CN114722595A - Micro-grid optimized operation method containing power conversion station - Google Patents

Micro-grid optimized operation method containing power conversion station Download PDF

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CN114722595A
CN114722595A CN202210311359.5A CN202210311359A CN114722595A CN 114722595 A CN114722595 A CN 114722595A CN 202210311359 A CN202210311359 A CN 202210311359A CN 114722595 A CN114722595 A CN 114722595A
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宋蓓蓓
陈文韬
程俊超
马妍
姚冲城
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Shanghai Dianji University
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Abstract

The invention provides a micro-grid optimized operation method containing a power conversion station, which comprises the following steps: analyzing a behavior sequence of a vehicle using a charging and replacing station and a charging and replacing demand based on a travel chain of the electric vehicle, and establishing a charging and replacing demand model; establishing a battery charging rule matrix and a vehicle battery replacement demand matrix according to the behavior data set, and performing matching decision of the vehicle and the battery; establishing an economic optimal upper layer model of the power station; and considering that the operating load fluctuation of the power conversion station can generate impact on the microgrid, and establishing a lower-layer model with the aim of minimizing the load fluctuation variance. The method can ensure the operation economy of the power change station and reduce the carbon emission of the micro-grid system in which the power change station is positioned.

Description

Micro-grid optimized operation method containing power conversion station
Technical Field
The invention relates to the technical field of optimal matching of batteries and vehicles, in particular to a micro-grid optimal operation method with a battery replacement station.
Background
At present, the interactive operation of the electric automobile and the power grid mainly takes V2G (vehicle to grid), and as the name suggests, V2G refers to that the electric automobile connected with the power grid is used as a distributed load and power supply, and can release the electric energy stored in a power battery of the electric automobile to the power grid, thereby providing support for optimizing the operation and safety of the power grid. In recent years, a great deal of research has been conducted by academia on the interaction of electric vehicles with the power grid in the V2G mode. The electric automobile is integrally regarded as a movable distributed energy storage unit, and the electric automobile is explained in the aspects of load balance, harmonic pollution, power grid frequency modulation and automobile network communication respectively.
At present, a research on a B2G (battery to grid) mode is also carried out, namely a power battery charging mode is used for replacing a whole vehicle charging mode, a vehicle owner can continue to travel only by replacing a power battery when the electric quantity of the battery is insufficient without waiting at a charging place, the replaced power battery is intensively charged in a large charging and replacing power station, the whole process can be accurately controlled, a power utilization plan of the large charging and replacing power station can be orderly adjusted by matching with the actual operation condition of a power grid, the safe and economic operation of the power grid is guaranteed, and the focus of B2G is decoupling between the electric vehicle and the power battery thereof.
However, the electric vehicle has a fixed departure schedule and a fixed departure route during operation, the operation regularity is strong, but because factors such as weather, route road conditions, driver driving habits and carrying capacity are uncertain, the actual running time and power consumption of the vehicle have certain deviation from the running under the ideal condition in the day ahead, if the sequence problem of changing the electric power of the vehicle in the power changing station and the charging and discharging are carried out in a non-sequential manner, certain economic losses can be generated for the power changing station operating company and the electric vehicle company, and only a uniform matching strategy is not formed for the planning of battery charging and vehicle changing rules.
In the current research, the state of charge of a battery to be charged is generally low, parameters are normally distributed, sufficient electric quantity cannot be provided actually, even over-discharge of the battery may be caused, and the service life of the battery is shortened, while B2G service is provided by selecting the battery being charged, but in practice, the time for full charge is difficult to predict due to frequent charging and discharging of the battery, the service reliability is affected due to insufficient number of fully charged batteries in a charging station, and the difficulty in making a charging plan is increased. In addition, the existing research is insufficient in planning the operating power condition of the power conversion station, the phenomenon of large peak-valley difference fluctuation exists, the impact on a power grid is large, the line damage and the voltage mutation are easily caused, and the potential safety hazard exists.
Disclosure of Invention
Aiming at the defects in the existing image privacy protection technology, the invention aims to provide the microgrid optimization operation method containing the power change station, which can ensure the operation economy of the power change station, reduce the carbon emission of the microgrid system where the power change station is located and realize the double-layer benefits of cost and environment.
In order to solve the problems, the technical scheme of the invention is as follows:
a microgrid operation optimization method comprising a power conversion station, the method comprising the following steps:
analyzing a behavior sequence of a vehicle using a power change station and a power charging and changing demand based on a travel chain of the electric vehicle, and establishing a power charging and changing demand model;
establishing a battery charging rule matrix and a vehicle battery replacement demand matrix according to the behavior data set, and performing matching decision of the vehicle and the battery;
establishing an economic optimal upper layer model of the power station; and
and considering that the impact of the operating load fluctuation of the power conversion station on the microgrid is generated, and establishing a lower-layer model with the minimum load fluctuation variance as a target.
Optionally, the formula for establishing the electric vehicle trip chain model is as follows:
Figure BDA0003568462840000021
wherein, TCtIs the travel chain of the electric vehicle t;
Figure BDA0003568462840000022
the departure place of the electric vehicle t in the ith trip chain is defined;
Figure BDA0003568462840000023
the departure time of the electric vehicle t in the ith trip chain is shown; Δ Mt,iThe driving mileage of the electric vehicle t in the ith trip chain is calculated; delta Tt,iThe driving time of the electric vehicle t in the ith trip chain is set;
Figure BDA0003568462840000024
the arrival place of the electric vehicle t in the ith trip chain is shown;
Figure BDA0003568462840000025
the arrival time of the electric vehicle t in the ith trip chain is shown;
Figure BDA0003568462840000026
the time length of the electric vehicle t after the ith trip link is ended.
Optionally, the SOC formula of the electric vehicle is:
Figure BDA0003568462840000027
wherein: SOCr,tSOC (%) when the electric vehicle r returns to the charging station at the t-th time; SOCr,0Setting an initial value (%) of SOC for the electric vehicle r at the initial time of the simulation process; srIs the nominal mileage of the electric vehicle r; dr,tIs the running distance of the electric vehicle r in the t period; delta SOCr,tThe SOC increment of the electric vehicle r at the t-th time is 0 if the electric vehicle r is not charged or replaced within the t-th time period; etar,tThe battery capacity fade coefficient at the t-th time of the electric vehicle r can be simplified to 0.
Optionally, the SOC of the electric vehicle r returning to the charging point at the t-th time may be obtained according to the SOC state conversion function, and in combination with the travel distance expectation of the next time period, the criterion that may define the battery pack and vehicle charging and battery changing requirement is:
Figure BDA0003568462840000031
wherein，Dr,t+1The expected travel distance of the electric vehicle r at t + 1; lr,t+1And (4) considering whether the electric vehicle r needs to be charged or not and the mileage allowance reserved when the electric vehicle r needs to be charged or replaced.
Optionally, the process of the battery replacement requirement is as follows:
initializing a given electric vehicle departure sequence table, driving energy consumption and initial SOC information;
simulating one-wheel departure, and sequentially starting the departure vehicle and the equipped battery from 1 to netNumbered and the remaining battery backups are in order from net+1To nbpNumbering;
simulating a trip chain according to the departure timetable, and calculating the SOC of a battery carried by each electric vehicle when the electric vehicle returns to the station according to the driving distance and road conditions;
sequentially judging whether the battery pack needs to be charged and whether the vehicle needs to be charged, if the SOC of the battery pack meets the charging requirement, detaching the battery pack, adding the battery pack into the tail end of a standby battery pack sequence for queuing for charging, and simultaneously recording the current SOC information of the battery pack;
the fully charged battery at the front of the standby battery pack sequence is loaded into the electric vehicle, wherein the front and back sequences of the standby battery pack sequence are arranged in an ascending order according to subscripts, the lowest subscript is positioned at the foremost end, the charging is carried out preferentially, and then the vehicle is added into a departure sequence to wait for the next round of departure;
and counting the SOC and time information of the battery pack to be replaced, and combining the scheduling of the quantity and power of the rechargeable batteries and a matching strategy of the subsequent battery charging and replacing requirements to obtain a daily power change curve of the battery charging and replacing station.
Optionally, the step of establishing a battery charging rule matrix and a vehicle battery replacement demand matrix according to the behavior data set, and performing a matching decision specifically includes: and establishing a charging requirement and battery replacement requirement matching index, matching the charging requirement and the battery replacement requirement by adopting a good and bad solution distance method sorting method to obtain a charging and battery replacement matching scheme, and selecting an optimal battery pack and a vehicle with a corresponding number to perform charging and battery replacement operation.
Optionally, the formula of the upper layer model is:
Figure BDA0003568462840000032
wherein:
Figure BDA0003568462840000033
in order to achieve the cost of electricity purchase,
Figure BDA0003568462840000034
for the cost of the configuration of the battery to store energy,
Figure BDA0003568462840000035
in order to participate in the auxiliary service to sell revenue,
Figure BDA0003568462840000036
which is a carbon market income.
Optionally, the formula of the lower layer model is:
Figure BDA0003568462840000037
wherein: pequ,tThe real-time equivalent load power of the power station in the dispatching cycle.
Compared with the prior art, the invention has the beneficial effects that:
1. aiming at the problems of low battery replacement efficiency and influence on economic benefit caused by mismatching of battery replacement and battery charging operations of a battery replacement station vehicle, a battery replacement demand optimal matching method based on good and bad solution sequencing evaluation is provided;
2. establishing an economic optimal model of the operation of the power exchange station, taking charge and discharge power as a decision target, taking vehicle operation as constraint, comparing the economic optimal model with a scene based on demand response and disordered charge from power fluctuation and peak-valley transfer angles, and making an optimal distribution scheme of the charge and discharge power of the power exchange station;
3. aiming at the problems of low economic benefit and large power peak-valley difference in the operation process of a power exchanging station, a strategy model for the power exchanging station to participate in a third-party peak regulation auxiliary service market based on a B2G mode is provided, and an optimal scheduling strategy of the power exchanging station is obtained.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic structural diagram of a microgrid system including a power swapping station provided in an embodiment of the present invention;
fig. 2 is a flow chart of a microgrid optimization operation method including a power swapping station according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for simulating the SOC distribution information of the condensed electric vehicle cluster by sliding according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention provides an optimized operation method of a micro-grid containing a power change station, which is based on a system structure containing the micro-grid containing the power change station as shown in figure 1, can meet the operation rule of a vehicle in the power change station, realize the optimal matching of battery pack charging and vehicle power change, realize the economic target with optimal cost for the power change station, reasonably schedule energy factors in all aspects for the micro-grid where the power change station is located, and realize the reasonable distribution of the power time of the power change station.
Specifically, fig. 2 is a flowchart of a method for optimizing operation of a microgrid including a power conversion station according to an embodiment of the present invention, where the following assumptions are made: 1. the battery pack and the electric vehicle only carry out battery charging and replacing operation at a battery replacing station of the travel chain; 2. all batteries in the battery replacement station have the same specification, and the actual performance difference of each battery is not considered; 3. the battery is installed in a charging cabinet, and full charging operation is carried out under the condition that B2G, carbon quota and peak shaving auxiliary service requirements are met; 4. charging the power battery pack at constant speed and constant power; 5. the running power consumption of the electric vehicle is in direct proportion to the running time, and the charging cabinets and the chargers in the charging station are sufficient; 6. the running speed of the electric vehicle is kept unchanged in the operation process, and after the electric vehicle reaches the battery replacement station, if the battery replacement is carried out, the stay time is the battery replacement time, namely the unloading time, and the electric vehicle immediately enters a charging process after the power battery pack carried by the electric vehicle is replaced; 7. the internal power demand and the power interface capacity of each module of the micro-grid are fixed, and the specific tide and charging and discharging control problem inside the nano-grid are not considered; based on the hypothesis, the optimal operation method of the micro-grid with the power conversion station comprises the following steps:
s1: analyzing a behavior sequence of a vehicle using a charging and battery replacing station and a charging and battery replacing demand based on a trip chain of the electric vehicle, and establishing a charging and battery replacing demand model;
specifically, an electric vehicle travel chain is constructed by referring to related data of a traditional fuel automobile, sliding simulation is carried out on the running electric energy consumption and the electric energy charging and replacing processes of each automobile in the cluster by adopting a Latin hypercube sampling method, and the battery charge state information is aggregated according to the arrival stage operation of the automobiles and the battery charging and replacing transfer process in the charging and replacing process.
Establishing an electric vehicle trip chain model as shown in the following formula (1):
Figure BDA0003568462840000051
wherein, TCtIs the travel chain of the electric vehicle t;
Figure BDA0003568462840000052
the departure place of the electric vehicle t in the ith trip chain is defined;
Figure BDA0003568462840000053
the departure time of the electric vehicle t in the ith trip chain is shown; Δ Mt,iThe driving mileage of the electric vehicle t in the ith trip chain is obtained; delta Tt,iThe driving time of the electric vehicle t in the ith trip chain is set;
Figure BDA0003568462840000054
the arrival place of the electric vehicle t in the ith trip chain is shown;
Figure BDA0003568462840000055
the arrival time of the electric vehicle t in the ith trip chain is shown;
Figure BDA0003568462840000056
the time length of the electric vehicle t after the ith trip link is ended.
Specifically, the arrival place of the previous trip chain is the departure place of the next trip chain; the driving mileage of the ith trip chain of the electric vehicle is a function of departure time, departure place and arrival place; the travel time of the ith trip chain of the electric vehicle is a function of departure time, a place and a station arrival place; the arrival time of the ith trip chain of the electric vehicle is the ith departure time plus the running time of the ith trip chain; the departure time of the ith +1 th trip chain of the electric vehicle is the arrival time of the ith trip chain plus the stopping time after the ith trip chain is ended, and the formulas are respectively shown as the following formula (2):
Figure BDA0003568462840000057
wherein the content of the first and second substances,
Figure BDA0003568462840000061
a mileage function for completing the ith behavior process for the electric vehicle t;
Figure BDA0003568462840000062
and (4) performing the ith action process for the electric vehicle t.
According to the simplified electric vehicle trip chain model, a model describing the battery charging and replacing demand by three key indexes of the starting time of the returning process, the running distance of the returning process and the arrival time of the returning process is established, and the samples are screened and sorted by adopting a Latin hypercube sampling method more suitable for small sample quantity.
The SOC variation of the electric vehicle is an accumulated process along with the driving power consumption and the power charging and discharging cycles during the parking, that is, the SOC of the electric vehicle is determined by the trip chain of the electric vehicle in a long operation period (for example, one day, one week), and can be expressed as the following formula (3):
Figure BDA0003568462840000063
therein, SOCr,tThe SOC (%) when the electric vehicle r returns to the charging station at the t-th time; SOCr,0Setting an initial value (%) of SOC for the electric vehicle r at the initial time of the simulation process; srIs the nominal range of the electric vehicle r; dr,tIs the running distance of the electric vehicle r in the t period; delta SOCr,tThe SOC increment of the electric vehicle r at the t-th time is 0 if the electric vehicle r is not charged or replaced within the t-th time period; etar,tThe battery capacity fade coefficient at the t-th time of the electric vehicle r can be simplified to 0.
The SOC of the electric vehicle r returning to the charging point at the t-th time can be obtained according to the SOC state conversion function, and the criterion for defining the battery pack and vehicle charging and battery changing requirements is shown in formula (4) in combination with the travel distance expectation of the next time period:
Figure BDA0003568462840000064
wherein D isr,t+1Is the expected travel distance of the electric vehicle r at t + 1; lr,t+1And (4) considering whether the electric vehicle r needs to be charged or not and the mileage allowance reserved when the electric vehicle r needs to be charged or replaced.
After the above relationship is clearly defined, a method of aggregating the electric vehicle cluster SOC distribution information by sliding simulation based on the established electric vehicle trip chain is shown in fig. 3. From a given initial value of charge (SOC)r,0) And starting to perform sliding cycle simulation on the processes of electric energy consumption and electric energy charging and replacing for each individual vehicle in the electric vehicle cluster. For simplicity of analysis, assume a battery change mode or charge-up (until full)
Figure BDA0003568462840000065
)。
In the sliding simulation process, gradually obvious characteristics of the electric vehicle cluster rule can be captured and stored, so that SOC distribution which is more in line with the characteristics of the electric vehicle cluster to be researched is obtained. For example, the SOC distribution of the simulated electric vehicle cluster at each moment is stored in units of days, and after each day is finished, the SOC distribution rule characteristics of the electric vehicle cluster at any two days in the last k days are compared by using indexes such as the expectation, variance and variance coefficient distribution of the SOC at each moment. If the SOC distribution variation between days is controlled within a certain threshold range within k consecutive days, it is considered that the SOC distribution information of the sample electric vehicle group has already become stable. At this time, from the perspective of the electric vehicle cluster, the current SOC aggregation distribution can be considered to conform to the relevant characteristics of the electric vehicle cluster under study, so that the SOC aggregation distribution can be applied to the modeling of the charging and replacing demand, and the reasonability and accuracy of the result can be improved.
Defining the total number of power batteries in the station as nbpGroup, total number of electric vehicles netAnd s is the spare battery number (SBP) obtained by the difference of the two, and the flow of the battery replacement requirement is as follows:
initializing a given electric vehicle departure sequence table, driving energy consumption, initial SOC and other information;
simulating one-wheel departure, and sequentially starting the departure vehicle and the equipped battery from 1 to netNumbered and the remaining battery backups are in order from net+1To nbpNumbering;
simulating a trip chain according to the departure schedule, and calculating the SOC of a battery loaded when each electric vehicle returns to the station according to the driving distance and road conditions;
sequentially judging whether the battery pack needs to be charged and whether the vehicle needs to be charged, if the SOC of the battery pack meets the charging requirement, detaching the battery pack, adding the battery pack into the tail end of a standby battery pack sequence for queuing for charging, and simultaneously recording the current SOC information of the battery pack;
the fully charged battery at the front of the standby battery pack sequence is loaded into the electric vehicle, wherein the front and the back of the standby battery pack sequence are arranged in an ascending order according to subscripts, the lowest subscript is positioned at the foremost end and is charged preferentially, and then the vehicle is added into a departure sequence to wait for the next departure;
and counting the SOC and the time information of the battery pack to be replaced, and combining the scheduling of the number and the power of the rechargeable batteries and a matching strategy of the subsequent battery charging and replacing requirements, so that a daily power change curve of the battery charging and replacing station can be obtained.
S2: establishing a battery charging rule matrix and a vehicle battery replacement demand matrix according to the behavior data set, and performing matching decision of the vehicle and the battery;
specifically, a charging demand and battery replacement demand matching index is established, the charging demand and the battery replacement demand are matched by adopting a good-bad solution distance method sorting method to obtain a charging and battery replacement matching scheme, and an optimal battery pack and a vehicle with a corresponding number are selected to perform charging and battery replacement operation.
Different from the characteristic that vehicles correspond to battery packs one to one in an electric vehicle charging mode, the vehicles are separated from the battery packs in a battery changing mode, and the corresponding relation influences the charging scheduling space of each battery pack, so that the safety and the economy of a charging scheme in a station are influenced. Therefore, the vehicle battery changing demand and battery pack charging demand matching model is provided based on the vehicle battery changing demand and battery pack charging demand model of the battery charging and changing station: and screening out the optimal battery pack charging requirements from the battery pack charging requirements for matching according to different vehicle battery changing requirements. The matching model of the vehicle battery replacement requirement and the battery pack charging requirement mainly comprises two parts: (1) establishing a matching index; (2) and (3) providing a matching strategy based on an entropy weight-TOPSIS method, and obtaining the matching between the optimal rechargeable battery and the vehicle battery replacement according to the rules.
S3: establishing an economic optimal upper layer model of the power station;
specifically, an economic optimal upper-layer model of the battery replacement station is established, and a power distribution curve of the battery replacement station is obtained by taking the electricity purchase cost, the battery loss cost, the battery configuration cost, the carbon quota income and the income of participating in the peak regulation auxiliary service into consideration and using the vehicle operation limit and the power balance of other output units in the microgrid as constraints.
F1Is the daily operating cost of the fleet of vehicles,
Figure BDA0003568462840000081
Figure BDA0003568462840000082
Figure BDA0003568462840000083
Figure BDA0003568462840000084
wherein:
Figure BDA0003568462840000085
in order to achieve the cost of electricity purchase,
Figure BDA0003568462840000086
for the cost of the configuration of the battery to store energy,
Figure BDA0003568462840000087
in order to participate in the auxiliary service to sell revenue,
Figure BDA0003568462840000088
for carbon market income, fp,timeAs a function of time of day power price, NyIs the number of days of the year, NcFor the life of the battery, CinFor the cost of battery purchase, SVIs the remaining value of the battery, M is the total number of vehicles operated by the company on the current day.
And introducing a B2G mode, and taking the battery replacement battery as energy storage to participate in peak-valley scheduling of the power grid. And taking the power conversion station as a third party main body to participate in the power conversion and peak regulation auxiliary service market and participate in the excitation type demand response.
S4: and considering that the impact of the operating load fluctuation of the power conversion station on the microgrid is generated, and establishing a lower-layer model with the minimum load fluctuation variance as a target.
Specifically, F2Is a lower layer target load fluctuation function and is composed of real-time load in a scheduling period TLoad and variance of average load.
Figure BDA0003568462840000089
Figure BDA00035684628400000810
Pequ,tThe real-time equivalent load power of the power station in the dispatching cycle.
In order to ensure that the operation cost after the lower layer optimization is consistent with the upper layer optimization result, constraint conditions are added on the basis of the upper layer constraint conditions: the charging and discharging amounts in each time period are consistent, and the battery loss in each time period is consistent.
And comparing the peak-valley changes of the obtained upper-layer power curve and the lower-layer power curve, and comparing the operation cost of the power conversion station, the battery loss cost, the carbon quota income, the income of the peak-shaving auxiliary service market, the consumption of wind energy, the load variance and the carbon emission before and after the optimization of the upper layer and the lower layer, so as to verify the economy and the low carbon performance of the model.
The double-layer optimization model is established, because the solution after the upper-layer optimization is not smooth and stable, the influence of the power utilization behavior habit of a power purchasing party, namely a fleet operation company along with time is caused, the phenomenon that the load of a charging and replacing power station fluctuates along with three periods of peak, valley and level possibly causes large fluctuation of the load of a power grid, and the introduction of the lower-layer optimization model is considered. The decision variable optimized by the lower layer model is the charge and discharge power, and on the basis of the upper optimization, the constraint conditions such as load variance, power limitation and the like are added to further constrain the charge and discharge power of the battery of the power conversion station by taking the minimum power grid load fluctuation caused by the operation of the power station as a target.
Compared with the prior art, the invention has the beneficial effects that:
1. aiming at the problems of low battery replacement efficiency and influence on economic benefit caused by mismatching of battery replacement and battery charging operations of a battery replacement station vehicle, a battery replacement demand optimal matching method based on good and bad solution sequencing evaluation is provided;
2. establishing an economic optimal model of the operation of the battery replacement station, taking charge and discharge power as a decision target, taking vehicle operation as constraint, comparing the economic optimal model with a scene based on demand response and disordered charging from power fluctuation and peak-valley transfer angles, and making an optimal distribution scheme of the charge and discharge power of the battery replacement station;
3. aiming at the problems of low economic benefit and large power peak-valley difference in the operation process of a power exchanging station, a strategy model for the power exchanging station to participate in a third-party peak regulation auxiliary service market based on a B2G mode is provided, and an optimal scheduling strategy of the power exchanging station is obtained.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (8)

1. A microgrid optimal operation method comprising a power swapping station is characterized by comprising the following steps:
analyzing a behavior sequence of a vehicle using a charging and replacing station and a charging and replacing demand based on a travel chain of the electric vehicle, and establishing a charging and replacing demand model;
establishing a battery charging rule matrix and a vehicle battery replacement demand matrix according to the behavior data set, and making a matching decision between the vehicle and the battery;
establishing an economic optimal upper layer model of the power station; and
and considering that the impact of the operating load fluctuation of the power conversion station on the microgrid is generated, and establishing a lower-layer model with the minimum load fluctuation variance as a target.
2. The microgrid optimal operation method with a power swapping station as claimed in claim 1, wherein the formula for establishing the electric vehicle trip chain model is as follows:
Figure FDA0003568462830000011
wherein, TCtIs the trip chain of the electric vehicle t;
Figure FDA0003568462830000012
the departure place of the electric vehicle t in the ith trip chain is defined;
Figure FDA0003568462830000013
the departure time of the electric vehicle t in the ith trip chain is shown; Δ Mt,iThe driving mileage of the electric vehicle t in the ith trip chain is obtained; delta Tt,iThe driving time of the electric vehicle t in the ith trip chain is set;
Figure FDA0003568462830000014
the arrival place of the electric vehicle t in the ith trip chain is shown;
Figure FDA0003568462830000015
the arrival time of the electric vehicle t in the ith trip chain is shown;
Figure FDA0003568462830000016
the time length of the electric vehicle t after the ith trip link is ended.
3. The microgrid optimized operation method with a power conversion station according to claim 2, wherein the SOC formula of the electric vehicle is as follows:
Figure FDA0003568462830000017
wherein: SOCr,tSOC (%) when the electric vehicle r returns to the charging station at the t-th time; SOCr,0Setting an initial value of SOC for the electric vehicle r at the initial time of the simulation process; srIs the nominal range of the electric vehicle r; dr,tIs the running distance of the electric vehicle r in the t period; delta SOCr,tThe SOC increment of the electric vehicle r at the t-th time is 0 if the electric vehicle r is not charged or replaced within the t-th time period; etar,tFor electric vehicles r at tThe battery capacity fading coefficient of (2) can be simplified to 0.
4. The method as claimed in claim 3, wherein the SOC of the electric vehicle r returning to the charging point at the t-th time is obtained according to the SOC state conversion function, and the criterion for defining the battery pack and vehicle charging and battery replacement requirements is defined by combining the trip distance expectation of the next time period:
Figure FDA0003568462830000021
wherein D isr,t+1The expected travel distance of the electric vehicle r at t + 1; lr,t+1And (4) considering whether the electric vehicle r needs to be charged or not and the mileage allowance reserved when the electric vehicle r needs to be charged or replaced.
5. The microgrid optimal operation method containing a power swapping station as claimed in claim 4, wherein the flow of the power swapping requirement is as follows:
initializing an departure sequence table, driving energy consumption and initial SOC information of a given electric vehicle;
simulating one-round departure, and sequentially starting the departure vehicle and the equipped battery from 1 to netNumbered and the remaining battery backups are in order from net+1To nbpNumbering;
simulating a trip chain according to the departure schedule, and calculating the SOC of a battery loaded when each electric vehicle returns to the station according to the driving distance and road conditions;
sequentially judging whether the battery pack needs to be charged and whether the vehicle needs to be charged, if the SOC of the battery pack meets the charging requirement, detaching the battery pack, adding the battery pack into the tail end of a standby battery pack sequence for queuing for charging, and simultaneously recording the current SOC information of the battery pack;
the fully charged battery at the front of the standby battery pack sequence is loaded into the electric vehicle, wherein the front and the back of the standby battery pack sequence are arranged in an ascending order according to subscripts, the lowest subscript is positioned at the foremost end and is charged preferentially, and then the vehicle is added into a departure sequence to wait for the next round of departure;
and counting the SOC and the time information of the battery pack to be replaced, and combining the scheduling of the number and the power of the rechargeable batteries and a matching strategy of the subsequent battery charging and replacing requirements, so that a daily power change curve of the battery charging and replacing station can be obtained.
6. The microgrid optimal operation method comprising a swapping station as claimed in claim 1, wherein the step of establishing a battery charging rule matrix and a vehicle swapping demand matrix according to the behavior data set and performing a matching decision specifically comprises: and establishing a charging requirement and battery replacement requirement matching index, matching the charging requirement and the battery replacement requirement by adopting a good and bad solution distance method sorting method to obtain a charging and battery replacement matching scheme, and selecting an optimal battery pack and a vehicle with a corresponding number to perform charging and battery replacement operation.
7. The microgrid optimized operation method with a swapping station as claimed in claim 1, wherein the formula of the upper model is as follows:
Figure FDA0003568462830000022
wherein:
Figure FDA0003568462830000023
in order to achieve the cost of electricity purchase,
Figure FDA0003568462830000024
for the cost of the configuration of the battery to store energy,
Figure FDA0003568462830000025
in order to participate in the ancillary service in selling revenue,
Figure FDA0003568462830000026
which is a carbon market income.
8. The microgrid optimized operation method with a swapping station as claimed in claim 1, wherein the formula of the lower layer model is as follows:
Figure FDA0003568462830000031
wherein: pequ,tThe real-time equivalent load power of the power station in the dispatching cycle.
CN202210311359.5A 2022-03-28 2022-03-28 Micro-grid optimized operation method containing power conversion station Pending CN114722595A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116720714A (en) * 2023-08-07 2023-09-08 北京玖行智研交通科技有限公司 Intelligent scheduling method and device for charging and power changing of electric vehicle
CN116760030A (en) * 2023-08-16 2023-09-15 天津港电力有限公司 Low-carbon port micro-grid load regulation and control economic optimization method considering logistics characteristics
CN117608306A (en) * 2024-01-23 2024-02-27 上海邻里邻外信息科技有限公司 AGV trolley control system and method for mobile charging and vehicle moving

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116720714A (en) * 2023-08-07 2023-09-08 北京玖行智研交通科技有限公司 Intelligent scheduling method and device for charging and power changing of electric vehicle
CN116720714B (en) * 2023-08-07 2023-10-20 北京玖行智研交通科技有限公司 Intelligent scheduling method and device for charging and power changing of electric vehicle
CN116760030A (en) * 2023-08-16 2023-09-15 天津港电力有限公司 Low-carbon port micro-grid load regulation and control economic optimization method considering logistics characteristics
CN116760030B (en) * 2023-08-16 2023-10-31 天津港电力有限公司 Low-carbon port micro-grid load regulation and control economic optimization method considering logistics characteristics
CN117608306A (en) * 2024-01-23 2024-02-27 上海邻里邻外信息科技有限公司 AGV trolley control system and method for mobile charging and vehicle moving
CN117608306B (en) * 2024-01-23 2024-03-29 上海邻里邻外信息科技有限公司 AGV trolley control system and method for mobile charging and vehicle moving

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