CN111461522B - Micro-grid economic optimization scheduling method based on electric vehicle running state - Google Patents
Micro-grid economic optimization scheduling method based on electric vehicle running state Download PDFInfo
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Abstract
The invention discloses a microgrid economic optimization scheduling method based on an electric vehicle running state, which is used for considering the problem of microgrid running cost when an electric vehicle is subjected to charge-discharge scheduling; then, establishing an electric automobile running state model according to the running data of the electric automobile and the connection state with the power grid; and finally, considering the running requirements of users while considering the running state of the electric automobile, and combining the electricity purchasing price and the electricity selling price of each time interval of the power grid to reduce the running cost of the microgrid as a target, and obtaining the charge-discharge power and the photovoltaic output curve of the electric automobile after optimized dispatching, thereby achieving the effect of reducing the running cost of the microgrid. The invention can reduce the operation cost of the micro-grid and improve the economical efficiency of the system while ensuring the reliability of the system and considering the operation state of the electric automobile.
Description
Technical Field
The invention relates to the technical field of electric vehicle optimized dispatching, in particular to a micro-grid economic optimized dispatching method based on an electric vehicle running state.
Background
With the development of Electric Vehicle (EV) technology, the number of Electric vehicles is increasing. However, a large number of electric vehicles are connected to the power grid, so that a power grid load is adversely affected, for example, load peak-valley difference is increased, and in addition, as the charging load of the electric vehicles has nonlinear and unstable characteristics, harmonic waves are introduced, so that the power quality is affected. Research shows that most passenger vehicles only have about 1h of daily average running time, and the passenger vehicles are in a stop state for 95% of the time. Therefore, the electric automobile with enough large scale is connected to the power grid, and the electric automobile can be used as the distributed energy storage equipment. On the basis of meeting the driving requirements of the electric automobile, redundant electric energy is fed back to the power grid, and conversely, when the electric automobile needs to be charged, the electric automobile can obtain the electric energy from the power grid, which is a V2G (Vehicle-to-grid, V2G) technology; in addition, in which excess electric energy is fed back to the remaining vehicles on the basis of satisfying their own driving demands, is the V2V (Vehicle-to-Vehicle, V2V) technology.
An important content in the research of the microgrid is economic optimization scheduling of the energy of the microgrid, and as the charging and discharging behaviors of electric vehicles have uncertainty in time and space, when large-scale electric vehicles are connected into the microgrid without being controlled, the power supply burden of the microgrid is increased, the operation control difficulty of the microgrid is increased, and the economic scheduling of the microgrid is more complicated.
In the economic dispatch of electric vehicles studied so far, there is a deficiency in considering both V2G and V2V technologies. Therefore, by reasonably controlling the V2G and V2V technologies, the driving requirements of the electric automobile are considered on the basis of the running state of the electric automobile, the charging and discharging behaviors of the electric automobile are reasonably guided, users of the electric automobile and a power grid benefit together according to a set charging and discharging strategy, and the safe and stable running of a power system is maintained.
Disclosure of Invention
The invention aims to provide a micro-grid economic optimization scheduling method based on the running state of an electric vehicle, which considers the running state of the electric vehicle and the running requirements of users at the same time, performs economic optimization scheduling on a system and provides favorable technical support for the combination of the electric vehicle and an intelligent power grid.
The technical solution for realizing the purpose of the invention is as follows: a microgrid economic optimization scheduling method based on an electric vehicle running state comprises the following steps:
step 1, establishing an operation state model of the electric automobile in one day based on the driving data of the electric automobile and the connection state of the electric automobile and a power grid, and dividing the state of the electric automobile into a driving energy consumption state, a parking vehicle-network interaction state, a parking vehicle-vehicle interaction state and a parking non-chargeable and dischargeable state;
step 2, establishing an electric automobile ordered charging and discharging optimization scheduling model, including establishing a target function and establishing constraint conditions;
and 3, carrying out model solution on the established model by utilizing matlab software.
Compared with the prior art, the invention has the following remarkable advantages:
(1) the electric automobile is modeled based on the connection state of the electric automobile and a power grid, the electric automobile is divided into a driving energy consumption state, a parking vehicle-grid interaction state, a parking vehicle-vehicle interaction state and a parking non-charging and discharging state, and the operation state of the electric automobile is considered more comprehensively compared with the previous research;
(2) according to the invention, on the basis of considering a plurality of running states of the electric automobile, the running requirements of users are considered for the charging and discharging scheduling of the electric automobile cluster, so that the discharging scheduling is considered on the basis of meeting the next running requirements, and the residual electric quantity can still meet the use requirements of the residual travel of the users after the electric automobile participates in scheduling;
(3) according to the method, when an optimized dispatching model is established, only a single technology of V2V or V2G is avoided being considered, but a V2V technology is considered on the basis of the V2G technology, and in the process of electric vehicle charging and discharging dispatching, on the basis of ensuring stable operation of a system, benefits of a micro-grid system and electric vehicle users can be maximized.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
Fig. 1 is a load diagram of a microgrid in summer.
Fig. 2 is a summer photovoltaic force diagram.
Fig. 3 is a disordered charging diagram of the electric automobile.
Fig. 4 is a load diagram of a microgrid in summer after optimization.
Fig. 5 is an optimized summer photovoltaic force diagram.
FIG. 6 is a graph of optimized electric vehicle load.
Detailed Description
A microgrid economic optimization scheduling model based on an electric vehicle running state comprises the following steps:
step 1, establishing an operation state model of the electric automobile in one day based on the driving data of the electric automobile and the connection state of the electric automobile and a power grid, and dividing the state of the electric automobile into a driving energy consumption state, a parking vehicle-network interaction state, a parking vehicle-vehicle interaction state and a parking non-chargeable and dischargeable state; the method specifically comprises the following steps:
when the electric automobile is parked and the Vehicle-to-Grid (V2G) technology can be carried out, a parking available-to-Grid interaction state is defined; when the electric automobile is parked and no V2G technical support is arranged around, but Vehicle-to-Vehicle (V2V) and technical conditions can be carried out to define a parking available-Vehicle interaction state; defining a driving energy consumption state when the electric automobile drives; when the electric automobile is parked and no V2G and V2V technical supports are around, the electric automobile is defined as a parking non-chargeable and dischargeable state.
Step 2, establishing an electric automobile ordered charging and discharging optimization scheduling model, including establishing a target function and establishing constraint conditions; the method specifically comprises the following steps:
1) constructing an objective function
minF＝C Fuel +C OM +C Buy/Sell (1)
In the formula (1), F represents the operation cost, and the unit is element;
in the formula (1), C OM Representing the running management cost, in units of elements, can be expressed as:
in the formula (2), n represents the number of electric vehicles participating in scheduling; k OM Representing an operation management coefficient; p i (t) represents the power of the ith electric vehicle at time t, in kW, and can be expressed as:
in the formula (3), the reaction mixture is,
a charging flag indicating charging of the ith electric vehicle;
a discharge flag indicating the i-th electric vehicle; when the electric vehicle is charged, the charging mark value
The flag is 1, the other cases are 0; when the electric automobile discharges, the discharge mark value
The label is-1, and the other cases are 0; p is N The rated charging power of the electric automobile is kW;
in the formula (1), C Fuel Representing fuel cost, in units of elements, can be expressed as:
in the formula (4), K OM Representing a fuel cost factor;
in the formula (1), C Buy/Sell The net electricity purchase cost of the electric automobile is represented by the following units:
in the formula (5), c Buy (t) the electricity price of the electric automobile is purchased at the moment t, and the unit is yuan; c. C Sell·G (t) selling the electricity price of the electric automobile to the power grid at the moment t, wherein the unit is yuan; c. C Sell·V (t) selling electricity prices of the electric automobile to other electric automobiles at the moment t, wherein the unit is Yuan; p is d The rated charging power of the electric automobile is kW; v (t) represents that the ith electric automobile is marked with electricity selling to the power grid at the time t, if the ith electric automobile sells electricity to the power grid at the time t, V (t) is 1, otherwise, V (t) is 0; w (t) represents a mark for selling electricity to the rest of the electric automobiles at the time t for the ith electric automobile, wherein if the mark sells electricity to the rest of the electric automobiles at the time t, w (t) is 1, and if not, the mark is 0.
2) Building constraints
Power balance constraint:
in formula (6), P Load Representing a base load; p is EV·Load Represents the charging and discharging load of the electric automobile, and P is the charging state of the electric automobile EV·Load The value is negative, and P is in the discharging state of the electric vehicle EV·Load The value is positive.
State of charge constraint:
0.3≤SOC i (t)≤0.95 (7)
in the formula (7), SOC i (t) represents the state of charge of the ith electric vehicle at time t; in order to prevent the energy storage unit from being irreversibly damaged by the overcharge or the overdischarge of the electric vehicle, the battery energy of the electric vehicle should be always kept within a certain range.
And thirdly, restraint is not carried out when charging and discharging are carried out:
when each electric automobile is optimally scheduled, the charging behavior and the discharging behavior cannot occur at the same time; therefore, the charging flag value
And discharge mark value
Cannot be simultaneously non-0 values.
Fourthly, restraining the active power output of each micro source:
P i·min ≤P i (t)≤P i·max (9)
in formula (9), P i·min 、P i·max Respectively represent the upper and lower limits of the output power of the ith electric automobile.
Step 3, carrying out model solution on the established model by utilizing matlab software; the method specifically comprises the following steps:
the economic optimization scheduling model of the electric automobile is characterized in that the charging and discharging power and photovoltaic output of the electric automobile are optimized according to the size of a target function, when the motion state of the electric automobile is considered, the operation cost of a micro-grid system is obtained to be the lowest, a particle swarm algorithm is used for solving, and the key point is that the optimization operation is repeatedly carried out on the particle swarm, so that the global optimal solution is obtained.
The invention is further described below with reference to specific embodiments and the accompanying drawings.
Examples
Referring to fig. 1, the load of a certain microgrid in summer is taken as an example for analysis, and the scheduling strategy of the invention is verified.
Fig. 2 is photovoltaic output in summer, and fig. 3 is a load diagram of disordered charging of the electric automobile.
The real automobile running data of the American traffic department are read, and 20 electric automobiles are randomly extracted for analysis and calculation.
Some parameter settings when establishing the microgrid economic model are shown in tables 1, 2 and 3.
TABLE 1 price for buying and selling electricity from microgrid to large power grid
TABLE 2 price for buying and selling electricity between different electric vehicles
TABLE 3 micro-Source parameters
And solving the economic optimization model by using a particle swarm method.
Fig. 4 is a load graph of a microgrid after optimization in summer.
Fig. 5 is a graph of the optimized summer photovoltaic output.
And fig. 6 is a load curve diagram of the optimized electric automobile.
The improvement effect is shown in table 4.
TABLE 4 cost comparison
Through analyzing the comparison of the optimization result graph and the table data, it can be seen that the scheduling cost is reduced and the operation cost of the micro-grid is further reduced by reducing the peak-valley difference of the micro-grid load, the electric vehicle load and the photovoltaic output when the scheduling model is used for carrying out charging and discharging scheduling on the electric vehicle on the basis of ensuring the stable operation of the power system.
The above discussion is merely an example of the present invention, and any equivalent variations on the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. A microgrid economic optimization scheduling method based on an electric vehicle running state is characterized by comprising the following steps:
step 1, establishing an operation state model of the electric automobile in one day based on the driving data of the electric automobile and the connection state of the electric automobile and a power grid, and dividing the state of the electric automobile into a driving energy consumption state, a parking vehicle-network interaction state, a parking vehicle-vehicle interaction state and a parking non-chargeable and dischargeable state;
step 2, establishing an electric automobile ordered charging and discharging optimization scheduling model, comprising the following steps:
1) constructing an objective function
min F＝C Fuel +C OM +C Buy/Sell (1)
In the formula (1), F represents the running cost; c OM Represents the running management cost:
in the formula (2), n represents the number of electric vehicles participating in scheduling, K OM Representing the operation management coefficient, P i (t) represents the power of the ith electric vehicle at time t, and is represented as:
in the formula (3), the reaction mixture is,
a charging flag indicating charging of the ith electric vehicle;
a discharge flag indicating the i-th electric vehicle; when the electric automobile is charged, the charging mark value
The flag is 1, the other cases are 0; when the electric automobile discharges, the discharge mark value
The label is-1, the other cases are 0; p N Is the rated charging power of the electric automobile;
in the formula (1), C Fuel Represents the fuel cost:
in the formula (4), K Fuel Representing a fuel cost factor;
in the formula (1), C Buy/Sell Representing the net electricity purchase cost of the electric automobile:
in the formula (5), c Buy (t) the electricity price of the electric automobile is purchased at the moment t; c. C Sell·G (t) selling electricity price to the power grid by the electric automobile at the moment t; c. C Sell·V (t) selling electricity prices from the electric automobile to other electric automobiles at the moment t; v (t) represents that the ith electric automobile is marked for selling electricity to the power grid at the time t, if the ith electric automobile sells electricity to the power grid at the time t, V (t) is 1, and if not, the ith electric automobile is 0; w (t) represents that the ith electric automobile sells electricity to the rest of the electric automobiles at the time t, if the ith electric automobile sells electricity to the rest of the electric automobiles at the time t, w (t) is 1, otherwise, the ith electric automobile is 0;
2) building constraints
Power balance constraint:
in the formula (6), P Load Representing the base load; p EV·Load Represents the charging and discharging load of the electric automobile, and P is the charging state of the electric automobile EV·Load The value is negative, and P is in the discharging state of the electric vehicle EV·Load The value is positive;
state of charge constraint:
0.3≤SOC i (t)≤0.95 (7)
in the formula (7), SOC i (t) represents the state of charge of the ith electric vehicle at time t;
and thirdly, restraint is not carried out when charging and discharging are carried out:
when each electric automobile is optimally scheduled, the charging behavior and the discharging behavior cannot occur at the same time; therefore, the charging flag value
And discharge mark value
Cannot be simultaneously non-0 values;
fourthly, restraining the active power output of each micro source:
P i·min ≤P i (t)≤P i·max (9)
in the formula (9), P i·min 、P i·max Respectively representing the upper limit and the lower limit of the output power of the ith electric automobile;
and 3, carrying out model solution on the established model by utilizing matlab software.
2. The microgrid economic optimization scheduling method based on the running state of the electric vehicle as claimed in claim 1, wherein the specific method for establishing the running state model of the electric vehicle in one day in the step 1 is as follows:
when the electric automobile is parked and the V2G technology can be carried out, the parking available-network interaction state is defined; when the electric automobile is parked and no V2G technical support is arranged around, the parking available-vehicle interaction state is defined when the V2V technical support can be carried out; defining the driving energy consumption state when the electric automobile drives; when the electric automobile is parked and no V2G and V2V technical supports are around, the electric automobile is defined as a parking non-chargeable and dischargeable state.
3. The economic optimization scheduling method for the microgrid based on the running state of the electric vehicle as claimed in claim 1, characterized in that the model solving method in step 3 is as follows: and solving by using a particle swarm algorithm, and repeatedly carrying out optimization operation on the particle swarm so as to obtain a global optimal solution.
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| CN106712093A (en) * | 2017-01-23 | 2017-05-24 | 南京理工大学 | Island parallel operation control method based on high-capacity energy storage system |
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| CN110890763A (en) * | 2019-08-26 | 2020-03-17 | 南京理工大学 | Electric automobile and photovoltaic power generation cooperative scheduling method for limiting charge-discharge state switching |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106712093A (en) * | 2017-01-23 | 2017-05-24 | 南京理工大学 | Island parallel operation control method based on high-capacity energy storage system |
| CN107499165A (en) * | 2017-09-01 | 2017-12-22 | 北京友信宏科电子科技股份有限公司 | A kind of vehicle-mounted All-in-One control device and system based on cascade magnetic coupling technology |
| CN110110895A (en) * | 2019-04-08 | 2019-08-09 | 国网河北省电力有限公司经济技术研究院 | The method and terminal device of electric car Optimized Operation |
| CN110890763A (en) * | 2019-08-26 | 2020-03-17 | 南京理工大学 | Electric automobile and photovoltaic power generation cooperative scheduling method for limiting charge-discharge state switching |
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