CN115378016A - Method and system for generating day-ahead charging plan of multi-electric automobile cluster - Google Patents
Method and system for generating day-ahead charging plan of multi-electric automobile cluster Download PDFInfo
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Abstract
A day-ahead charging plan generation method and a system for a multi-electric automobile cluster fully consider the regulation and control requirements of a power grid and the charging economy of an electric automobile cluster, and are combined with a particle swarm algorithm improved based on a genetic algorithm to generate a day-ahead plan for charging and discharging of the multi-electric automobile cluster. Example analysis can prove that compared with disordered charging and peak-valley price demand response participation of electric vehicles, the method disclosed by the invention can optimize the running indexes of a distribution network system, reduce the comprehensive cost of multi-electric vehicle cluster charging and reduce the calculation dimensionality and the calculation amount of the electric vehicles in the power grid regulation and control process.
Description
Technical Field
The invention 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
With the reduction of fossil energy reserves and global climate change, various countries put forward targets of carbon peak reaching and carbon neutralization, and the electric automobile is a representative of terminal electrification, so that the utilization of fossil energy can be reduced, the power generation fluctuation of new energy can be stabilized, and the consumption of new energy power generation can be assisted. However, with the scale development of electric vehicles, the safety and stability of the operation of the urban power grid will meet great challenges. On one hand, the proportion of the charging load of the electric automobile in the overall load of the urban power grid is improved, the load peak-valley difference of the system is increased, and the distribution transformer is subjected to heavy load or overrun operation risk; on the other hand, the large-scale electric vehicles are charged simultaneously, and the problems of voltage offset of nodes and branches of the electric vehicle charging stations are also caused. Therefore, the ordered charging of the large-scale electric automobile is realized through the V2G technology, the negative influence of the electric automobile on a power grid is avoided, and the distributed energy storage resources are fully utilized. Meanwhile, when the regional power distribution network regulates and controls the electric automobile, the traditional direct control mode cannot adapt to the characteristics of large quantity, small distribution, small monomer capacity, strong randomness and the like of the electric automobile. Therefore, a clustering control mode is generally adopted for mass electric vehicles to reduce the operation dimension.
On the basis of the existing research, the centralized distribution of the charging power of the multiple electric automobile clusters in a certain power distribution network area is researched from the perspective of a power grid, and the generation method of the charging plan of the multiple electric automobile clusters considering the power supply capacity of the area power distribution network is formed, so that the cluster charging cost is optimized under the condition that the safe and stable operation of the power grid is ensured, and meanwhile, the risk of unbalanced supply and demand caused by the disorder of electric automobile charging can be avoided.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method and a system for generating a day-ahead charging plan of a multi-electric automobile cluster, which optimize the cluster charging cost under the condition of ensuring the safe and stable operation of a power grid and can avoid the risk of unbalanced supply and demand caused by the disorder of electric automobile charging.
The invention adopts the following technical scheme.
A method for generating a day-ahead charging plan of a multi-electric automobile cluster comprises the following steps:
and 4, constructing a charging scheduling model based on the electric automobile clusters, and generating a total electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
Preferably, in step 1, the electric vehicle cluster is divided according to the electric vehicle charging position, that is, the charging station connected to the electric vehicle is taken as the minimum unit of cluster division.
Preferably, in step 2, the regulation and control requirements of the power distribution network include peak regulation and voltage regulation, wherein the peak regulation includes a peak regulation period and a load reduction amount; the pressure regulation includes: and the voltage weak node position, the active load reduction amount and the reactive compensation putting amount.
Preferably, in step 2, the time of use electricity price includes: the peak electricity price of charging the electric automobile, the valley electricity price of charging the electric automobile, and the ultra-low valley electricity price of charging the electric automobile.
Preferably, in the step 3, the unordered 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 economic optimization charging cost is the charging cost obtained by locally adjusting the initial charging time and the corresponding charging power of the electric vehicles in the cluster with the aim of minimizing the overall charging cost of the cluster.
Preferably, in step 4, the charging scheduling model based on the electric vehicle cluster is as follows:
in the formula (I), the compound is shown in the specification,
minF represents that the minimum load variance of the regional distribution network is taken as an optimization target;
t represents the number of time segments in one period;
P n,t representing the load level without electric vehicle output at the node n;
P k,t representing the electric vehicle charging load of cluster k;
n represents the total number of nodes of the system;
N clu the number of electric vehicle clusters in the region is represented;
Preferably, in step 4, the electric vehicle cluster day-ahead plan is generated by combining constraint conditions, where the constraint conditions include: unordered charging cost, economic optimization cost, electric vehicle cluster schedulable capacity constraint, electric vehicle 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, the charging and discharging power of each electric vehicle cluster point is taken as a solving target, and the solving is carried out by utilizing the improved particle swarm optimization algorithm under the condition of considering a constraint function, wherein the position of each particle corresponds 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 cross operation formula is as follows:
in the formula (I), the compound is shown in the specification,
alpha is a parameter, and the intersection operation becomes uniform arithmetic intersection when alpha is a constant; when α is a variable, non-uniform arithmetic interleaving is performed at this time;
step 4.2, the speed and position updating expression of the mutation operator is as follows:
in the formula (I), the compound is shown in the specification,
c 1 and c 2 Represents a learning factor;
r 1 and r 2 Is a group of random numbers of 0-1;
A multi-electric automobile cluster day-ahead charging plan generation system comprises: an acquisition module, a demand regulation and control module, a cost calculation module, a charging scheduling model module and a plan execution module, wherein,
the acquisition module is used for acquiring cluster information of the electric automobile, acquiring the overall charging demand and charging load of the electric automobile cluster and generating the day-ahead schedulable capacity of the electric automobile cluster;
the demand regulation and control module is used for predicting regional basic loads, formulating a regional power distribution network dispatching plan by combining the day-ahead dispatching capability of the electric vehicle cluster, obtaining the regional power distribution network regulation and control demand and issuing corresponding charging time-of-use electricity price of the electric vehicle;
the cost calculation module is used for performing on-site electric vehicle charging plan adjustment by the electric vehicle cluster according to the time-of-use electricity price issued by the power grid according to the cluster charging cost minimum to obtain disordered 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 a total 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; a storage medium to store instructions;
the processor is used for operating according to the instruction to execute the steps of the multi-electric automobile cluster day-ahead charging plan generation method.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for generating a multi-electric vehicle cluster day-ahead charging plan.
Compared with the prior art, the method has the advantages that the load peak-valley difference of the system can be effectively reduced, the weak node voltage is optimized, the line blocking risk is reduced, and meanwhile the charging cost of the multi-electric automobile cluster 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 load in a certain residential area and local economy of charging of an electric vehicle thereof after optimization;
FIG. 3 is a flow chart of a method for generating a multi-electric vehicle cluster day-ahead charging plan;
FIG. 4 is a diagram of an implementation scenario for an IEEE33 node power distribution network;
FIG. 5 is a diagram of schedulable capability bounds of each electric vehicle cluster in the embodiment;
FIG. 6 is a graph of the overall load generated by the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are only some embodiments of the invention, and not all embodiments. All other embodiments obtained by a person skilled in the art without any inventive step based on the spirit of the present invention are within the scope of the present invention.
Example 1.
As shown in fig. 3, a method for generating a day-ahead charging plan of multiple electric vehicle clusters includes the following steps:
Preferably, the present embodiment collects cluster information through a bidirectional charge-discharge pile connected to the electric vehicle for each time interval. Each electric automobile cluster predicts the total charging demand and charging load day ahead to form the day ahead scheduling capability of the electric automobile cluster, and uploads the information to the power distribution network scheduling department;
preferably, in the embodiment, for the division of the electric vehicle cluster, the charging electrical position of the electric vehicle is naturally divided, that is, the charging station connected to the electric vehicle is used as the minimum unit of the cluster division, and the reporting of the information and the interaction capacity of the electric vehicle is performed by using the charging station as the unit.
And 2, the power distribution network dispatching department predicts the regional basic load, and formulates a power distribution network dispatching plan by combining the received electric vehicle charging information, and performs security check. And obtaining regulation and control requirements of the regional power distribution network, such as peak regulation (including peak regulation time period and load reduction), voltage regulation (including voltage weak node position, active load reduction and reactive compensation input amount) and the like, and distributing corresponding charging time-sharing electricity price of the electric automobile.
The present embodiment preferably provides the time-of-use electricity prices including: the peak electricity price of charging the electric automobile, the valley electricity price of charging the electric automobile, and the ultra-low valley electricity price of charging the electric automobile. For the release of the time-sharing charging electricity price, the time-sharing electricity price of the power grid is divided into 3 sections of peak, valley and average according to the electricity utilization peak, and each 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 comes in and goes out from the real valley area of the power grid, on the basis of the peak-valley time-sharing electricity price of the original residents of the power grid at a certain place, 24h a day is divided into 3 sections, namely, the peak-valley section, the valley section and the super-valley section, and the corresponding sections are respectively 6. The peak to valley electricity rate settings are shown in table 1.
TABLE 1 setting of peak-to-valley electricity rates in the present invention
| Time period | Peak-to-valley electricity price of power grid in certain place | Charging 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, the electric automobile cluster performs local electric automobile charging plan adjustment according to the time-of-use electricity price issued by the power grid and with the cluster charging cost minimum to obtain disordered charging cost and economic optimization charging cost information, and uploads the disordered charging cost and economic optimization charging cost information to a power distribution network dispatching department.
Wherein, the unordered 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 c dch (ii) a The charging cost optimized by economy means that the electric automobiles in the cluster are locally regulated by taking the lowest integral charging cost of the cluster as a targetInitial charging time and corresponding charging power, resulting in a charging cost c opt 。
The local adjustment of the charging plan of the electric automobile is realized by taking a single electric automobile cluster as a main body, locally adjusting the initial charging time of the electric automobiles in the cluster by taking the minimum total cluster charging cost as a target according to the time-of-use electricity price issued by the power grid regulation and control department in the step 2, and thus obtaining the economic optimization charging cost c opt Therefore, cost of charging disorderly c dch ＞c opt . Starting as 2. The large-scale electric automobile can be charged in the ultra-low valley period of the power grid according to the power price guide, so that the low valley peak load regulation pressure of the power grid can be reduced, and the power consumption cost of a user can be reduced.
However, such local adjustment based on peak-to-valley electricity prices also presents a certain problem, as shown in fig. 2, which is a curve of local economy optimization of load and charging of electric vehicles in a certain residential area, it can be seen that load peaks of electric vehicles in the residential area occur at about 20 hours, but after the local economy optimization, the load peaks are shifted to 22 hours, but the peak situation is not fundamentally reduced. Therefore, the local economic optimization still has certain blindness, and a unified scheduling department is needed to generate the charging plan.
And 4, constructing a charging scheduling model based on the electric automobile clusters, and generating a total 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 a total electric vehicle charging plan of each electric vehicle cluster by taking meeting the system regulation and control requirements as a main target according to the information such as the node position of the electric vehicle cluster and the trend relation.
In the process, the disordered charging cost and the economic optimization cost uploaded by the cluster are used as constraint conditions to ensure that the economic performance of the generated plan is superior to that of the disordered charging of the electric automobiles in the region, so that the cluster of the electric automobiles is attracted to be charged according to the plan of the power distribution network; the power distribution network dispatching department issues a charging plan to the electric automobile cluster;
for the generation of a day-ahead charging plan of a multi-electric automobile cluster, due to the characteristics of small monomer capacity, large quantity, dispersion and the like of large-scale electric automobiles, a control mode of directly controlling an original power distribution network is not applicable any more, and when the multi-level regulation and control mode is adopted, electric automobile clusters are often involved in regulation and control.
Preferably, the charging station-level electric vehicle cluster is used as a regulation and control 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 framework:
in the formula (I), the compound is shown in the specification,
minF represents that the minimum load variance of the regional distribution network is taken as the optimization target of the embodiment;
t represents the number of time segments in one period;
P n,t representing the load level without electric vehicle output at the node n;
P k,t representing the electric vehicle charging load of the cluster k;
n represents the total number of nodes of the system;
N clu the number of electric vehicle clusters in the region is represented;
While generating a day-ahead plan for an electric vehicle cluster, some constraints need to be considered, including: the system comprises electric automobile cluster schedulable capacity constraint, electric automobile cluster charging demand constraint, distribution transformer capacity constraint, distribution line capacity constraint and the like.
(1) Electric automobile cluster schedulable ability and charging demand constraints: the charging power plan of the electric automobile cluster does not exceed the upper limit and the lower limit of the schedulable capacity, the charging power change at adjacent moments is within an allowable range, and the generated charging plan meets the charging requirement of a user. 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 formula (I), the compound is shown in the specification,
P k,max and P k,min The adjustable capacity upper limit and the adjustable capacity lower limit are respectively set for the k electric automobiles in the cluster;
EV k representing the total number of electric vehicles with adjustable capacity in the cluster k;
S i,exp 、S i,dep and S i,max The State of Charge (SOC) of the electric vehicle i at the expected end of charging, the actual SOC when leaving the charging station, and the SOC upper limit set for the vehicle battery are respectively.
(2) Distribution transformer and line capacity constraints: when a large-scale electric automobile is charged simultaneously, a large pressure is generated on a distribution transformer and a connected distribution line of the node. The sum of the total charging power and the basic load of the network cannot exceed the power supply capacity of the distribution transformer of the network at a certain moment, and the distribution line connected with a charging node should not be overloaded or blocked, namely:
λ l ·(P n,t +P k,t )＜P l (l＝1,2,...,L) (6)
in the formula (I), the compound is shown in the specification,
P T representing the rated operating power of the regional distribution transformer;
λ l indicating lineThe ratio of electrical power passed by way l;
P l representing the rated transmission power of line i.
Preferably, in this embodiment, a particle swarm optimization algorithm improved based on a genetic algorithm is adopted to generate the electric vehicle cluster charging plan. And taking the charge and discharge power of each electric vehicle cluster point as a solving target, and solving by using 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 cluster. Because the standard particle swarm optimization algorithm does not have selection, intersection and variation operations, the problems of more convergent algebra, local optimal values and the like can be caused. Therefore, the invention introduces three genetic operations of the genetic algorithm in the particle swarm algorithm, and improves the capability of the particle swarm algorithm to get rid of local optimal values and the capability of improving the search precision by improving the segmentation strategy of the population.
The crossover is an operation of generating a new individual by replacing and recombining partial structures of two parent individuals, so that the searching capability of a genetic algorithm is improved. Crossover in genetic operations is achieved by arithmetic crossover operators. Two individuals set at the time n
And with
An arithmetic crossover is performed, then the two new individuals generated at time n +1 after the crossover are:
in the formula (I), the compound is shown in the specification,
alpha is a parameter, and the intersection operation becomes uniform arithmetic intersection when alpha is a constant; when α is a variable, non-uniform arithmetic interleaving is performed at this time.
Preferably, in the present invention, α =2 is adopted in order to achieve both randomness and speed of the search.
The mutation operation simulates the process of gene mutation in biological evolution, and a certain gene on a gene sequence is mutated into an allele. Herein, use
Replacing the position of the ith particle in the particle swarm in D-dimensional space, namely x id Using historically optimized individuals
Replacing individual optima in particle swarm optimization
Optimizing populations using history
Replacement of global optima
By using
Accumulated difference of
Instead of the former
Wherein
The following equation (9) is obtained:
substituting the above formulas into the speed and position updating expressions of the basic particle swarm, namely (10) - (11), obtaining the speed and position updating expressions (12) - (13) for introducing mutation operators:
in the formula (I), the compound is shown in the specification,
c 1 and c 2 Is a learning factor;
r 1 and r 2 Is a group of random numbers of 0-1;
The electric automobile cluster charges according to the distribution network plan in the day.
Example 2.
A multi-level electric automobile participation power grid regulation and control system is shown in figure 1. When the regional power distribution network regulates and controls the electric automobile, the traditional direct control mode cannot adapt to the characteristics of large quantity, distribution, small monomer capacity, strong randomness and the like of the electric automobile. Therefore, a clustering control mode is generally adopted for mass electric vehicles to reduce the operation dimension. The electric vehicle cluster control generally adopts three layers of scheduling systems, namely a scheduling layer, a cluster layer and an electric vehicle layer.
Example 3.
An IEEE33 node system is selected as an implementation scenario, as shown in fig. 4, the whole distribution network area is divided into a residential area, a commercial area and an industrial area, and power curves of each node are set respectively. In this area, 5 electric vehicle clusters are set, and the charging station number, the type of the area where the electric vehicle clusters are located, and the configuration charging pile condition are shown in table 2.
Table 2 arrangement of charging stations
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 single automobile is 60kWh. The upper and lower schedulable capability limits of each electric vehicle cluster are shown in figure 5. By adopting the method disclosed by the invention to generate the ordered charging and discharging plan of 5 electric automobiles, after the ordered charging and discharging plan is superposed with the regional basic load, the ordered charging and discharging plan can be compared with a total system load curve during the unordered charging, as shown in an attached figure 6.
The chain line in the attached figure 6 is the total load curve of the electric automobile in the region of the distribution network in the disordered charging state, the total curve presents obvious peak-valley difference, the peak-valley difference is larger when the low-valley time period is 0 to 6 and when the peak time period is 9 to 21. The dotted line in the figure is a base load curve in the region, and the phenomenon of peak adding caused by disordered charging of the electric automobile can be seen. The solid line in figure 6 is the total load curve generated by the method of the present invention, the peak-to-valley difference of the generated curve is better than the curve containing the disordered charging load, the electric energy resource in the valley period is effectively utilized to charge the electric vehicle, the fluctuation of the load curve in the peak period is smaller, and the peak load is also lower. Therefore, the method provided by the invention can effectively optimize the overall load peak-valley difference of the distribution network system.
In the above description, three strategies are provided for electric vehicle cluster charging, namely, an unordered charging strategy, a local economic optimization strategy, and the optimization strategy provided by the present invention, and the advantages and disadvantages of the three strategies will be compared in terms of the influence of electric vehicle cluster charging on the power distribution network system and the comprehensive cost of electric vehicle cluster charging. The parameter pairs for the specific generated curves are shown in table 3.
TABLE 3 comparison of parameters of curves generated by the methods
| Disordered charging | In situ economic optimization | The method of the invention | |
| Peak to valley rate of total load | 63.744％ | 58.494％ | 60.547％ |
| Maximum voltage offset of node | 0.0743p.u. | 0.0913p.u. | 0.0614p.u. |
| Line maximum load rate | 85.45％ | 108.11％ | 75.67％ |
| Total charge cost of cluster | 4346.02 yuan | 3321.97 yuan | 3768.38 yuan |
As can be seen from table 3, in terms of the total load peak-valley difference rate and the total cluster charging cost, the local economic optimization effect is better, because the local economic optimization is performed according to the peak-valley electricity price formulated by the power grid, which is performed according to the peak-peak adjustment target of the power grid essentially, and each constraint of system operation is not considered, the peak-valley difference rate result is better, but the performance is poorer in terms of the node maximum voltage deviation, the line maximum load rate, and the like. The method provided by the invention meets all operation constraints of the system on the one hand, and on the other hand, the total load peak-valley difference rate 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 the economy is achieved, and certain superiority is achieved.
Example 4.
A multi-electric automobile cluster day-ahead charging plan generation system comprises: an acquisition module, a demand regulation and control module, a cost calculation module and a charging scheduling model module, wherein,
the acquisition module is used for acquiring cluster information of the electric automobile, acquiring the overall charging demand and charging load of the electric automobile cluster and generating the day-ahead schedulable capacity of the electric automobile cluster;
the demand regulation and control module is used for predicting regional basic loads, formulating a regional distribution network dispatching plan by combining the day-ahead dispatching capability of the electric vehicle cluster to obtain regional distribution network regulation and control demands, and issuing corresponding electric vehicle charging time-of-use electricity price;
the cost calculation module is used for performing on-site electric vehicle charging plan adjustment by the electric vehicle cluster according to the time-of-use electricity price issued by the power grid at the minimum cluster charging cost to obtain disordered 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 a total electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
Example 5.
A computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in a method for regulating participation of a multi-level electric vehicle in a power grid according to a first embodiment of the present invention.
The detailed steps are the same as those of the multi-level electric vehicle participating in the power grid regulation and control method provided by the embodiment 1, and are not described herein again.
Example 6.
An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the method for regulating and controlling the participation of a multi-level electric vehicle in a power grid according to the first embodiment of the present invention.
The detailed steps are the same as those of the multi-level electric vehicle participation power grid regulation method provided in embodiment 1, and are not described again here.
Compared with the prior art, the method has the advantages that the load peak-valley difference of the system can be effectively reduced, the weak node voltage is optimized, the line blocking risk is reduced, and meanwhile, the charging cost of the multi-electric automobile cluster is reduced.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory 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: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical 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 via 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 transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter 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.
The computer program instructions for carrying out operations of the present disclosure may be assembler 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 execute 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
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 storing the instructions comprises 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 flowchart 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 used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (11)
1. A method for generating a day-ahead charging plan of a multi-electric automobile cluster is characterized by comprising the following steps:
step 1, collecting cluster information of an electric automobile to obtain the overall charging requirement and charging load of the electric automobile cluster, and generating the day-ahead schedulable capacity of the electric automobile cluster;
step 2, forecasting regional basic loads, combining the day-ahead schedulable capacity of the electric vehicle cluster, formulating a regional distribution network scheduling plan, obtaining the regional distribution network regulation and control requirements, and issuing corresponding charging time-of-use electricity prices of the electric vehicles;
step 3, the electric automobile cluster performs on-site electric automobile charging plan adjustment according to the time-of-use electricity price issued by the power grid and with the cluster charging cost minimum to obtain disordered charging cost and economic optimization charging cost information;
and 4, constructing a charging scheduling model based on the electric automobile cluster, and generating a total electric automobile charging plan of the electric automobile cluster by combining constraint conditions and an improved particle swarm optimization algorithm.
2. The method for generating a day-ahead charging plan for a cluster of multi-electric vehicles according to claim 1,
in the step 1, the electric vehicle cluster is divided according to the charging electric position of the electric vehicle, namely, a charging station connected with the electric vehicle is used as the minimum unit for cluster division.
3. The method for generating the day-ahead charging plan of multiple electric automobile clusters according to claim 1,
in the step 2, the regulation and control requirements of the power distribution network comprise peak regulation and voltage regulation, wherein the peak regulation comprises a peak regulation time period and a load reduction amount; the pressure regulating includes: and the voltage weak node position, the active load reduction amount and the reactive compensation putting amount.
4. The method for generating the day-ahead charging plan of multiple electric automobile clusters according to claim 1,
in step 2, the time-of-use electricity price comprises: the peak electricity price of charging the electric automobile, the valley electricity price of charging the electric automobile, and the ultra-low valley electricity price of charging the electric automobile.
5. The method for generating the day-ahead charging plan of multiple electric automobile clusters according to claim 1,
in the step 3, the unordered charging cost refers to the initial charging time and the corresponding charging power of the electric automobiles in the local unregulated cluster, so that the charging cost is obtained; the economic optimization charging cost is the charging cost obtained by locally adjusting the initial charging time and the corresponding charging power of the electric vehicles in the cluster with the aim of minimizing the overall charging cost of the cluster.
6. The method for generating the day-ahead charging plan of multiple electric automobile clusters according to claim 1,
in step 4, the charging scheduling model based on the electric vehicle cluster is as follows:
in the formula (I), the compound is shown in the specification,
minF represents that the minimum load variance of the regional distribution network is taken as an optimization target;
t represents the number of time segments in one period;
P n,t indicating that node n does not contain an electric vehicle exitLoad level of force;
P k,t representing the electric vehicle charging load of the cluster k;
n represents the total number of nodes of the system;
N clu the number of electric vehicle clusters in the region is represented;
7. The method for generating the day-ahead charging plan of multiple electric automobile clusters according to claim 6,
in step 4, generating a day-ahead plan of the electric vehicle cluster by combining constraint conditions, wherein the constraint conditions comprise: unordered charging cost, economic optimization cost, electric vehicle cluster schedulable capacity constraint, electric vehicle cluster charging demand constraint, distribution transformer capacity constraint and distribution line capacity constraint.
8. The method for generating the day-ahead charging plan of multiple electric automobile clusters according to claim 7,
step 4, generating a charging plan by adopting a particle swarm optimization algorithm improved based on a genetic algorithm, taking the charging and discharging power of each electric vehicle cluster point as a solving target, and solving by using the improved particle swarm optimization algorithm under the condition of considering a constraint function, wherein the position of each particle corresponds 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 cross operation formula is as follows:
in the formula (I), the compound is shown in the specification,
alpha is a parameter, and the intersection operation becomes uniform arithmetic intersection when alpha is a constant; when α is a variable, non-uniform arithmetic interleaving is performed at this time;
step 4.2, the speed and position updating expression of the mutation operator is as follows:
in the formula (I), the compound is shown in the specification,
c 1 and c 2 Represents a learning factor;
r 1 and r 2 Is a group of random numbers of 0 to 1;
9. A system for generating a multi-electric vehicle cluster day-ahead charging plan using the method of any one of claims 1 to 8, comprising: an acquisition module, a demand regulation and control module, a cost calculation module and a charging scheduling model module, which is characterized in that,
the acquisition module is used for acquiring cluster information of the electric automobile, acquiring the overall charging demand and charging load of the electric automobile cluster and generating the day-ahead schedulable capacity of the electric automobile cluster;
the demand regulation and control module is used for predicting regional basic loads, formulating a regional power distribution network dispatching plan by combining the day-ahead dispatching capability of the electric vehicle cluster, obtaining the regional power distribution network regulation and control demand and issuing corresponding charging time-of-use electricity price of the electric vehicle;
the cost calculation module is used for performing on-site electric vehicle charging plan adjustment by the electric vehicle cluster according to the time-of-use electricity price issued by the power grid at the minimum cluster charging cost to obtain disordered 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 a total electric automobile charging plan of each electric automobile cluster by combining an improved particle swarm optimization algorithm.
10. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is to store instructions;
the processor is configured to operate according to the instructions to perform the steps of the method for generating a multi-electric vehicle cluster day-ahead charging plan according to any one of claims 1 to 9.
11. Computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of a method for generating a day-ahead charging plan for a cluster of multiple electric vehicles according to any one of claims 1 to 9.
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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 |
-
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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, 电力自动化设备, vol. 41, no. 6, pages 45 - 56 * |
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