CN115378016B - Multi-electric-vehicle cluster day-ahead charging plan generation method and system - Google Patents
Multi-electric-vehicle cluster day-ahead charging plan generation method and system Download PDFInfo
- Publication number
- CN115378016B CN115378016B CN202211110108.7A CN202211110108A CN115378016B CN 115378016 B CN115378016 B CN 115378016B CN 202211110108 A CN202211110108 A CN 202211110108A CN 115378016 B CN115378016 B CN 115378016B
- Authority
- CN
- China
- Prior art keywords
- charging
- cluster
- electric
- electric vehicle
- electric automobile
- 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.)
- Active
Links
- 238000007600 charging Methods 0.000 title claims abstract description 194
- 238000000034 method Methods 0.000 title claims abstract description 65
- 239000002245 particle Substances 0.000 claims abstract description 55
- 238000009826 distribution Methods 0.000 claims abstract description 54
- 230000005611 electricity Effects 0.000 claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 26
- 230000008569 process Effects 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 230000002068 genetic effect Effects 0.000 claims abstract description 8
- 238000005457 optimization Methods 0.000 claims description 40
- 238000003860 storage Methods 0.000 claims description 23
- 230000006870 function Effects 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 230000009467 reduction Effects 0.000 claims description 7
- 238000007599 discharging Methods 0.000 claims description 5
- 230000035772 mutation Effects 0.000 claims description 5
- 230000014509 gene expression Effects 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 230000002829 reductive effect Effects 0.000 abstract description 11
- 238000010586 diagram Methods 0.000 description 13
- 238000012545 processing Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 4
- 239000000178 monomer Substances 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000011065 in-situ storage Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 108700028369 Alleles Proteins 0.000 description 1
- 201000004569 Blindness Diseases 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 206010064571 Gene mutation Diseases 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010353 genetic engineering Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Power Engineering (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
A generation method and a generation system of a daily charge plan of a multi-electric-vehicle cluster fully consider the regulation and control requirements of a power grid and the charge economy of an electric-vehicle cluster, and combine a particle swarm algorithm improved based on a genetic algorithm to generate the daily charge and discharge plan of the multi-electric-vehicle cluster. According to the method, compared with unordered charging of the electric vehicles and participation in peak-valley electricity price demand response, the running index of the distribution network system can be optimized, the comprehensive cost of multi-electric vehicle cluster charging can be reduced, and the calculation dimension and the calculation amount of the electric vehicles in the power grid regulation and control process can be reduced.
Description
Technical Field
The application belongs to the field of new energy automobile application, and particularly relates to a multi-electric automobile cluster charging plan generation method and system considering power supply capacity of a power distribution network.
Background
Along with the reduction of fossil energy reserves and global climate change, various countries propose targets of carbon peak and carbon neutralization, and electric automobiles are representatives of terminal electrification, so that new energy power generation fluctuation can be stabilized while fossil energy use is reduced, and new energy power generation and consumption are assisted. However, with the large-scale development of electric vehicles, the safety and stability of urban power grid operation will meet great challenges. On one hand, the improvement of the duty ratio of the charging load of the electric automobile in the whole load of the urban power grid increases the peak-valley difference of the system load, so that the distribution transformer is exposed to the risk of heavy load or overrun operation; on the other hand, the large-scale electric automobile is charged simultaneously, so that the problems of voltage offset of nodes and branches of the electric automobile charging station and the like are caused. Therefore, the V2G technology is needed to realize orderly charging of the large-scale electric automobile, avoid the negative influence of the electric automobile on a power grid, and fully utilize the distributed energy storage resources of the electric automobile. Meanwhile, when the regional power distribution network regulates and controls the electric automobile, the traditional direct control mode cannot adapt to the characteristics of mass, distribution, small monomer capacity, strong randomness and the like of the electric automobile. Clustered control modes are generally adopted for massive electric vehicles to reduce operation dimensionality.
Based on the existing research, the centralized distribution of the multi-electric-vehicle cluster charging power in a certain power distribution network area is researched from the perspective of a power grid, so that the multi-electric-vehicle cluster charging plan generation method considering the power supply capacity of the power distribution network in the area is formed, the cluster charging cost is optimized under the condition that the safe and stable operation condition of the power grid is ensured, and meanwhile, the supply and demand unbalance risk caused by the disorder of electric vehicle charging can be avoided.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a method and a system for generating a daily charging plan of a plurality of electric vehicles, which optimize the cluster charging cost under the condition of ensuring the safe and stable operation of a power grid and can avoid the supply and demand unbalance risk caused by the charge disorder of the electric vehicles.
The application adopts the following technical scheme.
A method for generating a daily charging plan of a multi-electric-vehicle cluster comprises the following steps:
step 1, acquiring electric vehicle cluster information, obtaining the overall charging demand and charging load of an electric vehicle cluster, and generating the day-ahead schedulable capacity of the electric vehicle cluster;
step 2, predicting regional base load, combining the daily scheduling capability of the electric vehicle clusters, making a regional power distribution network scheduling plan, obtaining regional power distribution network regulation and control requirements, and distributing corresponding electric vehicle charging time-of-use electricity prices;
step 3, the electric automobile clusters carry out on-site electric automobile charging schedule adjustment according to the time-of-use electricity price issued by the power grid and with the minimum cluster charging cost, so as to obtain unordered charging cost and economical optimization charging cost information;
and 4, constructing a charging scheduling model based on the electric automobile clusters, and generating an overall electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
Preferably, in step 1, the electric vehicle charging electric position is used for dividing the electric vehicle clusters, that is, the charging station to which the electric vehicle is connected is used as the minimum unit of the cluster division.
Preferably, in step 2, the power distribution network regulation and control requirements include peak regulation and voltage regulation, wherein the peak regulation includes peak regulation time period and load reduction amount; the pressure regulation includes: and the voltage weak node is provided with an active load reduction amount and a reactive compensation input amount.
Preferably, in step 2, the time-of-use electricity price includes: charging peak electricity price of electric automobile, charging valley electricity price of electric automobile and charging ultra-low valley electricity price of electric automobile.
Preferably, in step 3, the disordered charging cost refers to the initial charging time and the corresponding charging power of the electric vehicles in the local unregulated cluster, so as to obtain the charging cost; the economical optimization charging cost refers to the charging cost obtained by locally adjusting the initial charging time and the corresponding charging power of the electric vehicles in the cluster by taking the lowest overall charging cost of the cluster as a target.
Preferably, in step 4, the charging schedule model based on the electric automobile cluster is as follows:
in the method, in the process of the application,
the minF represents that the minimum load variance of the regional distribution network is used as an optimization target;
t represents the number of time periods within one cycle;
P n,t representing a load level at node n that does not include the electric vehicle output;
P k,t the electric vehicle charging load of the cluster k is represented;
n represents the total number of nodes of the system;
N clu the number of the electric automobile clusters in the area is represented;
representing the average load of the system over a period.
Preferably, in step 4, generating an electric vehicle cluster day-ahead plan in combination with constraint conditions, where the constraint conditions include: unordered charging cost, economic optimization cost, electric automobile cluster schedulability constraint, electric automobile cluster charging demand constraint, distribution transformer capacity constraint and distribution line capacity constraint.
Preferably, in step 4, a charging plan is generated by adopting a particle swarm optimization algorithm improved based on a genetic algorithm, charging and discharging power of each electric automobile cluster point is used as a solving target, under the condition of considering a constraint function, the improved particle swarm algorithm is used for solving, and the positions of particles correspond to the charging and discharging power of each cluster point; the improved particle swarm optimization algorithm comprises the following specific steps:
step 4.1, the crossover operation formula is as follows:
in the method, in the process of the application,
representing an ith individual particle in an nth iteration;
representing the jth individual particle in the nth iteration;
representing the ith individual particle in the n+1th iteration;
representing the jth individual particle in the n+1th iteration;
alpha is a parameter, and when alpha is a constant, the crossover operation becomes uniform arithmetic crossover; when α is a variable, non-uniform arithmetic crossover is performed at this time;
step 4.2, the speed and position update expression of the mutation operator is as follows:
in the method, in the process of the application,
c 1 and c 2 Representing a learning factor;
r 1 and r 2 Is a group of random numbers ranging from 0 to 1;
representing the speed of the x-th particle in the y-th dimension in the nth iteration;
representing the speed of the x-th particle in the y-th dimension in the n+1th iteration;
representing a history optimal particle swarm individual;
representing a historical optimal population of particle swarms;
representing the n-th iteration,/th>Is a cumulative difference of (2).
A multi-electric vehicle cluster day-ahead charging schedule generation system, comprising: the system comprises an acquisition module, a demand regulation module, a cost calculation module, a charging scheduling model module and a plan execution module, wherein,
the acquisition module is used for acquiring the electric vehicle cluster information to obtain the overall charging demand and charging load of the electric vehicle cluster and generate the day-ahead dispatching capability of the electric vehicle cluster;
the demand regulation and control module is used for predicting regional base load, combining the daily scheduling capability of the electric vehicle cluster, making a regional distribution network scheduling plan, obtaining regional distribution network regulation and control demands, and distributing corresponding electric vehicle charging time-of-use electricity prices;
the cost calculation module is used for carrying out on-site electric vehicle charging schedule adjustment according to the time-of-use electricity price issued by the electric vehicle cluster and the minimum cluster charging cost to obtain unordered charging cost and economic optimization charging cost information;
the charging scheduling model module is used for constructing a charging scheduling model based on the electric automobile clusters and generating an overall electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
A terminal comprising a processor and a storage medium; the storage medium is used for storing instructions;
the processor is used for operating according to the instruction to execute the steps of the method for generating the daily charging schedule of the multi-electric-vehicle cluster.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a method for generating a daily charge plan for a cluster of multiple electric vehicles.
Compared with the prior art, the application has the beneficial effects that the peak-valley difference of the system load can be effectively reduced, the weak node voltage is optimized, the risk of line blockage is reduced, and meanwhile, the charging cost of a plurality of electric automobile clusters is reduced.
Drawings
FIG. 1 is a diagram of a multi-level electric vehicle participating grid regulation system;
FIG. 2 is a graph of a residential load and its electric vehicle charge local economy optimization;
FIG. 3 is a flow chart of a method for generating a multi-electric vehicle cluster day-ahead charging schedule;
FIG. 4 is an IEEE33 node power distribution network implementation scenario set-up diagram;
fig. 5 is a diagram of upper and lower limit of schedulable ability of each electric automobile cluster in the embodiment;
fig. 6 is a graph of the overall load generated by the method of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present application.
Example 1.
As shown in fig. 3, a method for generating a daily charging plan of a multi-electric-vehicle cluster includes the following steps:
step 1, acquiring electric vehicle cluster information, obtaining the overall charging demand and charging load of the electric vehicle cluster, and generating the day-ahead dispatching capability of the electric vehicle cluster.
In this embodiment, preferably, for each time interval, the cluster information is collected through a bidirectional charging and discharging pile connected to the electric vehicle. Each electric vehicle cluster predicts the total charging demand and the charging load in the future, forms the capability of dispatching the electric vehicle cluster in the future, and uploads the information to a dispatching department of the power distribution network;
in this embodiment, preferably, for the division of the electric vehicle clusters, natural division is performed by using electric charging positions of electric vehicles, that is, charging stations connected with the electric vehicles are used as minimum units of the cluster division, and reporting of information and mutual capability of the electric vehicles is performed by using the charging stations as units.
And 2, predicting the regional base load by a power distribution network dispatching department, and making a power distribution network dispatching plan and carrying out safety check by combining the received electric vehicle charging information. And obtaining the regulation and control requirements of the regional distribution network, such as peak regulation (including peak regulation time period and load reduction amount), voltage regulation (including voltage weak node position, active load reduction amount and reactive compensation input amount), and the like, and distributing corresponding electric vehicle charging time-of-use electricity price.
In this embodiment, the time-of-use electricity price preferably includes: charging peak electricity price of electric automobile, charging valley electricity price of electric automobile and charging ultra-low valley electricity price of electric automobile. For the release of time-sharing charging electricity price, the time-sharing electricity price of the power grid is characterized in that the time period in each day is divided into 3 interval sections of peak, valley and flat according to electricity consumption peaks, and each interval section is given with different electricity price levels so as to guide and encourage residents to participate in peak clipping and valley filling of the power grid.
Preferably, in this embodiment, because the peak-valley time-sharing interval of the power grid and the real valley time interval of the power grid have access, on the basis of the original peak-valley time-sharing electricity price of a certain electric power grid, 24 hours a day is divided into 3 interval sections of peak, valley and ultra-valley, the corresponding time sections are respectively 6:00-22:00, 22:00-2:00 and 2:00-6:00, and different electricity price levels are given in different interval sections, so as to encourage the electric automobile user to better participate in peak-valley peak shaving. The peak-to-valley electricity prices are set forth in table 1.
TABLE 1 setting of Peak-to-Valley electric prices in the present application
| Time period | Peak-valley electricity price of certain ground network | Charging electricity price of electric automobile |
| 6:00-22:00 | 0.617 | 0.85 |
| 22:00-2:00 | 0.307 | 0.45 |
| 2:00-6:00 | 0.307 | 0.25 |
And 3, carrying out on-site electric vehicle charging schedule adjustment by the electric vehicle cluster according to the time-of-use electricity price issued by the power grid and with the minimum cluster charging cost to obtain unordered charging cost and economical optimization charging cost information, and uploading the unordered charging cost and economical optimization charging cost information to a power distribution network dispatching department.
The unordered charging cost refers to the initial charging time and corresponding charging power of the electric automobile in the local unregulated cluster, so as to obtain charging cost c dch The method comprises the steps of carrying out a first treatment on the surface of the The economical optimization of the charging cost refers to locally adjusting the initial charging time and the corresponding charging power of the electric vehicles in the cluster by taking the lowest overall charging cost of the cluster as a target, thereby obtaining the charging cost c opt 。
In-situ adjustment of an electric vehicle charging plan is to take a single electric vehicle cluster as a main body, and to locally adjust the initial charging time of electric vehicles in the cluster according to the time-of-use electricity price issued by a power grid regulation and control department in step 2 and with the minimum total cluster charging cost as a target, thereby obtaining the economic optimization charging cost c opt So the unordered charging cost c dch ＞c opt . If 2:00 starts to be the load off-peak period, the user can set the initial charging time to be 2:00 under the influence of the electricity price. The large-scale electric automobile can be charged in the ultra-low valley period of the power grid according to the electricity price guidance, so that the peak shaving pressure of the low valley of the power grid can be reduced, and the electricity consumption of a user can be reducedCost is increased.
However, this local adjustment based on peak-to-valley electricity prices also presents a certain problem, as shown in fig. 2, which is a curve of a load of a residential area and a local economic optimization of charging of an electric vehicle, it can be seen that the load peak of the electric vehicle of the residential area appears at about 20 hours, however, after the local economic optimization, the load peak shifts to 22 points, but the peak situation is not reduced basically. Therefore, the local economic optimization still has a certain blindness, and a unified dispatching department is required to generate a charging plan.
And 4, constructing a charging scheduling model based on the electric automobile clusters, and generating an overall electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
And the power distribution network dispatching department generates an overall electric vehicle charging plan of each electric vehicle cluster according to the information such as the node position of the electric vehicle cluster, the tide relation and the like and taking the system regulation and control requirement as a main target.
In the process, unordered charging cost and economic optimization cost uploaded by the clusters are used as constraint conditions, so that the economic efficiency of a generation plan is guaranteed to be superior to that of unordered charging of electric vehicles in an area, and the electric vehicle clusters are attracted to charge according to a power distribution network plan; the power distribution network dispatching department issues a charging plan to the electric automobile cluster;
for the generation of a daily charging plan of a plurality of electric automobile clusters, the control mode of the original power distribution network direct control is not applicable any more due to the characteristics of small monomer capacity, mass, dispersion and the like of large-scale electric automobiles, and the electric automobile clusters are involved in the regulation when the multi-level regulation mode is adopted.
Preferably, in the application, a charging station-level electric automobile cluster is used as a regulation unit, and system peak clipping and valley filling are used as main optimization targets.
In order to realize peak clipping and valley filling of a load curve of a power distribution system, a charging scheduling model based on an electric automobile cluster is established by referring to the variance of the total load level of the system according to a designed scheduling frame:
in the method, in the process of the application,
minF represents the minimum load variance of the regional distribution network as the optimization target of the embodiment;
t represents the number of time periods within one cycle;
P n,t representing a load level at node n that does not include the electric vehicle output;
P k,t the electric vehicle charging load of the cluster k is represented;
n represents the total number of nodes of the system;
N clu the number of the electric automobile clusters in the area is represented;
representing the average load of the system over a period.
While generating an electric vehicle cluster day-ahead plan, some constraints need to be considered, including: the electric automobile cluster can be scheduled for capacity constraint, electric automobile cluster charging demand constraint, distribution transformer capacity constraint, distribution line capacity constraint and the like.
(1) Electric automobile cluster schedulability and charging demand constraint: the electric automobile cluster charging power plan should not exceed the upper and lower limits of the schedulable capacity, the charging power variation at adjacent moments should be within the allowable range, and the generated charging plan should meet the charging requirements of users. Namely:
P k,min ≤P k,t ≤P k,max (3)
S i,exp ＜S i,dep ＜S i,max (i＝1,2,...EV k ) (4)
in the method, in the process of the application,
P k,max and P k,min Electric automobile respectively in cluster kUpper and lower limits of adjustability;
EV k representing the total number of electric vehicles with adjustable capability in the cluster k;
S i,exp 、S i,dep s and S i,max The State of Charge (SOC) at the end of the expected charging of the electric vehicle i, the actual SOC at the departure from the charging station, and the upper SOC limit set for the vehicle battery, respectively.
(2) Distribution transformer and line capacity constraints: when large-scale electric automobile charges simultaneously, will produce great pressure to the distribution transformer and the continuous distribution lines of this node. The sum of the total charging power and the basic load of the network at a certain moment cannot exceed the power supply capacity of the network distribution transformer, and overload or blocking phenomenon of a distribution line connected at a charging node does not occur, namely:
λ l ·(P n,t +P k,t )＜P l (l＝1,2,...,L) (6)
in the method, in the process of the application,
P T indicating the rated operating power of the regional distribution transformer;
λ l representing the proportion of electrical power passed by line l;
P l the rated power of line l is indicated.
In this embodiment, preferably, a particle swarm optimization algorithm modified based on a genetic algorithm is used to generate the electric automobile cluster charging plan. And taking the charge and discharge power of each electric automobile cluster point as a solving target, and solving by utilizing an improved particle swarm algorithm under the condition of considering the constraint function. The position of the particles corresponds to the charging power of each point of the respective clusters. Because the standard particle swarm optimization algorithm does not have the operations of selection, intersection and mutation, the problems of multiple convergence algebra, partial optimal value sinking and the like can be caused. Therefore, the application introduces three genetic operations of the genetic algorithm in the particle swarm algorithm, and improves the capability of the particle swarm algorithm for getting rid of local optimal values and the capability of improving search precision by improving the segmentation strategy of the population.
The crossover is the operation of replacing and recombining part of structures of two parent individuals to generate new individuals, so that the searching capability of the genetic algorithm is improved. Crossover in genetic manipulation is achieved by arithmetic crossover operators. Two individuals set at time nAnd->Arithmetic crossover is performed, then the two new individuals generated at time n+1 after crossover are:
in the method, in the process of the application,
representing an ith individual particle in an nth iteration;
representing the jth individual particle in the nth iteration;
representing the ith individual particle in the n+1th iteration;
representing the jth individual particle in the n+1th iteration;
alpha is a parameter, and when alpha is a constant, the crossover operation becomes uniform arithmetic crossover; when α is a variable, a non-uniform arithmetic crossover is performed at this time.
Preferably, α=2 is taken in the present application to give consideration to both randomness and speed of the search.
The mutation operation simulates the process of gene mutation in biological evolution, and changes a certain gene on the gene sequence into an allele. Here, use is made ofInstead of the position of the ith particle in the population of particles in D-dimensional space, x id By history of optimal individualsOptimal +.>Historical optimal population->Instead of global optimum->Use->Is tired of (1)>Replace->Wherein->The expression (9) is used to obtain:
substituting the above formula into the velocity and position updates of the basic particle swarm, namely (10) - (11), the velocity and position update expressions (12) - (13) of the introduced mutation operator can be obtained:
in the method, in the process of the application,
representing the speed of the x-th particle in the y-th dimension in the n+1th iteration;
representing the speed of the x-th particle in the y-th dimension in the nth iteration;
c 1 and c 2 Is a learning factor;
r 1 and r 2 Is a group of random numbers ranging from 0 to 1;
representing the location of the individual extremum of the xth particle in the y-th dimension;
representing the position of the global extremum of the population of particle swarms in the y-th dimension;
representing the speed of the x-th particle in the y-th dimension in the nth iteration;
representing the speed of the x-th particle in the y-th dimension in the n+1th iteration;
representing a history optimal particle swarm individual;
representing a historical optimal population of particle swarms;
representing the n-th iteration,/th>Is a cumulative difference of (2). According to the improvement of the formula, the particle swarm has learning capability, and meanwhile, the local and global searching capability of the particle swarm is improved. The flow of generating the cluster charging plan based on the particle swarm optimization algorithm improved by the genetic algorithm is shown in fig. 3.
The electric automobile clusters are charged in the day according to a power distribution network plan.
Example 2.
A multi-level electric automobile participates in a power grid regulation system, as shown in figure 1. When the regional distribution network regulates and controls the electric automobile, the traditional direct control mode cannot adapt to the characteristics of mass, distribution, small monomer capacity, strong randomness and the like of the electric automobile. Clustered control modes are generally adopted for massive electric vehicles to reduce operation dimensionality. The electric automobile cluster regulation and control generally adopts a three-layer dispatching system, namely a dispatching layer, a group layer and an electric automobile layer.
Example 3.
An IEEE33 node system is selected as an implementation scene, and as shown in fig. 4, the whole power distribution network area is divided into a residential area, a business area and an industrial area, and each node power curve is respectively set. In this area, 5 electric car clusters are set, and the charging station numbers, the types of the areas and the situations of configuring the charging piles are shown in table 2.
Table 2 configuration settings of each charging station
The implementation scene is established in the power distribution network area, the electric automobile adopts a constant power charging mode, and the energy storage capacity of a bicycle is 60kWh. The upper and lower limits of the schedulable capacity of each electric automobile cluster are shown in fig. 5. The method of the application is adopted to generate ordered charge and discharge plans for 5 electric vehicles, and after the ordered charge and discharge plans are overlapped with regional base load, the ordered charge and discharge plans can be compared with a system total load curve during disordered charge, as shown in figure 6.
The dash-dot line in fig. 6 is the overall load curve of the regional distribution network electric vehicle in the disordered charge state, the overall curve shows obvious peak-valley difference, the peak-valley difference is larger when the valley period is 0 to 6 and the peak period is 9 to 21. The broken line in the figure is the base load curve of the area, and it can be seen that the disordered charge of the electric automobile will cause the phenomenon of peak-to-peak addition. In the graph 6, the solid line is the total load curve generated by the method, the peak-valley difference of the generated curve is superior to the curve containing unordered charging load, the electric energy resource in the valley period is effectively utilized for charging the electric automobile, the load curve in the peak period has smaller fluctuation, and the peak load is lower. Therefore, the method provided by the application can effectively optimize the peak-valley difference of the overall load of the distribution network system.
In the description, three strategies are mentioned for electric vehicle cluster charging, namely, a disordered charging strategy, an on-site economic optimization strategy and an optimization strategy provided by the application, and the advantages and disadvantages of the three strategies are compared in the aspects of the influence of electric vehicle cluster charging on a power distribution network system and the comprehensive cost of electric vehicle cluster charging. The parameter pairs of the specific generated curves are shown in table 3.
Table 3 parameter comparison of the curves generated by the methods
| Disordered charging | In situ economic optimization | The method of the application | |
| Peak of total load Gu Chalv | 63.744％ | 58.494％ | 60.547％ |
| Node maximum voltage offset | 0.0743p.u. | 0.0913p.u. | 0.0614p.u. |
| Maximum load rate of line | 85.45％ | 108.11％ | 75.67％ |
| Total cost of charging of clusters | 4346.02 yuan | 3321.97 yuan | 3768.38 yuan |
As can be seen from table 3, the overall load peak Gu Chalv and the overall cluster charging cost are better in the on-site economy optimization, because the on-site economy optimization is performed according to the peak-to-valley electricity price formulated by the power grid, which is performed essentially according to the peak regulation target of the power grid, and the various constraints of the system operation are not considered, the peak-to-valley difference rate results are better, but the performance is poorer in the aspects of the node maximum voltage offset, the line maximum load rate and the like. On one hand, the method provided by the application meets various operation constraints of the system in system performance, and on the other hand, the total load peak Gu Chalv and the total cluster charging cost are superior to those of the disordered charging method, so that the comprehensive satisfaction of the system performance and economy is achieved, and certain superiority is achieved.
Example 4.
A multi-electric vehicle cluster day-ahead charging schedule generation system, comprising: the system comprises an acquisition module, a demand regulation module, a cost calculation module and a charging scheduling model module, wherein,
the acquisition module is used for acquiring the electric vehicle cluster information to obtain the overall charging demand and charging load of the electric vehicle cluster and generate the day-ahead dispatching capability of the electric vehicle cluster;
the demand regulation and control module is used for predicting regional base load, combining the daily scheduling capability of the electric vehicle cluster, making a regional distribution network scheduling plan, obtaining regional distribution network regulation and control demands, and distributing corresponding electric vehicle charging time-of-use electricity prices;
the cost calculation module is used for carrying out on-site electric vehicle charging schedule adjustment according to the time-of-use electricity price issued by the electric vehicle cluster and the minimum cluster charging cost to obtain unordered charging cost and economic optimization charging cost information;
the charging scheduling model module is used for constructing a charging scheduling model based on the electric automobile clusters and generating an overall electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
Example 5.
Embodiment 5 of the present application provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a program which when executed by a processor performs a step in a multi-level electric vehicle participation grid regulation method according to the first embodiment of the present application.
The detailed steps are the same as those of the method for regulating and controlling the electric network of the multi-level electric vehicle provided in embodiment 1, and are not repeated here.
Example 6.
The embodiment 6 of the application provides an electronic device.
An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements steps in a multi-level electric vehicle participation power grid regulation method according to the first embodiment of the application when executing the program.
The detailed steps are the same as those of the method for regulating and controlling the electric network of the multi-level electric vehicle provided in embodiment 1, and are not repeated here.
Compared with the prior art, the application has the beneficial effects that the peak-valley difference of the system load can be effectively reduced, the weak node voltage is optimized, the risk of line blockage is reduced, and meanwhile, the charging cost of a plurality of electric automobile clusters is reduced.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (8)
1. The method for generating the daily charging schedule of the multi-electric-vehicle cluster is characterized by comprising the following steps of:
step 1, acquiring electric vehicle cluster information, obtaining the overall charging demand and charging load of an electric vehicle cluster, and generating the day-ahead schedulable capacity of the electric vehicle cluster;
step 2, predicting regional base load, combining the daily scheduling capability of the electric vehicle clusters, making a regional power distribution network scheduling plan, obtaining regional power distribution network regulation and control requirements, and distributing corresponding electric vehicle charging time-of-use electricity prices;
step 3, the electric automobile clusters carry out on-site electric automobile charging schedule adjustment according to the time-of-use electricity price issued by the power grid and with the minimum cluster charging cost, so as to obtain unordered charging cost and economical optimization charging cost information;
step 4, constructing a charging scheduling model based on the electric automobile cluster, and generating an overall electric automobile charging plan of the electric automobile cluster by combining constraint conditions and an improved particle swarm optimization algorithm; the charging scheduling model based on the electric automobile cluster is as follows:
in the method, in the process of the application,the method comprises the steps of representing that the minimum load variance of a regional distribution network is used as an optimization target; />Representing the number of time periods within one cycle; />Representing nodesnThe position does not contain the load level of the electric automobile output; />Representation clusterskIs a charging load of an electric automobile;representing the total number of nodes of the system; />The number of the electric automobile clusters in the area is represented; />Representing the average load of the system in one period;
the constraint conditions include: unordered charging cost, economic optimization cost, electric vehicle cluster schedulability constraint, electric vehicle cluster charging demand constraint, distribution transformer capacity constraint and distribution line capacity constraint;
generating a charging plan by adopting a particle swarm optimization algorithm based on genetic algorithm improvement, taking charging and discharging power of each electric automobile cluster point as a solving target, and solving by utilizing the improved particle swarm algorithm under the condition of considering a constraint function, wherein the positions of particles correspond to the charging and discharging power of each cluster point; the improved particle swarm optimization algorithm comprises the following specific steps:
step 4.1, the crossover operation formula is as follows:
in the method, in the process of the application,represent the firstnThe first iterationiIndividual particles; />Represent the firstnThe first iterationjIndividual particles;represent the firstn+1 iteration of the first iterationiIndividual particles; />Represent the firstn+1 iteration of the first iterationjIndividual particles; />Is a parameter, when->The crossover operation becomes uniform arithmetic crossover when constant; when->When the variable is variable, non-uniform arithmetic crossover is performed at the moment;
step 4.2, the speed and position update expression of the mutation operator is as follows:
in the method, in the process of the application,and->Representing a learning factor; />And->Is a group of random numbers of 0-1; />Represent the firstnIn the second iterationxParticle NoyDimension(s)A speed; />Represent the firstn+1 iteration of the processxParticle NoyThe speed of the dimension; />Representing a history optimal particle swarm individual; />Representing a historical optimal population of particle swarms; />Representing the n-th iteration,/th>Is a cumulative difference of (2).
2. The method for generating a daily charge schedule for a plurality of electric vehicles according to claim 1, wherein,
in step 1, the electric vehicle charging electric position is used for dividing the electric vehicle clusters, namely, the charging station connected with the electric vehicle is used as the minimum unit of the cluster division.
3. The method for generating a daily charge schedule for a plurality of electric vehicles according to claim 1, wherein,
in the step 2, the power distribution network regulation and control requirements comprise peak regulation and voltage regulation, wherein the peak regulation comprises peak regulation time period and load reduction amount; the pressure regulation includes: and the voltage weak node is provided with an active load reduction amount and a reactive compensation input amount.
4. The method for generating a daily charge schedule for a plurality of electric vehicles according to claim 1, wherein,
in step 2, the time-of-use electricity price includes: charging peak electricity price of electric automobile, charging valley electricity price of electric automobile and charging ultra-low valley electricity price of electric automobile.
5. The method for generating a daily charge schedule for a plurality of electric vehicles according to claim 1, wherein,
in step 3, the disordered charging cost refers to the initial charging time and the corresponding charging power of the electric automobile in the local unregulated cluster, so as to obtain the charging cost; the economical optimization charging cost refers to the charging cost obtained by locally adjusting the initial charging time and the corresponding charging power of the electric vehicles in the cluster by taking the lowest overall charging cost of the cluster as a target.
6. A multi-electric cluster day-ahead charging schedule generation system utilizing the method of any one of claims 1-5, comprising: the system comprises an acquisition module, a demand regulation module, a cost calculation module and a charging scheduling model module, and is characterized in that,
the acquisition module is used for acquiring the electric vehicle cluster information to obtain the overall charging demand and charging load of the electric vehicle cluster and generate the day-ahead dispatching capability of the electric vehicle cluster;
the demand regulation and control module is used for predicting regional base load, combining the daily scheduling capability of the electric vehicle cluster, making a regional distribution network scheduling plan, obtaining regional distribution network regulation and control demands, and distributing corresponding electric vehicle charging time-of-use electricity prices;
the cost calculation module is used for carrying out on-site electric vehicle charging schedule adjustment according to the time-of-use electricity price issued by the electric vehicle cluster and the minimum cluster charging cost to obtain unordered charging cost and economic optimization charging cost information;
the charging scheduling model module is used for constructing a charging scheduling model based on the electric automobile clusters and generating an overall electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
7. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of a method for generating a multi-electric vehicle cluster day-ahead charging plan according to any one of claims 1-5.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of a method for generating a daily charge plan for a cluster of multiple electric vehicles according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211110108.7A CN115378016B (en) | 2022-09-13 | 2022-09-13 | Multi-electric-vehicle cluster day-ahead charging plan generation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211110108.7A CN115378016B (en) | 2022-09-13 | 2022-09-13 | Multi-electric-vehicle cluster day-ahead charging plan generation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115378016A CN115378016A (en) | 2022-11-22 |
| CN115378016B true CN115378016B (en) | 2023-10-27 |
Family
ID=84072310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211110108.7A Active CN115378016B (en) | 2022-09-13 | 2022-09-13 | Multi-electric-vehicle cluster day-ahead charging plan generation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115378016B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009148121A (en) * | 2007-12-17 | 2009-07-02 | Denso Corp | Charging system for plug-in vehicle |
| CN114462854A (en) * | 2022-02-08 | 2022-05-10 | 国网江苏省电力有限公司苏州供电分公司 | Hierarchical scheduling method and system containing new energy and electric vehicle grid connection |
| CN114629148A (en) * | 2022-03-14 | 2022-06-14 | 国网江苏省电力有限公司苏州供电分公司 | Electric power system scheduling framework method containing electric vehicle resources and system thereof |
-
2022
- 2022-09-13 CN CN202211110108.7A patent/CN115378016B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009148121A (en) * | 2007-12-17 | 2009-07-02 | Denso Corp | Charging system for plug-in vehicle |
| CN114462854A (en) * | 2022-02-08 | 2022-05-10 | 国网江苏省电力有限公司苏州供电分公司 | Hierarchical scheduling method and system containing new energy and electric vehicle grid connection |
| CN114629148A (en) * | 2022-03-14 | 2022-06-14 | 国网江苏省电力有限公司苏州供电分公司 | Electric power system scheduling framework method containing electric vehicle resources and system thereof |
Non-Patent Citations (1)
| Title |
|---|
| 蔡子龙等.计及行车计划编制的电动公交车有序充电策略.电力自动化设备.2021,第41卷(第6期),45-56. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115378016A (en) | 2022-11-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review | |
| Rivera et al. | Distributed convex optimization for electric vehicle aggregators | |
| Ma et al. | Optimal charging of plug-in electric vehicles for a car-park infrastructure | |
| Li et al. | Optimization of bi-directional V2G behavior with active battery anti-aging scheduling | |
| Yang et al. | A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem | |
| CN110378548B (en) | Electric automobile virtual power plant multi-time scale response capability assessment model construction method | |
| Lu et al. | Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm | |
| Zhang et al. | Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO | |
| Rezaeimozafar et al. | A self-optimizing scheduling model for large-scale EV fleets in microgrids | |
| CN113258581B (en) | Source-load coordination voltage control method and device based on multiple intelligent agents | |
| CN112952847B (en) | Multi-region active power distribution system peak regulation optimization method considering electricity demand elasticity | |
| Ahmadi et al. | Optimal use of vehicle-to-grid technology to modify the load profile of the distribution system | |
| JP2020524325A (en) | A method of constructing a displaceable load model considering both environmental costs and rear-time electricity bill | |
| Darabi et al. | Intelligent charge rate optimization of PHEVs incorporating driver satisfaction and grid constraints | |
| Ma et al. | Real-time plug-in electric vehicles charging control for V2G frequency regulation | |
| Wu et al. | Electric vehicle charging scheduling considering infrastructure constraints | |
| CN112308373A (en) | Electric automobile ordered charging and discharging scheduling model and algorithm based on space-time double-layer optimization | |
| Bazmohammadi et al. | Optimal operation management of a microgrid based on MOPSO and Differential Evolution algorithms | |
| Chen et al. | Real-time optimal scheduling of large-scale electric vehicles: A dynamic non-cooperative game approach | |
| CN109149589B (en) | Flexible load management method and system based on multi-agent mechanism | |
| Wang et al. | Coordinated planning of electric vehicle charging infrastructure and renewables in power grids | |
| Ren et al. | Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price | |
| Wu et al. | On optimal charging scheduling for electric vehicles with wind power generation | |
| CN114612001A (en) | Regulation and control instruction decomposition method and system for cluster electric vehicle participating in power grid peak clipping | |
| Yi et al. | Research of building load optimal scheduling based on multi-objective estimation of distributed algorithm |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |