Power-traffic network dynamic traffic flow optimal scheduling method based on optimal capacity distribution of charging stations

Abstract

The invention discloses a power-traffic network dynamic traffic flow optimal scheduling method based on optimal capacity distribution of a charging station, and the method achieves the optimal scheduling of the power-traffic network through the optimal capacity distribution of a power distribution network to the charging station on the basis of the precise simulation of the operation of the traffic network through the dynamic traffic flow. Firstly, an optimal capacity distribution model is constructed, charging station capacity distribution is preferentially carried out by a power distribution network, risks are actively reduced to the minimum, the supply capacity of the power distribution network is brought into full play on the basis of ensuring absolute safety of the power distribution network, and maximum electric energy supply is provided for a traffic network charging station. The charging current and the optimal capacity distribution strategy of the electric vehicle are considered in the traffic network, the travel cost of the electric vehicle changing along with the flowing process is finely simulated and calculated, the electric power-traffic network dynamic traffic flow optimal scheduling method based on the optimal capacity distribution of the charging station is constructed, and the optimal power price strategy is formulated. And the benefit maximization of the power distribution-traffic coupling network is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of electricity-transportation network collaborative optimization, and specifically relates to an optimal scheduling method for dynamic traffic flow in an electricity-transportation network based on optimal capacity allocation of charging stations. Background Art

[0002] As the number of electric vehicles increases year by year, they are expected to replace fuel vehicles and become the main means of transportation in the near future. Due to the mobility and convenience of charging and discharging, electric vehicles play a role in peak load reduction and valley filling in vehicle-grid interaction. On the other hand, with the rapid growth of the number of electric vehicles, in the absence of unified management and dispatch, large-scale electric vehicles are connected to the grid for charging at the same time, which will inevitably cause a certain impact on the stability of the power system. How to ensure the stability and safety of the power system remains to be solved.

[0003] In order to cope with the impact and challenges of large-scale access of electric vehicles to the power grid, it is necessary to set restrictions on the access of charging stations. The commonly used method is to set price signals in the transportation network to induce and motivate the charging behavior of electric vehicles, such as setting electricity prices at charging stations to induce charging behavior, or influencing the charging choice of vehicles by charging queue time. However, these methods have obvious limitations. The uncertainty and lag of electric vehicles in responding to electricity prices will still bring risks to the stability of the power system. It is obviously not a wise move to leave the safety and stability of the distribution network to the control of the transportation network. In order to ensure the safety of the distribution network, it is necessary to prioritize the optimal allocation of charging station capacity, ensure absolute safety within the margin range, and let the distribution network actively minimize risks. Summary of the invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an optimal scheduling method for dynamic traffic flow in a power-transportation network based on the optimal capacity allocation of charging stations in response to the deficiencies in the prior art. The present invention takes into account the coordination of the power system and the transportation system. In order to ensure the safety and stability of the distribution network, the capacity of charging stations is optimally allocated in the distribution network to ensure absolute safety within the safety margin. On this basis, the optimal electricity price strategy is formulated for the transportation network charging stations in combination with the improved dynamic traffic allocation model. The present invention can not only achieve the safety of the distribution network when supplying power to the transportation network charging stations, but also consider the maximization of the charging revenue of the charging stations, thereby ensuring the coordinated operation of the power-transportation network.

[0005] Technical solution: In order to solve the above technical problems, the present invention provides a method for optimal scheduling of dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations, comprising the following steps:

[0006] Step 1: Obtain the network coefficient and operation coefficient of the power grid model, wherein the network coefficient includes the power grid topology, line resistance and impedance, and the operation coefficient includes the power generation coefficient of the generator set, the power generation coefficient of the distributed power source, the charging and discharging coefficient of the energy storage system, the photovoltaic inverter coefficient, and the charging station parameters;

[0007] Step 2: Obtain scenario data on load demand at each node of the power grid, photovoltaic output, current energy storage capacity, and upper and lower limits of capacity demand for charging stations in the transportation network;

[0008] Step 3: Taking the current load demand of each node in the distribution network and the photovoltaic output as known quantities, taking the energy storage charging and discharging and the output of distributed power sources as control quantities, taking the grid operation constraints and the energy storage operation constraints as constraints, and taking the maximum total charging supply capacity of the distribution network to the charging station as the objective function, an optimal capacity allocation model for the distribution network charging station is established, and the model is solved using a mixed integer nonlinear solver to obtain the optimal capacity allocation strategy for the distribution network for the charging station;

[0009] Step 4: Obtain the network topology, road section capacity, travel demand in each time period, network coefficient and scenario data of the penetration rate of vehicles that need to be charged of the traffic network model, and establish a dynamic traffic allocation model;

[0010] Step 5: Based on the optimal capacity allocation strategy of the distribution network charging stations in step 3 and the dynamic traffic allocation model in step 4, a dynamic traffic allocation model considering the optimal capacity allocation of the distribution network is established, and the mobility characteristics of the electric vehicle charging flow are considered to obtain an improved dynamic traffic allocation model;

[0011] Step 6: Based on the dynamic traffic distribution model considering the optimal capacity allocation of the distribution network in step 5, the maximum revenue of the charging station is taken as the objective function, the model is solved using a nonlinear solver, and the optimal pricing strategy is formulated to obtain the optimal scheduling method for dynamic traffic flow in the power-transportation network based on the optimal capacity allocation of charging stations.

[0012] Furthermore, the relevant operating constraints of the optimal capacity allocation model of the distribution network in step 3 are as follows:

[0013] (1) Grid operation constraints:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, i, j, and m are distribution network nodes. and represents the active injection power and reactive injection power of nodes j and i respectively; N is the set of distribution network nodes; P jm (t) and Q jm (t) represent the active power and reactive power of line jm respectively; r ij 、x ij Respectively represent the resistance and reactance of line ij; P ij (t) and Q ij (t) represent the active power and reactive power of line ij respectively; l ij represents the square of the current in circuit ij; v i (t) and v j (t) represents the square of the voltage amplitude at nodes i and j at time t; and They represent the active output and reactive output of the generator at node i at time t respectively; and They represent the active power output and reactive power output of the photovoltaic power plant at node i at time t respectively; and They represent the active output and reactive output of the distributed generation at node i at time t respectively; and They represent the discharge and charge of energy storage at node i at time t respectively; represents the load demand of the charging station at node i at time t; and Respectively represent the active and reactive loads of node i at time t; |V i (t)| represents the absolute value of the voltage amplitude at node i at time t, V i and Respectively represent the lower and upper limits of the voltage amplitude at node i; is the complex power generated by the generator, including active and reactive Two parts, and represents the lower and upper limits of the generator output at node i; |I ij (t)| represents the absolute value of the current amplitude of the circuit ij at time t, Represents the upper limit of the current amplitude of circuit ij; represents the square of the capacity upper limit of line ij;

[0025] (2) Energy storage operation constraints

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] In the formula, and are the charging and discharging state 0-1 variables of ESS on node i at time t; is the discharge power at time t; is the charging power at node i at time t; is the maximum value of charge and discharge power; is the storage capacity of ESS on node i at time t; is the maximum value of the amount of electricity that can be stored; α and β are the charging and discharging efficiencies respectively;

[0032] (3) PV inverter and distributed power constraints:

[0033]

[0034]

[0035]

[0036]

[0037] In the formula, is the active and reactive output of PV at node i at time t; is the rated capacity of the PV inverter at node i; is the reactive capacity of the PV inverter at node i; is the active and reactive output of distributed generation at node i at time t; is the active and reactive capacity of distributed generation at node i.

[0038] Furthermore, in step 3, the maximum optimization objective function of the total charging supply capacity of the distribution network to the charging station is:

[0039]

[0040] In the formula, It represents the charging power allocated to the charging station on node i at time t.

[0041] Furthermore, in step 4, a dynamic traffic assignment model is established as:

[0042] (1) Section status constraints

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] E rs (t+1)=E rs (t)+e rs (t)(A-26)

[0049]

[0050] In the formula, a is the road segment, k is the path, r and s are the starting node and destination node in the traffic network, T(A) represents the road segment set, They represent the inflow, outflow and state traffic flow on section a at time t for the kth path with r as the starting node and s as the destination node; u a (t), v a (t), x a (t) represents the aggregated traffic flow on the road section a at time t; F(r) represents the set of road sections a starting from the starting node r, is the set of paths without charging, is the set of paths passing through the charging station, f rs (t) is the travel demand between the starting and destination node pair rs at time t, γ is the penetration rate of vehicles that need to be charged; D(s) represents the set of road sections a leading to the destination node s; e rs (t) represents the arrival traffic flow of the origin-destination node pair rs at time t; E rs (t) is the cumulative arrival traffic flow of the start-destination node pair rs up to time t; is the cumulative arrival traffic flow of the kth path selected;

[0051] (2) Flow Conservation and Flow Propagation Constraints

[0052]

[0053]

[0054] The flow conservation constraint states that the traffic flow entering node j at time t is equal to the traffic flow leaving node j at time t. In the formula, F(j) represents the set of road segments a starting from node j, and D(j) represents the set of road segments a leading to node j. represents the estimated travel time of section a at time t, Indicates that segment b is the selected path k∈K rs All sections after section a; They represent the state traffic flow on section b at time t under the kth path with r as the starting node and s as the destination node; the traffic propagation constraint indicates that the state traffic flow on section a at time t will be Enter the selected path k∈K rs The subsequent section b, such as the first item of (A-29) As shown, or through Arrive at the destination node, as in the second item of (A-29) As shown;

[0055] (3) Travel time and cost constraints

[0056]

[0057]

[0058] t a (u a (t))=0,a∈T(A D )(A-32)

[0059]

[0060]

[0061] In the formula, t a (u a (t)) indicates that the road section a has u a (t) travel time in case of vehicle inflow; is the free travel time when there is no congestion on section a; u a (t) is the inflow of vehicles entering road section a at time t; is the traffic capacity of section a; T(A O ) is a set of common road sections, T(A E ) is the charging section set, T(A D ) is a set of virtual road segments; t ch The average charging time for charging vehicles; The maximum waiting time at the charging station that is acceptable to users; The charging capacity of the charging station is determined by the charging interface configuration in the charging station; c a (t) is the travel cost of section a at time t; ω t is the unit time cost; is the unit charging electricity price of the charging station located on road section a at time t; ch is the average charging power; t ch is the average charging time; ordinary road section A O The travel cost is determined by the travel time on this section. E Charging cost must be added to the travel time. D There is no travel time and no travel cost; It represents the travel cost of the kth path starting at time t with r as the starting node and s as the destination node; Indicates that segment b is the selected path k∈K rs All sections before section a, represents the estimated travel time of segment b at time t; calculate The arrival toll cost of each road section is adopted; is a 0-1 parameter for determining the correspondence between a section and a path. If the path passes through section a, then If not,

[0062] (4) DUE principle constraints

[0063]

[0064]

[0065]

[0066] In the formula, is the vehicle flow of the route k between the start-destination node pair rs at time t; is the minimum travel cost between the start-destination node pair rs at time t;

[0067] (5) Charging station capacity constraints and initial conditions:

[0068]

[0069]

[0070]

[0071]

[0072] In the formula, and They represent the state traffic flow on section a at time t=0 and time t=1 respectively, with r as the starting node and s as the destination node under the kth path; They represent the inflow traffic volume on section a at time t=0 under the kth path with r as the starting node and s as the destination node; It represents the cumulative arrival traffic flow of the kth path selected with r as the starting node and s as the destination node at time t = 0;

[0073] Furthermore, in step 5, the improved dynamic traffic assignment model that incorporates the optimal capacity allocation strategy of the distribution network and the mobility characteristics of the electric vehicle charging flow is:

[0074] (1) Section status constraints: (A-21)-(A-27)

[0075] (2) Flow conservation and flow propagation constraints: (A-28)-(A-29)

[0076] (3) Travel time and cost constraints:

[0077]

[0078]

[0079] t a (u a (t))=0,a∈T(A D )(A-44)

[0080]

[0081]

[0082] In the formula, It represents the number of electric vehicles that can be accommodated by the power allocated by the distribution network to the charging station on the charging section a at time t; represents the minimum value between the configuration capacity of the charging station and the supply capacity of the distribution network, and the rest of the definitions are the same as (A-30)-(A-34);

[0083] (4) DUE principle constraints: (A-35)-(A-37)

[0084] (5) Charging station capacity constraints and initial conditions:

[0085]

[0086]

[0087]

[0088]

[0089] P ch,a (t) = x a (t+1)P ch ,a∈T(A E )(A-51)

[0090]

[0091]

[0092] Where P ch,a (t) represents the charging load required by the charging station located on road section a; represents the charging load that the distribution network can provide to the charging station located on section a at time t; represents the charging capacity provided by the distribution network node i at time t; It is used to control the one-to-one correspondence between charging load nodes in the distribution network and charging sections in the transportation network.

[0093] Furthermore, in step 6, the objective function for optimizing the maximum revenue of the charging station is:

[0094]

[0095] The above formula represents the total charging revenue of the entire transportation network charging station at all time intervals, P ch is the average charging power, t ch is the average charging time.

[0096] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0097] Compared with the basic scheme of optimizing the dispatch of charging stations in the traditional distribution-transportation network, the technical scheme of the present invention prioritizes the optimal capacity allocation of charging stations in the distribution network, and optimizes the dispatch based on the optimal capacity allocation by considering the dynamic traffic allocation model of the charging flow. The results of the example test show that the method proposed by the present invention can effectively avoid the load shedding of the distribution network caused by the overload of the charging station, improve the safety and stability of the distribution network, and also improve the economy of the entire distribution-transportation coupling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 is a flow chart of the method of the present invention;

[0099] Figure 2 It is the coupling topology diagram of distribution network and transportation network;

[0100] Figure 3It is the heat map of load shedding in the distribution network caused by not adopting capacity allocation;

[0101] Figure 4 It is the charging load of each charging station in each period obtained by using different models. DETAILED DESCRIPTION

[0102] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0103] like Figure 1 As shown, the present invention provides a method for optimal scheduling of dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations, comprising the following steps:

[0104] Step 1: Obtain the network coefficient and operation coefficient of the power grid model, wherein the network coefficient includes the power grid topology, line resistance and impedance, and the operation coefficient includes the power generation coefficient of the generator set, the power generation coefficient of the distributed power source, the charging and discharging coefficient of the energy storage system, the photovoltaic inverter coefficient, and the charging station parameters;

[0105] Step 2: Obtain scenario data on load demand at each node of the power grid, photovoltaic output, current energy storage capacity, and upper and lower limits of capacity demand for charging stations in the transportation network;

[0106] Step 3: Taking the current load demand of each node in the distribution network and the photovoltaic output as known quantities, taking the energy storage charging and discharging and the output of distributed power sources as control quantities, taking the grid operation constraints and the energy storage operation constraints as constraints, and taking the maximum total charging supply capacity of the distribution network to the charging station as the objective function, an optimal capacity allocation model for the distribution network charging station is established, and the model is solved using a mixed integer nonlinear solver to obtain the optimal capacity allocation strategy for the distribution network for the charging station;

[0107] Step 4: Obtain the network topology, road section capacity, travel demand in each time period, network coefficient and scenario data of the penetration rate of vehicles that need to be charged of the traffic network model, and establish a dynamic traffic allocation model;

[0108] Step 5: Based on the optimal capacity allocation strategy of the distribution network charging stations in step 3 and the dynamic traffic allocation model in step 4, a dynamic traffic allocation model considering the optimal capacity allocation of the distribution network is established, and the mobility characteristics of the electric vehicle charging flow are considered to obtain an improved dynamic traffic allocation model;

[0109] Step 6: Based on the dynamic traffic distribution model considering the optimal capacity allocation of the distribution network in step 5, the maximum revenue of the charging station is taken as the objective function, the model is solved using a nonlinear solver, and the optimal pricing strategy is formulated to obtain the optimal scheduling method for dynamic traffic flow in the power-transportation network based on the optimal capacity allocation of charging stations.

[0110] Furthermore, the relevant operating constraints of the optimal capacity allocation model of the distribution network in step 3 are as follows:

[0111] (1) Grid operation constraints:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122] In the formula, i, j, and m are distribution network nodes. and represents the active injection power and reactive injection power of nodes j and i respectively; N is the set of distribution network nodes; P jm (t) and Q jm (t) represent the active power and reactive power of line jm respectively; r ij 、x ij Respectively represent the resistance and reactance of line ij; P ij (t) and Q ij (t) represent the active power and reactive power of line ij respectively; l ij represents the square of the current in circuit ij; v i (t) and v j (t) represents the square of the voltage amplitude at nodes i and j at time t; and They represent the active output and reactive output of the generator at node i at time t respectively; and They represent the active power output and reactive power output of the photovoltaic power plant at node i at time t respectively; and They represent the active output and reactive output of the distributed generation at node i at time t respectively; and They represent the discharge and charge of energy storage at node i at time t respectively; represents the load demand of the charging station at node i at time t; and Respectively represent the active and reactive loads of node i at time t; V i (t)| represents the absolute value of the voltage amplitude at node i at time t, V i and Respectively represent the lower and upper limits of the voltage amplitude at node i; is the complex power generated by the generator, including active and reactive Two parts, and represents the lower and upper limits of the generator output at node i; |I ij (t)| represents the absolute value of the current amplitude of the circuit ij at time t, Represents the upper limit of the current amplitude of circuit ij; represents the square of the capacity upper limit of line ij;

[0123] (2) Energy storage operation constraints

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] In the formula, and are the charging and discharging state 0-1 variables of ESS on node i at time t; is the discharge power at time t; is the charging power at node i at time t; is the maximum value of charge and discharge power; is the storage capacity of ESS on node i at time t; is the maximum value of the amount of electricity that can be stored; α and β are the charging and discharging efficiencies respectively;

[0130] (3) PV inverter and distributed power constraints:

[0131]

[0132]

[0133]

[0134]

[0135] In the formula, is the active and reactive output of PV at node i at time t; is the rated capacity of the PV inverter at node i; is the reactive capacity of the PV inverter at node i; is the active and reactive output of distributed generation at node i at time t; is the active and reactive capacity of distributed generation at node i.

[0136] Furthermore, in step 3, the maximum optimization objective function of the total charging supply capacity of the distribution network to the charging station is:

[0137]

[0138] In the formula, It represents the charging power allocated to the charging station on node i at time t.

[0139] Furthermore, in step 4, a dynamic traffic assignment model is established as:

[0140] (1) Section status constraints

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] E rs (t+1)=E rs (t)+e rs (t)(A-26)

[0147]

[0148] In the formula, a is the road segment, k is the path, r and s are the starting node and destination node in the traffic network, T(A) represents the road segment set, They represent the inflow, outflow and state traffic flow on section a at time t for the kth path with r as the starting node and s as the destination node; u a (t), v a (t), x a (t) represents the aggregated traffic flow on the road section a at time t; F(r) represents the set of road sections a starting from the starting node r, is the set of paths without charging, is the set of paths passing through the charging station, f rs (t) is the travel demand between the starting and destination node pair rs at time t, γ is the penetration rate of vehicles that need to be charged; D(s) represents the set of road sections a leading to the destination node s; e rs (t) represents the arrival traffic flow of the origin-destination node pair rs at time t; E rs (t) is the cumulative arrival traffic flow of the start-destination node pair rs up to time t; is the cumulative arrival traffic flow of the kth path selected;

[0149] (2) Flow Conservation and Flow Propagation Constraints

[0150]

[0151]

[0152] The flow conservation constraint states that the traffic flow entering node j at time t is equal to the traffic flow leaving node j at time t. In the formula, F(j) represents the set of road segments a starting from node j, and D(j) represents the set of road segments a leading to node j. represents the estimated travel time of section a at time t, Indicates that segment b is the selected path k∈K rs All sections after section a; They represent the state traffic flow on section b at time t under the kth path with r as the starting node and s as the destination node; the traffic propagation constraint indicates that the state traffic flow on section a at time t will be Enter the selected path k∈K rs The subsequent section b, such as the first item of (A-29) As shown, or through Arrive at the destination node, as in the second item of (A-29) As shown;

[0153] (3) Travel time and cost constraints

[0154]

[0155]

[0156] t a (u a (t))=0,a∈T(A D )(A-32)

[0157]

[0158]

[0159] In the formula, t a (u a (t)) indicates that the road section a has u a (t) travel time in case of vehicle inflow; is the free travel time when there is no congestion on section a; u a (t) is the inflow of vehicles entering road section a at time t; is the traffic capacity of section a; T(A O ) is a set of common road sections, T(A E ) is the charging section set, T(A D ) is a set of virtual road segments; t ch The average charging time for charging vehicles; The maximum waiting time at the charging station that is acceptable to users; The charging capacity of the charging station is determined by the charging interface configuration in the charging station; c a (t) is the travel cost of section a at time t; ω t is the unit time cost; is the unit charging electricity price of the charging station located on road section a at time t; ch is the average charging power; t ch is the average charging time; ordinary road section A O The travel cost is determined by the travel time on this section. E Charging cost must be added to the travel time. D There is no travel time and no travel cost; It represents the travel cost of the kth path starting at time t with r as the starting node and s as the destination node; Indicates that segment b is the selected path k∈K rs All sections before section a, represents the estimated travel time of segment b at time t; calculate The arrival toll cost of each road section is adopted; is a 0-1 parameter for determining the correspondence between a section and a path. If the path passes through section a, then If not,

[0160] (4) DUE principle constraints

[0161]

[0162]

[0163]

[0164] In the formula, is the vehicle flow of the route k between the start-destination node pair rs at time t; is the minimum travel cost between the start-destination node pair rs at time t;

[0165] (5) Charging station capacity constraints and initial conditions:

[0166]

[0167]

[0168]

[0169]

[0170] In the formula, and They represent the state traffic flow on section a at time t=0 and time t=1 respectively, with r as the starting node and s as the destination node under the kth path; They represent the inflow traffic volume on section a at time t=0 under the kth path with r as the starting node and s as the destination node; It represents the cumulative arrival traffic flow of the kth path selected with r as the starting node and s as the destination node at time t = 0;

[0171] Furthermore, in step 5, the improved dynamic traffic assignment model that incorporates the optimal capacity allocation strategy of the distribution network and the mobility characteristics of the electric vehicle charging flow is:

[0172] (1) Section status constraints: (A-21)-(A-27)

[0173] (2) Flow conservation and flow propagation constraints: (A-28)-(A-29)

[0174] (3) Travel time and cost constraints:

[0175]

[0176]

[0177] t a (u a(t))=0,a∈T(A D )(A-44)

[0178]

[0179]

[0180] In the formula, It represents the number of electric vehicles that can be accommodated by the power allocated by the distribution network to the charging station on the charging section a at time t; represents the minimum value between the configuration capacity of the charging station and the supply capacity of the distribution network, and the rest of the definitions are the same as (A-30)-(A-34);

[0181] (4) DUE principle constraints: (A-35)-(A-37)

[0182] (5) Charging station capacity constraints and initial conditions:

[0183]

[0184]

[0185]

[0186]

[0187] P ch,a (t) = x a (t+1)P ch ,a∈T(A E )(A-51)

[0188]

[0189]

[0190] Where P ch,a (t) represents the charging load required by the charging station located on road section a; represents the charging load that the distribution network can provide to the charging station located on section a at time t; represents the charging capacity provided by the distribution network node i at time t; It is used to control the one-to-one correspondence between charging load nodes in the distribution network and charging sections in the transportation network.

[0191] Furthermore, in step 6, the objective function for optimizing the maximum revenue of the charging station is:

[0192]

[0193] The above formula represents the total charging revenue of the entire transportation network charging station at all time intervals, P ch is the average charging power, t ch is the average charging time.

[0194] Case Analysis

[0195] The following example illustrates the superiority of the optimal scheduling method for dynamic traffic flow in the power-transportation network based on the optimal capacity allocation of charging stations described in the present invention. Figure 2 The IEEE 33-node power system and 13-node transportation system coupling network shown in FIG. In order to compare the superiority of the method proposed in the present invention, the power-transportation network optimization scheduling method with the optimal electricity price strategy of the charging station in the priority transportation network and the power-transportation network dynamic traffic flow optimal scheduling method based on the optimal capacity allocation of charging stations proposed in the present invention are respectively used for power-transportation network collaborative scheduling. The present invention is implemented through the GAMS optimization platform, and the Dicopt solver is used to solve the mixed integer nonlinear programming problem, and the Baron solver is used to solve the nonlinear programming problem.

[0196] Based on this example, the power-transportation network optimization scheduling method with the optimal electricity price strategy for charging stations in the priority transportation network may cause unnecessary load shedding losses in the distribution network (see the results for details). Figure 3 ), the optimal capacity allocation results of the optimal dispatching method of the power-transportation network dynamic traffic flow based on the optimal capacity allocation of charging stations (see Table 1), and the comparison of the benefits of each charging station formulating the optimal electricity price strategy through the improved dynamic allocation model, static allocation model, and equilibrium pricing model on the basis of capacity allocation (see Figure 4 ). It shows that the optimal capacity allocation of charging stations by the priority distribution network can effectively avoid the load shedding loss caused by overload of charging stations and improve the stability of the power system. At the same time, the optimal electricity price strategy is formulated in combination with the improved dynamic traffic allocation model. Compared with the static allocation model, the economic benefit is improved by 22.35%. It shows that the optimal scheduling method of dynamic traffic flow in the power-transportation network based on the optimal capacity allocation of charging stations not only improves the safety and stability of the distribution network, but also improves the economy of the distribution-transportation coupling system.

[0197] Table 1 Results of optimal capacity allocation of charging stations

[0198]

[0199]

[0200] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. An optimal scheduling method for dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations, characterized in that: The method comprises the following steps: Step 1: Obtain the network coefficient and operation coefficient of the power grid model, wherein the network coefficient includes the power grid topology, line resistance and impedance, and the operation coefficient includes the power generation coefficient of the generator set, the power generation coefficient of the distributed power source, the charging and discharging coefficient of the energy storage system, the photovoltaic inverter coefficient, and the charging station parameters; Step 2: Obtain scenario data on load demand at each node of the power grid, photovoltaic output, current energy storage capacity, and upper and lower limits of capacity demand for charging stations in the transportation network; Step 3: Taking the current load demand of each node in the distribution network and the photovoltaic output as known quantities, taking the energy storage charging and discharging and the output of distributed power sources as control quantities, taking the grid operation constraints and the energy storage operation constraints as constraints, and taking the maximum total charging supply capacity of the distribution network to the charging station as the objective function, an optimal capacity allocation model for the distribution network charging station is established, and the model is solved using a mixed integer nonlinear solver to obtain the optimal capacity allocation strategy for the distribution network for the charging station; Step 4: Obtain the network topology, road section capacity, travel demand in each time period, network coefficient and scenario data of the penetration rate of vehicles that need to be charged of the traffic network model, and establish a dynamic traffic allocation model; Step 5: Based on the optimal capacity allocation strategy of the distribution network charging stations in step 3 and the dynamic traffic allocation model in step 4, a dynamic traffic allocation model considering the optimal capacity allocation of the distribution network is established, and the mobility characteristics of the electric vehicle charging flow are considered to obtain an improved dynamic traffic allocation model; Step 6: Based on the dynamic traffic distribution model considering the optimal capacity allocation of the distribution network in step 5, the maximum revenue of the charging station is taken as the objective function, the model is solved using a nonlinear solver, and the optimal pricing strategy is formulated to obtain the optimal scheduling method for dynamic traffic flow in the power-transportation network based on the optimal capacity allocation of charging stations.

2. The optimal scheduling method for dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations according to claim 1, characterized in that: The relevant operating constraints of the optimal capacity allocation model of the distribution network in step 3 are as follows: (1) Grid operation constraints: In the formula, i, j, and m are distribution network nodes. and represents the active injection power and reactive injection power of nodes j and i respectively; N is the set of distribution network nodes; P jm (t) and Q jm (t) represent the active power and reactive power of line jm respectively; rij, xij represent the resistance and reactance of line ij respectively; P ij (t) and Q ij (t) represent the active power and reactive power of circuit ij respectively; lij represents the square of the current of circuit ij; v i (t) and v j (t) represents the square of the voltage amplitude at nodes i and j at time t; and They represent the active output and reactive output of the generator at node i at time t respectively; and They represent the active power output and reactive power output of the photovoltaic power plant at node i at time t respectively; and They represent the active output and reactive output of the distributed generation at node i at time t respectively; and They represent the discharge and charge of energy storage at node i at time t respectively; represents the load demand of the charging station at node i at time t; and Respectively represent the active and reactive loads of node i at time t; V i (t)| represents the absolute value of the voltage amplitude at node i at time t, V i and Respectively represent the lower and upper limits of the voltage amplitude at node i; is the complex power generated by the generator, including active and reactive Two parts, and represents the lower and upper limits of the generator output at node i; |I ij (t)| represents the absolute value of the current amplitude of the circuit ij at time t, Represents the upper limit of the current amplitude of circuit ij; represents the square of the capacity upper limit of line ij; (2) Energy storage operation constraints In the formula, and are the charging and discharging state 0-1 variables of ESS on node i at time t; is the discharge power at time t; is the charging power at node i at time t; is the maximum value of charge and discharge power; is the storage capacity of ESS on node i at time t; is the maximum value of the amount of electricity that can be stored; α and β are the charging and discharging efficiencies respectively; (3) PV inverter and distributed power constraints: In the formula, is the active and reactive output of PV at node i at time t; is the rated capacity of the PV inverter at node i; is the reactive capacity of the PV inverter at node i; is the active and reactive output of distributed generation at node i at time t; is the active and reactive capacity of distributed generation at node i.

3. The optimal scheduling method for dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations according to claim 2, characterized in that: In step 3, the maximum optimization objective function of the total charging supply capacity of the distribution network to the charging station is: In the formula, It represents the charging power allocated to the charging station on node i at time t.

4. The optimal scheduling method for dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations according to claim 3 is characterized in that: In step 4, the dynamic traffic assignment model is established as: (1) Section status constraints E rs (t+1)=E rs (t)+e rs (t)(A-26) In the formula, a is the road segment, k is the path, r and s are the starting node and destination node in the traffic network, T(A) represents the road segment set, They represent the inflow, outflow and state traffic flow on section a at time t for the kth path with r as the starting node and s as the destination node; u a (t), v a (t), x a (t) represents the aggregated traffic flow on the road section a at time t; F(r) represents the set of road sections a starting from the starting node r, is the set of paths without charging, is the set of paths passing through the charging station, f rs (t) is the travel demand between the starting and destination node pair rs at time t, γ is the penetration rate of vehicles that need to be charged; D(s) represents the set of road sections a leading to the destination node s; e rs (t) represents the arrival traffic flow of the origin-destination node pair rs at time t; E rs (t) is the cumulative arrival traffic flow of the start-destination node pair rs up to time t; is the cumulative arrival traffic flow of the k-th path selected; (2) Flow Conservation and Flow Propagation Constraints The flow conservation constraint states that the traffic flow entering node j at time t is equal to the traffic flow leaving node j at time t. In the formula, F(j) represents the set of road segments a starting from node j, and D(j) represents the set of road segments a leading to node j. represents the estimated travel time of section a at time t, Indicates that segment b is the selected path k∈K rs All sections after section a; They represent the state traffic flow on section b at time t under the kth path with r as the starting node and s as the destination node; the traffic propagation constraint indicates that the state traffic flow on section a at time t will be Enter the selected path k∈K rs The subsequent section b, such as the first item of (A-29) As shown, or through Arrive at the destination node, as in the second item of (A-29) As shown; (3) Travel time and cost constraints t a (u a (t))=0,a∈T(A D )(A-32) Where, t a (u a (t)) indicates that the road section a has u a (t) travel time in case of vehicle inflow; is the free travel time when there is no congestion on section a; u a (t) is the inflow of vehicles entering road section a at time t; is the traffic capacity of section a; T(A O ) is a set of common road sections, T(A E ) is the charging section set, T(A D ) is a set of virtual road segments; t ch The average charging time for charging vehicles; The maximum waiting time at the charging station that is acceptable to users; The charging capacity of the charging station is determined by the charging interface configuration in the charging station; c a (t) is the travel cost of section a at time t; ω t is the unit time cost; is the unit charging electricity price of the charging station located on road section a at time t; ch is the average charging power; t ch is the average charging time; ordinary road section A O The travel cost is determined by the travel time on this section. E The charging cost must be added to the travel time. D There is no travel time and no travel cost; It represents the travel cost of the kth path starting at time t with r as the starting node and s as the destination node; Indicates that segment b is the selected path k∈K rs All sections before section a, represents the estimated travel time of segment b at time t; calculate The arrival toll cost of each road section is adopted; is a 0-1 parameter for determining the correspondence between a section and a path. If the path passes through section a, then If not, (4) DUE principle constraints In the formula, is the vehicle flow of the route k between the start-destination node pair rs at time t; is the minimum travel cost between the start-destination node pair rs at time t; (5) Charging station capacity constraints and initial conditions: In the formula, and They represent the state traffic flow on section a at time t=0 and time t=1 respectively, with r as the starting node and s as the destination node under the kth path; They represent the inflow traffic volume on section a at time t=0 under the kth path with r as the starting node and s as the destination node; It represents the cumulative arrival traffic flow of the kth path selected with r as the starting node and s as the destination node at time t=0.

5. The optimal scheduling method for dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations according to claim 4, characterized in that: In step 5, the improved dynamic traffic assignment model that incorporates the optimal capacity allocation strategy of the distribution network and the mobility characteristics of the electric vehicle charging flow is: (1) Section status constraints: (A-21)-(A-27) (2) Flow conservation and flow propagation constraints: (A-28)-(A-29) (3) Travel time and cost constraints: t a (u a (t))=0,a∈T(A D )(A-44) In the formula, It represents the number of electric vehicles that can be accommodated by the power allocated by the distribution network to the charging station on the charging section a at time t; represents the minimum value between the configuration capacity of the charging station and the supply capacity of the distribution network, and the rest of the definitions are the same as (A-30)-(A-34); (4) DUE principle constraints: (A-35)-(A-37) (5) Charging station capacity constraints and initial conditions: P ch,a (t)=x a (t+1)P ch ,a∈T(A E )(A-51) Where P ch,a (t) represents the charging load required by the charging station located on road section a; represents the charging load that the distribution network can provide to the charging station located on section a at time t; represents the charging capacity provided by the distribution network node i at time t; It is used to control the one-to-one correspondence between charging load nodes in the distribution network and charging sections in the transportation network.

6. The optimal scheduling method for dynamic traffic flow in a power-transportation network based on optimal capacity allocation of charging stations according to claim 5, characterized in that: In step 6, the objective function for optimizing the charging station revenue is: The above formula represents the total charging revenue of the entire transportation network charging station at all time intervals, P ch is the average charging power, t ch is the average charging time.

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