CN112257907A - Electric vehicle load modeling method considering electricity price sensitivity - Google Patents

Electric vehicle load modeling method considering electricity price sensitivity Download PDF

Info

Publication number
CN112257907A
CN112257907A CN202011062605.5A CN202011062605A CN112257907A CN 112257907 A CN112257907 A CN 112257907A CN 202011062605 A CN202011062605 A CN 202011062605A CN 112257907 A CN112257907 A CN 112257907A
Authority
CN
China
Prior art keywords
user
time
load
charging
electricity price
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011062605.5A
Other languages
Chinese (zh)
Other versions
CN112257907B (en
Inventor
范晋衡
刘琦颖
曲大鹏
吴子俊
辛蕊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority to CN202011062605.5A priority Critical patent/CN112257907B/en
Publication of CN112257907A publication Critical patent/CN112257907A/en
Application granted granted Critical
Publication of CN112257907B publication Critical patent/CN112257907B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention belongs to the technical field of demand response of a power system, and particularly relates to an electric vehicle load modeling method considering electricity price sensitivity, which comprises the following steps of: s1: establishing a sensitivity analysis model of the user to the electricity price based on the psychology of the consumer; s2: establishing a user travel demand model based on user travel and state of charge constraints; s3: establishing a response model of user load transfer based on electricity price sensitivity based on a sensitivity analysis model and a user travel demand model; s4: based on the response model of the user load transfer established in step S3, the load level after the power rate guidance is calculated by combining the monte carlo method, that is, the EV user load response model after the power rate guidance is obtained. The method can more accurately simulate the limitation and the probability of charging period transfer of the EV user, and has very important significance for improving the accuracy of the guided charging load prediction.

Description

Electric vehicle load modeling method considering electricity price sensitivity
Technical Field
The invention belongs to the technical field of demand response of a power system, and particularly relates to an electric vehicle load modeling method considering electricity price sensitivity.
Background
Demand responses can be classified into an electricity price type and an incentive type according to policies, wherein participating users obtain profits mainly through price differences in electricity price type demand responses. Therefore, in the electricity price type demand response, a higher price difference can produce a stronger attraction to the user. The electric vehicle can generate direct or indirect scheduling interaction with a power grid as a charging load to assist the power grid in peak clipping and valley filling, so that the electric vehicle is a more flexible scheduling resource in demand response, for example, Chinese patent with publication number CN104966127B and publication date 2018.6.1, an economic scheduling method of the electric vehicle based on the demand response, the patent establishes an economic scheduling model of the demand response by predicting the behavior characteristics of electric vehicle users, optimizes the charging model according to the constraints of the charging state, the power, the charging state, the user trip and the like, and achieves the purposes of transferring the charging load and improving the income of a power grid company. However, the patent does not consider the sensitivity of the electricity price, the charging load is not comprehensive, and the accuracy is not high enough.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a modeling method of a response load model of an Electric Vehicle (EV) user for demand response by considering factors such as charging price, user planned travel demand, temporary travel electric quantity margin and the like.
In order to achieve the purpose, the invention is realized by the following technical scheme:
an electric automobile load modeling method considering electricity price sensitivity comprises the following steps:
s1: establishing a sensitivity analysis model of the user to the electricity price based on the psychology of the consumer;
s2: establishing a user travel demand model based on user travel and state of charge constraints;
s3: establishing a response model of user load transfer based on electricity price sensitivity based on a sensitivity analysis model and a user travel demand model;
s4: based on the response model of the user load transfer established in step S3, the load level after the power rate guidance is calculated by combining the monte carlo method, that is, the EV user load response model after the power rate guidance is obtained.
Further, the process of establishing the sensitivity analysis model in the step S1 is that a response model of the user to the price difference is established according to the psychological principle of the consumer so as to reflect the sensitivity of the user to the price;
the calculation method of the price sensitivity of the user is as follows:
Figure BDA0002712861660000021
kp＝αmax/(Δc2-Δc1)
wherein p isp(λ1λ) is the rate of price difference transfer for the user; Δ c1The dead zone threshold value is the electricity price difference when the user starts to respond; Δ c2A saturation region threshold value, namely the electricity price difference when the user has no response any more; Δ c is the electrovalence difference; alpha is alphamaxA saturation value that is a degree of response; k is a radical ofpThe slope of the linear region of the price response curve.
Further, the process of establishing the user travel demand model in step S2 is as follows:
including user travel and state of charge constraints, wherein the user travel constraints include a user queuing time transfer willingness FP(i) User trip demand diversion willingness FD(i) The state of charge constraints include a user charge transfer desire FS(i) User wish for charging transfer in the morningsh(i)。
Further, the user queue time transfer will FP(i) Determination of (1):
Figure BDA0002712861660000022
wherein, FP(i) Queuing time transfer intention for ith EV userHope; t isp(i) For actual queuing time, Tp_maxA queuing time limit that is acceptable to the user.
Further, the user's travel demand diversion will FD(i) Determination of (1):
Figure BDA0002712861660000023
wherein, FD(i) Transferring the travel demand transfer willingness after the ith EV charging time is transferred; t isy_maxAccepting the limit time of travel delay for EV users; t isy(i) The trip delay time of the EV is the trip delay time of a user caused by the conflict between the charging activity and the planned trip time after the charging time is transferred, and the charging activity is determined by the charging time period (t) for the nth planned trip delay in one dayc,tcw) And nth planned trip travel time period (t)d_n,tdw_n) To judge.
Further, the user's desire to transfer power FS(i) Determination of (1):
and measuring the satisfaction degree of the EV user to the electric quantity after the charging time is transferred by adopting a piecewise function based on the electric quantity margin requirement and the minimum SOC value constraint which does not damage the service life of the battery, wherein when the minimum SOC value one day after the charging time is transferred is larger than that before the transfer, the satisfaction degree of the electric quantity is 1, otherwise, the satisfaction degree of the electric quantity is:
Figure BDA0002712861660000031
wherein, FS(i) Satisfaction degree of the ith EV after the charging time is transferred to the electric quantity; smin(i) The minimum SOC value in one day after the ith EV charging time is transferred; smIs the lowest SOC value that does not impair battery life; sspAnd the SOC threshold value is used for meeting the EV electric quantity margin requirement.
Further, the user wishes to charge and transfer in the morning Fsh(i) Determination of (1):
Figure BDA0002712861660000032
further, the process of establishing the response model of the user load shifting based on the electricity price sensitivity in step S3 is as follows:
EV charge time from period λ1The transition probability to the time period λ is denoted pz(λ1λ), where λ ≠ λ1And then obtaining:
pz(λ1,λ)＝FP(i)FD(λ1,λ)FS(λ1,λ)Fsh(i)pp(λ1,λ)
considering the selection of the charging start time by the user of the EV, respectively calculating transition probabilities corresponding to the charging start times of the EV in the 0 th, 10 th, 20 th, 30 th, 40 th and 50 th minutes of the lambda period, selecting an optimal time point according to the transition probabilities as the charging start time in the period, if a plurality of optimal time points exist, randomly selecting one of the time points as the charging start time in the period, and taking the transition probability of the time point as the transition probability of the period, thereby obtaining a load transition probability matrix among the time periods;
the load transfer probability matrix of the EV user i in different time periods is as follows:
Figure BDA0002712861660000041
wherein p isz(λ1,λ)∈Ptr；
Figure BDA0002712861660000042
Further, in the current corresponding period λ1To randomly generate a transition probability prA 1 is to prComparing with elements in the load transfer probability matrix, if:
Figure BDA0002712861660000043
and is
Figure BDA0002712861660000044
The load will be limited by the time period lambda1Shifting to time period lambda2。
Further, the process of obtaining the EV user load response model guided by the electricity price in step S4 is as follows:
according to the time interval transfer probability of each user, Monte Carlo analog sampling is adopted to extract the charging time after each user is transferred;
suppose that user i is obtained by sampling from time period λjGo to lambdakAt a time period λkThe power of the load aggregate quotient is:
Figure BDA0002712861660000045
wherein, Pz,i(λj,λk) For user i during a time period λjTo lambdakCharging power of, NuzNumber of users;
sampling M times according to the process to obtain charging station load data sampled M times, and then calculating the average value of the charging station data sampled M times as a charging station predicted load;
continuously sampling the process by a Monte Carlo method to obtain predicted load P of the EV charging station at t moment after guidanceES(t)；
Figure BDA0002712861660000046
Wherein the content of the first and second substances,
Figure BDA0002712861660000047
the charging station load at the time t obtained by the first Monte Carlo sampling; m is Monte Carlo analog sampling times;
through the process, the EV user charging load response model guided by the electricity price can be obtained.
The invention has the beneficial effects that:
according to the method, the response degree of the EV user to the electricity price is inspected based on the psychology of a consumer, the sensitivity of the charging load to the electricity price is further inspected, the willingness of the EV user to select other periods for charging is considered by integrating the charging price, the planned travel demand, the travel electric quantity state and the like, the willingness is converted into the probability of charging period transfer, and finally the transfer condition of the charging load is simulated through a Monte Carlo method to obtain an EV user load response model guided by the electricity price. The method can more accurately simulate the limitation and the probability of charging period transfer of the EV user, and has very important significance for improving the accuracy of the guided charging load prediction.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a system diagram of an improved IEEE33 node in one embodiment;
FIG. 3 is a schematic illustration of different kinds of electricity prices;
FIG. 4 is a schematic diagram of the load change of the EV in each period after the different kinds of electricity price optimization guidance.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
Example 1:
as shown in fig. 1, an electric vehicle load modeling method considering power rate sensitivity includes the following steps:
s1: establishing a sensitivity analysis model of the user to the electricity price based on the psychology of the consumer;
s2: establishing a user travel demand model based on user travel and state of charge constraints;
s3: establishing a response model of user load transfer based on electricity price sensitivity based on a sensitivity analysis model and a user travel demand model;
s4: based on the response model of the user load transfer established in step S3, the load level after the power rate guidance is calculated by combining the monte carlo method, that is, the EV user load response model after the power rate guidance is obtained.
In this embodiment, the process of establishing the sensitivity analysis model in step S1 is to establish a response model of the user to the price difference according to the psychological principle of the consumer to reflect the sensitivity of the user to the price;
the calculation method of the price sensitivity of the user is as follows:
Figure BDA0002712861660000061
kp＝αmax/(Δc2-Δc1)
wherein p isp(λ1λ) is the rate of price difference transfer for the user; Δ c1The dead zone threshold value is the electricity price difference when the user starts to respond; Δ c2A saturation region threshold value, namely the electricity price difference when the user has no response any more; Δ c is the electrovalence difference; alpha is alphamaxA saturation value that is a degree of response; k is a radical ofpThe slope of the linear region of the price response curve.
In this embodiment, the process of establishing the user travel demand model in step S2 is as follows:
including user travel and state of charge constraints, wherein the user travel constraints include a user queuing time transfer willingness FP(i) User trip demand diversion willingness FD(i) The state of charge constraints include a user charge transfer desire FS(i) User wish for charging transfer in the morningsh(i)。
User queue time transfer willingness FP(i) Determination of (1):
Figure BDA0002712861660000062
wherein, FP(i) Queuing a time transfer intention for the user of the ith EV; t isp(i) For actual queuing time，Tp_maxA queuing time limit that is acceptable to the user.
User trip demand diversion willingness FD(i) Determination of (1):
Figure BDA0002712861660000063
wherein, FD(i) Transferring the travel demand transfer willingness after the ith EV charging time is transferred; t isy_maxAccepting the limit time of travel delay for EV users; t isy(i) The trip delay time of the EV is the trip delay time of a user caused by the conflict between the charging activity and the planned trip time after the charging time is transferred, and the charging activity is determined by the charging time period (t) for the nth planned trip delay in one dayc,tcw) And nth planned trip travel time period (t)d_n,tdw_n) To judge.
User's desire to transfer electric quantity FS(i) Determination of (1):
and measuring the satisfaction degree of the EV user on the electric quantity after the charging time is transferred by adopting a piecewise function based on the electric quantity margin requirement and the constraint of the lowest SOC (state of charge) value which does not damage the service life of the battery, wherein when the minimum SOC value one day after the charging time is transferred is larger than that before the transfer, the satisfaction degree of the electric quantity is 1, otherwise, the satisfaction degree of the electric quantity is as follows:
Figure BDA0002712861660000071
wherein, FS(i) Satisfaction degree of the ith EV after the charging time is transferred to the electric quantity; smin(i) The minimum SOC value in one day after the ith EV charging time is transferred; smIs the lowest SOC value that does not impair battery life; sspAnd the SOC threshold value is used for meeting the EV electric quantity margin requirement.
User willingness to charge transfer in the morning Fsh(i) Determination of (1):
Figure BDA0002712861660000072
in this embodiment, the process of establishing the response model of the user load shift based on the electricity rate sensitivity in step S3 is as follows:
the method comprises the steps of analyzing and calculating the load transfer probability of EV users in different time periods according to the travel demands of the EV users and the sensitivity of the EV users to electricity prices, and simulating the transfer condition of charging loads by a Monte Carlo method after determining the transfer probability of the EV charging time periods under a certain charging price scheme.
The calculation method of the transition probability of the charging period of the EV user is as follows:
EV charge time from period λ1The transition probability to the time period λ is denoted pz(λ1λ), where λ ≠ λ1And then obtaining:
pz(λ1,λ)＝FP(i)FD(λ1,λ)FS(λ1,λ)Fsh(i)pp(λ1,λ)
considering the selection of the charging start time by the user of the EV, respectively calculating transition probabilities corresponding to the charging start times of the EV in the 0 th, 10 th, 20 th, 30 th, 40 th and 50 th minutes of the lambda period, selecting an optimal time point according to the transition probabilities as the charging start time in the period, if a plurality of optimal time points exist, randomly selecting one of the time points as the charging start time in the period, and taking the transition probability of the time point as the transition probability of the period, thereby obtaining a load transition probability matrix among the time periods;
the load transfer probability matrix of the EV user i in different time periods is as follows:
Figure BDA0002712861660000081
wherein p isz(λ1,λ)∈Ptr；
Figure BDA0002712861660000082
At the current corresponding time period lambda1Random ofOne-to-one transition probability prA 1 is to prComparing with elements in the load transfer probability matrix, if:
Figure BDA0002712861660000083
and is
Figure BDA0002712861660000084
The load will be limited by the time period lambda1Shifting to time period lambda2。
In this embodiment, the process of obtaining the EV user load response model after the power rate guidance in step S4 is as follows:
according to the time interval transition probability of each user, Monte Carlo analog sampling is adopted to extract the charging time after each user is transferred;
suppose that user i is obtained by sampling from time period λjGo to lambdakAt a time period λkThe power of the load aggregate quotient is:
Figure BDA0002712861660000085
wherein, Pz,i(λj,λk) For user i during a time period λjTo lambdakCharging power of, NuzNumber of users;
sampling M times according to the process to obtain charging station load data sampled M times, and then calculating the average value of the charging station data sampled M times as a charging station predicted load;
continuously sampling the process by a Monte Carlo method to obtain predicted load P of the EV charging station at t moment after guidanceES(t)；
Figure BDA0002712861660000086
Wherein the content of the first and second substances,
Figure BDA0002712861660000087
obtained for the first Monte Carlo sampleCharging station load at time t; m is Monte Carlo analog sampling times;
through the process, the EV user charging load response model guided by the electricity price can be obtained.
Example 2:
the present embodiment is simulated by applying the EV user charging load response model in embodiment 1.
The simulation network selects the IEEE33 node power distribution system, as shown in fig. 2, and the line selects LGJ-150. Node 1 is a balanced node, the voltage is set to be 1.05p.u., and the peak-to-valley electricity price of the power grid is shown in table 1.
TABLE 1
Figure BDA0002712861660000091
And the normal load ratio of each node accessed is the original load ratio of each node of the IEEE33 node standard power distribution system. On the basis, the charging station load is connected to a node 8, the charging station is large in scale, 35 charging piles are arranged, each pile can be used for one EV to perform fast charging or two EVs to perform slow charging simultaneously, the charging station only provides fast charging service at 6: 00-24: 00, and users at 0: 00-6: 00 can perform slow charging according to charging requirements.
The rest parameters of the implementation are set as follows:
in the simulation, Δ t ═ 1h, lead ahead service price c'serThe value of 0.8 yuan/kW.h, delta c1、Δc2Can respectively take 0.1 yuan/kW.h and 1 yuan/kW.h, SmAnd SenRespectively taking 0.2 and 0.4, taking 500 as M, and taking 1200 as EV user number; the simulation results are shown in fig. 3 and 4.
The response degree of the user is increased along with the increase of the peak-to-valley electricity price difference, the response effect of the user is obvious in the peak charge period under the guidance of the three electricity prices, and the electricity price sensitivity is more obvious. At ordinary times and at valley times, the load transfer effects of users at three electricity prices are not greatly different, and generally, users prefer to transfer the load at peak time to eight points in the morning or ten points in the evening, so that the load at valley time is filled well, and the time period with the largest price difference is in the early morning. Therefore, the load transfer behavior of the user is influenced by two aspects of the travel demand and the electricity price guide.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. An electric vehicle load modeling method considering electricity price sensitivity is characterized by comprising the following steps:
s1: establishing a sensitivity analysis model of the user to the electricity price based on the psychology of the consumer;
s2: establishing a user travel demand model based on user travel and state of charge constraints;
s3: establishing a response model of user load transfer based on electricity price sensitivity based on a sensitivity analysis model and a user travel demand model;
s4: and calculating the load level after the power price guide based on the response model of the user load transfer and combining a Monte Carlo method, namely obtaining the EV user load response model after the power price guide.
2. The method for modeling electric vehicle load considering sensitivity to electricity prices as claimed in claim 1, wherein the process of establishing the sensitivity analysis model in step S1 is to establish a response model of the user to price difference according to the psychological principle of the consumer to reflect the sensitivity of the user to price;
the calculation method of the price sensitivity of the user is as follows:
Figure FDA0002712861650000011
kp＝αmax/(Δc2-Δc1)
wherein p isp(λ1λ) is the rate of price difference transfer for the user; Δ c1The dead zone threshold value is the electricity price difference when the user starts to respond; Δ c2A saturation region threshold value, namely the electricity price difference when the user has no response any more; Δ c is the electrovalence difference; alpha is alphamaxA saturation value that is a degree of response; k is a radical ofpThe slope of the linear region of the price response curve.
3. The electric vehicle load modeling method considering the electricity price sensitivity according to claim 2, wherein the process of establishing the user travel demand model in step S2 is as follows:
including user travel and state of charge constraints, wherein the user travel constraints include a user queuing time transfer willingness FP(i) User trip demand diversion willingness FD(i) The state of charge constraints include a user charge transfer desire FS(i) User wish for charging transfer in the morningsh(i)。
4. The electric vehicle load modeling method considering electricity price sensitivity according to claim 3, wherein a user queuing time shift will FP(i) Determination of (1):
Figure FDA0002712861650000021
wherein, FP(i) Queuing a time transfer intention for the user of the ith EV; t isp(i) For actual queuing time, Tp_maxA queuing time limit that is acceptable to the user.
5. The electric vehicle load modeling method considering electricity price sensitivity according to claim 3, wherein user trip demand diversion will FD(i) Determination of (1):
Figure FDA0002712861650000022
wherein, FD(i) Transferring the travel demand transfer willingness after the ith EV charging time is transferred; t isy_maxAccepting the limit time of travel delay for EV users; t isy(i) The trip delay time of the EV is the trip delay time of a user caused by the conflict between the charging activity and the planned trip time after the charging time is transferred, and the charging activity is determined by the charging time period (t) for the nth planned trip delay in one dayc,tcw) And nth planned trip travel time period (t)d_n,tdw_n) To judge.
6. The electric vehicle load modeling method considering electricity price sensitivity according to claim 3, wherein user's willingness to transfer electricity FS(i) Determination of (1):
and measuring the satisfaction degree of the EV user to the electric quantity after the charging time is transferred by adopting a piecewise function based on the electric quantity margin requirement and the minimum SOC value constraint which does not damage the service life of the battery, wherein when the minimum SOC value one day after the charging time is transferred is larger than that before the transfer, the satisfaction degree of the electric quantity is 1, otherwise, the satisfaction degree of the electric quantity is:
Figure FDA0002712861650000023
wherein, FS(i) Satisfaction degree of the ith EV after the charging time is transferred to the electric quantity; smin(i) The minimum SOC value in one day after the ith EV charging time is transferred; smIs the lowest SOC value that does not impair battery life; sspAnd the SOC threshold value is used for meeting the EV electric quantity margin requirement.
7. The electric vehicle load modeling method considering electricity price sensitivity as claimed in claim 3, wherein a user wishes to charge and transfer F early in the morningsh(i) Determination of (1):
Figure FDA0002712861650000031
8. the method for modeling electric vehicle load considering sensitivity to electricity price according to claim 3, wherein the step S3 of establishing the response model of user load shift based on sensitivity to electricity price is as follows:
EV charge time from period λ1The transition probability to the time period λ is denoted pz(λ1λ), where λ ≠ λ1And then obtaining:
pz(λ1,λ)＝FP(i)FD(λ1,λ)FS(λ1,λ)Fsh(i)pp(λ1,λ)
considering the selection of the charging start time by the user of the EV, respectively calculating transition probabilities corresponding to the charging start times of the EV in the 0 th, 10 th, 20 th, 30 th, 40 th and 50 th minutes of the lambda period, selecting an optimal time point according to the transition probabilities as the charging start time in the period, if a plurality of optimal time points exist, randomly selecting one of the time points as the charging start time in the period, and taking the transition probability of the time point as the transition probability of the period, thereby obtaining a load transition probability matrix among the time periods; the load transfer probability matrix of the EV user i in different time periods is as follows:
Figure FDA0002712861650000032
wherein p isz(λ1,λ)∈Ptr；
Figure FDA0002712861650000033
9. The electric vehicle load modeling method considering electricity price sensitivity according to claim 8,characterised by the fact that in the current corresponding time period lambda1To randomly generate a transition probability prA 1 is to prComparing with elements in the load transfer probability matrix, if:
Figure FDA0002712861650000034
and is
Figure FDA0002712861650000035
The load will be limited by the time period lambda1Shifting to time period lambda2。
10. The method for modeling electric vehicle load considering sensitivity of electricity price according to claim 9, wherein the step S4 is to obtain the EV user load response model guided by electricity price: according to the time interval transfer probability of each user, Monte Carlo analog sampling is adopted to extract the charging time after each user is transferred;
suppose that user i is obtained by sampling from time period λjGo to lambdakAt a time period λkThe power of the load aggregate quotient is:
Figure FDA0002712861650000041
wherein, Pz,i(λj,λk) For user i during a time period λjTo lambdakCharging power of, NuzNumber of users;
sampling M times according to the process to obtain charging station load data sampled M times, and then calculating the average value of the charging station data sampled M times as a charging station predicted load;
continuously sampling the process by a Monte Carlo method to obtain predicted load P of the EV charging station at t moment after guidanceES(t)；
Figure FDA0002712861650000042
Wherein the content of the first and second substances,
Figure FDA0002712861650000043
the charging station load at the time t obtained by the first Monte Carlo sampling; m is Monte Carlo analog sampling times;
through the process, the EV user charging load response model guided by the electricity price can be obtained.
CN202011062605.5A 2020-09-30 2020-09-30 Electric vehicle load modeling method considering electricity price sensitivity Active CN112257907B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011062605.5A CN112257907B (en) 2020-09-30 2020-09-30 Electric vehicle load modeling method considering electricity price sensitivity

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011062605.5A CN112257907B (en) 2020-09-30 2020-09-30 Electric vehicle load modeling method considering electricity price sensitivity

Publications (2)

Publication Number Publication Date
CN112257907A true CN112257907A (en) 2021-01-22
CN112257907B CN112257907B (en) 2023-04-07

Family

ID=74233487

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011062605.5A Active CN112257907B (en) 2020-09-30 2020-09-30 Electric vehicle load modeling method considering electricity price sensitivity

Country Status (1)

Country Link
CN (1) CN112257907B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117937570A (en) * 2024-03-18 2024-04-26 南方电网科学研究院有限责任公司 Adjustable margin optimization method and system for distributed charging facility
CN117937570B (en) * 2024-03-18 2024-06-11 南方电网科学研究院有限责任公司 Adjustable margin optimization method and system for distributed charging facility

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104966127A (en) * 2015-06-03 2015-10-07 东南大学 Electric vehicle economic dispatching method based on demand response
CN106855960A (en) * 2016-12-27 2017-06-16 国网福建省电力有限公司 A kind of charging electric vehicle load forecasting method under Peak-valley TOU power price guiding
CN109088454A (en) * 2018-08-24 2018-12-25 国网天津市电力公司 A kind of electric car charging method based on automatic demand response and Spot Price
CN110533222A (en) * 2019-07-29 2019-12-03 国网河南省电力公司经济技术研究院 Electric car charging load forecasting method and device based on peak Pinggu electricity price
CN110728396A (en) * 2019-09-25 2020-01-24 国网山西省电力公司经济技术研究院 Electric vehicle charging load comprehensive modeling method considering space-time distribution
CN110956329A (en) * 2019-12-02 2020-04-03 国网浙江省电力有限公司绍兴供电公司 Load prediction method based on distributed photovoltaic and electric vehicle space-time distribution
CN111160639A (en) * 2019-12-21 2020-05-15 杭州电子科技大学 Electric vehicle charging load prediction method based on user travel time-space distribution characteristics

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104966127A (en) * 2015-06-03 2015-10-07 东南大学 Electric vehicle economic dispatching method based on demand response
CN106855960A (en) * 2016-12-27 2017-06-16 国网福建省电力有限公司 A kind of charging electric vehicle load forecasting method under Peak-valley TOU power price guiding
CN109088454A (en) * 2018-08-24 2018-12-25 国网天津市电力公司 A kind of electric car charging method based on automatic demand response and Spot Price
CN110533222A (en) * 2019-07-29 2019-12-03 国网河南省电力公司经济技术研究院 Electric car charging load forecasting method and device based on peak Pinggu electricity price
CN110728396A (en) * 2019-09-25 2020-01-24 国网山西省电力公司经济技术研究院 Electric vehicle charging load comprehensive modeling method considering space-time distribution
CN110956329A (en) * 2019-12-02 2020-04-03 国网浙江省电力有限公司绍兴供电公司 Load prediction method based on distributed photovoltaic and electric vehicle space-time distribution
CN111160639A (en) * 2019-12-21 2020-05-15 杭州电子科技大学 Electric vehicle charging load prediction method based on user travel time-space distribution characteristics

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
常方宇等: ""分时充电价格下电动汽车有序充电引导策略"", 《电网技术》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117937570A (en) * 2024-03-18 2024-04-26 南方电网科学研究院有限责任公司 Adjustable margin optimization method and system for distributed charging facility
CN117937570B (en) * 2024-03-18 2024-06-11 南方电网科学研究院有限责任公司 Adjustable margin optimization method and system for distributed charging facility

Also Published As

Publication number Publication date
CN112257907B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
Zhang et al. Daily electric vehicle charging load profiles considering demographics of vehicle users
CN111682567B (en) Ordered charging and discharging control method considering user evaluation based on fuzzy control technology
Stiasny et al. Sensitivity analysis of electric vehicle impact on low-voltage distribution grids
CN110378548B (en) Electric automobile virtual power plant multi-time scale response capability assessment model construction method
CN110363362B (en) Multi-target day-ahead economic dispatching model building and solving method for flexible load
Zeng et al. Solving overstay and stochasticity in PEV charging station planning with real data
CN109947496A (en) Application program preloads method, apparatus, storage medium and mobile terminal
CN113829934B (en) Electric vehicle cluster aggregation response capacity determining method and scheduling method
CN110232219B (en) Electric vehicle schedulable capacity verification method based on data mining
Lu et al. Charging and relocating optimization for electric vehicle car-sharing: an event-based strategy improvement approach
CN108876052A (en) Electric car charging load forecasting method, device and computer equipment
Hipolito et al. Charging, steady-state SoC and energy storage distributions for EV fleets
CN115081743A (en) Charging station system configuration method, device, equipment and computer readable storage medium
Li et al. V2G multi-objective dispatching optimization strategy based on user behavior model
Xue et al. Research on load forecasting of charging station based on XGBoost and LSTM model
Jha et al. Coordinated effect of PHEVs with DGs on distribution network
CN112257907B (en) Electric vehicle load modeling method considering electricity price sensitivity
Sun et al. Charging stations‐oriented electric vehicle charging strategy based on battery characteristics
Terada et al. Ev charging simulator for public infrastructure considering smart charging and price policies
CN110866650B (en) Load optimization method based on load demand coefficient and time-span peak staggering and peak clipping
CN117353346A (en) Demand response method and device for communication base station and power system
CN112001564A (en) User leaving time prediction method based on long-term and short-term memory network
CN116502964A (en) Method and device for monitoring and modeling carbon emission space-time distribution of electric automobile
CN115564495A (en) Comprehensive evaluation method suitable for site selection of new energy power station
CN115395613A (en) Battery charging management method and device, electronic equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant