CN114884133B

CN114884133B - Micro-grid economic dispatching optimization method and system considering electric automobile

Info

Publication number: CN114884133B
Application number: CN202210431143.2A
Authority: CN
Inventors: 潘崇超; 秦建华; 李悦; 李天奇
Original assignee: University of Science and Technology Beijing USTB
Current assignee: Beijing Kailaimei Climate Technology Co ltd
Priority date: 2022-04-22
Filing date: 2022-04-22
Publication date: 2023-10-10
Anticipated expiration: 2042-04-22
Also published as: CN114884133A

Abstract

The invention relates to a micro-grid economic dispatch optimization method and system considering an electric automobile, wherein the method comprises the following steps: establishing an output model of each device contained in the micro-grid system; establishing a multi-target economic dispatch model with the minimum charging cost of the electric automobile user and the minimum comprehensive operation management cost of the micro-grid as well as the minimum peak-valley difference of the power of the connecting lines as targets; establishing constraint conditions under normal working conditions of each device; determining the weight of each objective function in the multi-objective economic dispatch model, performing linear weighting, and constructing a new objective function; setting three charging schemes, namely a disordered charging scheme, an ordered charging scheme and a V2G scheme; solving the new objective function to obtain a charging and discharging state, charging and discharging power, a charging and discharging state of energy storage equipment and power in the charging station; and comparing the change conditions of the objective functions of different charging schemes to determine the optimal charging scheme. The method can determine the optimal charging scheme and realize multi-party win-win.

Description

Micro-grid economic dispatching optimization method and system considering electric automobile

Technical Field

The invention relates to the field of micro-grids, in particular to a micro-grid economic dispatch optimization method and system considering electric automobiles.

Background

With the increasing progress of economic strength in China, the electric energy demand shows a year-by-year rising trend, and the daily electricity demand of residents is particularly obvious. However, the region of China is vast, if only depends on the electric energy transmission service of the national power grid company, on one hand, the problem of large consumption of electric energy can be caused by long-distance electric energy transmission, on the other hand, the fund and personnel requirements of large-scale power grid construction are too large, the construction is difficult to complete in a short time, and the problem of the micro-grid system can be well relieved.

The micro-grid is a relatively independent whole from the system perspective, and can provide power requirements for users. The system comprises a plurality of power generation units, load units, energy storage units and a dispatching control platform. The micro-grid generally comprises a photovoltaic array, a wind driven generator, a micro gas engine and the like, wherein the pollution damage caused to the environment in the power generation process of the photovoltaic array and the wind driven generator is extremely small, and the micro-grid is representative of clean energy, and meanwhile, due to the development and progress of the scientific technology in recent years, the power generation cost of the photovoltaic array and the wind driven generator is continuously reduced, and an advantage is created for the output of a renewable energy generator set in the future. The energy storage unit is generally composed of a plurality of storage batteries, and when the output of the wind driven generator and the photovoltaic array is unstable, the electric quantity in the batteries is released, and when the generated power is larger than the power load of residents, the energy storage unit is used for storing energy, so that the energy storage unit is an indispensable part of the micro-grid system.

The use of micro-grids can facilitate the utilization of distributed energy sources. Meanwhile, the cost of the micro-grid is relatively low compared with the capacity expansion construction cost of the grid in terms of economic cost, and the micro-grid can cope with the increasing power load demands of residents or enterprises. The power supply pressure of a large power grid can be obviously reduced, and the maintenance cost of power grid equipment is reduced.

The problem of disordered charging of the large-scale electric automobile can seriously influence safe and stable operation of a power grid, the development of a micro-grid can relieve the power supply pressure of the power grid and promote the utilization of renewable energy sources, so that the electric automobile is reasonably scheduled according to the charging and discharging behaviors of the electric automobile by depending on the micro-grid and the power grid, and the electric automobile has stronger research value and practical significance for ensuring the economic and stable operation of owners of the electric automobile, the micro-grid and the power grid. In comparison, the method optimizes three dimensions of the owner, the micro-grid and the power grid of the electric automobile, and in addition, the charging loss model of the electric automobile is added, so that the method is more in line with the actual situation. In the future, more constraint conditions can be considered, and optimization can be performed from the angles of network loss and the like.

Disclosure of Invention

The invention aims to provide a micro-grid economic dispatching optimization method and system considering an electric automobile, and an optimal charging scheme is determined.

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

a microgrid economic dispatch optimization method taking into account electric vehicles, the optimization method comprising:

establishing an output model of each device contained in the micro-grid system; the output model of each device comprises: an output model of a wind driven generator, an output model of a photovoltaic array, an output model of energy storage equipment, an electric vehicle charge-discharge model and a battery loss model;

establishing a multi-target economic dispatch model which aims at minimum charging cost of an electric automobile user, minimum comprehensive operation and management cost of a micro-grid and minimum peak-valley difference of power of a connecting line based on the output model of each device;

establishing constraint conditions under normal working conditions of each device; the constraint conditions include: the method comprises the following steps of power balance constraint, wind driven generator output constraint, photovoltaic array output constraint, energy storage device discharge uniqueness constraint, energy storage device charge and discharge power constraint, energy storage device capacity state constraint, power grid purchase and sale uniqueness constraint, electric vehicle charge and discharge state uniqueness constraint, electric vehicle charge and discharge power constraint, electric vehicle capacity constraint, electric vehicle non-schedulable period constraint and electric vehicle charge and electric quantity consistency constraint before and after optimization;

Determining the weight of each objective function in the multi-objective economic dispatch model, performing linear weighting, and constructing a new objective function;

setting three charging schemes, namely a disordered charging scheme, an ordered charging scheme and a V2G scheme;

solving the new objective function based on constraint conditions of each device under normal working conditions to obtain a charging and discharging state, charging and discharging power, a charging and discharging state and power of energy storage devices in the charging station;

and comparing the change conditions of the objective functions of different charging schemes to determine the optimal charging scheme.

Optionally, the expression of the output model of the wind driven generator is as follows:

wherein P is _WT,i Representing the power generation amount of the wind turbine; p (P) _WT Representing the rated power of the wind generating set; v _i The actual wind speed at the moment i is represented; v ⁱⁿ Indicating cut-in wind speed; v ^out Indicating the cut-out wind speed; v ⁰ Indicating a rated wind speed;

the expression of the output model of the photovoltaic array is as follows:

T _S (t)＝T _amd (t)+0.0138(1+0.031T _amd (t))(1-0.042V(t))G _S (t)

wherein K is a power temperature coefficient; p (P) _PV (t) is the actual output power of the photovoltaic array at time t; p (P) _{pv_rated} Maximum power of the photovoltaic array under standard rated conditions; g _S (t) is the actual illumination intensity at time t; g _STC The value of the light radiation density under standard rated conditions is 1 kW.h/m ² ；T _STC The rated ambient temperature is 298K; t (T) _S (t) is the actual temperature of the battery plate at the moment t; t (T) _amd (t) is the actual ambient temperature at time t;

the expression of the output model of the energy storage device is as follows:

the output model of the energy storage device comprises the following steps: the capacity of the energy storage device, the charge state of the energy storage device and the charge quantity;

the capacity of the energy storage device is represented by C;

the expression of the state of charge of the energy storage device is:

C _remain c is the residual electric quantity _battery I is the discharge current, I is the total capacity;

the expression of the charge amount is as follows:

Q(t+1)＝Q(t)×(1-α)+Snj _{bat_char} (t)×P(t)

Q(t+1)＝Q(t)×(1-α)-Snj _{bat_dischar} (t)×P(t)

wherein Q (t+1) represents the charge amount at the next moment, Q (t) represents the charge amount at the current moment, and alpha represents the self-consumption coefficient of the energy storage device, snj _{bat_char} (t) and Snj _{bat_dischar} (t) represents the charging state and the discharging state of the energy storage device at the moment t, and P (t) represents the charging and discharging power of the energy storage device;

the expression of the electric automobile charge-discharge model is as follows:

the expression when the electric vehicle is in a charged state is as follows:

E _EV (t+1)＝E _EV (t)×(1-α)+Snj _char (t)×P _{EV_char} (t)×η _char

the expression when the electric automobile is in a discharge state is as follows:

wherein E is _EV Represents the electric quantity at a certain moment, alpha represents the self-consumption coefficient, P _{EV_char} 、P _{EV_dischar} Respectively charge and discharge power, eta _char 、η _dischar Respectively, charging and discharging efficiency, snj _char 、Snj _dischar Respectively representing the charge and discharge states at a certain moment;

The expression of the battery loss model is as follows:

wherein l=ad ^-b L represents the cycle number of the battery, K _{EV_b} Represents the battery loss cost at a depth of discharge of D, b is a constant, k is a scaling factor, C _{EV_battery} Representing the cost of the battery.

Optionally, the objective function of the charging cost of the electric automobile user is:

f ₁ ＝min(cost _char -cost _dischar +cost _{battery_loss} -cost _subside )

wherein, cost _char Representing the charge cost, cost _dischar Represents profit obtained by V2G discharge, cost _{battery_loss} Representing battery loss cost, cost _subside Representing the subsidy costs of the government in V2G mode of participation by the user.

Optionally, the objective function of the comprehensive operation management cost of the micro-grid is as follows:

f ₂ ＝min(cost _f +cost _w +cost _{grid_buy} +cost _pol )

wherein, cost _f Representing the cost of power generation _w Representing maintenance costs, costs _{grid_buy} Representing the total cost of electricity purchase and sale, cost _pol Indicating the cost of pollutant remediation.

Optionally, the objective function of the peak-valley difference of the power of the tie is:

f ₃ ＝min(max(P _grid (t))-min(P _grid (t)))

wherein P is _grid And (t) is the peak-valley difference of the power of the connecting line.

Optionally, the expression of the power balance constraint is as follows:

P _w (t)+P _pv (t)+P _{bat_dc} (t)+P _{grid_buy} (t)＝P _{bat_c} (t)+P _EV (t)+P _basic (t)+P _{grid_sell} (t)

wherein P is _w For the actual output of the wind-driven generator, P _pv For the actual output of the photovoltaic array, P _{bat_c} 、P _{bat_dc} Respectively charge and discharge power of the energy storage device, P _{grid_buy} And P _{grid_sell} Real-time power purchased and sold from the power grid, P _EV Charging load of electric automobile, P _basic Basic power load is used for users;

the expression of the output constraint of the wind driven generator is as follows:

P _w (t)≤P _w,t

wherein P is _w,t Representing a predicted force value of the wind power generator;

the expression of the photovoltaic array output constraint is as follows:

P _pv (t)≤P _pv,t

wherein P is _pv,t Representing a predicted force value of the photovoltaic array;

the energy storage device discharge uniqueness constraint is expressed as follows:

0≤Snj _{bat_c} (t)+Snj _{bat_dc} (t)≤1

therein, snj _{bat_c} (t) represents the state of charge of the energy storage device at a certain time, snj _{bat_dc} (t) represents a discharge state of the energy storage device at a certain moment;

the energy storage device charge and discharge power constraint expression is as follows:

Snj _{bat_c} (t)×P _{bat_min_c} ≤P _{bat_c} (t)≤Snj _{bat_c} (t)×P _{bat_max_c}

Snj _{bat_dc} (t)×P _{bat_min_dc} ≤P _{bat_dc} (t)≤Snj _{bat_dc} (t)×P _{bat_max_dc}

P _{bat_c,max} ＝C _bat ×γ _bat,c

P _{bat_dc,max} ＝C _bat ×γ _bat,dc

wherein P is _{bat_min_c} Representing a minimum charging power; p (P) _{bat_max_c} Representing the maximum discharge power; p (P) _{bat_min_dc} Representing a minimum discharge power; p (P) _{bat_max_dc} Representing the maximum discharge power; gamma ray _bat,c Indicating a maximum charge rate at a certain time; gamma ray _bat,dc Indicating the maximum discharge rate at a certain moment; c (C) _bat Representing full electrical energy of the energy storage device;

the energy storage device capacity state constraint is expressed as follows:

E _{bat_start} ＝SOC _{bat_start} ×C _bat

SOC _min ×C _bat ≤E _bat (t)≤SOC _max ×C _bat

E _{bat_start} ＝E _{bat_end}

wherein SOC is _{bat_start} Representing the state of charge of the energy storage device when the energy storage device is started to be used, C _bat Representing the full charge of the energy storage device, E _{bat_start} Indicating when the energy storage device is put into useElectric quantity E of (E) _bat (t) represents the electric quantity of the energy storage device at a certain moment, E _{bat_end} Representing the power of the energy storage device at the end time of one day and SOC _min 、SOC _max Respectively representing the minimum and maximum nuclear charge states of the electric automobile;

the expression of the unique constraint of the electricity purchasing and selling of the power grid is as follows:

0≤Snj _{grid_buy} (t)+Snj _{grid_sell} (t)≤1

therein, snj _{grid_buy} (t)、Snj _{grid_sell} (t) respectively representing the electricity purchasing and selling states of the micro-grid at a certain moment;

the expression of the unique constraint of the charge and discharge states of the electric automobile is as follows:

0≤Snj _char (t)+Snj _dischar (t)≤1

therein, snj _char (t)、Snj _dischar (t) respectively representing the charge and discharge states of the electric automobile at all times;

the expression of the charge and discharge power constraint of the electric automobile is as follows:

Snj _char (t)×P _{EV_c,min} ≤P _{EV_char} (t)≤Snj _char (t)×P _{EV_c,max}

Snj _dischar (t)×P _{EV_dc,min} ≤P _{EV_dischar} (t)≤Snj _dischar (t)×P _{EV_dc,max}

P _{EV_c,max} ＝C _EV ×γ _EV,c

P _{EV_dc,max} ＝C _EV ×γ _EV,dc

wherein, gamma _EV,c 、γ _EV,dc Respectively representing the maximum charge and discharge multiplying power of the electric automobile;

the expression of the capacity constraint of the electric automobile is as follows:

SOC _min ×C _EV ≤E _EV (t)≤SOC _max ×C _EV

E _{EV_out} ≥SOC _expect ×C _EV

wherein SOC is _min 、SOC _max Respectively representing the minimum and maximum nuclear charge states of the electric automobile, E _{EV_out} Representing an amount of electricity when the electric vehicle leaves the charging station; SOC (State of Charge) _expect Representing the state of charge of the battery when the owner of the electric vehicle desires to leave the charging station, C _EV Representing the electric quantity of the electric automobile;

the expression of the non-schedulable time constraint of the electric automobile is as follows:

P _{EV_char} (t)＝0 (t≥T _end or T is less than or equal to T _start )

P _{EV_dischar} (t)＝0 (t≥T _end Or T is less than or equal to T _start )

P _{EV_char} (t)＝0 (T _start ≤t≤T _end )

P _{EV_dischar} (t)＝0 (T _start ≤t≤T _end )

Wherein P is _{EV_char} (t)、P _{EV_dischar} (T) each represents the charge/discharge power at a certain time of the electric vehicle, T _start Indicating the arrival time of the electric automobile, T _end The outbound time of the electric automobile is represented;

the expression of the electric vehicle optimization front and rear charging electric quantity consistency constraint is as follows:

Wherein P is _i,wx (t) represents the charging power at a certain time in the unordered charging state of the ith vehicle in the charging station, P _i,yx (t) represents the charging power of the ith vehicle at a certain moment in the orderly charging state in the charging station; p (P) _i,V2G,char (t) represents the charging power at a certain time in the V2G mode of the ith vehicle, P _{i,V2G,dischar} (t) represents the discharge power of the ith vehicle at a certain time in the V2G mode.

Optionally, the disordered charging scheme is as follows: the electric automobile owner does not accept the scheduling scheme, and the electric automobile owner carries out unordered charging, namely the electric automobile owner starts charging immediately after arriving at the charging station, and immediately leaves the charging station after the charging is finished.

Optionally, the ordered charging scheme is:

and the electric automobile owners accept the scheduling scheme to carry out ordered charging, namely the electric automobile is charged according to a charging scheduling strategy, and the charging behavior occurs in the period of low electric load preferentially. When the electric energy provided by the wind driven generator and the photovoltaic array cannot meet the requirements, electricity is purchased from the power grid through a connecting line between the micro-grid and the power grid, so that the gap of the electric power requirement is filled.

Optionally, the V2G scheme is: the electric automobile owner accepts the scheduling scheme, carries out V2G with the electric network, namely the electric automobile is according to the scheduling strategy of charging and discharging, the charging behavior preferentially occurs in the period of low electricity load, the discharging behavior preferentially occurs in the period of high electricity load, the comprehensive operation management cost of the micro-grid is reduced, meanwhile, the power peak-valley difference of the connecting line is reduced, the investment of power generating equipment at the side of the electric network is reduced, the electric quantity of the electric automobile owner when the electric automobile leaves after participating in the V2G service of the same system is not less than 80% of the total electric quantity, and the electric quantity of the electric automobile is not less than 20% of the total electric quantity in the discharging process.

Based on the method in the invention, the invention further provides a micro-grid economic dispatching optimization system considering the electric automobile, which is characterized in that the optimization system comprises:

the output model construction module of each device is used for establishing an output model of each device contained in the micro-grid system; the output model of each device comprises: an output model of a wind driven generator, an output model of a photovoltaic array, an output model of energy storage equipment, an electric vehicle charge-discharge model and a battery loss model;

the multi-target economic dispatch module construction module is used for constructing a multi-target economic dispatch model which aims at minimum charging cost of electric automobile users, minimum comprehensive operation management cost of micro-grids and minimum peak-to-valley difference of tie power based on the output models of the equipment;

the constraint condition determining module is used for establishing constraint conditions under normal working conditions of each device; the constraint conditions include: the method comprises the following steps of power balance constraint, wind driven generator output constraint, photovoltaic array output constraint, energy storage device discharge uniqueness constraint, energy storage device charge and discharge power constraint, energy storage device capacity state constraint, power grid purchase and sale uniqueness constraint, electric vehicle charge and discharge state uniqueness constraint, electric vehicle charge and discharge power constraint, electric vehicle capacity constraint, electric vehicle non-schedulable period constraint and electric vehicle charge and electric quantity consistency constraint before and after optimization;

The new objective function construction module is used for determining the weight of each objective function in the multi-objective economic dispatch model, carrying out linear weighting, and constructing a new objective function;

the charging scheme setting module is used for setting three charging schemes of an unordered charging scheme, an ordered charging scheme and a V2G scheme;

the solving module is used for solving the new objective function based on constraint conditions of each device under normal working conditions to obtain a charging and discharging state, charging and discharging power, a charging and discharging state of the energy storage device and power in the charging station;

and the comparison module is used for comparing the change conditions of the objective functions of different charging schemes and determining an optimal charging scheme.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

according to the method and the system, benefits of a user side, a power grid side and a micro-grid side are comprehensively considered, a multi-objective optimization model which is minimum in charging cost of a covered user, minimum in comprehensive operation and management cost of the micro-grid and minimum in peak-valley difference of a connecting line is established, and based on the actual condition, a battery loss model is introduced in the research aiming at V2G, so that the model is closer to reality and has practical significance.

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 schematic view of a micro-grid structure according to an embodiment of the present invention;

FIG. 2 is a flowchart of a micro-grid economic dispatch optimization method considering an electric automobile according to an embodiment of the invention;

FIG. 3 is a schematic view of a wind turbine according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a workflow structure of a photovoltaic array according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of an exemplary energy storage device according to the present invention;

FIG. 6 is a graph showing the relationship between the depth of discharge and the cycle life according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of wind and light prediction output at different times according to an embodiment of the present invention;

FIG. 8 is a comparative schematic diagram of charge costs under three schemes according to an embodiment of the present invention;

fig. 9 is a schematic diagram of comparison of comprehensive operation and management costs of a micro-grid under three schemes according to an embodiment of the present invention;

FIG. 10 is a graph showing the comparison of the peak-to-valley differences of the tie lines under three schemes according to the embodiments of the present invention;

FIG. 11 is a graph showing the comparison of link power according to various embodiments of the present invention;

fig. 12 is a schematic diagram showing comparison of charging power of an electric vehicle according to various embodiments of the present invention;

fig. 13 is a schematic structural diagram of an economic dispatch optimization system for a micro-grid considering an electric automobile according to an embodiment of the invention.

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.

The invention aims to provide a micro-grid economic dispatch optimization method and system considering an electric automobile, and an optimal charging scheme is determined

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.

The use of micro-grids can facilitate the utilization of distributed energy sources. Meanwhile, the cost of the micro-grid is relatively low compared with the capacity expansion construction cost of the grid in terms of economic cost, and the micro-grid can cope with the increasing power load demands of residents or enterprises. The power supply pressure of a large power grid can be obviously reduced, and the maintenance cost of power grid equipment is reduced, and particularly as shown in fig. 1, fig. 1 is a structural diagram of a micro power grid.

Fig. 2 is a flowchart of a micro-grid economic dispatch optimization method considering an electric automobile according to an embodiment of the present invention, as shown in fig. 2, the method includes:

step 101: establishing an output model of each device contained in the micro-grid system; the output model of each device comprises: an output model of a wind driven generator, an output model of a photovoltaic array, an output model of energy storage equipment, an electric automobile charging and discharging model and a battery loss model.

The description of the output model of the wind driven generator is as follows:

the wind driven generator converts the kinetic energy of wind in nature into mechanical energy through the blades, then converts the mechanical energy into electric energy through the generator, and transmits the electric energy to a user for use through a power distribution network, wherein the two conversion of the energy is involved in the process. For wind power generators, the selection of blades is critical. From the aspect of work efficiency and use cost, three blades are adopted in the wind driven generator, and meanwhile, the requirements for materials of the wind driven generator are as follows: the density is big, and the quality is light for the paddle is when receiving the natural wind effect, can provide huge kinetic energy, drives the work of motor. Fig. 3 is a structural view of a wind power generator.

The relationship between the actual output power and the wind speed of the wind driven generator is as follows:

Wherein P is _WT,i Representing the power generation amount of the wind turbine; p (P) _WT Representing wind power generationRated power of the unit; v _i The actual wind speed at the moment i is represented; v ⁱⁿ Indicating cut-in wind speed; v ^out Indicating the cut-out wind speed; v ⁰ Indicating the rated wind speed.

The description of the photovoltaic array output model is as follows:

a photovoltaic array refers to an integrated unit of a plurality of photovoltaic modules, which contains a plurality of photovoltaic cells. Among them, the photovoltaic cell is mostly made of semiconductor silicon material. The power generation principle of the photovoltaic array is approximately as follows: two types of semiconductors, namely P-type semiconductors and N-type semiconductors, exist on photovoltaic panels.

When sunlight reaches the photovoltaic panel, holes move from the P-pole to the N-pole, and electrons move from the N-pole to the P-pole to generate electricity. Under standard illumination, the output voltage of the solar cell is generally 0.5V, and in order to create a larger potential difference for users, a plurality of battery packs are generally connected in series, and meanwhile, monocrystalline silicon with higher conversion efficiency is used as a material. The workflow structure of the photovoltaic array is shown in fig. 4.

The actual output condition of the photovoltaic array can be expressed by the following formula:

T _S (t)＝T _amd (t)+0.0138(1+0.031T _amd (t))(1-0.042V(t))G _S (t)

wherein K is a power temperature coefficient; p (P) _PV (t) is the actual output power of the photovoltaic array at time t; p (P) _{pv_rated} Maximum power of the photovoltaic array under standard rated conditions; g _S (t) is the actual illumination intensity at time t; g _STC The value of the irradiation density is 1 kW.h/m < 2 >; t (T) _STC The rated ambient temperature is 298K; t (T) _S (t) is the actual temperature of the battery plate at the moment t; t (T) _amd And (t) is the actual ambient temperature at time t.

The energy storage device model is described as follows:

the energy storage device is one of the key devices indispensable in the whole micro-grid system, and is a device for converting electric energy into chemical energy in principle. In the micro-grid system, the actual output of the wind generating set and the photovoltaic array is difficult to determine due to environmental factors, so that the energy storage device can well solve the problem, the energy storage device can store the electric energy which cannot be used in the peak period of power generation of the wind generating set or the photovoltaic array, and the electric energy stored before is released in the low valley period of power generation of the wind generating set or the photovoltaic array to play a role of an energy carrier, so that the power supply reliability is further improved.

The following parameters of the energy storage device have a more important role in the operation of the microgrid.

(1) Energy storage device capacity

The capacity of the energy storage device refers to the electric quantity which is continuously discharged to a termination voltage and is output in total under the full-electric state, and is generally indicated by C and has the unit of Ah.

(2) State of charge of energy storage device

The state of charge of the energy storage device refers to the ratio of the remaining power to the total power at a certain moment, which is denoted by SOC hereinafter. For example, in the full state, soc=100%, and in the remaining amount of 0, soc=0%. The expression can be represented by the following formula:

wherein C is _remain C is the residual electric quantity _battery I is the discharge current, which is the total capacity.

(3) Charge capacity

The charge amount refers to the amount of electricity of the battery at a certain moment, and is generally represented by Q. The energy storage device has different charge amount representation methods under different charge and discharge states, and can be generally represented by the following formula:

Q(t+1)＝Q(t)×(1-α)+Snj _{bat_char} (t)×P(t)

Q(t+1)＝Q(t)×(1-α)-Snj _{bat_dischar} (t)×P(t)

wherein Q (t+1) Representing the charge at the next moment, Q (t) representing the charge at the current moment, and alpha representing the consumption coefficient of the energy storage device, snj _{bat_char} (t) and Snj _{bat_dischar} And (t) represents the charging state and the discharging state of the energy storage device at the moment t, and P (t) represents the charging and discharging power of the energy storage device. Fig. 5 is a model diagram of an energy storage device.

The introduction of the electric automobile charge-discharge model is as follows:

from the aspect of function positioning, the electric automobile has 'source-charge dual property', is used as a mobile energy storage device in a micro-grid system, is a novel controllable load for network access, can transmit electric energy to external loads through a V2G technology in a non-driving process, can be used as a load side, can absorb electric energy at the moment of low-valley of electric power demand, is used for daily travel demands of owners, and can realize benign bidirectional interaction with the micro-grid.

When the electric vehicle is in a charged state, it can be expressed by the following formula:

the expression when the electric vehicle is in a charged state is as follows:

E _EV (t+1)＝E _EV (t)×(1-α)+Snj _char (t)×P _{EV_char} (t)×η _char

wherein E is _EV Represents the electric quantity at a certain moment, alpha represents the self-consumption coefficient, P _{EV_char} 、P _{EV_dischar} Respectively charge and discharge power, eta _char 、η _dischar Respectively, charging and discharging efficiency, snj _char 、Snj _dischar Each of which indicates a charge/discharge state at a certain time.

The battery loss model of the electric automobile is introduced as follows:

when the electric automobile participates in the V2G discharging process, the aging of the battery can be accelerated. In general, there are many factors that affect the aging of lithium ion batteries. The method mainly comprises the following steps: the charge and discharge rate of the battery, the charge and discharge depth, the ambient temperature of the battery during charge and discharge, and the like. The invention starts from a classical loss model of the lithium ion battery, and only considers the influence of the depth of discharge on the service life of the battery.

On the premise of determining the battery capacity, the total discharge amount of the whole life cycle of the battery can be calculated through the cycle times of the battery, and the total discharge amount is shown as the following formula:

E _{EV_all} ＝L×E _EV ×D

wherein E is _{EV_all} Represents the total discharge capacity of the battery, L represents the cycle number of the battery, E _EV The battery capacity is represented, and D represents the depth of discharge of the battery.

Assuming that the depth of discharge of the electric automobile is unchanged, under the condition that the cost of a power battery of the electric automobile is known, the cost of battery loss caused by unit discharge amount can be calculated, as shown in the following formula:

in the cost _{EV_battery} Representing the total cost of the power battery of the electric automobile, E _{EV_all} Represents the total discharge amount, K _{EV_a} Representing the cost of battery loss due to the unit discharge amount. Since the above equation does not accurately reflect the relationship between the battery loss cost and the depth of discharge, it is expressed by the following equation.

L＝aD ^-b

Wherein L represents the cycle number of the battery, K _{EV_b} The battery loss cost at depth of discharge D is shown, a and b are constants, and can be obtained by fitting in fig. 6, k is a scaling factor.

And then, introducing the battery loss cost, and calculating to obtain the relation between the battery loss cost and the discharge depth caused by single discharge in the V2G mode, so as to perfect the mathematical model of the electric automobile participating in the V2G, and enable the model to be more in line with the actual situation.

The multi-objective economic dispatch model is introduced as follows:

the micro-grid economic dispatching model of the electric automobile is considered, different from the traditional economic dispatching model, the previous model is more than an optimized model built on one side or two sides, the problems are seen from the angles of an electric automobile owner, a micro-grid and a power grid respectively, objective functions representing benefits are formulated from different perspectives, the micro-grid economic dispatching model is composed of various constraint conditions, benefits of all sides are considered as far as possible from the global point of view, and the micro-grid economic dispatching model is a complex multi-objective optimized model, and aims to maximize benefits of the main side, the micro-grid side and the power grid side of the electric automobile and ensure safe, stable and economic operation of the whole system. In general, the mathematical model can be described by the following formula:

Wherein g _i (x) Representing m inequality constraints; h is a _j (x) Representing n equality constraints.

Step 102: and establishing a multi-target economic dispatch model which aims at minimum charging cost of the electric automobile user, minimum comprehensive operation and management cost of the micro-grid and minimum peak-valley difference of the power of the connecting lines based on the output model of each device.

The objective functions are as follows:

(1) Charging cost of electric automobile owner

The optimization target 1 is the charging cost f of the electric automobile owner ₁ Minimum.

In order to pursue maximization of benefits, the electric car owners can adjust the charge and discharge behaviors of the electric car owners according to the time-sharing electricity price of the time period, the electric car owners can aim at the maximum charge and discharge benefits according to the conditions of the electric car owners, and meanwhile, the charge and discharge behaviors of the electric car owners can be regarded as mutually independent, namely, the charge and discharge behaviors of the electric car owners cannot be mutually influenced. Can be represented by the following formula:

f ₁ ＝min(cost _char -cost _dischar +cost _{battery_loss} -cost _subside )

wherein, cost _char Representing the charge cost, cost _dischar Represents profit obtained by V2G discharge, cost _{battery_loss} Representing battery loss cost, cost _subside Representing subsidy costs, price of the government in V2G mode of participation by the user _service Representing charge service charge, according to order data, the invention takes a value of 0.65 yuan/kWh, price _subside Is the subsidy expense.

(2) Comprehensive operation management cost of micro-grid

The optimization target 2 is the comprehensive operation management cost f of the regional micro-grid ₂ Minimum.

Generally, the comprehensive operation management cost of the regional micro-grid includes the power generation cost, maintenance cost, electricity purchase and selling cost from the power grid and the cost required for pollutant control of each distributed power generation device. Can be represented by the following formula:

f ₂ ＝min(cost _f +cost _w +cost _{grid_buy} +cost _pol )

wherein, cost _f Representing the cost of power generation _w Representing maintenance costs, costs _{grid_buy} Representing the total cost of electricity purchase and sale, cost _pol Indicating the pollution treatment cost;

V _CO2 (t)＝P _{grid_buy} (t)×K _v,CO2

V _SO2 (t)＝P _{grid_buy} (t)×K _v,SO2

V _NOx (t)＝P _{grid_buy} (t)×K _v,NOx

wherein K is _{f_w} 、K _{f_pv} 、K _{f_bat} K represents the power generation cost coefficients of the wind driven generator, the photovoltaic array and the energy storage device respectively _{w_w} 、K _{w_pv} 、K _{w_bat} Respectively represent a wind driven generator, a photovoltaic array and energy storageThe maintenance cost factor of the device is that,V _NOx (t) represents CO ₂ 、SO ₂ 、NO _X Discharge at a certain moment, +.>K _NOx Representing the treatment cost of each gas respectively, +.>Representing the discharge coefficient of each gas generated by the unit electricity purchasing quantity.

(3) Power peak-valley difference of tie line

The optimization target 3 is the power peak-valley difference f of the connecting line ₃ Minimum.

The minimum peak-to-valley difference of the link power can be expressed by the following equation.

f ₃ ＝min(max(P _grid (t))-min(P _grid (t)))

When the distributed power generation equipment output inside the micro grid is far greater than the actual power load demand of the user, the supply and demand are caused. Or when the output of the distributed power generation equipment in the micro-grid is far smaller than the actual power load demand of a user, the micro-grid and the external large power grid can be generally subjected to electric energy exchange through the connecting lines when the supply is smaller than the demand, so that the waste of electric energy is prevented or the basic power demand of the user cannot meet the phenomenon. In general, the output condition of the power generation equipment in the regional micro-grid in the non-island mode is smaller than the actual power load demand of the user, so the peak-valley difference of the power of the connecting line can be regarded as the peak-valley difference of the power purchased from the grid. The expression can be represented by the following formula:

P _grid (t)＝P _load (t)-P _w (t)-P _pv (t)-P _{battery_dischar} (t)-P _{EV_dischar} (t)

Wherein P is _load (t) represents the total load at time t, P _w (t) represents the output of the wind driven generator at time t, P _pv (t) represents the output force of the photovoltaic array at the moment t, P _{battery_dischar} (t) represents the discharge power of the energy storage device at the time t, P _{EV_dischar} And (t) represents the total discharge power of all electric vehicles at the moment t in the V2G mode.

In the process of optimizing the multiple objective functions, because the objective functions have conflict and contradiction, the optimal solution of the single objective function is difficult to realize, most of the processing methods at present convert the multiple single objective functions into one single objective function through linear weighting, however, different single objective functions are likely to be unable to calculate because of different dimensions from each other due to consideration of the problem of different angles, and in this case, normalization processing should be performed first so that the dimensions between the multiple single objective functions are consistent, and then linear weighting is performed. The normalization formula is as follows:

wherein f _i,wx,max Representing the maximum value of each objective function under disordered charge condition, f _{_i} Representing the normalized objective function.

Step 103: establishing constraint conditions under normal working conditions of each device; the constraint conditions include: the method comprises the following steps of power balance constraint, wind driven generator output constraint, photovoltaic array output constraint, energy storage device discharge uniqueness constraint, energy storage device charge and discharge power constraint, energy storage device capacity state constraint, power grid purchase and sale uniqueness constraint, electric vehicle charge and discharge state uniqueness constraint, electric vehicle charge and discharge power constraint, electric vehicle capacity constraint, electric vehicle non-schedulable period constraint and electric vehicle charge and electric quantity consistency constraint before and after optimization.

(1) Power balance constraint:

during operation of the microgrid, power balance constraints of the various devices and the electric vehicle, the base electrical loads, and the power grid must be met for any time. The following is shown:

in the above formula: p (P) _w For the actual output of the wind-driven generator, P _pv For the actual output of the photovoltaic array, P _{bat_c} 、P _{bat_dc} Respectively charge and discharge power of the energy storage device, P _{grid_buy} And P _{grid_sell} Real-time power purchased and sold from the power grid, P _EV Charging load of electric automobile, P _basic The electric load is basically used for the user.

(2) Force constraint of wind driven generator:

the real-time output condition of the wind driven generator is easily affected by weather, and the output is unstable. Thus, in general, the actual output of the wind turbine is not higher than its predicted output value. In order to ensure stable operation of the wind turbine, in the actual operation process, the following constraints need to be carried out on the wind turbine, namely:

P _w (t)≤P _w,t

(3) Photovoltaic array output constraint:

the real-time output condition of the photovoltaic array is the same as that of the wind driven generator, and is greatly influenced by environmental factors. Similarly, in order to ensure stable operation of the photovoltaic array, in the actual operation process, the following constraints need to be carried out on the photovoltaic array, namely:

P _pv (t)≤P _pv,t

(4) Unique constraint of charging and discharging of energy storage equipment:

I.e. the energy storage device cannot be both charged and discharged at the same time. This can be represented by the following formula:

0≤Snj _{bat_c} (t)+Snj _{bat_dc} (t)≤1

(5) Energy storage device charge-discharge power constraint:

under normal working conditions, the charging and discharging power of the energy storage equipment cannot exceed the limit. This can be represented by the following formula:

P _{bat_c,max} ＝C _bat ×γ _bat,c

P _{bat_dc,max} ＝C _bat ×γ _bat,dc

wherein P is _{bat_min_c} Representing a minimum charging power; p (P) _{bat_max_c} Representing the maximum discharge power; p (P) _{bat_min_dc} Representing a minimum discharge power; p (P) _{bat_max_dc} Representing the maximum discharge power; gamma ray _bat,c Indicating a maximum charge rate at a certain time; gamma ray _bat,dc Indicating the maximum discharge rate at a certain time.

(6) Energy storage device capacity state constraints

When the energy storage device starts to be used, certain electric quantity can be given for subsequent use, and meanwhile, in the actual use process, the electric quantity of the energy storage device cannot exceed the maximum and minimum electric quantity requirements. In order to ensure the normal operation of the energy storage device in the next day, the electric quantity at the last moment of each day must be kept consistent with the electric quantity at the beginning, so that the energy storage device can be recycled.

P _{bat_c,max} ＝C _bat ×γ _bat,c

P _{bat_dc,max} ＝C _bat ×γ _bat,dc

(7) Unique constraint of electricity purchasing and selling of power grid

0≤Snj _{grid_buy} (t)+Snj _{grid_sell} (t)≤1

I.e. the micro-grid cannot both purchase and discharge power from and to the grid at the same time. Can be represented by the above, snj _{grid_buy} (t)、Snj _{grid_sell} (t) respectively representing the electricity purchasing and selling states of the micro-grid at a certain moment;

(8) Unique constraint of charge and discharge states of electric automobile

I.e. during charging stations, it is not possible for the electric vehicle to be both charged and discharged at the same time. This can be represented by the following formula:

0≤Snj _char (t)+Snj _dischar (t)≤1

therein, snj _char (t)、Snj _dischar (t) represents electric power respectivelyThe charge and discharge states of the automobile at all times;

(9) Electric automobile charge-discharge power constraint

In the actual charging and discharging process of the electric vehicle, in order to ensure the safe use of the electric vehicle, the charging and discharging power must be kept within a certain interval range

Snj _char (t)×P _{EV_c,min} ≤P _{EV_char} (t)≤Snj _char (t)×P _{EV_c,max}

P _{EV_c,max} ＝C _EV ×γ _EV,c

P _{EV_dc,max} ＝C _EV ×γ _EV,dc

(10) Capacity constraint of electric automobile

In order to prevent the phenomena of overcharge and overdischarge, the service life of the battery is ensured. The electric quantity of the electric automobile should be kept within a reasonable and safe interval range at any time. Meanwhile, in order to ensure the basic charging requirement of the electric vehicle owner, the SOC of the electric vehicle should be ensured to be higher than that of the electric vehicle when the owner leaves the charging station _expect Thereby meeting the basic travel demands of the users.

SOC _min ×C _EV ≤E _EV (t)≤SOC _max ×C _EV

E _{EV_out} ≥SOC _expect ×C _EV

(11) Non-schedulable time period constraint of electric automobile

The electric automobile can only accept the charge and discharge schedule at the charging station, and can not accept the schedule after leaving the charging station, and the charge and discharge power is zero. The expression can be expressed by the following formula:

P _{EV_char} (t)＝0 (t≥T _end or T is less than or equal to T _start )

P _{EV_dischar} (t)＝0 (t≥T _end Or T is less than or equal to T _start )

P _{EV_char} (t)＝0 (T _start ≤t≤T _end )

P _{EV_dischar} (t)＝0 (T _start ≤t≤T _end )

Wherein P is _{EV_char} (t)、P _{EV_dischar} (T) each represents the charge/discharge power at a certain time of the electric vehicle, T _start Indicating the arrival time of the electric automobile, T _end The outbound time of the electric automobile is represented; because the arrival time of the electric automobile is uncertain, two situations are generally classified: that is, the time of arrival and the time of departure are on the same day, and the time of arrival and the time of departure are not on the same day, for example, the time of arrival is the first day (evening) and the time of departure is the second day (early morning).

(12) Electric automobile optimizes front and back charge electric quantity uniformity constraint

Each electric vehicle must be guaranteed to be consistent with the charge level charged before the schedule is not accepted after the schedule is accepted for orderly charging. After charging and discharging, each electric automobile must be guaranteed to be consistent with the charging electric quantity before dispatching, and the charging electric quantity can be expressed by the following formula:

Wherein P is _i,wx (t) represents the charging power at a certain time in the unordered charging state of the ith vehicle in the charging station, P _i,yx (t) represents the firsti, charging power of the vehicle at a certain moment in an orderly charging state in the charging station; p (P) _i,V2G,char (t) represents the charging power at a certain time in the V2G mode of the ith vehicle, P _{i,V2G,dischar} (t) represents the discharge power of the ith vehicle at a certain time in the V2G mode.

Step 104: and determining the weight of each objective function in the multi-objective economic dispatch model, performing linear weighting, and constructing a new objective function.

Selection of objective function weights

Analytical Hierarchy Process (AHP) is hereinafter abbreviated. Is proposed by thomas-saidi, university of pittsburgh, usa. The method can be divided into three steps:

(1) Establishing a hierarchical model

The structural model comprises a target layer, a criterion layer and a scheme layer. For purposes of the present invention, the target layer refers to the minimum of the multiple target functions. The criterion layer comprises various objective functions, namely charging cost, comprehensive operation management cost of the micro-grid and the like. The scheme layer refers to a final scheme selected after giving different weights to the objective function.

(2) Constructing a judgment matrix

Constructing a judgment matrix for different objective functions, wherein the judgment matrix a _ij The scale method of (2) is shown in table 1:

table 1 scale arrangement table

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Table 2 judgment matrix table

The invention sets the electric power according to the way of expert scoringThe scale of the charging cost of the automobile owner is 5, the comprehensive operation management cost of the micro-grid is 3, the peak-valley difference of the power of the connecting line is 1, and the judgment matrix a is obtained according to the setting _ij The following is shown:

the judgment matrix is solved by a sum-product method, and the formula is shown as follows.

(3) Consistency check

And obtaining the maximum eigenvalue of the judgment matrix according to the following formula.

Wherein A is A-B judgment matrix, N is judgment matrix order, lambda _max And judging the maximum eigenvalue of the matrix. Then, the result is brought into the following equation to calculate the consistency index CI and the consistency ratio CR, and the consistency is checked. Wherein RI is a correction coefficient.

Table 3 may be referred to for the values of RI at different orders.

TABLE 3 correction coefficient table

When the value of CR is less than or equal to 0.1, the judgment matrix passes the consistency test. The weights corresponding to the three objective functions are respectively 0.6370, 0.2583 and 0.1047, the CI value is 0.0193, and the CR value is 0.0370, so that consistency test is satisfied.

The solution tool in the present invention is as follows:

in the scheduling model established by the invention, the decision variables comprise the charge and discharge states and the charge and discharge power of the electric vehicle in the charging station, the output states of the wind driven generator and the photovoltaic array in the micro-grid, the states of electricity purchase and electricity selling from the power grid, the charge and discharge states and the charge and discharge power of the energy storage device and the like. The equality constraints include: total power balance constraints of 24 hours a day, etc. Inequality constraints include: operational constraints of the individual devices, capacity constraints, etc. The invention converts the partial nonlinear part contained in the model into a linear model for processing through piecewise linearization. And (3) carrying out simulation modeling by adopting Matlab+Yalmip, and solving the model based on Cplex software.

Step 105: three charging schemes of a disordered charging scheme, an ordered charging scheme and a V2G scheme are set.

Step 106: and solving the new objective function based on constraint conditions of each device under normal working conditions to obtain a charging and discharging state, charging and discharging power, a charging and discharging state of the energy storage device and power in the charging station.

Step 107: and comparing the change conditions of the objective functions of different charging schemes to determine the optimal charging scheme.

In the following, the invention takes micro-grid in a certain region of the south as an example to carry out simulation calculation and case analysis verification. The micro-grid comprises a wind driven generator, a photovoltaic array and energy storage equipment. The rated power of the wind driven generator is 100kW, the real-time output condition of the wind driven generator in the region in winter on typical days can be obtained, the rated power of the photovoltaic array is 300kW, and the real-time output condition of the photovoltaic array in the region in winter on typical days can be obtained according to the information such as the temperature, the radiation intensity and the like of the region in 24 hours, as shown in fig. 7. The full capacity of the energy storage equipment is 50kW, and the charging and discharging efficiencies are 95%.

The electrical load requirements of a cell served by the microgrid are also known. The cell has 50 electric vehicles, the full charge capacity is 60kW, the maximum charge and discharge energy multiplying power of the electric vehicles is 0.1167, the maximum charge and discharge power is 7kW, the charge and discharge efficiency is 90%, and the state of charge limits of the battery are SOC respectively _max ＝1，SOC _min ＝0.2。

The electric quantity purchased by the micro-grid from the power grid is mostly generated by a thermal generator, so that pollutant emission including CO ₂ 、SO ₂ 、NO _X Etc. In order to minimize the damage and influence of pollutants to the environment,

the treatment of the contaminants should be performed, wherein the treatment costs for each contaminant are shown in table 4:

TABLE 4 cost of treatment of contaminants due to electricity purchasing

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Aiming at a wind driven generator, a photovoltaic array and energy storage equipment contained in a micro-grid. Wherein the electricity generation costs and maintenance costs of the respective devices are shown in table 5:

table 5 generation and maintenance costs for each device of the micro grid system

According to the invention, each device in the electric automobile and the micro-grid system is used as a research and optimization object, and under the condition that the micro-grid is in a grid-connected state, the change condition of each objective function of the electric automobile in different operation modes is compared and analyzed, so that the comprehensive benefit maximization of multiple parties is further realized.

The invention is divided into the following three schemes according to the running mode of the electric automobile:

scheme 1: the electric automobile owner does not accept the scheduling scheme, and the electric automobile owner carries out unordered charging, namely the electric automobile owner starts charging immediately after arriving at the charging station, and immediately leaves the charging station after the charging is finished. The system is only added in the form of a load and no discharge is performed to the grid.

Scheme 2: and the electric automobile owners accept the scheduling scheme to carry out ordered charging, namely the electric automobile is charged according to a charging scheduling strategy, and the charging behavior occurs in the period of low electric load preferentially. When the electric energy provided by the wind driven generator and the photovoltaic array cannot meet the requirements, electricity is purchased from the power grid through a connecting line between the micro-grid and the power grid, so that the gap of the electric power requirement is filled. In this solution, the electric vehicle is likewise only used as a load side and no discharge is carried out on the power grid.

Scheme 3: the electric automobile owner accepts the scheduling scheme and carries out V2G with the power grid, namely the electric automobile is charged and discharged according to the scheduling strategy, the charging behavior is preferentially generated in the low-valley period of the power load, the discharging behavior is preferentially generated in the peak period of the power load, the comprehensive operation and management cost of the micro-grid is reduced as much as possible, meanwhile, the power peak-valley difference of the connecting line is reduced, and the investment of power generating equipment at the power grid side is reduced. Meanwhile, in order to ensure the travel requirement of the electric automobile, after participating in the V2G service of the same system, the electric quantity of the electric automobile when the electric automobile leaves is not lower than 80% of the total electric quantity. In the discharging process, in order to prevent the overdischarge phenomenon from greatly damaging the service life of the battery, the electric quantity is not lower than 20% of the total electric quantity.

Analysis of results

According to the data of the case, calculation is carried out to obtain objective function comparison graphs under different schemes, as shown in fig. 8, under the condition that the electric automobile is in different charging modes, the charging cost is different, the highest charging cost of an electric automobile owner is 1533.79 yuan under the unordered charging condition, the charging cost of a user can be obviously reduced by ordered charging, the descending amplitude is 15.5%, compared with unordered charging, the charging cost of a V2G mode is 1247.94 yuan, the descending amplitude is 18.64%, and compared with ordered charging, the descending amplitude of the charging cost of the user is smaller in the V2G mode and is only 3.71%, and the main reason is that the discharging income under the V2G mode is limited by the peak-valley electricity price difference of the area, financial patches of the local government and the cost of a power battery. Because the electric quantity of the electric automobile is at least not lower than 80% of the full electric quantity when the electric automobile leaves the charging station, the electric automobile must be charged after the electric automobile is discharged to ensure the travel after the user, if the peak-valley electricity price difference is lower, the discharging income is too low after the loss cost of the battery is deducted, and if the charging cost of the user is required to be ensured to be the lowest, the enthusiasm of the user for participating in scheduling is improved, and the electric automobile must be correspondingly subsidized. Table 6 is a comparison of the costs in the different schemes (rate of change compared to chaotic charging in brackets).

Table 6 f in three schemes ₁ Comparison of the fees

As shown in fig. 9, compared with the ordered charging, the overall operation management cost of the micro-grid is slightly increased by 0.78% in the ordered charging mode, and the total cost in the V2G mode is reduced by 1.32%. The two modes can reduce the power generation cost of the micro-grid system, improve the power purchase cost, mainly because the charging behavior is mainly concentrated in the power consumption peak period during disordered charging, the power purchase cost is higher at the moment, and the power purchase cost is improved under ordered charging and V2G modes because the power purchase quantity is increased at the valley moment, so that the total power purchase cost is improved compared with disordered charging, and meanwhile, the pollution treatment cost is correspondingly increased due to the increase of the power purchase quantity, and the pollution treatment cost is respectively improved by 22.89% and 20.86%. In the pollutant treatment cost, three schemes are all CO ₂ Is up to about 59%, NO _X Next, about 32% SO ₂ Minimum, about 9%. Table 7 shows f in various schemes ₂ Comparison of various cost change rates.

Table 7 f in three schemes ₂ Comparison of various cost change rates

As can be seen from fig. 10, in the ordered charging mode, the peak value of the tie line can be effectively reduced from original 392.03kW to 263.00kW, the decreasing amplitude is 32.91%, the valley value of the tie line is increased from original 43.01kW to 160.07kW, the increasing amplitude is 272.19%, and the peak-valley difference is reduced from original 349.03kW to 102.93kW, and the decreasing amplitude is 70.51%. The variance of the connecting line is reduced from the original 10157.68 to 1462.44, and the amplitude reduction is 85.60%.

In the V2G mode, the peak value of the connecting line is reduced to 211.52kW from the original 392.03kW, the decreasing amplitude is 46.05%, the valley value of the connecting line is increased to 164.01kW from the original 43.01kW, and the increasing amplitude is 281.35%. The peak-valley difference of the connecting line is reduced to 47.51kW from 349.03kW, the reducing amplitude is 81.35%, the variance of the connecting line is reduced to 400.69 from 10157.68, and the reducing amplitude is 96.06%. Meanwhile, under the V2G mode, various indexes can be further optimized on the basis of the ordered charging mode. Compared with the ordered charging mode, the V2G can reduce the peak value of the connecting line, the amplitude reduction is 19.57%, the valley value of the connecting line is increased, the amplitude increase is 2.46%, the peak-valley difference of the connecting line is reduced, the amplitude reduction is 53.84%, the variance of the connecting line is reduced, and the amplitude reduction is 72.60%. Table 8 shows f in various schemes ₃ Comparison of various indicators (rate of change compared to disorder charge in brackets).

Table 8 f in three schemes ₃ Comparison of various indexes

Fig. 11 and 12 show the power change condition of the tie line and the charge load change condition of the electric vehicle in different schemes, respectively. The ordered charging mode and the V2G mode can be found to obviously improve the valley power of the connecting line, reduce peak power, greatly relieve the purchase power in the peak period, reduce the fund investment of power generation equipment of a power grid, slow down the service life loss of equipment such as a power distribution network and the like, and ensure the safe operation of the power grid.

As can be seen from fig. 12, in the unordered charging mode, since the electric car owner charges the electric car after reaching the company or returning home, two peaks appear near the two time points of 10:00 and 20:00. And the charging power at night is obviously higher than that at daytime. In the ordered charging mode, if the user completely responds to the scheduling policy to charge, at this time, the charging power can be found to be obviously weakened at 17:00-21:00 in the evening, and the charging power is found to be obviously weakened at 1 in the early morning: 00-5:00 the charging power is obviously increased, the charging load of the electric automobile is transferred from the peak period to the valley period of the electric load, the charging cost is obviously reduced, and meanwhile, the enough electric quantity can be ensured to be used for the next day. In the V2G mode, the charging power is further improved from 1:00 to 3:00, and the utilization of electric energy at valley time is improved. At 18:00-21:00, the electric automobile discharges in the electricity consumption peak period, the electricity price is highest, more discharge cost can be earned through V2G, meanwhile, the power of the connecting line in the period can be reduced, and the peak-valley difference of the power of the connecting line is reduced.

The development of the micro-grid can relieve the power supply pressure of the grid, promote the utilization of renewable energy sources, and has stronger research value and practical significance for the economic operation scheduling research of the micro-grid. The chapter adds an electric automobile battery loss model on the basis of the previous research, adds the electric automobile battery loss model into a V2G scheme, and simultaneously establishes an economic dispatch model of the micro-grid from three standpoints of an owner, the micro-grid and the power grid. The research process and conclusion of the invention are as follows:

(1) Establishing an output model, an electric vehicle charge-discharge model and a battery loss model of each device of the micro-grid system

The invention firstly introduces the working principles of the wind driven generator, the photovoltaic array and the energy storage equipment and the working value thereof, establishes respective output models, establishes a charge and discharge model of the electric automobile, and then establishes the relation between the depth of discharge and the loss cost to pave for the application in the V2G mode.

(2) Micro-grid economic dispatch model for electric automobile

On the basis of the device models, a multi-objective optimization model comprising the charging cost of the electric car owner, the comprehensive operation management cost of the micro-grid and the peak-valley difference of the power of the connecting line is established. And adopting matlab+Yalmip to carry out simulation modeling, and realizing solution calculation based on Cplex.

(3) Comparing optimized scheduling result changes for different schemes

Case verification is performed by taking a micro-grid in a city in the south as an example. Three schemes of disordered charging, ordered charging and V2G are respectively set for the electric automobile, and the change of the dispatching result is optimized by comparing different schemes. The results show that: the ordered charging can reduce the charging cost of users by 15.5%, increase the comprehensive operation and management cost of the micro-grid by 0.78% and reduce the peak-valley difference of the connecting lines by 85.6%. The V2G can reduce the charging cost of a user by 18.64%, the comprehensive operation and management cost of the micro-grid by 1.32%, and the peak-valley difference of the connecting lines by 96.06%.

Fig. 13 is a schematic diagram of an optimization system for economic dispatch of a micro-grid considering an electric vehicle according to an embodiment of the present invention, wherein the optimization system includes:

the output model construction module 201 of each device is configured to establish an output model of each device included in the micro-grid system; the output model of each device comprises: an output model of a wind driven generator, an output model of a photovoltaic array, an output model of energy storage equipment, an electric vehicle charge-discharge model and a battery loss model;

the multi-target economic dispatch module construction module 202 is configured to establish a multi-target economic dispatch model targeting minimum charging cost of the electric vehicle user, minimum comprehensive operation and management cost of the micro-grid and minimum peak-to-valley difference of the power of the interconnecting lines based on the output models of the devices;

a constraint condition determining module 203, configured to establish constraint conditions under normal operating conditions of each device; the constraint conditions include: the method comprises the following steps of power balance constraint, wind driven generator output constraint, photovoltaic array output constraint, energy storage device discharge uniqueness constraint, energy storage device charge and discharge power constraint, energy storage device capacity state constraint, power grid purchase and sale uniqueness constraint, electric vehicle charge and discharge state uniqueness constraint, electric vehicle charge and discharge power constraint, electric vehicle capacity constraint, electric vehicle non-schedulable period constraint and electric vehicle charge and electric quantity consistency constraint before and after optimization;

A new objective function construction module 204, configured to determine the weight of each objective function in the multi-objective economic dispatch model, and perform linear weighting to construct a new objective function;

the charging scheme setting module 205 is configured to set three charging schemes of a disordered charging scheme, an ordered charging scheme and a V2G scheme;

the solving module 206 is configured to solve the new objective function based on constraint conditions under the normal operating conditions of the respective devices, so as to obtain a charge and discharge state, charge and discharge power, a charge and discharge state of the energy storage device, and power in the charging station;

and the comparison module 207 is used for comparing the change conditions of the objective functions of different charging schemes to determine an optimal charging scheme.

In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. 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

1. An electric vehicle-considered micro-grid economic dispatch optimization method is characterized by comprising the following steps:

establishing an output model of each device contained in the micro-grid system; the output model of each device comprises: an output model of a wind driven generator, an output model of a photovoltaic array, an output model of energy storage equipment, an electric vehicle charge-discharge model and a battery loss model; the electric automobile battery is a lithium battery; the expression of the battery loss model is as follows:

wherein l=ad ^-b L represents the cycle number of the battery, K _{EV_b} Represents the battery loss cost at a depth of discharge of D, a and b are constants, k is a proportionality coefficient, C _{EV_battery} Representing battery cost;

the expression of the charge amount in the output model of the energy storage device is as follows:

Q(t+1)＝Q(t)×(1-α)+Snj _{bat_char} (t)×P(t)；

Q(t+1)＝Q(t)×(1-α)-Snj _{bat_dischar} (t)×P(t)；

wherein Q (t+1) represents the charge quantity at the time t+1, Q (t) represents the charge quantity at the time t, and alpha represents the self-consumption coefficient of the energy storage device, snj _{bat_char} (t) and Snj _{bat_dischar} (t) represents the charging state and the discharging state of the energy storage device at the moment t, and P (t) represents the charging and discharging power of the energy storage device;

when the electric automobile is in a charging state, the expression of the electric automobile charging and discharging model is as follows:

E _EV (t+1)＝E _EV (t)×(1-α′)+Snj _char (t)×P _{EV_char} (t)×η _char ；

when the electric automobile is in a discharging state, the expression of the electric automobile charging and discharging model is as follows:

Wherein E is _EV (t) represents the electric quantity at time t, E _EV (t+1) represents the electric quantity at time t+1, α' represents the consumption coefficient of the electric vehicle, and P _{EV_char} 、P _{EV_dischar} Respectively charge power, discharge power, eta _char 、η _dischar Respectively, charging efficiency, discharging efficiency, snj _char (t)、Snj _dischar (t) each represents a charge state and a discharge state at time t;

establishing a multi-target economic dispatch model which aims at minimum charging cost of an electric automobile user, minimum comprehensive operation and management cost of a micro-grid and minimum peak-valley difference of power of a connecting line based on the output model of each device; the objective function of the charging cost of the electric automobile user is as follows:

f ₁ ＝min(cost _char -cost _dischar +cost _{battery_loss} -cost _subside )；

wherein, cost _char Representing the charge cost, cost _dischar Represents profit obtained by V2G discharge, cost _{battery_loss} Representing battery loss cost, cost _subside Representing subsidy fees for the user to participate in the V2G mode;

Price _service representing charge service charge, price _subside To subsidize the cost, price _char 、Price _dischar Respectively representing charge cost and discharge cost, i represents an ith vehicle, and n represents the total number of vehicles;

the objective function of the peak-valley difference of the power of the connecting line is as follows:

f ₃ ＝min(max(P _grid (t))-min(P _grid (t)))；

P _grid (t) is the power at the moment of the tie-line t; p (P) _grid (t)＝P _load (t)-P _w (t)-P _pv (t)-P _{battery_dischar} (t)-P _{EV_dischar} (t)；P _load (t) represents the total load at time t, P _w (t) represents the output of the wind driven generator at time t, P _pv (t) represents the output force of the photovoltaic array at the moment t, P _{battery_dischar} (t) represents the discharge power of the energy storage device at time t;

setting three charging schemes, namely a disordered charging scheme, an ordered charging scheme and a V2G scheme; the V2G scheme is as follows: the electric automobile owner accepts a scheduling scheme, and carries out V2G with a power grid, namely the electric automobile is in a low-valley period of electric load according to a charging and discharging scheduling strategy, the charging behavior is in a peak period of electric load, the comprehensive operation and management cost of a micro-grid is reduced, meanwhile, the peak-valley difference of power of a connecting line is reduced, the investment of power generation equipment at the side of the power grid is reduced, the electric quantity of the electric automobile owner when the electric automobile leaves after participating in V2G service of the same system is not less than 80% of the total electric quantity, and the electric quantity of the electric automobile is not less than 20% of the total electric quantity in the discharging process; the disordered charging scheme is as follows: the electric automobile owner does not accept the scheduling scheme, and the electric automobile owner carries out disordered charging, namely, the electric automobile owner immediately starts charging after arriving at the charging station, and immediately leaves the charging station after the charging is finished;

2. The optimization method for economic dispatch of a micro-grid taking into account electric vehicles according to claim 1, wherein the expression of the output model of the wind driven generator is as follows:

wherein P is _WT,t Representing the generating capacity of the wind turbine at the moment t; p (P) _WT Representing the rated power of the wind generating set; v _t The actual wind speed at the time t is represented; v ⁱⁿ Indicating cut-in wind speed; v ^out Indicating the cut-out wind speed; v ⁰ Indicating a rated wind speed;

the expression of the output model of the photovoltaic array is as follows:

T _S (t)＝T _amd (t)+0.0138(1+0.031T _amd (t))(1-0.042V(t))G _S (t)

wherein K is a power temperature coefficient; p (P) _PV (t) is the output of the photovoltaic array at time t; p (P) _{pv_rated} Maximum power of the photovoltaic array under standard rated conditions; g _S (t) is the actual illumination intensity at time t; g _STC The value of the light radiation density under standard rated conditions is 1 kW.h/m ² ；T _STC The rated ambient temperature is 298K; t (T) _S (t) is the actual temperature of the battery plate at the moment t; t (T) _amd (t) is the actual ambient temperature at time t, and V (t) is the actual wind speed at time t;

the capacity of the energy storage device is represented by C;

the expression of the state of charge of the energy storage device is:

C _remain c is the residual electric quantity _battery For total capacity, I is discharge current and SOC is state of charge of the energy storage device.

3. The method for optimizing economic dispatch of a micro-grid for an electric vehicle according to claim 1, wherein the objective function of the comprehensive operation and management cost of the micro-grid is:

f ₂ ＝min(cost _f +cost _w +cost _{grid_buy} +cost _pol )

4. The method for optimizing economic dispatch of a microgrid for an electric vehicle according to claim 1, wherein the expression of the power balance constraint is as follows:

wherein P is _w For the actual output of the wind-driven generator, P _pv For the actual output of the photovoltaic array, P _{bat_c} 、P _{bat_dc} Respectively the charging power of the energy storage device and the discharging power of the energy storage device, P _{grid_buy} And P _{grid_sell} Real-time power purchased from the power grid and real-time power sold to the power grid, respectively, P _EV Charging load of electric automobile, P _basic Basic power load is used for users;

P _w (t)≤P _w,t

wherein P is _w,t The predicted force value of the wind driven generator at the moment t is represented;

the expression of the photovoltaic array output constraint is as follows:

P _pv (t)≤P _pv,t

wherein P is _pv,t Representing a predicted force value of the photovoltaic array at the time t;

0≤Snj _{bat_c} (t)+Snj _{bat_dc} (t)≤1

therein, snj _{bat_c} (t) represents the state of charge of the energy storage device at time t, snj _{bat_dc} (t) represents a discharge state of the energy storage device at time t;

P _{bat_max_c} ＝C _bat ×γ _bat,c

P _{bat_max_dc} ＝C _bat ×γ _bat,dc

the energy storage device capacity state constraint is expressed as follows:

E _{bat_start} ＝SOC _{bat_start} ×C _bat

SOC _min ×C _bat ≤E _bat (t)≤SOC _max ×C _bat

E _{bat_start} ＝E _{bat_end}

wherein SOC is _{bat_start} Representing the state of charge of the energy storage device when the energy storage device is started to be used, C _bat Representing the full charge of the energy storage device, E _{bat_start} Representing the electric quantity when the energy storage equipment starts to be used, E _bat (t) represents the electric quantity of the energy storage device at the moment t, E _{bat_end} Representing the power of the energy storage device at the end time of one day and SOC _min 、SOC _max Respectively representing the minimum charge state and the maximum charge state of the electric automobile;

0≤Snj _{grid_buy} (t)+Snj _{grid_sell} (t)≤1

therein, snj _{grid_buy} (t)、Snj _{grid_sell} (t) respectively representing the electricity purchasing state and the electricity selling state of the micro-grid at the time t;

0≤Snj _char (t)+Snj _dischar (t)≤1

Snj _char (t)×P _{EV_c,min} ≤P _{EV_char} (t)≤Snj _char (t)×P _{EV_c,max}

P _{EV_c,max} ＝C _EV ×γ _EV,c

P _{EV_dc,max} ＝C _EV ×γ _EV,dc

wherein, gamma _EV,c 、γ _EV,dc Respectively represents the maximum charge multiplying power and the maximum discharge multiplying power of the electric automobile, P _{EV_c,min} 、P _{EV_c,max} Respectively representing the minimum value and the maximum value of the charging power of the electric automobile, P _{EV_dc,min} 、P _{EV_dc,max} Respectively representing the minimum value and the maximum value of the discharge power of the electric automobileLarge value, C _EV Representing the electric quantity of the electric automobile;

SOC _min ×C _EV ≤E _EV (t)≤SOC _max ×C _EV

E _{EV_out} ≥SOC _expect ×C _EV

wherein E is _{EV_out} Representing an amount of electricity when the electric vehicle leaves the charging station; SOC (State of Charge) _expect Representing a state of charge of the battery when an electric vehicle owner desires to leave the charging station;

P _{EV_char} (t)＝0(t≥T _end or T is less than or equal to T _start )

P _{EV_dischar} (t)＝0(t≥T _end Or T is less than or equal to T _start )

P _{EV_char} (t)＝0(T _start ≤t≤T _end )

P _{EV_dischar} (t)＝0(T _start ≤t≤T _end )

Wherein P is _{EV_char} (t)、P _{EV_dischar} (T) represents the charging power and the discharging power of the electric automobile at the time T, T _start Indicating the arrival time of the electric automobile, T _end The outbound time of the electric automobile is represented;

Wherein P is _i,wx (t) represents the charging power at time t in the unordered charging state of the ith vehicle in the charging station, P _i,yx (t) shows the ith vehicle in the charging stationCharging power at time t in the ordered charging state; p (P) _i,V2G,char (t) represents the charging power at time t in the i-th vehicle, V2G mode, P _{i,V2G,dischar} (t) represents the discharge power at time t in the V2G mode of the i-th vehicle.

5. The method for optimizing economic dispatch of a micro-grid for an electric vehicle according to claim 1, wherein the ordered charging scheme is as follows:

an electric automobile owner accepts a scheduling scheme to carry out ordered charging, namely the electric automobile is charged according to a charging scheduling strategy, and charging behavior occurs preferentially in a low-valley period of electric load; when the electric energy provided by the wind driven generator and the photovoltaic array cannot meet the requirements, electricity is purchased from the power grid through a connecting line between the micro-grid and the power grid, so that the gap of the electric power requirement is filled.

6. A micro-grid economic dispatch optimization system that accounts for electric vehicles, the optimization system comprising:

the output model construction module of each device is used for establishing an output model of each device contained in the micro-grid system; the output model of each device comprises: an output model of a wind driven generator, an output model of a photovoltaic array, an output model of energy storage equipment, an electric vehicle charge-discharge model and a battery loss model; the electric automobile battery is a lithium battery; the expression of the battery loss model is as follows:

Q(t+1)＝Q(t)×(1-α)+Snj _{bat_char} (t)×P(t)；

Q(t+1)＝Q(t)×(1-α)-Snj _{bat_dischar} (t)×P(t)；

the multi-target economic dispatch module construction module is used for constructing a multi-target economic dispatch model which aims at minimum charging cost of electric automobile users, minimum comprehensive operation management cost of micro-grids and minimum peak-to-valley difference of tie power based on the output models of the equipment; the objective function of the charging cost of the electric automobile user is as follows:

f ₃ ＝min(max(P _grid (t))-min(P _grid (t)))；

the charging scheme setting module is used for setting three charging schemes of an unordered charging scheme, an ordered charging scheme and a V2G scheme; the V2G scheme is as follows: the electric automobile owner accepts a scheduling scheme, and carries out V2G with a power grid, namely the electric automobile is in a low-valley period of electric load according to a charging and discharging scheduling strategy, the charging behavior is in a peak period of electric load, the comprehensive operation and management cost of a micro-grid is reduced, meanwhile, the peak-valley difference of power of a connecting line is reduced, the investment of power generation equipment at the side of the power grid is reduced, the electric quantity of the electric automobile owner when the electric automobile leaves after participating in V2G service of the same system is not less than 80% of the total electric quantity, and the electric quantity of the electric automobile is not less than 20% of the total electric quantity in the discharging process; the disordered charging scheme is as follows: the electric automobile owner does not accept the scheduling scheme, and the electric automobile owner carries out disordered charging, namely, the electric automobile owner immediately starts charging after arriving at the charging station, and immediately leaves the charging station after the charging is finished;