Dynamic power-traffic coupling system optimal pricing method considering differentiated V2G

Abstract

The invention discloses a dynamic electric power-traffic coupling system optimal pricing method considering differentiation V2G, which fully considers electric vehicle user V2G differentiation factors on the background of traffic flow simulation of a dynamic traffic distribution model, and sets an optimal electricity price to realize optimal scheduling of an electric power-traffic coupling system. Firstly, a dynamic traffic distribution model considering traffic flow is established, and differentiated behaviors of users for V2G and changes of vehicle cost at any time and space movement are considered in the model. And then establishing a second-order cone DistFlow-based power distribution network optimal power flow model in the power distribution network to describe a power flow distribution condition, realizing coupling with a traffic network through a charging station and a V2G station, constructing a dynamic power-traffic coupling system optimal pricing method considering differentiated V2G, and guiding charging flow and V2G flow distribution through formulating an optimal charging electricity price and V2G reward so as to realize optimal pricing of the dynamic power-traffic coupling system. Load shedding loss caused by overload in the peak period of the power distribution network is reduced, and flexibility and reliability of the power distribution network are enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of coordinated optimization of the electric - transportation network, and particularly relates to an optimal pricing method for a dynamic power - transportation coupling system considering differentiated V2G. Background Art

[0002] Electric vehicles are considered an important direction to replace fuel vehicles and solve carbon emissions and the greenhouse effect. The large - scale access of electric vehicles to the power grid has made the connection between the transportation network and the distribution network closer. However, at the same time, it has also brought challenges to the stable operation of the distribution network. On the one hand, the surge in the charging load of electric vehicles puts pressure on the balance of the distribution network. On the other hand, the growth of electric vehicles will cause changes in the traffic flow distribution. If electric vehicles are not guided, it may lead to traffic congestion in the transportation network.

[0003] In order to reasonably guide electric vehicles and utilize the flexible charging and discharging characteristics of electric vehicles, enabling the power - transportation coupling system to better adapt to the rise of electric vehicles, a large number of studies have verified that price signals can be set in the transportation network to guide the distribution of traffic flow and alleviate traffic congestion in the transportation network. However, most studies only consider the charging demand of electric vehicles, but the discharging ability of electric vehicles as flexible energy storage has been ignored. The flexible charging and discharging ability of electric vehicles as mobile energy storage can be actively utilized, encouraging electric vehicles to perform vehicle - grid interaction during the peak load period of the power grid and guiding vehicles to go to V2G stations for discharging to relieve the power grid pressure. Summary of the Invention

[0004] Object of the Invention: The technical problem to be solved by the present invention is to provide an optimal pricing method for a dynamic power - transportation coupling system considering differentiated V2G in view of the deficiencies of the existing technology. The present invention takes into account the coordination of the power - transportation coupling system. First, a dynamic traffic assignment model considering traffic flow is established, in which the differentiated behavior of users towards V2G and the changes of vehicle costs with spatio - temporal movement are considered. Secondly, an optimal power flow model of the distribution network based on second - order cone DistFlow is established to describe the power flow distribution, and the coupling with the transportation network is realized through charging stations and V2G stations. By simulating the traffic flow and the power flow of the distribution network, the present invention formulates optimal electricity prices for charging stations and V2G stations to guide the charging and discharging of electric vehicles, relieve the load pressure of the distribution network, reduce the load shedding loss, and improve the stability and economy of the power - transportation coupling system.

[0005] Technical Solution: To solve the above - mentioned technical problem, the present invention provides an optimal pricing method for a dynamic power - transportation coupling system considering differentiated V2G, and the method includes the following steps:

[0006] Step 1: Obtain network parameters such as the traffic network topology, charging station / V2G station capacity, section capacity, travel demand, and vehicle-grid interaction penetration rate. Taking the charging / V2G price as the control variable and the traffic network operation constraints as the constraints, establish a dynamic traffic assignment model;

[0007] Step 2: Based on the dynamic traffic assignment model in Step 1, obtain the scenario data of the vehicle-grid interaction differentiated behavior parameters of traffic network users and the vehicle power distribution, and add the differentiated V2G selection and electricity price continuity constraints to establish a dynamic traffic assignment model considering differentiated V2G;

[0008] Step 3: Obtain the operation coefficients of the distribution network topology, line impedance, distributed power coefficient, and photovoltaic inverter coefficient. Taking the distributed power output as the control variable and the power grid operation constraints as the constraints, establish an optimal power flow model for the distribution network;

[0009] Step 4: Based on the optimal power flow model of the distribution network in Step 3, obtain the scenario data of the load demand of each node in the power grid, the upper and lower limits of the distributed power output, the photovoltaic output situation, the coupling structure of the charging station / V2G station, and the generation cost and load shedding loss coefficient, and establish an optimal power flow model for the distribution network considering load shedding;

[0010] Step 5: Based on the dynamic traffic assignment model considering differentiated V2G in Step 2 and the optimal power flow model of the distribution network considering load shedding in Step 4, establish an optimal pricing model for the dynamic power-traffic coupling system considering differentiated V2G. Taking the minimum total cost of the coupling system as the objective function, solve this model using a non-linear solver, and obtain the optimal pricing of the dynamic power-traffic coupling system considering differentiated V2G by setting the charging / V2G price to guide the traffic flow distribution.

[0011] Furthermore, the relevant operation constraints of the traffic network dynamic traffic assignment model in Step 1 are as follows:

[0012] (1) Section state equation:

[0013]

[0014] E rs (t + 1) = E rs (t) + e rs (t)(A - 8)

[0015]

[0016] In the formula, a is the section, k is the path, T(A) represents the section set. Each pair of starting node and destination node is called an O-D pair. Each O-D pair is connected by different optional paths, and the path is represented by K rsIt is indicated that \(r\in T(R)\) is the starting node, \(s\in T(S)\) is the destination node, and the travel demand between the O - D pair is represented by \(q\). rs It is indicated that in the transportation network considering the penetration rates of electric vehicles and V2G vehicles, the paths are divided into ordinary paths, charging paths, and V2G paths The section sets can be divided into the ordinary section set \(T(A O ), the charging section set \(T(A E ), the virtual section set \(T(A D ), and the V2G section set \(T(A V ) are respectively the inflow, outflow, and status vehicle flows on section \(a\) under the \(k\)-th path with \(r\) as the starting node and \(s\) as the destination node at time \(t\); \(u a (t)\), \(v a (t)\), \(x a (t)\) are respectively the inflow, outflow, and status aggregated vehicle flows on section \(a\) at time \(t\); \(F(r)\) is the set of sections \(a\) starting from the starting node \(r\); \(\gamma\) is the penetration rate of vehicles that need to be charged; \(\upsilon\) is the penetration rate of vehicles that can make V2G selections; \(f rs (t)\) is the travel demand between the O - D pair \(r - s\) at time \(t\); is the number of vehicles that differentially select ordinary paths with \(r\) as the starting node and \(s\) as the destination node; is the number of vehicles that differentially select V2G paths with \(r\) as the starting node rs point and \(s\) as the destination node; \(D(s)\) is the set of sections \(a\) leading to the destination node \(s\); \(e rs (t)\) is the arrival vehicle flow of the origin - destination node pair \(r - s\) at time \(t\); \(E rs (t)\) is the cumulative arrival vehicle flow of the origin - destination node pair \(r - s\) at time \(t\); is the cumulative arrival vehicle flow of the origin - destination node pair \(r - s\) that selects the \(k\)-th path at time \(t\);

[0017] (2) Flow conservation and flow propagation constraints

[0018]

[0019] In the formula, \(F(j)\) represents the set of sections \(a\) starting from node \(j\), and \(D(j)\) represents the set of sections \(a\) leading to node \(j\); is the free - flow time of section \(a\); indicates that section \(b\) is all the sections after section \(a\) in the selected path \(k\in K rs ; is the status vehicle flow on section \(b\) of the origin - destination node pair \(r - s\) at time is the state traffic flow on section b of the origin-destination node pair r-s at time t; is the cumulative arrival traffic flow of choosing the k-th path in the origin-destination node pair r-s at time t;

[0020] (3) Section travel time

[0021]

[0022] t a (u a (t)) = 0, a ∈ T(A D )(A - 16)

[0023] In the formula: t a (u a (t)) represents the travel time of section a when there is a traffic inflow of u a (t) vehicles; is the free travel time of section a; is the traffic capacity of section a; Charging section A E and V2G section A V adopt the M / M / c / K queuing model to describe the charging queuing behavior of charging vehicles at the charging station; t ch and t v2g are the average charging / V2G time of the vehicle; and are the maximum queuing time of the charging / V2G site capacity; and are the configured capacity of the charging station / V2G station, which is determined by the charging / V2G interface configuration in the station; x a (t + 1) are the state aggregated traffic flows on section a at time t + 1 respectively; Virtual section A D is actually a virtual section set for the connectivity of vehicle travel after expanding the traffic network section. Therefore, it is considered that the travel time of the vehicle on the virtual section A D is 0;

[0024] (4) User travel cost

[0025] c a (t) = ω t t a (u a (t)), a ∈ T(A O )(A - 17)

[0026]

[0027] c a (t) = 0, a ∈ T(A D)(A-20)

[0028]

[0029] Wherein, c a (t) is the traffic cost of road section a at time t; ω t is the cost per unit time; t a (u a (t)) represents the travel time of road section a with vehicle inflow of u a (t); is the unit charging price of the charging station on road section a at time t; p ch and p v2g are the average charging / V2G power; t ch and t v2g are the average charging / V2G time; is the unit discharge reward of the V2G station on road section a at time t; is the one-time subsidy of the V2G station on road section a at time t; is the travel cost of the k-th path starting from node r and ending at node s at time t, indicates that road section b is all road sections before road section a in the selected path k∈K rs When calculating , the traffic cost at the inflow time of each road section is adopted; is the free travel time of road section b; is a 0-1 parameter for judging the correspondence between road sections and paths. If path k passes through road section a, then If not, then

[0030] (5) Dynamic user equilibrium condition

[0031]

[0032] Wherein, 0≤a⊥b≥0 means a≥0, b≥0 and ab = 0; is the travel cost of the k-th path starting from node r and ending at node s at time t; are respectively the minimum travel costs of the ordinary path, charging path, and V2G path starting from node r and ending at node s at time t.

[0033] Furthermore, in step 2, the dynamic traffic assignment model adds differential V2G selection and electricity price continuity constraints:

[0034] (1) Differential V2G selection

[0035]

[0036] f o,p f(t) = 1 - f v,p (t)(A - 26)

[0037]

[0038] Where p is the power level of electric vehicle users; f v,p f(t) and f o,p f(t) are respectively the percentages of vehicles of users with power level p choosing the V2G path / ordinary path at time t; α p is the influence factor of V2G differential selection by the unit discharge reward; β p is the influence factor of V2G differential selection by the one-time subsidy; is the unit discharge reward of the V2G station on section a at time t; is the one-time subsidy of the V2G station on section a at time t; is the minimum V2G unit discharge reward; is the minimum V2G one-time subsidy time cost; δ p is the percentage of users with power level p; is the number of vehicles differentially choosing the ordinary path with r as the starting node and s as the destination node; is the number of vehicles differentially choosing the V2G path with r as the starting node and s as the destination node; υ is the vehicle penetration rate that can make V2G selection; f rs f(t) is the travel demand between O - D pair r - s at time t;

[0039] (2) Electricity price continuity constraint

[0040]

[0041] Where φ is the lower limit of price fluctuation; is the upper limit of price fluctuation; is the unit charging electricity price of the charging station on section a at time t; is the unit discharge reward of the V2G station on section a at time t; is the one-time subsidy of the V2G station on section a at time t; and are respectively the lower and upper limits of the charging electricity price, V2G discharge electricity price and V2G one-time subsidy; Equations (A - 29) - (A - 31) are price fluctuation constraints;

[0042] (3) Capacity constraint and initial value conditions

[0043]

[0044] In the formula, (A - 32) - (A - 33) is the capacity constraint of the charging station / V2G station; (A - 34) - (A - 35) is the initial value condition; x a (t) are the state - aggregated vehicle flows on road section a at time t respectively; and are the configured capacities of the charging station / V2G station; are the state vehicle flows on road section a under the k - th path starting from node r and ending at node s at time 0 and time 1 respectively; is the inflow vehicle flow on road section a under the k - th path starting from node r and ending at node s at time 0.

[0045] Furthermore, in step 3, the optimal power flow model of the distribution network is established as:

[0046] (1) Branch power flow equation

[0047]

[0048]

[0049] In the formula, N is the set of distribution network nodes; i, j, m are distribution network nodes; and are the active injection power and reactive injection power of nodes i and j respectively; r ij and x ij are the resistance and reactance of line ij respectively; P ij (t), Q ij (t) and P jm (t), Q jm (t) are the active power and reactive power of lines ij and jm respectively; l ij (t) is the square of the current of line ij; v i (t) and v j (t) are the squares of the voltages of nodes i and j respectively;

[0050] (2) Node injection power equation

[0051]

[0052] In the formula, are the active injection power and reactive injection power of node i respectively; and are the active and reactive powers sent by the superior power grid at node i respectively; and are the active and reactive powers generated by distributed power sources at node i respectively; and are the active and reactive powers generated by distributed photovoltaics at node i respectively; is the V2G power at node i; is the active load of the charging station at node i; and are the conventional active and reactive loads at node i respectively, and x a (t + 1) are the state-aggregated vehicle flows on road section a at time t + 1; is a parameter used to describe the one-to-one correspondence between the charging stations / V2G stations on road section a of the transportation network and the distribution network nodes;

[0053] (3) Photovoltaic inverter model and upper and lower limit constraints

[0054]

[0055]

[0056] In the formula, is the distributed photovoltaic capacity at node i; and are the active and reactive powers generated by the distributed photovoltaic at node i respectively; is the reactive power capacity of the photovoltaic inverter at node i, and |V i (t)| represents the absolute value of the voltage amplitude at node i at time t; V i and are the lower and upper limits of the voltage at node i respectively; |I ij (t)| represents the absolute value of the current amplitude of circuit ij at time t; is the current upper limit of line ij; and are the active and reactive powers generated by the superior power grid at node i respectively; and are the lower and upper limits of the active and reactive power transmission of the superior power grid at node i respectively; and are the active and reactive powers generated by the distributed power source at node i respectively; and are the lower and upper limits of the active and reactive powers generated by the distributed power source at node i respectively.

[0057] Furthermore, in step 4, the optimal cost model of the distribution network considering the load shedding loss is:

[0058]

[0059] In the formula, are the active injection power and reactive injection power of node i respectively; is the actual active load supplied by node i at time t, The reactive power load actually supplied to node i at time t; The conventional active power demand at node i; The load shedding power of node i at time t.

[0060] Furthermore, in step 5, the optimal pricing model of the dynamic power-transportation coupling system considering differentiated V2G is:

[0061] minF PDN = F gen + F DG - F ch + F v2g + F cut (A-52)

[0062]

[0063]

[0064] In the formula, F PDN is the total cost of the distribution network system; F gen is the cost of purchasing electricity from the superior power grid; F DG is the power generation cost of distributed power sources; F ch is the total revenue of the charging station; F v2g is the total V2G reward paid by the distribution network; F cut is the load shedding loss of the distribution network; ω(t) is the time-of-use electricity price; Δt is the time span; p ch and p v2g are the average charging / V2G power; t ch and t v2g are the average charging / V2G time; is the reference charging electricity price, taking 1.5 yuan / kWh; is the unit charging electricity price of the charging station on road section a at time t; is the unit discharge reward of the V2G station on road section a at time t; is the one-time subsidy of the V2G station on road section a at time t; Formulas (A-55) and (A-56) use u a (t) instead of x a (t + 1), because the charging cost / V2G price of each vehicle is determined according to the entry time of the station; ω cut (t) is the unit load shedding loss of the distribution network; is the load shedding power of node i at time t.

[0065] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0066] Compared with the basic solution of the optimal scheduling of electric vehicles in the traditional power distribution - transportation coupling system, in the context of accurately simulating traffic flow by the dynamic traffic assignment model of the present invention, the flexible charging and discharging capabilities and differentiated behavior characteristics of the vehicle - grid interaction of electric vehicles are fully considered, and the optimal electricity price is set to guide charging vehicles and V2G vehicles to charge and discharge. The results of the case study show that the method proposed in the present invention can effectively provide additional benefits for V2G users while effectively reducing the load shedding loss of the distribution network, and improve the stability and economy of the power distribution - transportation coupling system. Description of the Drawings

[0067] Figure 1 is the flowchart of the method of the present invention;

[0068] Figure 2 is the topology diagram of the power distribution - transportation coupling system;

[0069] Figure 3 is the minimum passing cost of three paths in scenario b;

[0070] Figure 4 is the comparison of the operation costs of the power distribution network in scenarios a and b;

[0071] Figure 5 is the comparison of the load shedding situations in scenarios a, b, and d. Detailed Embodiments

[0072] The present invention will be further clarified below in conjunction with the drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent forms of modification of the present invention by those skilled in the art all fall within the scope defined by the appended claims of this application.

[0073] As Figure 1 shown, the present invention provides an optimal pricing method for a dynamic power - transportation coupling system considering differentiated V2G, and the method includes the following steps:

[0074] Step 1: Obtain network parameters such as the topology of the transportation network, the capacity of charging stations / V2G stations, the capacity of road sections, travel demand, and vehicle - grid interaction penetration rate. Taking the charging / V2G price as the control variable and the operation constraints of the transportation network as the constraint conditions, establish a dynamic traffic assignment model;

[0075] Step 2: Based on the dynamic traffic assignment model in Step 1, obtain the scenario data of the differentiated behavior parameters of vehicle - grid interaction of transportation network users and the vehicle power distribution, and add differentiated V2G selection and electricity price continuity constraints to establish a dynamic traffic assignment model considering differentiated V2G;

[0076] Step 3: Obtain the operating coefficients of the distribution network topology, line impedance, distributed power coefficient, and photovoltaic inverter coefficient. Using the distributed power output as the control variable and the grid operation constraints as the constraint conditions, establish an optimal power flow model for the distribution network;

[0077] Step 4: Based on the optimal power flow model of the distribution network in Step 3, obtain the scenario data of the load demand of each node in the grid, the upper and lower limits of the distributed power output, the photovoltaic output situation, the coupling structure of the charging station / V2G station, as well as the generation cost and the load shedding loss coefficient, and establish an optimal power flow model for the distribution network considering load shedding;

[0078] Step 5: Based on the dynamic traffic assignment model considering differentiated V2G in Step 2 and the optimal power flow model of the distribution network considering load shedding in Step 4, establish an optimal pricing model for the dynamic power - traffic coupling system considering differentiated V2G. Taking the minimum total cost of the coupling system as the objective function, solve this model using a nonlinear solver, and guide the traffic flow distribution by setting the charging / V2G price to obtain the optimal pricing of the dynamic power - traffic coupling system considering differentiated V2G.

[0079] Furthermore, the relevant operation constraints of the dynamic traffic assignment model of the transportation network in Step 1 are as follows:

[0080] (1) Link state equation:

[0081]

[0082]

[0083] E rs (t + 1)=E rs (t)+e rs (t)(A - 8)

[0084]

[0085] In the formula, a is the link, k is the path, T(A) represents the set of links. Each pair of origin and destination nodes is called an O - D pair. Each O - D pair is connected by different alternative paths, and the path is represented by K rs r ∈ T(R) is the origin node, s ∈ T(S) is the destination node, and the travel demand between O - D pairs is represented by q rs In the transportation network considering the penetration rate of electric vehicles and V2G vehicles, the paths are divided into ordinary paths charging paths and V2G paths The set of links can be divided into the set of ordinary links T(A O ), the set of charging links T(A E ), and the set of virtual links T(AD ) and the set of V2G road segments \(T(A)\) V ); are the inflow, outflow, and status traffic volumes on road segment \(a\) under the \(k\)-th path from the starting node \(r\) to the destination node \(s\) at time \(t\); \(u\) a (t), \(v\) a (t), \(x\) a (t) are the aggregated inflow, outflow, and status traffic volumes on road segment \(a\) at time \(t\); \(F(r)\) is the set of road segments \(a\) starting from the starting node \(r\); \(\gamma\) is the penetration rate of vehicles in need of charging; \(\upsilon\) is the penetration rate of vehicles capable of making V2G selections; \(f\) rs (t) is the travel demand between the O - D pair \(r - s\) at time \(t\); is the number of vehicles that differentially choose ordinary paths from the starting node \(r\) to the destination node \(s\); is the starting node \(f\) rs (t) point, the number of vehicles that differentially choose V2G paths from the starting node \(r\) to the destination node \(s\); \(D(s)\) is the set of road segments \(a\) leading to the destination node \(s\); \(e\) rs (t) is the arrival traffic volume of the origin - destination node pair \(r - s\) at time \(t\); \(E\) rs (t) is the cumulative arrival traffic volume of the origin - destination node pair \(r - s\) at time \(t\); is the cumulative arrival traffic volume of the origin - destination node pair \(r - s\) that selects the \(k\)-th path at time \(t\);

[0086] (2) Flow conservation and flow propagation constraints

[0087]

[0088]

[0089] 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 \(j\); is the free - flow time of road segment \(a\); indicates that road segment \(b\) is all road segments after road segment \(a\) in the selected path \(k\in K\) rs ; is the status traffic volume on road segment \(b\) of the origin - destination node pair \(r - s\) at time is the status traffic volume on road segment \(b\) of the origin - destination node pair \(r - s\) at time \(t\); is the cumulative arrival traffic volume of the origin - destination node pair \(r - s\) that selects the \(k\)-th path at time

[0090] (3) Road segment travel time

[0091]

[0092] t a (u a (t)) = 0, a ∈ T(A D )(A - 16)

[0093] In the formula: t a (u a (t)) represents the travel time of section a when there is an inflow of u a (t) vehicles; is the free travel time of section a; is the traffic capacity of section a; Charging section A E and V2G section A V adopt the M / M / c / K queuing model to describe the charging queue behavior of charging vehicles at the charging station; t ch and t v2g are the average charging / V2G time of the vehicle; and are the maximum queuing times for the charging / V2G station capacity; and are the configured capacities of the charging station / V2G station, which are determined by the charging / V2G interface configuration in the station; x a (t + 1) are the aggregated traffic flows on section a at time t + 1 respectively; Virtual section A D is actually a virtual section set for the connectivity of vehicle travel after the expansion of the traffic network section. Therefore, it is considered that the travel time of the vehicle on virtual section A D is 0;

[0094] (4) User travel cost

[0095] c a (t) = ω t t a (u a (t)), a ∈ T(A O )(A - 17)

[0096]

[0097] c a (t) = 0, a ∈ T(A D )(A - 20)

[0098]

[0099] In the formula, c a (t) is the travel cost of section a at time t; ω t is the unit time cost; t a (u a (t)) represents that there is u on section aa (t) Travel time in the case of vehicle inflow; Unit charging price of the charging station on road section a at time t; p ch and p v2g Is the average charging / V2G power; t ch and t v2g Is the average charging / V2G time; Unit discharge reward of the V2G station on road section a at time t; One-time subsidy of the V2G station on road section a at time t; Travel cost of the k-th path starting from r and ending at s at time t, Indicates that road section b is the selected path k∈K rs All road sections before road section a in, when calculating , the travel cost at the inflow time of each road section is adopted; Free travel time of road section b; Is a 0-1 parameter for judging the correspondence between road sections and paths. If path k passes through road section a, then Does not pass through, then

[0100] (5) Dynamic user equilibrium condition

[0101]

[0102] In the formula, 0≤a⊥b≥0 means a≥0, b≥0 and ab = 0; Travel cost of the k-th path starting from r and ending at s at time t; Are the minimum travel costs of the ordinary path, charging path, and V2G path starting from r and ending at s at time t, respectively.

[0103] Furthermore, the dynamic traffic assignment model in step 2 adds differential V2G selection and electricity price continuity constraints:

[0104] (1) Differential V2G selection

[0105]

[0106] f o,p (t) = 1 - f v,p (t) (A - 26)

[0107]

[0108]

[0109] Where \(p\) is the power level of electric vehicle users; \(f\) v,p (t) and \(f\) o,p (t) are respectively the percentages of vehicles of users with power level \(p\) choosing V2G paths / ordinary paths at time \(t\); \(\alpha\) p is the influence factor of V2G differential selection affected by the unit discharge reward; \(\beta\) p is the influence factor of V2G differential selection affected by the one-time subsidy; is the unit discharge reward of the V2G station on road section \(a\) at time \(t\); is the one-time subsidy of the V2G station on road section \(a\) at time \(t\); is the minimum V2G unit discharge reward; is the minimum V2G one-time subsidy time cost; \(\delta\) p is the percentage of users with power level \(p\); is the number of vehicles differentially choosing the ordinary path with \(r\) as the starting node and \(s\) as the destination node; is the number of vehicles differentially choosing the V2G path with \(r\) as the starting node and \(s\) as the destination node; \(\upsilon\) is the vehicle penetration rate capable of making V2G selections; \(f\) rs (t) is the travel demand between O - D pair \(r - s\) at time \(t\);

[0110] (2) Electricity price continuity constraint

[0111]

[0112] Where \(\varphi\) is the lower limit of price fluctuation; is the upper limit of price fluctuation; is the unit charging electricity price of the charging station on road section \(a\) at time \(t\); is the unit discharge reward of the V2G station on road section \(a\) at time \(t\); is the one-time subsidy of the V2G station on road section \(a\) at time \(t\); and are respectively the lower and upper limits of the charging electricity price, V2G discharge electricity price and V2G one-time subsidy; Equations (A - 29) - (A - 31) are price fluctuation constraints;

[0113] (3) Capacity constraint and initial value conditions

[0114]

[0115] Where, Equations (A - 32) - (A - 33) are the charging station / V2G station capacity constraints; Equations (A - 34) - (A - 35) are the initial value conditions; \(x\) a (t) are respectively the state - aggregated vehicle flows on road section \(a\) at time \(t\); and are the configured capacities of the charging station / V2G station; They are the state traffic flows on section a under the k-th path with r as the starting node and s as the destination node at time 0 and time 1 respectively. It is the inflow traffic flow on section a under the k-th path with r as the starting node and s as the destination node at time 0.

[0116] Furthermore, in step 3, the optimal power flow model of the distribution network is established as follows:

[0117] (1) Branch power flow equation

[0118]

[0119] In the formula, N is the set of distribution network nodes; i, j, and m are distribution network nodes; and are the active injection power and reactive injection power of nodes i and j respectively; r ij and x ij are the resistance and reactance of line ij respectively; P ij (t), Q ij (t) and P jm (t), Q jm (t) are the active power and reactive power of lines ij and jm respectively; l ij (t) is the square of the current of line ij; v i (t) and v j (t) are the squares of the voltages of nodes i and j respectively;

[0120] (2) Node injection power equation

[0121]

[0122] In the formula, are the active injection power and reactive injection power of node i respectively; and are the active power and reactive power sent from the superior power grid at node i respectively; and are the active power and reactive power generated by distributed power sources at node i respectively; and are the active power and reactive power generated by distributed photovoltaics at node i respectively; is the V2G power at node i; is the active load of the charging station at node i; and are the conventional active and reactive loads at node i respectively, x a (t + 1) are the state aggregated traffic flows on section a at time t + 1; is a parameter used to describe the one-to-one correspondence between the charging station / V2G station on section a of the transportation network and the distribution network nodes;

[0123] (3) Photovoltaic inverter model and upper and lower limit constraints

[0124]

[0125] Wherein, is the distributed photovoltaic capacity at node i; and are the active and reactive powers generated by the distributed photovoltaic at node i, respectively; is the reactive power capacity of the photovoltaic inverter at node i, |V i (t)| represents the absolute value of the voltage amplitude at node i at time t; V i and are the lower and upper limits of the voltage at node i, respectively; |I ij (t)| represents the absolute value of the current amplitude of circuit ij at time t; is the current upper limit of line ij; and are the active and reactive powers generated by the superior power grid at node i, respectively; and are the lower and upper limits of the active and reactive power transmission of the superior power grid at node i, respectively; and are the active and reactive powers generated by the distributed power source at node i, respectively; and are the lower and upper limits of the active and reactive powers generated by the distributed power source at node i, respectively.

[0126] Furthermore, in step 4, the optimal cost model of the distribution network considering the load shedding loss is:

[0127]

[0128] Wherein, are the active injection power and reactive injection power of node i, respectively; is the active load actually supplied by node i at time t, is the reactive load actually supplied by node i at time t; is the conventional active demand at node i; is the load shedding power of node i at time t.

[0129] Furthermore, in step 5, the optimal pricing model of the dynamic power-transportation coupling system considering differentiated V2G is:

[0130] minF PDN = F gen + F DG - F ch + F v2g+F cut (A-52)

[0131]

[0132] Wherein, F PDN is the total cost of the distribution network system; F gen is the electricity purchase cost from the superior power grid; F DG is the power generation cost of distributed power sources; F ch is the total revenue of the charging station; F v2g is the total V2G remuneration paid by the distribution network; F cut is the load shedding loss of the distribution network; ω(t) is the time-of-use electricity price; Δt is the time span; p ch and p v2g are the average charging / V2G power; t ch and t v2g are the average charging / V2G time; is the reference charging electricity price, taking 1.5 yuan / kWh; is the unit charging electricity price of the charging station on road section a at time t; is the unit discharge remuneration of the V2G station on road section a at time t; is the one-time subsidy of the V2G station on road section a at time t; Equations (A-55) and (A-56) use u a (t) instead of x a (t + 1), because the charging cost / V2G price of each vehicle is determined according to the entry time into the station; ω cut (t) is the unit load shedding loss of the distribution network; is the load shedding power of node i at time t.

[0133] Case Study

[0134] The superiority of the optimal pricing method for the dynamic power-traffic coupling system considering differentiated V2G described in the present invention will be illustrated by the following case study. The present invention uses Figure 2 the coupled system of the 13-node transportation network and the 33-node distribution network shown as a case study. To compare the superiority of the method proposed in the present invention, a total of the following four scenarios are considered. Scenario a: Optimized scheduling of the dynamic traffic assignment model without considering V2G participation; Scenario b: Optimized scheduling of the dynamic traffic assignment model considering V2G participation; Scenario c: Optimized scheduling of the static traffic assignment model considering V2G participation; Scenario d: Substituting the results of the static traffic assignment model considering V2G into the dynamic traffic assignment model to obtain the real situation of the power distribution-transportation coupling system. The present invention is implemented through the GAMS optimization platform and uses the Baron solver to solve the non-linear programming problem.

[0135] Based on this example, the distribution system operator in Scenario b formulates reasonable one-time V2G remuneration and discharging electricity prices to attract electric vehicle users to perform V2G discharging during peak load periods. Electric vehicle users can not only offset their own time costs during the journey (including V2G time), but also obtain a certain income (for the comparison of travel costs, see Figure 3 ); Comparing Scenario a and Scenario b, attracting electric vehicle users to discharge during peak load periods by paying V2G remuneration can effectively reduce the proportion of load shedding in the distribution network, from 25.98% of the original total cost to 9.98% of the current total cost. The V2G remuneration of 3,747.6 yuan avoids a load shedding loss of 12,598 yuan. While ensuring the discharging income of V2G vehicle owners, it improves the ability of the power grid to avoid risks (see Figure 4 ), indicating that the optimal pricing method of the dynamic power-traffic coupling system with differentiated V2G can give full play to the flexible charging and discharging capabilities of electric vehicle mobile energy storage, formulate the optimal electricity price strategy to guide traffic flow, and relieve the load pressure on the power grid; Comparing Scenario a, Scenario b, and Scenario d, Scenario b significantly reduces the total load shedding and reduces the distribution network loss by reasonably dispatching V2G vehicles to discharge during peak load periods. Since Scenario c uses a static traffic assignment model to simulate traffic flow, all vehicles complete their journeys during the flat load period of the distribution network from t1 to t6, and ignores the spatio-temporal differences in vehicle journeys, charging, and V2G. The optimized result shows that the load shedding of the distribution network is 0. However, the actual situation should be as shown in Scenario d, where vehicles will charge during peak load periods, and the final result is that the load on the distribution network peaks on top of the peak. Due to the unreasonable pricing strategy, the total load shedding even slightly exceeds that of Scenario a (see Figure 5 ), indicating that the optimal pricing method of the dynamic power-traffic coupling system with differentiated V2G under the background of the dynamic traffic assignment model can more accurately simulate traffic flow, and the price-guided charging and discharging behavior of electric vehicles is more in line with the expected effect.

[0136] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. An optimal pricing method for a dynamic power-transportation coupling system considering differentiated V2G, characterized in that The method includes the following steps: Step 1: Obtain network parameters such as the topology of the transportation network, the capacities of charging stations / V2G stations, the capacities of road sections, travel demands, and the vehicle-grid interaction penetration rate. Taking the charging / V2G price as the control variable and the operation constraints of the transportation network as the constraints, establish a dynamic traffic assignment model. Step 2: Based on the dynamic traffic assignment model in Step 1, obtain the vehicle-grid interaction differential behavior parameters of transportation network users and the scenario data of vehicle power distribution. Incorporate differential V2G selection and electricity price continuity constraints to establish a dynamic traffic assignment model considering differential V2G. Step 3: Obtain the operation coefficients of the distribution network topology, line impedance, distributed power coefficient, and photovoltaic inverter coefficient. Taking the distributed power output as the control variable and the grid operation constraints as the constraints, establish an optimal power flow model for the distribution network. Step 4: Based on the optimal power flow model of the distribution network in Step 3, obtain the scenario data of the load demands of each node in the grid, the upper and lower limits of distributed power output, photovoltaic output conditions, the coupling structure of charging stations / V2G stations, and the generation cost and load shedding loss coefficient, and establish an optimal power flow model for the distribution network considering load shedding. Step 5: Based on the dynamic traffic assignment model considering differential V2G in Step 2 and the optimal power flow model of the distribution network considering load shedding in Step 4, establish an optimal pricing model for the dynamic power-transportation coupling system considering differential V2G. Taking the minimum total cost of the coupling system as the objective function, solve this model using a nonlinear solver, and guide the traffic flow distribution by setting the charging / V2G price to obtain the optimal pricing of the dynamic power-transportation coupling system considering differential V2G.

2. The optimal pricing method for a dynamic power-transportation coupling system considering differentiated V2G according to claim 1, wherein The relevant operation constraints of the dynamic traffic assignment model of the transportation network in Step 1 are as follows: (1) Road section state equation: E rs (t + 1)= E rs (t)+ e rs (t)(A - 8) In the formula, a represents a road section, k represents a path, T(A) represents the set of road sections. Each pair of starting node and destination node is called an O-D pair. Each O-D pair is connected by different alternative paths, and the path is represented by K rs r ∈ T(R) represents the starting node, s ∈ T(S) represents the destination node, and the travel demand between O-D pairs is represented by q rs In the transportation network considering the penetration rates of electric vehicles and V2G vehicles, the paths are divided into ordinary paths charging paths and V2G paths The set of road sections can be divided into the set of ordinary road sections T(A O ), the set of charging road sections T(A E ), the set of virtual road sections T(A D ), and the set of V2G road sections T(A V ); are respectively the inflow, outflow, and status vehicle flows on road section a under the k-th path with r as the starting node and s as the destination node at time t; u a (t), v a (t), x a (t) are respectively the inflow, outflow, and status aggregated vehicle flows on road section a at time t; F(r) is the set of road sections starting from the starting node r; γ is the penetration rate of vehicles that need to be charged; υ is the penetration rate of vehicles that can make V2G selections; f rs (t) is the travel demand between the O-D pair r-s at time t; is the number of vehicles that differentially select ordinary paths with r as the starting node and s as the destination node; is the number of vehicles that differentially select V2G paths with r as the starting rs (t) node and s as the destination node; D(s) is the set of road sections leading to the destination node s; e rs (t) is the arrival vehicle flow of the origin-destination node pair r-s at time t; E rs (t) is the cumulative arrival vehicle flow of the origin-destination node pair r-s at time t; is the cumulative arrival vehicle flow of the origin-destination node pair r-s that selects the k-th path at time t; (2) Flow conservation and flow propagation constraints Wherein, F(j) represents the set of section a starting from node j, and D(j) represents the set of section a leading to node j; is the free passage time of section a; represents that section b is the section after section a in the selected path k∈K rs in all sections; is the state traffic flow on section b in the origin-destination node pair r-s at time is the state traffic flow on section b in the origin-destination node pair r-s at time t; is the cumulative arrival traffic flow of the k-th path selected in the origin-destination node pair r-s at time (3) Road section travel time t a (u a (t)) = 0, a ∈ T(A D )(A - 16) where: t a (u a (t)) represents the travel time of section a when there is a vehicle inflow of u a (t); is the free travel time of section a; is the traffic capacity of section a; Charging section A E and V2G section A V adopt the M / M / c / K queuing model to describe the charging queue behavior of charging vehicles at the charging station; t ch and t v2g is the average charging / V2G time of the vehicle; and is the maximum queuing time of the charging / V2G station capacity; and is the configured capacity of the charging station / V2G station, which is determined by the charging / V2G interface configuration in the station; x a (t + 1) are the aggregated vehicle flows on road section a at time t + 1; virtual road section A D is actually a virtual road section set for the connectivity of vehicle passage after the expansion of the traffic network section. Therefore, it is considered that the passage time of the vehicle on the virtual road section A D is 0; (4) User travel cost c a \(\theta(t)=\omega\) t t a (u a (t)), a ∈ T(A O )(A - 17) c a (t) = 0, a ∈ T(A D )(A - 20) where c a (t) is the travel cost of road section a at time t; ω t is the unit time cost; t a (u a (t)) represents the travel time of road section a with u a (t) vehicles flowing in; is the unit charging price of the charging station on road section a at time t; p ch and p v2g are the average charging / V2G power; t ch and t v2g are the average charging / V2G time; is the unit discharge reward of the V2G station on road section a at time t; is the one-time subsidy of the V2G station on road section a at time t; is the travel cost of the k-th path starting from r and ending at s at time t, indicates that road section b is all the road sections before road section a in the selected path k ∈ K rs When calculating , the travel costs at the inflow times of each road section are used; is the free travel time of road section b; is a 0-1 parameter for judging the correspondence between road sections and paths. If path k passes through road section a, then does not pass through, then (5) Dynamic user equilibrium condition Where 0≤a⊥b≥0 means a≥0, b≥0 and ab=0; is the travel cost of the k-th path starting from node r and ending at node s at time t; are respectively the minimum travel costs of the ordinary path, the charging path, and the V2G path starting from node r and ending at node s at time t.

3. The optimal pricing method for a dynamic power-transportation coupling system considering differentiated V2G according to claim 2, wherein, In Step 2, the differential V2G selection and electricity price continuity constraints are incorporated into the dynamic traffic assignment model: (1) Differential V2G selection f o,p f(t) = 1 - f v,p f(t)(A - 26) where p is the power level of electric vehicle users; f v,p (t) and f o,p (t) are the percentages of vehicles of users with power level p choosing the V2G path / ordinary path at time t; α p is the influence factor of V2G differential selection affected by the unit discharge reward; β p is the influence factor of V2G differential selection affected by the one-time subsidy; is the unit discharge reward of the V2G station on road section a at time t; is the one-time subsidy of the V2G station on road section a at time t; is the minimum V2G unit discharge reward; is the minimum V2G one-time subsidy time cost; δ p is the percentage of users with power level p; is the number of vehicles differentially choosing the ordinary path with r as the starting node and s as the destination node; is the number of vehicles differentially choosing the V2G path with r as the starting node and s as the destination node; υ is the vehicle penetration rate capable of making V2G selection; f rs (t) is the travel demand between O-D pair r-s at time t; (2) Electricity price continuity constraint where φ is the lower limit of price fluctuation; is the upper limit of price fluctuation; is the unit charging price of the charging station on road section a at time t; is the unit discharging reward of the V2G station on road section a at time t; is the one-time subsidy for the V2G station on road section a at time t; and are the lower and upper limits of the charging price, V2G discharging price, and V2G one-time subsidy, respectively; Equations (A-29)-(A-31) are price fluctuation constraints; (3) Capacity constraint and initial value condition In the formula, formula (A-32) - formula (A-33) are the capacity constraints of the charging station / V2G station; formula (A-34) - formula (A-35) are the initial conditions; x a (t) are the state-aggregated vehicle flows on section a at time t; and are the configured capacities of the charging station / V2G station; are the state vehicle flows on section a under the k-th path with r as the starting node and s as the destination node at time 0 and time 1 respectively; is the inflow vehicle flow on section a under the k-th path with r as the starting node and s as the destination node at time 0.

4. The optimal pricing method for a dynamic power-transportation coupling system considering differentiated V2G according to claim 3, wherein In Step 3, the optimal power flow model for the distribution network is established as: (1) Branch power flow equation Wherein, N is the set of distribution network nodes; i, j, and m are distribution network nodes; and are respectively the active injection power and reactive injection power of nodes i and j; r ij and x ij are respectively the resistance and reactance of line ij; P ij (t), Q ij (t) and P jm (t), Q jm (t) are respectively the active power and reactive power of lines ij and jm; l ij (t) is the square of the current of line ij; v i (t) and v j (t) are the squares of the voltages of nodes i and j; (2) Node injection power equation In the formula, are the active injection power and reactive injection power of node i respectively; and are the active power and reactive power sent by the superior power grid at node i respectively; and are the active power and reactive power generated by distributed power sources at node i respectively; and are the active power and reactive power generated by distributed photovoltaics at node i respectively; is the V2G power at node i; is the active load of the charging station at node i; and are the conventional active and reactive loads at node i respectively, x a (t + 1) are the state-aggregated traffic flows on section a at time t + 1 respectively; is a parameter used to describe the one-to-one correspondence between the charging stations / V2G stations on section a of the transportation network and the distribution network nodes; (3) Photovoltaic inverter model and upper and lower limit constraints In the formula, is the distributed photovoltaic capacity at node i; and are the active and reactive powers generated by the distributed photovoltaic at node i, respectively; is the reactive power capacity of the photovoltaic inverter at node i, and |V i (t)| represents the absolute value of the voltage amplitude at node i at time t; V i and are the lower and upper limits of the voltage at node i, respectively; |I ij (t) represents the absolute value of the current amplitude of circuit ij at time t; is the upper limit of the current of line ij; and are the active and reactive powers respectively generated by the superior power grid at node i; and are respectively the lower and upper limits of the active and reactive powers transmitted by the superior power grid at node i; and are the active and reactive powers respectively generated by the distributed power source at node i; and are respectively the lower and upper limits of the active and reactive powers generated by the distributed power source at node i.

5. The optimal pricing method for a dynamic power-transportation coupling system considering differentiated V2G according to claim 4, characterized in that, In Step 4, the cost-optimal model for the distribution network considering load shedding loss is: wherein, are respectively the active injection power and the reactive injection power of node i; is the active load actually supplied by node i at time t, is the reactive load actually supplied by node i at time t; is the conventional active demand at node i; is the load shedding power of node i at time t.

6. The optimal pricing method for a dynamic power-transportation coupling system considering differentiated V2G according to claim 5, wherein In Step 5, the optimal pricing model for the dynamic power-transportation coupling system considering differential V2G is: minF PDN = F gen + F DG - F ch + F v2g + F cut (A - 52) Wherein, F PDN is the total cost of the distribution network system; F gen is the power purchase cost from the superior power grid; F DG is the power generation cost of distributed power sources; F ch is the total revenue of the charging station; F v2g is the total V2G remuneration paid by the distribution network; F cut is the load shedding loss of the distribution network; ω(t) is the time-of-use electricity price; Δt is the time span; p ch and p v2g are the average charging / V2G power; t ch and t v2g are the average charging / V2G time; is the reference charging electricity price, taking 1.5 yuan / kWh; is the unit charging electricity price of the charging station on road section a at time t; is the unit discharge remuneration of the V2G station on road section a at time t; is the one-time subsidy of the V2G station on road section a at time t; Equations (A-55) and (A-56) adopt u a (t) instead of x a (t + 1) because the charging cost / V2G price of each vehicle is determined according to the entry time into the station; ω cut (t) is the unit load shedding loss of the distribution network; is the load shedding power of node i at time t.

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