CN112487560B - Electric power traffic coupling system coordinated game scheduling method based on EV (electric vehicle) owner intention - Google Patents

Abstract

The invention provides an electric power traffic coupling system coordinated game scheduling method based on EV vehicle owner intention, which comprises the following steps: (1): establishing a traffic network model, including a traffic network topology model and an EV owner charging behavior model which are established based on graph theory; (2): establishing a double-layer coordination game scheduling model of the electric power traffic coupling network, wherein an upper-layer electric power network takes the lowest power distribution cost as an optimization target, an optimization model is established by considering network constraints, and each charging station in a lower-layer traffic network aims at maximizing self income to establish a non-cooperative game model; (3): solving a double-layer optimization model, solving an upper-layer power grid optimal power flow problem by adopting a second-order cone planning method, obtaining power grid node electricity prices by adopting a power flow tracking method, solving by adopting a greedy algorithm aiming at a non-cooperative game problem of a charging station in a lower-layer traffic network, and alternately iterating optimization variables of upper-layer and lower-layer sub-problems until the problem is converged, thereby realizing unified scheduling and optimized operation of the system.

Description

Electric power traffic coupling system coordinated game scheduling method based on EV vehicle owner intention

Technical Field

The invention belongs to the technical field of electric power detection, and particularly relates to an EV (electric vehicle) owner intention-based coordinated game scheduling method for an electric power traffic coupling system.

Background

In order to reduce the dependence on fossil energy to be exhausted and reduce the emission of greenhouse gases, the electrification in the transportation field has been supported by the policy of each country and has become a research focus in recent years. In 2017, the british government announced that new gasoline and diesel vehicles will be banned from sale by 2040. In recent years, norway is the country with the highest global Electric Vehicle (EV) popularity, and Electric vehicles account for 48% of new sales in the first quarter of 2019. Incentives are being actively enacted in the united states, particularly in california, to increase the market proportion of electric vehicles and to promote investment in charging infrastructure. Some asian countries, such as china and japan, also set ambitious targets for market share of electric vehicles.

Compared with the traditional automobile, the electric automobile has more diversified energy sources, particularly renewable energy power generation, and the action speed of an internal electrification system is faster and more controllable. In addition, advanced communication technologies (such as 5G cellular networks), GPS facilities and smart metering facilities enable two-way data exchange between the traffic network and EV car owners. In this context, EVs are reinforcing the coupling between power and traffic networks as flexible dynamic load and energy buffering vehicles. The penetration of large numbers of electric vehicles may reduce the stability of the grid, but if managed properly, they can also increase grid flexibility and promote renewable energy consumption. By adopting an efficient and reasonable charging strategy for the EV, the power grid and the traffic network are expected to realize excellent cooperation, so that a multi-win situation of the power grid, the traffic network and an EV owner is formed.

Currently, research and study on practical and effective charging strategies for EVs have been conducted by various national scholars. Where a centralized policy enables scheduling policies to be made from the system level to coordinate charging behavior of all EVs. While distributed policies are inferior to centralized policies in terms of global optimality and grid stability, distributed policies do protect user privacy and permissions, and reduce computation and communication costs.

Charging price is one of the most significant reasons affecting the efficiency of charging stations and EV users. According to the literature, in recent years, many scholars develop researches aiming at the influence of different electricity price mechanisms on the stability of a power distribution system and the operation efficiency of an electrified traffic system, and gradually aim at the formulation of dynamic price strategies, such as a charging mode based on the residence time of an EV owner and a pricing mode of jointly scheduling electric vehicles and storing energy, but generally, the research of the existing dynamic price strategies is ideal in general research objects and has a long distance from practical application.

In terms of modeling charging behavior, some studies have considered technical, environmental and economic factors. In general, the main reasons influencing the charging behavior and the driving manner of an electric vehicle are mainly studied from three perspectives, namely transportation, vehicle technology and power systems. The influence of factors such as charging service cost, user waiting time cost and battery life related cost on the charging decision of the EV owner is comprehensively considered. Furthermore, different types of EV users (e.g., business users and private users) have different willingness to charge on different typical days (weekdays and weekends). However, most of the existing research ignores various factors influencing the charging behavior, particularly in centralized dispatching, and the factors are ignored along with permission deprivation of EV owners.

The coupling of the power network and the traffic network enables the related planning and scheduling of the electric vehicle to become a multi-benefit subject problem, most of the problems are modeled based on a game theory, and generally, a heuristic or analytic optimization method is adopted to solve between the power network and the traffic network, between charging stations and between the charging stations and EV owners, so as to coordinate the economic operation of the power network and the traffic network.

However, the above studies rarely consider the coupling relationship between the price policy and the electric traffic network at the same time, which may result in somewhat poor practical feasibility.

Disclosure of Invention

The invention provides a coordinated game scheduling method of an electric power traffic coupling system based on EV owner intention, which balances the income between charging stations and the utilization rate of charging facilities by using a non-cooperative game method and promotes local renewable energy consumption, thereby promoting the harmonious development of the electric power traffic coupling system.

The invention particularly relates to an EV (electric vehicle) owner intention-based electric power traffic coupling system coordinated game scheduling method, which comprises the following steps of:

step (1): establishing a traffic network model, including a traffic network topology model and an EV vehicle owner charging behavior model which are established based on graph theory;

step (2): establishing a double-layer coordination game scheduling model of the electric power traffic coupling network, wherein an upper-layer electric power network takes the lowest power distribution cost as an optimization target, an optimization model is established by considering network constraints, and each charging station in a lower-layer traffic network aims at maximizing self income to establish a non-cooperative game model;

and (3): solving a double-layer optimization model, solving an upper-layer power grid optimal power flow problem by adopting a second-order cone planning method, obtaining power grid node electricity prices by adopting a power flow tracking method, solving by adopting a greedy algorithm aiming at a non-cooperative game problem of a charging station in a lower-layer traffic network, and alternately iterating optimization variables of upper-layer and lower-layer sub-problems until the problem is converged, thereby realizing unified scheduling and optimized operation of the system.

The process of establishing the traffic network model in the step (1) is as follows:

(1) establishing a topological model of the traffic network, and characterizing the traffic network topology by a graph G (V, E) (t) based on graph theory knowledge, wherein a node set V represents all intersections, and an edge set E represents all road sections; considering the road length and the congestion situation, the traffic network G at time t is considered as a directed weighted graph, and the weight of each road segment is: weight _e,t ＝lr _e (1+bs _e,t )，lr _e Indicates the length, bs, of the section e _e,t A blocking signal of the road section e at the time t is shown;

(2) the EV vehicle owner charging behavior model comprises a charging probability model and a charging willingness model:

the EV vehicle main charging probability model is

SOC _m Represents the state of charge of the EV m,

a minimum value of state of charge that indicates protection against over-discharge of the battery; consider SOC _m Is less than

When EV m must go to the charging station for charging, i.e. charging probability A _m Is 1; SOC _m Between SOCmin m and 100%, the charging probability A _m And SOC _m Negative correlation, τ represents a charging probability related parameter;

the charging will model is as follows:

determining a distance parameter by adopting a Dijkstra algorithm: d is Dijkstra (G (V, E), weight, source, target);

firstly, the detour distance d for charging to a charging station k is calculated _m,k ＝d _SPm,k +d _k,TPm -d _SPm,TPm ，d _SPm,k ,d _k,TPm And d _SPm,TPm Respectively representing the shortest travel mileage from the starting point SPm to the charging station k, from the charging station k to the destination TPm, and from the starting point SPm to the destination TPm;

secondly, based on d _SPm,k And calculating the state of charge of the battery when the EV m runs to a charging station k:

gammam represents EVm unit mileage power consumption, kWh/km;

thirdly, whether d is satisfied is judged _SPm,k ＜d _SPm,TPm ，d _k,TPm ＜d _SPm,TPm ，

Thirdly, calculating the charging willingness of the EV m to select the charging station k

α ₀ ,α ₁ ,α ₂ ,α ₃ Is composed of

A related parameter;

thirdly, the relation between the willingness to charge and the charge price is

α ₄ ,α ₅ To represent

The parameters that are relevant are set to the parameters,

means representing a regional charge price;

finally, the charging willingness of the EV owner to select the charging station is comprehensively expressed as

ω ₁ ,ω ₂ And omega ₃ The weights occupied by the three factors are respectively,

the loyalty of the charging station k is selected for EV m, and when the charging level of the charging station k is consistent with the preference of the owner,

equal to 1, otherwise 0.

The specific steps of establishing the double-layer coordination game scheduling model are as follows:

firstly, in order to ensure the effect of the proposed scheduling strategy, before a double-layer coordination game scheduling model is constructed, the following six basic assumptions are made:

assume that 1: data interaction between the EV owner and the charging station is realized by means of advanced wireless communication technology and GPS (global positioning system) facilities;

assume 2: it is believed that historical data for traffic flow and renewable energy generation may be obtained from local weather bureaus and traffic departments, respectively; the obtained data is processed in an advanced data aggregator and is corresponding to the prediction, and the data aggregator is deployed on a cloud computer server;

assume that 3: based on the vehicle condition on the road and the charging station running state broadcasted in real time, the data aggregator processes the traffic flow information and broadcasts road blocking information to all EVs on the traffic network;

assume 4: all charging stations and EVs are considered as rational participants, i.e. pursuing self-benefit or utility maximization;

assume that 5: the renewable energy power generation amount referred to in the invention refers to wind power or photoelectricity which cannot be consumed nearby a charging station, and is considered to be free or low in cost;

assume 6: after the EV charging process is considered to be completed, the charging station can charge the overtime fee to the EV owner, so that the waiting time after the EV arrives at the charging station is ignored;

secondly, building a power flow optimization model in an upper-layer power network, including

(1) The objective function of the upper-layer power system is that the total cost of power generation and distribution is the lowest:

C _Gn cost per unit of power generation, C _Tij Unit power distribution cost;

(2) the upper layer model needs to satisfy the power flow equality constraint

j _u A set of nodes for power flow direction inflow j; j is a function of _d A set of nodes flowing from j for the power flow direction; p is _Lj,t And Q _Lj,t Active and reactive electric power consumed for node j; p _ij,t And Q _ij,t Is the power flow between nodes i and j; r _ij And X _ij Representing the impedance of the line between nodes i and jResistance parameters; u shape _i,t And U _j,t Representing the voltages at nodes i and j; i is _ij,t Represents the current between nodes i and j;

(3) voltage current constraint U _i,min ≤U _i,t ≤U _i,max ，|I _ij,t |≤I _ij,max ，U _i,min And U _i,max The minimum value and the maximum value of the node voltage are obtained; I.C. A _ij,max Is the maximum value of the line current;

thirdly, establishing a non-cooperative game model by aiming at maximizing the self income of each charging station in the lower-layer traffic network:

(1) constructing a charging station optimization strategy:

the revenue for each charging station in the lower transportation network is equal to the revenue for providing charging service to the EV less the cost of purchasing power to the upper grid, maxP _k,t ＝p _k,t ·D _k,t -LMP _k,t ·max(D _k,t -RE _k,t ,0)；

The charging price of the charging station still needs to satisfy:

and

upper and lower limits of the charge price for the charging station;

(2) constructing a non-cooperative game model:

the definition is as follows:

the participants: all K charging stations in total;

strategy: for each charging station k, a charging price strategy is selected

δ _k A set of price policies for charging station k;

the benefits are as follows: the k charging station receives a profit P _k,t (p _k,t ,p _-k,t )，p _-k,t Refers to the price policy of all charging stations except charging station k;

if a non-cooperative game model is found

The lower layer traffic network can reach a balanced state; generalized Nash equilibrium is defined as

The solving process of the optimal power flow problem of the upper-layer power grid and the non-cooperative game problem of the lower-layer traffic network comprises the following steps:

firstly, in the load flow equation constraint of an upper-layer power grid, the current, the voltage and the active power and the reactive power are in a nonlinear relation, linearization processing is needed, and according to an optimization result, a load flow tracking method is needed to calculate the node electricity price, and the specific process is as follows:

(1) processing formula by adopting second-order cone relaxation method

The nonlinear problem is converted into a convex optimization linear problem by a square term in (1):

solving by a commercial solver: two variables are introduced to relax the square term in the power flow constraint, and alpha is led to _ij,t ＝I2 ij,t，β _i,t U2 i, t; thus the equation I2 ij, t ═ P2 ij, t + Q2 ij, t)/U2I, t translates into

Will be provided with

Is converted into

(2) Calculating the electricity price of the upper-layer power grid node by adopting a power flow tracking method:

based on the result of the optimal power flow, the node marginal price of each charging station node is obtained by a power flow tracking method:

in order to make up the cost of the power generation section,

for the cost composition of the distribution part, A _u Is an up-tracking matrix of the grid, A _d Is a down-tracking matrix of the power grid,

secondly, solving the non-cooperative game of the lower-layer traffic network by adopting a greedy algorithm, wherein the specific process is as follows:

(1) to find a generalized Nash equilibrium solution, an equivalent definition is proposed:

defining: if p is _t ^* Is the optimization problem minF (p) _t ) Is best solution F (p) _t ^* ) And the optimal solution equals 0, then p _t ^* Also non-cooperative gaming problems

Generalized Nash equilibrium solution of (2);

F(p _t ) The optimization problem of (2) is defined as:

the necessity proves that: if p is _t ^* Non-cooperative gaming problem

Then for any K belonging to K, there is

Is equal to

Thus, F (p) _t ^* ) Equal to 0; and also has F (p) _t ) Must be greater than or equal to 0, obviously p _t ^* Is the optimization problem minF (p) _t ) The optimal solution of (a);

and (3) the sufficiency proves that: for theAny K is K, apparently P _k,t (p _k,t ) Is greater than or equal to minP _k,t (·,p _-k,t ) (ii) a Thus if p is _t ^* Is the optimization problem minF (p) _t ) An optimal solution of, and

equal to 0, there must be K for any K,

is equal to

Thus, p _t ^* Also non-cooperative gaming problems

Generalized nash equilibrium solution of (2);

(2) and solving the non-cooperative game problem of the lower-layer traffic network by adopting a greedy algorithm.

Compared with the prior art, the beneficial effects are that: according to the coordination game scheduling method of the electric power traffic coupling system based on the EV owner intention, firstly, a traffic network topology model is built based on a graph theory, three factors such as a detour distance, a charging price and a charging grade are comprehensively considered, a charging intention model of the EV owner is built, and compared with the existing research, the charging decision-making behavior of the EV owner is more comprehensively and truly described; then a double-layer coordination game scheduling model of the electric power traffic coupling system is established, an upper-layer power grid establishes an optimization scheduling model taking the lowest power generation and distribution cost as an optimization target, second-order cone relaxation is adopted to convert the lowest power generation and distribution cost into a stable linear power flow to solve, a power flow tracking method is adopted to obtain node marginal power price, a lower layer establishes a non-cooperative game model taking charging stations as a main body, each charging station influences the charging behavior of an EV owner by adjusting the price strategy of the charging station, so that the distribution result of the charging demand is generated and fed back to the upper-layer power grid, and the steps are repeated and iterated until the system is balanced to obtain a unified scheduling strategy.

Secondly, the penetration of renewable energy in the power system is considered, and the wind power and the photovoltaic can be promoted by the aid of a charging station in a traffic network, so that the method is suitable for areas with insufficient renewable energy consumption; in addition, the method and the system can balance the income of the charging stations in the traffic network, balance the utilization condition of charging facilities of the charging stations, obviously improve the daily income of the charging stations with the positions occupying the disadvantages and the surplus wind power nearby, dredge the traffic flow to a certain extent, and relieve the traffic jam condition in the morning and evening.

Finally, the method has good solving efficiency, is suitable for optimized scheduling of a large-scale electric power traffic coupling system, can be used for scheduling at an interval of one hour or less, and has a great application prospect in future energy traffic scenes with high-proportion renewable energy penetration and developed data processing and communication technologies.

Drawings

Fig. 1 is a schematic structural diagram of a coordinated game scheduling method of an electric power traffic coupling system based on EV vehicle owner's will according to the present invention.

Fig. 2 is a structural diagram of an electric power traffic coupling system in dublin area in an embodiment of the present invention.

Detailed Description

The specific implementation of the coordinated game scheduling method for the electric power traffic coupling system based on the EV owner will be described in detail below with reference to the accompanying drawings.

The structure of the electric power traffic coupling system in the Dublin area in the embodiment of the invention is shown in figure 2, and the system comprises an upper-layer power distribution network, an offshore wind farm, a land wind farm, a photovoltaic power station, a charging station, an electric vehicle and the like. The parameters of each charging station are shown in a table 1, and the parameters of each wind power plant and each photovoltaic power station are shown in a table 2. The power network in the example adopts an IEEE 33 node test system, and specific parameters are shown in Table 3. Based on historical data of Dublin traffic administration and meteorological departments, an artificial neural network is adopted for data prediction, and the obtained traffic flow and renewable energy output data are shown in tables 4 and 5. In order to compare and analyze the performance of the scheduling strategy in the embodiment of the invention, two price comparison mechanisms are constructed, namely time-of-use electricity price and unified electricity price, and specific parameters are shown in a table 6.

Table 1 each charging station parameter in the calculation

TABLE 2 parameters of wind farms and photovoltaic plants in the example

Table 3 parameters of the power network in the example

TABLE 4 traffic flow prediction values in the calculation example

Photovoltaic and fan output predicted values in table 5 calculation example

TABLE 6 pricing mechanism for comparison in the example

As shown in fig. 1, the coordinated game scheduling method of the electric power traffic coupling system of the present invention includes the following steps:

step S1, establishing a traffic network model, including a traffic network topology model and an EV vehicle owner charging behavior model which are established based on graph theory;

step S2, establishing a double-layer coordination game scheduling model of the electric power traffic coupling network, wherein the upper layer electric power network takes the lowest power distribution cost as an optimization target, the network constraint is considered to establish the optimization model, and each charging station in the lower layer traffic network aims at maximizing the self income to establish a non-cooperative game model;

and S3, solving a double-layer optimization model, solving the optimal power flow problem of the upper-layer power grid by adopting a second-order cone planning method, acquiring the power price of the power grid node by adopting a power flow tracking method, solving by adopting a greedy algorithm aiming at the non-cooperative game problem of the charging station in the lower-layer traffic network, and alternately iterating the optimization variables of the sub-problems of the upper layer and the lower layer until the problem is converged, thereby realizing the unified scheduling and the optimized operation of the system.

In the foregoing embodiment, as a preferred example, in step S1, the process of establishing the inter-regional cooling, heating and power combined supply system model includes steps S11 to S12, where the scheduling time Δ t is selected to be 1 h:

and step S11, establishing a topological model of the traffic network, and characterizing the traffic network topology by using a graph G (V, E) (t) based on graph theory knowledge, wherein a node set V represents all intersections, and an edge set E represents all road sections. Considering the road length and the congestion situation, the traffic network G at time t is considered as a directed weighted graph, and the weight of each road segment is:

weight _e,t ＝lr _e (1+bs _e,t ) (1)

in the formula, lr _e Indicates the length, bs, of the section e _e,t A blocking signal of the road section e at the time t is shown;

step S12, the charging behavior of the EV vehicle owner can be characterized in terms of charging probability and charging willingness; the EV vehicle owner charging probability model building process is as follows:

step S121, considering the state of charge of the EV battery, dividing the charging probability into two intervals according to the lowest value of the state of charge for consideration:

in the formula, SOC _m Represents the state of charge of the EV m,

representing the lowest state of charge that prevents over-discharge of the battery. Consider SOC _m Is less than

At the same time, EV m must go to the charging station for charging, i.e. charging probability A _m Is 1; SOC _m Between

_m And between 100%, the charging probability A _m And SOC _m Negative correlation, expression is shown in (2).

And S122, different EV owners have different charging preferences, partially pay attention to time cost, and preferentially select the nearest charging station, partially pay attention to charging cost, and preferentially select the charging station with lower charge. Furthermore, EV owners also have different preferences regarding the charging level of the charging station. Without loss of generality, the three factors are integrated, and the charging willingness of the EV owner is considered to be influenced by the detour distance on the spatial attribute and the charging price and the charging level on the non-spatial attribute.

As shown in equation (3), all distance-related parameters are determined by Dijkstra's algorithm:

d＝Dijkstra(G(V,E),weight,source,target) (3)

for EV m with a trip starting point SPm and a destination TPm, the detour distance to charging station k for EV m is defined as shown in equation (4):

d _m,k ＝d _SPm,k +d _k,TPm -d _SPm,TPm (4)

in the formula, d _SPm,k ,d _k,TPm And d _SPm,TPm Respectively, from the starting point SPm to the charging station k, and from the charging station k to the destinationTPm, and the shortest traveled miles from the starting point SPm to the destination TPm.

Based on d _SPm,k The state of charge of the battery when driving to charging station k can be obtained EVm:

to avoid meaningless deviation from the original route, the detour distance is also constrained by equations (6) to (8):

d _SPm,k ＜d _SPm,TPm (6)

d _k,TPm ＜d _SPm,TPm (7)

before calculating the charging will of an EV owner for a certain charging station, it is necessary to determine whether the constraints of equations (6) to (8) are satisfied. So that the candidate set of charging stations can curtail to charging stations with sufficient electric quantity when the EV arrives. Otherwise, a too low state of charge at EV arrival may compromise battery health.

Considering only the detour distance, EV m selects the charging willingness of charging station k

As shown in formulas (9) and (10):

f(d _m,k )＝α ₁ ·(1-(α ₂ ·d _m,k +α ₃ ) ^-1 ) (10)

in the formula, alpha ₀ ,α ₁ ,α ₂ ,α ₃ To represent

The relevant parameters.

The charging price refers to a charging standard when the charging station provides the charging service for the EV. The relationship between the willingness to charge and the charge price is characterized by equations (11) and (12):

in the formula (I), the compound is shown in the specification,

represents the average of the regional charge prices.

Standard SAE J1772 standardizes EV charging levels, including AC charging and DC charging, based on the type of power transfer, maximum power. Taking into account the impact of the charge level on the charge duration and the battery health. Different EV car owners have different preferences. By using

Indicating the loyalty of EV m in selecting charging station k, when the charging level of charging station k is consistent with the preference of the vehicle owner,

equal to 1, otherwise 0.

The influence of the three factors is combined, and the charging willingness of the EV owner to select the charging station is comprehensively expressed as:

it is noted that the time index t of the parameter and the variable in the EV characteristic modeling are omitted for simplicity.

As a preferred example, the step S2 of constructing the double-layer coordinated game scheduling model specifically includes steps S21 to S23:

step S21, in order to ensure the effect of the scheduling strategy, before constructing a double-layer coordination game scheduling model, the following six basic assumptions are made:

assume that 1: data interaction between the EV vehicle owner and the charging station is achieved by means of advanced wireless communication technology and GPS facilities.

Assume 2: it is believed that historical data for traffic flow and renewable energy generation may be obtained from local weather stations and traffic departments, respectively. The obtained data will be processed and subject to prediction in an advanced data aggregator deployed on the cloud computer servers.

Assume 3: based on the vehicle conditions on the road and the charging station operating status of the real-time broadcast, the data aggregator processes the traffic flow information and broadcasts the road congestion information to all EVs on the traffic network, i.e., bs in equation (1) _e,t 。

Assume 4: all charging stations and EVs are considered as rational participants, i.e. pursuing self-benefit or utility maximization.

Assume that 5: the renewable energy power generation amount referred to in the invention refers to wind power or photoelectricity which cannot be consumed in the vicinity of the charging station, and is considered to be free or low in cost.

Assume that 6: after the EV charging process is considered to be completed, the charging station charges the EV owner for a timeout fee, so that the waiting time after the EV arrives at the charging station is ignored.

Step S22, the process of building the power flow optimization model in the upper layer power network is as follows:

the objective function of the upper-layer power system is that the total cost of power generation and distribution is the lowest:

the upper layer model needs to satisfy the power flow equation constraint, as shown in equation (10):

in the formula, j _u A set of nodes representing a power flow direction flowing into j; j is a function of _d A set of nodes representing the power flow direction flowing from j; p is _Lj,t And Q _Lj,t Representing the active and reactive electrical power consumed by node j; p is _ij,t And Q _ij,t Representing the power flow between nodes i and j; r is _ij And X _ij Representing an impedance parameter of the line between nodes i and j; u shape _i,t And U _j,t Representing the voltages at nodes i and j; i is _ij,t Represents the current between nodes i and j;

voltage and current restraint:

0.9*12.66≤U _i,t ≤1.1*12.66 (16)

|I _ij,t |≤500 (17)

in step S23, the charging stations in the lower transportation network are regarded as different benefit subjects, and benefit conflicts exist among them, and all seek to maximize their benefits:

step S231, constructing a charging station optimization strategy:

the revenue for each charging station in the lower transportation network is equal to the revenue for providing charging services to the EV minus the cost of purchasing electricity from the upper grid, as shown in equation (18):

maxP _k,t ＝p _k,t ·D _k,t -LMP _k,t ·max(D _k,t -RE _k,t ,0) (18)

the charging price of the charging station also needs to satisfy the constraint of equation (19):

0.2≤p _k,t ≤2 (19)

step S232, constructing a non-cooperative game model:

each charging station pursues self income maximization by adjusting charging price, and the charging stations do not have a relationship of membership, and the interests conflict with each other. Therefore, the invention models the price adjustment behavior of each charging station at the lower layer into a non-cooperative game model

The definition is as follows:

the participants: all together K13 charging stations.

Strategy: for each charging station k, a charging price strategy is selected

δ _k Is the set of price policies for charging station k.

And (4) yield: the k charging station receives the profit of

The expression is shown in formula (18).

It is to be noted that p _-k,t Refers to the price strategy of all charging stations except charging station k.

If a non-cooperative game model is found

Generalized Nash equalization

The underlying traffic network may reach a state of equilibrium. In this case, any charging station changing the charging price reduces its own profit, and the upper grid also tends to be stable. The definition for generalized nash equilibrium is shown as equation (20):

as a preferred example, in the step S3, the process of performing the compressor linearization modeling includes steps S31 to S32:

in step S3, the solving process of the optimal power flow problem of the upper power grid and the non-cooperative game problem of the lower traffic network is as follows:

step S31, in the flow equation constraint of the upper layer power grid, the nonlinear relation among current, voltage and active and reactive power needs to be processed in a linearization way, and according to the optimization result, a flow tracking method needs to be adopted to calculate the node electricity price, and the specific process is as follows:

step S311, the quadratic term in the formula (15) is processed by using a second-order cone relaxation method, and the nonlinear problem is converted into a convex optimization linear problem, which can be solved by a commercial solver (e.g., CPLEX):

two variables are introduced to relax the square term in the flow constraint so that

Thereby equation

Can be converted into formula (21):

formula (15) can thus be converted to the form of formula (22):

step S312, calculating the electricity price of the upper-layer power grid node by adopting a power flow tracking method:

based on the result of the optimal power flow, the node marginal price of each charging station node can be obtained by a power flow tracking method:

in the formula, the first term on the right represents the cost component of the power generation section, the second term represents the cost component of the power distribution section, A _u Representing the up-tracking matrix of the grid, A _d A backward matrix representing the power grid, specifically expressed as formulas (24) and (2)5) As shown.

Step S32, solving the non-cooperative game of the lower layer traffic network by adopting a greedy algorithm, wherein the specific process comprises the following steps:

step S321, in order to find a generalized nash equilibrium solution, an equivalent definition is proposed:

defining: if p is _t ^* Is the optimization problem minF (p) _t ) Is best solution F (p) _t ^* ) And the optimal solution equals 0, then p _t ^* Also non-cooperative gaming problems

Generalized nash equilibrium solution of (1).

F(p _t ) The optimization problem of (2) is defined as:

the necessity proves that: if p is _t ^* Non-cooperative gaming problem

Then for any K belonging to K, there is

Is equal to

Thus, F (p) _t ^* ) Equal to 0. And also has F (p) _t ) Must be greater than or equal to 0, obviously p _t ^* Is the optimization problem minF (p) _t ) The optimal solution of (1).

And (3) the sufficiency proves that: for any K belonging to K, it is apparent that

Greater than or equal to

Thus, if p _t ^* Is the optimization problem minF (p) _t ) An optimal solution of, and

equal to 0, there must be K for any K,

is equal to

Thus, p _t ^* Also non-cooperative gaming problems

Generalized nash equilibrium solution of (1).

Step S322, solving the non-cooperative game problem of the lower layer traffic network by adopting a greedy algorithm:

one sub-decision of generalized nash equalization is the optimal solution when the other sub-decisions are fixed, so the generalized nash equalization problem has an optimal sub-structure. Considering that the greedy algorithm is suitable for solving the optimization problem with the optimal substructure, the generalized Nash equilibrium of the lower-layer traffic network selects the greedy algorithm for solving.

The results are shown in Table 7:

TABLE 7 comparative analysis of daily cumulative revenue for all charging stations under different cases

As can be seen from table 9, under the three price schemes, the daily cumulative revenue of C3, C6, C8 and C13 with surplus renewable energy sources nearby is significantly higher than that of other ordinary charging stations. Since these charging stations preferentially use nearby wind or photovoltaic electricity, the cost of purchasing electricity from the upper grid is reduced. However, the daily cumulative revenue for C4 and C14, which are also located near the wind farm, is significantly lower than for other charging stations. On the one hand, the installed wind capacity in the vicinity of the two charging stations is relatively small, and the early peak traffic hours coincide with the valley of the fan output, which results in limited wind power utilization. On the other hand, C4 is located on a road with sparse traffic, while C14 is located far from the center of Dublin City. The disadvantage of their geographic location is unattractive to EV owners in terms of detour distance. In contrast, the general charging stations C9 and C10 can obtain good benefits through good locations.

The transverse comparison shows that when the dynamic game strategy is adopted, compared with other two price mechanisms, the daily accumulated income of C4, C6 and C14 is obviously improved in areas rich in wind energy. When uniform electricity prices are adopted, the charging willingness of the EV owners is only influenced by the detour distance, and the charging stations on light-load roads are not attractive to the EV owners. Particularly C14, located at the end of the distribution network, which needs to pay higher power transmission costs, its daily cumulative revenue will be much lower than other charging stations. In contrast, the dynamic gaming strategy balances the benefits of all charging stations to some extent, particularly those charging stations near the wind farm but in poor geographic locations, by adjusting the charging prices of the charging stations. In the embodiment, the charging prices of the C4, the C6 and the C14 are all reduced, so that the willingness of EV owners to select the charging prices is improved, and the daily accumulated income of the EV owners is improved. As can also be seen from table 7, the profit of C4 increased by 19.72% and 18.77%, respectively, the profit of C6 increased by 6.17% and 5.45%, respectively, and the profit of C14 increased by 26.75% and 26.88%, respectively, compared to the time-of-use electricity rate and the uniform electricity rate. At the same time, the daily cumulative revenue for the other charging stations fluctuates to an acceptable level, indicating that the dynamic betting strategy can increase the revenue for these charging stations without unduly reducing the revenue for the other charging stations.

TABLE 8 renewable energy consumption under dynamic Game strategy

TABLE 9 renewable energy consumption under time-of-use electricity price mechanism

TABLE 10 renewable energy consumption under unified electricity price mechanism

Tables 8 to 10 show the renewable energy consumption under three pricing regimes. For the wind power consumption situation, from 0:00 to 7:00, the wind power output is in the valley period, the EV to be charged on the road is very few, and therefore the wind power consumption rates of C4, C6 and C14 are low. When the traffic early peak time (7:00 to 9:00) comes, the wind power consumption rate is all increased and can even reach 100% in some cases. Subsequently, as the wind power output increases and the traffic flow decreases, the rate of consumption gradually decreases. During late peak traffic hours (16:00 to 18:00), wind energy consumption increases slightly. And then, the traffic flow is rapidly reduced, the wind power output is in a descending trend, and the wind power consumption rate is further reduced.

For the photoelectric absorption case, there is no photoelectric consumption after sunset. During the morning and evening peak traffic hours, the solar energy consumption of C3, C8 and C13 can reach 100%. Although 9:00 to 16:00 are peak periods of solar output, the rate of consumption is limited due to the small number of EVs to be charged on the road. Among them, C13 shows a higher overall acceptance rate than the other two charging stations due to the relatively small installed capacity of the blower.

On the whole, under the dynamic game strategy, the overall consumption level of renewable energy is superior to other two price mechanisms. Charging stations in areas rich in wind energy or solar energy can utilize the power generation advantages of free renewable energy sources, attract more EV owners by adjusting charging prices, and accordingly the consumption of the renewable energy sources is promoted.

TABLE 11 utilization of charging station charging location under different pricing schemes

Table 11 lists the average utilization of the charging facilities in the renewable energy rich charging station for the three pricing mechanisms. It can be seen that under the dynamic game strategy, the utilization rate of the charging stations in the areas with rich wind energy is generally improved, and the utilization rate of the charging stations in the areas with rich solar energy resources is generally reduced. Under the other two pricing mechanisms, the charging stations related to the photoelectricity have better performance in the utilization rate of the charging facilities due to the geographic advantages, and the charging stations related to the wind power have much lower utilization rate. Through adjusting the charging price, the dynamic game strategy can not only promote the consumption of renewable energy sources, but also balance the utilization rate of charging stations with surplus wind power and surplus photoelectric.

In summary, in the embodiment of the invention, a traffic network topology model is firstly constructed based on a graph theory, three factors such as a detour distance, a charging price and a charging grade are comprehensively considered, a charging willingness model of an EV vehicle owner is constructed, and compared with the existing research, a charging decision of the EV vehicle owner is more comprehensively and truly depicted. Then a double-layer coordination game scheduling model of the electric power traffic coupling system is established, an upper-layer power grid establishes an optimization scheduling model taking the lowest power generation and distribution cost as an optimization target, second-order cone relaxation is adopted to convert the lowest power generation and distribution cost into a stable linear power flow to solve, a power flow tracking method is adopted to obtain node marginal electricity prices, a lower layer establishes a non-cooperative game model taking a charging station as a main body, each non-cooperative game model influences the charging behavior of an EV owner by adjusting the price strategy of the non-cooperative game model, so that the distribution result of the charging demand is generated and fed back to the upper-layer power grid, and the steps are repeated until the system is balanced to obtain a unified scheduling strategy. Two comparison cases are set for comparison and analysis, and results show that the strategy can balance the benefits and the utilization rate of the charging station and effectively promote the consumption of surrounding renewable energy sources. Especially for charging stations near but in poor locations of the wind farm, the performance of the wind farm in all aspects can be significantly improved.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same. It will be understood by those skilled in the art that various modifications and equivalents may be made to the embodiments of the invention as described herein, and such modifications and variations are intended to be within the scope of the claims appended hereto.

Claims (1)

1. The coordination game scheduling method of the electric power traffic coupling system based on EV owner intention is characterized by comprising the following steps of:

step (1): establishing a traffic network model, including a traffic network topology model and an EV owner charging behavior model which are established based on graph theory;

step (2): establishing a double-layer coordination game scheduling model of the electric power traffic coupling network, wherein an upper-layer electric power network takes the lowest power distribution cost as an optimization target, an optimization model is established by considering network constraints, and each charging station in a lower-layer traffic network aims at maximizing self income to establish a non-cooperative game model;

and (3): solving a double-layer optimization model, solving an upper-layer power grid optimal power flow problem by adopting a second-order cone planning method, obtaining power grid node electricity prices by adopting a power flow tracking method, solving by adopting a greedy algorithm aiming at a non-cooperative game problem of a charging station in a lower-layer traffic network, and alternately iterating optimization variables of upper-layer and lower-layer sub-problems until the problem is converged to realize unified scheduling and optimized operation of the system;

the process of establishing the traffic network model in the step (1) is as follows:

(1) establishing a topological model of the traffic network, and characterizing the traffic network topology by using a graph G (V, E) (t) based on graph theory knowledge, wherein a node set V represents all intersections, and an edge set E represents all road sections; considering the road length and the congestion situation, the traffic network G at time t is considered as a directed weighted graph, and the weight of each road segment is: weight _e,t ＝lr _e (1+bs _e,t )，lr _e Indicates the length, bs, of the section e _e,t A blocking signal indicating a section e at time t;

(2) the EV vehicle owner charging behavior model comprises a charging probability model and a charging intention model:

the EV vehicle main charging probability model is

SOC _m The state of charge of EV m is shown,

a minimum value of state of charge that indicates prevention of over-discharge of the battery; consider SOC _m Is less than

When EV m must go to the charging station for charging, i.e. charging probability A _m Is 1; SOC _m Between

And between 100%, the charging probability A _m And SOC _m Negative correlation, τ represents a charging probability related parameter;

the charging intention model is as follows:

determining a distance parameter by adopting a Dijkstra algorithm: d is Dijkstra (G (V, E), weight, source, target);

firstly, the detour distance d for charging to a charging station k is calculated _m,k ＝d _SPm,k +d _k,TPm -d _SPm,TPm ，d _SPm,k ,d _k,TPm And d _SPm,TPm Respectively representing the shortest travel mileage from the starting point SPm to the charging station k, from the charging station k to the destination TPm, and from the starting point SPm to the destination TPm;

secondly, based on d _SPm,k And calculating EVm to obtain the battery state of charge when the vehicle runs to a charging station k:

γ _m representing EVm mileage power consumption, kWh/km;

thirdly, whether d is satisfied is judged _SPm,k ＜d _SPm,TPm ，d _k,TPm ＜d _SPm,TPm ，

Thirdly, calculating the charging willingness of the EV m to select the charging station k

f(d _m,k )＝α ₁ ·(1-(α ₂ ·d _m,k +α ₃ ) ^-1 )，α ₀ ,α ₁ ,α ₂ ,α ₃ Is composed of

A related parameter;

thirdly, the relation between the willingness to charge and the charge price is

α ₄ ,α ₅ Represent

The parameters that are to be correlated with each other,

means representing a regional charge price;

finally, the charging willingness of the EV owner to select the charging station is comprehensively expressed as

ω ₁ ,ω ₂ And ω ₃ The weights occupied by the three factors are respectively,

the loyalty of the charging station k is selected for EV m, and when the charging level of the charging station k is consistent with the preference of the owner,

equal to 1, otherwise 0;

the specific steps of establishing the double-layer coordination game scheduling model are as follows:

firstly, in order to ensure the effect of the scheduling strategy, before a double-layer coordination game scheduling model is constructed, the following six basic assumptions are made:

assume that 1: data interaction between the EV owner and the charging station is realized by means of advanced wireless communication technology and GPS (global positioning system) facilities;

assume 2: it is considered that the historical data of the traffic flow and the renewable energy power generation can be obtained from the local weather bureau and the traffic department, respectively; the obtained data is processed in a high-level data aggregator, and the data aggregator is deployed on a cloud computer server and responds to the prediction;

assume that 3: based on the vehicle condition on the road and the charging station running state broadcasted in real time, the data aggregator processes the traffic flow information and broadcasts road blocking information to all EVs on the traffic network;

assume 4: all charging stations and EVs are considered as rational participants, i.e. pursuing self-benefit or utility maximization;

assume that 5: the renewable energy power generation amount referred to in the invention refers to wind power or photoelectricity which cannot be consumed nearby a charging station, and is considered to be free or low in cost;

assume 6: after the EV charging process is considered to be completed, the charging station can charge the overtime fee to the EV owner, so that the waiting time after the EV arrives at the charging station is ignored;

secondly, building a power flow optimization model in an upper-layer power network, including

(1) The objective function of the upper-layer power system is that the total cost of power generation and distribution is the lowest:

C _Gn cost per unit of power generation, C _Tij Unit power distribution cost;

(2) the upper layer model needs to satisfy the equation constraint of power flow

j _u A set of nodes flowing into j for the power flow direction; j is a unit of a group _d A set of nodes flowing from j for the power flow direction; p _Lj,t And Q _Lj,t Active and reactive electrical power consumed for node j; p _ij,t And Q _ij,t Is the power flow between nodes i and j; r is _ij And X _ij Representing an impedance parameter of the line between nodes i and j; u shape _i,t And U _j,t Representing the voltages at nodes i and j; I.C. A _ij,t Represents the current between nodes i and j;

(3) voltage current constraint U _i,min ≤U _i,t ≤U _i,max ，|I _ij,t |≤I _ij,max ，U _i,min And U _i,max The minimum value and the maximum value of the node voltage are obtained; I.C. A _ij,max Is the maximum value of the line current;

thirdly, establishing a non-cooperative game model by aiming at maximizing the self income of each charging station in the lower-layer traffic network:

(1) constructing a charging station optimization strategy:

the revenue for each charging station in the lower transportation network is equal to the revenue for providing charging service to the EV less the cost of purchasing power to the upper grid, maxP _k,t ＝p _k,t ·D _k,t -LMP _k,t ·max(D _k,t -RE _k,t ,0)；

The charging price of the charging station still needs to satisfy:

and

upper and lower limits of the charge price for the charging station;

(2) constructing a non-cooperative game model:

the definitions are as follows:

the participants: all K charging stations in total;

strategy: for each charging station k, a charging price strategy p is selected _k,t ,

δ _k A set of price policies for charging station k;

and (4) yield: the k charging station receives a profit P _k,t (p _k,t ,p _-k,t )，p _-k,t Refers to the price policy of all charging stations except charging station k;

if a non-cooperative game model is found

The lower layer traffic network can reach a balanced state; generalized Nash equilibrium is defined as

The solving process of the optimal power flow problem of the upper-layer power grid and the non-cooperative game problem of the lower-layer traffic network comprises the following steps:

firstly, in the load flow equation constraint of an upper-layer power grid, the current, the voltage and the active power and the reactive power are in a nonlinear relation, linearization processing is needed, and according to an optimization result, a load flow tracking method is needed to calculate the node electricity price, and the specific process is as follows:

(1) processing formula by adopting second-order cone relaxation method

The non-linear problem is converted into a convex optimization linear problem by a square term in (1):

solving by a commercial solver: two variables are introduced to relax the square term in the power flow constraint, and alpha is led to _ij,t ＝I2 ij,t，β _i,t U2 i, t; thus the equation I2 ij, t ═ P2 ij, t + Q2 ij, t)/U2I, t translates into

Will be provided with

Is converted into

(2) Calculating the electricity price of the upper-layer power grid node by adopting a power flow tracking method:

based on the result of the optimal power flow, the node marginal price of each charging station node is obtained by a power flow tracking method:

in order to make up the cost of the power generation section,

for cost components of the distribution section, A _u Is an up-tracking matrix of the grid, A _d Is a down-tracking matrix of the power grid,

secondly, solving the non-cooperative game of the lower-layer traffic network by adopting a greedy algorithm, wherein the specific process is as follows:

(1) to find a generalized Nash equilibrium solution, an equivalent definition is proposed:

defining: if p is _t ^* Is the optimization problem minF (p) _t ) Is best solution F (p) _t ^* ) And the optimal solution equals 0, then p _t ^* Also non-cooperative gaming problems

Generalized Nash equilibrium solution of (2);

F(p _t ) The optimization problem of (2) is defined as:

the necessity proves that: if p is _t ^* Non-cooperative gaming problem

Then for any K belonging to K, there is

Is equal to

Thus, F (p) _t ^* ) Equal to 0; and also has F (p) _t ) Must be greater than or equal to 0, obviously p _t ^* Is the optimization problem minF (p) _t ) The optimal solution of (2);

and (3) the sufficiency proves that: for any K belonging to K, it is apparent that there is P _k,t (p _k,t ) Is greater than or equal to minP _k,t (·,p _-k,t ) (ii) a Thus if p is _t ^* Is the optimization problem minF (p) _t ) An optimal solution of, and

equal to 0, there must be a K for any K,

is equal to

Thus, p _t ^* Also non-cooperative gaming problems

Generalized Nash equilibrium solution of (2);

(2) and solving the non-cooperative game problem of the lower-layer traffic network by adopting a greedy algorithm.

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