CN116029453A - Electric automobile charging pile configuration method, recording medium and system - Google Patents
Electric automobile charging pile configuration method, recording medium and system Download PDFInfo
- Publication number
- CN116029453A CN116029453A CN202310127683.6A CN202310127683A CN116029453A CN 116029453 A CN116029453 A CN 116029453A CN 202310127683 A CN202310127683 A CN 202310127683A CN 116029453 A CN116029453 A CN 116029453A
- Authority
- CN
- China
- Prior art keywords
- charging
- cost
- micro
- grid
- electric vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 12
- 239000002245 particle Substances 0.000 claims abstract description 12
- 238000012546 transfer Methods 0.000 claims abstract description 12
- 238000009826 distribution Methods 0.000 claims description 142
- 230000005611 electricity Effects 0.000 claims description 89
- 230000009194 climbing Effects 0.000 claims description 57
- 238000004146 energy storage Methods 0.000 claims description 28
- 238000010276 construction Methods 0.000 claims description 19
- 238000012423 maintenance Methods 0.000 claims description 19
- 239000003344 environmental pollutant Substances 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 18
- 231100000719 pollutant Toxicity 0.000 claims description 18
- 238000003860 storage Methods 0.000 claims description 13
- 239000000446 fuel Substances 0.000 claims description 12
- 238000010248 power generation Methods 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 6
- 238000009434 installation Methods 0.000 claims description 6
- 230000010354 integration Effects 0.000 claims description 3
- 238000004378 air conditioning Methods 0.000 claims 2
- 230000002123 temporal effect Effects 0.000 claims 1
- 238000005457 optimization Methods 0.000 abstract description 9
- 230000007246 mechanism Effects 0.000 abstract description 2
- 230000001052 transient effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 8
- 238000000342 Monte Carlo simulation Methods 0.000 description 7
- 230000002776 aggregation Effects 0.000 description 7
- 238000004220 aggregation Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 7
- 238000005265 energy consumption Methods 0.000 description 6
- 230000029305 taxis Effects 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 230000002411 adverse Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000031700 light absorption Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000004513 sizing Methods 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention belongs to the technical field of electric automobile charging, and discloses an electric automobile charging pile configuration method, which is characterized in that a user and a micro-grid operator are layered based on an electric automobile charging load prediction model, a multi-target double-layer optimization scheduling method is adopted, the running efficiency of a micro-grid layer is fully considered on the basis of ensuring the charging efficiency of the user, the space-time transfer of the electric automobile charging requirement is realized by utilizing a pricing mechanism, the model is solved by utilizing a particle swarm algorithm, the limitation of the traditional optimization method is overcome, and the optimizing performance is improved. The invention also provides a non-transient readable recording medium storing the configuration program of the charging pile of the electric automobile and a system comprising the medium, and the processing circuit can call the program to execute the method, thereby being applicable to the configuration work of the charging pile of the electric automobile.
Description
Technical Field
The invention belongs to the technical field of electric automobile charging, and particularly discloses an electric automobile charging pile configuration method, a non-transitory readable recording medium and a data processing system.
Background
Along with the continuous consumption of fossil energy and the increasing severity of environmental problems caused by the continuous consumption of fossil energy, an electric automobile is widely focused and applied as a low-carbon and environment-friendly travel tool. The charging pile is used as a medium for coupling the load of the electric vehicle and the micro-grid, and the quick and slow charging configuration mode and capacity of the charging pile have great influence on the safe and stable operation of the grid and the travel of the electric vehicle. Therefore, the influence mechanism of the large-scale aggregation charging of the electric vehicle on the climbing performance requirement of the micro-grid power supply, the load fluctuation characteristic and the charging efficiency of the electric vehicle is analyzed, the optimal configuration of the fast/slow charging piles in the multi-micro-grid is realized by utilizing the technical means, and the method has very important practical significance for ensuring the safe and stable operation of the micro-grid and improving the charging efficiency of the electric vehicle.
At present, on one hand, a large number of students develop researches on the space-time distribution of the electric vehicle load, but the respective operation characteristics of the quick charge and the slow charge cannot be deeply analyzed, the time shifting characteristic of the multi-area slow charge and the space transfer characteristic of the quick charge are not fully considered, so that the formed electric vehicle load model has high distortion degree, and a good reference basis cannot be provided for the quantity and the capacity of the quick charge and the slow charge of the charging pile; on the other hand, a great deal of researches are carried out on the problem of locating and sizing the charging station, but charging piles are configured according to the space-time distribution of the charging demands of users in the related researches, the influence of the aggregation characteristics of the electric vehicles caused by the position and capacity configuration of the charging piles on the safety and stability of the running of the micro-grid cannot be fully considered, and the charging of the users of the electric vehicles and the optimal running efficiency of the micro-grid cannot be realized. Therefore, the charging characteristics and configuration capacity of the charging piles in the multi-micro-grid area are not fully considered in the prior art, so that the charging piles are not matched in quick/slow charging configuration mode and capacity, and the charging efficiency of the electric vehicle and the operation efficiency of the micro-grid are low.
Disclosure of Invention
In order to solve the background art, the invention provides a configuration method of an electric vehicle charging pile, which comprises the following steps:
s1, analyzing a plurality of factors, wherein the factors influence the charging mode, the charging place or the charging time of an electric vehicle, simulating and generating a fast/slow charging function of the electric vehicle in a region to be researched, and describing initial space-time distribution characteristics of the electric vehicle load;
s2, establishing an electric vehicle load space-time distribution rule model by taking the highest charging efficiency of a user as a target and combining the electricity price and the initial space-time distribution characteristics;
s3, determining constraint conditions in the electric vehicle load space-time distribution rule model, wherein the constraint conditions comprise the electric vehicle storage battery state of charge, the upper limit and the lower limit of electricity price and charging time;
s4, calculating fast charge and slow charge loads of the electric vehicles in the micro-grids of all operators in jurisdictions, and establishing a net load model of each micro-grid, wherein the net load model describes risks faced by the micro-grids when the charging loads of the electric vehicles are aggregated according to the net load fluctuation amplitude and the climbing performance demand;
s5, determining the output and climbing constraint conditions of the micro-grid power generation unit and the power balance constraint conditions of each region in the net load model;
S6, integrating the characteristics of the electric vehicle load space-time distribution rule model and the net load model, and iteratively adjusting the electricity price of the electric vehicle for fast/slow charging to transfer the space-time distribution of the electric vehicle load demand with the aim of improving the running efficiency of the micro-grid;
s7, substituting the adjusted electricity price into a net load model, wherein the charging load of the electric automobile is represented by the capacity and the quantity of the fast/slow charging piles, and solving the electric automobile by a multi-target particle swarm algorithm to obtain an optimal configuration scheme of the charging piles of the electric automobile.
Preferably, the electric automobile load space-time distribution rule model characterizes the user charging efficiency according to user satisfaction, wherein the user satisfaction is the sum of travel satisfaction and charging cost satisfaction, and the specific expression is as follows:
wherein f is an objective function;
travel satisfaction and charging cost satisfaction of the electric automobile j to the charging station i are respectively; d (D) max and Dmin The farthest distance and the nearest distance of the electric vehicle in the road network from the charging demand node to the charging station are respectively; />
The distance from the electric automobile j to the charging station i at the moment t; />
and />
The highest charge cost and the lowest charge cost of the corresponding vehicle model of the charging station i are respectively; />
The charging electricity price of the electric automobile j at the charging station i at the moment t; / >
For the j-th vehicle>
Percentage of remaining charge at the moment of arrival at the charging station.
Preferably, the payload model comprises:
micro-grid net load fluctuation minimum submodel:
wherein ,
in the formula ,
for each micro-grid net load mean value +.>
Is->
Respectively the charging power of the electric automobile at the time t, the power of the basic load, the power of the energy storage equipment and the output of the wind/light unit,
and />
For transmission of parts in a scheduling periodAverage value of power; t represents a scheduling period, herein a time period in units of 1h, i.e., t=24;
micro-grid comprehensive operation cost minimum submodel:
in the formula ,S2 The comprehensive operation cost of the micro-grid is; s is S i,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region; s is S i,2 The running cost of the output unit of each area is calculated; s is S i,3 Climbing cost for the power output units of the micro-grids in each region;
in the formula ,Si,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region;
is->
The price and the installation quantity of the quick/slow charging piles are respectively, n is the operation life of the charging piles, d is the discount rate, and eta is the percentage of the operation maintenance cost to the investment cost;
in the formula ,
is->
The running cost of the diesel engine set, the running cost of the energy storage equipment and the running cost of the power distribution network tie line of each regional micro-grid are respectively; / >
Is->
The operation and maintenance cost, the fuel cost and the environmental treatment cost of the diesel engine set are respectively; c (C) de,om Maintenance factor for the operation of a diesel engine system, +.>
For i region t moment diesel engine unit output power, a, b and C are fuel coefficients, C k 、λ de,k The cost for treating the k-type pollutant and the emission amount for generating the k-type pollutant during operation are respectively; c (C) ES,om and />
Respectively obtaining an operation cost coefficient of the energy storage device and charging and discharging power of the energy storage device at the moment of the region t; />
and />
The method is characterized in that the distribution network interconnecting line electric energy transaction cost and the distribution network environment treatment cost are respectively +.>
For the electricity price of the distribution network at time t, +.>
For the power of the interconnection line of the distribution network, the positive and negative values of the power respectively represent the buying and selling electric quantity of the micro-grid to the main network, lambda DN,k Generating the discharge amount of the kth pollutant for the operation of the distribution network interconnecting line;
in the formula ,
and />
The climbing cost and the tie climbing cost of the micro-grid diesel engine unit in each region are respectively; c (C) de and CDN The cost coefficient of the output climbing of the diesel engine set and the power distribution network tie line is +.>
and />
The power is transmitted by the diesel engine set and the power distribution network tie line at the moment t of each regional micro-grid respectively; />
Spare cost coefficients are rotated for distribution network tie lines.
Preferably, the method for transferring the space-time distribution of the load demand of the electric automobile comprises the steps of transferring the time distribution of the load through slow charge delay; and transferring the spatial distribution of the load through the fast-charged point position switching.
The invention further provides an electric vehicle charging pile configuration system, which comprises the following functional modules:
the charging demand analysis module is used for analyzing a plurality of factors, wherein the factors influence the charging mode, the charging place or the charging time of the electric vehicle, simulate and generate a fast/slow charging function of the electric vehicle in the area to be researched, and describe the initial space-time distribution characteristics of the electric vehicle load;
the charging demand model building module is used for building an electric vehicle load space-time distribution rule model by taking the highest charging efficiency of a user as a target and combining the electricity price and the initial space-time distribution characteristics; determining constraint conditions in the electric vehicle load space-time distribution rule model, wherein the constraint conditions comprise the state of charge of a storage battery of the electric vehicle, the upper limit and the lower limit of electricity price and charging time;
the micro-grid load model construction module is used for calculating the fast charge and slow charge of the electric vehicle in the micro-grid of each operator district and establishing a net load model of each micro-grid, wherein the net load model describes the risk faced by the micro-grid when the charging load of the electric vehicle is gathered according to the net load fluctuation range and the climbing performance demand; determining the output and climbing constraint conditions of the micro-grid power generation unit and the power balance constraint conditions of each region in the net load model;
The power price adjusting module is used for integrating the characteristics of the electric vehicle load space-time distribution rule model and the net load model description, iteratively adjusting the power price of the electric vehicle for quick/slow charging with the aim of improving the running efficiency of the micro-grid, and transferring the space-time distribution of the electric vehicle load demands;
and the scheme integration module is used for substituting the adjusted electricity price into a net load model, wherein the electric vehicle charging load is represented by the capacity and the number of the fast/slow charging piles, and the optimal configuration scheme of the electric vehicle charging piles is obtained through solving by a multi-target particle swarm algorithm.
Preferably, an electric vehicle load space-time distribution rule model is built in the charging demand model building module; the electric automobile load space-time distribution rule model characterizes user charging efficiency according to user satisfaction, wherein the user satisfaction is the sum of travel satisfaction and charging cost satisfaction, and the specific expression is as follows:
wherein f is an objective function;
travel satisfaction and charging cost satisfaction of the electric automobile j to the charging station i are respectively; d (D) max and Dmin The farthest distance and the nearest distance of the electric vehicle in the road network from the charging demand node to the charging station are respectively; />
The distance from the electric automobile j to the charging station i at the moment t; / >
and />
The highest charge cost and the lowest charge cost of the corresponding vehicle model of the charging station i are respectively; />
The charging electricity price of the electric automobile j at the charging station i at the moment t; />
For the j-th vehicle>
Percentage of remaining charge at the moment of arrival at the charging station.
Preferably, a payload model of each micro-grid is built in the micro-grid load model building module, and the payload model comprises:
micro-grid net load fluctuation minimum submodel:
wherein ,
in the formula ,
for each micro-grid net load mean value +.>
Is->
Respectively the charging power of the electric automobile at the time t, the power of the basic load, the power of the energy storage equipment and the output of the wind/light unit,
and />
An average value of transmission power of each part in a scheduling period; t represents a scheduling period, herein a time period in units of 1h, i.e., t=24;
micro-grid comprehensive operation cost minimum submodel:
in the formula ,S2 The comprehensive operation cost of the micro-grid is; s is S i,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region; s is S i,2 The running cost of the output unit of each area is calculated; s is S i,3 Climbing cost for the power output units of the micro-grids in each region;
in the formula ,Si,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region;
is->
The price and the installation quantity of the quick/slow charging piles are respectively, n is the operation life of the charging piles, d is the discount rate, and eta is the percentage of the operation maintenance cost to the investment cost;
in the formula ,
is->
The running cost of the diesel engine set, the running cost of the energy storage equipment and the running cost of the power distribution network tie line of each regional micro-grid are respectively; />
Is->
The operation and maintenance cost, the fuel cost and the environmental treatment cost of the diesel engine set are respectively; c (C) de,om Maintenance factor for the operation of a diesel engine system, +.>
For i region t moment diesel engine unit output power, a, b and C are fuel coefficients, C k 、λ de,k The cost for treating the k-type pollutant and the emission amount for generating the k-type pollutant during operation are respectively; c (C) ES,om and />
Respectively obtaining an operation cost coefficient of the energy storage device and charging and discharging power of the energy storage device at the moment of the region t; />
and />
The method is characterized in that the distribution network interconnecting line electric energy transaction cost and the distribution network environment treatment cost are respectively +.>
For the electricity price of the distribution network at time t, +.>
For the power of the interconnection line of the distribution network, the positive and negative values of the power respectively represent the buying and selling electric quantity of the micro-grid to the main network, lambda DN,k Is a power distribution network tie-lineOperation produces emissions of type k pollutants;
in the formula ,
and />
The climbing cost and the tie climbing cost of the micro-grid diesel engine unit in each region are respectively; c (C) de and CDN The cost coefficient of the output climbing of the diesel engine set and the power distribution network tie line is +.>
and />
The power is transmitted by the diesel engine set and the power distribution network tie line at the moment t of each regional micro-grid respectively; / >
Spare cost coefficients are rotated for distribution network tie lines.
Preferably, the electricity price adjustment module relates to a method for transferring the space-time distribution of the load demand of the electric automobile, and the method for transferring the space-time distribution of the load demand of the electric automobile comprises the step of transferring the time distribution of the load through slow charge delay; and transferring the spatial distribution of the load through the fast-charged point position switching.
Another aspect of the present invention is to provide a non-transitory readable recording medium storing one or more programs including a plurality of instructions, which when executed, cause a processing circuit to perform steps included in the method for configuring a charging pile for an electric vehicle.
The present invention also provides a data processing device, including a processing circuit and a memory electrically coupled thereto, wherein the memory is configured to store at least one program, the program includes a plurality of instructions, and the processing circuit runs the program to execute the steps included in the method for configuring the charging pile of the electric vehicle.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the method, the charge demand space-time distribution of different types of electric vehicles is fully considered, the electric vehicle charge load prediction model is established, the change situation of the charge demand of the electric vehicles is more accurately described through the charge scheduling strategy of the electric vehicles, the charge demand of each region is optimized by utilizing the operation characteristics of the charge of the electric vehicles, and the formed electric vehicle load model can well provide reference for the configuration capacity of the charge pile.
(2) The method comprises the steps of layering users and micro-grid operators structurally, adopting a multi-target double-layer optimization scheduling method, fully considering the running efficiency of the micro-grid layer on the basis of guaranteeing the charging efficiency of the users, coupling the power climbing risk brought by the huge impact of electric vehicle load to the micro-grid into the formulation of charging electricity price, fully absorbing the wind/light output of each region by utilizing the space-time transfer characteristic of the electric vehicle, reducing the climbing output of each region unit, and improving the charging efficiency of the electric vehicle users and the running efficiency of the multi-micro-grid.
(3) According to the method, charging stations in different areas are selected in a fast charging mode, and charging time in different areas is selected in a slow charging mode to serve as variables for simulation, a particle swarm algorithm is used for solving a model, the defect that an optimization method and the like are prone to being involved in local optimal solutions is overcome, and optimizing performance is improved.
Drawings
FIG. 1 is a frame diagram of an embodiment of the present invention;
FIG. 2 is a road network diagram of an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an embodiment of the present invention;
FIG. 4 is a graph showing the comparison of the fast/slow charge and the net load of each region before and after optimization in the embodiment of the invention;
FIG. 5 shows dynamic electricity prices of the micro-grids in each region according to the embodiment of the present invention;
Fig. 6 is a graph showing a comparison of charging efficiency in the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention, where the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are obtained by a person skilled in the art without innovative work, are intended to be within the scope of the invention.
As shown in fig. 1, an embodiment of a method for configuring a charging pile of an electric automobile includes the following steps:
various factors influencing the charging mode, the charging place and the charging time of the electric vehicle are analyzed, the quick/slow charging load of the electric vehicle in each area is generated by Monte Carlo simulation, and the initial space-time distribution characteristics of the electric vehicle load are described;
the invention mainly researches the charging load demands of private cars, taxis and buses of class 3. Specifically analyzing the selection of the charging mode, the charging place and the charging time of the corresponding type of vehicle, and setting various parameters of the load prediction model based on the selection;
charging demand initial time: the charging loads of different types of electric automobiles have randomness in time and space, and according to NHTS statistical analysis, the charging demand initial moments of 3 types of user groups are approximately considered to meet normal distribution in different time periods, and the charging states of the charging demand initial moments also meet normal distribution:
Wherein t is the initial moment of the charging demand, i.e. the moment when the electric vehicle user considers to go to the charging station; sigma (sigma) s and μs (s＝1,2),σ 1 and μ1 For the expected and standard deviation, sigma, of the initial moment of charge demand 2 and μ2 The expected and standard deviations of the state of charge at the initial moment of the charging demand, respectively.
As shown in fig. 2, the road network nodes 1-12 are residential areas, wherein 6 nodes are residential area micro-grid charging station nodes; the road network nodes 13-19 are commercial areas, wherein the 14 nodes are commercial area micro-grid charging station nodes; the road network nodes 20-25 are office areas, wherein 6 nodes are office area micro-grid charging station nodes;
private car load-most users of private cars have charging demands at different time periods and at different places. The charging sites can be in working areas, business areas and residential areas, and the charging probabilities of the working areas, the business areas and the residential areas are respectively set to be 0.4, 0.2 and 0.4. In the working area, the charging probability of 0.2 is that slow charging is selected from 06:00-18:00, the probability of 0.2 is that fast charging is selected from 07:00-11:00 and 12:00-16:00, and the initial moments of charging requirements respectively obey normal distribution N (12, 3) 2 )、N(9，1 2 )、N(14，1 2 ) The method comprises the steps of carrying out a first treatment on the surface of the In commercial areas, the probability of 0.1 is that the quick charge is selected from 06:00 to 18:00, the probability of 0.1 is that the quick charge is selected from 17:00 to 21:00, and the initial moments of the charging demands respectively obey the normal distribution N (12, 3 2 )、N(19，1 2 ) The method comprises the steps of carrying out a first treatment on the surface of the In residential areas, the probability of 0.1 is that quick charge is selected from 08:00 to 20:00, and slow charge is selected from 17:00 to 03:00, and the initial moments of the charging demands respectively obey normal distribution N (14, 3 2 )、N(23，2 2 ) The method comprises the steps of carrying out a first treatment on the surface of the The state of charge at the initial time of the charging demand of the private car obeys the normal distribution N (0.3,0.1) 2 )；
The taxi load is that the operation time of the taxi is whole day, so that a mode of two charges per day is generally adopted, and the charging time is selected in a period with less traffic, namely a midday period and a midnight period. Charging of taxisThe time intervals are 01:00-05:00, 10:00-14:00, respectively obey normal distribution N (3, 1) 2 ) And N (12, 1) 2 ). Because the journey of the taxi is not fixed, the distribution of charging demand nodes is random, and a quick charging pile is selected for charging after the distance and the charging price are considered; the state of charge at the initial moment of the charging demand of the taxi obeys normal distribution N (0.2,0.1) 2 )；
The bus load is that the operation time of the bus is approximately 06:00-22:00, the operation time and the route of the bus are relatively concentrated, the bus can be charged in a concentrated mode, the charging nodes are fixed, a mode of two charging in one day is generally adopted, the fast charging is carried out in the midday period, and the slow charging is carried out after the bus is off duty in the evening. The charging period of the bus is 12:00-16:00, 18:00 to 02:00 the next day, respectively obeys normal distribution N (14, 1) 2 ) And N (22, 2) 2 ). The fast charging piles in the commercial area are selected for fast charging in the midday period, and the slow charging is selected for the charging station in the residential area in the night period; the state of charge at the initial moment of the charging demand of the bus obeys normal distribution N (0.5, 0.1) 2 )。
Table 1 electric vehicle charging load prediction parameters
Analyzing the influence of the fast/slow charging of the electric vehicle on the safe and stable operation of each micro-grid, and establishing an upper-layer electric vehicle load space-time distribution rule model by using load time transfer and space transfer technology and taking the highest charging efficiency of a user as a target;
electric automobile fast/slow charging scheduling strategy: part of private cars select to be slowly charged in a working area in daytime and in a residential area at night, and the electric cars are regarded as load which can be transferred in time due to long slow charging time; because the quick charging time is shorter, the dispatching is more flexible, and the quick charging modes of private cars and taxis can select different charging stations for quick charging in the charging period, so that the load can be regarded as a load which can be transferred in space; in addition, the charging time and the place of the bus are relatively fixed, so that the charging mode of the bus is switched under the related constraint, and the charging time can be flexibly arranged within a fixed period of time;
and (3) taking the highest charging efficiency of a user as a target, establishing a load space-time distribution rule model of the upper-layer electric automobile, wherein the objective function is as follows:
The upper layer characterizes the user charging efficiency by the user satisfaction degree, wherein the user satisfaction degree is the sum of travel satisfaction degree and charging cost satisfaction degree, and the specific expression is as follows:
wherein f is an objective function;
travel satisfaction and charging cost satisfaction of the electric automobile j to the charging station i are respectively; d (D) max and Dmin The farthest distance and the nearest distance of the electric vehicle in the road network from the charging demand node to the charging station are respectively; />
The distance from the electric automobile j to the charging station i at the moment t; />
and />
The highest charge cost and the lowest charge cost of the corresponding vehicle model of the charging station i are respectively; />
For t time electricityCharging electricity price of the motor vehicle j at the charging station i; />
For the j-th vehicle>
Percentage of remaining charge at the moment of arrival at the charging station.
Determining constraint conditions of the SOC, the upper limit and the lower limit of electricity price and charging time of the storage battery of the electric automobile:
(1) Battery SOC constraint
In order to prevent the electric quantity of the electric automobile from being too low, a lower limit of the SOC is set so as to ensure that the electric automobile meets the driving energy consumption requirement from a charging demand node to a selected charging station; meanwhile, in order to cause serious adverse effects on battery life due to overcharge, an upper limit of SOC is set, that is:
in the formula ,
and />
Respectively the maximum value and the minimum value of the allowable storage battery SOC;
(2) Upper and lower limit constraint of electricity price
In order to ensure the benefits of electric automobile users and micro-grid operators, the charging electricity price of the electric automobile needs to be kept within reasonable upper and lower limits, namely:
in the formula ,
is->
Respectively charging the minimum value and the maximum value of the charge price of the charging station i;
(3) Charging time constraint
In order to prevent the user from charging too long, resulting in reduced satisfaction of the user charging time, an upper charging time limit is set, namely:
0≤T i f,t ≤T i f,max
0≤T i s,t ≤T i s,max
in the formula ,Ti f,max and Ti s,max Respectively the minimum value and the maximum value of the fast/slow charging time of the charging station i; t (T) i f,t and Ti s,t The charging time of the fast/slow charging of the charging station i at the time t is respectively.
Considering the fast charging and slow charging of the electric automobile, establishing a net load model of each micro-grid, and describing the risk faced by the micro-grid when the charging load of the electric automobile is gathered according to the fluctuation range of the net load and the climbing performance demand level;
electric automobile charging electricity price pricing strategy based on micro-grid running state: after the electric automobile generates a charging demand, the electric automobile is charged to the nearest charging station, and the electric automobile does not participate in optimal scheduling at the moment, and the charging electricity price is set to be the peak electricity price; when the electric automobile selects a charging position and a charging starting time according to the charging electricity price, the electric automobile participates in optimal scheduling; setting a dynamic electricity price based on an electric vehicle charging electricity price pricing strategy of the running state of the micro-grid;
Dividing the peak-to-valley electricity prices into two sections:
in the formula ,C1,i C (C) 2,i Respectively the electricity price interval 1 and the electricity price interval 2, C v 、C n C (C) p Respectively a valley value, a flat value and a peak value of the electricity price;
the net load size at the time t of each micro-grid;
firstly, judging whether the net load at each moment is negative, if so, indicating that wind/light output is not absorbed, and mapping electricity prices in an electricity price interval 1 according to the proportion of wind and light absorption:
in the formula ,
scheduling payload dips within a period for each microgrid;
if the net load is positive, the wind/light output is completely absorbed, and the electricity price is mapped in the electricity price interval 2 according to the net load climbing rate:
in the formula ,
is->
The maximum value of the net load climbing of each micro-grid at the time t and the net load climbing in the scheduling period is respectively obtained.
Taking the running risk of the micro-grids caused by aggregation and charging of the electric automobile into consideration, taking factors such as the fluctuation range of the net load, the climbing requirement, new energy consumption and the like, and taking the highest running efficiency of each micro-grid as a target, establishing a lower-layer multi-micro-grid fast/slow pile charging configuration model, wherein the objective function is as follows:
(1) The micro-grid net load fluctuation is minimal:
in the formula ,
for each micro-grid net load mean value +. >
Is->
Charging power of the electric automobile, power of the basic load, power of the energy storage equipment and output of the wind/light unit at the moment t respectively are +.>
and />
An average value of transmission power of each part in a scheduling period; t represents a scheduling period, herein a time period in units of 1h, i.e., t=24;
(2) The comprehensive operation cost of the micro-grid is the smallest:
in the formula ,S2 The comprehensive operation cost of the micro-grid is; s is S i,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region; s is S i,2 The running cost of the output unit of each area is calculated; s is S i,3 Climbing cost for the power output units of the micro-grids in each region;
in the formula ,Si,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region;
is->
The price and the installation quantity of the quick/slow charging piles are respectively, n is the operation life of the charging piles, d is the discount rate, and eta is the percentage of the operation maintenance cost to the investment cost;
in the formula ,
is->
The running cost of the diesel engine set, the running cost of the energy storage equipment and the running cost of the power distribution network tie line of each regional micro-grid are respectively; />
Is->
The operation and maintenance cost, the fuel cost and the environmental treatment cost of the diesel engine set are respectively; c (C) de,om Maintenance factor for the operation of a diesel engine system, +.>
For i region t moment diesel engine unit output power, a, b and C are fuel coefficients, C k 、λ de,k The cost for treating the k-type pollutant and the emission amount for generating the k-type pollutant during operation are respectively; c (C) ES,om and />
Respectively obtaining an operation cost coefficient of the energy storage device and charging and discharging power of the energy storage device at the moment of the region t; />
and />
The method is characterized in that the distribution network interconnecting line electric energy transaction cost and the distribution network environment treatment cost are respectively +.>
For the electricity price of the distribution network at time t, +.>
For the power of the interconnection line of the distribution network, the positive and negative values of the power respectively represent the buying and selling electric quantity of the micro-grid to the main network, lambda DN,k Operating product for power distribution network tie lineGenerating the discharge amount of the k-th pollutant;
in the formula ,
and />
The climbing cost and the tie climbing cost of the micro-grid diesel engine unit in each region are respectively; c (C) de and CDN The cost coefficient of the output climbing of the diesel engine set and the power distribution network tie line is +.>
and />
The power is transmitted by the diesel engine set and the power distribution network tie line at the moment t of each regional micro-grid respectively; />
Spare cost coefficients are rotated for distribution network tie lines.
Determining the output and climbing constraint of a micro-grid power generation unit, and the power balance constraint conditions of each region:
(1) Power generation unit output constraint
in the formula ,
and />
and />
and />
Respectively the minimum value and the maximum value of the output force of the diesel engine set, the energy storage equipment and the power distribution network tie line;
(2) Climbing constraint of power generation unit
in the formula ,
the maximum value of the output climbing of the diesel engine set and the power distribution network tie line is respectively;
(3) Power balance constraint
According to the characteristics of the load space-time distribution of the electric automobile, the running risk of the micro-grid caused by the aggregation and charging of the electric automobile is considered, factors such as the fluctuation range of net load, climbing requirements, new energy consumption and the like are considered, the running efficiency of the micro-grid is improved, the charging electricity price of the electric automobile is formulated, and a capacity optimizing configuration model of a multi-micro-grid quick/slow charging pile is established; solving the model through a multi-target particle swarm algorithm to obtain a charging pile optimal configuration scheme, and improving the charging efficiency of a user and the running efficiency of a micro-grid;
as shown in fig. 3, the model is solved using monte carlo simulation and a multi-objective particle swarm algorithm. The upper layer uses Monte Carlo simulation to generate charge demand nodes of various users, charge demand initial time and charge states of the time, load curves of all users are overlapped to obtain fast/slow charge load curves of all areas to be transmitted into the lower layer, electricity prices are formulated by combining operation states of micro-grids of all areas, different charging stations are selected by fast charging modes, charging time points are selected by slow charging modes to serve as variables, a multi-objective particle swarm algorithm is used for solving a double-layer model, and the optimal configuration of the fast/slow charge load of the user with highest charge efficiency and the fast/slow charge pile of all areas with highest operation efficiency is obtained.
The invention sets two comparison scenes to perform calculation analysis, scene 1: the charging electricity price is not considered to guide the space-time distribution of the charging demands of the electric automobile, and the fast/slow charging demands of the electric automobile user are obtained according to the load prediction parameters. Scenario 2: according to the method provided by the invention, the fast/slow charging demands of the users of the electric automobile are optimized by considering the guidance of the charging electricity price of the micro-grid in each region on the fast/slow charging load.
Parameter setting: assume that 1000 private cars, 100 taxis and 50 buses are charged in a planned area every day; the average running speed of the vehicle is 30km/h; the battery parameters of each type of electric vehicle are shown in table 2.
Table 2 different types of electric vehicle battery parameters
The quick charging pile in the micro-grid is a direct current charging pile, the power is 60kW, the unit price is 5 ten thousand yuan, the slow charging pile is an alternating current charging pile, the power is 12kW, the unit price is 1 ten thousand yuan, the operation period is 10 years, the sticking rate is 10%, and the operation and maintenance cost accounts for 10% of the construction cost; the charging peak valley flat electricity price of the micro-grid is 1.3 yuan/kWh, 0.8 yuan/kWh and 0.3 yuan/kWh respectively; the parameters of the diesel engine set, the distribution network interconnecting lines, the energy storage equipment, the wind/light set, the basic load and other equipment in each area are shown in tables 3, 4 and 5; the device numbers 1/2/3 are office/business/residential micro-grid devices, respectively.
Table 3 microgrid diesel engine set and distribution network tie line parameters
Table 4 microgrid energy storage device parameters
TABLE 5 microgrid wind/light unit and base load parameters
According to various electric vehicle load prediction parameters, probability sampling of parameters such as charging position, charging demand initial time, SOC and the like is carried out by using a Monte Carlo method, and fast/slow charging loads of all areas are obtained through simulation, wherein the result is shown as a scene I in FIG. 4; in the second scenario in fig. 4, the fast/slow charging load of the office area in the first scenario is rapidly increased in the 07:00-11:00 period according to the fast/slow charging load and the net load comparison chart of each area, so that the net load of the office area in the first scenario climbs more, the fast charging load is guided to the business area and the residential area for fast charging through the electricity price, the slow charging load is transferred on time-space distribution by changing the charging starting time, and the net load fluctuation of the working area in the second scenario is greatly reduced, and meanwhile, the influence on the net load fluctuation of the business area and the residential area is smaller; in the period of 11:00-18:00, more wind/light output is not consumed in the micro-grid of the residential area in the scene one, at the moment, partial fast charging loads of the office area and the business area select the residential area to be fast charged, the scene two-step load transfer realizes effective consumption of the wind/light output, and meanwhile, the fluctuation of the net load of the business area is reduced, but the fluctuation of the net load of the office area is slightly increased; in the period of 18:00-24:00, the photovoltaic stops generating electricity, the wind power output is reduced, the base load of each area is in a peak period, the net load of the business area in the scene one climbs the maximum, partial quick charge load of the business area in the period selects the working area and the residential area to be quickly charged, the net load fluctuation of the office area and the residential area in the scene two is slightly increased, and the impact of the net load fluctuation of the business area on the micro-grid is effectively relieved; in the period of 24:00-07:00, the wind power output is gradually increased, the power consumption of the basic load is reduced, partial slow charge loads of the working area and the residential area are transferred to the period from the period of 18:00-24:00 for charging, and partial fast charge loads of the business area are also transferred to the working area and the residential area for fast charging; and through analysis of each period, the fast/slow charging load transfers the load according to the electricity price formulated by the running state of each region, so that the optimal running efficiency of the region of the multi-micro power grid is realized.
Table 6 comparison of payload data for each region
Table 6 shows that for comparison of the micro-grid payload data before and after charge optimization, the payload peak Gu Chafen of each region was reduced by 28.3%, 15.4% and 22.4%, and the payload variance was reduced by 24.5%, 15.7% and 2.1%, respectively; the method has the advantages that the net load fluctuation and peak-valley difference of the micro-grids in each region are reduced to different degrees through the space-time distribution of the charging demands of the electric vehicles under the guidance of electricity prices, and the safe and stable operation of the multi-micro-grid region is realized;
according to the electric vehicle charging electricity price pricing strategy based on the running state of the micro-grid, the charging electricity price of each area is formulated, and the optimized electricity price result is shown in fig. 5; in combination with the analysis of the net load curve of each area, the office area only has the condition that wind/light is not completely absorbed at 17, so that the electricity price at 17 is in a section 1, and the net load at other moments is positive, so that mapping is carried out in an electricity price section 2 according to the net load climbing; similarly, in commercial and residential areas, when the net load is negative, electricity prices are mapped in the interval 1 to guide the electric vehicle load to absorb wind/light output, when the net load is positive, electricity prices are mapped in the interval 2 to guide the electric vehicle load to charge in the area with smaller output climbing. The electricity price strategy provided by the method further optimizes peak-valley flat electricity price, and guides the space-time distribution of the electric vehicle load by using the electricity price according to the output of each regional micro-grid and the heterogeneity of the load, so that the influence of large-scale electric vehicle load access on each regional micro-grid is minimum, and the safe and stable operation of the multi-regional micro-grid is facilitated.
TABLE 7 fast/slow fill pile configuration results
The results of configuring the charging piles according to the fast/slow charging loads before and after the optimization are shown in table 7; the peak value of the slow charging load is reduced by optimizing the charging time of the slow charging vehicles in the working area and the residential area, so that the configuration quantity of the slow charging piles in the working area and the residential area is reduced from 68 and 87 to 66 and 80 respectively, and the configuration quantity of the slow charging piles is reduced on the basis of meeting the requirements of users, so that the idle rate of the slow charging piles can be reduced, and the configuration cost can be reduced; the quick charge load of the three areas is flexibly selected to be charged, the peak value of the quick charge load of the working area and the business area is reduced, and the number of the configured quick charge piles is reduced from 18 and 18 to 13 and 16 respectively; because the residential area has no slow charging load in the midday period and the self basic load is smaller, the fast charging vehicles of the residential area are increased in the midday period, the peak value of the fast charging load of the residential area is increased, the number of the configured fast charging piles is increased from 10 to 13, but the integral number of the fast charging piles is reduced from 46 to 42, compared with the configuration of the fast charging piles in the first scenario, the configuration of the fast charging piles in the second scenario is more balanced and reasonable, the influence of the aggregation characteristic of the electric automobile caused by the position and capacity configuration of the charging piles on the safe and stable operation of the micro-grid is fully considered, and the integral fast/slow charging pile optimal configuration is realized.
In the first scenario, as shown in fig. 6, after the electric vehicle user generates the charging demand, the electric vehicle user goes to the nearest charging station to charge, and as the user does not participate in the electricity price demand response, the charging cost satisfaction of the user is lower, but the travel satisfaction is the largest, and the user satisfaction is 871.5; when the user participates in the electricity price demand response, different charging time and charging position are needed to be selected, and the travel satisfaction degree of the user is greatly reduced, but the charge electricity price is reduced, so that the cost satisfaction degree of the user is greatly improved relative to the first situation, and the overall satisfaction degree is improved by 22.1% relative to the first situation.
In the method, users and micro-grid operators are layered, a multi-target double-layer optimization scheduling method is adopted, the charging demand space-time distribution of different types of electric vehicles is considered, an electric vehicle charging load prediction model is established, the running efficiency of a micro-grid layer is fully considered on the basis of guaranteeing the charging efficiency of the users, the power climbing risk brought by the huge impact of the electric vehicle load to the micro-grid is coupled into the establishment of charging electricity prices, the space-time transfer characteristic of the electric vehicles is utilized, the wind/light output of each area is fully consumed, the climbing output of each area unit is reduced, and the optimal configuration of the multi-micro-grid speed/charging pile with the highest efficiency of the electric vehicle users and the micro-grid operators is obtained.
An embodiment of an electric automobile fills electric pile configuration system, this system includes that the demand analysis module that charges, the demand model construction module that charges, little electric wire netting load model construction module, price of electricity adjustment module and scheme integrate the module:
the charging demand analysis module is used for analyzing factors affecting the charging mode, the charging place and the charging time of the electric vehicle, generating the fast/slow charging load of the electric vehicle in each area by utilizing Monte Carlo simulation, and describing the initial space-time distribution characteristics of the electric vehicle load.
The invention mainly researches the charging load demands of private cars, taxis and buses of class 3. Specifically analyzing the selection of the charging mode, the charging place and the charging time of the corresponding type of vehicle, and setting various parameters of the load prediction model based on the selection;
charging demand initial time: the charging loads of different types of electric automobiles have randomness in time and space, and according to NHTS statistical analysis, the charging demand initial moments of 3 types of user groups are approximately considered to meet normal distribution in different time periods, and the charging states of the charging demand initial moments also meet normal distribution:
wherein t is the initial moment of the charging demand, i.e. the moment when the electric vehicle user considers to go to the charging station; sigma (sigma) s and μs (s＝1,2),σ 1 and μ1 For the expected and standard deviation, sigma, of the initial moment of charge demand 2 and μ2 The expected and standard deviations of the state of charge at the initial moment of the charging demand, respectively.
As shown in fig. 2, the road network nodes 1-12 are residential areas, wherein 6 nodes are residential area micro-grid charging station nodes; the road network nodes 13-19 are commercial areas, wherein the 14 nodes are commercial area micro-grid charging station nodes; the road network nodes 20-25 are office areas, wherein 6 nodes are office area micro-grid charging station nodes;
private car load-most users of private cars have charging demands at different time periods and at different places. The charging sites can be in working areas, business areas and residential areas, and the charging probabilities of the working areas, the business areas and the residential areas are respectively set to be 0.4, 0.2 and 0.4. In the working area, the charging probability of 0.2 is that slow charging is selected from 06:00-18:00, the probability of 0.2 is that fast charging is selected from 07:00-11:00 and 12:00-16:00, and the initial moments of charging requirements respectively obey normal distribution N (12, 3) 2 )、N(9，1 2 )、N(14，1 2 ) The method comprises the steps of carrying out a first treatment on the surface of the In commercial areas, the probability of 0.1 is that the quick charge is selected from 06:00 to 18:00, the probability of 0.1 is that the quick charge is selected from 17:00 to 21:00, and the initial moments of the charging demands respectively obey the normal distribution N (12, 3 2 )、N(19，1 2 ) The method comprises the steps of carrying out a first treatment on the surface of the In residential areas, the probability of 0.1 is that quick charge is selected from 08:00 to 20:00, and slow charge is selected from 17:00 to 03:00, and the initial moments of the charging demands respectively obey normal distribution N (14, 3 2 )、N(23，2 2 ) The method comprises the steps of carrying out a first treatment on the surface of the The state of charge at the initial time of the charging demand of the private car obeys the normal distribution N (0.3,0.1) 2 )；
The taxi load is that the operation time of the taxi is whole day, so that a mode of two-day charging is generally adopted, and the charging time is selected in a period with less traffic flow, and the charging time is respectivelyIs the midday period and the midnight period. The charging period of the taxis is 01:00-05:00, 10:00-14:00, respectively obeys normal distribution N (3, 1) 2 ) And N (12, 1) 2 ). Because the journey of the taxi is not fixed, the distribution of charging demand nodes is random, and a quick charging pile is selected for charging after the distance and the charging price are considered; the state of charge at the initial moment of the charging demand of the taxi obeys normal distribution N (0.2,0.1) 2 )；
The bus load is that the operation time of the bus is approximately 06:00-22:00, the operation time and the route of the bus are relatively concentrated, the bus can be charged in a concentrated mode, the charging nodes are fixed, a mode of two charging in one day is generally adopted, the fast charging is carried out in the midday period, and the slow charging is carried out after the bus is off duty in the evening. The charging period of the bus is 12:00-16:00, 18:00 to 02:00 the next day, respectively obeys normal distribution N (14, 1) 2 ) And N (22, 2) 2 ). The fast charging piles in the commercial area are selected for fast charging in the midday period, and the slow charging is selected for the charging station in the residential area in the night period; the state of charge at the initial moment of the charging demand of the bus obeys normal distribution N (0.5, 0.1) 2 )。
Table 1 electric vehicle charging load prediction parameters
The charging demand model construction module is used for analyzing the influence of the fast/slow charging of the electric automobile on the safe and stable operation of each micro-grid, and establishing an upper-layer electric automobile load space-time distribution rule model by using load time transfer and space transfer technology and taking the highest charging efficiency of a user as a target.
Electric automobile fast/slow charging scheduling strategy: part of private cars select to be slowly charged in a working area in daytime and in a residential area at night, and the electric cars are regarded as load which can be transferred in time due to long slow charging time; because the quick charging time is shorter, the dispatching is more flexible, and the quick charging modes of private cars and taxis can select different charging stations for quick charging in the charging period, so that the load can be regarded as a load which can be transferred in space; in addition, the charging time and the place of the bus are relatively fixed, so that the charging mode of the bus is switched under the related constraint, and the charging time can be flexibly arranged within a fixed period of time;
and (3) taking the highest charging efficiency of a user as a target, establishing a load space-time distribution rule model of the upper-layer electric automobile, wherein the objective function is as follows:
the upper layer characterizes the user charging efficiency by the user satisfaction degree, wherein the user satisfaction degree is the sum of travel satisfaction degree and charging cost satisfaction degree, and the specific expression is as follows:
Wherein f is an objective function;
travel satisfaction and charging cost satisfaction of the electric automobile j to the charging station i are respectively; d (D) max and Dmin The farthest distance and the nearest distance of the electric vehicle in the road network from the charging demand node to the charging station are respectively; />
The distance from the electric automobile j to the charging station i at the moment t; />
and />
The highest charge cost and the lowest charge cost of the corresponding vehicle model of the charging station i are respectively; />
The charging electricity price of the electric automobile j at the charging station i at the moment t; />
For the j-th vehicle>
Percentage of remaining charge at the moment of arrival at the charging station.
Determining constraint conditions of the SOC, the upper limit and the lower limit of electricity price and charging time of the storage battery of the electric automobile:
(1) Battery SOC constraint
In order to prevent the electric quantity of the electric automobile from being too low, a lower limit of the SOC is set so as to ensure that the electric automobile meets the driving energy consumption requirement from a charging demand node to a selected charging station; meanwhile, in order to cause serious adverse effects on battery life due to overcharge, an upper limit of SOC is set, that is:
in the formula ,
and />
Respectively the maximum value and the minimum value of the allowable storage battery SOC;
(2) Upper and lower limit constraint of electricity price
In order to ensure the benefits of electric automobile users and micro-grid operators, the charging electricity price of the electric automobile needs to be kept within reasonable upper and lower limits, namely:
in the formula ,
is->
Respectively charging the minimum value and the maximum value of the charge price of the charging station i;
(3) Charging time constraint
In order to prevent the user from charging too long, resulting in reduced satisfaction of the user charging time, an upper charging time limit is set, namely:
0≤T i f,t ≤T i f,max
0≤T i s,t ≤T i s,max
in the formula ,Ti f,max and Ti s,max Respectively the minimum value and the maximum value of the fast/slow charging time of the charging station i; t (T) i f,t and Ti s,t The charging time of the fast/slow charging of the charging station i at the time t is respectively.
The micro-grid load model construction module is used for considering the fast charging and the slow charging of the electric automobile, establishing a net load model of each micro-grid, and describing the risk faced by the micro-grid when the charging load of the electric automobile is gathered according to the fluctuation range of the net load and the climbing performance demand;
electric automobile charging electricity price pricing strategy based on micro-grid running state: after the electric automobile generates a charging demand, the electric automobile is charged to the nearest charging station, and the electric automobile does not participate in optimal scheduling at the moment, and the charging electricity price is set to be the peak electricity price; when the electric automobile selects a charging position and a charging starting time according to the charging electricity price, the electric automobile participates in optimal scheduling; setting a dynamic electricity price based on an electric vehicle charging electricity price pricing strategy of the running state of the micro-grid;
Dividing the peak-to-valley electricity prices into two sections:
in the formula ,C1,i C (C) 2,i Respectively the electricity price interval 1 and the electricity price interval 2, C v 、C n C (C) p Respectively a valley value, a flat value and a peak value of the electricity price;
the net load size at the time t of each micro-grid;
firstly, judging whether the net load at each moment is negative, if so, indicating that wind/light output is not absorbed, and mapping electricity prices in an electricity price interval 1 according to the proportion of wind and light absorption:
in the formula ,
scheduling payload dips within a period for each microgrid;
if the net load is positive, the wind/light output is completely absorbed, and the electricity price is mapped in the electricity price interval 2 according to the net load climbing rate:
in the formula ,
is->
The maximum value of the net load climbing of each micro-grid at the time t and the net load climbing in the scheduling period is respectively obtained.
Taking the running risk of the micro-grids caused by aggregation and charging of the electric automobile into consideration, taking factors such as the fluctuation range of the net load, the climbing requirement, new energy consumption and the like, and taking the highest running efficiency of each micro-grid as a target, establishing a lower-layer multi-micro-grid fast/slow pile charging configuration model, wherein the objective function is as follows:
(1) The micro-grid net load fluctuation is minimal:
in the formula ,
for each micro-grid net load mean value +. >
Is->
Charging power of the electric automobile, power of the basic load, power of the energy storage equipment and output of the wind/light unit at the moment t respectively are +.>
and />
An average value of transmission power of each part in a scheduling period; t represents a scheduling period, herein a time period in units of 1h, i.e., t=24;
(2) The comprehensive operation cost of the micro-grid is the smallest:
in the formula ,S2 The comprehensive operation cost of the micro-grid is; s is S i,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region; s is S i,2 The running cost of the output unit of each area is calculated; s is S i,3 Climbing cost for the power output units of the micro-grids in each region;
in the formula ,Si,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region;
is->
The price and the installation quantity of the quick/slow charging piles are respectively, n is the operation life of the charging piles, d is the discount rate, and eta is the percentage of the operation maintenance cost to the investment cost;
in the formula ,
is->
The running cost of the diesel engine set, the running cost of the energy storage equipment and the running cost of the power distribution network tie line of each regional micro-grid are respectively; />
Is->
The operation and maintenance cost, the fuel cost and the environmental treatment cost of the diesel engine set are respectively; c (C) de,om Maintenance factor for the operation of a diesel engine system, +.>
For i region t moment diesel engine unit output power, a, b and C are fuel coefficients, C k 、λ de,k The cost for treating the k-type pollutant and the emission amount for generating the k-type pollutant during operation are respectively; c (C) ES,om and />
Respectively obtaining an operation cost coefficient of the energy storage device and charging and discharging power of the energy storage device at the moment of the region t; />
and />
The method is characterized in that the distribution network interconnecting line electric energy transaction cost and the distribution network environment treatment cost are respectively +.>
For the electricity price of the distribution network at time t, +.>
For the power of the interconnection line of the distribution network, the positive and negative values of the power respectively represent the buying and selling electric quantity of the micro-grid to the main network, lambda DN,k Generating the discharge amount of the kth pollutant for the operation of the distribution network interconnecting line;
in the formula ,
and />
The climbing cost and the tie climbing cost of the micro-grid diesel engine unit in each region are respectively; c (C) de and CDN The cost coefficient of the output climbing of the diesel engine set and the power distribution network tie line is +.>
and />
The power is transmitted by the diesel engine set and the power distribution network tie line at the moment t of each regional micro-grid respectively; />
Spare cost coefficients are rotated for distribution network tie lines.
Determining the output and climbing constraint of a micro-grid power generation unit, and the power balance constraint conditions of each region:
(1) Power generation unit output constraint
in the formula ,
and />
and />
and />
Respectively the minimum value and the maximum value of the output force of the diesel engine set, the energy storage equipment and the power distribution network tie line;
(2) Climbing constraint of power generation unit
in the formula ,
the maximum value of the output climbing of the diesel engine set and the power distribution network tie line is respectively;
(3) Power balance constraint
The electric charge price adjusting module considers the running risk of the micro-grid caused by aggregation and charging of the electric vehicle according to the load space-time distribution characteristics of the electric vehicle, considers factors such as the fluctuation range of the net load, the climbing requirement, new energy consumption and the like, aims at improving the running efficiency of the micro-grid, and formulates the charging electric charge price of the electric vehicle;
establishing a capacity optimization configuration model of the multi-micro-grid fast/slow charging piles through a scheme integration module: solving the model through a multi-target particle swarm algorithm to obtain a charging pile optimal configuration scheme, and improving the charging efficiency of a user and the running efficiency of a micro-grid;
as shown in fig. 3, the model is solved using monte carlo simulation and a multi-objective particle swarm algorithm. The upper layer uses Monte Carlo simulation to generate charge demand nodes of various users, charge demand initial time and charge states of the time, load curves of all users are overlapped to obtain fast/slow charge load curves of all areas to be transmitted into the lower layer, electricity prices are formulated by combining operation states of micro-grids of all areas, different charging stations are selected by fast charging modes, charging time points are selected by slow charging modes to serve as variables, a multi-objective particle swarm algorithm is used for solving a double-layer model, and the optimal configuration of the fast/slow charge load of the user with highest charge efficiency and the fast/slow charge pile of all areas with highest operation efficiency is obtained.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the invention may take the form of a computer program product embodied on one or more computers, usable storage media (including but not limited to disk storage, CD-ROM, optical storage, and the like) having computer usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Assembling the above method steps into a program and storing the program in a hard disk or other non-transitory storage medium, thus forming an embodiment of the "non-transitory readable recording medium" of the present invention; the storage medium is electrically connected with the computer processor, and the electric vehicle charging pile is configured through data processing, so that the embodiment of the 'data processing system' of the invention is formed.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The electric automobile charging pile configuration method is characterized by comprising the following steps of:
s1, analyzing a plurality of factors, wherein the factors influence the charging mode, the charging place or the charging time of an electric vehicle, simulating and generating a fast/slow charging function of the electric vehicle in a region to be researched, and describing initial space-time distribution characteristics of the electric vehicle load;
s2, establishing an electric vehicle load space-time distribution rule model by taking the highest charging efficiency of a user as a target and combining the electricity price and the initial space-time distribution characteristics;
s3, determining constraint conditions in the electric vehicle load space-time distribution rule model, wherein the constraint conditions comprise the electric vehicle storage battery state of charge, the upper limit and the lower limit of electricity price and charging time;
S4, calculating fast charge and slow charge loads of the electric vehicles in the micro-grids of all operators in jurisdictions, and establishing a net load model of each micro-grid, wherein the net load model describes risks faced by the micro-grids when the charging loads of the electric vehicles are aggregated according to the net load fluctuation amplitude and the climbing performance demand;
s5, determining the output and climbing constraint conditions of the micro-grid power generation unit and the power balance constraint conditions of each region in the net load model;
s6, integrating the characteristics of the electric vehicle load space-time distribution rule model and the net load model, and iteratively adjusting the electricity price of the electric vehicle for fast/slow charging to transfer the space-time distribution of the electric vehicle load demand with the aim of improving the running efficiency of the micro-grid;
s7, substituting the adjusted electricity price into a net load model, wherein the charging load of the electric automobile is represented by the capacity and the quantity of the fast/slow charging piles, and solving the electric automobile by a multi-target particle swarm algorithm to obtain an optimal configuration scheme of the charging piles of the electric automobile.
2. The electric vehicle charging pile configuration method according to claim 1, wherein the electric vehicle load space-time distribution rule model characterizes the user charging efficiency by user satisfaction, the user satisfaction is a sum of travel satisfaction and charging cost satisfaction, and the specific expression is:
Wherein f is an objective function;
travel satisfaction and charging cost satisfaction of the electric automobile j to the charging station i are respectively; d (D) max and Dmin The farthest distance and the nearest distance of the electric vehicle in the road network from the charging demand node to the charging station are respectively; />
The distance from the electric automobile j to the charging station i at the moment t; />
and />
The highest charge cost and the lowest charge cost of the corresponding vehicle model of the charging station i are respectively; />
The charging electricity price of the electric automobile j at the charging station i at the moment t; />
For the jth vehicle in
Time of day reaching chargingThe remaining power percentage of the station.
3. The electric vehicle charging pile configuration method according to claim 2, wherein the payload model includes:
micro-grid net load fluctuation minimum submodel:
/>
wherein ,
in the formula ,
for each micro-grid net load mean value +.>
Is->
Respectively the charging power of the electric automobile at the time t, the power of the basic load, the power of the energy storage equipment and the output of the wind/light unit,
and />
An average value of transmission power of each part in a scheduling period; t represents a scheduling period, herein a time period in units of 1h, i.e., t=24;
micro-grid comprehensive operation cost minimum submodel:
in the formula ,S2 The comprehensive operation cost of the micro-grid is; s is S i,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region; s is S i,2 The running cost of the output unit of each area is calculated; s is S i,3 Climbing cost for the power output units of the micro-grids in each region;
in the formula ,Si,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region;
a kind of electronic device with high-pressure air-conditioning system
The price and the installation quantity of the quick/slow charging piles are respectively, n is the operation life of the charging piles, d is the discount rate, and eta is the percentage of the operation maintenance cost to the investment cost;
in the formula ,
is->
The running cost of the diesel engine set, the running cost of the energy storage equipment and the running cost of the power distribution network tie line of each regional micro-grid are respectively; />
Is->
The operation and maintenance cost, the fuel cost and the environmental treatment cost of the diesel engine set are respectively; c (C) de,om Maintenance factor for the operation of a diesel engine system, +.>
For i region t moment diesel engine unit output power, a, b and C are fuel coefficients, C k 、λ de,k The cost for treating the k-type pollutant and the emission amount for generating the k-type pollutant during operation are respectively; c (C) ES,om and />
Respectively obtaining an operation cost coefficient of the energy storage device and charging and discharging power of the energy storage device at the moment of the region t; />
and />
The method is characterized in that the distribution network interconnecting line electric energy transaction cost and the distribution network environment treatment cost are respectively +.>
For the electricity price of the distribution network at time t, +. >
For the power of the interconnection line of the distribution network, the positive and negative values of the power respectively represent the buying and selling electric quantity of the micro-grid to the main network, lambda DN,k Generating the discharge amount of the kth pollutant for the operation of the distribution network interconnecting line; />
in the formula ,
and />
The climbing cost and the tie climbing cost of the micro-grid diesel engine unit in each region are respectively; c (C) de and CDN The cost coefficient of the output climbing of the diesel engine set and the power distribution network tie line is +.>
and />
The power is transmitted by the diesel engine set and the power distribution network tie line at the moment t of each regional micro-grid respectively; />
Spare cost coefficients are rotated for distribution network tie lines.
4. The electric vehicle charging pile configuration method according to claim 3, wherein the method of transferring the space-time distribution of the electric vehicle load demand includes transferring the time distribution of the load by slow-charge delay; and transferring the spatial distribution of the load through the fast-charged point position switching.
5. The electric automobile fills electric pile configuration system, characterized by that includes following functional module:
the charging demand analysis module is used for analyzing a plurality of factors, wherein the factors influence the charging mode, the charging place or the charging time of the electric vehicle, simulate and generate a fast/slow charging function of the electric vehicle in the area to be researched, and describe the initial space-time distribution characteristics of the electric vehicle load;
The charging demand model building module is used for building an electric vehicle load space-time distribution rule model by taking the highest charging efficiency of a user as a target and combining the electricity price and the initial space-time distribution characteristics; determining constraint conditions in the electric vehicle load space-time distribution rule model, wherein the constraint conditions comprise the state of charge of a storage battery of the electric vehicle, the upper limit and the lower limit of electricity price and charging time;
the micro-grid load model construction module is used for calculating the fast charge and slow charge of the electric vehicle in the micro-grid of each operator district and establishing a net load model of each micro-grid, wherein the net load model describes the risk faced by the micro-grid when the charging load of the electric vehicle is gathered according to the net load fluctuation range and the climbing performance demand; determining the output and climbing constraint conditions of the micro-grid power generation unit and the power balance constraint conditions of each region in the net load model;
the power price adjusting module is used for integrating the characteristics of the electric vehicle load space-time distribution rule model and the net load model description, iteratively adjusting the power price of the electric vehicle for quick/slow charging with the aim of improving the running efficiency of the micro-grid, and transferring the space-time distribution of the electric vehicle load demands;
And the scheme integration module is used for substituting the adjusted electricity price into a net load model, wherein the electric vehicle charging load is represented by the capacity and the number of the fast/slow charging piles, and the optimal configuration scheme of the electric vehicle charging piles is obtained through solving by a multi-target particle swarm algorithm.
6. The electric vehicle charging pile configuration system according to claim 5, wherein an electric vehicle load space-time distribution rule model is built in the charging demand model building module; the electric automobile load space-time distribution rule model characterizes user charging efficiency according to user satisfaction, wherein the user satisfaction is the sum of travel satisfaction and charging cost satisfaction, and the specific expression is as follows:
wherein f is an objective function;
travel satisfaction and charging cost satisfaction of the electric automobile j to the charging station i are respectively; d (D) max and Dmin The farthest distance and the nearest distance of the electric vehicle in the road network from the charging demand node to the charging station are respectively; />
The distance from the electric automobile j to the charging station i at the moment t; />
and />
The highest charge cost and the lowest charge cost of the corresponding vehicle model of the charging station i are respectively; />
The charging electricity price of the electric automobile j at the charging station i at the moment t; />
For the jth vehicle in 
Percentage of remaining charge at the moment of arrival at the charging station.
7. The electric vehicle charging pile configuration system according to claim 6, wherein a payload model of each micro-grid is built in the micro-grid load model building module, the payload model comprising:
micro-grid net load fluctuation minimum submodel:
wherein ,
in the formula ,
for each micro-grid net load mean value +.>
Is->
Respectively the charging power of the electric automobile at the time t, the power of the basic load, the power of the energy storage equipment and the output of the wind/light unit,
and />
An average value of transmission power of each part in a scheduling period; t represents a scheduling period, herein a time period in units of 1h, i.e., t=24;
micro-grid comprehensive operation cost minimum submodel:
in the formula ,S2 The comprehensive operation cost of the micro-grid is; s is S i,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region; s is S i,2 The running cost of the output unit of each area is calculated; s is S i,3 Climbing cost for the power output units of the micro-grids in each region;
in the formula ,Si,1 (i=1, 2, 3) is the construction cost (yuan/day) of the fast/slow charging piles of the micro-grid in each region;
a kind of electronic device with high-pressure air-conditioning system
The price and the installation quantity of the quick/slow charging piles are respectively, n is the operation life of the charging piles, d is the discount rate, and eta is the percentage of the operation maintenance cost to the investment cost;
in the formula ,
is->
The running cost of the diesel engine set, the running cost of the energy storage equipment and the running cost of the power distribution network tie line of each regional micro-grid are respectively; />
Is->
The operation and maintenance cost, the fuel cost and the environmental treatment cost of the diesel engine set are respectively; c (C) de,om Maintenance factor for the operation of a diesel engine system, +.>
For i region t moment diesel engine unit output power, a, b and C are fuel coefficients, C k 、λ de,k The cost for treating the k-type pollutant and the emission amount for generating the k-type pollutant during operation are respectively; c (C) ES,om and />
Respectively obtaining an operation cost coefficient of the energy storage device and charging and discharging power of the energy storage device at the moment of the region t; />
and />
The method is characterized in that the distribution network interconnecting line electric energy transaction cost and the distribution network environment treatment cost are respectively +.>
For the electricity price of the distribution network at time t, +.>
For the power of the interconnection line of the distribution network, the positive and negative values of the power respectively represent the buying and selling electric quantity of the micro-grid to the main network, lambda DN,k Generating the discharge amount of the kth pollutant for the operation of the distribution network interconnecting line;
in the formula ,
and />
The climbing cost and the tie climbing cost of the micro-grid diesel engine unit in each region are respectively; c (C) de and CDN The cost coefficient of the output climbing of the diesel engine set and the power distribution network tie line is +.>
and />
The power is transmitted by the diesel engine set and the power distribution network tie line at the moment t of each regional micro-grid respectively; / >
Spare cost coefficients are rotated for distribution network tie lines.
8. The electric vehicle charging pile configuration system according to claim 7, wherein the electricity price adjustment module relates to a method of transferring a temporal-spatial distribution of electric vehicle load demands, the method of transferring a temporal-spatial distribution of electric vehicle load demands including transferring a temporal distribution of loads by a slow charge delay; and transferring the spatial distribution of the load through the fast-charged point position switching.
9. A non-transitory readable recording medium storing one or more programs including a plurality of instructions, wherein the programs include the steps included in the electric vehicle charging pile configuration method of any one of claims 1 to 4.
10. A data processing system comprising a processing circuit and a memory electrically coupled thereto, wherein the memory arrangement stores at least one program comprising a plurality of instructions, the processing circuit running the program to perform the steps included in a method of configuring a charging pile for an electric vehicle as claimed in any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310127683.6A CN116029453A (en) | 2023-02-14 | 2023-02-14 | Electric automobile charging pile configuration method, recording medium and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310127683.6A CN116029453A (en) | 2023-02-14 | 2023-02-14 | Electric automobile charging pile configuration method, recording medium and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116029453A true CN116029453A (en) | 2023-04-28 |
Family
ID=86079551
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310127683.6A Pending CN116029453A (en) | 2023-02-14 | 2023-02-14 | Electric automobile charging pile configuration method, recording medium and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116029453A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116843045A (en) * | 2023-09-01 | 2023-10-03 | 南京特拉利精密电子科技有限公司 | Vehicle charging pile management method and system |
| CN117172516A (en) * | 2023-11-03 | 2023-12-05 | 深圳航天科创泛在电气有限公司 | Charging pile dynamic scheduling decision-making method, device, equipment and storage medium |
| CN117635220A (en) * | 2024-01-26 | 2024-03-01 | 南京邮电大学 | Electric taxi charging cost optimization method and system |
-
2023
- 2023-02-14 CN CN202310127683.6A patent/CN116029453A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116843045A (en) * | 2023-09-01 | 2023-10-03 | 南京特拉利精密电子科技有限公司 | Vehicle charging pile management method and system |
| CN116843045B (en) * | 2023-09-01 | 2023-11-14 | 南京特拉利精密电子科技有限公司 | Vehicle charging pile management method and system |
| CN117172516A (en) * | 2023-11-03 | 2023-12-05 | 深圳航天科创泛在电气有限公司 | Charging pile dynamic scheduling decision-making method, device, equipment and storage medium |
| CN117172516B (en) * | 2023-11-03 | 2024-03-05 | 深圳航天科创泛在电气有限公司 | Charging pile dynamic scheduling decision-making method, device, equipment and storage medium |
| CN117635220A (en) * | 2024-01-26 | 2024-03-01 | 南京邮电大学 | Electric taxi charging cost optimization method and system |
| CN117635220B (en) * | 2024-01-26 | 2024-05-24 | 南京邮电大学 | Electric taxi charging cost optimization method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ma et al. | Optimal charging of plug-in electric vehicles for a car-park infrastructure | |
| CN103241130B (en) | Energy management method and system for electric bus charging and swap station | |
| Kumar et al. | V2G capacity estimation using dynamic EV scheduling | |
| CN105160451B (en) | A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle | |
| CN116029453A (en) | Electric automobile charging pile configuration method, recording medium and system | |
| CN108944531A (en) | A kind of orderly charge control method of electric car | |
| CN109492791B (en) | Inter-city expressway network light storage charging station constant volume planning method based on charging guidance | |
| CN111310966A (en) | Micro-grid site selection and optimal configuration method containing electric vehicle charging station | |
| CN109050284B (en) | Electric automobile charging and discharging electricity price optimization method considering V2G | |
| CN109657993A (en) | A kind of automatic demand response method of energy local area network energy-storage system based on non-cooperative game | |
| CN111079971A (en) | Charging station pricing method considering vehicle, station and network | |
| CN111799827A (en) | Method for regulating and controlling load of transformer area containing optical storage charging station | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| CN112550047B (en) | Optimal configuration method and device for light charging and storage integrated charging station | |
| Gorbunova et al. | Assessment of the use of renewable energy sources for the charging infrastructure of electric vehicles | |
| Rajani et al. | A hybrid optimization based energy management between electric vehicle and electricity distribution system | |
| CN114022046B (en) | Comprehensive energy system optimization scheduling method considering traffic balance | |
| Shi et al. | An economic evaluation of electric vehicles balancing grid load fluctuation, new perspective on electrochemical energy storage alternative | |
| He et al. | Expansion planning of electric vehicle charging stations considering the benefits of peak‐regulation frequency modulation | |
| CN110861508B (en) | Charging control method and system shared by residential area direct current chargers and storage medium | |
| CN112396223A (en) | Electric vehicle charging station energy management method under interactive energy mechanism | |
| CN112260274A (en) | Panoramic theory-based virtual power plant construction method | |
| CN110826210A (en) | Power interconnection-based multi-zone building virtual power plant modeling and optimization coordination method | |
| CN114884133B (en) | Micro-grid economic dispatching optimization method and system considering electric automobile | |
| Lei et al. | Coordinated scheduling of electric vehicles and wind power generation considering vehicle to grid mode |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |