CN116111579A - Electric automobile access distribution network clustering method - Google Patents

Electric automobile access distribution network clustering method Download PDF

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CN116111579A
CN116111579A CN202211593378.8A CN202211593378A CN116111579A CN 116111579 A CN116111579 A CN 116111579A CN 202211593378 A CN202211593378 A CN 202211593378A CN 116111579 A CN116111579 A CN 116111579A
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张俊成
谭靖
罗天禄
张启炬
谭晓虹
陶毅刚
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Guangxi Power Grid Co Ltd
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Abstract

The invention discloses a method for accessing an electric automobile into a power distribution network cluster, which comprises the following steps: s1, acquiring data of an electric automobile connected into a power distribution network; s2, calculating the load model parameters of the single electric automobile based on the acquired data; s3, extracting electric vehicle load deterministic model parameters equivalent to the estimated value of the number of electric vehicles connected in an actual power distribution network by adopting a Monte Carlo simulation method based on the single electric vehicle load model parameters; and S4, summing the load deterministic model parameters of each electric automobile through a Minkowski to obtain electric automobile cluster model parameters. According to the invention, the EV clusters are aggregated by an EV aggregator (such as an EV charging service provider) and then participate in power system dispatching, so that the problem that a single EV directly reports to generate a large amount of high-dimensional variable data which is unfavorable for dispatching planning is solved, and the aggregation of a massive EV monomer decision space is realized from the perspective of optimizing a feasible region through Minkowski summation, so that the solving speed of the dispatching problem of the electric automobile can be improved.

Description

Electric automobile access distribution network clustering method
Technical Field
The invention belongs to the technical field of distribution networks, and particularly relates to a method for accessing an electric automobile into a distribution network cluster.
Background
In recent years, sales of new energy automobiles in China are rapidly increased, wherein electric automobiles are dominant, and the electric automobiles become important choices for traveling in urban areas. The large-scale use of electric automobiles also brings higher requirements to power grid dispatching. The electric automobile is accessed into the power distribution network on a large scale, and the power distribution network possibly faces the problems of increased peak-valley difference, difficult scheduling and the like. The electric power system is provided with a large number of electric vehicles EV, if charging data of all EV users are directly reported to participate in system adjustment, a large number of high-dimensional variable data can be generated in the dispatching process, and optimization solving of a dispatching plan is not facilitated; meanwhile, the capacity of a single EV is difficult to reach the admission threshold of the electric power market; disclosing data of a large number of EV users to the power system also creates user privacy security issues. Therefore, it is necessary to cluster electric vehicles accessing the power distribution network to guide the orderly access of the electric vehicles.
Disclosure of Invention
An object of the application is to provide an electric automobile access distribution network clustering method to solve current electric automobile and insert the distribution network on a large scale, cause the distribution network peak Gu Cha increase, the scheduling difficulty's problem, satisfy electric automobile orderly access's demand, improve electric automobile's scheduling efficiency, adapt to the development of novel distribution network.
In order to solve or improve the problems, the invention provides a method for accessing an electric automobile into a power distribution network cluster, which comprises the following specific technical scheme:
the invention provides a user power failure event fault prediction method, which comprises the following steps:
step S1, acquiring data of an electric automobile connected into a power distribution network, wherein the data comprise initial electric quantity, charging starting time and charging ending time of the electric automobile;
step S2, calculating the load model parameters of the single electric automobile based on the acquired data;
step S3, based on the single electric vehicle load model parameters, extracting electric vehicle load deterministic model parameters equivalent to the estimated value of the number of electric vehicles connected in an actual power distribution network by adopting a Monte Carlo simulation method;
and S4, summing the load deterministic model parameters of each electric automobile through Minkowski to obtain electric automobile cluster model parameters.
Preferably, in the step S1, the data of the electric vehicle connected to the power distribution network further includes: upper battery power limit, lower battery power limit, maximum charge power and maximum discharge power.
Preferably, in the step S2, the calculation method of the load model parameter of the single electric automobile is as follows:
Figure SMS_1
Figure SMS_2
wherein:
Figure SMS_4
the charging power and the discharging power of an nth electric automobile EV at the t moment are respectively; />
Figure SMS_8
The battery power of the nth electric automobile EV at the t moment; />
Figure SMS_10
The maximum charging power and the maximum discharging power of the nth electric automobile EV are respectively; />
Figure SMS_5
The upper limit and the lower limit of the electric quantity of the EV battery of the nth electric automobile are respectively; />
Figure SMS_7
The parking start time and the parking end time of the nth electric automobile EV are respectively, and the electric automobile EV is charged and discharged between the parking start time and the parking end time so as to meet the energy consumption requirement of a user; />
Figure SMS_9
In order to represent the Boolean variable of the EV parking state of the nth electric automobile at the t moment, the value is 1 when the electric automobile is in the parking state, and the value is 0 when the electric automobile is not in the parking state; />
Figure SMS_11
Respectively representing the Boolean variables of the EV charging state and discharging state of the nth electric automobile at the t moment, wherein +.>
Figure SMS_3
In a discharge state with
Figure SMS_6
Not more than 1 indicates that the nth electric automobile EV cannot be charged and discharged at the same time at the moment t; Δt is a unit time interval.
The electric power system is provided with a large number of electric vehicles EV, if charging data of all EV users are directly reported to participate in system adjustment, a large number of high-dimensional variable data can be generated in the dispatching process, and optimization solving of a dispatching plan is not facilitated; meanwhile, the capacity of a single EV is difficult to reach the admission threshold of the electric power market; disclosing data of a large number of EV users to the power system also creates user privacy security issues. Therefore, EV clusters should be aggregated by an aggregator typified by an EV charging service provider and then participate in power system scheduling.
Preferably, in the step S3, the method for extracting the electric vehicle load deterministic model parameter corresponding to the estimated value of the number of electric vehicles connected to the actual power distribution network by using the monte carlo simulation method includes:
random sampling was performed according to the following parametric model:
Figure SMS_12
wherein: p is any uncertainty parameter and represents the maximum charging power P of the ith electric automobile i,ch,max Maximum discharge power P of ith electric automobile i,dis,max Initial charge quantity S of ith electric automobile i (t i,start ) Or the charging time t of the ith electric automobile i,end -t i,start The method comprises the steps of carrying out a first treatment on the surface of the Mu is the mean value corresponding to p, and sigma is the standard deviation corresponding to p.
Preferably, in the step S4, the method for obtaining the electric vehicle cluster model parameters by summing the electric vehicle load deterministic model parameters through minkowski includes:
Figure SMS_13
Figure SMS_14
Figure SMS_15
/>
wherein:
Figure SMS_16
the battery power of the nth electric automobile EV at the parking start time and the parking end time respectively.
Based on Minkowski summation theory, a large number of EV monomers of the electric automobile can be polymerized into EV cluster GES equipment with larger capacity and charging and discharging energy power, integrated scheduling management of a large number of EV monomers is realized, and the admission threshold of an electric market is met. The aggregate procedure in which the capacity, power boundary minkowski summation actually increases the feasible domain of the power, and is therefore a relaxed minkowski summation procedure. The loose Minkowski summation process can accurately express the total power of the EV cluster, and has feasibility. Through the summation process, an EV cluster GES model with physical significance can be established, and aggregation of a mass EV monomer decision space is realized from the perspective of optimizing a feasible region.
The aggregation provider can enable the dispatching center to master the operation situation of the GES and the available up-and-down adjustment resources and conduct transaction by reporting the parameter set, and finally quantification and exploitation of the schedulable potential of the EV cluster are realized.
The beneficial effects of the invention are as follows:
1. according to the invention, the electric vehicle EV charging service provider is used for representing the aggregation of the EV clusters, and then the aggregation is used for participating in power system dispatching, so that a large amount of high-dimensional variable data generated by single direct report is solved, the optimization solving problem of a dispatching plan is not facilitated, and the solving speed of the electric vehicle dispatching problem can be effectively improved.
2. The invention can accurately express the total power of the EV cluster through the Minkowski summation, and has feasibility. Through the Minkowski summation process, an EV cluster GES model with physical significance can be established, and aggregation of massive EV monomer decision spaces is realized from the perspective of optimizing a feasible domain. Through the aggregation process, an aggregator only needs to report a small amount of GES model parameters in the electric power market transaction process, so that on one hand, the data transmission volume is reduced, the data processing pressure and the optimal scheduling calculation pressure of a power system scheduling center are reduced, on the other hand, the disclosure range of EV monomer energy data in the power system is limited, and the protection of user privacy is enhanced.
Drawings
Fig. 1 is a flowchart of a method for an electric automobile to access a power distribution network cluster according to an embodiment of the invention.
Fig. 2 is an EV cluster map.
Fig. 3 is a minkowski summation schematic.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. 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.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Examples
In order to solve or improve the problems of complex power failure fault prediction, low precision and insufficient corresponding time of the existing user, a user power failure event fault prediction method shown in fig. 1 is provided, which comprises the following steps:
step S1, acquiring data of an electric vehicle connected into a power distribution network, wherein the data comprise initial electric quantity, charging starting time, charging ending time, upper limit of battery electric quantity, lower limit of battery electric quantity, maximum charging power, maximum discharging power and the like of the electric vehicle.
Step S2, calculating the load model parameters of the single electric automobile based on the acquired data;
step S3, based on the single electric vehicle load model parameters, extracting electric vehicle load deterministic model parameters equivalent to the estimated value of the number of electric vehicles connected in an actual power distribution network by adopting a Monte Carlo simulation method;
and S4, summing the load deterministic model parameters of each electric automobile through Minkowski to obtain electric automobile cluster model parameters.
(1) Overview of the schedulable potential of EV clusters
According to the definition of the schedulable potential of the electric automobile EV cluster, the charging load generated by the EV to meet the future driving energy requirement is called a self-scheduling load, and the capacity and the charging and discharging power of the vehicle-mounted battery which can participate in the requirement response are called the schedulable potential of the EV on the basis of the self-scheduling load.
As shown in fig. 2, the EV cluster constitutes a generalized energy store (GeneralizedEnergyStorage, GES) that generates charge power or discharge power during a single period by self-scheduling, thereby generating rescheduling power during demand response to take advantage of the schedulable potential. The rescheduling power that causes the GES to act to reduce the system load is referred to as up-regulated power, and the rescheduling power that causes the GES to act to increase the system load is referred to as down-regulated power. In the process of the GES rescheduling, 3 conditions of the self-dispatching charge state changing to the discharge state through rescheduling, rescheduling to reduce the charge power and rescheduling to increase the discharge power generate up-regulating power, and 3 conditions of the self-dispatching charge state changing to the charge state through rescheduling, rescheduling to reduce the discharge power and rescheduling to increase the charge power generate down-regulating power.
The schedulable potential calculation of the EV cluster is to predict and calculate the self-scheduling load and the maximum charge/discharge power of the GES, further determine the envelope space of the re-scheduling power and capacity of the GES, and provide a reference for the aggregator to guide the GES to participate in the electric market transaction.
(2) Modeling of single EV
The corresponding load model can be established by defining the initial charge state SOC (StateOfCharge), the parking start time and the parking end time of the single EV, and the following formula is shown:
Figure SMS_17
Figure SMS_18
wherein:
Figure SMS_20
charging and discharging power of the nth EV at the time t respectively; />
Figure SMS_24
The battery power of the nth EV at the t moment; />
Figure SMS_25
Maximum charge and discharge power of the nth EV; />
Figure SMS_21
Figure SMS_23
The upper limit and the lower limit of the electric quantity of the nth EV battery are respectively set; />
Figure SMS_27
The parking start time and the parking end time of the nth EV are respectively, and the EV is charged and discharged between the parking start time and the parking end time so as to meet the energy requirement of a user; />
Figure SMS_29
In order to represent the Boolean variable of the nth EV parking state at the moment t, the value is 1 when the vehicle is in a parking state, and the value is 0 when the vehicle is not in a parking state; />
Figure SMS_19
Respectively representing the n th EV charging state and discharging state at t time, and the charging state is +.>
Figure SMS_22
In a discharge state with
Figure SMS_26
Figure SMS_28
No more than 1 indicates that the nth EV cannot be charged and discharged at the same time at the moment t; Δt is a unit time interval.
The electric power system is provided with a large number of EVs, if charging data of all EV users are directly reported to participate in system adjustment, a large number of high-dimensional variable data can be generated in the dispatching process, and the optimization solution of a dispatching plan is not facilitated; meanwhile, the capacity of a single EV is difficult to reach the admission threshold of the electric power market; disclosing data of a large number of EV users to a power system creates user privacy security issues. Therefore, EV clusters should be aggregated by an aggregator typified by an EV charging service provider and then participate in power system scheduling.
(3) EV cluster GES modeling based on Minkowski summation theory
The minkowski summation theory is a summation theory applicable to Euclidean space, and can be used for solving expansion sets of variable spaces with the same definition domain. The minkowski summation diagram is shown in fig. 3.
For variable spaces M and N with the same definition domain, the Minkowski summing process is:
Figure SMS_30
wherein:
Figure SMS_31
minkowski sums for 2 variable spaces, containing a larger space; m and n are elements in the variable space M, N, respectively. M and N are spaces formed by the external power characteristics of the electric automobile cluster, and specifically comprise: charging power
Figure SMS_32
Discharge power->
Figure SMS_33
Maximum charging power->
Figure SMS_34
Maximum discharge power +.>
Figure SMS_35
Etc.
For a single EV, boolean variable
Figure SMS_36
The EV grid-connected time extension with the original variability is unified to the same time feasibility domain, so that the model of a single EV has Minkowski additivity. For EV cluster N under certain aggregator management EV For example, the aggregate model can be expressed as:
Figure SMS_37
Figure SMS_38
Figure SMS_39
wherein:
Figure SMS_40
the battery power at the nth EV parking start time and the parking end time respectively.
The influence of single EV grid connection and grid disconnection in the EV cluster on cluster charge and discharge power limit and battery electric quantity is considered. From the formula, it can be known that a large number of EV monomers can be polymerized into an EV cluster GES device with larger capacity and charging and discharging energy based on Minkowski summation theory, so that integrated scheduling management of a large number of EV monomers is realized, and the admission threshold of the electric power market is met. The aggregate process of summing capacity, power boundary minkowski in the equation is effectively a power-added feasible domain, and is therefore a relaxed minkowski summing process. The loose Minkowski summation process can accurately express the total power of the EV cluster, and has feasibility. Through the summation process, an EV cluster GES model with physical significance can be established, and aggregation of a mass EV monomer decision space is realized from the perspective of optimizing a feasible region.
The aggregation of EV clusters is realized through a Minkowski summation process at the aggregation business level, and then the variables and parameters of GES are extracted, wherein the variables and parameters are shown in the following formula:
Figure SMS_41
wherein:
Figure SMS_42
respectively the charge and discharge power of GES at the moment t; s is S GES (t) is the electric quantity of the GES at the moment t; />
Figure SMS_43
Maximum charge and discharge power of GES at time t respectively; />
Figure SMS_44
Figure SMS_45
Respectively the upper limit and the lower limit of the electric quantity of the GES at the moment t; ΔS GES And (t) is the electric quantity change amount of the GES at the time t caused by EV stopping factors. The final GES parameter set obtained by extraction is
Figure SMS_46
Through the loose Minkowski summation process and the GES parameter extraction process, the aggregator can aggregate massive EV monomers into GES and compress massive EV charging data into GES model parameters. Through the aggregation process, an aggregator only needs to report a small amount of GES model parameters in the electric power market transaction process, so that on one hand, the data transmission volume is reduced, the data processing pressure and the optimal scheduling calculation pressure of a power system scheduling center are reduced, on the other hand, the disclosure range of EV monomer energy data in the power system is limited, and the protection of user privacy is enhanced.
Based on the known capacity boundary and power boundary of the GES model and the self-dispatching load of the EV cluster obtained by calculation, the rescheduling power and rescheduling capacity of the GES are further obtained by calculation, and the quantification of the rescheduling potential of the GES is realized.
The rescheduling power includes an up-regulated power and a down-regulated power, as shown in the following formula:
Figure SMS_47
/>
wherein:
Figure SMS_48
the power is respectively adjusted up and down at t moment GES;
Figure SMS_49
and respectively regulating charge and discharge power at t time GES.
The rescheduling capacity comprises an up-regulation capacity and a down-regulation capacity, and is shown in the following formula:
Figure SMS_50
wherein:
Figure SMS_51
the upper adjustment capacity and the lower adjustment capacity of the GES at the moment t are respectively; />
Figure SMS_52
And (5) self-adjusting the electric quantity for the GES at the time t. Further integrating the schedulable potential quantization parameter set by combining with the GES parameter set
Figure SMS_53
The aggregation provider can enable the dispatching center to master the operation situation of the GES and the available up-and-down adjustment resources and conduct transaction by reporting the parameter set, and finally quantification and exploitation of the schedulable potential of the EV cluster are realized.
In summary, the electric vehicle EV aggregation (such as an EV charging service provider) aggregates the EV clusters and then participates in power system dispatching, so that the problem that a single EV directly reports a large amount of high-dimensional variable data and is unfavorable for optimizing and solving a dispatching plan is solved; through the Minkowski summation, aggregation of a mass EV monomer decision space is realized from the perspective of optimizing a feasible domain, and the solving speed of the scheduling problem of the electric automobile can be improved. The method comprises the steps that an aggregator only needs to report a small amount of GES model parameters in the electric power market transaction process, so that on one hand, the data transmission volume is reduced, the data processing pressure and the optimal scheduling calculation pressure of a power system scheduling center are reduced, on the other hand, the disclosure range of EV monomer energy data in an electric power system is limited, and the protection of user privacy is enhanced.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; 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 or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.

Claims (5)

1. A user outage event fault prediction method, comprising:
step S1, acquiring data of an electric automobile connected into a power distribution network, wherein the data comprise initial electric quantity, charging starting time and charging ending time of the electric automobile;
step S2, calculating the load model parameters of the single electric automobile based on the acquired data;
step S3, based on the single electric vehicle load model parameters, extracting electric vehicle load deterministic model parameters equivalent to the estimated value of the number of electric vehicles connected in an actual power distribution network by adopting a Monte Carlo simulation method;
and S4, summing the load deterministic model parameters of each electric automobile through Minkowski to obtain electric automobile cluster model parameters.
2. The method for predicting a failure event of a user according to claim 1, wherein in step S1, the accessing data of the electric vehicle in the power distribution network further includes: upper battery power limit, lower battery power limit, maximum charge power and maximum discharge power.
3. The method for predicting a failure event of a user according to claim 1, wherein in the step S2, the method for calculating the load model parameters of the single electric automobile is as follows:
Figure FDA0003995846770000011
Figure FDA0003995846770000012
wherein:
Figure FDA0003995846770000013
the charging power and the discharging power of an nth electric automobile EV at the t moment are respectively; />
Figure FDA0003995846770000014
The battery power of the nth electric automobile EV at the t moment; />
Figure FDA0003995846770000015
The maximum charging power and the maximum discharging power of the nth electric automobile EV are respectively; />
Figure FDA0003995846770000016
The upper limit and the lower limit of the electric quantity of the EV battery of the nth electric automobile are respectively;
Figure FDA0003995846770000017
Figure FDA0003995846770000018
the parking start time and the parking end time of the nth electric vehicle EV are respectively, and the electric vehicle EV is charged and discharged between the parking start time and the parking end timeTo meet the energy demand of the user;
Figure FDA0003995846770000019
in order to represent the Boolean variable of the EV parking state of the nth electric automobile at the t moment, the value is 1 when the electric automobile is in the parking state, and the value is 0 when the electric automobile is not in the parking state; />
Figure FDA00039958467700000110
Respectively representing the Boolean variables of the EV charging state and discharging state of the nth electric automobile at the t moment, wherein +.>
Figure FDA0003995846770000021
In a discharge state with
Figure FDA0003995846770000022
Figure FDA0003995846770000023
Not more than 1 indicates that the nth electric automobile EV cannot be charged and discharged at the same time at the moment t; Δt is a unit time interval.
4. The method for predicting a failure event of a user according to claim 3, wherein in step S3, the method for extracting the electric vehicle load deterministic model parameters corresponding to the estimated value of the number of electric vehicles connected in the actual power distribution network by using the monte carlo simulation method is as follows:
random sampling was performed according to the following parametric model:
Figure FDA0003995846770000024
/>
wherein: p is any uncertainty parameter and represents the maximum charging power P of the ith electric automobile i,ch,max Maximum discharge power P of ith electric automobile i,dis,max Initial charge quantity S of ith electric automobile i (t i,start ) Or the charging time t of the ith electric automobile i,end -t i,start The method comprises the steps of carrying out a first treatment on the surface of the Mu is the mean value corresponding to p, and sigma is the standard deviation corresponding to p.
5. The method for predicting a failure event of a user as claimed in claim 1, wherein in the step S4, the method for summing the load deterministic model parameters of each electric vehicle by minkowski to obtain the cluster model parameters of the electric vehicle is as follows:
Figure FDA0003995846770000025
Figure FDA0003995846770000026
Figure FDA0003995846770000027
wherein:
Figure FDA0003995846770000028
the battery power of the nth electric automobile EV at the parking start time and the parking end time respectively. />
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CN116894163A (en) * 2023-09-11 2023-10-17 国网信息通信产业集团有限公司 Charging and discharging facility load prediction information generation method and device based on information security
CN117973071A (en) * 2024-03-13 2024-05-03 四川大学 Aggregation modeling and adjustable potential evaluation method for electric automobile under multiple scenes

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116894163A (en) * 2023-09-11 2023-10-17 国网信息通信产业集团有限公司 Charging and discharging facility load prediction information generation method and device based on information security
CN116894163B (en) * 2023-09-11 2024-01-16 国网信息通信产业集团有限公司 Charging and discharging facility load prediction information generation method and device based on information security
CN117973071A (en) * 2024-03-13 2024-05-03 四川大学 Aggregation modeling and adjustable potential evaluation method for electric automobile under multiple scenes

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