CN110991881B - Cooperative scheduling method and system for electric vehicle battery exchange station and electric company - Google Patents

Abstract

The invention discloses a cooperative scheduling method and system for an electric vehicle battery exchange station and an electric company. The method comprises the following steps: constructing an interaction model of an electric company and a power exchange station; the power company and power exchange station interaction model comprises a first optimization model and a second optimization model; constructing a power exchange station and an electric automobile scheduling model; the power exchange station and electric automobile scheduling model comprises a third optimization model and a fourth optimization model; solving an interaction model of the electric company and the power exchange station and a dispatching model of the electric vehicle by adopting a game iterative algorithm to obtain an optimal dispatching result; the optimal scheduling result consists of an optimal electricity selling price of an electric power company, an optimal electricity purchasing amount of a battery replacement station, an optimal battery exchange price and an optimal battery exchange requirement. The invention can reduce the cost of the power exchange station, improve the benefits of the power exchange station and the power company and improve the satisfaction degree of the users of the electric automobile.

Description

Cooperative scheduling method and system for electric vehicle battery exchange station and electric company

Technical Field

The invention relates to the technical field of electric vehicle optimized dispatching, in particular to a collaborative dispatching method and system for an electric vehicle power exchange station and an electric company.

Background

At present, under the condition of no government policy and price guidance, unordered charging and changing potential of a large-scale electric automobile can have great influence on the aspects of operation, planning, safety and the like of a power system. Therefore, bidirectional interaction between the electric automobile and the power grid is promoted, an effective electric energy transaction mechanism is formulated to guide ordered charging and discharging of the electric automobile on the basis of ensuring that the requirements of the electric automobile are met, peak-valley difference of the power grid can be reduced, income of a power exchange station and a power grid company is increased, user satisfaction is improved, and energy conservation and emission reduction effects are achieved. The power exchange station is used as an intermediary of an electric automobile and a power system, and the optimal scheduling of the power exchange station is particularly important for the economic operation of the whole power grid.

The electric automobile charging and replacing station is the most main place for supplying energy to the electric automobile, and has direct effect on charging and replacing load regulation and control. In order to better ensure the market rule of the operation of the electric automobile charging and exchanging station, systematic research on the operation strategy of the charging and exchanging station is required. And (3) formulating a reasonable operation strategy of the charging and replacing station, and coordinating the interest and disadvantage relation between the charging and replacing station and the practicability pursued by the electric automobile user. Meanwhile, the economic benefits of the operation main body of the electric automobile charging and replacing station are guaranteed, more investment is attracted, the construction of the charging and replacing station is promoted, the belief that consumers purchase electric automobiles is increased, and further the progress of the electric automobile technology is promoted.

Therefore, how to reduce the cost of the power exchange station, improve the benefits of the power exchange station and the power company, and improve the satisfaction of the users of the electric automobile is a problem to be solved at present.

Disclosure of Invention

Based on this, it is necessary to provide a method and a system for collaborative scheduling of an electric vehicle power exchange station and an electric company, so as to improve the benefits of the power exchange station and the electric company and improve the satisfaction degree of users of the electric vehicle while reducing the cost of the power exchange station.

In order to achieve the above object, the present invention provides the following solutions:

a cooperative scheduling method for an electric automobile power exchange station and an electric company comprises the following steps:

acquiring a first initial parameter, a second initial parameter, a third initial parameter and a fourth initial parameter; the first initial parameters comprise thermal power generation cost, photovoltaic power generation cost, wind power generation cost, thermal power generation power, photovoltaic power generation power, wind power generation power, environmental calculation cost of carbon dioxide generated by thermal power generation, unit punishment cost of abandoned wind and abandoned light and power generation coal consumption coefficient; the second initial parameters comprise unit maintenance cost of the battery in the power exchange station and unit lease cost of the battery in the power exchange station; the third initial parameters include purchase cost of a single battery, capacity of the battery, and the number of times of recycling of the battery; the fourth initial parameter includes a battery exchange price and a battery exchange demand;

According to the first initial parameters, the electricity selling price of the electric power company and the electricity purchasing quantity of the power exchange station, taking the maximum income of the electric power company as a target, taking the electricity selling price of the electric power company as a decision variable, and taking total power balance, the thermal power generation power, the photovoltaic power generation power, the wind power generation power and the electricity selling price of the electric power company as constraint conditions, constructing a first optimization model;

according to the second initial parameters, the electricity selling price of the electric company and the electricity purchasing quantity of the electricity changing station, the minimum cost of the electricity changing station is taken as a target, the electricity purchasing quantity of the electricity changing station is taken as a decision variable, and the electricity purchasing quantity of the electricity changing station is taken as a constraint condition, a second optimization model is constructed;

according to the third initial parameter, the battery exchange price, the electricity selling price of the electric company and the electricity purchasing quantity of the battery exchange station, taking the maximum income of the battery exchange as a target, taking the battery exchange price as a decision variable, and taking the battery exchange price, the total charging power of the battery exchange station and the electricity exchanging requirement of the battery exchange station as constraint conditions, establishing a third optimization model;

according to the fourth initial parameters, a fourth optimization model is established by taking the maximum user satisfaction degree as a target, the battery exchange requirement as a decision variable and the user satisfaction degree and the user electricity cost as constraint conditions;

Solving the first optimization model and the second optimization model by adopting a game iterative algorithm to obtain a first group of optimal solutions; the first group of optimal solutions comprise optimal power company electricity selling prices and optimal power exchange station electricity purchasing quantity;

inputting the purchase electric quantity of the optimal battery replacement station to the third optimization model, and solving the third optimization model and the fourth optimization model by adopting a game iteration algorithm to obtain a second group of optimal solutions; the second set of optimal solutions includes an optimal battery exchange price and an optimal battery exchange demand;

determining an optimal scheduling result; the optimal scheduling result is composed of the first group of optimal solutions and the second group of optimal solutions.

Optionally, the first optimization model is:

wherein ,

C _Gen,t ＝C _Coal,unit (a·P _coal,t ² +b·P _coal,t +c)，

C _PV,t ＝C _PV,unit P _PV,t ，

C _WT,t ＝C _WT,unit P _WT,t ，

wherein ,I_E For the benefit of the power company, T is the time, T is the total time, and pri _Sell,t Price of electricity selling for electric power company, Q _BSS,t For power purchase of power exchange station, C _Gen,t C is the thermal power generation cost _PV,t C is the cost of photovoltaic power generation _WT,t C is the wind power generation cost _CO2,t Cost is reduced for environmental conversion of carbon dioxide generated by thermal power generation, C _Pun,PV,WT,t C, punishing cost of wind and light discarding of electric power company _Coal,unit Is the power generation cost of the thermal power unit, C _PV,unit C is the power generation cost of the photovoltaic unit _WT,unit For the power generation cost of wind power unit, P _coal,t Is thermal power, P _PV,t For photovoltaic power generation, P _WT,t For wind power generation, a, b and c are all the coal consumption coefficients of power generation,cost per unit coal consumption to produce carbon dioxide;

the optimization conditions of the first optimization model are as follows:

P _coal,t +P _PV,t +P _WT,t ＝P _BSS,t +P _base,t ，

P _i,min ≤P _i,t ≤P _i,max i∈coal,pv,wt，

pri _Sell,min ≤pri _Sell,t ≤pri _Sell,max ，

P _BSS,t for the total charging power of the power exchange station, P _base,t P is the power company which does not consider the resident load of the electric car _i,min Is the minimum output power of thermal power generation, the minimum output power of photovoltaic power generation or the minimum output power of wind power generation, P _i,max The maximum output power of thermal power generation, the maximum output power of photovoltaic power generation or the maximum output power of wind power generation; pri (pri) _Sell,min Minimum electricity price for electric power company, pri _Sell,max And the maximum electricity selling price is the electric power company.

Optionally, the second optimization model is:

wherein ,C_BSS For the cost of the power exchange station, C _m For unit maintenance cost of battery in power exchange station, C _b The rental cost of the unit of the battery in the power exchange station;

the constraint conditions of the second optimization model are as follows:

Q _BSS,t ≥P _BSS,t ·Δt，

Δt is the unit charging time of the power exchange station.

Optionally, the third optimization model is:

wherein ,

wherein ,pri_swap,t Exchange price for battery, C _total For the purchase cost of single battery, B is the capacity of the battery, N is the recycling frequency of the battery, P _av To optimize the average charging power of the pre-exchange station, P _DA,t Step for optimizing the total charging power of a pre-exchange station _p Step is the power step _pri For price step length, P _B ' _SS,t For the slope of the total charging power of the station, P _t To optimize the charge power of the single battery D _t The power-exchanging requirement is met for a user;

the constraint conditions of the third optimization model are as follows:

pri _swap,min ≤pri _swap,t ≤pri _swap,max ，

P _BSS,min ≤P _BSS,t ≤P _BSS,max ，

E _min ≤D _t ≤E _max ，

wherein ,pri_swap,min Pri for minimum exchange price of exchange station _swap,max For maximum exchange price of exchange station, P _BSS,min For minimum output power of the power exchange station, P _BSS,max For maximum output power of the power-exchange station E _min For providing minimum value of number of service equipment for power exchange station, E _max The maximum number of service devices for the power exchange can be provided.

Optionally, the fourth optimization model is:

wherein ,

C _EV,t ＝P _t ·D _t ·pri _DA,Sell,t ·Δt，

C _EV,0 ＝P _t ·D _DA,t ·pri _swap,t ·Δt，

wherein ,M_EV,t For the satisfaction degree of the electric automobile user, C _EV,0 C for optimizing the power conversion cost of the electric automobile before _EV,t D for the optimized battery power conversion cost _DA,t Pri for optimizing pre-battery exchange requirements _DA,Sell,t Exchanging prices for the battery before optimization;

constraint conditions of the fourth optimization model are as follows:

0≤M _EV,t ≤1，

C _EV,0 ≥C _EV,t 。

the invention also provides a co-scheduling system of the electric automobile power exchange station and the electric company, which comprises the following steps:

the parameter acquisition module is used for acquiring a first initial parameter, a second initial parameter, a third initial parameter and a fourth initial parameter; the first initial parameters comprise thermal power generation cost, photovoltaic power generation cost, wind power generation cost, thermal power generation power, photovoltaic power generation power, wind power generation power, environmental calculation cost of carbon dioxide generated by thermal power generation, unit punishment cost of abandoned wind and abandoned light and power generation coal consumption coefficient; the second initial parameters comprise unit maintenance cost of the battery in the power exchange station and unit lease cost of the battery in the power exchange station; the third initial parameters include purchase cost of a single battery, capacity of the battery, and the number of times of recycling of the battery; the fourth initial parameter includes a battery exchange price and a battery exchange demand;

The first model building module is used for building a first optimization model according to the first initial parameters, the electricity selling price of the electric power company and the electricity purchasing quantity of the power exchange station, taking the electricity selling price of the electric power company as a decision variable and taking total power balance, the thermal power generation power, the photovoltaic power generation power, the wind power generation power and the electricity selling price of the electric power company as constraint conditions;

the second model construction module is used for constructing a second optimization model by taking the minimum cost of the power exchange station as a target, taking the power exchange station purchase quantity as a decision variable and taking the power exchange station purchase quantity as a constraint condition according to the second initial parameter, the power selling price of the power company and the power exchange station purchase quantity;

the third model building module is used for building a third optimization model according to the third initial parameter, the battery exchange price, the electricity selling price of the power company and the electricity purchasing quantity of the power exchange station, taking the maximum profit of the power exchange station as a target, taking the battery exchange price as a decision variable, and taking the battery exchange price, the total charging power of the power exchange station and the electricity exchange requirement of the power exchange station as constraint conditions;

the fourth model construction module is used for constructing a fourth optimization model according to the fourth initial parameter, taking the maximum user satisfaction degree as a target, taking the battery exchange requirement as a decision variable and taking the user satisfaction degree and the user electricity cost as constraint conditions;

The first solving module is used for solving the first optimizing model and the second optimizing model by adopting a game iterative algorithm to obtain a first group of optimal solutions; the first group of optimal solutions comprise optimal power company electricity selling prices and optimal power exchange station electricity purchasing quantity;

the second solving module is used for inputting the electricity purchasing quantity of the optimal battery replacement station to the third optimizing model, and solving the third optimizing model and the fourth optimizing model by adopting a game iteration algorithm to obtain a second group of optimal solutions; the second set of optimal solutions includes an optimal battery exchange price and an optimal battery exchange demand;

the scheduling result determining module is used for determining an optimal scheduling result; the optimal scheduling result is composed of the first group of optimal solutions and the second group of optimal solutions.

Optionally, the first optimization model in the first model building module is:

wherein ,

C _Gen,t ＝C _Coal,unit (a·P _coal,t ² +b·P _coal,t +c)，

C _PV,t ＝C _PV,unit P _PV,t ，

C _WT,t ＝C _WT,unit P _WT,t ，

wherein ,I_E For the benefit of the power company, T is the time, T is the total time, and pri _Sell,t Price of electricity selling for electric power company, Q _BSS,t For power purchase of power exchange station, C _Gen,t C is the thermal power generation cost _PV,t C is the cost of photovoltaic power generation _WT,t C is the wind power generation cost _CO2,t Cost is reduced for environmental conversion of carbon dioxide generated by thermal power generation, C _Pun,PV,WT,t C, punishing cost of wind and light discarding of electric power company _Coal,unit Is the power generation cost of the thermal power unit, C _PV,unit C is the power generation cost of the photovoltaic unit _WT,unit For the power generation cost of wind power unit, P _coal,t Is thermal power, P _PV,t For photovoltaic power generation, P _WT,t For wind power generation, a, b and c are all the coal consumption coefficients of power generation,cost per unit coal consumption to produce carbon dioxide;

the optimization conditions of the first optimization model are as follows:

P _coal,t +P _PV,t +P _WT,t ＝P _BSS,t +P _base,t ，

P _i,min ≤P _i,t ≤P _i,max i∈coal,pv,wt，

pri _Sell,min ≤pri _Sell,t ≤pri _Sell,max ，

P _BSS,t for the total charging power of the power exchange station, P _base,t P is the power company which does not consider the resident load of the electric car _i,min Is the minimum output power of thermal power generation, the minimum output power of photovoltaic power generation or the minimum output power of wind power generation, P _i,max The maximum output power of thermal power generation, the maximum output power of photovoltaic power generation or the maximum output power of wind power generation; pri (pri) _Sell,min Minimum electricity price for electric power company, pri _Sell,max And the maximum electricity selling price is the electric power company.

Optionally, the second optimization model in the second model building module is:

wherein ,C_BSS For the cost of the power exchange station, C _m For unit maintenance cost of battery in power exchange station, C _b The rental cost of the unit of the battery in the power exchange station;

the constraint conditions of the second optimization model are as follows:

Q _BSS,t ≥P _BSS,t ·Δt，

Δt is the unit charging time of the power exchange station.

Optionally, the third optimization model in the third model building module is:

wherein ,

wherein ,pri_swap,t Exchange prices for batteries，C _total For the purchase cost of single battery, B is the capacity of the battery, N is the recycling frequency of the battery, P _av To optimize the average charging power of the pre-exchange station, P _DA,t Step for optimizing the total charging power of a pre-exchange station _p Step is the power step _pri For price step length, P' _BSS,t For the slope of the total charging power of the station, P _t To optimize the charge power of the single battery D _t The power-exchanging requirement is met for a user;

the constraint conditions of the third optimization model are as follows:

pri _swap,min ≤pri _swap,t ≤pri _swap,max ，

P _BSS,min ≤P _BSS,t ≤P _BSS,max ，

E _min ≤D _t ≤E _max ，

wherein ,pri_swap,min Pri for minimum exchange price of exchange station _swap,max For maximum exchange price of exchange station, P _BSS,min For minimum output power of the power exchange station, P _BSS,max For maximum output power of the power-exchange station E _min For providing minimum value of number of service equipment for power exchange station, E _max The maximum number of service devices for the power exchange can be provided.

Optionally, the fourth optimization model in the fourth model building module is:

wherein ,

C _EV,t ＝P _t ·D _t ·pri _DA,Sell,t ·Δt，

C _EV,0 ＝P _t ·D _DA,t ·pri _swap,t ·Δt，

wherein ,M_EV,t For the satisfaction degree of the electric automobile user, C _EV,0 C for optimizing the power conversion cost of the electric automobile before _EV,t D for the optimized battery power conversion cost _DA,t To optimizeFront battery exchange demand, pri _DA,Sell,t Exchanging prices for the battery before optimization;

constraint conditions of the fourth optimization model are as follows:

0≤M _EV,t ≤1，

C _EV,0 ≥C _EV,t 。

compared with the prior art, the invention has the beneficial effects that:

the invention provides a cooperative scheduling method and system for an electric vehicle battery exchange station and an electric company. The method comprises the following steps: constructing an interaction model of an electric company and a power exchange station; the power company and power exchange station interaction model comprises a first optimization model and a second optimization model; constructing a power exchange station and an electric automobile scheduling model; the power exchange station and electric automobile scheduling model comprises a third optimization model and a fourth optimization model; solving an interaction model of the electric company and the power exchange station and a dispatching model of the electric vehicle by adopting a game iterative algorithm to obtain an optimal dispatching result; the optimal scheduling result consists of an optimal electricity selling price of an electric power company, an optimal electricity purchasing amount of a battery replacement station, an optimal battery exchange price and an optimal battery exchange requirement. The invention can reduce the cost of the power exchange station, improve the benefits of the power exchange station and the power company and improve the satisfaction degree of the users of the electric automobile.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a diagram showing the interaction relationship among electric power companies, power stations and users of electric vehicles according to an embodiment of the present invention;

FIG. 2 is a flowchart of a co-scheduling method for an electric vehicle battery exchange station and an electric company according to an embodiment of the present invention;

FIG. 3 is a block diagram of an interactive model of an electric company and a power exchange station according to an embodiment of the present invention;

FIG. 4 is a block diagram of a power plant and electric vehicle scheduling model according to an embodiment of the present invention;

FIG. 5 is a flow chart of an embodiment of the invention for optimizing solutions using a game iteration algorithm.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

Electric automobile trades power station: the electric automobile power exchanging station is the most main place for supplying energy to the electric automobile; unlike conventional automobiles, electric automobiles are new energy automobiles powered by electric energy driving motors, and therefore, battery charging and replacing stations are "gas stations" of new energy electric automobiles. The user can replace the empty battery with the full battery by paying corresponding cost, compared with a charging station, the electric vehicle scheduling system has the advantages that the user time is saved to a great extent, and the scheduling flexibility of the electric vehicle is improved.

Electric power company: the power company comprises a thermal power plant, a photovoltaic power plant and a wind power plant, and the power company performs unified dispatching to realize the economic operation of the whole power generation system, reduce the phenomenon of wind and light abandoning and improve the utilization rate of photovoltaic and wind power generation.

The interaction relationship among the electric company, the power exchange station and the electric automobile user is shown in fig. 1. The concept of the co-scheduling method of the electric vehicle battery exchange station and the electric company in the embodiment is as follows:

1) And establishing an interaction model of the power company and the power exchange station, and realizing bidirectional interaction between the power company and the power exchange station. In the process of bidirectional interaction between the electric power company and the power grid, the electric power company is often a main role formulated by an electric energy transaction mechanism, the electric power company firstly determines an electricity selling price, then the electric power station responds to the electricity price determined by the electric power company, and the electric power purchasing quantity is determined on the basis of meeting the exchange requirement so as to minimize the electric power station cost. In actual interaction, the power purchase decision of the power exchange station can adversely affect the establishment of the trading power price of the power company, a game model is formed, and finally dynamic balance is formed. The two actions are sequential, so that in actual interaction, the power grid company is a leader, the power exchange station is a follower, and the strategy selection of one party can directly influence the income and the cost of the other party. And finally, the income of the electric company and the cost of the power exchange station are balanced. And transmitting the generated equalization solution to the second layer, and scheduling by the second layer. The utility and station interaction model includes a first optimization model established with the utility as a leader and a second optimization model established with the station as a follower.

2) And establishing a power exchange station and electric automobile scheduling model. The power exchange station serves as an intermediary between an electric company and an electric automobile, firstly, electricity is purchased from the electric company, and then the electricity is sold to electric automobile users, so that a gap is earned. The power exchange station sells power according to exchange price, and higher exchange price can lead to less user demand, and too low price can lead to the power exchange station to be in a defect state. In actual interaction, the user demand of the electric automobile can adversely affect the establishment of exchange prices of the exchange station, and the two exchange prices are repeatedly played, so that dynamic equilibrium is finally formed. The actions between the two are also sequential, so that in actual interaction, the power exchange station is a leader, and the electric automobile user is a follower.

In the interaction process of the power exchange station and the electric automobile user, the power exchange station firstly receives the iterative equilibrium solution transmitted by the first layer, determines an initial battery exchange electricity price according to the self operation condition, and expects the maximum benefit under the quotation. After receiving the initial battery exchange electricity price, the user makes an electricity exchange decision according to the own electricity demand and feeds back to the electricity exchange station. And then the exchange price of the exchange station is redetermined according to the exchange requirement of the electric automobile user, the exchange price again influences the exchange requirement of the user, and the exchange price and the exchange requirement are iterated repeatedly, so that dynamic balance is finally formed. The power exchange station and the electric vehicle dispatching model comprise the power exchange station as a leader to establish a third optimization model and the electric vehicle user as a follower to establish a fourth optimization model.

In this embodiment, the co-scheduling method for the electric vehicle battery exchange station and the electric company is as follows.

Fig. 2 is a flowchart of a co-scheduling method for an electric vehicle battery exchange station and an electric company according to an embodiment of the present invention. Referring to fig. 2, the co-scheduling method for the electric vehicle battery exchange station and the electric company according to the embodiment includes:

step S1: the method comprises the steps of obtaining a first initial parameter, a second initial parameter, a third initial parameter and a fourth initial parameter.

The first initial parameters comprise thermal power generation cost, photovoltaic power generation cost, wind power generation cost, thermal power generation power, photovoltaic power generation power, wind power generation power, environmental calculation cost of carbon dioxide generated by thermal power generation, unit punishment cost of abandoned wind and abandoned light and power generation coal consumption coefficient; the second initial parameters comprise unit maintenance cost of the battery in the power exchange station and unit lease cost of the battery in the power exchange station; the third initial parameters include purchase cost of a single battery, capacity of the battery, and the number of times of recycling of the battery; the fourth initial parameter includes a battery exchange price and a battery exchange demand.

Step S2: and according to the first initial parameters, the electricity selling price of the electric power company and the electricity purchasing quantity of the power exchange station, taking the maximum income of the electric power company as a target, taking the electricity selling price of the electric power company as a decision variable, and taking total power balance, thermal power generation power, photovoltaic power generation power, wind power generation power and the electricity selling price of the electric power company as constraint conditions, constructing a first optimization model.

In the step, an electric power company consisting of firepower, wind power and photovoltaic is an electric energy supply source of the power exchange station, and the electric power company uniformly coordinates and dispatches the electric energy. The optimization model considers the thermal power, wind power and photovoltaic power generation cost, and pollution to the environment caused by carbon dioxide generated by fire coal in the thermal power generation process is considered as the environmental conversion cost. Because of uncertainty of photovoltaic and wind energy, electric energy cannot be sold in real time, and waste of electric energy is formed, and therefore punishment is made on the wind and light discarding phenomenon of an electric company. The utilization of thermal power is reduced as much as possible, the high-efficiency utilization of renewable energy is realized, and the electricity selling price is formulated by the electric company to sell electric energy to the power exchange station.

In the interaction process of the electric power company and the power exchange station, for the electric power company, the decision variable is the price pri of electricity selling _Sell,t The first optimization model established is as follows:

wherein ,

C _Gen,t ＝C _Coal,unit (a·P _coal,t ² +b·P _coal,t +c)，

C _PV,t ＝C _PV,unit P _PV,t ，

C _WT,t ＝C _WT,unit P _WT,t ，

wherein ,I_E For the benefit of the power company, T is the time, T is the total time, and pri _Sell,t Price of electricity selling for electric company (in terms of/kw.h), Q _BSS,t For the electricity purchasing quantity (kw.h) of the power exchange station, C _Gen,t C is the thermal power generation cost (this) _PV,t C is the cost of photovoltaic power generation _WT,t For wind power generation cost (this), C _CO2,t Cost is reduced for environment for generating carbon dioxide by thermal power generation (C) _Pun,PV,WT,t Punishment cost for wind and light discarding of electric company (this), C _Coal,unit Is the power generation cost of the thermal power unit (the per kw.h), C _PV,unit C is the power generation cost of the photovoltaic unit (the per kw.h) _WT,unit The power generation cost of the wind power unit (the wind power unit/kw.h) and the P _coal,t Is thermal power (kw), P _PV,t For photovoltaic power generation (kw), P _WT,t Is wind power generation power (kw), a, b and c are allThe coal consumption coefficient of the power generation,cost per kg of carbon dioxide produced per unit of coal consumption.

The optimization conditions of the first optimization model are as follows:

total power balance constraint

P _coal,t +P _PV,t +P _WT,t ＝P _BSS,t +P _base,t ，

Power generation constraint

P _i,min ≤P _i,t ≤P _i,max i∈coal,pv,wt，

Electricity selling price constraint of electric company

pri _Sell,min ≤pri _Sell,t ≤pri _Sell,max ，

P _BSS,t For total charging power (kw) of the station, P _base,t For electric power companies irrespective of the resident load (kw), P _i,min Is the minimum output power (kw) of thermal power generation, the minimum output power (kw) of photovoltaic power generation or the minimum output power (kw) of wind power generation, P _i,max The maximum output power (kw) of thermal power generation, the maximum output power (kw) of photovoltaic power generation or the maximum output power (kw) of wind power generation; pri (pri) _Sell,min Minimum electricity selling price (this/kw.h) for electric power company, pri _Sell,max The maximum electricity selling price (the/kw.h) of the electric power company is achieved.

Step S3: and constructing a second optimization model according to the second initial parameters, the electricity selling price of the electric company and the electricity purchasing quantity of the electricity changing station, taking the minimum cost of the electricity changing station as a target, taking the electricity purchasing quantity of the electricity changing station as a decision variable and taking the electricity purchasing quantity of the electricity changing station as a constraint condition.

For a power exchange station, the decision variable is the electricity purchase quantity Q _BSS,t On the basis of meeting the power conversion requirement, the second optimization model is as follows:

wherein ,C_BSS For the cost of the power exchange station, C _m For unit maintenance cost of battery in power exchange station (this/kw.h), C _b The cost per rental of the batteries in the battery exchange station is (/ kw.h).

The constraint conditions of the second optimization model are as follows:

Q _BSS,t ≥P _BSS,t ·Δt，

Δt is the unit charging time of the power exchange station.

And (3) obtaining an interaction model of the electric power company and the power exchange station, which comprises the first optimization model and the second optimization model, through the step (S2) and the step (S3), wherein the interaction model of the electric power company and the power exchange station is shown in figure 3.

Step S4: and establishing a third optimization model according to the third initial parameter, the battery exchange price, the electricity selling price of the electric company and the electricity purchasing quantity of the exchange station, taking the maximum income of the exchange station as a target, taking the battery exchange price as a decision variable, and taking the battery exchange price, the total charging power of the exchange station and the electricity exchanging requirement of the exchange station as constraint conditions.

The power exchange station is a main place for electric energy exchange, and can lead to a large amount of load aggregation without reasonable policy and price guidance, increase peak-valley difference and cause damage to the whole system. The electric automobile is guided to orderly exchange electricity through reasonable exchange price by the exchange station, so that the economic operation of the exchange station can be realized, and the exchange requirement of a user can be ensured. The decision variable is the battery exchange price pri _swap,t The third optimization model is established as follows:

wherein ,pri_swap,t Exchange price for battery (/ kw.h), C _total The purchase cost of a single battery (this) is represented by B, which is the capacity (kw.h) of the battery, and N, which is the number of times the battery is recycled. The first part of the third optimization model is the electricity exchanging gain of the electricity exchanging station, the second part is the electricity purchasing cost of the electricity exchanging station, and the third part is the battery damage expense.

In order to make the load fluctuation of the electric automobile smaller, the electric automobile fluctuates up and down at the average power; firstly judging whether the total charging power of a battery exchange station is larger than a power step length of average charging power before optimization and whether the power of the battery exchange station presents an increasing trend, if so, increasing the battery exchange price by a step length; if the total charging power of the power exchange station is larger than the average charging power before optimization by one power step length but the power of the power exchange station is in a decreasing trend, the exchange price is reduced by one step length, and otherwise, the exchange price is kept unchanged.

Battery exchange price pri _swap,t The functional relationship of (2) is as follows:

wherein ,P_av To optimize the average charging power (kw) of the pre-exchange station, P _DA,t Step for optimizing the total charging power (kw) of a pre-exchange station _p Step is the power step (kw) _pri For price step size (, P' _BSS,t For the slope of the total charging power of the station, P _t To optimize the charge power (kw) of the individual cells, D _t The power change needs for the user (block).

The constraint conditions of the third optimization model are as follows:

exchange price constraint of exchange station

pri _swap,min ≤pri _swap,t ≤pri _swap,max ，

Total power constraint of power exchange station

P _BSS,min ≤P _BSS,t ≤P _BSS,max ，

Constraint of power exchange requirement of power exchange station

E _min ≤D _t ≤E _max ，

wherein ,pri_swap,min Pri for minimum exchange price of exchange station _swap,max For maximum exchange price of exchange station, P _BSS,min For minimum output power of the power exchange station, P _BSS,max For maximum output power of the power-exchange station E _min For providing minimum value of number of service equipment for power exchange station, E _max The maximum number of service devices for the power exchange can be provided.

Step S5: and according to the fourth initial parameters, establishing a fourth optimization model by taking the maximum user satisfaction degree as a target, taking the battery exchange requirement as a decision variable and taking the user satisfaction degree and the user electricity cost as constraint conditions.

Under the guidance of market environment and government strategies, the sales of electric vehicles are increasing year by year. Unlike conventional automobiles, electric automobiles are new energy automobiles powered by electric energy driving motors. And the user evaluates the service quality of the power exchange station by exchanging the charge quantity of the battery and exchanging time. The decision variable of the fourth optimization model is the battery power changing requirement D _t The electric automobile user focuses on the cost, user satisfaction with the cost as a reference is established, and the fourth optimization model is as follows:

wherein ,

C _EV,t ＝P _t ·D _t ·pri _DA,Sell,t ·Δt，

C _EV,0 ＝P _t ·D _DA,t ·pri _swap,t ·Δt，

wherein ,M_EV,t For the satisfaction degree of the electric automobile user, C _EV,0 C, for optimizing the power conversion cost (the electric vehicle) of the electric vehicle before _EV,t D, changing electricity cost (the cost) for the optimized battery _DA,t To optimize the battery exchange requirementBlock), pri _DA,Sell,t To optimize the battery exchange price (this/kw.h). The term "before optimization" refers to a mode that an electric automobile arrives at a power exchange station and is replaced and charged, namely, unordered charging of the electric automobile is achieved under the condition that the electric automobile has no reasonable exchange price. The term "optimized" refers to orderly charging of the electric vehicle at reasonable exchange price after optimized dispatching.

Constraint conditions of the fourth optimization model are as follows:

0≤M _EV,t ≤1，

C _EV,0 ≥C _EV,t 。

and (4) obtaining a power exchange station and electric automobile dispatching model comprising a third optimization model and a fourth optimization model through the steps (S4) and (S5), wherein the power exchange station and the electric automobile dispatching model are shown in fig. 4.

And the interactive model of the electric company and the power exchange station and the scheduling model of the electric vehicle are optimized and solved by adopting a game iterative algorithm, the solving thought is shown in step S6 and step S7, and the specific process of optimizing and solving is shown in FIG. 5. And solving an interaction strategy through game iteration, and interacting through the price and the requirement of the price response. The interaction model of the electric power company and the power exchange station, and the scheduling model of the electric power station and the electric vehicle can well promote benign interaction between the electric vehicle and the system, so that win-win is realized among the vehicle users, the power exchange station and the electric power company.

Step S6: solving the first optimization model and the second optimization model by adopting a game iterative algorithm to obtain a first group of optimal solutions; the first group of optimal solutions comprises optimal power company electricity selling prices and optimal power exchange station electricity purchasing quantity.

Step S7: inputting the purchase electric quantity of the optimal battery replacement station to the third optimization model, and solving the third optimization model and the fourth optimization model by adopting a game iteration algorithm to obtain a second group of optimal solutions; the second set of optimal solutions includes an optimal battery exchange price and an optimal battery exchange demand.

Step S8: determining an optimal scheduling result; the optimal scheduling result is composed of the first group of optimal solutions and the second group of optimal solutions.

The electric vehicle battery exchange station and electric company collaborative scheduling method of the embodiment has the following advantages:

in the prior art, an exchange model is established between an electric vehicle and a power exchange station, and the charging requirement and the time-of-use electricity price of the electric vehicle are mainly considered, so that the charging station power, the battery charging power and the battery charging quantity of the electric vehicle are optimized. And establishing a utility function which aims at charging station benefits and reducing peak-valley differences, and comparing and analyzing influences of unordered charging and ordered charging on the charging station benefits and the whole power grid load.

The embodiment aims at the cooperative scheduling of the electric automobile power exchange station and the electric company, the benefits of the electric automobile power exchange station and the electric company are considered from different angles, and the optimization model and most of researches of the electric automobile power exchange station and the electric company are quite different. The electric company firstly sells electric energy to the power exchange station through the electricity selling price, and the power exchange station sells the purchased electric energy to electric automobile users through the price exchanging mechanism. And under the consideration of the power generation cost, the environment conversion cost and the wind and light discarding multi-factors, establishing a first optimization model with the maximum income of the electric company. In one aspect, the second optimization model is configured to establish a minimum cost of the battery exchange station as an objective function in consideration of multiple factors of purchase cost, battery maintenance and rental cost. On the other hand, in order to make the load fluctuation of the power exchange station smaller, a battery exchange price model is established, and a third optimization model is established by taking the maximum profit of the power exchange station as an objective function. The electric automobile user is an object of the whole coordination scheduling service, and on the basis of meeting the power change requirement, a fourth optimization model aiming at user satisfaction is established in consideration of the user payment cost.

For the electric company, obtaining benefits through an electric energy transaction mechanism; and the power exchange station is reasonably guided to purchase electric energy, so that peak load is avoided, and peak-valley difference is reduced. The pressure of the whole power system is reduced to a certain extent, and the safe operation of the system is facilitated. For the power exchange station, the electric power is purchased through the demand response of exchange price, so that the real-time exchange demand is met, the cost can be reduced, the income is increased, and the long-term profit of the power exchange station is realized. For electric automobile users, the users are reasonably guided to exchange electricity through exchange prices of the exchange stations, so that the use cost of the users is reduced, and the user satisfaction is increased. The optimized power change plays a role in peak clipping and valley filling on the system load to a certain extent, so that the running stability of the power grid is improved. The optimization model can well promote benign interaction between the electric automobile and the system, so that win-win is realized among automobile users, the power exchange station and the power company.

The invention also provides a co-scheduling system of the electric automobile power exchange station and the electric company, which comprises the following steps:

the parameter acquisition module is used for acquiring a first initial parameter, a second initial parameter, a third initial parameter and a fourth initial parameter; the first initial parameters comprise thermal power generation cost, photovoltaic power generation cost, wind power generation cost, thermal power generation power, photovoltaic power generation power, wind power generation power, environmental calculation cost of carbon dioxide generated by thermal power generation, unit punishment cost of abandoned wind and abandoned light and power generation coal consumption coefficient; the second initial parameters comprise unit maintenance cost of the battery in the power exchange station and unit lease cost of the battery in the power exchange station; the third initial parameters include purchase cost of a single battery, capacity of the battery, and the number of times of recycling of the battery; the fourth initial parameter includes a battery exchange price and a battery exchange demand.

The first model construction module is used for constructing a first optimization model according to the first initial parameters, the electricity selling price of the electric power company and the electricity purchasing quantity of the power exchange station, taking the electricity selling price of the electric power company as a decision variable and taking total power balance, thermal power generation power, photovoltaic power generation power, wind power generation power and the electricity selling price of the electric power company as constraint conditions.

And the second model construction module is used for constructing a second optimization model by taking the electricity purchasing quantity of the electricity changing station as a decision variable and taking the electricity purchasing quantity of the electricity changing station as a constraint condition according to the second initial parameter, the electricity selling price of the electric company and the electricity purchasing quantity of the electricity changing station and taking the minimum cost of the electricity changing station as a target.

And the third model construction module is used for constructing a third optimization model by taking the maximum profit of the power exchange station as a target and taking the battery exchange price as a decision variable and taking the battery exchange price, the total charging power of the power exchange station and the power exchange demand of the power exchange station as constraint conditions according to the third initial parameter, the battery exchange price, the power selling price of the power company and the power exchange station power purchase quantity.

And the fourth model construction module is used for constructing a fourth optimization model by taking the maximum user satisfaction degree as a target, the battery exchange requirement as a decision variable and the user satisfaction degree and the user electricity cost as constraint conditions according to the fourth initial parameter.

The first solving module is used for solving the first optimizing model and the second optimizing model by adopting a game iterative algorithm to obtain a first group of optimal solutions; the first group of optimal solutions comprises optimal power company electricity selling prices and optimal power exchange station electricity purchasing quantity.

The second solving module is used for inputting the electricity purchasing quantity of the optimal battery replacement station to the third optimizing model, and solving the third optimizing model and the fourth optimizing model by adopting a game iteration algorithm to obtain a second group of optimal solutions; the second set of optimal solutions includes an optimal battery exchange price and an optimal battery exchange demand.

The scheduling result determining module is used for determining an optimal scheduling result; the optimal scheduling result is composed of the first group of optimal solutions and the second group of optimal solutions.

As an optional implementation manner, the first optimization model in the first model building module is:

wherein ,

C _Gen,t ＝C _Coal,unit (a·P _coal,t ² +b·P _coal,t +c)，

C _PV,t ＝C _PV,unit P _PV,t ，

C _WT,t ＝C _WT,unit P _WT,t ，

wherein ,I_E For the benefit of the power company, T is the time, T is the total time, and pri _Sell,t Price of electricity selling for electric power company, Q _BSS,t For power purchase of power exchange station, C _Gen,t C is the thermal power generation cost _PV,t C is the cost of photovoltaic power generation _WT,t For the cost of wind power generation,cost is reduced for environmental conversion of carbon dioxide generated by thermal power generation, C _Pun,PV,WT,t C, punishing cost of wind and light discarding of electric power company _Coal,unit Is the power generation cost of the thermal power unit, C _PV,unit C is the power generation cost of the photovoltaic unit _WT,unit For the power generation cost of wind power unit, P _coal,t Is thermal power, P _PV,t For photovoltaic power generation, P _WT,t For wind power generation power, a, b and c are all power generation coal consumption coefficients, and +. >Cost per unit coal consumption to produce carbon dioxide;

the optimization conditions of the first optimization model are as follows:

P _coal,t +P _PV,t +P _WT,t ＝P _BSS,t +P _base,t ，

P _i,min ≤P _i,t ≤P _i,max i∈coal,pv,wt，

pri _Sell,min ≤pri _Sell,t ≤pri _Sell,max ，

P _BSS,t for the total charging power of the power exchange station, P _base,t P is the power company which does not consider the resident load of the electric car _i,min Is the minimum output power of thermal power generation, the minimum output power of photovoltaic power generation or the minimum output power of wind power generation, P _i,max The maximum output power of thermal power generation, the maximum output power of photovoltaic power generation or the maximum output power of wind power generation; pri (pri) _Sell,min Minimum electricity price for electric power company, pri _Sell,max And the maximum electricity selling price is the electric power company.

As an optional implementation manner, the second optimization model in the second model building module is:

wherein ,C_BSS For the cost of the power exchange station, C _m For unit maintenance cost of battery in power exchange station, C _b The rental cost of the unit of the battery in the power exchange station;

the constraint conditions of the second optimization model are as follows:

Q _BSS,t ≥P _BSS,t ·Δt，

Δt is the unit charging time of the power exchange station.

As an optional implementation manner, the third optimization model in the third model building module is:

/>

wherein ,

wherein ,pri_swap,t Exchange price for battery, C _total For the purchase cost of single battery, B is the capacity of the battery, N is the recycling frequency of the battery, P _av To optimize the average charging power of the pre-exchange station, P _DA,t Step for optimizing the total charging power of a pre-exchange station _p Step is the power step _pri For price step length, P' _BSS,t For the slope of the total charging power of the station, P _t To optimize the charge power of the single battery D _t The power-exchanging requirement is met for a user;

the constraint conditions of the third optimization model are as follows:

pri _swap,min ≤pri _swap,t ≤pri _swap,max ，

P _BSS,min ≤P _BSS,t ≤P _BSS,max ，

E _min ≤D _t ≤E _max ，

wherein ,pri_swap,min Pri for minimum exchange price of exchange station _swap,max For maximum exchange price of exchange station, P _BSS,min For minimum output power of the power exchange station, P _BSS,max For maximum output power of the power-exchange station E _min For providing minimum value of number of service equipment for power exchange station, E _max The maximum number of service devices for the power exchange can be provided.

As an optional implementation manner, the fourth optimization model in the fourth model building module is:

wherein ,

C _EV,t ＝P _t ·D _t ·pri _DA,Sell,t ·Δt，

C _EV,0 ＝P _t ·D _DA,t ·pri _swap,t ·Δt，

wherein ,M_EV,t For the satisfaction degree of the electric automobile user, C _EV,0 C for optimizing the power conversion cost of the electric automobile before _EV,t D for the optimized battery power conversion cost _DA,t Pri for optimizing pre-battery exchange requirements _DA,Sell,t Exchanging prices for the battery before optimization;

constraint conditions of the fourth optimization model are as follows:

0≤M _EV,t ≤1，

C _EV,0 ≥C _EV,t 。

the electric automobile power exchange station and electric company collaborative scheduling system can reduce the cost of the power exchange station, improve the benefits of the power exchange station and the electric company, and improve the satisfaction degree of electric automobile users

For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.

The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (10)

1. The method for cooperatively scheduling the electric automobile power exchange station and the electric company is characterized by comprising the following steps of:

acquiring a first initial parameter, a second initial parameter, a third initial parameter and a fourth initial parameter; the first initial parameters comprise thermal power generation cost, photovoltaic power generation cost, wind power generation cost, thermal power generation power, photovoltaic power generation power, wind power generation power, environmental calculation cost of carbon dioxide generated by thermal power generation, unit punishment cost of abandoned wind and abandoned light and power generation coal consumption coefficient; the second initial parameters comprise unit maintenance cost of the battery in the power exchange station and unit lease cost of the battery in the power exchange station; the third initial parameters include purchase cost of a single battery, capacity of the battery, and the number of times of recycling of the battery; the fourth initial parameter includes a battery exchange price and a battery exchange demand;

According to the first initial parameters, the electricity selling price of the electric power company and the electricity purchasing quantity of the power exchange station, taking the maximum income of the electric power company as a target, taking the electricity selling price of the electric power company as a decision variable, and taking total power balance, the thermal power generation power, the photovoltaic power generation power, the wind power generation power and the electricity selling price of the electric power company as constraint conditions, constructing a first optimization model;

according to the second initial parameters, the electricity selling price of the electric company and the electricity purchasing quantity of the electricity changing station, the minimum cost of the electricity changing station is taken as a target, the electricity purchasing quantity of the electricity changing station is taken as a decision variable, and the electricity purchasing quantity of the electricity changing station is taken as a constraint condition, a second optimization model is constructed;

according to the third initial parameter, the battery exchange price, the electricity selling price of the electric company and the electricity purchasing quantity of the battery exchange station, taking the maximum income of the battery exchange as a target, taking the battery exchange price as a decision variable, and taking the battery exchange price, the total charging power of the battery exchange station and the electricity exchanging requirement of the battery exchange station as constraint conditions, establishing a third optimization model;

according to the fourth initial parameters, a fourth optimization model is established by taking the maximum user satisfaction degree as a target, the battery exchange requirement as a decision variable and the user satisfaction degree and the user electricity cost as constraint conditions;

Solving the first optimization model and the second optimization model by adopting a game iterative algorithm to obtain a first group of optimal solutions; the first group of optimal solutions comprise optimal power company electricity selling prices and optimal power exchange station electricity purchasing quantity;

inputting the purchase electric quantity of the optimal battery replacement station to the third optimization model, and solving the third optimization model and the fourth optimization model by adopting a game iteration algorithm to obtain a second group of optimal solutions; the second set of optimal solutions includes an optimal battery exchange price and an optimal battery exchange demand;

determining an optimal scheduling result; the optimal scheduling result is composed of the first group of optimal solutions and the second group of optimal solutions.

2. The method for co-scheduling an electric vehicle battery exchange station and an electric company according to claim 1, wherein the first optimization model is:

wherein ,

C _Gen,t ＝C _Coal,unit (a·P _coal,t ² +b·P _coal,t +c)，

C _PV,t ＝C _PV,unit P _PV,t ，

C _WT,t ＝C _WT,unit P _WT,t ，

C _CO2,t ＝F _CO2 (a·P _coal,t ² +b·P _coal,t +c)，

wherein ,I_E For the benefit of the power company, T is the time, T is the total time, and pri _Sell,t Price of electricity selling for electric power company, Q _BSS,t For power purchase of power exchange station, C _Gen,t C is the thermal power generation cost _PV,t C is the cost of photovoltaic power generation _WT,t C is the wind power generation cost _CO2,t Cost is reduced for environmental conversion of carbon dioxide generated by thermal power generation, C _Pun,PV,WT,t C, punishing cost of wind and light discarding of electric power company _Coal,unit Is the power generation cost of the thermal power unit, C _PV,unit C is the power generation cost of the photovoltaic unit _WT,unit For the power generation cost of wind power unit, P _coal,t Is thermal power, P _PV,t For photovoltaic power generation, P _WT,t For wind power generation power, a, b and c are all the coal consumption coefficients of power generation, F _CO2 Cost per unit coal consumption to produce carbon dioxide;

the optimization conditions of the first optimization model are as follows:

P _coal,t +P _PV,t +P _WT,t ＝P _BSS,t +P _base,t ，

P _i,min ≤P _i,t ≤P _i,max i∈coal,pv,wt，

pri _Sell,min ≤pri _Sell,t ≤pri _Sell,max ，

P _BSS,t for the total charging power of the power exchange station, P _base,t P is the power company which does not consider the resident load of the electric car _i,min Minimum output power of thermal power generation and photovoltaic power generationMinimum electric output or minimum wind power output, P _i,max The maximum output power of thermal power generation, the maximum output power of photovoltaic power generation or the maximum output power of wind power generation; pri (pri) _Sell,min Minimum electricity price for electric power company, pri _Sell,max And the maximum electricity selling price is the electric power company.

3. The method for co-scheduling an electric vehicle battery exchange station and an electric company according to claim 2, wherein the second optimization model is:

wherein ,C_BSS For the cost of the power exchange station, C _m For unit maintenance cost of battery in power exchange station, C _b The rental cost of the unit of the battery in the power exchange station;

the constraint conditions of the second optimization model are as follows:

Q _BSS,t ≥P _BSS,t ·Δt，

Δt is the unit charging time of the power exchange station.

4. The method for co-scheduling an electric vehicle battery exchange station and an electric company according to claim 3, wherein the third optimization model is:

wherein ,

wherein ,pri_swap,t Exchange price for battery, C _total For the purchase cost of single battery, B is the capacity of the battery, N is the recycling frequency of the battery, P _av To optimize the average charging power of the pre-exchange station, P _DA,t Step for optimizing the total charging power of a pre-exchange station _p Step is the power step _pri For price step length, P _B ' _SS,t For the slope of the total charging power of the station, P _t To optimize the charge power of the single battery D _t The power-exchanging requirement is met for a user;

the constraint conditions of the third optimization model are as follows:

pri _swap,min ≤pri _swap,t ≤pri _swap,max ，

P _BSS,min ≤P _BSS,t ≤P _BSS,max ，

E _min ≤D _t ≤E _max ，

wherein ,pri_swap,min Pri for minimum exchange price of exchange station _swap,max For maximum exchange price of exchange station, P _BSS,min For minimum output power of the power exchange station, P _BSS,max For maximum output power of the power-exchange station E _min For providing minimum value of number of service equipment for power exchange station, E _max The maximum number of service devices for the power exchange can be provided.

5. The method for collaborative scheduling of an electric vehicle battery exchange station and an electric company according to claim 4, wherein the fourth optimization model is:

wherein ,

C _EV,t ＝P _t ·D _t ·pri _DA,Sell,t ·Δt，

C _EV,0 ＝P _t ·D _DA,t ·pri _swap,t ·Δt，

wherein ,M_EV,t For the satisfaction degree of the electric automobile user, C _EV,0 C for optimizing the power conversion cost of the electric automobile before _EV,t D for the optimized battery power conversion cost _DA,t Pri for optimizing pre-battery exchange requirements _DA,Sell,t Exchanging prices for the battery before optimization;

constraint conditions of the fourth optimization model are as follows:

0≤M _EV,t ≤1，

C _EV,0 ≥C _EV,t 。

6. an electric automobile trades power station and electric company and cooperates dispatch system, characterized by comprising:

the parameter acquisition module is used for acquiring a first initial parameter, a second initial parameter, a third initial parameter and a fourth initial parameter; the first initial parameters comprise thermal power generation cost, photovoltaic power generation cost, wind power generation cost, thermal power generation power, photovoltaic power generation power, wind power generation power, environmental calculation cost of carbon dioxide generated by thermal power generation, unit punishment cost of abandoned wind and abandoned light and power generation coal consumption coefficient; the second initial parameters comprise unit maintenance cost of the battery in the power exchange station and unit lease cost of the battery in the power exchange station; the third initial parameters include purchase cost of a single battery, capacity of the battery, and the number of times of recycling of the battery; the fourth initial parameter includes a battery exchange price and a battery exchange demand;

the first model building module is used for building a first optimization model according to the first initial parameters, the electricity selling price of the electric power company and the electricity purchasing quantity of the power exchange station, taking the electricity selling price of the electric power company as a decision variable and taking total power balance, the thermal power generation power, the photovoltaic power generation power, the wind power generation power and the electricity selling price of the electric power company as constraint conditions;

The second model construction module is used for constructing a second optimization model by taking the minimum cost of the power exchange station as a target, taking the power exchange station purchase quantity as a decision variable and taking the power exchange station purchase quantity as a constraint condition according to the second initial parameter, the power selling price of the power company and the power exchange station purchase quantity;

the third model building module is used for building a third optimization model according to the third initial parameter, the battery exchange price, the electricity selling price of the power company and the electricity purchasing quantity of the power exchange station, taking the maximum profit of the power exchange station as a target, taking the battery exchange price as a decision variable, and taking the battery exchange price, the total charging power of the power exchange station and the electricity exchange requirement of the power exchange station as constraint conditions;

the fourth model construction module is used for constructing a fourth optimization model according to the fourth initial parameter, taking the maximum user satisfaction degree as a target, taking the battery exchange requirement as a decision variable and taking the user satisfaction degree and the user electricity cost as constraint conditions;

the first solving module is used for solving the first optimizing model and the second optimizing model by adopting a game iterative algorithm to obtain a first group of optimal solutions; the first group of optimal solutions comprise optimal power company electricity selling prices and optimal power exchange station electricity purchasing quantity;

The second solving module is used for inputting the electricity purchasing quantity of the optimal battery replacement station to the third optimizing model, and solving the third optimizing model and the fourth optimizing model by adopting a game iteration algorithm to obtain a second group of optimal solutions; the second set of optimal solutions includes an optimal battery exchange price and an optimal battery exchange demand;

the scheduling result determining module is used for determining an optimal scheduling result; the optimal scheduling result is composed of the first group of optimal solutions and the second group of optimal solutions.

7. The co-scheduling system of an electric vehicle battery exchange station and an electric company according to claim 6, wherein the first optimization model in the first model building module is:

wherein ,

C _Gen,t ＝C _Coal,unit (a·P _coal,t ² +b·P _coal,t +c)，

C _PV,t ＝C _PV,unit P _PV,t ，

C _WT,t ＝C _WT,unit P _WT,t ，

wherein ,I_E For the benefit of the power company, T is the time, T is the total time, and pri _Sell,t Price of electricity selling for electric power company, Q _BSS,t For power purchase of power exchange station, C _Gen,t C is the thermal power generation cost _PV,t C is the cost of photovoltaic power generation _WT,t For the cost of wind power generation,cost is reduced for environmental conversion of carbon dioxide generated by thermal power generation, C _Pun,PV,WT,t C, punishing cost of wind and light discarding of electric power company _Coal,unit Is the power generation cost of the thermal power unit, C _PV,unit C is the power generation cost of the photovoltaic unit _WT,unit For the power generation cost of wind power unit, P _coal,t Is thermal power, P _PV,t For photovoltaic power generation, P _WT,t For wind power generation power, a, b and c are all the coal consumption coefficients of power generation, F _CO2 Cost per unit coal consumption to produce carbon dioxide;

the optimization conditions of the first optimization model are as follows:

P _coal,t +P _PV,t +P _WT,t ＝P _BSS,t +P _base,t ，

P _i,min ≤P _i,t ≤P _i,max i∈coal,pv,wt，

pri _Sell,min ≤pri _Sell,t ≤pri _Sell,max ，

P _BSS,t for the total charging power of the power exchange station, P _base,t P is the power company which does not consider the resident load of the electric car _i,min Is the minimum output power of thermal power generation, the minimum output power of photovoltaic power generation or the minimum output power of wind power generation, P _i,max The maximum output power of thermal power generation, the maximum output power of photovoltaic power generation or the maximum output power of wind power generation; pri (pri) _Sell,min Minimum electricity price for electric power company, pri _Sell,max And the maximum electricity selling price is the electric power company.

8. The co-scheduling system for an electric vehicle battery exchange station and an electric company according to claim 7, wherein the second optimization model in the second model building module is:

wherein ,C_BSS For the cost of the power exchange station, C _m For unit maintenance cost of battery in power exchange station, C _b The rental cost of the unit of the battery in the power exchange station;

the constraint conditions of the second optimization model are as follows:

Q _BSS,t ≥P _BSS,t ·Δt，

Δt is the unit charging time of the power exchange station.

9. The electric vehicle battery exchange station and electric company collaborative scheduling system according to claim 8, wherein the third optimization model in the third model building module is:

wherein ,

wherein ,pri_swap,t Exchange price for battery, C _total For the purchase cost of single battery, B is the capacity of the battery, N is the recycling frequency of the battery, P _av To optimize the average charging power of the pre-exchange station, P _DA,t Step for optimizing the total charging power of a pre-exchange station _p Step is the power step _pri For price step length, P' _BSS,t For the slope of the total charging power of the station, P _t To optimize the charge power of the single battery D _t The power-exchanging requirement is met for a user;

the constraint conditions of the third optimization model are as follows:

pri _swap,min ≤pri _swap,t ≤pri _swap,max ，

P _BSS,min ≤P _BSS,t ≤P _BSS,max ，

E _min ≤D _t ≤E _max ，

wherein ,pri_swap,min Pri for minimum exchange price of exchange station _swap,max For maximum exchange price of exchange station, P _BSS,min For minimum output power of the power exchange station, P _BSS,max For maximum output power of the power-exchange station E _min For providing minimum value of number of service equipment for power exchange station, E _max The maximum number of service devices for the power exchange can be provided.

10. The electric vehicle battery and electric company co-scheduling system according to claim 9, wherein the fourth optimization model in the fourth model building module is:

wherein ,

C _EV,t ＝P _t ·D _t ·pri _DA,Sell,t ·Δt，

C _EV,0 ＝P _t ·D _DA,t ·pri _swap,t ·Δt，

wherein ,M_EV,t For the satisfaction degree of the electric automobile user, C _EV,0 C for optimizing the power conversion cost of the electric automobile before _EV,t D for the optimized battery power conversion cost _DA,t Pri for optimizing pre-battery exchange requirements _DA,Sell,t Exchanging prices for the battery before optimization;

constraint conditions of the fourth optimization model are as follows:

0≤M _EV,t ≤1，

C _EV,0 ≥C _EV,t 。