CN117937570A

CN117937570A - Adjustable margin optimization method and system for distributed charging facility

Info

Publication number: CN117937570A
Application number: CN202410302843.0A
Authority: CN
Inventors: 林晓明; 顾衍璋; 林伟斌; 雷一勇; 肖勇; 张帆; 钱斌; 唐建林
Original assignee: China South Power Grid International Co ltd; China Southern Power Grid Co Ltd
Current assignee: China South Power Grid International Co ltd; China Southern Power Grid Co Ltd
Priority date: 2024-03-18
Filing date: 2024-03-18
Publication date: 2024-04-26
Anticipated expiration: 2044-03-18
Also published as: CN117937570B

Abstract

The invention relates to the technical field of charging facility optimization and discloses an adjustable margin optimization method and system of a distributed charging facility.

Description

Adjustable margin optimization method and system for distributed charging facility

Technical Field

The invention relates to the technical field of charging facility optimization, in particular to an adjustable margin optimization method and system for a distributed charging facility.

Background

With the rapid increase of emerging loads such as electric vehicles and the like, the electric vehicles (ELECTRIC VEHICLE, EV) are connected in a large scale to enlarge the peak-valley difference of the loads of the power distribution network, increase network loss, reduce voltage quality and reliability of the power system, and can fill valleys in low electricity consumption to promote new energy consumption and improve the stability of the power system. The novel power system taking the new energy as a main body needs to excavate more flexible and adjustable resources to realize new energy consumption, peak clipping and valley filling and improve the power supply reliability.

Meanwhile, the electric power investment can be delayed by adjusting the electric automobile, and the economic benefit is obvious. Therefore, the adjustable margin of the electric automobile is excavated, the adjustment capacity of the power system is improved, and the electric automobile is a new important way for guaranteeing the safe and economic operation of the novel power system.

Current charging facility adjustable margin execution algorithms mainly include centralized or hierarchical control algorithms. The centralized control algorithm has higher requirements on the operation and communication capacity of the control center, the optimization problem scale is increased rapidly along with the nonlinearity of decision duration and EV quantity, and EV users need to report complete information to the central controller, and the complete information is difficult to guarantee due to limitations of communication bottleneck, bandwidth, infrastructure capacity expansion cost, data explosiveness increase caused by EV quick access and the like, so that when a large number of EVs are connected, the centralized control is easy to cause poor control efficiency and control precision, and the actual requirements are difficult to be met. The hierarchical control strategy is to share the centralized control function to multiple aggregators to share, each of which coordinates a set of EVs while affecting the decisions of other aggregators. That is, the EV is divided into a plurality of different groups, and the charging strategy is solved by the sub-groups. Both centralized and hierarchical control strategies rely on complex communication systems, which are costly and difficult to meet practical requirements, and outdated information can affect control efficiency and control accuracy.

Disclosure of Invention

The invention provides an adjustable margin optimization method and an adjustable margin optimization system for a distributed charging facility, which solve the technical problems that the control efficiency and the control precision of an adjustable margin execution algorithm of the existing charging facility are poor, and the actual requirements are difficult to meet.

In view of this, a first aspect of the present invention provides a method for optimizing an adjustable margin of a distributed charging facility, comprising the steps of:

responding to an adjustable margin optimization request of a charging facility of a target platform area, and determining decision parameters according to historical load time sequence data of the target platform area, wherein the decision parameters comprise daily load valley time periods, the number of basic charging units, electric quantity margins of all the basic charging units, slow charging power and fast charging power, and the basic charging units are obtained by dividing the daily load valley time periods;

Determining the charging time length of the electric automobile to be charged according to the battery state information of the electric automobile to be charged, comparing the charging time length of the electric automobile to be charged with the daily load valley period, and determining a charging sub-period initial set and a charging start time initial set corresponding to the charging sub-period initial set according to the basic charging unit;

Screening a plurality of candidate charging starting moment sets corresponding to preset charging modes respectively and a candidate charging sub-period set corresponding to the candidate charging starting moment set based on the initial charging starting moment set;

Determining the sum of the electric quantity margins of the charge subinterval candidate sets corresponding to the preset charging modes respectively according to the electric quantity margins of the basic charging units;

determining a comprehensive candidate charging start time set according to candidate charging start time sets respectively corresponding to a plurality of preset charging modes;

Determining the selection probability of each charging start time in the comprehensive candidate charging start time set according to the sum of the electric quantity margin and the preset selection probability of the charging mode based on the comprehensive candidate charging start time set;

determining an optimal charging starting time and optimal charging power corresponding to the optimal charging starting time according to the selection probability of the charging starting time, the slow charging power and the fast charging power;

And determining an adjustable power margin of the charging facility according to the optimal charging power corresponding to the optimal charging starting time of the charging facility and the pre-acquired initial charging power before optimization.

Preferably, the determining a decision parameter according to historical load time sequence data of the target area in response to an adjustable margin optimization request of a charging facility of the target area, where the decision parameter includes a daily load valley period, the number of basic charging units, an electric quantity margin of each basic charging unit, slow charging power and fast charging power, and the basic charging units are obtained by dividing based on the daily load valley period specifically includes:

responding to an adjustable margin optimization request of a charging facility of a target platform area, and acquiring historical load time sequence data of the target platform area;

performing cluster analysis on the historical load time sequence data of the target platform area to determine a daily load valley period;

dividing the daily load valley period into a plurality of basic charging units according to the duration of the daily load valley period and the preset charging shortest duration;

Performing integral operation according to the basic load and the reference load of each basic charging unit to determine the electric quantity margin of each basic charging unit;

Generating decision parameters according to the daily load valley period, the number of basic charging units, the electric quantity margin of each basic charging unit, preset slow charging power and preset fast charging power, and transmitting the decision parameters to charging facilities of the target station area.

Preferably, the step of determining the charging duration of the electric vehicle to be charged according to the battery state information of the electric vehicle to be charged, comparing the charging duration of the electric vehicle to be charged with the daily load valley period, and determining the initial set of charging sub-periods and the initial set of charging start moments corresponding to the initial set of charging sub-periods according to the basic charging unit specifically includes:

determining the charging duration of the electric automobile to be charged according to the battery state information of the electric automobile to be charged, wherein the charging duration of the electric automobile to be charged is as follows:

；

wherein T _ch,i represents the charging time of the ith electric automobile, 、Respectively represent the initial charge state of charge and the expected charge state at the end of chargeRepresents a battery rated capacity, η _ch represents a charging efficiency, and P _ch represents a charging power;

comparing the charging time length of the electric automobile to be charged with the daily load valley period;

If the charging duration of the electric vehicle to be charged is less than or equal to the daily load valley period, determining a charging sub-period initial set according to the number of the basic charging units and the charging duration of the electric vehicle to be charged, wherein the charging sub-period initial set comprises a plurality of charging initial sub-periods, and each charging initial sub-period meets the charging duration of the electric vehicle to be charged;

Generating a charging start time initial set according to the charging sub-period initial set, wherein the charging start time initial set comprises a plurality of charging start initial times, and each charging start initial time is the starting time of the charging initial sub-period.

Preferably, after the step of determining the charging duration of the electric vehicle to be charged according to the battery state information of the electric vehicle to be charged, comparing the charging duration of the electric vehicle to be charged with the intra-day load valley period, and determining the initial set of charging sub-periods and the initial set of charging start moments corresponding to the initial set of charging sub-periods according to the basic charging unit, the method further includes:

Judging whether the arrival time of the electric automobile to be charged at the charging facility of the target platform area is the whole point time or not;

If the arrival time of the electric automobile to be charged reaching the charging facility of the target platform area is not the whole point time, rounding the arrival time, and eliminating the rounded arrival time of the initial set of the charging starting time.

Preferably, the step of screening a plurality of candidate charging start time sets corresponding to preset charging modes respectively based on the initial charging start time set and a candidate charging sub-period set corresponding to the candidate charging start time set specifically includes:

Determining the charging duration in a preset charging mode according to the preset charging mode and the charging power corresponding to the preset charging mode, wherein the preset charging mode comprises a slow charging mode and a fast charging mode;

determining a plurality of candidate charging start moments in the preset charging mode according to the charging duration in the preset charging mode based on the initial set of charging start moments, and forming a set of candidate charging start moments;

And generating a charge sub-period candidate set based on the candidate charge start time set and the charge duration in the preset charge mode.

Preferably, the method further comprises:

the selection probability of each charging start time in the comprehensive candidate charging start time set is determined by the following formula:

；

In the method, in the process of the invention, Representing the probability of selection of the charge start time,The kth candidate charge start time of the ith electric automobile in the comprehensive candidate charge start time set is represented, t represents the charge start time,Electric quantity margin of the kth candidate charging starting moment of the ith electric automobile in the slow charging mode is represented, and the electric quantity margin is/Representing the probability of a user selecting a slow charge mode charge,Index indicating candidate charge start time in slow charge mode,Indicates the number of candidate charge start times in the slow charge mode,Electric quantity margin sum representing candidate charging starting time set of ith electric automobile in slow charging mode,Representing the electric quantity margin of the kth candidate charging start time of the ith electric automobile in the fast charging mode, b representing the index of the candidate charging start time in the fast charging mode,The number of candidate charge start times in the fast charge mode is indicated,And the sum of the electric quantity margins of the candidate charging starting moment set of the ith electric automobile in the fast charging mode is represented.

Preferably, the step of determining the optimal charging start time and the optimal charging power corresponding to the optimal charging start time according to the selection probability of the charging start time, the slow charging power and the fast charging power specifically includes:

Dividing the comprehensive candidate charging start time set into a plurality of numerical intervals according to the selection probability of each charging start time, wherein the length of each numerical interval is determined by the size of the selection probability of the charging start time corresponding to the numerical interval;

randomly generating random numbers which are in [0,1] and uniformly distributed, and determining optimal charging starting time and optimal charging power corresponding to the optimal charging starting time based on the random numbers, wherein the optimal charging starting time and the optimal charging power corresponding to the optimal charging starting time are as follows:

；

In the method, in the process of the invention, Indicating optimal charge start timeRepresenting the optimal charging power,Representing slow charge power,Representing fast charge power,Representing uniformly distributed random numbers ranging from [0,1 ]The probability of selecting the fast charge mode is represented, wherein,

。

In a second aspect, the present invention also provides an adjustable margin optimization system for a distributed charging facility, comprising:

The decision determining module is used for responding to an adjustable margin optimization request of a charging facility of the target platform area, determining decision parameters according to historical load time sequence data of the target platform area, wherein the decision parameters comprise daily load valley time periods, the number of basic charging units, electric quantity margins of all the basic charging units, slow charging power and fast charging power, and the basic charging units are obtained by dividing the daily load valley time periods;

The charging initial module is used for determining the charging time length of the electric automobile to be charged according to the battery state information of the electric automobile to be charged, comparing the charging time length of the electric automobile to be charged with the daily load valley period, and determining a charging sub-period initial set and a charging start time initial set corresponding to the charging sub-period initial set according to the basic charging unit;

The charging screening module is used for screening a plurality of candidate charging starting time sets corresponding to preset charging modes respectively and a charging sub-period candidate set corresponding to the candidate charging starting time set based on the charging starting time initial set;

The electric quantity margin determining module is used for determining the electric quantity margin sum of the charging sub-period candidate sets corresponding to the preset charging modes respectively according to the electric quantity margin of each basic charging unit;

The charging candidate determining module is used for determining a comprehensive candidate charging starting time set according to candidate charging starting time sets respectively corresponding to a plurality of preset charging modes;

The charging probability calculation module is used for determining the selection probability of each charging start time in the comprehensive candidate charging start time set based on the comprehensive candidate charging start time set through the sum of the electric quantity margin and the preset selection probability of the charging mode;

The charging power calculation module is used for determining an optimal charging starting time and an optimal charging power corresponding to the optimal charging starting time according to the selection probability of the charging starting time, the slow charging power and the fast charging power;

And the power margin calculation module is used for determining the adjustable power margin of the charging facility according to the optimal charging power corresponding to the optimal charging starting time of the charging facility and the pre-acquired initial charging power before optimization.

In a third aspect, the present invention also provides an electronic device including a memory and a processor;

the memory is used for storing programs;

the processor executes the program to implement the method described above.

In a fourth aspect, the present invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the method described above.

From the above technical scheme, the invention has the following advantages:

According to the method, decision parameters are determined through historical load time sequence data of a target platform region, a comprehensive candidate charging start time set is determined through determining candidate charging start time sets corresponding to a plurality of preset charging modes respectively, the selection probability of each charging start time in the comprehensive candidate charging start time set is determined through the sum of electric quantity margin and the selection probability of the preset charging modes, so that the optimal charging start time and the optimal charging power are determined, the adjustable power margin of the charging facility is determined according to the optimal charging power of the charging facility and the pre-acquired initial charging power before optimization, the control efficiency and the control precision of the execution of the adjustable margin of the charging facility are improved, the risk of load peaks formed due to overlarge EV scale is avoided, and the adaptability is high.

Drawings

Fig. 1 is a flowchart of an adjustable margin optimization method for a distributed charging facility according to an embodiment of the present invention;

FIG. 2 is a flowchart of determining decision parameters according to historical load time series data of a target area according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a dividing scheme of a load valley period in a day according to an embodiment of the present invention;

fig. 4 is a schematic diagram of a network node structure of a distributed charging facility under limited data sharing according to an embodiment of the present invention;

FIG. 5 is a flowchart of step S3 according to an embodiment of the present invention;

Fig. 6 is a schematic diagram of a division result of a numerical interval of a charging start time according to an embodiment of the present invention;

fig. 7 is a schematic structural diagram of an IEEE33 node power distribution system according to an embodiment of the present invention;

fig. 8 is a schematic diagram of a load clustering center result calculated by each clustering algorithm of a residential area according to an embodiment of the present invention;

Fig. 9 is a schematic diagram of a distributed charge control effect under different load scenarios according to an embodiment of the present invention;

fig. 10 is a schematic diagram of a distributed charge control effect under different electric vehicle scales according to an embodiment of the present invention;

fig. 11 is a schematic diagram of a distributed charge control effect under different fast charge power according to an embodiment of the present invention;

fig. 12 is a schematic diagram of a distributed charge control effect under different slow charge probabilities according to an embodiment of the present invention;

Fig. 13 is a schematic structural diagram of an adjustable margin optimization system of a distributed charging facility according to an embodiment of the present invention.

Detailed Description

In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The embodiment can be applied to the situation of optimizing the adjustable margin of the distributed charging facility, the method can be executed by an adjustable margin optimizing device of the distributed charging facility, the adjustable margin optimizing device of the distributed charging facility of the power distribution network can be realized in a form of hardware and/or software, and the adjustable margin optimizing device of the distributed charging facility of the power distribution network can be configured in computer equipment.

In view of this, for easy understanding, please refer to fig. 1, fig. 1 illustrates a flow of an adjustable margin optimization method for a distributed charging facility according to the present invention.

The invention provides an adjustable margin optimization method of a distributed charging facility, which comprises the following steps S1-S8:

Step S1, responding to an adjustable margin optimization request of a charging facility of a target platform area, and determining decision parameters according to historical load time sequence data of the target platform area, wherein the decision parameters comprise daily load valley time periods, the number of basic charging units, electric quantity margins of all the basic charging units, slow charging power and fast charging power, and the basic charging units are obtained by dividing the daily load valley time periods.

In one embodiment, as shown in fig. 2, fig. 2 illustrates a flow for determining decision parameters based on historical load timing data of a target zone. The step S1 specifically includes the following steps S101 to S105:

step S101, responding to an adjustable margin optimization request of a charging facility of a target platform area, and acquiring historical load time sequence data of the target platform area.

And S102, performing cluster analysis on historical load time sequence data of the target platform area, and determining a daily load valley period.

In practical application, after historical load time sequence data of a target platform area are acquired, determining a daily valley period, and starting time and ending time of the daily valley period through cluster analysis. The typical day and its load can be determined in particular by means of integrated clustering. The clustering effect under different clustering numbers is measured by using a DB (Davies-Bouldin index) index. And obtaining a load curve of a typical scene through a clustering algorithm, and determining a valley period in a day, and starting time and ending time of the valley period in a day through the valley period load curve. The clustering algorithm may be K-means, fuzzy C-means or SOM clustering algorithm, which is not limited herein.

And step 103, dividing the daily load valley period into a plurality of basic charging units according to the duration of the daily load valley period and the preset charging shortest duration.

Illustratively, the daily load valley period is subdivided into J basic charging units according to the duration of the daily load valley period and the shortest time for charging the electric vehicle in the target area. If the shortest continuous charging time is 15 minutes, 15 minutes is taken as a basic charging unit. Note that the J-th basic charging unit divided is Tj, j=1, 2, …, J. The value of J determines the accuracy requirement of the distributed control strategy, and the larger J is, the finer the control is. In this embodiment, the basic charging unit takes 60 minutes, and the value of J takes 8, so that the daily load valley period can be divided into 8 basic charging units, and the scheme of dividing the daily load valley period as shown in fig. 3 is obtained.

And step S104, carrying out integration operation according to the basic load and the reference load of each basic charging unit to determine the electric quantity margin of each basic charging unit.

The electric quantity margin is an index for measuring the acceptable load capacity of the power grid in a certain time period. The power margin E _margin,j of the j-th sub-period T _avail,j can be expressed as:

；

Wherein P (t) represents a regional base load whose value varies with time; the maximum value of the base load in the night valley period is used as the reference load P _ref, and can be provided by the regional load control center. When the valley period is determined, the reference load P _ref is then determined. And (3) integrating the difference value between the reference load P _ref and the regional base load P (T) in the time period from T _j-1 to T _j to obtain the electric quantity margin of the time period T _j.

Step S105, generating decision parameters according to the daily load valley period, the number of basic charging units, the electric quantity margin of each basic charging unit, the preset slow charging power and the preset fast charging power, and transmitting the decision parameters to the charging facilities of the target station area.

The slow charging power and the fast charging power can be set by self, for example, the slow charging power is 7kW, and the fast charging power is 14kW. In practical applications, the charging power may be an interval value.

In practical application, as shown in fig. 4, the network node structure of the distributed charging facility under the limited data sharing is that charging piles are connected to each user access point, and each regional load control center is configured with a distributed adjustable margin execution policy generator (i.e. decision maker), and the region can be a district, a city or even a larger range. The negative control center transmits electricity price, historical load and basic load information to the decision maker. The decision maker divides the basic charging units for the valley period in one day, generates a decision parameter table comprising the valley period duration, the charging starting time set, the basic charging units and the electric quantity margin thereof, and sends the decision parameter table to the charging piles.

And as a charging decision maker and a basic information collector, the decision maker calculates the basic load and the load trough period of a certain period of the area according to the electricity price and the historical load information of the corresponding area. Meanwhile, each load valley period is divided into a plurality of basic charging units, and the electric quantity bearing capacity of each basic unit is calculated respectively. And generates a decision parameter table, as shown in table 1, which is provided to the charging stake for charging decisions. The decision parameter table may be updated on a annual, quarterly, monthly or daily basis. For convenience of description, the decision period is divided into 3 periods, that is, defined as T _p、T_l and T _v,T_p、T_l and T _v, respectively representing the peak load period, the normal period and the valley period of the region, and T _p+T_l+T_v =24 hours.

Table 1 decision parameter table of adjustable margin execution decision maker

Step S2, determining the charging time length of the electric vehicle to be charged according to the battery state information of the electric vehicle to be charged, comparing the charging time length of the electric vehicle to be charged with the daily load valley period, and determining an initial set of charging time periods and an initial set of charging starting moments corresponding to the initial set of charging sub-period according to the basic charging unit.

Specifically, the step S2 specifically includes the following steps S201 to S204:

Step S201, determining a charging duration of the electric vehicle to be charged according to battery state information of the electric vehicle to be charged, where the charging duration of the electric vehicle to be charged is:

；

wherein T _ch,i represents the charging time of the ith electric automobile, 、Respectively represent the initial charge state of charge and the expected charge state at the end of chargeRepresents the battery rated capacity, η _ch represents the charging efficiency, and P _ch represents the charging power.

Step S202, comparing the charging time of the electric automobile to be charged with the daily load valley period;

Step S203, if the charging duration of the electric vehicle to be charged is less than or equal to the daily load valley period, determining a charging sub-period initial set according to the number of basic charging units and the charging duration of the electric vehicle to be charged, where the charging sub-period initial set includes a plurality of charging initial sub-periods, and each charging initial sub-period satisfies the charging duration of the electric vehicle to be charged.

Wherein, the initial set of charging subintervals is:

；

Wherein T _avail,i represents an initial set of charging sub-period, m represents element index in the initial set of charging sub-period, J represents the number of basic charging units, 、、Respectively representing the 1 st charging period of the ith electric automobile, the m th charging period of the ith electric automobile and the/>, of the ith electric automobileTime period of charge,As a round-up function.

In one example, each charge sub-period in the initial set of charge sub-periods is equal to or greater than a charge duration of the electric vehicle to be charged, and the charge sub-period is typically determined in units of basic charging units.

Step S204, a charging start time initial set is generated according to the charging sub-period initial set, wherein the charging start time initial set comprises a plurality of charging start initial times, and each charging start initial time is the starting time of the charging initial sub-period.

The initial set of charging start moments T _st,i corresponding to the initial set of charging subintervals T _avail,i is:

；

In the method, in the process of the invention, The charging start time of the mth charging sub-period of the ith electric vehicle is indicated.

Illustratively, assuming a valley period of 23:00 a day to 7:00 a next day, the basic charging unit takes 60 minutes, with 8 basic charging units. If the EV charging duration satisfies 0<T _ch,i -1, the corresponding possible charging start time is 8 full-point times contained in T _start, and the initial set of charging sub-periods is {23:00-0:00, 0:00-1:00: 00 …,06:00-07:00}. If the charging duration satisfies 1<T _ch,i < 2 >, it is assumed that the time for starting charging of the EV is 06:00, and it can be deduced that charging cannot be completed in the valley period, so that the charging start time selectable by the EV is 7 elements remaining after the set T _start removes the charging time 06:00. Similarly, the optional charging start time of the vehicle meeting 7<T _ch,i.ltoreq.8 is only 23:00, and the initial set of charging sub-periods is {23:00-7:00 }.

When the charging time of the electric vehicle to be charged is longer than the daily load valley period, the EV is arranged to cover the whole daily load valley period for charging, and meanwhile, according to the vehicle taking time set by a user, the charging is performed in advance before the daily load valley period starts or is continued after the daily load valley period ends, so that the set target electric quantity is ensured to be reached when the vehicle is driven away.

Step S3, screening out a plurality of candidate charging start time sets corresponding to the preset charging modes respectively and a candidate charging sub-period candidate set corresponding to the candidate charging start time set based on the initial charging start time set.

Specifically, as shown in fig. 5, the step S3 specifically includes the following steps S301 to S303:

Step S301, determining a charging duration in a preset charging mode according to the preset charging mode and a charging power corresponding to the preset charging mode, where the preset charging mode includes a slow charging mode and a fast charging mode.

The charging duration of the slow charging mode is calculated as follows:

；

In the method, in the process of the invention, The charging duration of the slow charge mode is indicated, and P _cs indicates the slow charge charging power.

The charge duration of the quick charge mode is calculated as:

；

In the method, in the process of the invention, The charge duration of the fast charge mode is indicated, and P _cf indicates the fast charge power.

Step S302, determining a plurality of candidate charging start times in a preset charging mode according to a charging duration in the preset charging mode based on the initial set of charging start times, and forming a set of candidate charging start times.

Step S303, a charge sub-period candidate set is generated based on the candidate charge start time set and a preset charge duration in a charge mode.

Exemplary, the electric vehicle has q ^s selectable charging start times in the slow charging mode, forming a charging sub-period candidate setThe method comprises the following steps:

；

In the method, in the process of the invention, The q ^s th optional charging starting time of the electric automobile in the slow charging mode is indicated.

Charging sub-period candidate set corresponding to candidate charging start time set of electric automobile in slow charging modeThe method comprises the following steps:

；

In the method, in the process of the invention, The q ^s charge sub-period of the electric automobile in the slow charge mode is represented, and the charge sub-period candidate setThe charging sub-periods of the battery are in the unit of basic charging units and are greater than or equal to the charging duration in the slow charging mode.

Candidate set of charging time periodElectric quantity margin set/>, corresponding to each sub-periodThe method comprises the following steps:

；

In the method, in the process of the invention, And the electric quantity margin of the q ^s th charging sub-period of the electric automobile in the slow charging mode is shown.

Meanwhile, the electric automobile has q ^f selectable charging starting moments in a quick charging mode to form a charging sub-period candidate setThe method comprises the following steps:

；

In the method, in the process of the invention, The q ^f th optional charging starting time of the electric automobile in the fast charging mode is indicated.

Charging sub-period candidate set corresponding to candidate charging start time set of electric automobile in fast charging modeThe method comprises the following steps:

；

In the method, in the process of the invention, The q ^f charge sub-period of the electric automobile in the fast charge mode is represented, and the charge sub-period candidate setThe charging sub-periods of the battery are in units of basic charging units and are greater than or equal to the charging duration in the fast charging mode.

；

In the method, in the process of the invention, And the electric quantity margin of the q ^f th charging sub-period of the electric automobile in the fast charging mode is shown.

And S4, determining the sum of the electric quantity margins of the charge subinterval candidate sets respectively corresponding to a plurality of preset charging modes according to the electric quantity margins of the basic charging units.

For example, the electric vehicle is set to start charging at the (a) charging start time selected from the (q ^s) charging start times, that is, the electric vehicle is selected to be atSub-period combined charging, whereinIncluding h basic charging units, thenSum of electric quantity marginsThe method comprises the following steps:

；

where q is the index of the basic charging unit.

The electric automobile is set to select the b charging starting time to start charging in the q ^f th charging starting time, namely the electric automobile is selected to start chargingSub-period combined charging, wherein-Comprising d basic charging units,Sum of electric quantity marginsThe method comprises the following steps:

；

it can be understood that, since the power margin of the basic charging unit is a constant value, the power margin value of the area corresponding to each possible charging period is a constant value, but the power margin values are different when the charging periods are different.

And S5, determining a comprehensive candidate charging start time set according to the candidate charging start time sets respectively corresponding to the plurality of preset charging modes.

In one example, the probability of selecting each combination of sub-periods is that of selecting the corresponding charging start time, and since the number of possible start times of the fast charging mode is greater than the number of start times of the slow charging mode, the comprehensive candidate charging start time set of the charging pile is a union of candidate charging start time sets corresponding to the fast charging mode and the slow charging mode respectively, that is:

；

And S6, determining the selection probability of each charging start time in the comprehensive candidate charging start time set based on the comprehensive candidate charging start time set through the sum of the electric quantity margin and the preset selection probability of the charging mode.

The electric automobile user generally does not limit the selection probability of the fast charging mode and the slow charging mode. If the electric automobile user has a requirement, the probability of the electric automobile user to select slow charge and fast charge can be limited by the following calculation formula

；

In the method, in the process of the invention,、And selecting the probabilities of slow charge and fast charge for the ith electric automobile user respectively, wherein the sum of the probabilities is equal to 1. The charging speed and the charging speed can be obtained through statistics of historical data of the charging pile, and can also be manually input by a user of the electric automobile.

Specifically, the selection probability of each charging start time in the comprehensive candidate charging start time set is determined by the following equation:

；

Wherein, the sum of the selection probabilities of q ^f charging start moments is 1.

And S7, determining the optimal charging starting time and the optimal charging power corresponding to the optimal charging starting time according to the selection probability of the charging starting time, the slow charging power and the fast charging power.

Specifically, the step S7 specifically includes steps S701 to S702:

step S701, dividing the comprehensive candidate charging start time set into a plurality of numerical intervals according to the selection probability of each charging start time, wherein the length of the numerical intervals is determined by the magnitude of the selection probability of the charging start time corresponding to the numerical intervals.

It can be understood that, in order to reasonably arrange the distribution of the charging time periods of the electric automobile in the valley time periods, the charging pile converts the electric quantity margin of each sub-time period in the valley time period into the charging probability based on the acquired decision parameter table, and calculates the selection probability corresponding to each selectable charging starting time. Meanwhile, the length of the numerical section is determined according to the magnitude of the selection probability corresponding to each selectable charging start time, wherein the longer the selection probability corresponding to the charging start time is, the longer the length of the numerical section is, namely the greater the possibility of charging at the charging start time is.

Step S702, randomly generating random numbers with the range of [0,1] and uniform distribution, and determining the optimal charging start time and the optimal charging power corresponding to the optimal charging start time based on the random numbers by the following formula:

；

In the method, in the process of the invention, Indicating optimal charge start timeRepresenting the optimal charging power,Representing slow charge power,Representing fast charge power,Representing uniformly distributed random numbers ranging from [0,1 ]Representing the probability of selecting fast charge mode, wherein/>

。

For example, the probability delta that the ith electric vehicle selects the slow charge mode is 60%, the probability that the ith electric vehicle selects the fast charge mode is 40%, and the possible charging start time and the corresponding probability of the ith electric vehicle in the fast charge mode and the slow charge mode can be shown in table 2.

TABLE 2 charging start time and corresponding probability distribution

The charging probability corresponding to the charging start time set T _start,i of the ith electric automobile is: 40%, 30%, 10%, 12% and 8%. In practical application, the selection probability of the selectable charging start time is divided into a plurality of value intervals, as shown in fig. 6, fig. 6 illustrates the division result of the value intervals of the charging start time, and a random number which is uniformly distributed and is in the range of [0,1] is randomly generatedJudging the random numberAt the position of the numerical value interval, the electric automobile is charged with slow charge or fast charge power at the charging start time corresponding to the numerical value interval.

For example whenAt 0.48, the electric vehicle is arranged to start charging at a slow charge rate at a time of 00:00 based on the following equation. The decision processes among the charging pile individuals are mutually independent, and information transmission is not needed.

；

And S8, determining an adjustable power margin of the charging facility according to the optimal charging power corresponding to the optimal charging starting time of the charging facility and the pre-obtained initial charging power before optimization.

Wherein the adjustable power margin of the charging facilityThe calculation formula of (2) is as follows:

；

In the method, in the process of the invention, For pre-acquired pre-optimized initial charge power.

The invention determines decision parameters through historical load time sequence data of the target platform area, determines a comprehensive candidate charging start time set through determining candidate charging start time sets corresponding to a plurality of preset charging modes respectively, determines the selection probability of each charging start time in the comprehensive candidate charging start time set by utilizing the sum of electric quantity margin and the selection probability of the preset charging modes, thereby determining the optimal charging start time and the optimal charging power, and determining the adjustable power margin of the charging facility according to the optimal charging power of the charging facility and the pre-acquired initial charging power before optimization, thereby improving the control efficiency and control precision of the execution of the adjustable margin of the charging facility, avoiding the risk of load peaks formed by overlarge EV scale, and having stronger adaptability.

In a specific embodiment, after step S2, the method further includes:

Step S21, judging whether the arrival time of the electric automobile to be charged at the charging facility of the target platform area is the whole point time.

Step S22, if the arrival time of the electric vehicle to be charged reaching the charging facility of the target platform area is not the whole time, rounding the arrival time, and eliminating the rounded arrival time of the initial set of the charging starting time.

It should be noted that, for the case that the basic charging unit is 1 hour, because the access time of the electric vehicle is not necessarily exactly the whole point time, the charging pile needs to be rounded and certain elements in the initial set of the charging start time corresponding to the electric vehicle are removed. For example, if an electric vehicle arrives at 23:15, the 23:00 time in the initial set of charging start time should be removed. After this step, the possible charging period of the EV is corrected, and the remaining elements constitute the initial set of corrected charging start times. Wherein, the initial set/>, of corrected charging start timeWhere the number of elements is p, then p may be expressed as:

；

the following is an example of an adjustable margin optimization method for a distributed charging facility provided in connection with the present embodiment.

In this example, an IEEE33 node power distribution system is used, and as shown in fig. 7, fig. 7 illustrates the structure of the IEEE33 node power distribution system. The electric automobile is connected into low-voltage distribution areas such as 220V/380V residential communities, commercial buildings or industrial parks, and the low-voltage distribution areas are connected into an IEEE33 node distribution system through 2 transformers which are operated in parallel. The charging load of the electric automobile is accessed to 2-6 nodes in the IEEE33 node system according to the ratio of 3:3:2:1:1. The EV power battery according to the example had a capacity of 42kWh, a charging efficiency of 0.95, a charge start SOC value of 1.0, a slow charge power of 3.5kW, and a fast charge power of 14kW, following a normal distribution N (0.35,0.02). Since resident commutes EVs are typically charged during night stops, only night off-peak periods are considered. The residential area sets the peak, valley and halving electricity prices according to the industrial electricity price of a certain province, and the specific electricity price is shown in table 3.

TABLE 3 general industrial time-of-use electricity price in certain provinces

The basic load data set in the example obtains three typical scenes through a clustering algorithm, the clustering result of the residential areas is shown in fig. 8, and fig. 8 illustrates the load clustering center result calculated by each clustering algorithm of the residential areas.

Three exemplary scene classifications are shown in Table 4. The load change trend of the first typical scene is the same as that of the third typical scene, the load peaks are in the time periods of 7:00-9:00 and 18:00-22:00, and the second typical scene has only one load peak in the time period of 17:00-22:00, which is approximately a straight line. The load dips for three typical scenarios are all at night for periods 23:00-7:00.

TABLE 4 classification of typical scenes for populated areas

To facilitate analysis, the present example makes the following assumptions:

(1) The study period was 24 hours a day, i.e., t=24 hours;

(2) Each electric automobile is not interrupted until the charging reaches the expected value;

(3) All electric automobile charging power is only two kinds of slow charging and fast charging;

(4) All electric car users participate in the charging control.

In order to verify the influence of different load scenes, the electric automobile is firstly set to be 100 in scale, the quick charge power is 14kW, the slow charge probability is 0.5, and 3 typical scenes are 3 groups of cluster centers of integrated clusters. The distributed charge control effect under different load scenarios is shown in fig. 9.

As can be seen from fig. 9, the first, second and third scenes have a night valley period of 23:00-7:00, and the total night load is increased after the distributed charging control. However, under different load scenes, the control effect of the distributed strategy in the night valley period is different, because the night valley load of the different load scenes is different, namely the electric quantity margin of each period is different, so that the charging probability of each period related to the electric quantity margin is changed, and the adjustable power distribution of the electric automobile is changed.

In order to verify the influence of different electric automobile scales, a load scene is set as a scene I, the quick charge power is 14kW, the slow charge probability is 0.5, and the distributed charge control effect under different electric automobile scales is shown in fig. 10.

As can be seen from fig. 10, 1) when the electric vehicle scale is 100, the strategy provided by the present invention can effectively distribute the charging load in the valley period, so that the peak-valley difference and the load variance of 24 hours a day are reduced compared with those before the electric vehicle is connected, and the load curve after superposition is more relaxed. 2) At an EV scale of 300 vehicles, the night off-peak load is already higher than the daytime off-peak load. In combination with the variance data shown in table 5, it is known that the overall load curve becomes more gentle as the electric vehicle scale involved in the ordered control increases in a certain number of electric vehicles, and the more the adjustable power increases, but there is a risk of forming a new peak. 3) When the E electric automobile scale is 500, a large number of electric automobile users access the power grid in the valley period, so that the valley period is even the highest load in 24 hours, such as 3 am. 4) Under a distributed control strategy, the increase in electric vehicle load is not linearly increasing because the charging probability is affected by the load margin and random number sampling.

TABLE 5 peak-to-valley differences and load variances for 24 hours throughout the day at different EV scales

Firstly, setting a load scene as a scene I, wherein the scale of the electric automobile is 100, the slow charge probability is 0.5, and the distributed charge control effect under different fast charge and charge powers is shown in fig. 11.

As can be seen from fig. 11, when the fast charge power is 32kW, the charging time of the electric vehicle is short, and at this time, the load of the electric vehicle is concentrated near the time when the power margin is large, for example, 3 to 4 points; when the quick charging power is 7kW, the charging time of the electric automobile is longer, and the charging starting time is mainly concentrated in the first half of the valley period, so that the time point with larger adjustable power of the electric automobile is mainly concentrated in 24 points-2 am.

In order to verify the influence of different slow charge probabilities, a load scene is first assumed, the electric vehicle scale is 100, the fast charge power is 14kW, and the distributed charge control effect under different slow charge probabilities is shown in fig. 12.

In fig. 12, when the slow charge probability is 1, that is, the electric vehicle user selects the slow charge power for charging, because the charging duration is longer, the number of optional charging sub-periods of the electric vehicle is smaller, most electric vehicles are concentrated in the 23:00-0:00 period to start charging, and the adjustable power in the whole valley period is relatively average. When the slow charge probability is 0, namely the electric automobile user selects the fast charge power for charging, the adjustable power in the 3-4-point period is higher due to the length of the charging time. The slow charge probability is 0.5, namely the probability that the user of the electric automobile selects the fast charge and the slow charge is equal, and the total load is located between two extreme conditions and is not equal to the average value of the other two conditions.

It should be noted that, according to the present example, with respect to different influencing factors of the distributed control strategy, the influence degree of the load scene, the EV scale, the fast charge power, and the fast and slow charge probability of the adjustable margin execution control strategy is verified through theoretical analysis and simulation comparison. In particular, within a certain EV number, as the EV scale participating in the orderly control gradually increases, the better the adjustability of the entire charging facility is, but the risk of forming new load peaks due to the excessive EV scale is prevented.

Meanwhile, the method only depends on the base charging unit, the electric quantity margin value and the quick and slow charging power of the base charging unit to set 6 parameters, namely the charging power, the charging period and the adjustable power of the charging facility can be decided, and the dependence on communication and local computing capacity is low, so that the distributed charging control device has the advantages of low cost, simplicity, convenience, practicability, suitability for various V2G terminals and outstanding applicability and expandability.

The above is a detailed description of an embodiment of an adjustable margin optimization method of a distributed charging facility provided by the present invention, and the following is a detailed description of an embodiment of an adjustable margin optimization system of a distributed charging facility provided by the present invention.

For ease of understanding, referring to fig. 13, the present invention also provides an adjustable margin optimization system of a distributed charging facility, including:

The decision determining module 100 is configured to determine decision parameters according to historical load time sequence data of a target platform area in response to an adjustable margin optimization request of a charging facility of the target platform area, where the decision parameters include a daily load valley period, the number of basic charging units, an electric quantity margin of each basic charging unit, slow charging power and fast charging power, and the basic charging units are obtained by dividing the basic charging units based on the daily load valley period;

The charging initial module 200 is configured to determine a charging duration of the electric vehicle to be charged according to battery state information of the electric vehicle to be charged, compare the charging duration of the electric vehicle to be charged with a daily load valley period, and determine an initial set of charging sub-period and an initial set of charging start time corresponding to the initial set of charging sub-period according to the basic charging unit;

The charging screening module 300 is configured to screen a plurality of candidate charging start time sets corresponding to preset charging modes respectively and a charge sub-period candidate set corresponding to the candidate charging start time set based on the initial charging start time set;

The electric quantity margin determining module 400 is configured to determine an electric quantity margin sum of a plurality of charging sub-period candidate sets corresponding to a preset charging mode respectively according to the electric quantity margin of each basic charging unit;

The charging candidate determining module 500 is configured to determine a comprehensive candidate charging start time set according to candidate charging start time sets corresponding to a plurality of preset charging modes respectively;

The charging probability calculation module 600 is configured to determine, based on the comprehensive candidate charging start time set, a selection probability of each charging start time in the comprehensive candidate charging start time set through a sum of electric quantity margins and a preset selection probability of a charging mode;

The charging power calculation module 700 is configured to determine an optimal charging start time and an optimal charging power corresponding to the optimal charging start time according to the selection probability of the charging start time, the slow charging power and the fast charging power;

the power margin calculation module 800 is configured to determine an adjustable power margin of the charging facility according to an optimal charging power corresponding to an optimal charging start time of the charging facility and a pre-obtained initial charging power before optimization.

The invention also provides electronic equipment, which comprises a memory and a processor;

The memory is used for storing programs;

the processor executes the program to implement the method described above.

The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the method described above.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working processes of the above-described system, electronic device and computer storage medium may refer to corresponding processes in the foregoing method embodiments, which are not described herein again.

In several embodiments provided by the present invention, it will be understood that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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.

In the several embodiments provided herein, it should be understood that the disclosed system, electronic device, computer storage medium, and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for executing all or part of the steps of the method according to the embodiments of the present invention by means of a computer device (which may be a personal computer, a server, or a network device, etc.). And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adjustable margin optimization method of a distributed charging facility is characterized by comprising the following steps:

2. The method for optimizing adjustable margin of a distributed charging facility according to claim 1, wherein the determining a decision parameter according to historical load time series data of a target station area in response to an adjustable margin optimizing request of a charging facility of the target station area, the decision parameter including a daily load valley period, a number of basic charging units, a power margin of each basic charging unit, a slow charging power and a fast charging power, wherein the basic charging units are divided based on the daily load valley period, specifically comprises:

3. The method for optimizing an adjustable margin of a distributed charging facility according to claim 1, wherein the step of determining a charging duration of an electric vehicle to be charged according to battery state information of the electric vehicle to be charged, comparing the charging duration of the electric vehicle to be charged with the daily load valley period, and determining an initial set of charging sub-periods and an initial set of charging start times corresponding to the initial set of charging sub-periods according to the basic charging unit specifically includes:

；

4. The method for optimizing an adjustable margin of a distributed charging facility according to claim 3, wherein after the step of determining a charging duration of an electric vehicle to be charged according to battery state information of the electric vehicle to be charged, comparing the charging duration of the electric vehicle to be charged with the daily load valley period, and determining a charging sub-period initial set and a charging start time initial set corresponding to the charging sub-period initial set according to the basic charging unit, the method further comprises:

5. The method for optimizing an adjustable margin of a distributed charging facility according to claim 3 or 4, wherein the step of screening a plurality of candidate charging start time sets corresponding to preset charging modes respectively and a candidate set of charging subintervals corresponding to the candidate charging start time sets based on the initial set of charging start time sets specifically comprises:

6. The adjustable margin optimization method of a distributed charging facility of claim 5, further comprising:

;

In the method, in the process of the invention, Representing the probability of selection of the charge start time,The kth candidate charge start time of the ith electric automobile in the comprehensive candidate charge start time set is represented, t represents the charge start time,Electric quantity margin of the kth candidate charging starting moment of the ith electric automobile in the slow charging mode is represented, and the electric quantity margin is/Representing the probability of a user selecting a slow charge mode charge,Index indicating candidate charge start time in slow charge mode,Indicates the number of candidate charge start times in the slow charge mode,Electric quantity margin sum representing candidate charging starting time set of ith electric automobile in slow charging mode,Representing the electric quantity margin of the kth candidate charging start time of the ith electric automobile in the fast charging mode, b representing the index of the candidate charging start time in the fast charging mode,Indicates the number of candidate charge start times in the fast charge mode,And the sum of the electric quantity margins of the candidate charging starting moment set of the ith electric automobile in the fast charging mode is represented.

7. The method for optimizing an adjustable margin of a distributed charging facility according to claim 6, wherein the step of determining an optimal charging start time and an optimal charging power corresponding to the optimal charging start time according to a selection probability of a charging start time, the slow charging power and the fast charging power specifically comprises:

；

。

8. An adjustable margin optimization system for a distributed charging facility, comprising:

9. An electronic device comprising a memory and a processor;

the memory is used for storing programs;

the processor executing the program implements the method of any one of claims 1 to 7.

10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 7.