of the active power of the generator at bus i, respectively. is the active power of the generator at bus i, and are the lower and upper limits of the reactive power of the generator at bus i, respectively. is the reactive power of the generator at bus i, is the maximum value of bus voltage, is the minimum value of bus voltage; is the inherent load of busbar j, T A is the set of all links in the road network, represents the flow of electric vehicles on link a, E C is the charging demand of a single electric vehicle, x a is the total traffic on link a, t a is the driving time on the link, ψ is the charging power of the wireless charging device, and η is the charging efficiency.

5. The method for coordinated optimization operation of a transportation network and a power grid based on two-way different electricity prices according to claim 1 is characterized in that: The step 3 is specifically as follows: Step 3.1: According to the topological information of the traffic network, the overall traffic demand, and the ratio of fuel vehicles to electric vehicles, the traffic network model based on mixed user equilibrium is solved to minimize the total driving cost of fuel vehicles and electric vehicles, thereby obtaining the fuel vehicle and electric vehicle flow of each link; Step 3.2: Combine the differentiated connection structure and physical constraints of the state quantity of the distribution network and the electric vehicle flow of each link to solve the distribution network model and obtain the marginal electricity price of each bus; Step 3.3: If the grid cost corresponding to the current marginal electricity price of each bus is the lowest, the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data are obtained. Otherwise, based on the current fuel vehicle and electric vehicle traffic of each link, repeat steps 3.1 and 3.2, and continuously solve the transportation network model and distribution network model until the grid cost corresponding to the latest marginal electricity price of each bus is the lowest, and the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data are obtained to achieve the coordinated optimization operation of the transportation network-grid coupling network.

6. A transportation network-power grid coordinated optimization operation system based on two-way different electricity prices, characterized in that: include: A distribution network differentiated connection structure generation unit, used to determine the structure and connection mode of the distribution network according to the topological information of the transportation network; A traffic network topology information acquisition unit, used to acquire the topology information of the traffic network; An overall traffic demand acquisition unit, used for acquiring overall traffic demand; The solver is used to couple and optimize the traffic network model and the distribution network model according to the topological information of the traffic network, the overall traffic demand, the ratio of fuel vehicles and electric vehicles, the differentiated connection structure of the distribution network, and the physical constraints of the state quantity, so as to obtain the optimal traffic distribution of fuel vehicles and electric vehicles and the charging data of the power grid; The output display unit is used to output the optimal traffic distribution of fuel vehicles and electric vehicles and grid charging data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the transportation network-power grid coordinated optimization operation method based on bidirectional different electricity prices as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the transportation network-power grid coordinated optimization operation method based on two-way different electricity prices as described in any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the transportation network-power grid coordinated optimization operation method based on two-way different electricity prices as described in any one of claims 1 to 5 are implemented.

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